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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Modeling Brain Volume Using Deep Learning-Based Physical Activity Features in Patients With Dementia.

Bumhee Park^1,2, Byung Jin Choi¹, Heirim Lee²

¹Department of Biomedical Informatics, Ajou University School of Medicine, Suwon-si, South Korea.

Frontiers in Neuroinformatics

|March 31, 2022

Summary

This study shows activity data can predict regional brain volumes, aiding early detection of neurological disorders like Alzheimer's disease. This method accurately estimates 116 brain regions without clinical data.

Keywords:

accelerometer actigraphy autoencoder cognitive dysfunction deep learning dementia

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Published on: April 14, 2014

Area of Science:

Neuroscience
Medical Imaging
Machine Learning

Background:

Dementia severity correlates with reduced brain volumes, necessitating early detection methods.
Existing methods using activity data to estimate brain volume are limited by data interpretability and focus on total brain volume.
Previous research could not detect regional brain volume reductions crucial for characterizing disease progression.

Purpose of the Study:

To evaluate the prediction of 116 specific brain region volumes using activity data.
To develop a novel feature extraction method combining time-frequency domain and unsupervised deep learning.
To assess the feasibility of using activity data for early detection of neurological disorders.

Main Methods:

Developed an unsupervised deep learning feature extraction model using National Health and Nutrition Examination Survey (NHANES) activity data (n=14,482).
Applied the model and time-frequency domain methods to Biobank Innovations for chronic Cerebrovascular disease With ALZheimer's disease Study (BICWALZS) activity data (n=177).
Used linear regression to estimate 116 regional brain volumes from extracted activity features, validated against MRI data.

Main Results:

Achieved statistically significant regression models for all 116 brain regions.
Demonstrated a high average correlation coefficient of 0.990 ± 0.006 between predicted and actual regional brain volumes.
Observed correlations consistently above 0.964 across all regions, with temporal lobe regions showing the highest correlation (0.995).

Conclusions:

A combined deep learning and time-frequency domain approach effectively extracts activity features for regional brain volume prediction.
This method enables volume estimation using solely activity data, independent of clinical variables.
Findings suggest activity data holds potential for early detection of neurological disorders, including Alzheimer's disease.