Big connectome imaging data in psychiatric disorders

Abstract

Psychiatric disorders are a pressing public health challenge, posing a significant threat to the well-being of millions of people worldwide. Their elusive etiology, rooted in the complex interplay of genetic, environmental, and neural factors, requires innovative research approaches. The advent of advanced neuroimaging techniques and connectomics marks a transformative era, enabling researchers to delve into the structural and functional networks of the human brain. This transformation is underscored by the establishment of large brain datasets and a growing body of published findings, heralding a new era in neuroscience research that is poised to reshape our understanding of psychiatric disorders. Here, we review recent advances in connectome and neuroimaging big data in psychiatric disorders. First, we highlight several multisite neuroimaging datasets that hold immense potential for groundbreaking discoveries in understanding the intricate structural and functional network architecture of various psychiatric disorders. We then present innovative methods for multicenter and multidimensional data analysis, particularly connectome-based meta-analytic and multivariate analysis methods. Furthermore, we demonstrate the critical value of these methods in synthesizing findings from diverse published works or multisite data, and in exploring connectomics associations with demographics, symptomatology, behavioral and cognitive metrics, and genetic data in psychiatric disorders. Finally, we discuss the emerging issues and challenges that urgently need to be addressed in the field and that will shape the future trajectory of psychiatric research.