New AI method for neuroscience and brain disease research

New AI method for neuroscience and brain disease research


Source: Geralt/Pixabay

A new study shows how artificial intelligence (AI) is a powerful new paradigm for conducting research in neuroscience and diseases such as dementia, Alzheimer’s disease and other cognitive disorders.

This new research was led by Andrew McKenzie, MD, PhD, co-chief research resident in the Department of Psychiatry at Icahn Mount Sinai, in collaboration with scientists affiliated with Boston University School of Medicine in Boston, UT Southwestern Medical Center in Dallas, University of Texas Health Science Center in San Antonio and Newcastle University in Tyne, UK.

“To pave the way for better prevention and treatment options for age-related cognitive impairment, there is an urgent need to identify the structural features of brain microanatomy that are strongly associated with the disease using diagnostic protocols. ‘unbiased evaluation,’ the researchers wrote. “One approach to identify structural correlates of cognitive impairment is to perform clinicopathologic correlation in postmortem human brains.”

Alzheimer’s disease (AD) is the most common type of dementia and a progressive neurodegenerative disease that destroys brain cells over time and is characterized by changes in the brain such as amyloid plaques and neurofibrillary tangles called tau which can impact memory, cognition and the ability for a person to live independently.

Globally, an estimated 47 million people are living with Alzheimer’s disease (AD), a figure that is expected to reach 76 million by 2030 according to the Alzheimer’s Association. The exact cause of Alzheimer’s disease as well as other cognitive disorders is not yet fully understood.

In the United States alone, there are 5.8 million Americans with Alzheimer’s disease, two-thirds of whom are women according to a report by AARP and the Women’s Alzheimer’s Movement (WAM). By 2050, the number of Americans living with Alzheimer’s disease is expected to reach 16 million according to the Harvard NeuroDiscovery Center at Harvard Medical School.

Artificial intelligence is the field of computing that aims to enable machines to perform tasks associated with the human brain, such as cognition, learning, and problem solving. Machine learning is a subset of artificial intelligence, where computer systems are able to perform tasks without explicit hard-coding of instructions. The ubiquity of AI is largely due to advances in the pattern recognition capabilities of deep learning, a type of machine learning whose deep neural network architecture is somewhat inspired by the biological brain. . Supervised learning refers to the process of training AI deep learning algorithms with labeled data. Supervised learning is typically used for classification and regression.

To conduct the study, the team used a loosely supervised AI deep learning algorithm and a combination of Python, Pytorch, R, ggplot2, NVIDIA v100 GPUs, and the ImageNet database. Specifically, they repurposed an existing AI classification called Constrained Attention Multi-Instance Learning (CLAM) to analyze slide imaging of human brain autopsy tissue from over 700 donors to predict whether there is had signs of cognitive impairment.

The AI ​​was trained to identify areas in brain samples where there is a reduction in Luxol Fast Blue (LFB) staining, a copper-based dye. The Luxol Rapid Blue Stain is a stain widely used to identify myelin in the brain under light microscopy for neuroanatomical analysis.

The whiteness of white matter in the brain is due to the fatty layer of myelin that surrounds nerve fibers called axons. Myelination allows action potentials to travel along the axon faster than an unmyelinated potential. The gray color of gray matter comes from the neuronal cell bodies it contains and has mostly unmyelinated axons. LFB staining differentiates white matter from gray matter in brain tissue sections by transforming abundant lipoproteins in the myelin sheaths into blue. Demyelination can be caused by traumatic brain injury, multiple sclerosis (MS), and other diseases and disorders.

The researchers trained two sets of models on 10 cross-validation folds. “Although the models had modest accuracy as a pure classification task, in both brain regions the classification accuracy was significantly higher than chance, suggesting that the models have utility for inferring the pathophysiology,” the researchers wrote.

“Our results demonstrate a scalable platform with interpretable deep learning to identify unexpected aspects of pathology in cognitive disorders that can be translated into the study of other neurobiological disorders,” the researchers concluded.

Copyright © 2022 Cami Rosso All rights reserved.

#method #neuroscience #brain #disease #research

Leave a Comment

Your email address will not be published. Required fields are marked *