Affiliation:
1. Faculty of Society and Design, School of Psychology Bond University Gold Coast Queensland Australia
2. Department of Electronic Engineering NED University of Engineering & Technology Karachi Sindh Pakistan
3. The Faculty of Health Sciences, Department of Human Anatomy and Physiology University of Johannesburg Auckland Park South Africa
Abstract
AbstractBackgroundDeep‐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.Objectives and MethodsIn 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.ResultsIn turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.ConclusionsWe are confident that this study can help more researchers enter the field and create accessible fMRI and deep‐learning diagnostic models for neurological disorders.