Machine Learning Models for Predicting Neurological Disorders from Brain Imaging Data

Abstract

Neurological disorders present a significant challenge to healthcare systems worldwide due to their complex etiology and diverse manifestations. Recent advancements in neuroimaging techniques have provided valuable insights into the structural and functional characteristics of the brain, offering a promising avenue for understanding and predicting neurological disorders. Machine learning (ML) algorithms, particularly deep learning models, have emerged as powerful tools for analyzing brain imaging data and extracting meaningful patterns that can aid in disease diagnosis and prognosis. This article provides an overview of the current state-of-the-art in using machine learning models for predicting neurological disorders from brain imaging data. We begin by discussing the importance of neuroimaging in capturing structural and functional abnormalities associated with various neurological conditions, including Alzheimer's disease, Parkinson's disease, schizophrenia, and epilepsy. Next, we review different types of brain imaging modalities, such as structural MRI, functional MRI, diffusion tensor imaging (DTI), positron emission tomography (PET), and electroencephalography (EEG), highlighting their respective strengths and limitations in capturing different aspects of brain function and pathology.