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Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example.

Authors :
Warren SL
Khan DM
Moustafa AA
Source :
Brain and behavior [Brain Behav] 2024 Jun; Vol. 14 (6), pp. e3554.
Publication Year :
2024

Abstract

Background: Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.<br />Objectives and Methods: In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.<br />Results: In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.<br />Conclusions: We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.<br /> (© 2024 The Author(s). Brain and Behavior published by Wiley Periodicals LLC.)

Details

Language :
English
ISSN :
2162-3279
Volume :
14
Issue :
6
Database :
MEDLINE
Journal :
Brain and behavior
Publication Type :
Academic Journal
Accession number :
38841732
Full Text :
https://doi.org/10.1002/brb3.3554