Advertisements
Feeds:
Posts
Comments

Archive for the ‘MRI’ Category


Disentangling molecular alterations from water-content changes in the aging human brain using quantitative MRI

Reporter: Dror Nir, PhD

 

Authors’ list: Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger, Sagiv Shifman & Aviv A. Mezer
Published in: Nature Communications volume 10, Article number: 3403 (2019)

Abstract

It is an open question whether aging-related changes throughout the brain are driven by a common factor or result from several distinct molecular mechanisms. Quantitative magnetic resonance imaging (qMRI) provides biophysical parametric measurements allowing for non-invasive mapping of the aging human brain. However, qMRI measurements change in response to both molecular composition and water content. Here, we present a tissue relaxivity approach that disentangles these two tissue components and decodes molecular information from the MRI signal. Our approach enables us to reveal the molecular composition of lipid samples and predict lipidomics measurements of the brain. It produces unique molecular signatures across the brain, which are correlated with specific gene-expression profiles. We uncover region-specific molecular changes associated with brain aging. These changes are independent from other MRI aging markers. Our approach opens the door to a quantitative characterization of the biological sources for aging, that until now was possible only post-mortem.

 

Introduction

The biology of the aging process is complex, and involves various physiological changes throughout cells and tissues1. One of the major changes is atrophy, which can be monitored by measuring macroscale brain volume reduction1,2. In some cases, atrophy can also be detected as localized microscale tissue loss reflected by increased water content3. This process is selective for specific brain regions and is thought to be correlated with cognitive decline in Alzheimer’s disease2,4,5. In addition to atrophy, there are molecular changes associated with the aging of both the normal and pathological brain5,6. Specifically, lipidome changes are observed with age, and are associated with several neurological diseases7,8,9,10,11.

It is an open question as to whether there are general principles that govern the aging process, or whether each system, tissue, or cell deteriorates with age for different reasons12,13. On one hand, the common-cause hypothesis proposes that different biological aging-related changes are the result of a single underlying factor14,15. This implies that various biomarkers of aging will be highly correlated16. On the other hand, the mosaic theory of aging suggests that there are several distinct aging mechanisms that have a heterogenous effect throughout the brain12,13. According to this latter view, combining different measurements of brain tissue is crucial in order to fully describe the state of the aging brain. To test these two competing hypotheses in the context of volumetric and molecular aging-related changes, it is essential to measure different biological aspects of brain tissue. Unfortunately, the molecular correlates of aging are not readily accessible by current in vivo imaging methods.

The main technique used for non-invasive mapping of the aging process in the human brain is magnetic resonance imaging (MRI)2,17,18,19. Advances in the field have led to the development of quantitative MRI (qMRI). This technique provides biophysical parametric measurements that are useful in the investigation and diagnosis of normal and abnormal aging20,21,22,23,24,25,26,27. qMRI parameters have been shown to be sensitive to the microenvironment of brain tissue and are therefore named in vivo histology28,29,30. Nevertheless, an important challenge in applying qMRI measurements is increasing their biological interpretability. It is common to assume that qMRI parameters are sensitive to the myelin fraction20,23,30,31,32,33, yet any brain tissue including myelin is a mixture of multiple lipids and proteins. Moreover, since water protons serve as the source of the MRI signal, the sensitivity of qMRI parameters to different molecular microenvironments may be confounded by their sensitivity to the water content of the tissue34,35. We hypothesized that the changes observed with aging in MRI measurements20,23,30,31,32,33,36 such as R1, R2, mean diffusivity (MD), and magnetization transfer saturation (MTsat)37, could be due to a combination of an increase in water content at the expense of tissue loss, and molecular alterations in the tissue.

Here, we present a qMRI analysis that separately addresses the contribution of changes in molecular composition and water content to brain aging. Disentangling these two factors goes beyond the widely accepted “myelin hypothesis” by increasing the biological specificity of qMRI measurements to the molecular composition of the brain. For this purpose, we generalize the concept of relaxivity, which is defined as the dependency of MR relaxation parameters on the concentration of a contrast agent38. Instead of a contrast agent, our approach exploits the qMRI measurement of the local non-water fraction39 to assess the relaxivity of the brain tissue itself. This approach allows us to decode the molecular composition from the MRI signal. In samples of known composition, our approach provides unique signatures for different brain lipids. In the live human brain, it produces unique molecular signatures for different brain regions. Moreover, these MRI signatures agree with post-mortem measurements of the brain lipid and macromolecular composition, as well as with specific gene-expression profiles. To further validate the sensitivity of the relaxivity signatures to molecular composition, we perform direct comparison of MRI and lipidomics on post-mortem brains. We exploit our approach for multidimensional characterization of aging-related changes that are associated with alterations in the molecular composition of the brain. Finally, we evaluate the spatial pattern of these changes throughout the brain, in order to compare the common-cause and the mosaic theories of aging in vivo.

 

Results

Different brain lipids have unique relaxivity signatures
The aging process in the brain is accompanied by changes in the chemophysical composition, as well as by regional alterations in water content. In order to examine the separate pattern of these changes, we developed a model system. This system was based on lipid samples comprising common brain lipids (phosphatidylcholine, sphingomyelin, phosphatidylserine, phosphatidylcholine-cholesterol, and phosphatidylinositol-phosphatidylcholine)7. Using the model system, we tested whether accounting for the effect of the water content on qMRI parameters provides sensitivity to fine molecular details such as the head groups that distinguish different membrane phospholipids. The non-water fraction of the lipid samples can be estimated by the qMRI measurement of lipid and macromolecular tissue volume (MTV, for full glossary of terms see Supplementary Table 1)39. By varying the concentration of the lipid samples, we could alter their MTV and then examine the effect of this manipulation on qMRI parameters. The parameters we estimated for the lipid samples were R1, R2, and MTsat. The potential ambiguity in the biological interpretation of qMRI parameters is demonstrated in Fig. 1a. On one hand, samples with similar lipid composition can present different R1 measurements (Fig. 1a, points 1 & 2). On the other hand, scanning samples with different lipid compositions may result in similar R1 measurements (Fig. 1a, points 2 & 3). This ambiguity stems from the confounding effect of the water content on the MR relaxation properties.

Screenshot 2019-08-01 at 14.36.20

We evaluated the dependency of different qMRI parameters on the non-water fraction estimated by MTV. This analysis revealed strong linear dependencies (median R2 = 0.74, Fig. 1a, b and Supplementary Fig. 1a, b). These linear MTV dependencies change as a function of the lipid composition, reflecting the inherent relaxivity of the different lipids. We could therefore use the MTV derivatives of qMRI parameters (dqMRIdMTV, i.e., the slope of the linear relationship between each qMRI parameter and MTV) as a measure that is sensitive to molecular composition. By accounting for the Multidimensional Dependency on MTV (“MDM”) of several qMRI parameters, a unique MRI relaxivity signature was revealed for each lipid (Fig. 1c). This implies that the water-related ambiguity demonstrated in the inset of Fig. 1a can be removed by measuring the MTV dependencies (Fig. 1c). Creating mixtures of several lipids provided supportive evidence for the generality of our framework. Figure 1d and Supplementary Fig. 1c show that the qMRI measurements of a mixture can be predicted by summing the MTV dependencies of pure lipids (for further details see Supplementary Note 1 and Supplementary Fig. 2). Furthermore, we used this biophysical model to predict the lipid composition of a mixture from its MDM measurements (Fig. 1e). This model provided a good estimation of the sphingomyelin (Spg) and phosphatidylserine (PS) content (R2 > 0.64) but failed to predict phosphatidylcholine (PtdCho) content (for further details see Supplementary Note 2). While lipids are considered to be a major source of the MRI signal in the brain 40,41,42,43,44,45, our approach can be applied to other compounds to reveal differences in the MRI signal between different proteins, sugars, and ions (Supplementary Fig. 1d). Hence, the relationships between qMRI parameters and MTV account for the effect of water on MRI measurements and could be of use in quantifying the biological and molecular contributions to the MRI signal of water protons.

The tissue relaxivity of the human brain is region-specific.
In order to target age-related changes in molecular composition, we applied the same approach for the human brain (Fig. 2a).

Screenshot 2019-08-01 at 14.41.35

We found that the linear dependency of qMRI parameters on MTV is not limited to in vitro samples and a similar relationship was also evident in the human brain (Fig. 2b and Supplementary Figs. 3–5). Importantly, different brain regions displayed a distinct dependency on MTV. Therefore, the relaxivity of brain tissue is region-specific. Figure 2b provides an example for the regional linear trends of R1 and MTsat in a single subject. Remarkably, while the thalamus and the pallidum presented relatively similar R1 dependencies on MTV, their MTsat dependencies were different (p < 0.001, two-sample t-test). Compared to these two brain regions, frontal white-matter demonstrated different dependencies on MTV (p < 0.001, two-sample t-test). A better separation between brain regions can therefore be achieved by combining the MTV dependencies of several qMRI parameters (MTsat, MD, R1 and R2). The MTV derivatives of qMRI parameters are consistent across subjects (Fig. 2c and Supplementary Fig. 6), with good agreement between hemispheres (Supplementary Fig. 5). Moreover, they provide a novel pattern of differentiation between brain regions, which is not captured by conventional qMRI methods (Supplementary Fig. 7). In our lipid sample experiments, the MDM approach revealed unique relaxivity signatures of different lipids (Fig. 1c). Therefore, we attribute the observed diversity in the MTV derivatives of qMRI parameters across brain regions to the intrinsic heterogeneity in the chemophysical microenvironment of these regions. The multidimensional dependency of various qMRI parameters on MTV can be represented by the space of MTV derivatives to reveal a unique chemophysical MDM signature for different brain regions (Fig. 2d, see explanatory scheme of the MDM method in Supplementary Fig. 8). Fig. 2 figure2 The MDM method provides region-specific signatures in the in vivo human brain. a Representative MTV, MTsat, and R1 maps. b Calculating the MDM signatures. The dependency of R1 (left) and MTsat (right) on MTV in three brain regions of a single subject. For each region, MTV values were pooled into bins (dots are the median of each bin; shaded area is the median absolute deviation), and a linear fit was calculated (colored lines). The slopes of the linear fit represent the MTV derivatives of R1 and MTsat and vary across brain regions. c The reliability of the MDM method across subjects. Variation in the MTV derivatives of R1 (left) and MTsat (right) in young subjects (N = 23). Different colors represent 14 brain regions (see legend). Edges of each box represent the 25th, and 75th percentiles, median is in black, and whiskers extends to extreme data points. Different brain regions show distinct MTV derivatives. d Unique MDM signatures for different brain regions (in different colors). Each axis is the MTV derivative (“MDM measurements”) of a different qMRI parameter (R1, MTsat, R2, and MD). The range of each axis is in the legend. Colored traces extend between the MDM measurements, shaded areas represent the variation across subjects (N = 23). An overlay of all MDM signatures is marked with dashed lines Full size image The in vivo MDM approach captures ex vivo molecular profiles To validate that the MDM signatures relate to the chemophysical composition of brain tissue, we compared them to a previous study that reported the phospholipid composition of the human brain7. First, we established the comparability between the in vivo MRI measurements and the reported post-mortem data. MTV measures the non-water fraction of the tissue, a quantity that is directly related to the total phospholipid content. Indeed, we found good agreement between the in vivo measurement of MTV and the total phospholipid content across brain regions (R2 = 0.95, Fig. 3a). Söderberg et al.7 identified a unique phospholipid composition for different brain regions along with diverse ratios of phospholipids to proteins and cholesterol. We compared this regional molecular variability to the regional variability in the MDM signatures. To capture the main axes of variation, we performed principal component analysis (PCA) on both the molecular composition of the different brain regions and on their MDM signatures. For each of these two analyses, the first principal component (PC) explained >45% of the variance. The regional projection on the first PC of ex vivo molecular composition was highly correlated (R2 = 0.84, Fig. 3b) with the regional projection on the first PC of in vivo MDM signatures. This confirms that brain regions with a similar molecular composition have similar MDM. Supplementary Fig. 9a provides the correlations of individual lipids with MDM. Importantly, neither MTV nor the first PC of standard qMRI parameters was as strongly correlated with the ex vivo molecular composition as the MDM (Supplementary Fig. 9b, c). We next used the MDM measurements as predictors for molecular properties of different brain regions. Following our content predictions for lipids samples (Fig. 1e), we constructed a weighted linear model for human data (for further details see Supplementary Note 3). To avoid over fitting, we reduced the number of fitted parameters by including only the MDM and the molecular features that accounted for most of the regional variability. The MTV derivatives of R1 and MTsat accounted for most of the variance in MDM. Thus, we used these parameters as inputs to the linear model, while adjusting their weights through cross validation. We tested the performance of this model in predicting the three molecular features that account for most of the variance in the ex vivo molecular composition. Remarkably, MRI-driven MDM measurements provided good predictions for the regional sphingomyelin composition (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c) and the regional ratio of phospholipids to proteins (R2 = 0.56, p < 0.05 for the F-test, Fig. 3c).

Screenshot 2019-08-01 at 14.44.06
Last, we compared the cortical MDM signatures to a gene co-expression network based on a widespread survey of gene expression in the human brain46. Nineteen modules were derived from the gene network, each comprised of a group of genes that co-varies in space. Six out of the nineteen gene modules were significantly correlated with the first PC of MDM. Interestingly, the first PC of MDM across the cortex was correlated most strongly with the two gene modules associated with membranes and synapses (Fig. 4, for further details see Supplementary Note 4 and Supplementary Figs. 10 and 11).

Screenshot 2019-08-01 at 14.47.04

Post-mortem validation for the lipidomic sensitivity of MDM.
The aforementioned analyses demonstrate strong agreement between in vivo MDM measurements and ex vivo molecular composition based on a group-level comparison of two different datasets. Strikingly, we were able to replicate this result at the level of the single brain. To achieve this we performed MRI scans (R1, MTsat, R2, MD, and MTV mapping) followed by histology of two fresh post-mortem porcine brains (Fig. 5a, b). First, we validated the qMRI estimation of MTV using dehydration techniques. MTV values estimated using MRI were in agreement with the non-water fraction found histologically (adjusted R2 = 0.64, p < 0.001 for the F-test, Fig. 5c).

Screenshot 2019-08-01 at 14.50.12
Next, we estimated the lipid composition of different brain regions. Thin-layer chromatography (TLC) was employed to quantify seven neutral and polar lipids (Supplementary Table 2 and Supplementary Fig. 12a). In accordance with the analysis in Fig. 3, we performed PCA to capture the main axes of variation in lipidomics, standard qMRI parameters, and MDM. Figure 5d shows that MTV did not correlate with the molecular variability across the brain, estimated by the 1st PC of lipidomics. Likewise, the molecular variability did not agree with the 1st PC of standard qMRI parameters (Fig. 5e).

Last, we applied the MDM approach to the post-mortem porcine brain. Similar to the human brain, different porcine brain regions have unique MDM signatures (Fig. 5f, g and Supplementary Fig. 12b). Remarkably, we found that agreement between lipid composition and MRI measurements emerges at the level of the MDM signatures. The molecular variability across brain regions significantly correlated with the regional variability in the MDM signatures (adjusted R2 = 0.3, p < 0.01 for the F-test, Fig. 5h). Excluding from the linear regression five outlier brain regions where the histological lipidomics results were 1.5 standard deviations away from the center yielded an even stronger correlation between MDM signatures and lipid composition (adjusted R2 = 0.55, p < 0.001 for the F-test, Supplementary Fig. 12c). This post-mortem analysis validates that the MDM approach allows us to capture molecular information using MRI at the level of the individual brain.

Disentangling water and molecular aging-related changes.
After establishing the sensitivity of the MDM signatures to the molecular composition of the brain, we used them to evaluate the chemophysical changes of the aging process. To assess aging-related changes across the brain, we scanned younger and older subjects (18 older adults aged 67 ± 6 years and 23 younger adults aged 27 ± 2 years). First, we identified significant molecular aging-related changes in the MDM signatures of different brain regions (Figs. 6 and 7, right column; Supplementary Fig. 13). Next, we tested whether the changes in MRI measurements, observed with aging, result from a combination of changes in the molecular composition of the tissue and its water content. We found that although it is common to attribute age-related changes in R1 and MTsat to myelin28,30,36, these qMRI parameters combine several physiological aging aspects. For example, using R1 and MTsat we identified significant aging-related changes in the parietal cortex, the thalamus, the parietal white-matter and the temporal white-matter (Figs. 6 and 7, left column). However, the MDM approach revealed that these changes have different biological sources (Figs. 6 and 7, middle columns; see Supplementary Figs. 14–17 for more brain regions).

Screenshot 2019-08-01 at 14.51.53

Screenshot 2019-08-01 at 14.54.44

Screenshot 2019-08-01 at 14.56.06

In agreement with the mosaic hypothesis, we identified distinct aging patterns for different brain regions. For example, in the hippocampus we found a change in R2* values related to a higher iron concentration with age, along with significant reduction in the total hippocampal volume (Fig. 8a). This age-related shrinkage was not accompanied by lower MTV values, indicating conserved tissue density (Fig. 7b). In addition, there was no significant difference in the hippocampal MDM signature with age (Fig. 7b). Cortical gray-matter areas also exhibited similar trends of volume reduction without major loss in tissue density (Fig. 8a). Unlike the gray matter, in the white matter we did not find volume reduction or large iron accumulation with age (Fig. 8a). However, we did find microscale changes with age in tissue composition, as captured by the MDM signature (Figs. 6a and 7c, and Supplementary Fig. 13), accompanied by a significant density-related decline in MTV (Fig. 8a). These findings are consistent with previous histological studies49,50,51 (see Discussion), and provide the ability to monitor in vivo the different components of the aging mosaic.

Last, to test whether the different biological aging trajectories presented in Fig. 8a share a common cause, we evaluated the correlations between them (Fig. 8b). Importantly, the chemophysical trajectory did not correlate significantly with the iron or volume aging patterns. The spatial distribution of water-related changes was found to correlate with iron content alterations (R2 = 0.27) and chemophysical alterations (R2 = 0.25). However, the strongest correlation between aging-related changes was found in volume and iron content (R2 = 0.77). As shown previously, this correlation may be explained to some extent by a systematic bias in automated tissue classification23. Additional analysis revealed that the different dimensions of the MDM signature capture distinct patterns of aging-related changes (Supplementary Fig. 30). Hence, complementary information regarding the various chemophysical mechanisms underlying brain aging could be gained by combining them.

 

Discussion

Normal brain aging involves multiple changes, at both the microscale and macroscale level. MRI is the main tool for in vivo evaluation of such age-related changes in the human brain. Here, we propose to improve the interpretation of MRI findings by accounting for the fundamental effect of the water content on the imaging parameters. This approach allows for non-invasive mapping of the molecular composition in the aging human brain.

Our work is part of a major paradigm shift in the field of MRI toward in vivo histology30,36,52. The MDM approach contributes to this important change by providing a hypothesis-driven biophysical framework that was rigorously developed. We demonstrated the power of our framework, starting from simple pure lipid phantoms to more complicated lipid mixtures, and from there, to the full complexity of the brain. In the brain, we show both in vivo and post-mortem validations for the molecular sensitivity of the MDM signatures. Early observations relate different qMRI parameters to changes in the fraction of myelin20,23,30,31,32,33,36. The current approach enriches this view and provides better sensitivity to the molecular composition and fraction of myelin and other cellular tissues.

We developed a unique phantom system of lipid samples to validate our method. While the phantom system is clearly far from the complexity of brain tissue, its simplicity allowed us to verify the specificity of our method to the chemophysical environment. Remarkably, our approach revealed unique signatures for different lipids, and is therefore sensitive even to relatively subtle details that distinguish one lipid from another. We chose to validate our approach using membrane lipids based on previous experiments40,41,42,43,44,45. Nevertheless, we do acknowledge the fact that brain tissue comprises many other compounds beside lipids, such as proteins, sugars, and ions. As we have shown, these other compounds also exhibit unique dependency on MTV. The effect of such compounds, along with other factors such as microstructure, and multi-compartment organization28 is probably captured when we apply the MDM approach to the in vivo human brain. Therefore, the phantoms were made to examine the MRI sensitivity for the chemophysical environment, and the human brain data was used to measure the true biological effects in a complex in vivo environment.

Our relaxivity approach captures the molecular signatures of the tissue, but is limited in its abilities to describe the full complexity of the chemophysical environment of the human brain. For example, R1 and R2, which are used to generate the MDM signatures, are also sensitive to the iron content23,48,52. However, we found that most of our findings cannot be attributed to alterations in iron content as measured with R2* (for more details see Supplementary Note 5). While there is great importance in further isolating different molecular components, we argue that accounting for the major effect of water on qMRI parameters (for R2 distributions see Supplementary Fig. 5) is a crucial step towards more specific qMRI interpretation.

We provide evidence from lipids samples and post-mortem data for the sensitivity of the MDM signatures to the molecular environment (Figs. 1e, 3b, and 5h). The variability of MDM values between human brain regions also correlated with specific gene-expression profiles (Fig. 4). While the comparison of in vivo human brain measurements to previously published ex vivo findings is based on two different datasets, these measurements are highly stable across normal subjects and the intersubject variabilities are much smaller than the regional variability. The agreement between the modalities provides strong evidence for the ability of our method to capture molecular information.

Remarkably, we were able to demonstrate the sensitivity of MDM signatures to lipid composition using direct comparison on post-mortem porcine brains. Even though there are many challenges in scanning post-mortem tissue, segmenting it, and comparing it to anatomically relevant histological results, we were able to replicate our in vivo findings. We provide histological validation for the MRI estimation of MTV. Moreover, we find that while standard qMRI parameters and MTV do not explain the lipidomic variability across the brain, the MDM signatures are in agreement with histological results. Lipids constitute the majority of the brain’s dry weight and are known to be important for maintaining neural conduction and chemical balance53,54. The brain lipidome was shown to have a great deal of structural and functional diversity and was found to vary according to age, gender, brain region, and cell type55. Disruptions of the brain lipid metabolism have been linked to different disorders, including Alzheimer’s disease, Parkinson’s disease, depression, and anxiety7,8,11,54,55,56,57. Our results indicate that the MDM approach enhances the consistency between MRI-driven measurements and lipidomics, compared with standard qMRI parameters.

The simplicity of our model, which is based on a first-order approximation of qMRI dependencies, has great advantages in the modeling of complex environments. Importantly, we used lipids samples to show that the contributions of different mixture-components can be summed linearly (Fig. 1d). For contrast agents, the relaxivity is used to characterize the efficiency of different agents. Here, we treated the tissue itself, rather than a contrast material, as an agent to compute the relaxivity of the tissue. While relaxivity is usually calculated for R1 and R2, we extended this concept to other qMRI parameters. Our results showed that the tissue relaxivity changes as a function of the molecular composition. This suggests that the relaxivity of the tissue relates to the surface interaction between the water and the chemophysical environment. A theoretical formulation for the effect of the surface interaction on proton relaxation has been proposed before58,59. Specifically, a biophysical model for the linear relationship between R1 and R2 to the inverse of the water content (1/WC = 1/(1 – MTV)) was suggested by Fullerton et al.43. Interestingly, 1/WC varies almost linearly with MTV in the physiological range of MTV values. Applying our approach with 1/WC instead of MTV produces relatively similar results (Supplementary Fig. 28). However, using MTV as a measure of tissue relaxivity allowed us to generalize the linear model to multiple qMRI parameters, thus producing multidimensional MDM signatures.

We show that the MDM signatures allow for better understanding of the biological sources for the aging-related changes observe with MRI. Normal brain aging involves multiple changes, at both the microscale and macroscale levels. Measurements of macroscale brain volume have been widely used to characterize aging-associated atrophy. Our method of analysis can complement such findings and provide a deeper understanding of microscale processes co-occurring with atrophy. Moreover, it allows us to test whether these various microscale and macroscale processes are caused by a common factor or represent the aging mosaic. Notably, we discovered that different brain regions undergo different biological aging processes. Therefore, combining several measurements of brain tissue is crucial in order to fully describe the state of the aged brain. For example, the macroscale aging-related volume reduction in cortical gray areas was accompanied by conserved tissue density, as estimated by MTV, and region-specific chemophysical changes, as estimated by the MDM. In contrast, in white-matter areas both MDM and MTV changed with age. These microscale alterations were not accompanied by macroscale volume reduction. Our in vivo results were validated by previous histological studies, which reported that the cortex shrinks with age, while the neural density remains relatively constant49,50. In contrast, white matter was found to undergo significant loss of myelinated nerve fibers during aging51. In addition, we found that the shrinkage of the hippocampus with age is accompanied with conserved tissue density and chemophysical composition. This is in agreement with histological findings, which predict drastic changes in hippocampal tissue composition in neurological diseases such as Alzheimer, but not in normal aging49,50,60,61. In contrast, hippocampal macroscale volume reduction was observed in both normal and pathological aging2.

It should be noted that most of the human subjects recruited for this study were from the academic community. However, the different age groups were not matched for variables such as IQ and socioeconomic status. In addition, the sample size in our study was quite small. Therefore, the comparison we made between the two age groups may be affected by variables other than age. Our approach may benefit from validation based on larger quantitative MRI datasets27,62. Yet, we believe we have demonstrated the potential of our method to reveal molecular alterations in the brain. Moreover, the agreement of our findings with previous histological aging studies supports the association between the group differences we measured and brain aging. Our results suggest that the MDM approach may be very useful in differentiating the effects of normal aging from those of neurodegenerative diseases. There is also great potential for applications in other brain research fields besides aging. For example, our approach may be used to advance the study and diagnosis of brain cancer, in which the lipidomic environment undergoes considerable changes63,64,65.

To conclude, we have presented here a quantitative MRI approach that decodes the molecular composition of the aging brain. While common MRI measurements are primarily affected by the water content of the tissue, our method employed the tissue relaxivity to expose the sensitivity of MRI to the molecular microenvironment. We presented evidence from lipid samples, post-mortem porcine brains and in vivo human brains for the sensitivity of the tissue relaxivity to molecular composition. Results obtained by this method in vivo disentangled different biological processes occurring in the human brain during aging. We identified region-specific patterns of microscale aging-related changes that are associated with the molecular composition of the human brain. Moreover, we showed that, in agreement with the mosaic theory of aging, different biological age-related processes measured in vivo have unique spatial patterns throughout the brain. The ability to identify and localize different age-derived processes in vivo may further advance human brain research.

Methods

Phantom construction
The full protocol of lipids phantom preparation is described in Shtangel et al.66.

In short, we prepared liposomes from one of the following lipids: phosphatidylserine (PS), phosphatidylcholine (PtdCho), phosphatidylcholine-cholesterol (PtdCho-Chol), Phosphatidylinositol-phosphatidylcholine (PI-PtdCho), or sphingomyelin (Spg). These phantoms were designed to model biological membranes and were prepared from lipids by the hydration–dehydration dry film technique67. The lipids were dissolved over a hot plate and vortexed. Next, the solvent was removed to create a dry film by vacuum-rotational evaporation. The samples were then stirred on a hot plate at 65 °C for 2.5 h to allow the lipids to achieve their final conformation as liposomes. Liposomes were diluted with Dulbecco’s phosphate buffered saline (PBS), without calcium and magnesium (Biological Industries), to maintain physiological conditions in terms of osmolarity, ion concentrations and pH. To change the MTV of the liposome samples we varied the PBS to lipid volume ratios66. Samples were then transferred to the phantom box for scanning in a 4 mL squared polystyrene cuvettes glued to a polystyrene box, which was then filled with ~1% SeaKem Agarose (Ornat Biochemical) and ~0.0005 M Gd (Gadotetrate Melumine, (Dotarem, Guerbet)) dissolved in double distilled water (ddw). The purpose of the agar with Gd (Agar-Gd) was to stabilize the cuvettes, and to create a smooth area in the space surrounding the cuvettes that minimalized air–cuvette interfaces. In some of our experiments we used lipid mixtures composed of several lipids. We prepared nine mixtures containing different combinations of two out of three lipids (PtdChol, Spg and PS) in varying volume ratios (1:1,1:2,2:1). For each mixture, we prepared samples in which the ratio between the different lipid components remained constant while the water-to-lipid volume fraction varied.

For the bovine serum albumin (BSA) phantoms, samples were prepared by dissolving lyophilized BSA powder (Sigma Aldrich) in PBS. To change the MTV of these phantoms, we changed the BSA concentration. For the BSA + Iron phantoms, BSA was additionally mixed with a fixed concentration of 50 µg/mL ferrous sulfate heptahydrate (FeSO4*7H2O). Samples were prepared in their designated concentrations at room temperature. Prepared samples were allowed to sit overnight at 4 ℃ to ensure BSA had fully dissolved, without the need for significant agitation, which is known to cause protein cross-linking. Samples were then transferred to the phantom box for scanning.

For Glucose and Sucrose phantoms, different concentrations of D-( + )-Sucrose (Bio-Lab) and D-( + )-Glucose (Sigma) were dissolved in PBS at 40 ℃. Samples were allowed to reach room temperature before the scan.

MRI acquisition for phantoms

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1 & MTV mapping, three-dimensional (3D) Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 8°, 16°, and 30°). The TE/TR was 3.91/18 ms. The scan resolution was 1.1 × 1.1 × 0.9 mm. The same sequence was repeated with a higher resolution of 0.6 × 0.6 × 0.5 mm. The TE/TR was 4.45/18 ms. For calibration, we acquired an additional spin-echo inversion recovery (SEIR) scan. This scan was done on a single slice, with adiabatic inversion pulse and inversion times of TI = 2000, 1200, 800, 400, and 50. The TE/TR was 73/2540 ms. The scan resolution was 1.2 mm isotropic.

For quantitative T2 mapping, images were acquired with a multi spin-echo sequence with 15 equally spaced spin echoes between 10.5 ms and 157.5 ms. The TR was 4.94 s. The scan resolution was 1.2 mm isotropic. For quantitative MTsat mapping, images were acquired with the FLASH Siemens WIP 805 sequence. The TR was 23 ms for all samples except PI:PtdCho for which the TR was 72 ms. Six echoes were equally spaced between 1.93 ms to 14.58 ms. The on-resonance flip angle was 6°, the MT flip angle was 220°, and the RF offset was 700. We used 1.1-mm in-plane resolution with a slice thickness of 0.9 mm. For samples of sucrose and glucose, MTsat mapping was done similar to the human subjects, based on 3D Spoiled gradient (SPGR) echo image with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.91/28 ms. The scan resolution was 1 mm isotropic.

Estimation of qMRI parameters for phantoms

MTV and R1 estimations for the lipids samples were computed based on a the mrQ39 (https://github.com/mezera/mrQ) and Vista Lab (https://github.com/vistalab/vistasoft/wiki) software. The mrQ software was modified to suit the phantom system66. The modification utilizes the fact that the Agar-Gd filling the box around the samples is homogeneous and can, therefore, be assumed to have a constant T1 value. We used this gold standard T1 value generated from the SEIR scan to correct for the excite bias in the spoiled gradient echo scans. While the data was acquired in two different resolutions (see “MRI acquisition”), in our analysis we use the median R1 and MTV of each lipid sample and these are invariant to the resolution of acquisition (Supplementary Fig. 1e). Thus, we were able to use scans with different resolutions without damaging our results. T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68.

For quantitative MTsat mapping see the “MTsat estimation” section for human subjects.

MDM computation for phantoms

We computed the dependency of each qMRI parameter (R1, MTsat, and R2) on MTV in different lipids samples. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). To manipulate the MTV values, we scanned samples of the same lipid in varying concentrations. We computed the median MTV of each sample, along with the median of qMRI parameters. We used these data points to fit a linear model across all samples of the same lipid. The slope of this linear model represents the MTV derivative of the linear equation. We used this derivative estimate of three qMRI parameters (R1, R2, and MTsat) to compute the MDM signatures. The same procedure was used for the MDM computation of lipid mixtures.

 

MDM modeling of lipid mixtures

We tested the ability of MDM to predict the composition of lipid mixtures. For this analysis we used nine mixture phantoms (see “Phantom construction”), along with the three phantoms of the pure lipid constituents of the mixtures (PS, Spg, and Ptd-Cho).

In order to predict the qMRI parameters of a lipid mixture (Fig. 1d) we used Supplementary Eq. 1 (Supplementary Note 1). To further predict the composition of the mixtures (Fig. 1e) we used Supplementary Eq. 5 (Supplementary Note 2). We solved this equation using the QR factorization algorithm.

Ethics

Human experiments complied with all relevant ethical regations. The Helsinki Ethics Committee of Hadassah Hospital, Jerusalem, Israel approved the experimental procedure. Written informed consent was obtained from each participant prior to the procedure.

Human subjects

Human measurements were performed on 23 young adults (aged 27 ± 2 years, 11 females), and 18 older adults (aged 67 ± 6 years, five females). Healthy volunteers were recruited from the community surrounding the Hebrew University of Jerusalem.

MRI acquisition for human subjects

Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 3.34–14.02 ms) and the TR was 19 ms (except for six young subjects for which the scan included only one TE = 3.34 ms). The scan resolution was 1 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 200, 400, 1,200, and 2400 ms. We used 2-mm in-plane resolution with a slice thickness of 3 mm. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 ms and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic. T2 scans of four subjects (one young, three old) were excluded from the analysis due to motion.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 3.34/27 ms. The scan resolution was 1 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0). In addition, we collected non-diffusion-weighted images with reversed phase-encode blips. For five subjects (four young, one old) we failed to acquire this correction data and they were excluded from the diffusion analysis.

Anatomical images were acquired with 3D magnetization prepared rapid gradient echo (MP-RAGE) scans for 24 of the subjects (14 from the younger subjects, 10 from the older subjects). The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms. Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) scans were acquired for the rest of the subjects. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/5000 ms.

Estimation of qMRI parameters for human subjects

Whole-brain MTV and R1 maps, together with bias correction maps of B1 + and B1-, were computed using the mrQ software39,69 (https://github.com/mezera/mrQ). Voxels in which the B1 + inhomogeneities were extrapolated and not interpolated were removed from the MTV and R1 maps. While we did not correct our MTV estimates for R2*, we showed that employing such a correction does not significantly change our results (see Supplementary Note 6, Supplementary Figs. 20–27). MTV maps of four subjects had bias in the lower part of the brain and they were therefore excluded from the analysis presented in Fig. 3, which includes ROIs in the brainstem.

Whole-brain T2 maps were computed by implementing the echo‐modulation curve (EMC) algorithm68. To combine the MTV and T2 we co-registered the quantitative MTV map to the T2 map. We used the ANTS software package70 to calculate the transformation and to warp the MTV map and the segmentation. The registration was computed to match the T1 map to the T2 map. Next, we applied the calculated transformation to MTV map (since MTV and T1 are in the same imaging space) and resampled the MTV map to match the resolution of the T2 map. The same transformation was also applied to the segmentation. R2 maps were calculated as 1/T2.

Whole-brain MTsat maps were computed as described in Helms et al.37. The MTsat measurement was extracted from Eq. (1):

MTsat=𝑀0𝐵1𝛼𝑅1TR𝑆MT−(𝐵1𝛼)22−𝑅1TR
(1)
Where SMT is the signal of the SPGR scan with additional MT pulse, α is the flip angle and TR is the repetition time. Mo (the equilibrium magnetization parameter), B1 (the transmit inhomogeneity), and R1 estimations were computed from the non-MT weighted SPGR scans, during the pipeline described under “MTV & R1 estimation”. Registration of the SMT image to the imaging space of the MTV map was done using a rigid-body alignment (R1, B1, and MO are all in the same space as MTV).

Diffusion analysis was done using the FDT toolbox in FSL71,72. Susceptibility and eddy current induced distortions were corrected using the reverse phase-encode data, with the eddy and topup commands73,74. MD maps were calculated using vistasoft (https://github.com/vistalab/vistasoft/wiki). We used a rigid-body alignment to register the corrected dMRI data to the imaging space of the MTV map (Flirt, FSL). In order to calculate the MD-MTV derivatives, we resampled the MTV map and the segmentation to match the dMRI resolution.

We used the SPGR scans with multiple echoes to estimate R2*. Fitting was done through the MPM toolbox75. As we had four SPGR scans with variable flip angles, we averaged the R2* maps acquired from each of these scans for increased SNR.

Human brain segmentation

Whole-brain segmentation was computed automatically using the FreeSurfer segmentation algorithm76. For subjects who had an MP-RAGE scan, we used it as a reference. For the other subjects the MP2RAGE scan was used as a reference. These anatomical images were registered to the MTV space prior to the segmentation process, using a rigid-body alignment. Sub-cortical gray-matter structures were segmented with FSL’s FIRST tool77. To avoid partial volume effects, we removed the outer shell of each ROI and left only the core.

MDM computation in the human brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas. This process was implemented in MATLAB (MathWorks, Natwick, MI, USA). For each ROI, we extracted the MTV values from all voxels and pooled them into 36 bins spaced equally between 0.05 and 0.40. This was done so that the linear fit would not be heavily affected by the density of the voxels in different MTV values. We removed any bins in which the number of voxels was smaller than 4% of the total voxel count in the ROI. The median MTV of each bin was computed, along with the median of the qMRI parameter. We used these data points to fit the linear model across bins using Eq. (2):

qMRIparameters=𝑎∗MTV+𝑏
(2)
The slope of this linear model (“a”) represents the MTV derivative of the linear equation. We used this derivative estimate to compute the MDM signatures.

For each subject, ROIs in which the total voxel count was smaller than a set threshold of 500 voxels for the MTsat and R1 maps, 150 voxels for the MD map, and 50 voxels for the R2 map were excluded.

Principal component analysis (PCA) in the human brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. For each MDM dimension (MTV derivatives of R1, MTsat, MD, and R2), we evaluated the median of the different brain areas across the young subjects. As each MDM dimension has different units, we then computed the z-score of each dimension across the different brain area. Finally, we performed PCA. The variables in this analysis were the different MDM dimensions, and the observations were the different brain areas. From this analysis, we derived the first PC that accounts for most of the variability in MDM signatures across the brain. To estimate the median absolute deviations (MAD) across subjects of each MDM measurement in the PC basis, we applied the z-score transformation to the original MAD and then projected them onto the PC basis.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

For the first PC of molecular composition, we followed the same procedure, but used the phospholipid composition and the ratio between phospholipids to proteins and cholesterol as variables. The data was taken from eight post-mortem human brains7. Brains were obtained from individuals between 54 and 57 years of age, which were autopsied within 24 h after death.

Linear model for prediction of human molecular composition

We used MDM measurements in order to predict the molecular composition of different brain areas (Fig. 3c). For this analysis we used Supplementary Eq. 5 in the Supplementary Note 2. We solved this equation using QR factorization algorithm (for more details see Supplementary Note 3).

Gene-expression dataset

For the gene-expression analysis we followed the work of Ben-David and Shifman46. Microarray data was acquired from the Allen Brain Atlas (http://human.brain-map.org/well_data_files) and included a total of 1340 microarray profiles from donors H0351.2001 and H0351.2002, encompassing the different regions of the human brain. The donors were 24 and 39 years old, respectively, at the time of their death, with no known psychopathologies. We used the statistical analysis described by Ben-David and Shifman46. They constructed a gene network using a weighted gene co-expression network analysis. The gene network included 19 modules of varying sizes, from 38 to 7385 genes. The module eigengenes were derived by taking the first PC of the expression values in each module. In addition, we used the gene ontology enrichment analysis described by Ben-David and Shifman to define the name of each module. The colors of the different modules in the Fig. 4 and Supplementary Fig. 10 are the same as in the original paper.

Next, we matched between the gene-expression data and the MRI measurements. This analysis was done on 35 cortical regions extracted from FreeSurfer cortical parcellation. We downloaded the T1-weighted images of the two donors provided by the Allen Brain Atlas (http://human.brain-map.org/mri_viewers/data) and used them as a reference for FreeSurfer segmentation. We then found the FreeSurfer label of each gene-expression sample using the sample’s coordinates in brain space. We removed samples for which the FreeSurfer label and the label provided in the microarray dataset did not agree (there were 72 such samples out of 697 cortical samples). For each gene module, we averaged over the eigengenes of all samples from the same cortical area across the two donors.

Last, we compared the cortical eigengene of each module to the projection of cortical areas on the first PC of MDM. In addition, we compared the modules’ eigengenes to the MTV values of the cortical areas and to the projection of cortical areas on the first PC of standard qMRI parameters (Supplementary Fig. 10). These 57 correlations were corrected for multiple comparisons using the FDR method.

Brain region’s volume computation

To estimate the volume of different brain regions, we calculated the number of voxels in the FreeSurfer segmentation of each region (see “Brain segmentation”).

R2* correction for MTV
To correct the MTV estimates for R2* we used Eq. (3):

MTV𝐶=1−(1−MTV)⋅exp(TE⋅R2∗)
(3)
Where MTVC is the corrected MTV.

Statistical analysis

The statistical significance of the differences between the age groups was computed using an independent-sample t-test (alpha = 0.05, both right and left tail) and was corrected for multiple comparisons using the false-discovery rate (FDR) method. For this analysis, MRI measurements of both hemispheres of bilateral brain regions were joined together. R2 measurements were adjusted for the number of data points. All statistical tests were two-sided.

Post-mortem tissue acquisition

Two post-mortem porcine brains were purchased from BIOTECH FARM.

Post-mortem MRI acquisition

Brains were scanned fresh (without fixation) in water within 6 h after death. Data was collected on a 3 T Siemens MAGNETOM Skyra scanner equipped with a 32-channel head receive-only coil at the ELSC neuroimaging unit at the Hebrew University.

For quantitative R1, R2*, & MTV mapping, 3D Spoiled gradient (SPGR) echo images were acquired with different flip angles (α = 4°, 10°, 20°, and 30°). Each image included five equally spaced echoes (TE = 4.01 – 16.51 ms) and the TR was 22 ms. The scan resolution was 0.8 mm isotropic. For calibration, we acquired additional spin-echo inversion recovery scan with an echo-planar imaging (EPI) read-out (SEIR-epi). This scan was done with a slab-inversion pulse and spatial-spectral fat suppression. For SEIR-epi, the TE/TR was 49/2920 ms. TI were 50, 200, 400, 1200 ms. The scan resolution was 2 mm isotropic. The EPI read-out was performed using 2 × acceleration.

For quantitative T2 mapping, multi‐SE images were acquired with ten equally spaced spin echoes between 12 and 120 ms. The TR was 4.21 s. The scan resolution was 2 mm isotropic.

For quantitative MTsat mapping, 3D Spoiled gradient (SPGR) echo image were acquired with an additional MT pulse. The flip angle was 10°, the TE/TR was 4.01/40 ms. The scan resolution was 0.8 mm isotropic.

Whole-brain DTI measurements were performed using a diffusion-weighted spin-echo EPI sequence with isotropic 1.5-mm resolution. Diffusion weighting gradients were applied at 64 directions and the strength of the diffusion weighting was set to b = 2000 s/mm2 (TE/TR = 95.80/6000 ms, G = 45mT/m, δ = 32.25 ms, Δ = 52.02 ms). The data includes eight non-diffusion-weighted images (b = 0).

For anatomical images, 3D magnetization prepared rapid gradient echo (MP-RAGE) scans were acquired. The scan resolution was 1 mm isotropic, the TE/TR was 2.98/2300 ms.

Histological analysis

Following the MRI scans the brains were dissected. Total of 42 brain regions were identified. Four samples were excluded as we were not able to properly separate the WM from the GM. One sample was excluded as we could not properly identify its anatomical origin. Additional two samples were too small for TLC analysis.

The non-water fraction (MTV) was determined by desiccation, also known as the dry-wet method. A small fraction of each brain sample (~0.25 g) was weighed. In order to completely dehydrate the fresh tissues, they were left for several days in a vacuum dessicator over silica gel at 4 °C. The experiment ended when no further weight loss occurred. The MTV of each brain sample was calculated based on the difference between the wet (Wwet) and dry (Wdry) weights of the tissue (Eq. 4):

MTV=𝑊wet−𝑊dry𝑊wet
(4)
For lipid extraction and lipidomics analysis78, Brain samples were weighted and homogenized with saline in plastic tubes on ice at concentration of 1 mg/12.5 µL. Two-hundred fifty microliters from each homogenate were utilized for lipid extraction and analysis with thin-layer chromatography (TLC). The lipid species distribution was analyzed by TLC applying 150 µg aliquots. Samples were reconstituted in 10 µL of Folch mixture and spotted on Silica-G TLC plates. Standards for each fraction were purchased from Sigma Aldrich (Rehovot, Israel) and were spotted in separate TLC lanes, i.e., 50 µg of triacylglycerides (TG), cholesterol (Chol), cholesteryl esters (CE), free fatty acids (FFA), lysophospholipids (Lyso), sphingomyelin (Spg), phosphatidylcholine (PtdCho), phosphatidylinositol (PI), phosphatidylserine (PS), and phosphatidylethanolamine (PE). Plates were then placed in a 20 × 20 cm TLC chamber containing petroleum ether, ethyl ether, and acetic acid (80:20:1, v/v/v) for quantification of neutral lipids or chloroform, methanol, acetic acid, and water (65:25:4:2, v:v:v:v) for quantification of polar lipids and run for 45 min. TG, Chol, CE, FFA, phospholipids (PL), Lyso, Spg, PtdCho, PI, PS, and PE bands were visualized with Iodine, scanned and quantified by Optiquant after scanning (Epson V700). Lyso, CE, TG, and PI were excluded from further analysis as their quantification was noisy and demonstrated high variability across TLC plates. This analysis was conducted under the guidance of Prof. Alicia Leikin-Frenkel in the Bert Strassburger Lipid Center, Sheba, Tel Hashomer.

Estimation of qMRI parameters in the post-mortem brain

Similar to human subjects.

Brain segmentation of post-mortem brain

Brain segmentation was done manually. Five tissue samples were excluded as we could not identify their origin location in the MRI scans.

MDM computation in the post-mortem brain

We computed the dependency of each qMRI parameter (R1, MTsat, MD, and R2) on MTV in different brain areas similarly to the analysis of the human subjects.

Principal component analysis (PCA) in the post-mortem brain

To estimate the variability in the MDM signatures across the brain, we computed the first principal component (PC) of MDM. PCA analysis was performed with four variables corresponding to the MDM dimensions (MTV derivatives of R1, MTsat, MD, and R2), and 30 observations corresponding to the different brain regions. As each MDM dimension has different units, we first computed the z-score of each dimension across the different brain areas prior to the PCA. From this analysis we derived the first PC that accounts for most of the variability in MDM signatures across the brain.

To compute the first PC of standard qMRI parameters we followed the same procedure, but used R1, MTsat, MD, and R2 instead of their MTV derivatives.

To estimate the variability in the lipid composition across the brain, we computed the first principal component (PC) of lipidomics. PCA analysis was performed with seven variables corresponding to the different polar and neutral lipids (Chol, FFA, PL, Spg, PtdCho, PS, PE), and 30 observations corresponding to the different brain regions. From this analysis, we derived the first PC that accounts for most of the variability in lipid composition across the brain.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

Code availability

A toolbox for computing MDM signatures is available at [https://github.com/MezerLab/MDM_toolbox].

The code generating the figures of in the paper is available at [https://github.com/MezerLab/MDM_Gen_Figs].

References
1.
Peters, R. Ageing and the brain. Postgrad. Med. J. 82, 84–88 (2006).

2.
Lockhart, S. N. & DeCarli, C. Structural imaging measures of brain aging. Neuropsychol. Rev. 24, 271–289 (2014).

3.
Wozniak, J. R. & Lim, K. O. Advances in white matter imaging: a review of in vivo magnetic resonance methodologies and their applicability to the study of development and aging. Neurosci. Biobehav. Rev. 30, 762–774 (2006).

4.
Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6, 67–77 (2010).

5.
Mrak, R. E., Griffin, S. T. & Graham, D. I. Aging-associated changes in human brain. J. Neuropathol. Exp. Neurol. 56, 1269–1275 (1997).

6.
Yankner, B. A., Lu, T. & Loerch, P. The aging brain. Annu. Rev. Pathol. 3, 41–66 (2008).

7.
Söderberg, M., Edlund, C., Kristensson, K. & Dallner, G. Lipid compositions of different regions of the human brain during aging. J. Neurochem. 54, 415–423 (1990).

8.
Lauwers, E. et al. Membrane lipids in presynaptic function and disease. Neuron 90, 11–25 (2016).

9.
Li, Q. et al. Changes in lipidome composition during brain development in humans, chimpanzees, and Macaque monkeys. Mol. Biol. Evol. 34, 1155–1166 (2017).

10.
Müller, C. P. et al. Brain membrane lipids in major depression and anxiety disorders. Biochim. Biophys. Acta-Mol. Cell Biol. Lipids 1851, 1052–1065 (2015).

11.
Naudí, A. et al. Lipidomics of human brain aging and Alzheimer’s disease pathology. Int. Rev. Neurobiol. 122, 133–189 (2015).

12.
Walker, L. C. & Herndon, J. G. Mosaic aging. Med. Hypotheses 74, 1048–1051 (2010).

13.
Cole, J. H., Marioni, R. E., Harris, S. E. & Deary, I. J. Brain age and other bodily ‘ages’: implications for neuropsychiatry. Mol. Psychiatry 1 (2018). https://doi.org/10.1038/s41380-018-0098-1.

14.
Hayflick, L. Biological aging is no longer an unsolved problem. Ann. N. Y. Acad. Sci. 1100, 1–13 (2007).

15.
Christensen, H., Mackinnon, A. J., Korten, A. & Jorm, A. F. The ‘common cause hypothesis’; of cognitive aging: evidence for not only a common factor but also specific associations of age with vision and grip strength in a cross-sectional analysis. Psychol. Aging 16, 588–599 (2001).

16.
Cole, J. H. et al. Brain age predicts mortality. Mol. Psychiatry 23, 1385–1392 (2018).

17.
Sowell, E. R., Thompson, P. M. & Toga, A. W. Mapping changes in the human cortex throughout the span of life. Neuroscience 10, 372–392 (2004).

18.
Fjell, A. M. & Walhovd, K. B. Structural brain changes in aging: courses, causes and cognitive consequences. Rev. Neurosci. 21, 187–221 (2010).

19.
Gunning-Dixon, F. M., Brickman, A. M., Cheng, J. C. & Alexopoulos, G. S. Aging of cerebral white matter: a review of MRI findings. Int. J. Geriatr. Psychiatry 24, 109–117 (2009).

20.
Callaghan, M. F. et al. Widespread age-related differences in the human brain microstructure revealed by quantitative magnetic resonance imaging. Neurobiol. Aging 35, 1862–1872 (2014).

21.
Yeatman, J. D., Wandell, B. A. & Mezer, A. A. Lifespan maturation and degeneration of human brain white matter. Nat. Commun. 5, 4932 (2014).

22.
Cox, S. R. et al. Ageing and brain white matter structure in 3,513 UK Biobank participants. Nat. Commun. 7, 13629 (2016).

23.
Lorio, S. et al. Disentangling in vivo the effects of iron content and atrophy on the ageing human brain. Neuroimage 103, 280–289 (2014).

24.
Gracien, R.-M. et al. Evaluation of brain ageing: a quantitative longitudinal MRI study over 7 years. Eur. Radiol. 27, 1568–1576 (2017).

25.
Draganski, B. et al. Regional specificity of MRI contrast parameter changes in normal ageing revealed by voxel-based quantification (VBQ). Neuroimage 55, 1423–1434 (2011).

26.
Tardif, C. L. et al. Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies. Neuroimage 149, 233–243 (2017).

27.
Carey, D. et al. Quantitative MRI provides markers of intra-, inter-regional, and age-related differences in young adult cortical microstructure. Neuroimage 182, 429–440 (2017).

28.
Cercignani, M., Dowell, N. G. & Tofts, P. S. Quantitative MRI of the Brain: Principles of Physical Measurement. (CRC Press, United States, 2018).

29.
Basser, P. J. & Pierpaoli, C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. Ser. B 111, 209–219 (1996).

30.
Weiskopf, N., Mohammadi, S., Lutti, A. & Callaghan, M. F. Advances in MRI-based computational neuroanatomy. Curr. Opin. Neurol. 28, 313–322 (2015).

31.
Winklewski, P. J. et al. Understanding the physiopathology behind axial and radial diffusivity changes—what do we know? Front. Neurol. 9, 92 (2018).

32.
Heath, F., Hurley, S. A., Johansen-Berg, H. & Sampaio-Baptista, C. Advances in noninvasive myelin imaging. Dev. Neurobiol. 78, 136–151 (2018).

33.
Lutti, A., Dick, F., Sereno, M. I. & Weiskopf, N. Using high-resolution quantitative mapping of R1 as an index of cortical myelination. Neuroimage 93, 176–188 (2014).

34.
Filo, S. & Mezer, A. A. in Quantitative MRI of the Brain: Principles of Physical Measurement (eds Cercignani, M., Dowell, N. G. & Tofts, P. S.) 55–72 (CRC Press, United States, 2018).

35.
Fullerton, G. D., Cameron, I. L. & Ord, V. A. Frequency dependence of magnetic resonance spin-lattice relaxation of protons in biological materials. Radiology 151, 135–138 (1984).

36.
Does, M. D. Inferring brain tissue composition and microstructure via MR relaxometry. Neuroimage 182, 136–148 (2018).

37.
Helms, G., Dathe, H., Kallenberg, K. & Dechent, P. High-resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T 1 relaxation obtained from 3D FLASH MRI. Magn. Reson. Med. 60, 1396–1407 (2008).

38.
Rohrer, M., Bauer, H., Mintorovitch, J., Requardt, M. & Weinmann, H. -J. Comparison of magnetic properties of MRI contrast media solutions at different magnetic field strengths. Investig. Radiol. 40, 715–724 (2005).

39.
Mezer, A. et al. Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging. Nat. Med. 19, 1667–1672 (2013).

40.
Koenig, S. H. Cholesterol of myelin is the determinant of gray‐white contrast in MRI of brain. Magn. Reson. Med. 20, 285–291 (1991).

41.
Koenig, S. H., Brown, R. D., Spiller, M. & Lundbom, N. Relaxometry of brain: why white matter appears bright in MRI. Magn. Reson. Med. 14, 482–495 (1990).

42.
Kucharczyk, W., Macdonald, P. M., Stanisz, G. J. & Henkelman, R. M. Relaxivity and magnetization transfer of white matter lipids at MR imaging: importance of cerebrosides and pH. Radiology 192, 521–529 (1994).

43.
Fullerton, G. D., Potter, J. L. & Dornbluth, N. C. NMR relaxation of protons in tissues and other macromolecular water solutions. Magn. Reson. Imaging 1, 209–226 (1982).

44.
Morawski, M. et al. Developing 3D microscopy with CLARITY on human brain tissue: towards a tool for informing and validating MRI-based histology. Neuroimage 182, 417–428 (2018).

45.
Leuze, C. et al. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. Neuroimage 156, 412–422 (2017).

46.
Ben-David, E. & Shifman, S. Networks of neuronal genes affected by common and rare variants in autism spectrum disorders. PLoS Genet. 8, e1002556 (2012).

47.
Zecca, L., Youdim, M. B. H., Riederer, P., Connor, J. R. & Crichton, R. R. Iron, brain ageing and neurodegenerative disorders. Nat. Rev. Neurosci. 5, 863–873 (2004).

48.
Langkammer, C. et al. Quantitative MR imaging of brain iron: a postmortem validation study. Radiology 257, 455–462 (2010).

49.
Freeman, S. H. et al. Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease. J. Neuropathol. Exp. Neurol. 67, 1205–1212 (2008).

50.
Burke, S. N. & Barnes, C. A. Neural plasticity in the ageing brain. Nat. Rev. Neurosci. 7, 30–40 (2006).

51.
Bowley, M. P., Cabral, H., Rosene, D. L. & Peters, A. Age changes in myelinated nerve fibers of the cingulate bundle and corpus callosum in the rhesus monkey. J. Comp. Neurol. 518, 3046–3064 (2010).

52.
Callaghan, M. F., Helms, G., Lutti, A., Mohammadi, S. & Weiskopf, N. A general linear relaxometry model of R1 using imaging data. Magn. Reson. Med. 73, 1309–1314 (2015).

53.
Piomelli, D., Astarita, G. & Rapaka, R. A neuroscientist’s guide to lipidomics. Nat. Rev. Neurosci. 8, 743–754 (2007).

54.
Sethi, S., Hayashi, M. A., Sussulini, A., Tasic, L. & Brietzke, E. Analytical approaches for lipidomics and its potential applications in neuropsychiatric disorders. World J. Biol. Psychiatry 18, 506–520 (2017).

55.
Fantini, J. & Yahi, N. Brain Lipids in Synaptic Function and Neurological Disease: Clues to Innovative Therapeutic Strategies for Brain Disorders. (Academic Press, United States, 2015).

56.
Shinitzky, M. Patterns of lipid changes in membranes of the aged brain. Gerontology 33, 149–154 (1987).

57.
Martin, M., Dotti, C. G. & Ledesma, M. D. Brain cholesterol in normal and pathological aging. Biochim. Biophys. Acta-Mol. Cell Biol. Lipids 1801, 934–944 (2010).

58.
Calucci, L. & Forte, C. Proton longitudinal relaxation coupling in dynamically heterogeneous soft systems. Prog. Nucl. Magn. Reson. Spectrosc. 55, 296–323 (2009).

59.
Halle, B. Molecular theory of field-dependent proton spin-lattice relaxation in tissue. Magn. Reson. Med. 56, 60–72 (2006).

60.
West, M. J., Coleman, P. D., Flood, D. G. & Troncoso, J. C. Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet (Lond., Engl.) 344, 769–772 (1994).

61.
West, M. J., Kawas, C. H., Stewart, W. F., Rudow, G. L. & Troncoso, J. C. Hippocampal neurons in pre-clinical Alzheimer’s disease. Neurobiol. Aging 25, 1205–1212 (2004).

62.
Slater, D. A. et al. Evolution of white matter tract microstructure across the life span. Hum. Brain Mapp. 40, 2252–2268 (2019).

63.
Jarmusch, A. K. et al. Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS. Proc. Natl Acad. Sci. U.S.A. 113, 1486–1491 (2016).

64.
Wenk, M. R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 4, 594–610 (2005).

65.
Eberlin, L. S. et al. Classifying human brain tumors by lipid imaging with mass spectrometry. Cancer Res. 72, 645–654 (2012).

66.
Shtangel, O. & Mezer, A. A phantom system designed to assess the effects of membrane lipids on water proton relaxation. bioRxiv 387845 (2018). https://doi.org/10.1101/387845.

67.
Akbarzadeh, A. et al. Liposome: methods of preparation and applications. Liposome Technol. 6, 102 (2013).

68.
Ben-Eliezer, N., Sodickson, D. K. & Block, K. T. Rapid and accurate T 2 mapping from multi-spin-echo data using Bloch-simulation-based reconstruction. Magn. Reson. Med. 73, 809–817 (2015).

69.
Mezer, A., Rokem, A., Berman, S., Hastie, T. & Wandell, B. A. Evaluating quantitative proton-density-mapping methods. Hum. Brain Mapp. 37, 3623–3635 (2016).

70.
Avants, B. B., Tustison, N. & Song, G. Advanced normalization tools (ANTS). Insight J. (2009). http://hdl.handle.net/10380/3113

71.
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage (2004). https://doi.org/10.1016/j.neuroimage.2004.07.051.

72.
Behrens, T. E. J. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. (2003). https://doi.org/10.1002/mrm.10609.

73.
Andersson, J. L. R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage (2003). https://doi.org/10.1016/S1053-8119(03)00336-7.

74.
Andersson, J. L. R. & Sotiropoulos, S. N. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage (2016). https://doi.org/10.1016/j.neuroimage.2015.10.019.

75.
Weiskopf, N. et al. Quantitative multi-parameter mapping of R1, PD*, MT, and R2* at 3T: a multi-center validation. Front. Neurosci. (2013). https://doi.org/10.3389/fnins.2013.00095.

76.
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).

77.
Patenaude, B., Smith, S. M., Kennedy, D. N. & Jenkinson, M. A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage (2011). https://doi.org/10.1016/j.neuroimage.2011.02.046.

78.
Shomonov-Wagner, L., Raz, A. & Leikin-Frenkel, A. Alpha linolenic acid in maternal diet halts the lipid disarray due to saturated fatty acids in the liver of mice offspring at weaning. Lipids Health Dis. (2015). https://doi.org/10.1186/s12944-015-0012-7.

Download references

Acknowledgements

This work was supported by the ISF grant 0399306, awarded to A.A.M. We acknowledge Ady Zelman for the assistance in collecting the human MRI data. We thank Assaf Friedler for assigning research lab space and advising on the lipid sample experiments. We thank Inbal Goshen for assigning research lab space and advising on the protein and ion samples as well as the porcine brain experiments. We thank Magnus Soderberg for advising on histological data interpretation. We are grateful to Brian A. Wandell, Jason Yeatman, Hermona Soreq, Ami Citri, Mark Does, Yaniv Ziv, Ofer Yizhar, Shai Berman, Roey Schurr, Jonathan Bain, Asier Erramuzpe Aliaga, Menachem Gutman, and Esther Nachliel for their critical reading of the manuscript and very useful comments. We thank Prof. Alicia Leikin-Frenkel for her guidance with the TLC analysis. We thank Rona Shaharabani for guidance and support in the post-mortem experiments.

Affiliations

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Shir Filo, Oshrat Shtangel, Noga Salamon, Adi Kol, Batsheva Weisinger & Aviv A. Mezer
Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel
Sagiv Shifman
Contributions
S.F., O.S., and A.A.M. conceived of the presented idea. S.F. and A.A.M. wrote the manuscript and designed the figures. S.F. collected the human and non-human brain datasets and analyzed them. O.S. performed the phantom experiments and analyzed them. B.W. performed the phantom experiments for non-lipid compounds. N.S. performed the gene-expression analysis. S.S. assisted and instructed with the gene-expression analysis. A.K. performed the porcine brain dissection.

Corresponding author

Correspondence to Aviv A. Mezer.

Ethics declarations & Competing interests

A.A.M, S.F., O.S. and the Hebrew University of Jerusalem have filed a patent application describing the technology used to measure MDM in this work. The other authors declare no competing interests.

Advertisements

Read Full Post »


Artificial Intelligence and Cardiovascular Disease

Reporter and Curator: Dr. Sudipta Saha, Ph.D.

 

Cardiology is a vast field that focuses on a large number of diseases specifically dealing with the heart, the circulatory system, and its functions. As such, similar symptomatologies and diagnostic features may be present in an individual, making it difficult for a doctor to easily isolate the actual heart-related problem. Consequently, the use of artificial intelligence aims to relieve doctors from this hurdle and extend better quality to patients. Results of screening tests such as echocardiograms, MRIs, or CT scans have long been proposed to be analyzed using more advanced techniques in the field of technology. As such, while artificial intelligence is not yet widely-used in clinical practice, it is seen as the future of healthcare.

 

The continuous development of the technological sector has enabled the industry to merge with medicine in order to create new integrated, reliable, and efficient methods of providing quality health care. One of the ongoing trends in cardiology at present is the proposed utilization of artificial intelligence (AI) in augmenting and extending the effectiveness of the cardiologist. This is because AI or machine-learning would allow for an accurate measure of patient functioning and diagnosis from the beginning up to the end of the therapeutic process. In particular, the use of artificial intelligence in cardiology aims to focus on research and development, clinical practice, and population health. Created to be an all-in-one mechanism in cardiac healthcare, AI technologies incorporate complex algorithms in determining relevant steps needed for a successful diagnosis and treatment. The role of artificial intelligence specifically extends to the identification of novel drug therapies, disease stratification or statistics, continuous remote monitoring and diagnostics, integration of multi-omic data, and extension of physician effectivity and efficiency.

 

Artificial intelligence – specifically a branch of it called machine learning – is being used in medicine to help with diagnosis. Computers might, for example, be better at interpreting heart scans. Computers can be ‘trained’ to make these predictions. This is done by feeding the computer information from hundreds or thousands of patients, plus instructions (an algorithm) on how to use that information. This information is heart scans, genetic and other test results, and how long each patient survived. These scans are in exquisite detail and the computer may be able to spot differences that are beyond human perception. It can also combine information from many different tests to give as accurate a picture as possible. The computer starts to work out which factors affected the patients’ outlook, so it can make predictions about other patients.

 

In current medical practice, doctors will use risk scores to make treatment decisions for their cardiac patients. These are based on a series of variables like weight, age and lifestyle. However, they do not always have the desired levels of accuracy. A particular example of the use of artificial examination in cardiology is the experimental study on heart disease patients, published in 2017. The researchers utilized cardiac MRI-based algorithms coupled with a 3D systolic cardiac motion pattern to accurately predict the health outcomes of patients with pulmonary hypertension. The experiment proved to be successful, with the technology being able to pick-up 30,000 points within the heart activity of 250 patients. With the success of the aforementioned study, as well as the promise of other researches on artificial intelligence, cardiology is seemingly moving towards a more technological practice.

 

One study was conducted in Finland where researchers enrolled 950 patients complaining of chest pain, who underwent the centre’s usual scanning protocol to check for coronary artery disease. Their outcomes were tracked for six years following their initial scans, over the course of which 24 of the patients had heart attacks and 49 died from all causes. The patients first underwent a coronary computed tomography angiography (CCTA) scan, which yielded 58 pieces of data on the presence of coronary plaque, vessel narrowing and calcification. Patients whose scans were suggestive of disease underwent a positron emission tomography (PET) scan which produced 17 variables on blood flow. Ten clinical variables were also obtained from medical records including sex, age, smoking status and diabetes. These 85 variables were then entered into an artificial intelligence (AI) programme called LogitBoost. The AI repeatedly analysed the imaging variables, and was able to learn how the imaging data interacted and identify the patterns which preceded death and heart attack with over 90% accuracy. The predictive performance using the ten clinical variables alone was modest, with an accuracy of 90%. When PET scan data was added, accuracy increased to 92.5%. The predictive performance increased significantly when CCTA scan data was added to clinical and PET data, with accuracy of 95.4%.

 

Another study findings showed that applying artificial intelligence (AI) to the electrocardiogram (ECG) enables early detection of left ventricular dysfunction and can identify individuals at increased risk for its development in the future. Asymptomatic left ventricular dysfunction (ALVD) is characterised by the presence of a weak heart pump with a risk of overt heart failure. It is present in three to six percent of the general population and is associated with reduced quality of life and longevity. However, it is treatable when found. Currently, there is no inexpensive, noninvasive, painless screening tool for ALVD available for diagnostic use. When tested on an independent set of 52,870 patients, the network model yielded values for the area under the curve, sensitivity, specificity, and accuracy of 0.93, 86.3 percent, 85.7 percent, and 85.7 percent, respectively. Furthermore, in patients without ventricular dysfunction, those with a positive AI screen were at four times the risk of developing future ventricular dysfunction compared with those with a negative screen.

 

In recent years, the analysis of big data database combined with computer deep learning has gradually played an important role in biomedical technology. For a large number of medical record data analysis, image analysis, single nucleotide polymorphism difference analysis, etc., all relevant research on the development and application of artificial intelligence can be observed extensively. For clinical indication, patients may receive a variety of cardiovascular routine examination and treatments, such as: cardiac ultrasound, multi-path ECG, cardiovascular and peripheral angiography, intravascular ultrasound and optical coherence tomography, electrical physiology, etc. By using artificial intelligence deep learning system, the investigators hope to not only improve the diagnostic rate and also gain more accurately predict the patient’s recovery, improve medical quality in the near future.

 

The primary issue about using artificial intelligence in cardiology, or in any field of medicine for that matter, is the ethical issues that it brings about. Physicians and healthcare professionals prior to their practice swear to the Hippocratic Oath—a promise to do their best for the welfare and betterment of their patients. Many physicians have argued that the use of artificial intelligence in medicine breaks the Hippocratic Oath since patients are technically left under the care of machines than of doctors. Furthermore, as machines may also malfunction, the safety of patients is also on the line at all times. As such, while medical practitioners see the promise of artificial technology, they are also heavily constricted about its use, safety, and appropriateness in medical practice.

 

Issues and challenges faced by technological innovations in cardiology are overpowered by current researches aiming to make artificial intelligence easily accessible and available for all. With that in mind, various projects are currently under study. For example, the use of wearable AI technology aims to develop a mechanism by which patients and doctors could easily access and monitor cardiac activity remotely. An ideal instrument for monitoring, wearable AI technology ensures real-time updates, monitoring, and evaluation. Another direction of cardiology in AI technology is the use of technology to record and validate empirical data to further analyze symptomatology, biomarkers, and treatment effectiveness. With AI technology, researchers in cardiology are aiming to simplify and expand the scope of knowledge on the field for better patient care and treatment outcomes.

 

References:

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.bhf.org.uk/informationsupport/heart-matters-magazine/research/artificial-intelligence

 

https://www.medicaldevice-network.com/news/heart-attack-artificial-intelligence/

 

https://www.nature.com/articles/s41569-019-0158-5

 

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5711980/

 

www.j-pcs.org/article.asp

http://www.onlinejacc.org/content/71/23/2668

http://www.scielo.br/pdf/ijcs/v30n3/2359-4802-ijcs-30-03-0187.pdf

 

https://www.escardio.org/The-ESC/Press-Office/Press-releases/How-artificial-intelligence-is-tackling-heart-disease-Find-out-at-ICNC-2019

 

https://clinicaltrials.gov/ct2/show/NCT03877614

 

https://www.europeanpharmaceuticalreview.com/news/82870/artificial-intelligence-ai-heart-disease/

 

https://www.frontiersin.org/research-topics/10067/current-and-future-role-of-artificial-intelligence-in-cardiac-imaging

 

https://www.news-medical.net/health/Artificial-Intelligence-in-Cardiology.aspx

 

https://www.sciencedaily.com/releases/2019/05/190513104505.htm

 

Read Full Post »


2019 Trends in Cardiology

Reporter: Aviva Lev-Ari, PhD, RN

 

BLOG | DAVE FORNELL, DAIC EDITORDECEMBER 11, 2018

A 40,000 Foot View of Trends in Cardiology

A 40,000 Foot View of Trends in Cardiology

 

I was recently asked about my thoughts on the big picture, over arching trends effecting cardiology. Here is the outline I gave them.

 

Cardiology Cost Drivers

Reimbursements from Centers for Medicare and Medicaid Services (CMS) and insurance providers drive trends for the adoption of new technologies. However, new technologies that can show empirical evidence for being able to improve outcomes at lower costs are being moved up for better payments. CMS and other insurers are also using a carrot and stick approach with increased use of CMS bundled payments. These give a flat fee for diagnosing and treating a heart attack or heart failure, rather than hospitals being paid for all the tests and procedures they did. This approach makes the hospitals want to find new ways to be more cost effective to increase their bottom lines to capture more of the bundled payment as revenue.

 

Heart failure makes up about a third or more of the costs to Medicare. This has caused CMS to look closely at what is driving costs, and really high readmission rates are mainly to blame. There are penalties or no reimbursements for patients who come back for repeat treatments because they were not managed properly the first time. New technologies to address heart failure and other chronic diseases are of major interest to DAIC readers. Many of these include information technology (IT) solutions, rather than treatment device technologies.

 

Other conditions like atrial fibrillation (AF) also drive up costs, so vendors are attempting to find better ways to diagnose and treat this condition. Current treatments are only effective in the first attempt in about 60 percent of patients.

 

Consolidation of Hospitals and Outside Physicians

This is a continuing trend where single hospitals or smaller hospital systems are being bought up by bigger fish to create economy of scale with larger healthcare systems. These often cover specific geographic areas and often cast a wide net to include some luminary hospitals, smaller community hospitals, immediate care centers and minute clinics inside drug partner pharmacies. Duplicate staff and services are sometimes eliminated after mergers and consolidation. Outside physicians, including cardiologists and radiologists, are also being brought into the fold as employees of the health systems, rather than the old model as outside contractors who have access to the hospital’s amenities.

 

While there is fear about consolidation, it can also offer advantages in many cases. This includes faster access to the newest technologies and devices through the system’s luminary hospitals, which can train staff at other hospitals, and more complex cases can be referred to the larger hospital. Read about this in more detail in the article “Hospital Consolidation May Increase Access to TAVR, New Cardiac Technologies.”
Trends in Cardiovascular Technologies

Any techniques and technologies that can improve outcomes, cut costs, reduce hospital length of stay or prevent readmissions can capture hospital and cardiologist attention in today’s healthcare environment. There has been a massive movement over the past two decades away from traditional open heart or vascular surgical procedures to catheter-based interventional procedures. This includes improvements in the durability and complexity of percutaneous coronary intervention (PCI), reopening chronic total occlusions (CTOs)endovascular aortic repair (EVAR), expanded interest in treating peripheral artery disease (PAD), and structural heart cases that used to be the realm of the cardiac surgeon.

 

There is a major revolution and rapid uptake in transcatheter valve technologies to replace open heart surgery. Structural heart procedures to repair or replace failing heart valves have had positive clinical trial after positive trial over the last several years. Several key cardiac surgeons in the field say catheter based interventions will likely be the way of the future and surgical case volumes will see stead declines over the next decade.

 

The Role of Information Technology and AI in Cardiology

IT solutions are now increasingly being leveraged in more sophisticated ways since most hospitals have converted to integrated electronic medical records (EMRs) over the past decade. These allow all patient and departmental data to be accessible in one location. Analytics software is now being used to mine this data to identify workflow inefficiencies and areas to cut costs or improve charge capture. Clinical decision support (CDS) software to help hospitals and doctors better meet guideline-based care in all specialties is being introduced to help clinicians make better care decisions. This includes choosing appropriate tests and procedures in an effort to reduce costs or avoid tests that will not be reimbursed.

 

Artificial intelligence (AI) will be taking over many of the manual tasks for monitoring data and to answer questions more quickly. AI will also be used to alert administrators or doctors when it autonomously identifies a problem. Applications to watch also include AI to monitor population health in the background. This can identify patients at risk for various cardiovascular diseases before they present with any symptoms. The software also can identify patients who need extra care and counseling because of the high likelihood they will not be compliant with discharge orders and be readmitted. AI also will offer a second set of eyes on cardiac imaging to help identify anomalies or greatly reduce time by performing all the measurements automatically without human intervention.

 

This use of IT also includes patient portals to engage with patients and allow better access to their records and care. This is already starting to filter down to apps on smart phones to improve care, compliance with doctor’s orders and to aid diagnosis of conditions before they become problematic, such as heart failure and AF.

 

Cardiac Imaging Trends

Cardiac ultrasound (echo) remains the No.1 imaging modality in cardiology because of its broad availability, low cost and no radiation. However, computed tomography (CT) is poised to become the front-line imaging test for acute chest pain patients in the emergency department. It is also the gold standard for structural heart procedure planning, and the number of these cases is rapidly rising. CT fractional flow reserve (CT-FFR) technology is widely expected to become the main test for chest pain in the next decade, since it has the potential to save both time and money. CT-FFR also will become the primary gate-keeper to the cath lab to significantly lower, or possibly eliminate, the need for diagnostic catheter angiograms.

 

Cardiac MRI has seen numerous advances in recent years that cut imaging times by 50 percent and automate quantification, cutting the time to read and process these exams. MRI is expected to see and increase for cardiac exams in the coming years. MRI and CT-FFR may greatly reduce the number of nuclear exams, which are currently the gold standard for cardiac perfusion imaging.

Read Full Post »


Medical Applications of Nano Magnetite

Author: Danut Dragoi, PhD

Nano magnetite refers to small crystals of Fe3O4 in nano-metric range that preserves some specific magnetic properties of the magnetite bulk crystal such as the magnetism at saturation, Curie temperature, coercive magnetic force, hysteresis loop, etc. A discussion of medical applications of nano-magnetic particles is shown in here.

Opportunities for magnetite nanoparticles to be effectively incorporated into environmental contaminant removal and cell separation ([1] Honda et al., 1998;[2] Ebner et al.,1999; [3] Rikers et al., 1998; [4] Navratil, 2003), magnetically guided-drug delivery (Roger et al., 1999), magnetocytolysis ([5] Roger et al., 1999), sealing agents (liquid O-rings) ([6] Enzel et al., 1999), dampening and cooling mechanisms in loudspeakers ([6] Enzel et al., 1999), and contrasting agents for magnetic resonance imaging (MRI) ([7] Schütt, 2004). Advancement of synthesis and stabilization procedures towards production of uniformly sized, dispersed (potentially embedded) magnetite nanoparticles has clearly inspired creative imagination and application in various fields. The following subsections address two topics, magnetic guided drug delivery and magnetic resonance tomography which  helps us  better understanding the capabilities offered by magnetite nanoparticles.

Magnetically Guided Drug Delivery

Ferrofluids containing encapsulated (with biologically compatible surface chemistries) magnetite nanoparticles, as described above, can be employed for drug delivery to specific locations. Exploitation of superparamagnetic magnetization of magnetite nanoparticles allows for “magnetic dragging” of internal (present in bloodstream or elsewhere) magnetite nanoparticles carrying DNA, enzymes, drugs to target-areas. Similarly, biological effectors, which are proteins (containing DNA specific to target cells) incorporated into encapsulated nanoparticle surface functionality, allow for target cell specificity. Once biological effector carrying magnetic nanoparticles bind to target-cells, the applied magnetic field is fluctuated (approximately 1 MHz) causing magnetocytolysis, or cell destruction, which eliminates target-cells. Similarly, after being dragged to target areas, magnetocytolysis of encapsulated nanoparticles can release drugs. Research towards these ends is currently being heavily investigated as potential for novel drug/cancer treatment abounds. ([5] Roger et al., 1999). Picture below shows schematically drug-loaded magnetic nanoparticles targeting for tumor therapy in which the magnetic nanoparticles are noninvasively moved toward the target.

Drug loaded NanoParicles

Image SOURCE:https://books.google.com/books?hl=en&lr=&id=oX32CwAAQBAJ&oi=fnd&pg=PA425&ots=1EDRtu7mDx&sig=fYjckTZEyXCkOBb4sjRAuWSR_U4#v=onepage&q&f=false

Magnetic Resonance Tomography

Magnetic Resonance Tomography (MRT) permits noninvasive visualization of cross-sectional images of the human body, tissues, and organs ([7] Schütt, 2004). The MRT technique provides better tissue resolution than traditional radiation based technologies; with addition of contrasting agents, this resolution can be further enhanced ([8] Shao et al., 2005). Magnetite nanoparticles (in ferrofluid form) are powerful contrasting agents due to their paramagnetic magnetization. Ferrofluid physico-morphosis under magnetic field Blaney 65 Human bloodstreams readily reject the nanoparticle colloidal solution, which quickly passes into the liver ([8] Shao et al., 2005). Consequently, ferrofluids have thus far only been useful in distinguishing between healthy and malignant liver cells. This limitation can be overcome through functionalization of magnetite nanoparticles with various ligands that allows for organ-specific transport; therefore, MRT imaging of various bodily organs can be possible. Furthermore, polymeric (i.e., polyethylene oxide – PEO) coating of functionalized magnetite particles permits ferrofluids longer bloodstream retention. ([7] Schütt, 2004) PEO coatings are applied through magnetite interaction with copolymer PEO-polypeptide; polypeptides interact with the positively charged magnetite surface and provide nanoparticle masking to allow longer bloodstream residence. These coated magnetite nanoparticles could also be employed as extremely efficient capsules for drug delivery systems, which are discussed by ([7] Schütt, 2004).

References

[1] Honda H, Kawabe A, Shinkai M, and Kobayashi T (1998). Development of chitosan-conjugated magnetite for magnetic cell separation. Journal of Fermentation and Bioengineering 86, 191-196

[2] Ebner AD, Ritter JA, Ploehn HJ, Kochen RL, and Navratil JD (1999). New magnetic field-enhanced process for the treatment of aqueous wastes. Separation Science and Technology 34, 1277-1300

[3] Rikers RA, Voncken JHL, and Dalmijn WL (1998). Cr-polluted soil studied by high gradient magnetic separation and electron probe. Journal of Environmental Engineering 124, 1159-1164

[4] Navratil JD (2003). Adsorption and nanoscale magnetic separation of heavy metals from water. U.S. EPA workshop on managing arsenic risks to the environment: characterization of waste, chemistry, and treatment and disposal. Denver, CO

[5] Roger J, Pons JN, Massart R, Halbreich A, and Bacri JC (1999). Some biomedical applications of ferro fluids. Eur. Phys. J. AP 5, 321-325

[6] Enzel P, Adelman N, Beckman KJ, Campbell DJ, Ellis AB, Lisensky GC (1999). Preparation of an aqueous-based ferrofluid. J. Chem. Educ. 76, 943-948

[7] Schütt D (2004). Magnetite colloids for drug delivery and magnetic resonance imaging. Institute Angewandte Polymerforschung: thesis Selim MS, Cunningham LP, Srivastava R, Olson JM (1997). Preparation of nano-size magnetic gamma ferric oxide (γ-Fe2O3) and magnetite (Fe3O4) particles for toner and color imaging applications. Recent Progress in Toner Technologies, 108- 111

[8] Shao H, Lee H, Huang Y, Kwak BK, and Kim CO (2005). Synthesis of nano-size magnetite coated with chitosan for MRI contrast agent by sonochemistry. Magnetics Conference, 2005. INTERMAG Asia 2005. Digests of the IEEE International, 461-462

https://books.google.com/books?hl=en&lr=&id=oX32CwAAQBAJ&oi=fnd&pg=PA425&ots=1EDRtu7mDx&sig=fYjckTZEyXCkOBb4sjRAuWSR_U4#v=onepage&q&f=false

 

Read Full Post »


Schizophrenia, broken-links

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

Runs in the Family

 New findings about schizophrenia rekindle old questions about genes and identity.
BY Annals of Science MARCH 28, 2016 ISSUE      http://www.newyorker.com/magazine/2016/03/28/the-genetics-of-schizophrenia

http://www.newyorker.com/wp-content/uploads/2016/03/160328_r27877-690.jpg

The author and his father have seen several relatives succumb to mental illness.CREDIT PHOTOGRAPH BY DAYANITA SINGH FOR THE NEW YORKER

In the winter of 2012, I travelled from New Delhi, where I grew up, to Calcutta to visit my cousin Moni. My father accompanied me as a guide and companion, but he was a sullen and brooding presence, lost in a private anguish. He is the youngest of five brothers, and Moni is his firstborn nephew—the eldest brother’s son. Since 2004, Moni, now fifty-two, has been confined to an institution for the mentally ill (a “lunatic home,” as my father calls it), with a diagnosis of schizophrenia. He is kept awash in antipsychotics and sedatives, and an attendant watches, bathes, and feeds him through the day.

My father has never accepted Moni’s diagnosis. Over the years, he has waged a lonely campaign against the psychiatrists charged with his nephew’s care, hoping to convince them that their diagnosis was a colossal error, or that Moni’s broken psyche would somehow mend itself. He has visited the institution in Calcutta twice—once without warning, hoping to see a transformed Moni, living a secretly normal life behind the barred gates. But there was more than just avuncular love at stake for him in these visits. Moni is not the only member of the family with mental illness. Two of my father’s four brothers suffered from various unravellings of the mind. Madness has been among the Mukherjees for generations, and at least part of my father’s reluctance to accept Moni’s diagnosis lies in a grim suspicion that something of the illness may be buried, like toxic waste, in himself.

Rajesh, my father’s third-born brother, had once been the most promising of the Mukherjee boys—the nimblest, the most charismatic, the most admired. But in the summer of 1946, at the age of twenty-two, he began to behave oddly, as if a wire had been tripped in his brain. The most obvious change in his personality was a volatility: good news triggered uncontained outbursts of joy; bad news plunged him into inconsolable desolation. By that winter, the sine curve of Rajesh’s psyche had tightened in its frequency and gained in its amplitude. My father recalls an altered brother: fearful at times, reckless at others, descending and ascending steep slopes of mood, irritable one morning and overjoyed the next. When Rajesh received news of a successful performance on his college exams, he vanished, elated, on a two-night excursion, supposedly “exercising” at a wrestling camp. He was feverish and hallucinating when he returned, and died of pneumonia soon afterward. Only years later, in medical school, did I realize that Rajesh was likely in the throes of an acute manic phase. His mental breakdown was the result of a near-textbook case of bipolar disorder.

Jagu, the fourth-born of my father’s siblings, came to live with us in Delhi in 1975, when I was five years old and he was forty-five. His mind, too, was failing. Tall and rail thin, with a slightly feral look in his eyes and a shock of matted, overgrown hair, he resembled a Bengali Jim Morrison. Unlike Rajesh, whose illness had surfaced in his twenties, Jagu had been troubled from his adolescence. Socially awkward, withdrawn from everyone except my grandmother, he was unable to hold a job or live by himself. By 1975, he had visions, phantasms, and voices in his head that told him what to do. He was still capable of extraordinary bursts of tenderness—when I accidentally smashed a beloved Venetian vase at home, he hid me in his bedclothes and informed my mother that he had “mounds of cash” stashed away, enough to buy “a thousand” replacement vases. But this episode was symptomatic: even his love for me extended the fabric of his psychosis and confabulation.

Unlike Rajesh, Jagu was formally diagnosed. In the late nineteen-seventies, a physician in Delhi examined him and determined that he had schizophrenia. But no medicines were prescribed. Instead, Jagu continued to live at home, half hidden away in my grandmother’s room. (As in many families in India, my grandmother lived with us.) For nearly a decade, she and my father maintained a fragile truce, with Jagu living under her care, eating meals in her room and wearing clothes that she stitched for him. At night, when Jagu was consumed by his fears and fantasies, she put him to bed like a child, with her hand on his forehead. She was his nurse, his housekeeper, his only friend, and, more important, his public defender. When my grandmother died, in 1985, Jagu joined a religious sect in Delhi and disappeared, until his death, a dozen years later.

……

at schizophrenia runs in families was evident even to the person who first defined the illness. In 1911, Eugen Bleuler, a Swiss-German psychiatrist, published a book describing a series of cases of men and women, typically in their teens and early twenties, whose thoughts had begun to tangle and degenerate. “In this malady, the associations lose their continuity,” Bleuler wrote. “The threads between thoughts are torn.” Psychotic visions and paranoid thoughts flashed out of nowhere. Some patients “feel themselves weak, their spirit escapes, they will never survive the day. There is a growth in their heads. Their bones have turned liquid; their hearts have turned into stone. . . . The patient’s wife must not use eggs in cooking, otherwise he will grow feathers.” His patients were often trapped between flickering emotional states, unable to choose between two radically opposed visions, Bleuler noted. “You devil, you angel, you devil, you angel,” one woman said to her lover.

Bleuler tried to find an explanation for the mysterious symptoms, but there was only one seemingly common element: schizophrenic patients tended to have first-degree relatives who were also schizophrenic. He had no tools to understand the mechanism behind the heredity. The word “gene” had been coined just two years before Bleuler published his book. The notion that a mental illness could be carried across generations by unitary, indivisible factors—corpuscles of information threading through families—would have struck most of Bleuler’s contemporaries as mad in its own right. Still, Bleuler was astonishingly prescient about the complex nature of inheritance. “If one is looking for ‘theheredity,’ one can nearly always find it,” he wrote. “We will not be able to do anything about it even later on, unless the single factor of heredity can be broken down into many hereditary factors along specific lines.”

In the nineteen-sixties, Bleuler’s hunch was confirmed by twin studies. Psychiatrists determined that if an identical twin was schizophrenic the other twin had a forty-to-fifty-per-cent chance of developing the disease—fiftyfold higher than the risk in the general population. By the early two-thousands, large population studies had revealed a strong genetic link between schizophrenia and bipolar disorder. Some of the families described in these studies had a crisscrossing history that was achingly similar to my own: one sibling affected with schizophrenia, another with bipolar disorder, and a nephew or niece also schizophrenic.

“The twin studies clarified two important features of schizophrenia and bipolar disorder,” Jeffrey Lieberman, a Columbia University psychiatrist who has studied schizophrenia for thirty years, told me. “First, it was clear that there wasn’t a single gene, but dozens of genes involved in causing schizophrenia—each perhaps exerting a small effect. And, second, even if you inherited the entire set of risk genes, as identical twins do, you still might not develop the disease. Obviously, there were other triggers or instigators involved in releasing the illness.” But while these studies established that schizophrenia had a genetic basis, they revealed nothing about the nature of the genes involved. “For doctors, patients, and families in the schizophrenia community, genetics became the ultimate mystery,” Lieberman said. “If we knew the identity of the genes, we would find the causes, and if we found the causes we could find medicines.”

In 2006, an international consortium of psychiatric geneticists launched a genomic survey of schizophrenia, hoping to advance the search for the implicated genes. With 3,322 patients and 3,587 controls, this was one of the largest and most rigorous such studies in the history of the disease. Researchers scanned through the nearly seven thousand genomes to find variations in gene segments that were correlated with schizophrenia. This strategy, termed an “association study,” does not pinpoint a gene, but it provides a general location where a disease-linked gene may be found, like a treasure map with a large “X” scratched in a corner of the genome.

The results, reported in 2009 (and updated in 2014) in the journal Nature, were a dispiriting validation of Bleuler’s hunch about multiple hereditary factors: more than a hundred independent segments of the genome were associated with schizophrenia. “There are lots of small, common genetic effects, scattered across the genome,” one researcher said. “There are many different biological processes involved.” Some of the putative culprits made biological sense—if dimly. There were genes linked to transmitters that relay messages between neurons, and genes for molecular channels that move electrical signals up and down nerve cells. But by far the most surprising association involved a gene segment on chromosome 6. This region of the genome—termed the MHC region—carries hundreds of genes typically associated with the immune system.

“The MHC-segment finding was so strange and striking that you had to sit up and take notice,” Lieberman told me. “Here was the most definitive evidence that something in the immune system might have something to do with schizophrenia. There had been hints about an immunological association before, but this was impossible to argue with. It raised an endlessly fascinating question: what was the link between immune-response genes and schizophrenia?”

The Rogue Immune Cells That Wreck the Brain

Beth Stevens thinks she has solved a mystery behind brain disorders such as Alzheimer’s and schizophrenia.

by Adam Piore   April 4, 2016            https://www.technologyreview.com/s/601137/the-rogue-immune-cells-that-wreck-the-brain/

In the first years of her career in brain research, Beth Stevens thought of microglia with annoyance if she thought of them at all. When she gazed into a microscope and saw these ubiquitous cells with their spidery tentacles, she did what most neuroscientists had been doing for generations: she looked right past them and focused on the rest of the brain tissue, just as you might look through specks of dirt on a windshield.

“What are they doing there?” she thought. “They’re in the way.’”

Stevens never would have guessed that just a few years later, she would be running a laboratory at Harvard and Boston’s Children’s Hospital devoted to the study of these obscure little clumps. Or that she would be arguing in the world’s top scientific journals that microglia might hold the key to understanding not just normal brain development but also what causes Alzheimer’s, Huntington’s, autism, schizophrenia, and other intractable brain disorders.

Microglia are part of a larger class of cells—known collectively as glia—that carry out an array of functions in the brain, guiding its development and serving as its immune system by gobbling up diseased or damaged cells and carting away debris. Along with her frequent collaborator and mentor, Stanford biologist Ben Barres, and a growing cadre of other scientists, Stevens, 45, is showing that these long-overlooked cells are more than mere support workers for the neurons they surround. Her work has raised a provocative suggestion: that brain disorders could somehow be triggered by our own bodily defenses gone bad.

A type of glial cell known as an oligodendrocyte

In one groundbreaking paper, in January, Stevens and researchers at the Broad Institute of MIT and Harvard showed that aberrant microglia might play a role in schizophrenia—causing or at least contributing to the massive cell loss that can leave people with devastating cognitive defects. Crucially, the researchers pointed to a chemical pathway that might be targeted to slow or stop the disease. Last week, Stevens and other researchers published a similar finding for Alzheimer’s.

This might be just the beginning. Stevens is also exploring the connection between these tiny structures and other neurological diseases—work that earned her a $625,000 MacArthur Foundation “genius” grant last September.

All of this raises intriguing questions. Is it possible that many common brain disorders, despite their wide-ranging symptoms, are caused or at least worsened by the same culprit, a component of the immune system? If so, could many of these disorders be treated in a similar way—by stopping these rogue cells?

Nature. 2016 Feb 11;530(7589):177-83. http://dx.doi.org:/10.1038/nature16549. Epub 2016 Jan 27.   Schizophrenia risk from complex variation of complement component 4.

Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.

 

Science  31 Mar 2016;        http://dx.doi.org:/10.1126/science.aad8373      Complement and microglia mediate early synapse loss in Alzheimer mouse models.
Soyon Hong1Victoria F. Beja-Glasser1,*Bianca M. Nfonoyim1,*,…., Ben A. Barres6Cynthia A. Lemere,2Dennis J. Selkoe2,7Beth Stevens1,8,

 Synapse loss in Alzheimer’s disease (AD) correlates with cognitive decline. Involvement of microglia and complement in AD has been attributed to neuroinflammation, prominent late in disease. Here we show in mouse models that complement and microglia mediate synaptic loss early in AD. C1q, the initiating protein of the classical complement cascade, is increased and associated with synapses before overt plaque deposition. Inhibition of C1q, C3 or the microglial complement receptor CR3, reduces the number of phagocytic microglia as well as the extent of early synapse loss. C1q is necessary for the toxic effects of soluble β-amyloid (Aβ) oligomers on synapses and hippocampal long-term potentiation (LTP). Finally, microglia in adult brains engulf synaptic material in a CR3-dependent process when exposed to soluble Aβ oligomers. Together, these findings suggest that the complement-dependent pathway and microglia that prune excess synapses in development are inappropriately activated and mediate synapse loss in AD.
Genome-wide association studies (GWAS) implicate microglia and complement-related pathways in AD (1). Previous research has demonstrated both beneficial and detrimental roles of complement and microglia in plaque-related neuropathology (2, 3); however, their roles in synapse loss, a major pathological correlate of cognitive decline in AD (4), remain to be identified. Emerging research implicates microglia and immune-related mechanisms in brain wiring in the healthy brain (1). During development, C1q and C3 localize to synapses and mediate synapse elimination by phagocytic microglia (57). We hypothesized that this normal developmental synaptic pruning pathway is activated early in the AD brain and mediates synapse loss.

 

Complex machinery

It’s not surprising that scientists for years have ignored microglia and other glial cells in favor of neurons. Neurons that fire together allow us to think, breathe, and move. We see, hear, and feel using neurons, and we form memories and associations when the connections between different neurons strengthen at the junctions between them, known as synapses. Many neuroscientists argue that neurons create our very consciousness.

Glia, on the other hand, have always been considered less important and interesting. They have pedestrian duties such as supplying nutrients and oxygen to neurons, as well as mopping up stray chemicals and carting away the garbage.

Scientists have known about glia for some time. In the 1800s, the pathologist Rudolf Virchow noted the presence of small round cells packing the spaces between neurons and named them “nervenkitt” or “neuroglia,” which can be translated as nerve putty or glue. One variety of these cells, known as astrocytes, was defined in 1893. And then in the 1920s, the Spanish scientist Pio del Río Hortega developed novel ways of staining cells taken from the brain. This led him to identify and name two more types of glial cells, including microglia, which are far smaller than the others and are characterized by their spidery shape and multiple branches. It is only when the brain is damaged in adulthood, he suggested, that microglia spring to life—rushing to the injury, where it was thought they helped clean up the area by eating damaged and dead cells. Astrocytes often appeared on the scene as well; it was thought that they created scar tissue.

This emergency convergence of microglia and astrocytes was dubbed “gliosis,” and by the time Ben Barres entered medical school in the late 1970s, it was well established as a hallmark of neurodegenerative diseases, infection, and a wide array of other medical conditions. But no one seemed to understand why it occurred. That intrigued Barres, then a neurologist in training, who saw it every time he looked under a microscope at neural tissue in distress. “It was just really fascinating,” he says. “The great mystery was: what is the point of this gliosis? Is it good? Is it bad? Is it driving the disease process, or is it trying to repair the injured brain?”

 https://youtu.be/6DOYTpXkLOY

Barres began looking for the answer. He learned how to grow glial cells in a dish and apply a new recording technique to them. He could measure their electrical qualities, which determine the biochemical signaling that all brain cells use to communicate and coördinate activity.

“From the second I started recording the glial cells, I thought ‘Oh, my God!’” Barres recalls. The electrical activity was more dynamic and complex than anyone had thought. These strange electrical properties could be explained only if the glial cells were attuned to the conditions around them, and to the signals released from nearby neurons. Barres’s glial cells, in other words, had all the machinery necessary to engage in a complex dialogue with neurons, and presumably to respond to different kinds of conditions in the brain.

Why would they need this machinery, though, if they were simply involved in cleaning up dead cells? What could they possibly be doing? It turns out that in the absence of chemicals released by glia, the neurons committed the biochemical version of suicide. Barres also showed that the astrocytes appeared to play a crucial role in forming synapses, the microscopic connections between neurons that encode memory. In isolation, neurons were capable of forming the spiny appendages necessary to reach the synapses. But without astrocytes, they were incapable of connecting to one another.

Hardly anyone believed him. When he was a young faculty member at Stanford in the 1990s, one of his grant applications to the National Institutes of Health was rejected seven times. “Reviewers kept saying, ‘Nah, there’s no way glia could be doing this,’” Barres recalls. “And even after we published two papers in Science showing that [astrocytes] had profound, almost all-or-nothing effects in controlling synapses’ formation or synapse activity, I still couldn’t get funded! I think it’s still hard to get people to think about glia as doing anything active in the nervous system.”

Marked for elimination

Beth Stevens came to study glia by accident. After graduating from Northeastern University in 1993, she followed her future husband to Washington, D.C., where he had gotten work in the U.S. Senate. Stevens had been pre-med in college and hoped to work in a lab at the National Institutes of Health. But with no previous research experience, she was soundly rebuffed. So she took a job waiting tables at a Chili’s restaurant in nearby Rockville, Maryland, and showed up at NIH with her résumé every week.

After a few months, Stevens received a call from a researcher named Doug Fields, who needed help in his lab. Fields was studying the intricacies of the process in which neurons become insulated in a coating called myelin. That insulation is essential for the transmission of electrical impulses.

As Stevens spent the following years pursuing a PhD at the University of Maryland, she was intrigued by the role that glial cells played in insulating neurons. Along the way, she became familiar with other insights into glial cells that were beginning to emerge, especially from the lab of Ben Barres. Which is why, soon after completing her PhD in 2003, Stevens found herself a postdoc in Barres’s lab at Stanford, about to make a crucial discovery.

Barres’s group had begun to identify the specific compounds astrocytes secreted that seemed to cause neurons to grow synapses. And eventually, they noticed that these compounds also stimulated production of a protein called C1q.

Conventional wisdom held that C1q was activated only in sick cells—the protein marked them to be eaten up by immune cells—and only outside the brain. But Barres had found it in the brain. And it was in healthy neurons that were arguably at their most robust stage: in early development. What was the C1q protein doing there?

https://d267cvn3rvuq91.cloudfront.net/i/images/glia33.jpg?sw=590&cx=0&cy=0&cw=2106&ch=2106

A stained astrocyte.

The answer lies in the fact that marking cells for elimination is not something that happens only in diseased brains; it is also essential for development. As brains develop, their neurons form far more synaptic connections than they will eventually need. Only the ones that are used are allowed to remain. This pruning allows for the most efficient flow of neural transmissions in the brain, removing noise that might muddy the signal.

But it was unknown how exactly the process worked. Was it possible that C1q helped signal the brain to prune unused synapses? Stevens focused her postdoctoral research on finding out. “We could have been completely wrong,” she recalls. “But we went for it.”

It paid off. In a 2007 paper, Barres and Stevens showed that C1q indeed plays a role in eliminating unneeded neurons in the developing brain. And they found that the protein is virtually absent in healthy adult neurons.

Now the scientists faced a new puzzle. Does C1q show up in brain diseases because the same mechanism involved in pruning a developing brain later goes awry? Indeed, evidence was already growing that one of the earliest events in neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and Huntington’s was significant loss of synapses.

When Stevens and Barres examined mice bred to develop glaucoma, a neurodegenerative disease that kills neurons in the optic system, they found that C1q appeared long before any other detectable sign that the disease was taking hold. It cropped up even before the cells started dying.

This suggested the immune cells might in fact cause the disease, or at the very least accelerate it. And that offered an intriguing possibility: that something could be made to halt the process. Barres founded a company, Annexon Biosciences, to develop drugs that could block C1q. Last week’s paper published by Barres, Stevens, and other researchers shows that a compound being tested by Annexon appears to be able to prevent the onset of Alzheimer’s in mice bred to develop the disease. Now the company hopes to test it in humans in the next two years.

Paths to treatments

To better understand the process that C1q helps trigger, Stevens and Barres wanted to figure out what actually plays the role of Pac-Man, eating up the synapses marked for death. It was well known that white blood cells known as macrophages gobbled up diseased cells and foreign invaders in the rest of the body. But macrophages are not usually present in the brain. For their theory to work, there had to be some other mechanism. And further research has shown that the cells doing the eating even in healthy brains are those mysterious clusters of material that Beth Stevens, for years, had been gazing right past in the microscope—the microglia that Río Hortega identified almost 100 years ago.

Now Stevens’s lab at Harvard, which she opened in 2008, devotes half its efforts to figuring out what microglia are doing and what causes them to do it. These cells, it turns out, appear in the mouse embryo at day eight, before any other brain cell, which suggests they might help guide the rest of brain development—and could contribute to any number of neurodevelopmental diseases when they go wrong.

Meanwhile, she is also expanding her study of the way different substances determine what happens in the brain. C1q is actually just the first in a series of proteins that accumulate on synapses marked for elimination. Stevens has begun to uncover evidence that there is a wide array of protective “don’t eat me” molecules too. It’s the balance between all these cues that regulates whether microglia are summoned to destroy synapses. Problems in any one could, conceivably, mess up the system.

Evidence is now growing that microglia are involved in several neurodevelopmental and psychiatric problems. The potential link to schizophrenia that was revealed in January emerged after researchers at the Broad Institute, led by Steven McCarroll and a graduate student named Aswin Sekar, followed a trail of genetic clues that led them directly to Stevens’s work. In 2009, three consortia from around the globe had published papers comparing DNA in people with and without schizophrenia. It was Sekar who identified a possible pattern: the more a specific type of protein was present in synapses, the higher the risk of developing the disease. The protein, C4, was closely related to C1q, the one first identified in the brain by Stevens and Barres.

McCarroll knew that schizophrenia strikes in late adolescence and early adulthood, a time when brain circuits in the prefrontal cortex undergo extensive pruning. Others had found that areas of the prefrontal cortex are among those most ravaged by the disease, which leads to massive synapse loss. Could it be that over-pruning by rogue microglia is part of what causes schizophrenia?

To find out, Sekar and McCarroll got in touch with Stevens, and the two labs began to hold joint weekly meetings. They soon demonstrated that C4 also had a role in pruning synapses in the brains of young mice, suggesting that excessive levels of the protein could indeed lead to over-pruning—and to the thinning out of brain tissue that appears to occur as symptoms such as psychotic episodes grow worse.

If the brain damage seen in Parkinson’s and Alzheimer’s stems from over-pruning that might begin early in life, why don’t symptoms of those diseases show up until later? Barres thinks he knows. He notes that the brain can normally compensate for injury by rewiring itself and generating new synapses. It also contains a lot of redundancy. That would explain why patients with Parkinson’s disease don’t show discernible symptoms until they have lost 90 percent of the neurons that produce dopamine.

It also might mean that subtle symptoms could in fact be detected much earlier. Barres points to a study of nuns published in 2000. When researchers analyzed essays the nuns had written upon entering their convents decades before, they found that women who went on to develop Alzheimer’s had shown less “idea density” even in their 20s. “I think the implication of that is they could be lifelong diseases,” Barres says. “The disease process could be going on for decades and the brain is just compensating, rewiring, making new synapses.” At some point, the microglia are triggered to remove too many cells, Barres argues, and the symptoms of the disease begin to manifest fully.

Turning this insight into a treatment is far from straightforward, because much remains unclear. Perhaps an overly aggressive response from microglia is determined by some combination of genetic variants not shared by everyone. Stevens also notes that diseases like schizophrenia are not caused by one mutation; rather, a wide array of mutations with small effects cause problems when they act in concert. The genes that control the production of C4 and other immune-system proteins may be only part of the story. That may explain why not everyone who has a C4 mutation will go on to develop schizophrenia.

Nonetheless, if Barres and Stevens are right that the immune system is a common mechanism behind devastating brain disorders, that in itself is a fundamental breakthrough. Because we have not known the mechanisms that trigger such diseases, medical researchers have been able only to alleviate the symptoms rather than attack the causes. There are no drugs available to halt or even slow neurodegeneration in diseases like Alzheimer’s. Some drugs elevate neurotransmitters in ways that briefly make it easier for individuals with dementia to form new synaptic connections, but they don’t reduce the rate at which existing synapses are destroyed. Similarly, there are no treatments that tackle the causes of autism or schizophrenia. Even slowing the progress of these disorders would be a major advance. We might finally go after diseases that have run unchecked for generations.

“We’re a ways away from a cure,” Stevens says. “But we definitely have a path forward.”

Adam Piore is a freelance writer who wrote “A Shocking Way to Fix the Brain”  in November/December 2015.

 

Int Immunopharmacol. 2001 Mar;1(3):365-92.

Genetic, structural and functional diversities of human complement components C4A and C4B and their mouse homologues, Slp and C4.

Blanchong CA1Chung EKRupert KLYang YYang ZZhou BMoulds JMYu CY.

Author information

Abstract

The complement protein C4 is a non-enzymatic component of the C3 and C5 convertases and thus essential for the propagation of the classical complement pathway. The covalent binding of C4 to immunoglobulins and immune complexes (IC) also enhances the solubilization of immune aggregates, and the clearance of IC through complement receptor one (CR1) on erythrocytes. Human C4 is the most polymorphic protein of the complement system. In this review, we summarize the current concepts on the 1-2-3 loci model of C4A and C4B genes in the population, factors affecting the expression levels of C4 transcripts and proteins, and the structural, functional and serological diversities of the C4A and C4B proteins. The diversities and polymorphisms of the mouse homologues Slp and C4 proteins are described and contrasted with their human homologues. The human C4 genes are located in the MHC class III region on chromosome 6. Each human C4 gene consists of 41 exons coding for a 5.4-kb transcript. The long gene is 20.6 kb and the short gene is 14.2 kb. In the Caucasian population 55% of the MHC haplotypes have the 2-locus, C4A-C4B configurations and 45% have an unequal number of C4A and C4B genes. Moreover, three-quarters of C4 genes harbor the 6.4 kb endogenous retrovirus HERV-K(C4) in the intron 9 of the long genes. Duplication of a C4 gene always concurs with its adjacent genes RP, CYP21 and TNX, which together form a genetic unit termed an RCCX module. Monomodular, bimodular and trimodular RCCX structures with 1, 2 and 3 complement C4 genes have frequencies of 17%, 69% and 14%, respectively. Partial deficiencies of C4A and C4B, primarily due to the presence of monomodular haplotypes and homo-expression of C4A proteins from bimodular structures, have a combined frequency of 31.6%. Multiple structural isoforms of each C4A and C4B allotype exist in the circulation because of the imperfect and incomplete proteolytic processing of the precursor protein to form the beta-alpha-gamma structures. Immunofixation experiments of C4A and C4B demonstrate > 41 allotypes in the two classes of proteins. A compilation of polymorphic sites from limited C4 sequences revealed the presence of 24 polymophic residues, mostly clustered C-terminal to the thioester bond within the C4d region of the alpha-chain. The covalent binding affinities of the thioester carbonyl group of C4A and C4B appear to be modulated by four isotypic residues at positions 1101, 1102, 1105 and 1106. Site directed mutagenesis experiments revealed that D1106 is responsible for the effective binding of C4A to form amide bonds with immune aggregates or protein antigens, and H1106 of C4B catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens. The expression of C4 is inducible or enhanced by gamma-interferon. The liver is the main organ that synthesizes and secretes C4A and C4B to the circulation but there are many extra-hepatic sites producing moderate quantities of C4 for local defense. The plasma protein levels of C4A and C4B are mainly determined by the corresponding gene dosage. However, C4B proteins encoded by monomodular short genes may have relatively higher concentrations than those from long C4A genes. The 5′ regulatory sequence of a C4 gene contains a Spl site, three E-boxes but no TATA box. The sequences beyond–1524 nt may be completely different as the C4 genes at RCCX module I have RPI-specific sequences, while those at Modules II, III and IV have TNXA-specific sequences. The remarkable genetic diversity of human C4A and C4B probably promotes the exchange of genetic information to create and maintain the quantitative and qualitative variations of C4A and C4B proteins in the population, as driven by the selection pressure against a great variety of microbes. An undesirable accompanying byproduct of this phenomenon is the inherent deleterious recombinations among the RCCX constituents leading to autoimmune and genetic disorders.

 

C4A isotype is responsible for effective binding to form amide bonds with immune aggregates or protein antigens, while C4B isotype catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens.

Derived from proteolytic degradation of complement C4, C4a anaphylatoxin is a mediator of local inflammatory process.

 

Schizophrenia and the Synapse

Genetic evidence suggests that overactive synaptic pruning drives development of schizophrenia.

By Ruth Williams | January 27, 2016

http://www.the-scientist.com/?articles.view/articleNo/45189/title/Schizophrenia-and-the-Synapse/

Compared to the brains of healthy individuals, those of people with schizophrenia have higher expression of a gene called C4, according to a paper published inNature today (January 27). The gene encodes an immune protein that moonlights in the brain as an eradicator of unwanted neural connections (synapses). The findings, which suggest increased synaptic pruning is a feature of the disease, are a direct extension of genome-wide association studies (GWASs) that pointed to the major histocompatibility (MHC) locus as a key region associated with schizophrenia risk.

“The MHC [locus] is the first and the strongest genetic association for schizophrenia, but many people have said this finding is not useful,” said psychiatric geneticist Patrick Sullivan of the University of North Carolina School of Medicine who was not involved in the study. “The value of [the present study is] to show that not only is it useful, but it opens up new and extremely interesting ideas about the biology and therapeutics of schizophrenia.”

Schizophrenia has a strong genetic component—it runs in families—yet, because of the complex nature of the condition, no specific genes or mutations have been identified. The pathological processes driving the disease remain a mystery.

Researchers have turned to GWASs in the hope of finding specific genetic variations associated with schizophrenia, but even these have not provided clear candidates.

“There are some instances where genome-wide association will literally hit one base [in the DNA],” explained Sullivan. While a 2014 schizophrenia GWAS highlighted the MHC locus on chromosome 6 as a strong risk area, the association spanned hundreds of possible genes and did not reveal specific nucleotide changes. In short, any hope of pinpointing the MHC association was going to be “really challenging,” said geneticist Steve McCarroll of Harvard who led the new study.

Nevertheless, McCarroll and colleagues zeroed in on the particular region of the MHC with the highest GWAS score—the C4 gene—and set about examining how the area’s structural architecture varied in patients and healthy people.

The C4 gene can exist in multiple copies (from one to four) on each copy of chromosome 6, and has four different forms: C4A-short, C4B-short, C4A-long, and C4B-long. The researchers first examined the “structural alleles” of the C4 locus—that is, the combinations and copy numbers of the different C4 forms—in healthy individuals. They then examined how these structural alleles related to expression of both C4Aand C4B messenger RNAs (mRNAs) in postmortem brain tissues.

…………..

Schizophrenia risk from complex variation of complement component 4

Aswin Sekar, Allison R. Bialas, Heather de Rivera, …, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Mark J. Daly, Michael C. Carroll, Beth Stevens & Steven A. McCarroll

Nature (11 Feb 2016); 530: 177–183 http://dx.doi.org:/10.1038/nature16549

Schizophrenia is a heritable brain illness with unknown pathogenic mechanisms. Schizophrenia’s strongest genetic association at a population level involves variation in the major histocompatibility complex (MHC) locus, but the genes and molecular mechanisms accounting for this have been challenging to identify. Here we show that this association arises in part from many structurally diverse alleles of the complement component 4 (C4) genes. We found that these alleles generated widely varying levels of C4A and C4B expression in the brain, with each common C4 allele associating with schizophrenia in proportion to its tendency to generate greater expression of C4A. Human C4 protein localized to neuronal synapses, dendrites, axons, and cell bodies. In mice, C4 mediated synapse elimination during postnatal development. These results implicate excessive complement activity in the development of schizophrenia and may help explain the reduced numbers of synapses in the brains of individuals with schizophrenia.

  1. Cannon, T. D. et al. Cortex mapping reveals regionally specific patterns of genetic and disease-specific gray-matter deficits in twins discordant for schizophrenia. Proc. Natl Acad. Sci. USA 99, 3228–3233 (2002)
  1. Cannon, T. D. et al. Progressive reduction in cortical thickness as psychosis develops: a multisite longitudinal neuroimaging study of youth at elevated clinical risk. Biol. Psychiatry 77,147–157 (2015)
  1. Garey, L. J. et al. Reduced dendritic spine density on cerebral cortical pyramidal neurons in schizophrenia. J. Neurol. Neurosurg. Psychiatry 65, 446–453 (1998)
  1. Glantz, L. A. & Lewis, D. A. Decreased dendritic spine density on prefrontal cortical pyramidal neurons in schizophrenia. Arch. Gen. Psychiatry 57, 65–73 (2000)
  1. Glausier, J. R. & Lewis, D. A. Dendritic spine pathology in schizophrenia. Neuroscience 251,90–107 (2013)
  1. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014)
  1. Shi, J. et al. Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460, 753–757 (2009)
  1. Stefansson, H. et al. Common variants conferring risk of schizophrenia. Nature 460,744–747 (2009)
  1. International Schizophrenia Consortium et al. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748–752 (2009)
  1. Schizophrenia Psychiatric Genome-Wide Association Study Consortium. Genome-wide association study identifies five new schizophrenia loci. Nature Genet . 43, 969–976 (2011)

 

The strongest genetic association found in schizophrenia is its association to genetic markers across the major histocompatibility complex (MHC) locus, first described in three Nature papers in 2009. …

 

Schizophrenia: From genetics to physiology at last

Ryan S. DhindsaDavid B. Goldstein
Nature  (11 Feb 2016); 530:162–163   http://dx.doi.org:/10.1038/nature16874

  1. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Nature511,421–427 (2014).
  2. Stevens, B. et alCell131, 1164–1178 (2007).
  3. Cannon, T. D. et al Psychiatry77, 147–157 (2015).
  4. Glausier, J. R. & Lewis, D. A. Neuroscience251, 90–107 (2013).
  5. Glantz, L. A. & Lewis, D. A.  Gen. Psychiatry57, 65–73 (2000).

 

 Jianxin Shi1, et al.   Common variants on chromosome 6p22.1 are associated with schizophrenia.  Nature 460, 753-757 (6 August 2009) | doi:10.1038/nature08192; Received 29 May 2009; Accepted 10 June 2009; Published online 1 July 2009; Corrected 6 August 2009

Schizophrenia, a devastating psychiatric disorder, has a prevalence of 0.5–1%, with high heritability (80–85%) and complex transmission1. Recent studies implicate rare, large, high-penetrance copy number variants in some cases2, but the genes or biological mechanisms that underlie susceptibility are not known. Here we show that schizophrenia is significantly associated with single nucleotide polymorphisms (SNPs) in the extended major histocompatibility complex region on chromosome 6. We carried out a genome-wide association study of common SNPs in the Molecular Genetics of Schizophrenia (MGS) case-control sample, and then a meta-analysis of data from the MGS, International Schizophrenia Consortium and SGENE data sets. No MGS finding achieved genome-wide statistical significance. In the meta-analysis of European-ancestry subjects (8,008 cases, 19,077 controls), significant association with schizophrenia was observed in a region of linkage disequilibrium on chromosome 6p22.1 (P = 9.54 × 10-9). This region includes a histone gene cluster and several immunity-related genes—possibly implicating aetiological mechanisms involving chromatin modification, transcriptional regulation, autoimmunity and/or infection. These results demonstrate that common schizophrenia susceptibility alleles can be detected. The characterization of these signals will suggest important directions for research on susceptibility mechanisms.

Editor’s Summary   6 August 2009
Schizophrenia risk: link to chromosome 6p22.1

A genome-wide association study using the Molecular Genetics of Schizophrenia case-control data set, followed by a meta-analysis that included over 8,000 cases and 19,000 controls, revealed that while common genetic variation that underlies risk to schizophrenia can be identified, there probably are few or no single common loci with large effects. The common variants identified here lie on chromosome 6p22.1 in a region that includes a histone gene cluster and several genes implicated in immunity.

Letter

Hreinn Stefansson1,48, et al. Common variants conferring risk of schizophrenia.
Nature 460, 744-747 (6 August 2009) | doi:10.1038/nature08186; Received 16 March 2009; Accepted 5 June 2009; Published online 1 July 2009

Schizophrenia is a complex disorder, caused by both genetic and environmental factors and their interactions. Research on pathogenesis has traditionally focused on neurotransmitter systems in the brain, particularly those involving dopamine. Schizophrenia has been considered a separate disease for over a century, but in the absence of clear biological markers, diagnosis has historically been based on signs and symptoms. A fundamental message emerging from genome-wide association studies of copy number variations (CNVs) associated with the disease is that its genetic basis does not necessarily conform to classical nosological disease boundaries. Certain CNVs confer not only high relative risk of schizophrenia but also of other psychiatric disorders1, 2, 3. The structural variations associated with schizophrenia can involve several genes and the phenotypic syndromes, or the ‘genomic disorders’, have not yet been characterized4. Single nucleotide polymorphism (SNP)-based genome-wide association studies with the potential to implicate individual genes in complex diseases may reveal underlying biological pathways. Here we combined SNP data from several large genome-wide scans and followed up the most significant association signals. We found significant association with several markers spanning the major histocompatibility complex (MHC) region on chromosome 6p21.3-22.1, a marker located upstream of the neurogranin gene (NRGN) on 11q24.2 and a marker in intron four of transcription factor 4 (TCF4) on 18q21.2. Our findings implicating the MHC region are consistent with an immune component to schizophrenia risk, whereas the association with NRGN and TCF4 points to perturbation of pathways involved in brain development, memory and cognition.

 

Letter

The International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder.  Nature 460, 748-752 (6 August 2009) | doi:10.1038/nature08185; Received 11 February 2009; Accepted 8 June 2009; Published online 1 July 2009; Corrected 6 August 2009

Schizophrenia is a severe mental disorder with a lifetime risk of about 1%, characterized by hallucinations, delusions and cognitive deficits, with heritability estimated at up to 80%1, 2. We performed a genome-wide association study of 3,322 European individuals with schizophrenia and 3,587 controls. Here we show, using two analytic approaches, the extent to which common genetic variation underlies the risk of schizophrenia. First, we implicate the major histocompatibility complex. Second, we provide molecular genetic evidence for a substantial polygenic component to the risk of schizophrenia involving thousands of common alleles of very small effect. We show that this component also contributes to the risk of bipolar disorder, but not to several non-psychiatric diseases.

 

The Psychiatric GWAS Consortium Steering Committee. A framework for interpreting genome-wide association studies of psychiatric disorders.  Molecular Psychiatry (2009) 14, 10–17; doi:10.1038/mp.2008.126; published online 11 November 2008

Genome-wide association studies (GWAS) have yielded a plethora of new findings in the past 3 years. By early 2009, GWAS on 47 samples of subjects with attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder and schizophrenia will be completed. Taken together, these GWAS constitute the largest biological experiment ever conducted in psychiatry (59 000 independent cases and controls, 7700 family trios and >40 billion genotypes). We know that GWAS can work, and the question now is whether it will work for psychiatric disorders. In this review, we describe these studies, the Psychiatric GWAS Consortium for meta-analyses of these data, and provide a logical framework for interpretation of some of the conceivable outcomes.

Keywords: genome-wide association, attention-deficit hyperactivity disorder, autism, bipolar disorder, major depressive disorder, schizophrenia

The purpose of this article is to consider the ‘big picture’ and to provide a logical framework for the possible outcomes of these studies. This is not a review of GWAS per se as many excellent reviews of this technically and statistically intricate methodological approach are available.789101112 This is also not a review of the advantages and disadvantages of different study designs and sampling strategies for the dissection of complex psychiatric traits. We would like to consider how the dozens of GWAS papers that will soon be in the literature can be synthesized: what can integrated mega-analyses (meta-analysis is based on summary data (for example, odds ratios) from all available studies whereas ‘mega-analysis’ uses individual-level genotype and phenotype data) of all available GWAS data tell us about the etiology of these psychiatric disorders? This is an exceptional opportunity as positive or negative results will enable us to learn hard facts about these critically important psychiatric disorders. We suggest that it is not a matter of ‘success versus failure’ or ‘optimism versus pessimism’ but rather an opportunity for systematic and logical approaches to empirical data whereby both positive and appropriately qualified negative findings are informative.

The studies that comprise the Psychiatric GWAS Consortium (PGC; http://pgc.unc.edu) are shown in Table 1. GWAS data for ADHD, autism, bipolar disorder, major depressive disorder and schizophrenia from 42 samples of European subjects should be available for mega-analyses by early 2009 (>59 000 independent cases and controls and >7700 family trios). To our knowledge, the PGC will have access to the largest set of GWAS data available.

A major change in human genetics in the past 5 years has been in the growth of controlled-access data repositories, and individual phenotype and genotype data are now available for many of the studies in Table 1. When the PGC mega-analyses are completed, most data will be available to researchers via the NIMH Human Genetics Initiative (http://nimhgenetics.org). Although the ready availability of GWAS data is a benefit to the field by allowing rapid application of a wide range of analytic strategies to GWAS data, there are potential disadvantages. GWAS mega-analysis is complex and requires considerable care and expertise to be done validly. For psychiatric phenotypes, there is the additional challenge of working with disease entities based largely on clinical description, with unknown biological validity and having both substantial clinical variation within diagnostic categories as well as overlaps across categories.13 Given the urgent need to know if there are replicable genotype–phenotype associations, a new type of collaboration was required.

The purpose of the PGC is to conduct rigorous and comprehensive within- and cross-disorder GWAS mega-analyses. The PGC began in early 2007 with the principal investigators of the four GAIN GWAS,14 and within six months had grown to 110 participating scientists from 54 institutions in 11 countries. The PGC has a coordinating committee, five disease-working groups, a cross-disorder group, a statistical analysis and computational group, and a cluster computer for statistical analysis. It is remarkable that almost all investigators approached agreed to participate and that no one has left the PGC. Most effort is donated but we have obtained funding from the NIMH, the Netherlands Scientific Organization, Hersenstichting Nederland and NARSAD.

The PGC has two major specific aims. (1) Within-disorder mega-analyses: conduct separate mega-analyses of all available GWAS data for ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia to attempt to identify genetic variation convincingly associated with any one of these five disorders. (2) Cross-disorder mega-analyses: the clinically-derived DSM-IV and ICD-10 definitions may not directly reflect the fundamental genetic architecture.15 There are two subaims. (2a) Conduct mega-analysis to identify genetic variation convincingly associated with conventional definitions of two or more disorders. This nosological aim could assist in delineating the boundaries of this set of disorders. (2b) An expert working group will convert epidemiological and genetic epidemiological evidence into explicit hypotheses about overlap among these disorders, and then conduct mega-analyses based on these definitions (for example, to examine the lifetime presence of idiopathic psychotic features without regard to diagnostic context).

The goal of the PGC is to identify convincing genetic variation-disease associations. A convincing association would be extremely unlikely to result from chance, show consistent effect sizes across all or almost all samples and be impervious to vigorous attempts to disprove the finding (for example, by investigating sources of bias, confirmatory genotyping, and so on). Careful attention will be paid to the impact of potential sources of heterogeneity17 with the goal of assessing its impact without minimizing its presence.

Biological plausibility is not an initial requirement for a convincing statistical association, as there are many examples in human genetics of previously unsuspected candidate genes nonetheless showing highly compelling associations. For example, multiple SNPs in intron 1 of the FTO gene were associated with body mass index in 13 cohorts with 38 759 participants18 and yet ‘FTO’ does not appear in an exhaustive 116 page compilation of genetic studies of obesity.19 Some strong associations are in gene deserts: multiple studies have found convincing association between prostate cancer and a region on 8q24 that is ~250 kb from the nearest annotated gene.20 Both of these examples are being intensively investigated and we suspect that a compelling mechanistic ‘story’ will emerge in the near future. The presence of a compelling association without an obvious biological mechanism establishes a priority research area for molecular biology and neuroscience of a psychiatric disorder.

The PGC will use mega-analysis as the main analytic tool as individual-level data will be available from almost all samples. To wield this tool appropriately, a number of preconditions must be met. First, genotype data from different GWAS platforms must be made comparable as the direct overlap between platforms is often modest. This requires meticulous quality control for the inclusion of both SNPs and subjects and attention to the factors that can cause bias (for example, population stratification, cryptic relatedness or genotyping batch effects). Genotype harmonization can be accomplished using imputation (2122, for example) so that the same set of ~2 million2324 directly or imputed SNP genotypes are available for all subjects. Second, phenotypes need to be harmonized across studies. This is one of the most crucial components of the PGC and we are fortunate to have world experts directing the work. Third, the mega-analyses will assess potential heterogeneity of associations across samples.

A decision-tree schematic of the potential outcomes of the PGC mega-analyses is shown in Figure 1. Note that many of the possibilities in Figure 1 are not mutually exclusive and different disorders may take different paths through this framework. It is possible that there eventually will be dozens or hundreds of sequence variants strictly associated with these disorders with frequencies ranging from very rare to common.

………

 

GWAS has the potential to yield considerable insights but it is no panacea and may well perform differently for psychiatric disorders. Even if these psychiatric GWAS efforts are successful, the outcomes will be complex. GWAS may help us learn that clinical syndromes are actually many different things—for example, proportions of individuals with schizophrenia might evidence associations with rare CNVs of major effect,56 with more common genetic variation in dozens (perhaps hundreds) of genomic regions, between genetic variation strongly modified by environmental risk factors, and some proportion may be genetically indistinguishable from the general population. Moreover, as fuel to long-standing ‘lumper versus splitter’ debates in psychiatric nosology, empirical data might show that some clinical disorders or identifiable subsets of subjects might overlap considerably.

The critical advantage of GWAS is the search of a ‘closed’ hypothesis space. If the large amount of GWAS data being generated are analyzed within a strict and coherent framework, it should be possible to establish hard facts about the fundamental genetic architecture of a set of important psychiatric disorders—which might include positive evidence of what these disorders are or exclusionary evidence of what they are not. Whatever the results, these historically large efforts should yield hard facts about ADHD, autism, bipolar disorder, major depressive disorder and schizophrenia that may help guide the next era of psychiatric research.

  1. Pe’er I, Yelensky R, Altshuler D, Daly MJ. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet Epidemiol 2008; 32: 381–385. | Article | PubMed |
  2. Weiss LA, Shen Y, Korn JM, Arking DE, Miller DT, Fossdal R et al. Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med 2008; 358: 667–675. | Article | PubMed | ChemPort |

 

Letter

Hreinn Stefansson1,36, et al. Large recurrent microdeletions associated with schizophrenia. Nature 455, 232-236 (11 September 2008) | doi:10.1038/nature07229; Received 17 April 2008; Accepted 8 July 2008; Corrected 11 September 2008

Reduced fecundity, associated with severe mental disorders1, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism2, schizophrenia3 and mental retardation4. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation4, 5 and autism2. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novoCNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.

 

The C4 gene can exist in multiple copies (from one to four) on each copy of chromosome 6, and has four different forms: C4A-short, C4B-short, C4A-long, and C4B-long. The researchers first examined the “structural alleles” of the C4 locus—that is, the combinations and copy numbers of the different C4 forms—in healthy individuals. They then examined how these structural alleles related to expression of both C4Aand C4B messenger RNAs (mRNAs) in postmortem brain tissues.

From this the researchers had a clear picture of how the architecture of the C4 locus affected expression ofC4A and C4B. Next, they compared DNA from roughly 30,000 schizophrenia patients with that from 35,000 healthy controls, and a correlation emerged: the alleles most strongly associated with schizophrenia were also those that were associated with the highest C4A expression. Measuring C4A mRNA levels in the brains of 35 schizophrenia patients and 70 controls then revealed that, on average, C4A levels in the patients’ brains were 1.4-fold higher.

C4 is an immune system “complement” factor—a small secreted protein that assists immune cells in the targeting and removal of pathogens. The discovery of C4’s association to schizophrenia, said McCarroll, “would have seemed random and puzzling if it wasn’t for work . . . showing that other complement components regulate brain wiring.” Indeed, complement protein C3 locates at synapses that are going to be eliminated in the brain, explained McCarroll, “and C4 was known to interact with C3 . . . so we thought well, actually, this might make sense.”

McCarroll’s team went on to perform studies in mice that revealed C4 is necessary for C3 to be deposited at synapses. They also showed that the more copies of the C4 gene present in a mouse, the more the animal’s neurons were pruned.

Synaptic pruning is a normal part of development and is thought to reflect the process of learning, where the brain strengthens some connections and eradicates others. Interestingly, the brains of deceased schizophrenia patients exhibit reduced neuron density. The new results, therefore, “make a lot of sense,” said Cardiff University’s Andrew Pocklington who did not participate in the work. They also make sense “in terms of the time period when synaptic pruning is occurring, which sort of overlaps with the period of onset for schizophrenia: around adolescence and early adulthood,” he added.

“[C4] has not been on anybody’s radar for having anything to do with schizophrenia, and now it is and there’s a whole bunch of really neat stuff that could happen,” said Sullivan. For one, he suggested, “this molecule could be something that is amenable to therapeutics.”

 

 

UniProtKB

Derived from proteolytic degradation of complement C4, C4a anaphylatoxin is a mediator of local inflammatory process. It induces the contraction of smooth muscle, increases vascular permeability and causes histamine release from mast cells and basophilic leukocytes.

Non-enzymatic component of C3 and C5 convertases and thus essential for the propagation of the classical complement pathway. Covalently binds to immunoglobulins and immune complexes and enhances the solubilization of immune aggregates and the clearance of IC through CR1 on erythrocytes. C4A isotype is responsible for effective binding to form amide bonds with immune aggregates or protein antigens, while C4B isotype catalyzes the transacylation of the thioester carbonyl group to form ester bonds with carbohydrate antigens.

 

Related Articles in Pharmaceutical Intelligence 

Schizophrenia genomics

Identical Twin Brother Develops Schizophrenia

Imaging Schizophrenia Brain

Outstanding Achievement in Schizophrenia Research

Combining Genetic has Identified Schizophrenia Subtypes

A new relationship identified in preterm stress and development of autism or schizophrenia

Genomic Promise for Neurodegenerative Diseases, Dementias, Autism Spectrum, Schizophrenia, and Serious Depression

Brain Biobank

Protein binding to RNAs in brain

Brain Matters from iBiology

Sleep science

Dopamine-β-Hydroxylase Functional Variants

An inconvenient truth about dreams

Synapse activity in neurotransmission

Mindful Discoveries

Schizophrenic hallucinations

Neurons Reprogrammed

Role of Neurotransmitters and other such Neurosignaling Molecules

microglia and brain maintenance

sequential memory

To reduce symptoms of mental illness and retrain the brain

To understand what happens in the brain to cause mental illness

Icelandic Population Genomic Study Results by deCODE Genetics come to Fruition: Curation of Current genomic studies

The Colors of Life Function

3:15PM 11/12/2014 – Discussion Complex Disorders @10th Annual Personalized Medicine Conference at the Harvard Medical School, Boston

Searchable Database of almost 100,000 functional Brain Scans related to Behavior on patients from 111 countries will change Psychiatry practices set in 170 years

Social Behavior Traits Embedded in Gene Expression

News in Exploration of the Biological Causes of Mental Illness: Potential for New Treatments

Nobel Prize in Physiology or Medicine 2013 for Cell Transport: James E. Rothman of Yale University; Randy W. Schekman of the University of California, Berkeley; and Dr. Thomas C. Südhof of Stanford University

Translational Research on the Mechanism of Water and Electrolyte Movements into the Cell

Synaptotagmin functions as a Calcium Sensor: How Calcium Ions Regulate the fusion of vesicles with cell membranes during Neurotransmission

Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute

No dishonour in depression

MGH’s Largest-ever Genetic Study of Five Psychiatric Disorders: Variation in SNPs in Two Genes involved in Calcium-Channel Signaling

Modern Society, Risk and Mentally Disordered Offender

 

 

 

 

 

 

 

 

Read Full Post »


Dense Breast Mammogram

Larry H. Bernstein, MD, FCAP, Curator

LPBI

 

 

The Problem With Mammograms

http://forward.com/culture/324003/the-problem-with-mammograms/#ixzz3queBnx00

 

Hallie Leighton had dense breasts — a fact she discovered only in her late 30s, via a mammogram. She grew up in an Ashkenazi family in New York, pursued a career in writing and worked with organizations promoting peace between Israelis and Arabs. By 2013 she was making a documentary on her father Jan Leighton, an actor who set the record as an actor for appearing in the most roles (2,407 according to the 1985 Guinness Book of World Records). She was never able to complete it. She died that year, at the age of 42.

Every woman in Leighton’s family had breast cancer, so she began getting annual mammograms at 35 — five years earlier than the recommended age. In 2009 the results of Leighton’s mammogram came in as “negative” or “normal”; by 2013 she was bedridden, undergoing her final days of chemotherapy.

When Leighton was first diagnosed in 2010, her doctor told her, “You have breast cancer, and it was there in 2009.” The tumor in Leighton’s breast went undiscovered until it was palpable — and at that point, the cancer was already in stage 4.

Happygram,” a documentary which exposes some of the shortcomings in mammography, chronicles Leighton’s struggle with cancer and the implications of having dense breasts.

“Most women simply aren’t informed that they have dense breast tissue,” said Leighton’s best friend Julie Marron. She wrote and directed the documentary, which is currently screening at film festivals around the country.

Breast density is defined by the relative amount of fat in relation to the amount of connective and epithelial tissue (tissue that lines blood vessels and cavities). When more than 50% of breast tissue is connective and epithelial tissue, instead of fatty tissue, the breasts are considered dense. Mammography is the only way to determine breast density.

“If you have dense breasts, what looks dense on a mammogram looks the same as a cancer would look. It tends to confuse or confound the physician, and reduces the sensitivity of the mammogram,” said Gerald Kolb, founder and president of The Breast Group, which counsels clients on different technologies in breast care. “Hallie Leighton’s breasts looked like snowballs; there was no chance they were going to find anything with the mammogram.”

Forty percent of women who are screened for breast cancer have dense breast tissue. These women also account for more than 70% of all invasive cancers. “Mammograms are not very effective screening tools for these women, as they miss between 50% and 75% of all invasive cancers in dense breast tissue,” Marron said. “This is obviously a very critical issue when you are dealing with a population that is more likely to develop cancer.”

Ashkenazi women are even more at risk. They are 1.6 times more likely than the general population to have dense breast tissue, according to Kolb. Moreover, one in 40 Ashkenazi women will test positive for one or both of BRCA gene mutations responsible for breast cancer. For the general population, that number is between one in 350 and one in 800.The BRCA 1 or 2 genes don’t cause cancer, they fight cancer, Kolb says. But if the gene is mutated, the body is not as well equipped to fight the cancer.

“A woman with a BRCA mutation has a lifetime risk of around 33% to 87%, depending on the gene and mutation,” Marron said. “Compare this to a lifetime risk of 12% for developing breast cancer for the overall population.” BRCA gene mutations can be inherited from either or both parents, and therefore they can be present in men as well as in women.

Breast density and BRCA gene mutations are not directly related, but both independently present an increased susceptibility to breast cancer.

“The biggest risk is that a doctor is not going to find the cancer when it’s really small,” Kolb said. When a tumor is detected at a centimeter or smaller, there’s a 95% cure rate. But if the cancer is the size of a golf ball by the time it’s detected, Kolb says, the woman has a 60% chance of living for five years, and then her mortality increases dramatically.

The good news is that mammography isn’t the only method of detecting breast cancer; the bad news is that very few people know this. “What we’re trying to do in the density movement is give women enough information so they can ask appropriate questions of a doctor,” Kolb said.

Kolb advises high-risk women to get a genetic risk analysis, which can be performed by a genetic counselor or a radiologist. He advises getting the risk analysis as early as age 25, but doing so is a personal decision. Not every woman is emotionally prepared to know the results.

“Mammography is a starting point,” said Dr. Dennis McDonald, a California-based women’s imager. Additionally, doctors recommend that women with dense breasts get an MRI, which McDonald says is reserved for high-risk women. It’s an expensive, invasive and time-consuming procedure that requires the injection of fluid in order to read the MRI. As of yet, doctors do not know the side effects of getting an annual MRI.

“A doctor should have started [Leighton] on an MRI right away. She was high risk and they chose to just monitor with a mammogram,” Kolb said. “That’s insufficient.”

Breast ultrasound is another alternative for women with dense breast tissue. “Most of the time, breast density doesn’t present a problem [with ultrasounds],” McDonald said. Though the ultrasound is effective in detecting cancer, he says the downside is that radiologists are often not that comfortable with the technology, simply because they have little experience with it. There are also a lot of false positives, he adds, which result in unnecessary exams or biopsies.

As “Happygram” documents, informing women of their breast density and of alternatives to mammography is a highly charged political issue.

“The whole breast cancer industry has grown up around mammograms,” Marron said. “Physicians weren’t educated on [breast density], deliberately so to a certain extent, and refused to inform patients on this issue, which is really outrageous if you think about it.” Marron says that doctors are required by law and ethical guidelines to inform patients of “material” medical information. “There is no legitimate reason that women have not been informed of this information,” she noted.

After Leighton’s diagnosis, she wanted to ensure that other women didn’t suffer the same misfortune of all-too-late tumor discovery on account of dense breast tissue. She gave media interviews, lobbied in Albany and starred in “Happygram,” all the while undergoing chemotherapy. She died four months after the Breast Density Information Bill passed in New York.

The law requires that every mammography report given to a patient with dense breasts inform the patient in plain language that she has dense breast tissue and that she should talk to her physician about the possible benefits of additional screenings. In New York, the first state in the nation to pass this kind of law, at least 2,500 women with dense breasts and invasive breast cancer received “normal” or “negative” results on their mammograms.

Similar legislation has been passed in more than 20 states throughout the country, but not without objection. Many well-intentioned radiologists, poorly informed about alternative screening options, feared that telling women the limitations of mammography would cause them to lose faith in it altogether and not get tested. Others argued that the information would make women anxious, and that it wouldn’t be fair for those who couldn’t afford additional testing. And still further arguments against informing women were possibly impacted by financial considerations, Marron added.

“Women aren’t getting the benefit of full notification across the board yet,” Marron said. “I think that has to change through education. That’s the primary reason we made this movie. There’s been so much resistance within the medical community to telling women. Change isn’t going to come from the medical community, it has to come from the patients.”

Ashkenazi women shouldn’t panic, Kolb says, but they need to carefully examine their breast density and alternative screening options: “Anytime you have a preventative tragedy like that, you have to do everything in your power to stop it from happening.”

Madison Margolin is a freelance writer based in New York. She writes frequently for the Village Voice.

Read more: http://forward.com/culture/324003/the-problem-with-mammograms/#ixzz3qufQOSmn

Read Full Post »


Twitter Offers Valuable Insights Into The Experience Of MRI Patients, Charles Sturt University Study

Read at:

Twitter offers valuable insights into the experience of MRI patients

Tweets can give medical professionals a window into the minds of patients, according to a new study published in the Journal of Medical Imaging and Radiation Sciences

Philadelphia, PA, October 28, 2015 – Magnetic Resonance Imaging (MRI) can be a stressful experience for many people, but clinicians have few ways to track the thoughts and feelings of their patients regarding this procedure. While the social networking site Twitter is known for breaking news and celebrity tweets, it may also prove to be a valuable feedback tool for medical professionals looking to improve the patient experience, according to a new study published in the December issue of the Journal of Medical Imaging and Radiation Sciences.

Johnathan Hewis, MSc, PgCert (LTHE), PgCert (BE), BSc Hon, an investigator from Charles Sturt University in Australia, analyzed 464 tweets related to MRI over the course of one month and found that patients, their friends, and family members were sharing their thoughts and feelings about all aspects of the procedure through the microblogging site. Tweets were categorized into three themes: MRI appointment, scan experience, and diagnosis.

Twitter is a giant in the social media space. In 2014, 19% of the entire adult population of the U.S. used Twitter, with almost 90% of those individuals accessing the service from their mobile phones. Because it is so ubiquitous, Twitter can provide crucial new insights to which practitioners would otherwise not be privy. In the study, patients expressed anxiety about many aspects of the process, including a lot of stress over the possibility of bad news. “The findings of this study indicate that anticipatory anxiety can manifest over an extended time period and that the focus can shift and change along the MRI journey,” explained Hewis. “An appreciation of anxiety related to results is an important clinical consideration for MRI facilities and referrers.”

The study found that tweets encapsulated patient thoughts about many other parts of the procedure including the cost, the feelings of claustrophobia, having to keep still during the scan, and the sound the MRI machine makes. One particularly memorable tweet about the sound read, “Ugh, having an MRI is like being inside a pissed off fax machine!”

Not all the tweets were centered around stress. Many friends and family members expressed sentiments of support including prayers and offering messages of strength. Some patients used Twitter to praise their healthcare team or give thanks for good results. Others spoke about the fact they liked having an MRI because it gave them some time to themselves or offered them a chance to nap.

Twitter isn’t just words, it’s also a way to share pictures. “An unexpected discovery of the examination preparation process was the ‘MRI gown selfie,'” revealed Hewis. “Fifteen patients tweeted a self-portrait photograph taken inside the changing cubicle while posing in their MRI gown/scrubs. Anecdotally, the ‘MRI gown selfie’ seemed to transcend age.”

During the course of his analysis, Hewis discovered that many patients took issue with the fact that they were not allowed to select the music they listened to during the MRI. “Music choice,” said Hewis, “is a simple intervention that can provide familiarity within a ‘terrifying’ environment.’ The findings of this study reinforce the ‘good practice’ of enabling patients’ choice of music, which may alleviate procedural anxiety.”

With such a broad reach, social networks like Twitter offer medical practitioners the opportunity to access previously unavailable information from their patients, which can help them continuously improve the MRI experience. “MRI patients do tweet about their experiences and these correlate with published findings employing more traditional participant recruitment methods,” concluded Hewis. “This study demonstrates the potential use of Twitter as a viable platform to conduct research into the patient experience within the medical radiation sciences.”

Media Contact

Chris Baumle
hmsmedia@elsevier.com
215-239-3731

Read Full Post »

Older Posts »