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Archive for the ‘Alzheimer’s Disease’ Category


Gene Therapy could be a Boon to Alzheimer’s disease (AD): A first-in-human clinical trial proposed

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

A recent research work performed by the Researchers at the University of California San Diego School of Medicine has shared their first-in-human Phase I clinical trial to assess the safety and viability of gene therapy to deliver a key protein into the brains of persons with Alzheimer’s Disease (AD) or Mild Cognitive Impairment (MCI), a condition that often precedes full-blown dementia.  

Mark Tuszynski, M.D., Ph.D., Professor of Neuroscience and Director of the Translational Neuroscience Institute at UC San Diego and team predicted that Gene therapy could be a boon to potential treatments for the disorders like AD and MCI.

The study provides an insight into the genetic source of these mental diseases.

The roots of mental disorders have remained an enigma for so many years. Alzheimer’s disease (AD) is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. AD is a neurodegenerative condition. A buildup of plaques and tangles in the brain, along with cell death, causes memory loss and cognitive decline. In most people with the disease, those with the late-onset type – symptoms first appear in their mid-60s. Alzheimer’s disease is the mostly appearing type of dementia in patients.

Drawing comparing a normal aged brain (left) and the brain of a person with Alzheimer’s (right).
Image Source: https://en.wikipedia.org/wiki/Alzheimer%27s_disease

What the study impart?

Despite decades of effort and billions of dollars of research investment, there are just mere two symptomatic treatments for AD. There is no cure or approved way to slow or stop the progression of the neurological disorder that afflicts more than 5 million Americans and is the sixth leading cause of death in the United States.

Prof. Tuszynski said gene therapy has been tested on multiple diseases and conditions, represents a different approach to a disease that requires new ways of thinking about the disease and new attempts at treatments.

The research team found that delivering the BDNF to the part of the brain that is affected earliest in Alzheimer’s disease; the entorhinal cortex and hippocampus – was able to protect from ongoing cell degeneration by reversing the loss of connections. “These trials were observed in aged rats, amyloid mice, and aged monkeys.”

The protein, called Brain-Derived Neurotrophic Factor or BDNF, a family of growth factors found in the Brain and Central Nervous System that support the survival of existing neurons and promote growth and differentiation of new neurons and synapses. BDNF is especially important in brain regions susceptible to degeneration in AD. It is normally produced throughout life in the entorhinal cortex, an important memory center in the brain and one of the first places where the effects of AD typically appear in the form of short-term memory loss. Persons with AD have diminished levels of BDNF.

However, BDNF is a large molecule and cannot pass through the Blood-Brain Barrier. As a solution, researchers will use gene therapy in which a harmless Adeno-Associated Virus (AAV2) is modified to carry the BDNF gene and injected directly into targeted regions of the brain, where researchers hope it will prompt the production of therapeutic BDNF in nearby cells.

Precautions were taken precisely in injecting the patient to avoid exposure to surrounding degenerating neurons since freely circulating BDNF can cause adverse effects, such as seizures or epileptic conditions.

The recent research and study speculate a safe and feasible assessment of the AAV2-BDNF pathway in humans. A previous gene therapy trial from 2001 to 2012 using AAV2 and a different protein called Nerve Growth Factor (NGF) was carried out by Prof. Tuszynski and team where they observed immense growth, axonal sprouting, and activation of functional markers in the brains of participants.

He also shared that “The BDNF gene therapy trial in AD represents an advancement over the earlier NGF trial, BDNF is a more potent growth factor than NGF for neural circuits that degenerate in AD. Besides, new methods for delivering BDNF will more effectively deliver and distribute it into the entorhinal cortex and hippocampus.”

The research team hopes that the three-year-long trial will recruit 12 participants with either diagnosed AD or MCI to receive AAV2-BDNF treatment, with another 12 persons serving as comparative controls over that period.

The researchers have plans to build on recent successes of gene therapy in other diseases, including a breakthrough success in the treatment of congenital weakness in infants (spinal muscular atrophy) and blindness (Leber Hereditary Optic Neuropathy, a form of retinitis pigmentosa).”

Main Source

https://www.universityofcalifornia.edu/news/could-gene-therapy-halt-progression-alzheimers-disease-first-human-clinical-trial-will-seek?utm_source=fiat-lux

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https://pharmaceuticalintelligence.com/2016/02/15/alzheimers-disease-tau-art-thou-or-amyloid/

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Inhibitory CD161 receptor recognized as a potential immunotherapy target in glioma-infiltrating T cells by single-cell analysis

Reporter: Dr. Premalata Pati, Ph.D., Postdoc

 

Brain tumors, especially the diffused Gliomas are of the most devastating forms of cancer and have so-far been resistant to immunotherapy. It is comprehended that T cells can penetrate the glioma cells, but it still remains unknown why infiltrating cells miscarry to mount a resistant reaction or stop the tumor development.

Gliomas are brain tumors that begin from neuroglial begetter cells. The conventional therapeutic methods including, surgery, chemotherapy, and radiotherapy, have accomplished restricted changes inside glioma patients. Immunotherapy, a compliance in cancer treatment, has introduced a promising strategy with the capacity to penetrate the blood-brain barrier. This has been recognized since the spearheading revelation of lymphatics within the central nervous system. Glioma is not generally carcinogenic. As observed in a number of cases, the tumor cells viably reproduce and assault the adjoining tissues, by and large, gliomas are malignant in nature and tend to metastasize. There are four grades in glioma, and each grade has distinctive cell features and different treatment strategies. Glioblastoma is a grade IV glioma, which is the crucial aggravated form. This infers that all glioblastomas are gliomas, however, not all gliomas are glioblastomas.

Decades of investigations on infiltrating gliomas still take off vital questions with respect to the etiology, cellular lineage, and function of various cell types inside glial malignancies. In spite of the available treatment options such as surgical resection, radiotherapy, and chemotherapy, the average survival rate for high-grade glioma patients remains 1–3 years (1).

A recent in vitro study performed by the researchers of Dana-Farber Cancer Institute, Massachusetts General Hospital, and the Broad Institute of MIT and Harvard, USA, has recognized that CD161 is identified as a potential new target for immunotherapy of malignant brain tumors. The scientific team depicted their work in the Cell Journal, in a paper entitled, “Inhibitory CD161 receptor recognized in glioma-infiltrating T cells by single-cell analysis.” on 15th February 2021.

To further expand their research and findings, Dr. Kai Wucherpfennig, MD, PhD, Chief of the Center for Cancer Immunotherapy, at Dana-Farber stated that their research is additionally important in a number of other major human cancer types such as 

  • melanoma,
  • lung,
  • colon, and
  • liver cancer.

Dr. Wucherpfennig has praised the other authors of the report Mario Suva, MD, PhD, of Massachusetts Common Clinic; Aviv Regev, PhD, of the Klarman Cell Observatory at Broad Institute of MIT and Harvard, and David Reardon, MD, clinical executive of the Center for Neuro-Oncology at Dana-Farber.

Hence, this new study elaborates the effectiveness of the potential effectors of anti-tumor immunity in subsets of T cells that co-express cytotoxic programs and several natural killer (NK) cell genes.

The Study-

IMAGE SOURCE: Experimental Strategy (Mathewson et al., 2021)

 

The group utilized single-cell RNA sequencing (RNA-seq) to mull over gene expression and the clonal picture of tumor-infiltrating T cells. It involved the participation of 31 patients suffering from diffused gliomas and glioblastoma. Their work illustrated that the ligand molecule CLEC2D activates CD161, which is an immune cell surface receptor that restrains the development of cancer combating activity of immune T cells and tumor cells in the brain. The study reveals that the activation of CD161 weakens the T cell response against tumor cells.

Based on the study, the facts suggest that the analysis of clonally expanded tumor-infiltrating T cells further identifies the NK gene KLRB1 that codes for CD161 as a candidate inhibitory receptor. This was followed by the use of 

  • CRISPR/Cas9 gene-editing technology to inactivate the KLRB1 gene in T cells and showed that CD161 inhibits the tumor cell-killing function of T cells. Accordingly,
  • genetic inactivation of KLRB1 or
  • antibody-mediated CD161 blockade

enhances T cell-mediated killing of glioma cells in vitro and their anti-tumor function in vivo. KLRB1 and its associated transcriptional program are also expressed by substantial T cell populations in other forms of human cancers. The work provides an atlas of T cells in gliomas and highlights CD161 and other NK cell receptors as immune checkpoint targets.

Further, it has been identified that many cancer patients are being treated with immunotherapy drugs that disable their “immune checkpoints” and their molecular brakes are exploited by the cancer cells to suppress the body’s defensive response induced by T cells against tumors. Disabling these checkpoints lead the immune system to attack the cancer cells. One of the most frequently targeted checkpoints is PD-1. However, recent trials of drugs that target PD-1 in glioblastomas have failed to benefit the patients.

In the current study, the researchers found that fewer T cells from gliomas contained PD-1 than CD161. As a result, they said, “CD161 may represent an attractive target, as it is a cell surface molecule expressed by both CD8 and CD4 T cell subsets [the two types of T cells engaged in response against tumor cells] and a larger fraction of T cells express CD161 than the PD-1 protein.”

However, potential side effects of antibody-mediated blockade of the CLEC2D-CD161 pathway remain unknown and will need to be examined in a non-human primate model. The group hopes to use this finding in their future work by

utilizing their outline by expression of KLRB1 gene in tumor-infiltrating T cells in diffuse gliomas to make a remarkable contribution in therapeutics related to immunosuppression in brain tumors along with four other common human cancers ( Viz. melanoma, non-small cell lung cancer (NSCLC), hepatocellular carcinoma, and colorectal cancer) and how this may be manipulated for prevalent survival of the patients.

References

(1) Anders I. Persson, QiWen Fan, Joanna J. Phillips, William A. Weiss, 39 – Glioma, Editor(s): Sid Gilman, Neurobiology of Disease, Academic Press, 2007, Pages 433-444, ISBN 9780120885923, https://doi.org/10.1016/B978-012088592-3/50041-4.

Main Source

Mathewson ND, Ashenberg O, Tirosh I, Gritsch S, Perez EM, Marx S, et al. 2021. Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysis. Cell.https://www.cell.com/cell/fulltext/S0092-8674(21)00065-9?elqTrackId=c3dd8ff1d51f4aea87edd0153b4f2dc7

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New Treatment in Development for Glioblastoma: Hopes for Sen. John McCain

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Gamma Linolenic Acid (GLA) as a Therapeutic tool in the Management of Glioblastoma

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Contribution of Nervous System Functional Deterioration to late-life Mortality: The Role Neurofilament light chain (NfL) a Blood Biomarker for the Progression of Neurological Diseases and its Correlation to Age and Life Expectancy

 

Reporter: Aviva Lev-Ati, PhD, RN

 

A neuronal blood marker is associated with mortality in old age

Abstract

Neurofilament light chain (NfL) has emerged as a promising blood biomarker for the progression of various neurological diseases. NfL is a structural protein of nerve cells, and elevated NfL levels in blood are thought to mirror damage to the nervous system. We find that plasma NfL levels increase in humans with age (n = 122; 21–107 years of age) and correlate with changes in other plasma proteins linked to neural pathways. In centenarians (n = 135), plasma NfL levels are associated with mortality equally or better than previously described multi-item scales of cognitive or physical functioning, and this observation was replicated in an independent cohort of nonagenarians (n = 180). Plasma NfL levels also increase in aging mice (n = 114; 2–30 months of age), and dietary restriction, a paradigm that extends lifespan in mice, attenuates the age-related increase in plasma NfL levels. These observations suggest a contribution of nervous system functional deterioration to late-life mortality.

SOURCE

How long will a healthy older person live? A substance in blood may provide a clue

Levels of a substance in nonagenerians’ and centenarians’ blood accurately predict how much longer they’re going to live. The substance comes from the brain.

The findings, in a study published in Nature Aging, could prove useful in developing life-extending drugs. They also raise questions about the brain’s role in aging and longevity.

The study, conducted by Stanford investigators including neuroscientist Tony Wyss-Coray, PhD, in collaboration with researchers in Denmark and Germany, zeroed in on a substance whose technical name is neurofilament light chain (abbreviated NfL). A structural protein produced in the brain, NfL is found in trace amounts in cerebrospinal fluids and blood, where it’s an indicator of damage to long extensions of nerve cells called axons.

Axons convey signals from one nerve cell to the next and are critical to all brain function. You’d rather they remain intact.

Too much NfL (different from the NFL)

High NfL levels in the blood have previously been associated with Alzheimer’s disease, multiple sclerosis, Huntington’s disease, amyotrophic lateral sclerosis (Lou Gehrig’s disease) and other neurological disorders. But the people monitored in the new study were generally pretty healthy for their age.

The researchers first looked at 122 people whose ages ranged from 21 to 107, and found increasing blood levels of NfL — as well as increasing variation among individuals — with increasing age.

Next, the scientists followed the fates of 135 people age 100 or over for a four-year period. Most of those centenarians were in good shape to begin with, as shown by their performance on standard tests of mental ability and by a measure of their capacity to meet the routine demands of daily living.

Not unexpectedly, those whose mental tests indicated impairment had more NfL in their blood than those with the sharpest minds did. And those with low levels were substantially likelier to live longer than those with high levels.

A look at people in their 90s confirmed the findings in the over-100 group. Blood NfL levels among 180 93-year-olds not only predicted the duration of these folks’ survival, but did so better than mental or daily-coping test scores did.

The investigators showed that mice’s blood NfL levels, too, increase with age. But cutting their caloric intake, beginning in young adulthood — already known to prolong the lives of mice and numerous other species — chopped the little creatures’ blood levels of this substance in half in old age. (This new finding doesn’t prove that lowering NfL blood levels causes increased longevity, but it’s consistent with it.)

Tie to life expectancy?

At a minimum, NfL appears to accurately flag mortality’s approach. That means it might be possible to monitor it as a surrogate marker for remaining life expectancy, much as blood cholesterol levels are used as proxies for cardiovascular health. If so, it could someday help drug developers assess life-extending interventions’ efficacy.

Clinical trials of interventions believed to enhance longevity have been impractical, because it would almost certainly take so long to get a statistically significant result that such trials would be hugely expensive — a major hang-up for pharmas considering investment in longevity drugs. But monitoring a proxy such as NfL could cut years off of such trials’ duration, perhaps encouraging drug developers to dive into the clinical arena with life-prolonging pharmacological candidates.

Possibly most intriguing of all: The new findings hint that maintaining a healthy brain in old age is the best route to a long life.

“It will be interesting to see how and why the brain might be so important in counting down our final years and months,” Wyss-Coray told me.

Photo by Pablo Bendandi

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Dysregulation of ncRNAs in association with Neurodegenerative Disorders

Curator: Amandeep Kaur

Research over the years has added evidences to the hypothesis of “RNA world” which explains the evolution of DNA and protein from a simple RNA molecule. Our understanding of RNA biology has dramatically changed over the last 50 years and rendered the scientists with the conclusion that apart from coding for protein synthesis, RNA also plays an important role in regulation of gene expression.

Figure: Overall Taxonomy of ncRNAs
Figure: Overall Taxonomy of ncRNAs
https://www.nature.com/articles/s42256-019-0051-2

The universe of non-coding RNAs (ncRNAs) is transcending the margins of preconception and altered the traditional thought that the coding RNAs or messenger RNAs (mRNAs) are more prevalent in our cells. Research on the potential use of ncRNAs in therapeutic relevance increased greatly after the discovery of RNA interference (RNAi) and provided important insights into our further understanding of etiology of complex disorders.

Figure: Atomic Structure of Non-coding RNA
https://en.wikipedia.org/wiki/Non-coding_RNA

Latest research on neurodegenerative disorders has shown the perturbed expression of ncRNAs which provides the functional association between neurodegeneration and ncRNAs dysfunction. Due to the diversity of functions and abundance of ncRNAs, they are classified into Housekeeping RNAs and Regulatory ncRNAs.

The best known classes of ncRNAs are the microRNAs (miRNAs) which are extensively studied and are of research focus. miRNAs are present in both intronic and exonic regions of matured RNA (mRNA) and are crucial for development of CNS. The reduction of Dicer-1, a miRNA biogenesis-related protein affects neural development and the elimination of Dicer in specifically dopaminergic neurons causes progressive degeneration of these neuronal cells in striatum of mice.

A new class of regulatory ncRNAs, tRNAs-derived fragments (tRFs) is superabundantly present in brain cells. tRFs are considered as risk factors in conditions of neural degeneration because of accumulation with aging. tRFs have heterogenous functions with regulation of gene expression at multiple layers including regulation of mRNA processing and translation, inducing the activity of silencing of target genes, controlling cell growth and differentiation processes.

The existence of long non-coding RNAs (lncRNAs) was comfirmed by the ENCODE project. Numerous studies reported that approximately 40% of lncRNAs are involved in gene expression, imprinting and pluripotency regulation in the CNS. lncRNA H19 is of paramount significance in neural viability and contribute in epilepsy condition by activating glial cells. Other lncRNAs are highly bountiful in neurons including Evf2 and MALAT1 which play important function in regulating neural differentiation and synapse formation and development of dendritic cells respectively.

Recently, a review article in Nature mentioned about the complex mechanisms of ncRNAs contributing to neurodegenerative conditions. The ncRNA-mediated mechanisms of regulation are as follows:

  • Epigenetic regulation: Various lncRNAs such as BDNF-AS, TUG1, MEG3, NEAT1 and TUNA are differentially expressed in brain tissue and act as epigenetic regulators.
  • RNAi: RNA interference includes post-transcriptional repression by small-interfering RNAs (siRNAs) and binding of miRNAs to target genes. In a wide spectrum of neurodegenerative diseases such as Alzheimer’s disease, Parkinson disease, Huntington’s disease, Amyotrophic lateral sclerosis, Fragile X syndrome, Frontotemporal dementia, and Spinocerebellar ataxia, have shown perturbed expression of miRNA.
  • Alternative splicing: Variation in splicing of transcripts of ncRNAs has shown adverse affects in neuropathology of degenerative diseases.
  • mRNA stability: The stability of mRNA may be affected by RNA-RNA duplex formation which leads to the degradation of sense mRNA or blocking the access to proteins involved in RNA turnover and modify the progression of neurodegenerative disorders.
  • Translational regulation: Numerous ncRNAs including BC200 directly control the translational process of transcripts of mRNAs and effect human brain of Alzheimer’s disease.
  • Molecular decoys: Non-coding RNAs (ncRNAs) dilute the expression of other RNAs by molecular trapping, also known as competing endogenous RNAs (ceRNAs) which hinder the normal functioning of RNAs. The ceRNAs proportion must be equivalent to the number of target miRNAs that can be sequestered by each ncRNAs in order to induce consequential de-repression of the target molecules.
Table: ncRNAs and related processes involved in neurodegenerative disorders
https://www.nature.com/articles/nrn.2017.90

The unknown functions of numerous annotated ncRNAs may explain the underlying complexity in neurodegenerative disorders. The profiling of ncRNAs of patients suffering from neurodevelopmental and neurodegenerative conditions are required to outline the changes in ncRNAs and their role in specific regions of brain and cells. Analysis of Large-scale gene expression and functional studies of ncRNAs may contribute to our understanding of these diseases and their remarkable connections. Therefore, targeting ncRNAs may provide effective therapeutic perspective for the treatment of neurodegenerative diseases.

References https://www.nature.com/scitable/topicpage/rna-functions-352/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6035743/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7695195/ https://link.springer.com/article/10.1007/s13670-012-0023-4 https://www.nature.com/articles/nrn.2017.90

 

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RNA in synthetic biology

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https://pharmaceuticalintelligence.com/2016/03/26/rna-in-synthetic-biology/

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Recent progress in neurodegenerative diseases and gliomas

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Connecting the Immune Response to Amyloid-β Aggregation in Alzheimer’s Disease via IFITM3

Reporter : Irina Robu, PhD

Alzheimer’s disease is a complex condition and it begins with slow aggregation of amyloid-β deposits over the course of years. This produces a mild cognitive impairment and a state of chronic inflammation enough to trigger harmful aggregation of the altered tau protein. Clearing amyloid-β from the brain hasn’t produced telling benefits to patients suggesting that it is not the key process in the development of the condition.

Recent research indicates that beta-amyloid has antiviral and antimicrobial properties, indicating a possible link between the immune response against infections and development of Alzheimer’s disease. Scientists have discovered evidence that protein interferon-induced transmembrane protein 3 (IFITM3) is involved in immune response to pathogens and play a key role in the accumulation of beta amyloid in plaques. IFITM3 is able to alter the activity of gamma-secretase enzyme, which breaks down the precursor proteins into fragments of beta-amyloid that make up plaques. 

Yet it was determined that the production of IFITM3 starts in reply to activation of the immune system by invading viruses and bacteria. Indeed, researchers found that the level of IFITM3 in human brain samples correlated with levels of certain viral infections as well as with gamma-secretase activity and beta-amyloid production. Age is the number one risk factor for Alzheimer’s and the levels of both inflammatory markers and IFITM3 increased with advancing age in mice.

Innate immunity is also correlated with Alzheimer’s disease1, but the influence of immune activation on the production of amyloid beta is unknown. They were able to identify IFITM3 as γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-β.

According to the current research, inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-β. The expression of IFITM3 is increased with ageing and in mouse models that express Alzheimer’s disease genes. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer’s disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer’s disease. These conclusions disclose a mechanism in which γ-secretase is controlled by neuroinflammation via IFITM3 and the risk of Alzheimer’s disease is thus amplified

SOURCE

https://www.nature.com/articles/s41586-020-2681-2

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Medical Device Technology for Alzheimer’s Diseases

Reporter: Danielle Smolyar

 

Alzheimer’s disease is said to be caused by a large number of proteins that are overproduced around a brain cell. Alzheimer’s is an irreversible disease that overtime decreases a person’s memory and the ability to perform tasks. With this disease, it is hard to function day to day life because it is hard to take on simple daily tasks or activities. It is a powerful and advanced disease that has not yet been found a cure. There have been many trials and scientists and researchers are still trying to figure out and find a cure for this disease because so many people, unfortunately, suffer from it.

This acute disease has no cure yet.  In an article titled, World Alzheimer’s Month: Exploring the latest research and devices for early detection, According to the Alzheimer’s Association,

“an estimated 5.3 million Americans are currently living with Alzheimer’s disease. By 2025, that number is expected to increase to more than seven million. Doctors diagnose dementia in around 10 million people every year, and 60–70% of these new diagnoses detect Alzheimer’s disease.”

The reality of the disease is tragic and the fact that the numbers keep growing calls for more urgency to find a cure and help innocent people fight off this disease. With society’s new technological and medical advancements, researchers have been working on finding a cure or developing a medical device to help people with Alzheimer’s. The article also states, ”Dr. Thom Wilcockson, from the UK’s Loughborough University, found that

eye-tracking technology could help identify mild cognitive impairment (MCI) in patients who might go on to develop Alzheimer’s disease in the future.”

With this technology and how advanced our society is, technology could eventually find a cure. With this device, it can help and make a considerable change in the number of people who develop Alzheimer’s. This new tool could help people prepare for the sickness or prevent future conditions from getting worse. Ultimately, if we have this technology, it can teach the world and educate the world on this condition and how we can take strides into preventing it from happening.

Dr. Thom Wilcosckon stated that looking for MCI can be a benchmark or sign for doctors to look for early development of Alzheimer’s:

Dr. Wilcockson and the research team worked with 42 patients with a diagnosis of aMCI, 47 with a diagnosis of naMCI, 68 people with dementia caused due to Alzheimer’s disease, and 92 healthy controls as part of their study. During the study, the participants were instructed to complete antisaccade tasks that are simple computer test where participants are told to look away from a distractor stimulus. The researchers found that they were able to differentiate between the two forms of MCI by looking at the eye-tracking results.

This modern technique of being able to pinpoint a specific aspect that would differentiate patients and their sicknesses from one another can cause a massive shift in the Alzheimer’s world. One step at a time, doctors, scientists, and researches are learning more about Alzheimers and are inching closer to hopefully finding a cure in the near future.

SOURCES:

World Alzheimer’s Month: Exploring latest research and devices for early detection

World Alzheimer’s Month: Exploring latest research and devices for early detection

 

https://www.nhs.uk/conditions/alzheimers-disease/causes/

https://www.nsmedicaldevices.com/news/world-alzheimers-month-medical-devices/

 

Other related articles published on this Open Access Online Scientific Journal include the following:

Alzheimer’s Disease: Novel Therapeutical Approaches — Articles of Note @PharmaceuticalIntelligence.com

Curators: Larry H. Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2016/04/05/alzheimers-disease-novel-therapeutical-approaches-articles-of-note-pharmaceuticalintelligence-com/

 

More …

Role of infectious agent in Alzheimer’s Disease?

Alzheimer’s disease, snake venome, amyloid and transthyretin

Alzheimer’s Disease – tau art thou, or amyloid

Breakthrough Prize for Alzheimer’s Disease 2016

Tau and IGF1 in Alzheimer’s Disease

Amyloid and Alzheimer’s Disease

Important Lead in Alzheimer’s Disease Model

BWH Researchers: Genetic Variations can Influence Immune Cell Function: Risk Factors for Alzheimer’s Disease,DM, and MS later in life

BACE1 Inhibition role played in the underlying Pathology of Alzheimer’s Disease

Late Onset of Alzheimer’s Disease and One-carbon Metabolism

Alzheimer’s Disease Conundrum – Are We Near the End of the Puzzle?

Ustekinumab New Drug Therapy for Cognitive Decline resulting from Neuroinflammatory Cytokine Signaling and Alzheimer’s Disease

New Alzheimer’s Protein – AICD

Developer of Alzheimer’s drug Exelon at Hebrew University’s School of Pharmacy: Israel Prize in Medicine awarded to Prof. Marta Weinstock-Rosin

TyrNovo’s Novel and Unique Compound, named NT219, selectively Inhibits the process of Aging and Neurodegenerative Diseases, without affecting Lifespan

@NIH – Discovery of Causal Gene Mutation Responsible for two Dissimilar Neurological diseases: Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD)

Introduction to Nanotechnology and Alzheimer disease

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

New ADNI Project to Perform Whole-genome Sequencing of Alzheimer’s Patients,

Brain Biobank

Removing Alzheimer plaques

Tracking protein expression

Schizophrenia genomics

Breakup of amyloid plaques

Mindful Discoveries

Beyond tau and amyloid

Serum Folate and Homocysteine, Mood Disorders, and Aging

Long Term Memory and Prions

Retromer in neurological disorders

Neurovascular pathways to neurodegeneration

Studying Alzheimer’s biomarkers in Down syndrome

Amyloid-Targeting Immunotherapy Targeting Neuropathologies with GSK33 Inhibitor

Brain Science

Sleep quality, amyloid and cognitive decline

microglia and brain maintenance

Notable Papers in Neurosciences

New Molecules to reduce Alzheimer’s and Dementia risk in Diabetic patients

The Alzheimer Scene around the Web

MRI Cortical Thickness Biomarker Predicts AD-like CSF and Cognitive Decline in Normal Adults

Read Full Post »


New Explanations for Evolution of Alzheimer’s Disease (AD): The Association between Brain neuroanatomy,  Brain Pathology and AD Biomarkers – Orientation and Attention are affected by the roles of Temporo-parietal junction (TPJ), Ventral attentional control network, Theory of mind, Inferior parietal cortex

 

Reporter and Curator: Aviva Lev-Ari, PhD, RN

 

UPDATED on 3/2/2020

Blood test method may predict Alzheimer’s protein deposits in brain

NIH-funded study reports advance in blood-based detection of ptau181, a biomarker of Alzheimer’s disease.

https://www.nih.gov/news-events/news-releases/blood-test-method-may-predict-alzheimers-protein-deposits-brain

 

On 2/27/2020 I attended an AFHU event with Prof. Shahar Arzi, MD, PhD as Speaker. The main argument was the Title of this curation: AD biomarkers of Amyloid and Tau deposits are found in the TPJ areas of the Brain where Attention, Theory of Mind and Empathy functions occur. Early detection of AD reveals symptoms of effects on Orientation and Attention. Application of Machine Learning (ML) on Brain imaging data allows for prediction of AD disease progression.

As an alternative or complementary Explanation we present The multiplex model of the genetics of Alzheimer’s disease”

  • The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

 

The Loss of Orientation and Attention are an outcome of Brain neuroanatomy,  Brain Pathology and AD Biomarkers

Dr. Shahar Arzy got his MD and MSc in neuroscience at the Hebrew University and PhD in neuroscience from the Swiss institute of Technology at the University of Geneva. He specialized in Neurology at Hadassah with subspecialty in cognitive neurology and epilepsy at Geneva University Hospital.

He now directs the Neuropsychiatry Lab at the Hebrew University and runs the neuropsychiatry clinic (with Dr. R. Eitan) and the epilepsy center (with Dr. D. Ekstein) in Hadassah Medical Center.

Dr. Arzy is a senior lecturer at the faculty of medicine, The Edmond And Lily Safra Brain Center and the Cognitive Science Program at the Hebrew University of Jerusalem.

Research Interests: Computational Neuropsychiatry Lab: Our lab of Computational Neuropsychiatry aims to bridge the gap between clinical practice and research, neurology, psychiatry, physics and psychology in order to re-formulate our understanding of the human self and its pathologies. To this aim we use newly developed computational methods (machine-learning algorithms, classifiers, network-approach and spectral analysis) applied directly on patients’ data (3T/7T fMRI, intracranial brain recordings, EEG, ECT), particularly tailored to improve clinical management and scientific understanding of neuropsychiatric disorders. The Neuropsychiatry Lab is located within the Department of Neurology and has a close collaboration with the Departments of Psychiatry, Neuroradiology and Neurosurgery, in order to develop approaches to address specific medical needs of neuropsychiatric patients and clinicians. Our main interests involve cortex-related functional conditions including epilepsy, neurodegenerative diseases, conversive and dissociative disorders, amnesias, disorientation states and different cognitive disturbances and misperceptions. By combining direct clinical involvement and cutting-edge computational methods we are able to challenge the customary context of the human “self” and to reframe neuropsychiatry, and at the same time to develop effective patient-tailored clinical tools to diagnose, monitor and treat these disorders.

SOURCE

https://scholars.huji.ac.il/jbc/people/dr-shahar-arzy

 

 

Position(s):
Associate Medical Director of Clinical Trials, Center for Alzheimer’s Research and Treatment, Brigham and Women’s Hospital
Associate Professor of Neurology, Harvard Medical School
Affiliation(s):
Brigham and Women’s HospitalMassachusetts General HospitalHarvard Medical School
Telephone:
(617) 732-8085
Interests:

I have a long-standing interest in clinical-pathologic and imaging correlates in Alzheimer’s disease.  Most recently, I have been using PET imaging to assess the relationship between apathy, executive function and instrumental activities of daily living, in vivo amyloid deposition (PiB PET) and synaptic integrity (FDG PET) in mild cognitive impairment and mild Alzheimer’s disease.

My other main research interest and involvement is in clinical trials for the treatment of Alzheimer’s disease.

More InformationMarshall Profile

Biography & Research:

During my medical education at the Boston University School of Medicine, medical internship/neurology residency at the University of Pittsburgh and dementia fellowship at the University of California, Los Angeles, I developed both my clinical and research interests in Alzheimer’s disease.  Along the way, I have collaborated with multiple investigators who encouraged and nurtured my drive to better understand this devastating disorder and find effective treatments.

I currently work as a behavioral neurologist at the Brigham and Women’s Hospital and the Massachusetts General Hospital, focusing on clinical trials and neuroimaging biomarkers in Alzheimer’s disease and its precursor stages.

Selected Publications:

Marshall GA, Kaufer DI, Lopez OL, Rao GR, Hamilton RL, DeKosky ST. Right Proscubiculum Amyloid Plaque Density Correlates with Anosognosia in Alzheimer’s Disease.  J Neurol Neurosurg Psychiatry 2004; 75:  1396-1400. [PMCID:  1738763].

Marshall GA, Hendrickson R, Kaufer DI, Ivanco LS, Bohnen NI. Cognitive Correlates of Brain MRI Subcortical Signal Hyperintensities in Non-Demented Elderly. Int J Geriatr Psychiatry 2006; 21:  32-35.

Marshall GA, Fairbanks LA, Tekin S, Vinters HV, Cummings JL. Neuropathologic Correlates of Activities of Daily Living in Alzheimer’s Disease. Alzheimer Dis Assoc Disord 2006; 20:  56-59.

Marshall GA, Fairbanks LA, Tekin S, Vinters HV, Cummings JL. Neuropathologic Correlates of Apathy in Alzheimer’s Disease. Dement Geriatr Cogn Disord 2006; 21:  144-147.

Marshall GA, Shchelchkov E, Kaufer DI, Ivanco LS, Bohnen NI. White Matter Hyperintensities and Cortical Acetylcholinesterase Activity in Parkinsonian Dementia. Act Neurol Scand 2006; 113:  87-91.

Marshall GA, Monserratt L, Harwood D, Mandelkern M, Cummings JL, Sultzer DL. Positron Emission Tomography Metabolic Correlates of Apathy in Alzheimer’s Disease. Arch Neurol 2007; 64:  1015-1020.

Sperling RA, Laviolette PS, OíKeefe K, OíBrien J, Rentz DM, Pihlajamaki M, Marshall G, Hyman BT, Selkoe DJ, Hedden T, Buckner RL, Becker JA, Johnson KA. Amyloid Deposition is associated with Impaired Default Network Function in Older Persons Without Dementia. Neuron 2009; 63:  178-188. [PMCID:  2738994].

Becker JA, Hedden T, Carmasin J, Maye J, Rentz DM, Putcha D, Fischl B, Greve D, Marshall GA, Salloway S, Marks D, Buckner RL, Sperling RA, Johnson KA. Amyloid-Beta Associated Cortical Thinning in Clinically Normal Elderly. Ann Neurol 2011; 69:  1032-1042. [PMCID:  3117980].

Marshall GA, Rentz DM, Frey MT, Locascio JJ, Johnson KA, Sperling RA, Alzheimerís Disease Neuroimaging Initiative. Executive Function and Instrumental Activities of Daily Living in Mild Cognitive Impairment and Alzheimerís Disease. Alzheimers Dementia 2011; 7:  300-308. [PMCID:  3096844].

Marshall GA, Olson LE, Frey MT, Maye J, Becker JA, Rentz DM, Sperling RA, Johnson KA, Alzheimerís Disease Neuroimaging Initiative. Instrumental Activities of Daily Living Impairment is associated with Increased Amyloid Burden. Dement Geriatr Cogn Disord 2011; 31:  443-450. [PMCID:  3150869].

More publications may be accessed at www.ncbi.nlm.nih.gov/sites/entrez?db=pubmed

SOURCE

https://www.madrc.org/members/10908

 

  • Perception and Multisensory Integration in Neurological Patients Using fMRI

https://clinicaltrials.gov/ct2/show/NCT01469858

02/11/2017 

Soc-Cog Colloq – Shahar Arzi

14:00

Wechsler Orientation and its role in Alzheimer’s disease: Mental-orientation is the cognitive function that manages the relationship between the individual and the environment in time (events), space (places) and person (people), as based on a distinct brain system. Observing the clinical similarities between mental-orientation domains and characteristics of Alzheimer’s disease (AD), as well as the striking neuroanatomical overlap between the orientation system and amyloid deposition and brain atrophy in AD, we hypothesized that disturbance of mental-orientation is a core-disorder in AD. In the presentation I will first present the ideas behind mental-orientation as well as its underlying brain system and its relation to the default mode network. I will present the current clinical understanding of Alzheimer’s disease and caveats it poses, and will supply behavioral and neuroimaging data supporting the central role of mental-orientation in the Alzheimer’s disease spectrum. I will conclude with reviewing current efforts in the unified research of space and time and its implication to Alzheimer’s disease.

 

Professor Ben Hur Tamir spoke to the Hebrew speaking group on the fascinating topic concerning ‘memory. As Director of the Brain Health and Neurology Department in Hadassah, his field is neurobiology. He told the audience that Dementia, and indeed Alzheimer’s is an illness that causes a loss of brain function due to sections of the brain being eaten away. In addition, depression or fear affect mental function. Once a patient is ready to admit to this, often memory improves. Alzheimer’s disease is not genetic, and it usually appears later in life. However one gene has been identified, and some genes carry risks. The illness can be triggered by other conditions such as blood pressure levels, Parkinsons, diabetes.

There are various tests that show whether a patient has the illness, as well as MRI scans, and there are medications that can remove antibodies, but by the time the antibodies are discovered it is too late to be effective.

Early detection of dementia or Alzheimer’s is the current key to thinking logically. Professor Ben Hur told us of research on a tribe in South America that showed that early hints of the disease show some 15 years before western testing can confirm its existence. The sooner the illness is found, symptoms can be attacked. Professor Shachar Arzi has developed a test that can be turned into an APP, and Professor Ruth Gabizon is working on oxidisation damage because weak anti-oxidants do not reach the brain.

He briefly suggested the use of Omega 3, Vitamin B and exercise to help protect against development of these illnesses.

SOURCE

https://www.hadassah-israel.org/index.php?option=com_content&view=article&id=122:mediscope-2018&catid=8:news&lang=en&Itemid=384

 

  • Alzheimer’s disease patients activate attention networks in a short-term memory task

 

Highlights

Patients with early AD succeeded in performing an fMRI short-term memory task.
Dorsal attention network activation did not differ between patients and controls.
Dorsal and ventral attention networks remained connected in high load task in AD.
DAN was necessary for the task, but not sufficient to reach normal performance.

Abstract

Network functioning during cognitive tasks is of major interest in Alzheimer’s disease (AD). Cognitive functioning in AD includes variable performance in short-term memory (STM). In most studies, the verbal STM functioning in AD patients has been interpreted within the phonological loop subsystem of Baddeley’s working memory model. An alternative account considers that domain-general attentional processes explain the involvement of frontoparietal networks in verbal STM beside the functioning of modality-specific subsystems. In this study, we assessed the functional integrity of the dorsal attention network (involved in task-related attention) and the ventral attention network (involved in stimulus-driven attention) by varying attentional control demands in a STM task. Thirty-five AD patients and twenty controls in the seventies performed an fMRI STM task. Variation in load (five versus two items) allowed the dorsal (DAN) and ventral attention networks (VAN) to be studied. ANOVA revealed that performance decreased with increased load in both groups. AD patients performed slightly worse than controls, but accuracy remained above 70% in all patients. Statistical analysis of fMRI brain images revealed DAN activation for high load in both groups. There was no between-group difference or common activation for low compared to high load conditions. Psychophysiological interaction showed a negative relationship between the DAN and the VAN for high versus low load conditions in patients. In conclusion, the DAN remained activated and connected to the VAN in mild AD patients who succeeded in performing an fMRI verbal STM task. DAN was necessary for the task, but not sufficient to reach normal performance. Slightly lower performance in early AD patients compared to controls might be related to maintained bottom-up attention to distractors, to decrease in executive functions, to impaired phonological processing or to reduced capacity in serial order processing.

 

  • Re-evaluating the role of TPJ in attentional control: Contextual updating?

https://www.sciencedirect.com/science/article/pii/S0149763413002005#fig0010

Abstract

The right temporo-parietal junction (TPJ) is widely considered as part of a network that reorients attention to task-relevant, but currently unattended stimuli (Corbetta and Shulman, 2002). Despite the prevalence of this theory in cognitive neuroscience, there is little direct evidence for the principal hypothesis that TPJ sends an early reorientation signal that “circuit breaks” attentional processing in regions of the dorsal attentional network (e.g., the frontal eye fields) or is completely right lateralized during attentional processing. In this review, we examine both functional neuroimaging work on TPJ in the attentional literature as well as anatomical findings. We first critically evaluate the idea that TPJ reorients attention and is right lateralized; we then suggest that TPJ signals might rather reflect post-perceptual processes involved in contextual updating and adjustments of top-down expectations; and then finally discuss how these ideas relate to the electrophysiological (P300) literature, and to TPJ findings in other cognitive and social domains. We conclude that while much work is needed to define the computational functions of regions encapsulated as TPJ, there is now substantial evidence that it is not specialized for stimulus-driven attentional reorienting.

image description

Fig. 1. Peak voxel coordinates for attention, theory of mind, and empathy. Coordinates were derived from the meta-analysis by Decety and Lamm, 2007aDecety and Lamm, 2007b. Additional data points from more recent studies have also been added to the visualization (see Table 1 for references of studies included). Images of the peak voxel coordinates in MNI space were created using GingerALE (www.brainmap.org) and are depicted on the MRIcroN (http://www.mccauslandcenter.sc.edu/mricro/mricron/) template brain.

Fig. 2. Illustration of the anatomical location of the parietal cortex from the Automatic Anatomical Labeling (AAL) atlas (Tzourio-Mazoyer et al., 2002) (A) and the cytoarchitectonic parietal maps of the Juelich atlas (Eickhoff et al., 2005) (B). The maps are depicted on the flattened brain surface of the PALS atlas as implemented in Caret 5.65 (Van Essen, 2005). SPL: superior parietal lobe, IPL: inferior parietal lobe, AG: angular gyrus, SMG: supramarginal gyrus, STG: superior temporal gyrus, MTG: middle temporal gyrus.

Review

 

  • The role of the right temporoparietal junction in attention and social interaction as revealed by ALE meta-analysis

Abstract

The right temporoparietal junction (rTPJ) is frequently associated with different capacities that to shift attention to unexpected stimuli (reorienting of attention) and to understand others’ (false) mental state [theory of mind (ToM), typically represented by false belief tasks]. Competing hypotheses either suggest the rTPJ representing a unitary region involved in separate cognitive functions or consisting of subregions subserving distinct processes. We conducted activation likelihood estimation (ALE) meta-analyses to test these hypotheses. A conjunction analysis across ALE meta-analyses delineating regions consistently recruited by reorienting of attention and false belief studies revealed the anterior rTPJ, suggesting an overarching role of this specific region. Moreover, the anatomical difference analysis unravelled the posterior rTPJ as higher converging in false belief compared with reorienting of attention tasks. This supports the concept of an exclusive role of the posterior rTPJ in the social domain. These results were complemented by meta-analytic connectivity mapping (MACM) and resting-state functional connectivity (RSFC) analysis to investigate whole-brain connectivity patterns in task-constrained and task-free brain states. This allowed for detailing the functional separation of the anterior and posterior rTPJ. The combination of MACM and RSFC mapping showed that the posterior rTPJ has connectivity patterns with typical ToM regions, whereas the anterior part of rTPJ co-activates with the attentional network. Taken together, our data suggest that rTPJ contains two functionally fractionated subregions: while posterior rTPJ seems exclusively involved in the social domain, anterior rTPJ is involved in both, attention and ToM, conceivably indicating an attentional shifting role of this region.

SOURCE

PMCID: PMC4791048
NIHMSID: NIHMS764322
PMID: 24915964
  • The multiplex model of the genetics of Alzheimer’s disease

Abstract

Genes play a strong role in Alzheimer’s disease (AD), with late-onset AD showing heritability of 58–79% and early-onset AD showing over 90%. Genetic association provides a robust platform to build our understanding of the etiology of this complex disease. Over 50 loci are now implicated for AD, suggesting that AD is a disease of multiple components, as supported by pathway analyses (immunity, endocytosis, cholesterol transport, ubiquitination, amyloid-β and tau processing). Over 50% of late-onset AD heritability has been captured, allowing researchers to calculate the accumulation of AD genetic risk through polygenic risk scores. A polygenic risk score predicts disease with up to 90% accuracy and is an exciting tool in our research armory that could allow selection of those with high polygenic risk scores for clinical trials and precision medicine. It could also allow cellular modelling of the combined risk. Here we propose the multiplex model as a new perspective from which to understand AD. The multiplex model reflects the combination of some, or all, of these model components (genetic and environmental), in a tissue-specific manner, to trigger or sustain a disease cascade, which ultimately results in the cell and synaptic loss observed in AD.

Hadassah International Symposium in Neurology

  • Seventieth Anniversary of the Department of Neurology and in Honor of Oded Abramsky

Magid Auditorium

Hadassah Hebrew University Medical Center, Ein Kerem, Jerusalem

June 3-5, 2007

The Department of Neurology at Hadassah University Hospital, is pleased to celebrate the 70th anniversary of its founding. The celebration will take place in Jerusalem, June 3-5, 2007.  The department was founded by the late Lipman Halpern who emigrated from Berlin to Jerusalem; He served as chairman until 1969, followed by Shaul Feldman who served until 1988 and Oded Abramsky who served until the end of  2005. The 70th anniversary is an historic event for Hadassah Medical Organization and the Hebrew University Hadassah Medical School. This occasion provides an opportunity to reflect on how the department came into being, to acknowledge the people who brought the department to this point, and the continuing role the department of neurology plays in patient care, research, education and community service. To honor seventy years of activities and achievements, the department is hosting an international forum of world-renowned neurologists. The event will honor the conclusion of Oded Abramsky’s term as chairman and the assumption of this role by Tamir Ben-Hur.

The scientific symposium includes overview presentations in various fields of clinical neurosciences by invited neurologists and Nobel Prize Laureates as well as presentations by members of the department of neurology.

We warmly welcome you to the symposium in Jerusalem.

Scientific Program

Sunday  June 3, 2007

09:00 – 09:45  Opening Session

Chairpersons:   Michael Sela (Weizmann Inst.)

Shaul Feldman (Hadassah)

           Oded Abramsky (Hadassah)

Welcome:        Tamir Ben-Hur  Chairman, Department of Neurology, Hadassah

Greetings:        Shlomo Mor-Yosef  Director General, Hadassah Medical Organization

                       Menachem Magidor  President, Hebrew University of Jerusalem

                       Ruth Arnon  Vice President, Israel Academy of Sciences

           Yoram Blachar  Chairman, Israel Medical Association

                       Avinoam Reches  Chairman, Israel Neurological Association

           Johan Aarli  President, World Federation of Neurology

09:45 – 11:15    Second Session

Neurodegeneration and protein degradation in disease

Chairpersons:  Burton Zweiman (Univ. Pennsylvania)

                       Douglas L. Arnold (McGill Univ.)

                       Bella Gross (Technion, Nahariya Hosp.)

09:45 – 10:05  Stanley B. Prusiner  Nobel Prize Laureate (UCSF)

                       Overview: Prion diseases

10:05 – 10:25   Aaron Ciechanover  Nobel Prize Laureate (Technion, Haifa)

     Ubiquitin-mediated protein degradation:

                       From basic mechanisms to  the patient bed

10:25 – 10:45   Roger Rosenberg (Univ. Texas)
Neurodegenerative diseases: New strategies in research and therapy

10:45 – 11:00    Scott A. Small (Columbia Univ.)

Alzheimer’s disease and aging

11:00 – 11:15    Howard L. Weiner (Harvard Univ.)

        Immunological treatments in neurodegenerative diseases

11:15 – 11:40    Coffee Break

11:40 – 13:05    Third Session

Paraneoplastic and Infectious diseases

Chairpersons:   Jack Antel (McGill Univ.)

                         Howard L. Lipton (Univ. Illinois Chicago)

                         Roni Milo (Ben-Gurion Univ., Barzilai Hosp.)

11:40 – 12:00    Jerome B. Posner (Memorial Sloan-Kettering)

                         Overview: Paraneoplastic syndromes

12:00 – 12:20    Richard T. Johnson (Johns Hopkins Univ.)

                        Overview: Neurovirology: State of the art

12:20 – 12:35    Donald H. Gilden (Univ. Colorado)

                        Antigen identification in MS

12:35 – 12:50    Peter G.E. Kennedy (Glasgow Univ.)

                       Neuropathogenesis of human trypanosomiasis (sleeping sickness)           

12:50 – 13:05    Francisco Gonzalez-Scarano  (Univ. Pennsylvania)

NeuroAIDS

13:05 – 13:55    Lunch Break

13:55 – 15:25    Fourth Session

Epilepsy, vascular and extrapyramidal disorders

Chairpersons:    Robert B. Daroff (Case Western Reserve Univ.)

                        Stephen Davis (Melbourne Univ.)

           Rivka Inzelberg (Tel Aviv Univ., Meir Hosp.)

13:55 – 14:15    Frederick Andermann (McGill Univ.)

                        Overview: Epilepsy: State of the art

14:15 – 14:30    Timothy A. Pedley (Columbia Univ.)

Understanding epileptogenesis: A beginning

14:30 – 14:50    Louis R. Caplan (Harvard Univ.)

                        Overview: Cerebrovascular diseases: State of the art

14:50 – 15:05    Vladimir Hachinsky  (London Univ., Ontario)

Vascular dementia

15:05 –  15:30Stanley Fahn (Columbia Univ.)

                        Overview: Parkinson’s disease and other extrapyramidal disorders

15:30 – 15:55    Coffee Break

15:55 – 17:15    Fifth Session

Neuromuscular disorders

Chairpersons:    Klaus V. Toyka (Univ. Wurzburg)

                        Aksel Siva (Istanbul Univ.)

David Yarnitsky (Technion, Rambam Hosp.)

15:55 – 16:20    George Karpati (McGill Univ.)

                        Overview:  Muscle diseases: State of the art

16:20 – 16:40    John Newsom-Davis (Oxford Univ.)

Neuromuscular junction disorders

16:40 – 17:00    Gerard Said (Bicetre Univ.)

                        Overview: Peripheral neuropathy:  State of the art

17:00 – 17:15    Robert P. Lisak (Wayne State Univ.)

Schwannopathies

Monday  June 4, 2007

09:00 – 10:10    Sixth Session

Sandy and Peter Collins Lectures on MS

Chairpersons:   Ioannis Milonas (Aristotle Univ.)

                         Tomas Olsson  (Karolinska Inst.)

                                    Ariel Miller (Technion, Carmel Hosp.)

09:00 – 09:20    Reinhard Hohlfeld (Munich Univ.)

                      Immunology of multiple sclerosis

09:20 – 09:40    Hans Lassmann  (Univ. Vienna)

Pathology of multiple sclerosis

09:40 – 09:55   Hans-Peter Hartung (Heinrich-Heine Univ.)

Current therapies in multiple sclerosis

09:55 – 10:10   Lawrence Steinman (Stanford Univ.)

Future therapies in multiple sclerosis

10:10 – 10:30   Coffee Break

10:30 – 11:40    Seventh Session

Stem cells and  Neurology

Chairpersons:   Leslie P. Weiner ( Univ. South Carolina)

                        Krzysztof Selmaj (Lodz Univ.)

Joab Chapman (Tel Aviv Univ., Sheba Hosp.)

10:30 – 10:50    Evan Y. Snyder (Burnham Inst., La Jolla)

Overview: stem cells therapy

10:50 – 11:10     Ian D. Duncan (Univ.  Wisconsin)

                         Remyelination in the CNS

11:10 – 11:25     Douglas Kerr (Johns Hopkins Univ.)

             Cell therapy for neurogenerative diseases

11:25 – 11:40     Jeff W.M. Bulte (Johns Hopkins Univ.)

 Molecular neuroimaging of cell therapy

11:40 – 12:50    Eight Session

            Agnes Ginges Lectures in Neurogenetics

Chairpersons:   Steve P. Ringel (Univ. Colorado)

                       Anna  Czlonkowska (Inst. Psychiatry-Neurology, Warsaw)

                       Boaz Weller (Technion, Bnai Zion Hosp.)

11:40 – 12:00    Stefano Di Donato (Carlo Besta Inst., Milano)

                        Overview: Neurogenetics:  State of the art

12:00 – 12:20    Salvatore DiMauro (Columbia Univ.)

Mitochondrial diseases

12:20 – 12:35    Stefan M. Pulst (UCLA)

Ion channels dysfunction in genetic spinocerebellar syndromes

12:35 – 12:50    Alastair D.S. Compston (Cambridge Univ.)

Genetics of MS and other demyelinating disorders

12:50 – 13:40    Lunch Break

13:40 – 16:35     Ninth Session

Department of Neurology, Hadassah: Research highlights

Chairpersons:   Yair Birnbaum (Director,  Hadassah Univ. Hosp., Ein Kerem)

Milton Alter (Temple Univ.)

                        Itzhak Wirguin (Ben-Gurion Univ.)

13:40 – 13: 55   Tamir Ben-Hur

                        Stem cell therapy in neurological diseases

13:55 – 14:10    Dimitrios Karussis

                        Neuroprotection in MS

14:10 – 14:25    Talma Brenner

            Pregnancy, alphafetoprotein, EAE and MS

14:25 – 14:40    Ruth Gabizon

                        When prions meet other pathological insults

14:40 – 14:55    Hanna Rosenmann

                        Novel animal models of Alzheimer’s disease and tauopathy

14:55 – 15:05    Tali Siegal

Longitudinal assessment of genetic and epigenetic markers  in progressive oligodendroglial tumors

15:05 – 15:15    Ronen R. Leker

                        Manipulation of endogenous neural stem cells in stroke

15:15 – 15:25    Netta Levin

      Plasticity in the human visual cortex: fMRI studies

15:25 – 15:35    Dana Ekstein

                        The role of zinc in epileptogenesis

15:35 – 15:45    Shahar Arzi

Remembering the future, predicting the past: 

                        An electrophysiological study of mental time travel

15:45 – 16:05    Coffee break

16:05 – 16:20    Zohar Argov

One gene is not enough: Lessons from hereditary neuromuscular disorders identified at Hadassah

16:20 – 16:35    Alex Lossos

        Adult genetic neurometabolic diseases: Hadassah’s experience

16:35 – 17:45Closing Session

Vision of future Neurology

Chairpersons:   Ehud Razin (Dean, Hebrew University Hadassah Medical School)

                        Shlomo Rotshenker (Chairman, Israel Neuroscience Society)

                        Eldad Melamed (Tel Aviv Univ., Beilinson Hosp.)

Greetings:         Ehud Olmert  Israel Prime Minister

                        Avi Israeli  Director General, Israel Ministry of Health

16:50 – 17:05    Natan M. Bornstein(Tel Aviv Univ., Ichilov Hosp.)

Neurology in Israel

17:05 – 17:25    Donald H. Silberberg (Univ. Pennsylvania)

Neurology in developing countries

17:25 – 17:45    Lewis P. Rowland (Columbia Univ.)

                        Prospects for neurology in the 21st century

Tuesday  June 5, 2007

Seventieth Meeting of the HebrewUniversity Board of Governors

13:30

Dedication of :

The Stanley B. PrusinerMedicalInformationCenter

Hebrew University Hadassah Medical School

Judah Magnes Square, Ein Kerem, Jerusalem

 

REFERENCE for the Genetics of Alzheimer’s Disease

  1. Goate, A. et al. Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 349, 704–706 (1991).
  2. Sherrington, R. et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375, 754–760 (1995).
  3. Rogaev, E. I. et al. Familial Alzheimer’s disease in kindreds with missense mutations in a gene on chromosome 1 related to the Alzheimer’s disease type 3 gene. Nature 376, 775–778 (1995).
  4. Hardy, J. & Selkoe, D. J. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science 297, 353–356 (2002).
  5. Ricciarelli, R. & Fedele, E. The amyloid cascade hypothesis in Alzheimer’s disease: it’s time to change our mind. Curr. Neuropharmacol. 15, 926–935 (2017).
  6. Doody, R. S., Farlow, M. & Aisen, P. S., Alzheimer’s Disease Cooperative Study Data Analysis and Publication Committee. Phase 3 trials of solanezumab and bapineuzumab for Alzheimer’s disease. N. Engl. J. Med. 370, 1460 (2014).
  7. Honig, L. S. et al. Trial of solanezumab for mild dementia due to Alzheimer’s disease. N. Engl. J. Med. 378, 321–330 (2018).
  8. Galimberti, D. & Scarpini, E. Disease-modifying treatments for Alzheimer’s disease. Ther. Adv. Neurol. Disord. 4, 203–216 (2011).
  9. Yiannopoulou, K. G. & Papageorgiou, S. G. Current and future treatments for Alzheimer’s disease. Ther. Adv. Neurol. Disord. 6, 19–33 (2013).
  10. Jagust, W. Imaging the evolution and pathophysiology of Alzheimer disease. Nat. Rev. Neurosci. 19, 687–700 (2018).
  11. Rajan, K. B., Wilson, R. S., Weuve, J., Barnes, L. L. & Evans, D. A. Cognitive impairment 18 years before clinical diagnosis of Alzheimer disease dementia. Neurology 85, 898–904 (2015).
  12. Rosenblum, W. I. Why Alzheimer trials fail: removing soluble oligomeric beta amyloid is essential, inconsistent, and difficult. Neurobiol. Aging 35, 969–974 (2014).
  13. Gatz, M. et al. Role of genes and environments for explaining Alzheimer disease. Arch. Gen. Psychiatry 63, 168–174 (2006).
  14. Wingo, T. S., Lah, J. J., Levey, A. I. & Cutler, D. J. Autosomal recessive causes likely in early-onset Alzheimer disease. Arch. Neurol. 69, 59–64 (2012).
  15. Saunders, A. M. et al. Apolipoprotein E epsilon 4 allele distributions in late-onset Alzheimer’s disease and in other amyloid-forming diseases. Lancet 342, 710–711 (1993).
  16. Strittmatter, W. J. et al. Apolipoprotein E: high-avidity binding to beta-amyloid and increased frequency of type 4 allele in late-onset familial Alzheimer disease. Proc. Natl Acad. Sci. USA 90, 1977–1981 (1993).
  17. Corder, E. H. et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat. Genet. 7, 180–184 (1994).
  18. Liao, F., Yoon, H. & Kim, J. Apolipoprotein E metabolism and functions in brain and its role in Alzheimer’s disease. Curr. Opin. Lipidol. 28, 60–67 (2017).
  19. Deane, R. et al. ApoE isoform-specific disruption of amyloid beta peptide clearance from mouse brain. J. Clin. Invest. 118, 4002–4013 (2008).
  20. Verghese, P. B., Castellano, J. M. & Holtzman, D. M. Apolipoprotein E in Alzheimer’s disease and other neurological disorders. Lancet Neurol. 10, 241–252 (2011).
  21. Harold, D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41, 1088–1093 (2009).
  22. Lambert, J. C. et al. Genome-wide association study identifies variants at CLU and CR1 associated with Alzheimer’s disease. Nat. Genet. 41, 1094–1099 (2009).

 

 

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Brain Health – Evidence that Lifestyle Habits can improve Brain Health – APOe4 gene in women appears to more often convert mild cognitive impairment to Alzheimer’s

 

Reporter: Aviva Lev-Ari, PhD, RN

 

Brain health is determined by how the organ is functioning; how much

  • blood flow
  • nutrients and
  • oxygen it is getting; and
  • how it is cleaning and filtering things like harmful proteins – high amyloid burden is decreasing in importance by climbing steps during excercise – proteins that increase the risk for Alzheimer’s can start to deposit in the brain 15-20 years before the onset of symptoms

Factors in boosting brain health

  • Exercise increases blood flow to the brain and increases the size of the anterior hippocampus, leading to improvements in spatial memory vs reduction in size and atrophy. Healthy lifestyle habits can reduce or negate risk — even in the presence of genetic predisposition
  • Diet – Mediterranean, heart-healthy diet can minimize adverse effects on memory and decrease the incidence of Alzheimer’s and dementia. It boost memory and cognition.
  • Exercise and diet increase release of endorphins which can stimulate cognitive functioning and mood improvements
  • Brain Derived Nerve Growth Factor (BDNF) — which can help memory, focus and attention — may increase as a result of physical activity.
  • Sleep and Mood are interconnected

Genetic Factors affecting Brain Health

  • ApoE4 gene carriers have an increased risk.
  • One copy of the gene can increase risk by 2-4 times the risk of the general population, and
  • Two copies of the gene may increase risk up to 10 times that of the general population. But that is risk, not cause.

Age

  • People over age 65, 1-2% have Alzheimer’s disease.
  • Above 85 years old, the prevalence is 30-50%.

Sex

  • Women have a higher risk of Alzheimer’s than do men.
  • Could this just be because women live longer than men?
  • Women may have more physiological risks than men.
  • For example, the APOe4 gene in women appears to more often convert mild cognitive impairment to Alzheimer’s.

Variability by Ethnicity and Race

  • Alzheimer’s disease within specific ethnic groups and races – has different disease profiles, more studies with diversity are needed.

Highest risk for developing Alzheimer’s and other neurodegenerative diseases

  • Genetic predisposition,
  • Low education,
  • High age and
  • Vascular risk factors

 

Minimizing Risk and induce Slower Progression – We can’t change age and we can’t change genetics. The modifiable risk factors are:

  • exercise,
  • sleep,
  • diet, and
  • cognitive stimulation

 

SOURCE

https://hip.stanford.edu/calendar-news/news/boosting-brain-health/

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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].

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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.

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Real Time Coverage of BIO 2019 International Convention, June 3-6, 2019 Philadelphia Convention Center, Philadelphia PA

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The BIO International Convention is the largest global event for the biotechnology industry and attracts the biggest names in biotech, offers key networking and partnering opportunities, and provides insights and inspiration on the major trends affecting the industry. The event features keynotes and sessions from key policymakers, scientists, CEOs, and celebrities.  The Convention also features the BIO Business Forum (One-on-One Partnering), hundreds of sessions covering biotech trends, policy issues and technological innovations, and the world’s largest biotechnology exhibition – the BIO Exhibition.

The BIO International Convention is hosted by the Biotechnology Innovation Organization (BIO). BIO represents more than 1,100 biotechnology companies, academic institutions, state biotechnology centers and related organizations across the United States and in more than 30 other nations. BIO members are involved in the research and development of innovative healthcare, agricultural, industrial and environmental biotechnology products.

 

Keynote Speakers INCLUDE:

Fireside Chat with Margaret (Peggy) Hamburg, MD, Foreign Secretary, National Academy of Medicine; Chairman of the Board, American Association for the Advancement of Science

Tuesday Keynote: Siddhartha Mukherjee (Author of the bestsellers Emperor of All Maladies: A Biography of Cancer and  The Gene: An Intimate History)

Fireside Chat with Jeffrey Solomon, Chief Executive Officer, COWEN

Fireside Chat with Christi Shaw, Senior Vice President and President, Lilly BIO-Medicines, Eli Lilly and Company

Wednesday Keynote: Jamie Dimon (Chairman JP Morgan Chase)

Fireside Chat with Kenneth C. Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co., Inc.

Fireside Chat: Understanding the Voices of Patients: Unique Perspectives on Healthcare

Fireside Chat: FDA Town Hall

 

ALSO SUPERSESSIONS including:

Super Session: What’s Next: The Landscape of Innovation in 2019 and Beyond

Super Session: Falling in Love with Science: Championing Science for Everyone, Everywhere

Super Session: Digital Health in Practice: A Conversation with Ameet Nathawani, Chief Digital Officer, Chief Medical Falling in Love with Science: Championing Science for Everyone, Everywhere

Super Session: Realizing the Promise of Gene Therapies for Patients Around the World

Super Session: Biotech’s Contribution to Innovation: Current and Future Drivers of Success

Super Session: The Art & Science of R&D Innovation and Productivity

Super Session: Dealmaker’s Intentions: 2019 Market Outlook

Super Session: The State of the Vaccine Industry: Stimulating Sustainable Growth

 

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The key benefits of attending the BIO International Convention are access to global biotech and pharma leaders via BIO One-on-One Partnering, exposure to industry though-leaders with over 1,500 education sessions at your fingertips, and unparalleled networking opportunities with 16,000+ attendees from 74 countries.

In addition, we produce BIOtechNOW, an online blog chronicling ‘innovations transforming our world’ and the BIO Newsletter, the organization’s bi-weekly email newsletter. Subscribe to the BIO Newsletter.

 

Membership with the Biotechnology Innovation Organization (BIO)

BIO has a diverse membership that is comprised of  companies from all facets of biotechnology. Corporate R&D members range from entrepreneurial companies developing a first product to Fortune 100 multinationals. The majority of our members are small companies – 90 percent have annual revenues of $25 million or less, reflecting the broader biotechnology industry. Learn more about how you can save with BIO Membership.

BIO also represents academic centers, state and regional biotech associations and service providers to the industry, including financial and consulting firms.

  • 66% R&D-Intensive Companies *Of those: 89% have annual revenues under $25 million,  4% have annual revenues between $25 million and $1 billion, 7% have annual revenues over $1 billion.
  • 16% Nonprofit/Academic
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Other posts on LIVE CONFERENCE COVERAGE using Social Media on this OPEN ACCESS JOURNAL and OTHER Conferences Covered please see the following link at https://pharmaceuticalintelligence.com/press-coverage/

 

Notable Conferences Covered THIS YEAR INCLUDE: (see full list from 2013 at this link)

  • Koch Institute 2019 Immune Engineering Symposium, January 28-29, 2019, Kresge Auditorium, MIT

https://calendar.mit.edu/event/immune_engineering_symposium_2019#.XBrIDc9Kgcg

http://kochinstituteevents.cvent.com/events/koch-institute-2019-immune-engineering-symposium/event-summary-8d2098bb601a4654991060d59e92d7fe.aspx?dvce=1

 

  • 2019 MassBio’s Annual Meeting, State of Possible Conference ​, March 27 – 28, 2019, Royal Sonesta, Cambridge

http://files.massbio.org/file/MassBio-State-Of-Possible-Conference-Agenda-Feb-22-2019.pdf

 

  • World Medical Innovation Forum, Partners Innovations, ARTIFICIAL INTELLIGENCE | APRIL 8–10, 2019 | Westin, BOSTON

https://worldmedicalinnovation.org/agenda-list/

https://worldmedicalinnovation.org/

 

  • 18th Annual 2019 BioIT, Conference & Expo, April 16-18, 2019, Boston, Seaport World Trade Center, Track 5 Next-Gen Sequencing Informatics – Advances in Large-Scale Computing

http://www.giiconference.com/chi653337/

https://pharmaceuticalintelligence.com/2019/04/22/18th-annual-2019-bioit-conference-expo-april-16-18-2019-boston-seaport-world-trade-center-track-5-next-gen-sequencing-informatics-advances-in-large-scale-computing/

 

  • Translating Genetics into Medicine, April 25, 2019, 8:30 AM – 6:00 PM, The New York Academy of Sciences, 7 World Trade Center, 250 Greenwich St Fl 40, New York

https://pharmaceuticalintelligence.com/2019/04/25/translating-genetics-into-medicine-april-25-2019-830-am-600-pm-the-new-york-academy-of-sciences-7-world-trade-center-250-greenwich-st-fl-40-new-york/

 

  • 13th Annual US-India BioPharma & Healthcare Summit, May 9, 2019, Marriott, Cambridge

https://pharmaceuticalintelligence.com/2019/04/30/13th-annual-biopharma-healthcare-summit-thursday-may-9-2019/

 

  • 2019 Petrie-Flom Center Annual Conference: Consuming Genetics: Ethical and Legal Considerations of New Technologies, May 17, 2019, Harvard Law School

http://petrieflom.law.harvard.edu/events/details/2019-petrie-flom-center-annual-conference

https://pharmaceuticalintelligence.com/2019/01/11/2019-petrie-flom-center-annual-conference-consuming-genetics-ethical-and-legal-considerations-of-new-technologies/

 

  • 2019 Koch Institute Symposium – Machine Learning and Cancer, June 14, 2019, 8:00 AM-5:00 PM  ET MIT Kresge Auditorium, 48 Massachusetts Ave, Cambridge, MA

https://pharmaceuticalintelligence.com/2019/03/12/2019-koch-institute-symposium-machine-learning-and-cancer-june-14-2019-800-am-500-pmet-mit-kresge-auditorium-48-massachusetts-ave-cambridge-ma/

 

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