Summary of Classification Of Alzheimer’s Dementia Vs. Healthy Subjects by Studying Structural Disparities in Fmri Time-series Of Dmn, By Sneha Noble et al.
Classification of Alzheimer’s Dementia vs. Healthy subjects by studying structural disparities in fMRI Time-Series of DMN
by Sneha Noble, Chakka Sai Pradeep, Neelam Sinha, Thomas Gregor Issac
First submitted to arxiv on: 29 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV); Neurons and Cognition (q-bio.NC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed methodology utilizes an autoencoder-based model to learn efficient representations of resting-state fMRI time series data from healthy individuals and those with Alzheimer’s Disease (AD). The goal is to characterize the level of structure in the time series, which can lead to discrimination between subject groups. The “deviation from stochasticity” (DS) measure is applied on fMRI data from 50 healthy individuals and 50 subjects with AD, obtained from the publicly available ADNI database. The results show that the DS measure for healthy fMRI is significantly different compared to that of AD. A peak classification accuracy of 95% was achieved using a Gradient Boosting classifier, applying the DS measure on 100 subjects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses brain scans to try and figure out if people are healthy or have Alzheimer’s disease. They use a special way to look at the brain activity patterns, which can be very different between healthy people and those with Alzheimer’s. The researchers used a machine learning model to see if they could tell the difference just by looking at these patterns. They found that it worked really well, getting 95% of the time correct! This could help doctors diagnose Alzheimer’s more easily in the future. |
Keywords
» Artificial intelligence » Autoencoder » Boosting » Classification » Machine learning » Time series
