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Using A.I. to Detect Lung Cancer gets an A!

Reporter: Irina Robu, PhD

Google researchers hypothesized that computers are as good or better than doctors at detecting tiny lung cancers on CT scans, since CT scan combines data from several X-rays to produce a detailed image of a structure inside the body. CT scans produce 2-dimensional images of a slice of the body and the data can also be used to construct 3-D images.

However, the technology published in Nature Medicine offers input in the future of artificial intelligence in medicine. By feeding vast amounts of data from medical imaging into systems called artificial neural networks, scientists can teach computers to identify patterns linked to a specific condition, like pneumonia, cancer or a wrist fracture that would be hard for a person to see. The system trails an algorithm, or set of instructions, and learns as it goes. The more data it receives, the better it becomes at interpretation.

The process, known as deep learning enables computers to identify objects and understand speech but it also created systems to help pathologists read microscope slides to diagnose cancer, and to help ophthalmologists detect eye disease in people with diabetes. In their recent study, the scientist used artificial intelligence to CT scans used to screen people for lung cancer, which caused 160,000 deaths in the United States last year, and 1.7 million worldwide. The scans are recommended for people at high risk because of a long history of smoking.

Screening studies showed that it can reduce the risk of dying from lung cancer and can also identify spots that might later become cancer, so that radiologists can categorize patients into risk groups and decide whether they need biopsies or more frequent follow-up scans to keep track of the suspect regions.

However, the test has errors. It can miss tumors or mistake benign spots for malignancies and shove patients into invasive, risky procedures like lung biopsies or surgery.

SOURCE

https://www.nytimes.com/2019/05/20/health/cancer-artificial-intelligence-ct-scans.html

Other related articles were published in this Online Scientific Open Access Journal including the following:

https://pharmaceuticalintelligence.com/2019/07/21/multiple-barriers-identified-which-may-hamper-use-of-artificial-intelligence-in-the-clinical-setting/

https://pharmaceuticalintelligence.com/2019/06/28/ai-system-used-to-detect-lung-cancer/

 

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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.

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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.


The Digital Age Gave Rise to New Definitions – New Benchmarks were born on the World Wide Web for the Intangible Asset of Firm’s Reputation: Pay a Premium for buying e-Reputation

Curator: Aviva Lev–Ari, PhD, RN

 

Direct reputation, feedback reputation and signaling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. … a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways.

Luıs M B Cabral, New York University and CEPR, 2005

 

 

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

  1.  Claiming Distinction
  2.  Recognition Bestowed
  3.  The Technology
  4.  The Sphere of Influence
  5.  The Industrial Benefactors in Potential
  6.  The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE
  7.  1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500
  8.  The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions
  9.  Growth in Twitter Followers and in Global Reach: Who are the NEW Followers? they are OUR COMPETITION   and   other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation
  10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Part 2:   Business Perspectives on Reputation

Part 3:   Economics Perspectives on Reputation

 

 

Part 1:   A Digital Business Defined and the Intangible Asset of Firm’s Reputation

This curation attempts to teach-by-example the new reality of the Intangible Asset of Firm’s Reputation when the business is 100% in the cloud, 100% electronic in nature (paperless), the customers are the Global Universe and the organization is 100% Global and 100% virtual.

 

A Case in Point: Intellectual Property Production Process of Health Care Digital Content using electronic Media Channels

 

Optimal Testimonial of e-Product Quality and Reputation for an Open Access Online Scientific Journal pharmaceuticalintelligence.com 

 

 1.   Claiming Distinction

Executive Summary

WHAT ARE LPBI Group’s NEEDS in June 2019: Aviva’s BOLD VISION on June 11, 2019

 

2.   Recognition Bestowed 

Our Books are here

  • On 8/17/2018, Dr. Lev-Ari, PhD, RN was contacted by the President elect of the Massachusetts Academy of Sciences (MAS), Prof. Katya Ravid of Boston University, School of Medicine, to join MAS in the role of Liaison to the Biotechnology and eScientific Publishing industries for the term of August 2018-July 2021. In the MAS, Dr. Lev-Ari serve as Board member, Fellow, and Advisor to the Governing Board.

http://www.maacadsci.org

MAS FELLOWS 

GOVERNING BOARD

ACTIVITIES

BUNDLED BY AMAZON.COM INTO A SIX-VOLUME SERIES FOR $515

https://lnkd.in/e6WkMgF

Sixteen Volumes ARE ON AMAZON.COM, average book length – 2,400 pages

https://lnkd.in/ekWGNqA

3.   The Technology

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

Detailed Technology Description

LPBI’s Pipeline Map: A Positioning Perspectives – An Outlook to the Future from the Present

 

4.   The Sphere of Influence 

LPBI Group’s Social Media Presence

JOURNAL Statistics on 2/24/2019

  • LPBI Platform is been used by GLOBAL Communities of Scientists for interactive dialogue of SCIENCE – Four case studies are presented in the link, below

Electronic Scientific AGORA: Comment Exchanges by Global Scientists on Articles published in the Open Access Journal @pharmaceuticalintelligence.com – Four Case Studies

Curator and Editor-in-Chief: Journal and BioMed e-Series, Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2018/04/10/electronic-scientific-agora-comment-exchanges-by-global-scientists-on-articles-published-in-the-open-access-journal-pharmaceuticalintelligence-com-four-case-studies/

 

5.   The Industrial Benefactors in Potential

Opportunities Map in the Acquisition Arena

Dynamic Contents for LPBI Group’s PowerPoint Presentation

Potential Use of LPBI IP as Value Price Driver by Potential Acquirer: Assumptions per Asset Class 

 

6.   The Actors at Play – Experts, Authors, Writers – Life Sciences & Medicine as it applies to HEALTH CARE

Founder’s Role in the Development of Venture’s Factors of Content Production – Biographical Notes by Aviva Lev-Ari, PhD, RN, LPBI Group

Top Authors by Number of eReaders Views

Top Articles by Number of e-Readers for All Days ending 2019-02-17

FIT Members Contribute to Opportunities Map

FINAL IMPROVEMENT TEAM (FIT): Definition of Active, Lapsing of Active Status, COMPs Formulas

FIT members – Who works on WHAT?

Summer 2019 Plan – Research Associates Tasks

 

7.   1st Level Connection on LinkedIn = +7,100 and Endorsements = +1,500

Connections First Level on LinkedIn: 500 CEOs, 200 Big Pharma Professionals, 7,000 in Total: LPBI Group Founder – Aviva Lev-Ari, PhD, RN

 

8.   The DIGITAL REPUTATION of our Venture – Twitter for the Professional and for Institutions

Mostly HONORED to be followed by [from an Excerpt of 117 Followers of the Twitter Account @AVIVA1950 from the List of 359 Followers] by the Number of their Followers on 2/24/2019

LPBI Group is mostly HONORED to be followed by [from an Excerpt of 136 Followers of the Twitter Account @pharma_BI from the List of 505 Followers] by the Number of their Followers on 3/20/2019

Excerpt of 136 Followers of @pharma_BI (from the List of 505 Followers) by the Number of their Followers on 3/20/2019

Excerpt of 117 Followers of @AVIVA1950 (from the List of 359 Followers) by the Number of their Followers

REACH – Two Handles on Twitter.com @AVIVA1950 @pharma_BI

9.   Growth in Twitter Followers and in Global Reach: Who are the NEW Followers: OUR COMPETITION and other Media Establishments – that is the definition of Trend Setter, Opinion Leader and Source for Emulation

@4openjournalFollows you

Follow

4open is a multi- & inter-disciplinary, online, peer-reviewed, open access journal publishing across a broad range of subjects in the STEM domain.

@roll_clausFollows you

Follow

Publishing Editor at 

@EDPSciences

@PubtextoPFollows you

Following

Pubtexto is an International online publishing organization that publishes Scientific literature through its different open access Journals.

@alexanderlabrieFollows you

Following

CEO 

@sphereinc

@BjoernBruecherFollows you

Following

THEODOR-BILLROTH-ACADEMY® 

(link: http://linkedin.com/in/bruecher)

linkedin.com/in/bruecher // 

(link: http://4open-sciences.org)

4open-sciences.org – Editor-in-Chief // Science Profile – 

(link: http://researchgate.net/profile/Bjoern)

researchgate.net/profile/Bjoern

@MPDexpertFollows you

Follow

translate research into life-changing Global manufactured Medical Products – drugs, devices, biotech, combination; anything requiring FDA approval#MedProdDev

@P_A_MORGONFollows you

Following

Life science expert & investor_travel, wine & golf amateur_Proud father of 2 girls_My Tweets are only mine 

@INmuneBioFollows you

Follow

INmune Bio, Inc. is developing therapies that harness patient’s #immunesystem to treat #cancer. Our focus is on #NKcells and #myeloid derived suppressor cells.

@sallyeavesFollows you

Following

Innovating #tech #education #business CEO CTO Advisor & Prof. #blockchain #AI 

@OxfordSBS

@Forbes

 #FinTech #speaker #SDGs #STEM #techforgood #sustainability

@sciencetracker2Follows you

You will hear more recent and cool scientific news here. Besides, some health and tech news. Follow us in

(link: http://facebook.com/sciencetracker2)

facebook.com/sciencetracker2

13.8K Following

24.6K Followers

Followed by Stanford Tweets, Biotech Week Boston, and 23 others you follow

@sgruenwaldFollows you

Following

MD, PhD, scientist, futurist, entrepreneur, managing director of 

(link: http://www.genautica.com)

genautica.com, co-founder 

(link: http://www.diagnomics.com)

diagnomics.com

(link: http://www.scoop.it/t/amazing-science)

scoop.it/t/amazing-scie…user

 

10.  Business Aspects of the Brick & Mortar World render OBSOLETE

Financial Valuation of Three Health Care Intellectual Property (IP) Content Asset Classes

Global Market Penetration Forecast for each Volume in the 16 Volume BioMed e-Series

2013-2019, On the Medical & Scientific Bookshelf in Kindle Store: eReader Behaviors: Browsing, Page Downloads and Buying e-Books – LPBI Group’s BioMed e-Series, Royalties Payment Analysis 

 

Part 2: BUSINESS PERSPECTIVES on Reputation

 

Warren Buffett on reputation: the economic value of values, integrity and corporate culture

Warren Buffett understands that reputation and integrity have economic value. Research that shows that a good reputation is worth real money — in fact, some research indicates that a good reputation might replace a line of credit at the bank. In his book Berkshire Beyond Buffett: The Enduring Value of Values, Lawrence Cunningham argues that one of Berkshire Hathaway’s greatest assets is reputation.

https://www.finn.agency/fr/warren-buffett-reputation-berkshire-hathaway

 

The Value of Reputation

Thomas Pfeiffer1,2,4,*, Lily Tran5, Coco Krumme5 and David G Rand1,3,* 1 Program for Evolutionary Dynamics, FAS, 2 School of Applied Sciences and Engineering, and 3 Department of Psychology, Harvard University, Cambridge MA 02138, USA 4 New Zealand Institute for Advanced Study, Massey University, Auckland 0745, New Zealand 5 MIT Media Laboratory, Cambridge MA 02139, USA

 

Reputation plays a central role in human societies.

Empirical and theoretical work indicates that a good reputation is valuable in that it increases one’s expected payoff in the future. Here, we explore a game that couples a repeated Prisoner’s Dilemma (PD), in which participants can earn and can benefit from a good reputation, with a market in which reputation can be bought and sold. This game allows us to investigate how the trading of reputation affects cooperation in the PD, and how participants assess the value of having a good reputation. We find that depending on how the game is set up, trading can have a positive or a negative effect on the overall frequency of cooperation. Moreover, we show that the more valuable a good reputation is in the PD, the higher the price at which it is traded in the market. Our findings have important implications for the use of reputation systems in practice.

Keywords: evolution of cooperation; reciprocal altruism; indirect reciprocity; reputation

http://decisionlab.harvard.edu/_content/research/papers/Krumme_Pfieffer_Tran_and_Rand_Value_of_Reputation.pdf

 

The Impact of Reputation on Market Value by Simon Cole

One of the most familiar, but least understood, intangible assets is a firm’s reputation.

Simon Cole is the founding partner of the corporate reputation and branding consultancy Reputation Dividend (www. reputationdividend.com).

http://www.reputationdividend.com/files/4713/4822/1479/Reputation_Dividend_WEC_133_Cole.pdf

 

Part 3:   ECONOMICS PERSPECTIVES on Reputation

 

The Economics of Trust and Reputation: A Primer

Luıs M B Cabral New York University and CEPR, June 2005, lecture series at the University of Zurich

lcabral@stern.nyu.edu

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

 

Notes on the literature

Important note: The notes in this section are essentially limited to the ideas discussed in the present version of these lectures notes. They cannot therefore be considered a survey of the literature. There are dozens of articles on the economics of reputation which I do not include here. In a future version of the text, I hope to provide a more complete set of notes on the literature. The notes below follow the order with which topics are presented.

Bootstrap models. The bootstrap mechanism for trust is based on a general result known as the folk theorem (known as such because of its uncertain origins). For a fairly general statement of the theorem (and its proof) see Fudenberg and Makin (1986). One of the main areas of application of the folk theorem has been the problem of (tacit or explicit) collusion in oligopoly. This is a typical problem of trust (or lack thereof): all firms would prefer prices to be high and output to be low; but each firm, individually, has an incentive to drop price and increase output. Friedman (1971) presents one of the earliest formal applications of the folk theorem to oligopoly collusion. He considers the case when firms set prices and history is perfectly observable. Both of the extensions presented in Section 2.2 were first developed with oligopoly collusion applications in mind. The case of trust with noisy signals (2.2.1) was first developed by Green and Porter (1984). A long series of papers have been written on this topic, including the influential work by Abreu, Pearce and Stacchetti (1990). Rotemberg and Saloner (1986) proposed a model of oligopoly collusion with fluctuating market demand. In this case, the intuition presented in Section 2.2.2 implies that firms collude on a lower price during periods of higher demand. This suggests that prices are counter-cyclical in markets where firms collude. Rotemberg and Saloner (1986) present supporting evidence from the cement industry. A number of papers have built on Rotemberg and Saloner’s analysis. Kandori (1992) shows that the i.i.d. assumption simplifies the analysis but is not crucial. Harrington (19??) considers a richer demand model and looks at how prices vary along the business cycle. The basic idea of repetition as a form of ensuring seller trustworthiness is developed in Klein and Leffler (1981). See also Telser (1980) and Shapiro (1983). When considering the problem of free entry, Klein and Leffler (1981) propose advertising as a solution, whereas Shapiro (1983) suggests low intro25 ductory prices. Section ?? is based on my own research notes. The general analysis of selfreinforcing agreements when there is an outside option of the kind considered here may be found in Ray (2002). Watson (1999, 2002) also considers models where the level of trust stars at a low level and gradually increases.

Bayesian models. The seminal contributions to the study of Bayesian models of reputation are Kreps and Wilson (1982) and Milgrom and Roberts (1982). The model in Section 3.2.1 includes elements from these papers as well as from Diamond (1989). H¨olmstrom (1982/1999) makes the point that separation leads to reduced incentives to invest in reputation. The issue of reputation with separation and changing types is treated in detail in the forthcoming book by Mailath and Samuelson (2006). In Section 3.3, I presented a series of models that deal with name as carriers of reputations. The part on changing names (Section 3.3.1) reflects elements from a variety of models, though, to the best of my knowledge, no study exists that models the process of secret, costless name changes in an infinite period adverse selection context. The study of markets for names follows the work by Tadelis (1999) and Mailath and Samuelson (2001). All of these papers are based on the Bayesian updating paradigm. Kreps (1990) presents an argument for trading reputations in a bootstrap type of model. The analysis of brand stretching (Section 3.3.3) is adapted from Cabral (2000). The paper considers a more general framework where the direct reputation, feedback reputation and signalling effects are present; and shows that better sellers are always more likely to brand stretch. The comparative statics with respect to the initial reputation level, however, are not obvious. As we saw above, a higher reputation firm can earn a higher direct reputation effect premium. But a higher reputation firm also has more to lose. The trade-off between using one’s reputation and protecting it can go both ways. For other papers on brand stretching and umbrella branding see Choi (1998), Anderson (2002).

Bibliography

Abreu, Dilip, David Pearce and Ennio Stacchetti (1990), “Toward a Theory of Discounted Repeated Games with Imperfect Monitoring,” Econometrica 58, 1041–1064. Andersson, Fredrik (2002), “Pooling reputations,” International Journal of Industrial Organization 20, 715–730. Bernhein, B. Douglas and Michael D. Whinston (1990), “Multimarket Contact and Collusive Behavior,” Rand Journal of Economics 21, 1–26. Cabral, Lu´ıs M B (2000), “Stretching Firm and Brand Reputation,” Rand Journal of Economics 31, 658-673. Choi, J.P. (1998), “Brand Extension and Informational Leverage,” Review of Economic Studies 65, 655–69. Diamond, Douglas W (1989), “Reputation Acquisition in Debt Markets,” Journal of Political Economy 97, 828–862. Ely, Jeffrey C., and Juuso Valim ¨ aki ¨ (2003), “Bad Reputation,” The Quarterly Journal of Economics 118, 785–814. Fishman, A., and R. Rob (2005), “Is Bigger Better? Customer Base Expansion through Word of Mouth Reputation,” forthcoming in Journal of Political Economy. Friedman, James (1971), “A Noncooperative Equilibrium for Supergames,” Review of Economic Studies 28, 1–12. Fudenberg, Drew and Eric Maskin (1986), “The Folk Theorem in Repeated Games with Discounting or with Imperfect Public Information,” Econometrica 54, 533–556. Green, Ed and Robert Porter (1984), “Noncooperative Collusion Under Imperfect Price Information,” Econometrica 52, 87–100. Holmstrom, Bengt ¨ (1999), “Managerial Incentive Problems: A Dynamic Perspective,” Review of Economic Studies 66, 169–182. (Originally (1982) in Essays in Honor of Professor Lars Wahlback.) Kandori, Michihiro (1992), “Repeated Games Played by Overlapping Generations of Players,” Review of Economic Studies 59, 81–92. Klein, B, and K Leffler (1981), “The Role of Market Forces in Assuring Contractual Performance,” Journal of Political Economy 89, 615–641. 27 Kreps, David (1990), “Corporate Culture and Economic Theory,” in J Alt and K Shepsle (Eds), Perspectives on Positive Political Economy, Cambridge: Cambridge University Press, 90–143. Kreps, David M., Paul Milgrom, John Roberts and Robert Wilson (1982), “Rational Cooperation in the Finitely Repeated Prisoners’ Dilemma,” Journal of Economic Theory 27, 245–252. Kreps, David M., and Robert Wilson (1982), “Reputation and Imperfect Information,” Journal of Economic Theory 27, 253–279. Mailath, George J, and Larry Samuelson (2001), “Who Wants a Good Reputation?,” Review of Economic Studies 68, 415–441. Mailath, George J, and Larry Samuelson (1998), “Your Reputation Is Who You’re Not, Not Who You’d Like To Be,” University of Pennsylvania and University of Wisconsin. Mailath, George J, and Larry Samuelson (2006), Repeated Games and Reputations: Long-Run Relationships, Oxford: Oxford University Press. Milgrom, Paul, and John Roberts (1982), “Predation, Reputation, and Entry Deterrence,” Journal of Economic Theory 27, 280–312. Phelan, Christopher (2001), “Public Trust and Government Betrayal,” forthcoming in Journal of Economic Theory. Ray, Debraj (2002), “The Time Structure of Self-Enforcing Agreements,” Econometrica 70, 547–582. Rotemberg, Julio, and Garth Saloner (1986), “A Supergame-Theoretic Model of Price Wars During Booms,” American Economic Review 76, 390–407. Shapiro, Carl (1983), “Premiums for High Quality Products as Rents to Reputation,” Quarterly Journal of Economics 98, 659–680. Tadelis, S. (1999), “What’s in a Name? Reputation as a Tradeable Asset,” American Economic Review 89, 548–563. Tadelis, Steven (2002), “The Market for Reputations as an Incentive Mechanism,” Journal of Political Economy 92, 854–882. Telser, L G (1980), “A Theory of Self-enforcing Agreements,” Journal of Business 53, 27–44. Tirole, Jean (1996), “A Theory of Collective Reputations (with applications to the persistence of corruption and to firm quality),” Review of Economic Studies 63, 1–22. 28 Watson, Joel (1999), “Starting Small and Renegotiation,” Journal of Economic Theory 85, 52–90. Watson, Joel (2002), “Starting Small and Commitment,” Games and Economic Behavior 38, 176–199. Wernerfelt, Birger (1988), “Umbrella Branding as a Signal of New Product Quality: An Example of Signalling by Posting a Bond,” Rand Journal of Economics 19, 458–466.

https://pdfs.semanticscholar.org/24e5/2f3bd22d4bfa86902e5ae07d57039480004f.pdf

 


Forbes Opinion: 13 Industries Soon To Be Revolutionized By Artificial Intelligence

Reporter: Aviva Lev-Ari, PhD, RN

13 Industries Soon To Be Revolutionized By Artificial Intelligence

7:00 am

30,112 views|Jan 16, 2019,

Post written by

Expert Panel, Forbes Technology Council

Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

Artificial intelligence (AI) and machine learning (ML) have a rapidly growing presence in today’s world, with applications ranging from heavy industry to education. From streamlining operations to informing better decision making, it has become clear that this technology has the potential to truly revolutionize how the everyday world works.

While AI and ML can be applied to nearly every sector, once the technology advances enough, there are many fields that are either reaping the benefits of AI right now or that soon will be. According to a panel of Forbes Technology Council members, here are 13 industries that will soon be revolutionized by AI.

1. Cybersecurity

The enterprise attack surface is massive. There are countless permutations and combinations in which the adversary can get in. It is exceptionally hard for organizations to analyze and improve their security posture. With its power to bring complex reasoning and self-learning in an automated fashion at massive scale, AI will be a game-changer in how we improve our cyber-resilience. – Gaurav Banga, Balbix

2. DevOps And Cloud Hosting

AI is starting to make its mark in DevOps. Currently, Amazon has rolled out machine learning for their Elastic Compute Cloud (EC2) instances, which applies to predictive instance autoscaling. Other cloud vendors are following suit with similar technology. Within the next 10 years, I see the same being applied to bigger things like code deployments and infrastructure provisioning. – Rick Conlee, Meticulosity

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

3. Manufacturing

Artificial intelligence in the world of manufacturing has limitless potential. From preventative maintenance to the automation of human tasks, AI will enable more efficient work that’s less prone to error and has higher quality. Initiatives from tech giants like Microsoft (AI for Accessibility) and smaller leading companies like AtBot will revolutionize AI for all information workers. – Dan Sonneborn, Aerie Consulting

4. Healthcare

Healthcare is only starting on its AI journey. Computer vision against X-rays shows promises to help pinpoint diseases; natural language processing (NLP) shows promises in drug safety; ML shows promises to find patterns within a population. Once we reach a point of true information interoperability, supporting the secure exchange of health data, all these promises will join forces to become breakthroughs for the patients. – Florian Quarré, Ciox Health

5. Construction

The construction industry has long been underserved by the technology and software sector. Many new startups like ours are using AI in a big way to slingshot the construction industry into tomorrow. Bringing AI and machine learning into this industry will make the construction process faster, safer and more cost effective by reducing human error and better utilizing big data.  – Karuna Ammireddy, Pype

6. Senior Care

With the aging Baby Boomer generation, we need solutions that provide continued efficiency for seniors to make them feel more confident about living alone or receiving support from their caregivers. While AI may not be able to understand the cultural, physical and emotional needs of people, it can provide updates to many outdated resources. – Abdullah Snobar, DMZ at Ryerson University

7. Retail

The retail industry will be one that is most impacted by AI. Its global spending is expected to grow to $7.3 billion per year by 2022. Retailers will use augmented and virtual reality functionality in advertising. Immersive product catalog visualization will grow dramatically, and shoppers will experience products before buying. It’s predicted that by 2020, chatbots will power 85% of all customer service interactions. – Nacho De Marco, BairesDev

8. Business Intelligence

Enterprises are overwhelmed by the volume of data generated by their customers, tools and processes. They are finding traditional business intelligence tools are failing. Spreadsheets and dashboards will be replaced by AI-powered tools that explore data, find insights and make recommendations automatically. These tools will change the way companies use data and make decisions. – Sean Byrnes, Outlier AI

9. City Planning

Infrastructure planning and development will get a big boost from AI. So much data can be processed and organized to help understand urban areas and how they are changing. AI data can also provide a different way of looking at growth and development, utility use, safety, and more. – Chalmers Brown, Due

10. Mental Health Diagnosis And Treatment

We are starting to see an increase in mental health issues among young people. Whether it is device addiction or withdrawal from the physical world, some are starting to isolate themselves online. This can ultimately lead to a breakdown of social cohesion. I see potential in using AI to identify people at risk and recommend therapy before they fall into a hole of depression and hopelessness. – Chris Kirby, Retired

11. Education

The basic concepts of education have not changed much across generations, and it is quite obvious that change is needed. The most pressing question is what that change should be and how to achieve it. Harnessing AI to create a personalized, dynamic and effective learning path for any subject can prove to be an amazing enabler for such a revolution. – Ofer Garnett, YouAPPi Inc.

12. Fashion

Using AI to learn about buying patterns of users across the world and predict fashion trends would be a great implementation. Having a great recommendation engine backed by AI would help users tremendously. – Amit Ojha, Diamond Foundry

13. Supply Chain Management

AI can account for more factors and complicated nonlinear and correlated dependencies of data much better than a human can do. AI can predict the future without human bias, but with a proper risk assessment, and find optimal decisions even under asymmetric cost profile. This leads to improvements in every decision. – Michael Feindt, Blue Yonder

Forbes Technology Council is an invitation-only, fee-based organization comprised of leading CIOs, CTOs and technology executives. Find out if you qualify at forbestechcoRead More

SOURCE

https://www.forbes.com/sites/forbestechcouncil/2019/01/16/13-industries-soon-to-be-revolutionized-by-artificial-intelligence/amp/?__twitter_impression=true


Broad@15 – In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health

Reporter: Aviva Lev-Ari, PhD, RN

 

THANK YOU @broadinstitute for following me @AVIVA1950

Following
A unique, collaborative community pioneering a new model of biomedical science

 

When I launched pharmaceuticalintelligence.com in April 2012, the first 26 categories of research where inspired by browsing the Broad Institute website.

Happy to report on 7/31/2019:

1,648,985 views

5,667 Posts

687 Categories – Our first 26 were in pursuit at the Broad Institute

10,105 Tags

 

#Broad@15

broadinstitute.org/15

 

In 2004, the Broad Institute of MIT and Harvard launched with a mission to improve human health.

This year marks our 15th anniversary. During that time, biology and medicine have evolved in astonishing ways, and so have we. Our community now includes more than four thousand scientists, software engineers, and more, with collaborations in more than three dozen countries.

We think the amazing pace of scientific progress is a story worth sharing. Beginning in the summer of 2019 and continuing through spring of 2020, we’ll host a series of public talks to trace the evolution of key fields of science and medicine over the last 15 years, and look ahead to how they might continue to evolve in the future.

These engaging discussions will be in place of our regular Midsummer Nights’ Science and Science for All Seasons series, which will return later in 2020. 

We hope you’ll join us in person or online! Sign up here to stay up to date!

 

Broad@15 Talk Series

Topics

The Human Genomic Revolution: Past, Present, and Future

Eric Lander 

Thursday, August 1, 2019

Over 15 years ago, the scientific community celebrated the sequencing of the first human genome. It’s time to ask how this monumental effort has transformed biomedical science, from basic research to the understanding and treatment of disease. Eric Lander, Broad Institute president and founding director and one of the principal leaders of the Human Genome Project, will survey the impact — what we’ve learned, and what lies ahead.

This lecture is presented in memory of Eliana Hechter and is supported by the Eliana Hechter Memorial Fund.

Cancer

Todd Golub

September 19, 2019

Mental Health

Benjamin Neale and Beth Stevens

October 7, 2019

Therapeutics

Anna Greka and Florence Wagner

Thursday, November 14, 2019

Genome Editing

David Liu and Feng Zhang

January 21, 2020

Infectious Disease

Deborah Hung and Pardis Sabeti

Thursday, February 13, 2020

Sequencing and Data Sciences

Jonathan Bloom and Stacey Gabriel

Wednesday, March 4, 2020

Single-cell Biology

Aviv Regev

May 5, 2020

SOURCE

https://www.broadinstitute.org/15


MinneBOS 2019, Field Guide to Data Science & Emerging Tech in the Boston Community

August 22, 2019, 8AM to 5PM at Boston University Questrom School of Business, 595 Commonwealth Avenue, Boston, MA

 

 

MinneBOS – Boston’s Field Guide to Data Science & Emerging Tech

Announcement

Leaders in Pharmaceutical Business Intelligence (LPBI) Group

 

REAL TIME Press Coverage for

 http://pharmaceuticalintelligence.com 

by

 Aviva Lev-Ari, PhD, RN

Director & Founder, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Editor-in-Chief, Open Access Online Scientific Journal, http://pharmaceuticalintelligence.com

Editor-in-Chief, BioMed e-Series, 16 Volumes in Medicine, https://pharmaceuticalintelligence.com/biomed-e-books/

@pharma_BI

@AVIVA1950

#MinneBos

 

Logo, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, Boston

Our BioMed e-series

WE ARE ON AMAZON.COM

 

https://lnkd.in/ekWGNqA

 

UPDATED AGENDA

Thursday, August 22 • 9:30am – 10:15am
Histopathological images are the gold standard tool for cancer diagnosis, whose interpretation requires manual inspection by expert pathologists. This process is time-consuming for the patients and subject to human error. Recent advances in deep learning models, particularly convolutional neural networks, combined with big databases of patient histopathology images will pave the path for cancer researchers to create more accurate guiding tools for pathologists. In this talk, I will review the latest advances of big data in healthcare analytics and focus on deep learning applications in cancer research. Targeted at a general audience, I will provide a high-level overview of technical concepts in deep learning image analysis, and describe a typical cloud-based workflow for tackling such big data problems. I will conclude my talk by sharing some of our most recent results based on a wide range of cancer types.

Speakers

avatar for Mohammad Soltanieh-ha, PhD

Mohammad Soltanieh-ha, PhD

Clinical Assistant Professor, Boston University – Questrom
Mohammad is a faculty at Boston University, Questrom School of Business, where he teaches data analytics and big data to master’s students. Mohammad’s current research area involves deep learning and its applications in cancer research.

10:15am

10:30am

Thursday, August 22 • 10:30am – 11:00am

Deep learning image recognition and classification models for fashion items

Large scale image recognition and classification is an interesting and challenging problem. This case study uses fashion-MNIST dataset that involves 60000 training images and 10000 testing images. Several popular deep learning models are explored in this study to arrive at a suitable model with high accuracy. Although convolutional neural networks have emerged as a gold-standard for image recognition and classification problems due to speed and accuracy advantages, arriving at an optimal model and making several choices at the time of specifying model architecture, is still a challenging task. This case study provides the best practices and interesting insights.

Speakers

avatar for Bharatendra Rai

Bharatendra Rai

Professor, UMass Dartmouth
Bharatendra Rai, Ph.D. is Professor of Business Analytics in the Charlton College of Business at UMass Dartmouth. His research interests include machine learning & deep learning applications.
  • Train data: 60,000
  • Test data: 10,000
  • Dataset available from Google MNIST Fashion Data – items in DB: data already labelled
  • Label and Description
  • Architecture: Input >> Conv >> Conv >> Pooling >> Dropout << Dense <<Flatten << Dropout >> Output
  • CNN vs Fully connected: 320 parameters: 3x3x1x32 + [32 BIAS TERM] = 320 vs
  • fully connected network parameters is 16 million
  • Train the model: 15 iterations – Training and Validation
  • Actual vs Predicted: 94% was classified correctly = Accuracy: 94% 5974 vs 4700 (78%)
  • Confusion Matrix – Test 720 correctly classified for item 6  – Probability va Actual Vs Predicted
  • Image generation: Noise . gnerator Network > fake Image vs Real image – GAN Loss va Discriminator Loss
  • CNN network help reduce # of parameter
  • Droppot layers can help reduce overfitting
  • validation split of x%chooses last x% of train data
  • Generation of new data is challenging

11:00am

11:15am

Thursday, August 22 • 11:15am – 12:00pm

Rapid Data Science

Most companies today require fast, traceable, and actionable answers to their data questions. This talk will present the structure of the data science process along with cutting edge developments in computing and data science technology (DST) with direct applications to real world problems (with a lot of pictures!). Everything from modeling to team building will be discussed, with clear business applications.

Speakers

avatar for Erez Kaminski

Erez Kaminski

Leaders Global Operations Fellow, MIT
Erez has spent his career helping companies solve problems using data science. He is currently a graduate student in computer science and business at MIT. Previously, he worked in data science at Amgen Inc. and as a technologist at Wolfram Research.

12:00pm

1:00pm

Thursday, August 22 • 1:00pm – 1:45pm

Health and Healthcare Data Visualization – See how you’re doing

Health and healthcare organizations are swimming in data but few have the skills to show and see the story in their data using the best practices of data visualization. This presentation raises awareness about the research that inform these best practice and stories from the front of groups who are embracing them and re-imagining how they display their data and information. These groups include the NYC Dept of Health & Mental Hygiene, The Centers for Medicare and Medicaid (CMS), and leading medical centers and providers across the country.

Speakers

avatar for Katherine Rowell

Katherine Rowell

Co-Founder & Principal, Health Data Viz
Katherine Rowell is a health, healthcare, and data visualization expert. She is Co-founder and Principal of HealthDataViz, a Boston firm that specializes in helping healthcare organizations organize, design and present visual displays of data to inform their decisions and stimulate… Read More →
  • dashboard for Hospital CEOs

1:45pm

2:00pm

Thursday, August 22 • 2:00pm – 2:45pm

AI in Healthcare

Benefits, challenges and impact of AI and Cybersecurity on medicine.

Speakers

avatar for Vinit Nijhawan

Vinit Nijhawan

Lecturer, Boston University
Vinit Nijhawan is an Entrepreneur, Academic, and Board Member with a track record of success, including 4 startups in 20 years.
  • US: Spends the most on Health Care (HC) death per 100K people is the highest
  • Eric Topol – Diagnosis is not done correctly, AI will help with diagnosis
  • Diagnosis — AI will have the most impact; VIRAL infections are diagnosed as bacterial infections and get antibiotics for treatment
  • Image Classification my ML – decline below to human misclassification
  • Training Data sets – Big data
  • Algorithms getting better
  • Data Capture getting better – HC as well
  • Investment in HC is the greatest
  • SECURITY related to Implentable Medical Devices = security attacks – hacking and sending signal to implentable devices

2:45pm

3:00pm

Empower

Thursday, August 22 • 3:00pm – 3:30pm

Patient centric AI: Saving lives with ML driven hospital interventions

This presentation will cover the use of machine learning for maximizing the impact of a hospital readmissions intervention program. With machine learning, clinical care teams can identify and focus their intervention efforts on patients with the highest risk of readmission. The talk will go over the goals, logistics, and considerations for defining, implementing, and measuring our ML driven intervention program. While covering some technical details, this presentation will focus on the business implementation of advanced technology for helping people live healthier lives.

Speakers

avatar for Miguel Martinez

Miguel Martinez

Data Scientist, Optum
Miguel Martinez is a Data Scientist at Optum Enterprise Analytics. Relied on as a tech lead in advancing AI healthcare initiatives, he is passionate about identifying and developing data science solutions for the benefit of organizations and people.

 

3:30pm

3:45pm

Thursday, August 22 • 3:45pm – 4:15pm

Using Ontologies to Power AI Systems

There’s a great deal of confusion about the role of a knowledge architecture in artificial intelligence projects. Some people don’t believe that any reference data is necessary. But in reality reference data is required- even if there is no metadata or architecture definitions outside defined externally for an AI algorithm, someone has made the decisions about architecture and classification within the program. However, this will not work for every organization because there are terms, workflows, product attributes, and organizing principles that are unique to the organization and that need to be defined for AI tools to work most effectively.

Speakers

avatar for Seth Earley

Seth Earley

CEO, Earley Information Science
Seth Earley is a published author and public speaker about artificial intelligence and information architecture. He wrote “There’s no AI without IA” which has become an industry catchphrase used by a number of people including Ginny Rometty, the CEO of IBM.
  • Ontology, taxonomies, thesauri – conceptual relationships
  • Object-Oriented Programming and Information Architecture using AI is Old wine in new bottles

4:15pm

Thursday, August 22

TBA

 Senior Leadership Panel: Future Directions of Analytics

This panel includes senior leaders from across industry, academia & government to discuss challenges they are tackling, needs they anticipate and goals they will achieve

Moderators

avatar for Bonnie Holub, PhD

Bonnie Holub, PhD

Industry & Business Data Science, Teradata
Bonnie has a PhD in Artificial Intelligence and specializes in correlating disparate sets of Big Data for actionable results.

The Sylvester Comprehensive Cancer Center of the University of Miami named 71st NCI designated Cancer Center

 

Reporter: Stephen J. Williams, PhD

As seen in the Cancer Letter at https://cancerletter.com/articles/20190729_1/

Conversation with The Cancer Letter

Sylvester becomes 71st NCI-designated cancer center

Stephen Nimer

Director,

Sylvester Comprehensive Cancer Center

 

After six years of  aggressively recruiting and spending more than $250 million to build up its programs, Sylvester Comprehensive Cancer Center has become the 71st NCI-designated cancer center in the US and the only such institution in South Florida.

The designation was announced July 29.

Sylvester, which is a part of the University of Miami Leonard M. Miller School of Medicine, is one of 64 cancer centers with the NCI Cancer Center designation in the nation. Fifty of these centers hold the Comprehensive Cancer Center designation. Seven more are designated as Basic Laboratory Cancer Centers.

“There are over 21 million people who live in the state of Florida. In 2014, Florida became the third largest state in the United States, surpassing New York—yet New York has seven NCI designated cancer centers and Florida had had only one,” Stephen D. Nimer, director of Sylvester, said to The Cancer Letter.

“There are over six million people in our catchment area, South Florida, and if they wanted to go to an NCI-designated cancer center they’d have to either get on a plane or drive nearly 300 miles—to Tampa.”

Public health programs that helped Sylvester secure the NCI designation include the Game Changer vehicle, which brings evidence-based interventions to underserved communities in the cancer center’s catchment area (The Cancer Letter, April 27, 2018). The center’s cancer control program also includes the Firefighter Cancer Initiative, a long-term study of exposures to carcinogens and ways to reduce and prevent cancer risks for Florida firefighters.

 

 

The cancer center is working on deploying another Game Changer vehicle. Recently, Peter Tunney, a New York and Miami-based artist and gallerist who donated a painting for the first Game Changer van, donated another painting that Sylvester can sell to raise money for its programs (The Cancer Letter, April 27, 2018).

 

“When they got that designation, they were walking on sunshine,” Tunney said to The Cancer Letter. “I think it’s a universal idea. I think that’s the goal for all of us—for all of mankind, for sick and healthy—to have that feeling that is so rare today: I am walking on sunshine. It’s almost like a thing of the past. Who can walk on sunshine today, in this crazy world filled with suffering and illness? And I just feel like we can, we can, it’s possible to be grateful for the things we have.

The intense yellow wallpaper motif reminds Tunney of the wallpaper in his grandmother’s house in the 1960s and 1970s, the time when American astronauts walked on the moon. “It’s somebody’s grandmother’s wallpaper from the sixties. We look back at that time, we look back at landing on the moon, and everyone is aflutter, ‘Oh, those were the good old days.’ No, these are the good old days.”

The word “comprehensive” in Sylvester’s name doesn’t refer to its level of NCI designation. When it was founded in 1973, the institution was known as the Comprehensive Cancer Center for the State of Florida. In 1992, after receiving a $27.5 million gift from the philanthropist Harcourt Sylvester Jr., it was renamed Sylvester Comprehensive Cancer Center.

 

Sylvester director Nimer spoke with Paul Goldberg, editor and publisher of The Cancer Letter.

 

Paul Goldberg:

First of all, congratulations.

Stephen Nimer: 

Thank you; it’s a big deal.

 

PG:

How long did it take to get this done?

SN:

I’d say, six years. I arrived in 2012, seven years ago, and the first year started by assessing what’s going on at Sylvester. We then developed our first five-year strategic plan, which ran from 2014 to 2018, and we submitted our [Cancer Center Support Grant] application in September 2018. We’re now in the midst of our second five-year plan.

 

PG:

And how much money did it require?

SN:

I’d have to add it all up. One of the most important things for us was that the state, in 2014, started giving us a bit over $16 million a year so that we could become NCI-designated. The health system, over a five-to-six-year period, probably gave us somewhere between $90 and $100 million. And then we’ve raised philanthropy. The philanthropy over five to six years, is maybe close to $100 million. So, it’s probably $250 -$270 million.

 

PG:

How many people did you have to recruit?

SN:

We went in [to NCI] with 124 members on our CCSG application, but over the last seven years we’ve recruited nearly 150 people. In addition to recruiting researchers I’ve been given the opportunity to build the clinical programs also.

Many of the clinical people are not included on the grant, because the grant has very specific requirements to be a member. For example, we’ve hired a couple of breast cancer surgeons, and they are not listed on the grant, because they are not yet doing significant research.

The NCI doesn’t want to know about people who don’t have grants or aren’t running clinical trials. So, out of the 124, which is what we went in with, I believe nearly 50 of our members were new.

 

PG:

How is your cancer center different from all others?

SN:

One of the things that we got the highest marks on is our community outreach and engagement efforts and how relevant the research we’re doing is to our catchment area.

A couple of examples:

We have a West Indies population, so we have an endemic HTLV-1-infected population, and thus a significant number of HTLV-1-related adult T-cell leukemia patients. So, one of our physician scientists has an R01 studying ATL. And we have a number of clinical trials for people with adult T-cell leukemia.

We also have a large burden of advanced cervical cancer patients in our region, especially in Little Haiti. And so, we have a lot of efforts on early detection of high-risk HPV, prevention and clinical treatment trials for women with cervical cancer.

Another thing that distinguishes us from many centers is the diversity of our faculty, our students, and the patients we put on clinical trials. In our CCSG application, roughly 30% of the patients on interventional trials were black and 40% were Hispanic—so both racial and ethnic diversity. We also have incredible socio-economic diversity.

What’s unique among the black population in our catchment area is that it is Afro-Caribbean more than African American—different genetics, different cultures.

The Hispanic population is unique as well. MD Anderson is probably largely Mexican Americans. New York is probably mostly Dominican and Puerto Rican. We have significant populations of Cuban Americans, Venezuelans, Brazilians, Argentinians, Colombians—an incredibly diverse group.

One example of how this plays out is in our prostate cancer research. The watch-and-wait approach is an appropriate strategy for many people. We found that our black population has more anterior prostate cancer lesions, so when you do blind biopsies, you’re more likely to miss lesions.

And then we’ve looked among the Hispanic populations as to who has a better or worse prognosis and we’ve identified subgroups within the Hispanic population that have different genetics and a different biology. So, we are tailoring our approach. Based on genetic ancestry as well as other factors.

The other thing is, we have a very strong cancer epigenetics programs, a very strong program on infections and cancer, including H. Pylori, HPV, and hepatitis viruses B and C.

We are very focused on developing programs that meet the needs of the people in this six-million-plus community.

Our catchment area is four counties, somewhat famous, because of the election news nearly every cycle: Broward, Palm Beach County, Miami Dade and Monroe County.

 

PG:

New York, where you come from, has an NCI-designated cancer center on every street corner. And Miami—make that South Florida—has just one now. How is Florida different? You would have thought that there would be multiple NCI-designated cancer centers in South Florida.

SN:

Your point is very well taken. There are over 21 million people who live in the state of Florida. In 2014, Florida became the third largest state in the United States, surpassing New York—yet New York has seven NCI designated cancer centers and Florida had had only one.

Moffitt had gotten a huge investment from the state in the past, and that enabled them to become NCI-designated. And upon designation, they could recruit more researchers, attract more patients, and get more philanthropy, and get all the positives from that. And for the longest time, Florida has only had one.

There are over six million people in our catchment area, South Florida, and if they wanted to go to an NCI-designated cancer center they’d have to either get on a plane or drive nearly 300 miles—to Tampa.

Now, one problem that we face in our region, which is very splintered in terms of market share, etc. is that there’s a lot of community hospitals here that have cancer centers, but they are not necessarily conducting cancer research in any way.

I’ve been reading Joe Simone’s Journal of Clinical Oncology paper from 2002, where he talks about the fact that there are no criteria to call yourself a cancer center. And because people may feel like you can get great care anywhere, they may not seek out the experts.

Probably, in many markets throughout the US, there’s still an ongoing process of trying to educate people as to what’s the difference between an NCI-designated cancer center and one that’s not. And, obviously, the designation is given, because of the research that’s going on. And so, people wonder: “What is the connection between the research and me being a patient there?”

A big part of educating our community is to tell people that oftentimes the doctors who are doing research on a specific cancer have a deeper knowledge about its management. Also, experts more often make the correct diagnosis and come up with more exact multidisciplinary treatment approaches for many cancers.

NCI-designated cancer centers have more clinical trials and more investigator-initiated clinical trials. Now, with NCI designation, we’ll have access to the [NCI Cancer Therapy Evaluation Program] drugs and treatments. Already, we have a very robust phase I clinical trials program, having put 161 patients on phase I trials last year.

This means that we are doing more innovative things, not accepting the status quo, which is what you often get in community hospitals.

I get asked all the time: “Don’t only complicated cancers need to get seen in Sylvester?” and I usually say, “Any cancer that you have is complicated.”

There are other things we need to stress:  Sometimes patients spend more time figuring out which flat screen TV they’re going to buy than they do figuring out who should be taking care of them. And so, we tell patients to ask: “How sure are you that you have made the correct diagnosis?”

So many people are misdiagnosed in the US each year, and sometimes people are treated who don’t need to be treated and vice-versa.

For instance, we are working with Moffitt and the University of Florida on pancreas cancer. We’re hoping to look at how many patients in our state are told that with radiation, chemotherapy, and surgery there’s a potential for cure, as opposed to being told that pancreatic cancer is terrible, and you better get your affairs in order.

While the NCI designation, of course, relates to multidisciplinary and collaborative research efforts, we have—given the diversity of our catchment area and community—an important task to educate people in culturally appropriate ways.

 

PG:

Well, there’s a lot happening that actually very good. Having the University of Florida on the path to designation is also wonderful for the state. There’s so much room in there for growth.

SN:

Absolutely. Absolutely.

 

PG:

Since we are talking about Joe Simone’s paper, the word “comprehensive” is in the name of your cancer center. Yet, you don’t—yet—have the NCI-koshered comprehensive designation. Can you change the name? Do you need to?

SN:

The University of Miami’s cancer center started in 1973 shortly after Nixon signed the National Cancer Act. Later, with a naming gift from the Sylvester family, we opened our doors as the Sylvester Comprehensive Cancer Center in 1992. The comprehensive in our name does not refer to an NCI designation. It’s been our name because we have always delivered comprehensive cancer care.

 

PG:

Let’s talk about the Game Changer. That’s such a cool thing. That was one of your center’s great ideas.

SN:

The Game Changer vehicle has been really incredible, already in its impact on our cancer education and early detection programs (The Cancer Letter, April 27, 2018). We’re accruing people for research, and we’re already following some of their health habits.

We’re in the process of delivering HPV vaccines. We have been working with our AIDS group, so you can get PrEP. And we go into communities, like Little Havana, Liberty City, Little Haiti. We are also going into areas to provide education on HIV. As you know, the incidence of HIV in the Miami Dade area is the highest in the nation. So, the vehicle is already having an impact in so many ways.

We’ve just gotten the second Game Changer!

Peter Tunney, the artist, is going to wrap this one also. And this one’s going to focus primarily on Monroe County, which has been hit hard by hurricanes, and also has very poor medical infrastructure.

If you travel to Miami, for business or pleasure, you don’t realize that it’s not that far to get to an extraordinarily rural area. The density of population in Monroe county is very low and access to health care is limited.

The areas that we’re trying to reach have so much socioeconomic gap and disparities. And the Game Changer vehicles are going to help us reach people who otherwise do not access traditional medical systems.

You asked me about the Game Changer vehicle as an idea, and I wanted to shout out the leadership team that we’ve been able to put together at Sylvester. They have been incredible. Our people have worked together in amazing ways. And so, when you say, “That’s a great idea of yours,” yours is the whole team, of course.

 

PG:

Of course.

SN:

It’s remarkable how much work it takes to build the research programs that allow us to even have a competitive application. There were so, so many people who spent so much time for the benefit of the cancer center, and not for their own research.

 

PG:

Can we talk about hurricanes? They have an impact on your mission.

SN:

It’s interesting, because the Sylvester Comprehensive Cancer Center opened its doors in 1992, which is just when Hurricane Andrew hit. I’ve looked through our archives: There are some great articles in the Miami newspaper, because we remained open and provided care right after Hurricane Andrew, which has been the most devastating hurricane here in, I don’t know exactly how many years, maybe 30 or 50 or whatever.

But even following the more recent hurricanes, we’ve been able to provide care for our patients. After Hurricane Irma, in one of our satellites we were open the next day, and we treated 30 patients with chemotherapy who needed it, even though many folks were without electricity.

It’s a unique challenge. We have hurricane preparedness for our laboratories. We have drills for the hospital. And we have a command center.

During Irma, because I live on Miami Beach, in a mandatory evacuation zone, I had to leave my home for a few days. And so, my wife and I slept in the hospital for three nights. There’s food, water, and air conditioning in the hospital. It’s not a bad place to be!

 

PG:

You’re driving now to one of the clinics, even as we speak; right? One of the satellite clinics?

SN:

Yes.

 

PG:

Can you tell me about that?

SN:

We have seven sites where we deliver clinical care. The main site in downtown Miami, and then we have three quite large facilities, one in Coral Gables, one in Plantation, one in Deerfield Beach. And we have three other satellites that are smaller, in Coral Springs, Hollywood, and Kendall.

And this allows us to deliver regional care. We’re all on the same EPIC electronic medical record. And we have patients enrolled on clinical trials in the satellites. Not all the satellites at the moment can have a research pharmacy. But the plan is we’re going to continue our expansion of facilities and services and increase the number of accruals and the sophistication of the trials that are available here. Everybody working in these satellites is a University of Miami employee.

The doctors are all part of our site disease groups, and they teleconference in to meetings and lectures. And many of them spend a day in Miami at the main satellite for education and clinical and other purposes.

Many of the doctors in the satellites are principal investigators on the clinical trials. And it’s important because people don’t want to travel necessarily on the freeways here to get to downtown Miami. And so, we can deliver academic care out in the community, which is always important and a challenging thing to do.

 

PG:

Is there anything we’ve forgotten, anything we need to address?

SN:

Maybe I can talk briefly about the state money for a minute. When Sen. [Rick] Scott [(R-FL)] was the governor, he got us together in his office, the University of Florida, Moffitt, and the University of Miami, and asked us what we needed to become major cancer centers and attain NCI designation so we could have three such facilities in the state.

The next year, the state gave us $10. 5 million to split three ways. So, we each got $3.5 million to bring in somebody from outside the state of Florida, a world-class scientist, and provide them with $500,000 a year for seven years.

We brought Ramin Shiekhattar from the Wistar Institute. He’s one of the leaders of our Cancer Epigenetics Program and a year and a half ago, Ramin won one of the highly prestigious NIH Director’s Pioneer Awards. I believe they give 10 out a year.

Next, the state set up a pool of $60 million to be shared between the three institutions each year for five and now six years. These funds are being used so that all three institutions can attain NCI designation. The directors of these cancer centers get along extremely well, and, in a pretty unique model, we created something called the Florida Academic Cancer Center Alliance.

It exists to promote collaborations across our institutions to conduct important cancer research and bring more federal research dollars to the state.

There are one or two other points I’d like to make: Another person we brought in, Gilberto Lopes, is the head of our Global Oncology Program and the editor of the Journal of Global Oncology for ASCO.

He just gave a plenary talk at 2018 ASCO, showing that immunotherapy is better than chemotherapy for the upfront treatment of certain subsets of lung cancer. His talk was one of four plenary talks we’ve recently given at important national cancer meetings.

I think the other message is just the level at which we’re operating on now. We are demonstrating to our community that we have people who are national leaders, and programs that are among the very best in the country. For this, I must thank the incredible team of researchers who work at Sylvester.

I think that, as we recruit more and more people, this designation is going to help us. I’m very pleased that when we submit NIH grants, the reviewers comment upon the environment in Miami, we now get the high scores for the research environment.

 

PG:

This brings up a problem that held back Sylvester for years, which was the lack of independence of the cancer center, or at least it was perceived to be that. Do you have the independence you need now?

SN:

First of all, I would never have left Sloan Kettering without the authority I needed from the leadership of the University of Miami, the health system and the Miller School of Medicine…

 

PG:

Yeah, that’s a good point.

SN:

I should point out, that I am the head of the cancer center, but I’m also the head of the oncology service line for UHealth health system. This arrangement allows me to align the clinical and the research missions in a way that many cancer center directors cannot.

It’s a real privilege, and I have great leadership and great people working on the service line to make our patient care and patient-related activities superb.

 

PG:

Well, that’s hugely important.

Copyright (c) 2018 The Cancer Letter Inc.

More on NCI Designated Cancer Centers can be found here: https://www.cancer.gov/research/nci-role/cancer-centers

Other articles on NCI Cancer Centers on the Open Access Online Journal include:

Salivary Gland Cancer – Adenoid Cystic Carcinoma: Mutation Patterns: Exome- and Genome-Sequencing @ Memorial Sloan-Kettering Cancer Center

Engineered Bacteria used as Trojan Horse for Cancer Immunotherapy

First Cost-Effectiveness Study of Multi-Gene Panel Sequencing in Advanced Non-Small Cell Lung Cancer Shows Moderate Cost-Effectiveness, Exposes Crucial Practice Gap