Deep Learning Tactics for Neuroimaging Genomics Investigations in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative condition that is hallmarked by senile dementia, worsens over time, and has no proven treatment. It causes a decline in cognitive abilities. Effective automated procedures are required for early prediction and diagnosis since it is imperative to stop the progression of the disease. The creation of precise computer diagnostic systems was made possible by the nature of several aspects of neural data, which were primarily retrieved from neuroimaging with computer-aided algorithms. In the field of computer vision, deep learning, a high-tech machine learning strategy, has demonstrated exceptional ability in identifying detailed structures in complicated, high-dimensional data. Presently, a rising body of research suggests that deep learning techniques can act as a crucial pillar for the diagnosis, categorization, and prediction of AD. In order to develop targeted therapies, it is crucial to comprehend the genetic aetiology of AD. Several researchers had tried to find possible biomarkers for future therapy by using machine learning techniques to analyze the expressed genes in AD patients. Technology advancements in genomic research, like genome-wide association studies (GWAS), which enable the identification of polymorphisms and have been extensively used in investigations of AD, have identified certain genes as significant clinical risk factors for AD. In addition, a number of deep learning models are currently being used in research investigations to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in light of the most recent developments in neuroimaging and genetics. The chapter enlightens many studies that apply deep learning algorithms to predict AD using genomes or neuroimaging data along with the supportive tactics. On the basis of combining both neuroimaging and genome data, pertinent integrative neuroimaging genomics studies that make use of deep learning techniques to forecast AD have been discussed. The limitations of the most recent deep learning combined with neuroimaging and genomics AD investigations have also been described. Lastly, a summary of the research findings, challenges, and future directions for integrating deep learning methods into therapeutic settings is deliberated.