Researchers at Worcester Polytechnic Institute (WPI) apply artificial intelligence to analyze brain scans and predict Alzheimer’s disease with 92.87% accuracy. Their study highlights anatomical changes, such as brain volume loss, that vary by age and sex. These findings, published in Neuroscience, could enable earlier diagnosis and treatment of this neurodegenerative condition affecting an estimated 6.9 million Americans aged 65 and older.
Challenges in Early Detection
Alzheimer’s disease damages neurons, causing cell death, brain tissue loss, and cognitive decline. Early symptoms often mimic normal aging, complicating diagnosis. Machine learning overcomes this by processing vast MRI data to spot subtle changes distinguishing healthy brains from those with mild cognitive impairment or Alzheimer’s.
Benjamin Nephew, assistant research professor in the Department of Biology and Biotechnology at WPI, notes, “Machine-learning technologies analyze large amounts of data from scans to identify subtle changes and accurately predict Alzheimer’s disease and related cognitive states.”
Study Methods and Results
The team, including PhD student Senbao Lu and recent MS graduate Bhaavin Jogeshwar, examined 815 MRI scans from the Alzheimer’s Disease Neuroimaging Initiative. These scans, from individuals aged 69 to 84, represent normal cognition, mild impairment, and Alzheimer’s.
They first measured volumes in 95 brain regions using machine learning. An algorithm then predicted disease states based on differences from healthy benchmarks, achieving 92.87% accuracy in distinguishing Alzheimer’s from normal or mildly impaired brains.
Key Brain Regions Implicated
Top predictors included volume loss in the hippocampus (vital for memory and learning), amygdala (emotion regulation), and entorhinal cortex (early Alzheimer’s target for memory, navigation, and perception).
Across ages and sexes, these regions showed consistent decline, particularly the right hippocampus in the youngest group (69-76 years), signaling its role in early detection.
Sex and Age Variations
Brain changes differed by sex. Females exhibited volume loss in the left middle temporal cortex, linked to language, memory, and visual perception. Males showed prominent loss in the right entorhinal cortex.
Nephew highlights the surprise in these disparities, potentially tied to sex hormone declines like estrogen in women and testosterone in men, which influence Alzheimer’s risk.
“The critical challenge is to build a generalizable machine-learning model that captures differences between healthy brains and those with mild cognitive impairment or Alzheimer’s,” Nephew states. “A generalizable model means the biomarkers are universal to all patients.”
Ongoing Research Efforts
The team advances with deep learning models and explores factors like diabetes. WPI’s interdisciplinary approach draws students from biology, neuroscience, psychology, computer science, and bioinformatics.
“This research exemplifies the strength of neuroscience at WPI, which is interdisciplinary and computational,” Nephew adds. “The brain is extremely complicated, and we need to think broadly to better understand, predict, and treat brain diseases.”
