Researchers have found that participants diagnosed with CTE post-mortem had shrinkage in regions of the brain associated with CTE, as well as other abnormalities, compared with healthy controls. “Specifically, those with CTE had shrinkage in the frontal and temporal lobes of the brain, the regions most impacted by CTE” explained corresponding author Jesse Mez, MD, MS, director of the Boston University (BU) Alzheimer’s Disease Center Clinical Core and a BU CTE Center Investigator.

CTE is a progressive brain disease associated with repetitive head impacts. It has been diagnosed after death in American football players and other contact sport athletes as well as members of the armed services and victims of physical abuse.

To learn how to diagnose neurodegenerative diseases like Alzheimer’s disease, scientists usually study a population during life and confirm a diagnosis after death, which can take decades. To shorten this timeline, Mez and colleagues worked backwards, reviewing the medical records of deceased brain donors and analyzing MRIs obtained during life an average of four years prior to death.

They compared the MRIs of 55 men diagnosed with CTE to 31 male healthy controls with normal cognition at the time of their scan. “MRI is commonly used to diagnose progressive brain diseases that are similar to CTE such as Alzheimer’s disease. Findings from this study show us what we can expect to see on MRI in CTE. This is very exciting because it brings us that much closer to detecting CTE in living people,” said first author Michael Alosco, PhD, associate professor of neurology at BU School of Medicine, co-director of the BU Alzheimer’s Disease Center Clinical Core, and a lead BU CTE Center investigator.

“While this finding is not yet ready for the clinic, it shows we are making rapid progress, and we encourage patients and families to continue to participate in research so we can find answers even faster,” adds Mez.

Alosco also added, “there is more to do as we still need to understand whether the patterns we saw on MRI are specific to CTE, that is, do they differentiate CTE from Alzheimer’s disease and other causes of dementia.”

Journal Reference:

1. Michael L. Alosco, Asim Z. Mian, Karen Buch, Chad W. Farris, Madeline Uretsky, Yorghos Tripodis, Zachary Baucom, Brett Martin, Joseph Palmisano, Christian Puzo, Ting Fang Alvin Ang, Prajakta Joshi, Lee E. Goldstein, Rhoda Au, Douglas I. Katz, Brigid Dwyer, Daniel H. Daneshvar, Christopher Nowinski, Robert C. Cantu, Neil W. Kowall, Bertrand Russell Huber, Victor E. Alvarez, Robert A. Stern, Thor D. Stein, Ronald J. Killiany, Ann C. McKee, Jesse Mez. Structural MRI profiles and tau correlates of atrophy in autopsy-confirmed CTE. Alzheimer’s Research & Therapy, 2021; 13 (1) DOI: 1186/s13195-021-00928-y

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The new study published  in the Frontiers in Aging Neuroscience (@FrontiersIn), discovered a sensing mechanism associated with the gut microbiome that explains how fenchol reduces neurotoxicity in the Alzheimer’s brain.

Emerging evidence indicates that short-chain fatty acids (SCFAs)- metabolites produced by beneficial gut bacteria and the primary source of nutrition for cells in your colon — contribute to brain health. The abundance of SCFAs is often reduced in older patients with mild cognitive impairment and Alzheimer’s disease, the most common form of dementia. However, how this decline in SCFAs contributes to Alzheimer’s disease progression remains largely unknown.

Gut-derived SCFAs that travel through the blood to the brain can bind to and activate free fatty acid receptor 2 (FFAR2), a cell signaling molecule expressed on brain cells called neurons.

“Our study is the first to discover that stimulation of the FFAR2 sensing mechanism by these microbial metabolites (SCFAs) can be beneficial in protecting brain cells against toxic accumulation of the amyloid-beta (Aβ) protein associated with Alzheimer’s disease,” said principal investigator Hariom Yadav, PhD, professor of neurosurgery and brain repair at the USF Health Morsani College of Medicine, where he directs the USF Center for Microbiome Research.

One of the two hallmark pathologies of Alzheimer’s disease is hardened deposits of Aβ that clump together between nerve cells to form amyloid protein plaques in the brain. The other is neurofibrillary tangles of tau protein inside brain cells. These pathologies contribute to the neuron loss and death that ultimately cause the onset of Alzheimer’s, a neurodegenerative disease characterized by loss of memory, thinking skills and other cognitive abilities.

Dr. Yadav and his collaborators delve into molecular mechanisms to explain how interactions between the gut microbiome and the brain might influence brain health and age-related cognitive decline. In this study, Dr. Yadav said, the research team set out to uncover the “previously unknown” function of FFAR2 in the brain.

The researchers first showed that inhibiting the FFAR2 receptor (thus blocking its ability to “sense” SCFAs in the environment outside the neuronal cell and transmit signaling inside the cell) contributes to the abnormal buildup of the Aβ protein causing neurotoxicity linked to Alzheimer’s disease.

Then, they performed large-scale virtual screening of more than 144,000 natural compounds to find potential candidates that could mimic the same beneficial effect of microbiota produced SCFAs in activating FFAR2 signaling. Identifying a natural compound alternative to SCFAs to optimally target the FFAR2 receptor on neurons is important, because cells in the gut and other organs consume most of these microbial metabolites before they reach the brain through blood circulation, Dr. Yadav noted.

Dr. Yadav’s team narrowed 15 leading compound candidates to the most potent one. Fenchol, a plant-derived compound that gives basil its aromatic scent, was best at binding to the FFAR’s active site to stimulate its signaling.

Further experiments in human neuronal cell cultures, as well as Caenorhabditis (C.) elegans (worm) and mouse models of Alzheimer’s disease demonstrated that fenchol significantly reduced excess Aβ accumulation and death of neurons by stimulating FFAR2 signaling, the microbiome sensing mechanism. When the researchers more closely examined how fenchol modulates Aβ-induced neurotoxicity, they found that the compound decreased senescent neuronal cells, also known as “zombie” cells, commonly found in brains with Alzheimer’s disease pathology.

Zombie cells stop replicating and die a slow death. Meanwhile, Dr. Yadav said, they build up in diseased and aging organs, create a damaging inflammatory environment, and send stress or death signals to neighboring healthy cells, which eventually also change into harmful zombie cells or die.

“Fenchol actually affects the two related mechanisms of senescence and proteolysis,” Dr. Yadav said of the intriguing preclinical study finding. “It reduces the formation of half-dead zombie neuronal cells and also increases the degradation of (nonfunctioning) Aβ, so that amyloid protein is cleared from the brain much faster.”

Before you start throwing lots of extra basil in your spaghetti sauce or anything else you eat to help stave off dementia, more research is needed — including in humans.

In exploring fenchol as a possible approach for treating or preventing Alzheimer’s pathology, the USF Health team will seek answers to several questions. A key one is whether fenchol consumed in basil itself would be more or less bioactive (effective) than isolating and administering the compound in a pill, Dr. Yadav said. “We also want to know whether a potent dose of either basil or fenchol would be a quicker way to get the compound into the brain.”

The USF Health-led research was supported in part by grants from the National Institutes of Health, the U.S. Department of Defense, and the NIH-funded Wake Forest Clinical and Translational Science Institute.

Journal Reference:

1. Atefeh Razazan, Prashantha Karunakar, Sidharth P. Mishra, Shailesh Sharma, Brandi Miller, Shalini Jain and Hariom Yadav. Activation of Microbiota Sensing – Free Fatty Acid Receptor 2 Signaling Ameliorates Amyloid-β Induced Neurotoxicity by Modulating Proteolysis-Senescence Axis. Frontiers in Aging Neuroscience, 2021 DOI: 3389/fnagi.2021.735933

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According to World Health Organisation, Alzheimer’s disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.

“Medical professionals all over the world attempt to raise awareness of an early Alzheimer’s diagnosis, which provides the affected with a better chance of benefiting from treatment. This was one of the most important issues for choosing a topic for Modupe Odusami, a PhD student from Nigeria,” says Rytis Maskeli?nas, a researcher at the Department of Multimedia Engineering, Faculty of Informatics, Kaunas University of Technology (KTU), Odusami’s PhD supervisor.

Image processing delegated to the machine

One of the possible Alzheimer’s first signs is mild cognitive impairment (MCI), which is the stage between the expected cognitive decline of normal ageing and dementia. Based on the previous research, functional magnetic resonance imaging (fMRI) can be used to identify the regions in the brain which can be associated with the onset of Alzheimer’s disease, according to Maskeli?nas. The earliest stages of MCI often have almost no clear symptoms, but in quite a few cases can be detected by neuroimaging.

However, although theoretically possible, manual analysing of fMRI images attempting to identify the changes associated with Alzheimer’s not only requires specific knowledge but is also time-consuming — application of Deep learning and other AI methods can speed this up by a significant time margin. Finding MCI features does not necessarily mean the presence of illness, as it can also be a symptom of other related diseases, but it is more of an indicator and possible helper to steer toward an evaluation by a medical professional.

“Modern signal processing allows delegating the image processing to the machine, which can complete it faster and accurately enough. Of course, we don’t dare to suggest that a medical professional should ever rely on any algorithm one-hundred-per cent. Think of a machine as a robot capable of doing the most tedious task of sorting the data and searching for features. In this scenario, after the computer algorithm selects potentially affected cases, the specialist can look into them more closely, and at the end, everybody benefits as the diagnosis and the treatment reaches the patient much faster,” says Maskeli?nas, who supervised the team working on the model.

We need to make the most of data

The deep learning-based model was developed as a fruitful collaboration of leading Lithuanian researchers in the Artificial Intelligence sector, using a modification of well-known fine-tuned ResNet 18 (residual neural network) to classify functional MRI images obtained from 138 subjects. The images fell into six different categories: from healthy through the spectre of mild cognitive impairment (MCI) to Alzheimer’s disease. In total, 51,443 and 27,310 images from The Alzheimer’s Disease Neuroimaging Initiative fMRI dataset were selected for training and validation.

The model was able to effectively find the MCI features in the given dataset, achieving the best classification accuracy of 99.99%, 99.95%, and 99.95% for early MCI vs. AD, late MCI vs. AD, and MCI vs. early MCI, respectively.

“Although this was not the first attempt to diagnose the early onset of Alzheimer’s from similar data, our main breakthrough is the accuracy of the algorithm. Obviously, such high numbers are not indicators of true real-life performance, but we’re working with medical institutions to get more data,” says Maskeli?nas.

According to him, the algorithm could be developed into software, which would analyse the collected data from vulnerable groups (those over 65, having a history of brain injury, high blood pressure, etc.) and notify the medical personnel about the anomalies related to the early onset of Alzheimer’s.

“We need to make the most of data,” says Maskeli?nas, “that’s why our research group focuses on the European open science principle, so anyone can use our knowledge and develop it further. I believe that this principle contributes greatly to societal advancement.”

The chief researcher, whose main area is focusing on the application of modern methods of artificial intelligence on signal processing and multimodal interfaces, says that the above-described model can be integrated into a more complex system, analysing several different parameters, for example, also monitoring eye movements’ tracking, face reading, voice analysing, etc. Such technology could then be used for self-check and alert to seek professional advice if anything is causing concern.

“Technologies can make medicine more accessible and cheaper. Although they will never (or at least not soon) truly replace the medical professional, technologies can encourage seeking timely diagnosis and help,” says Maskeli?nas.

Journal Reference:

1. Modupe Odusami, Rytis Maskeliūnas, Robertas Damaševičius, Tomas Krilavičius. Analysis of Features of Alzheimer’s Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network. Diagnostics, 2021; 11 (6): 1071 DOI: 3390/diagnostics11061071

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