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

Samuel L Warren¹, Danish M Khan², Ahmed A Moustafa^1,3

¹Faculty of Society and Design, School of Psychology, Bond University, Gold Coast, Queensland, Australia.

Brain and Behavior

|June 6, 2024

Summary

This study introduces assistive tools for deep learning and functional magnetic resonance imaging, creating an example autism spectrum disorder classification model to aid researchers in developing accessible diagnostic tools.

Keywords:

autism spectrum disorder (ASD)convolutional neural network (CNN)deep learning (DL)disease classification functional magnetic resonance imaging (fMRI)

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Area of Science:

Neuroscience
Computer Science
Medical Imaging

Background:

Deep learning (DL) is transforming neurological disorder classification using functional magnetic resonance imaging (fMRI).
Interdisciplinary knowledge gaps between brain sciences and medical sciences hinder fMRI and DL integration.
Complexity of fMRI and DL methods limits clinical adoption and accessibility for non-specialists.

Purpose of the Study:

To provide an introductory guide to popular DL and fMRI assistive tools.
To demonstrate an example autism spectrum disorder (ASD) classification model using fMRI and DL.
To streamline fMRI and DL pipelines for neurological disorder diagnosis.

Main Methods:

Utilized assistive tools such as Optuna and GIFT.
Employed the Autism Brain Imaging Data Exchange (ABIDE) preprocessed repository.
Developed a convolutional neural network model for ASD classification using fMRI data.

Main Results:

Presented a guide to assistive tools for fMRI and DL analysis.
Demonstrated a streamlined pipeline for fMRI and DL model development.
Provided a practical example of an ASD classification model.

Conclusions:

The study aims to lower barriers for researchers entering the fMRI and DL field.
The developed pipeline and guide facilitate the creation of accessible diagnostic models.
This work supports the advancement of neurological disorder diagnostics through accessible technology.