Data-driven approaches to neuroimaging biomarkers for neurological and psychiatric disorders: emerging approaches and examples

• Authors: Vince Calhoun, Emory University; Godfrey D Pearlson, Yale School of Medicine; Jing Sui, Emory University
• Journal Title: CURRENT OPINION IN NEUROLOGY
• Volume: Volume 34, Number 4
• Publisher: LIPPINCOTT WILLIAMS & WILKINS | 2021-08-01, Pages 469-479
• Type of Work: Article | Post-print: After Peer Review
• Publisher DOI: http://doi.org/10.1097/WCO.0000000000000967
• Subjects: Health Sciences, Mental Health
• Keywords: Science & Technology; Life Sciences & Biomedicine; Clinical Neurology; Neurosciences; Neurosciences & Neurology; data driven; data fusion; dynamics; multimodal; neuroimaging biomarkers; DYNAMIC FUNCTIONAL CONNECTIVITY; HUMAN CONNECTOME PROJECT; MULTIMODAL FUSION; PATTERNS; ICA; CLASSIFICATION; SUBJECT

Abstract

Purpose of reviewThe 'holy grail' of clinical applications of neuroimaging to neurological and psychiatric disorders via personalized biomarkers has remained mostly elusive, despite considerable effort. However, there are many reasons to continue to be hopeful, as the field has made remarkable advances over the past few years, fueled by a variety of converging technical and data developments.Recent findingsWe discuss a number of advances that are accelerating the push for neuroimaging biomarkers including the advent of the 'neuroscience big data' era, biomarker data competitions, the development of more sophisticated algorithms including 'guided' data-driven approaches that facilitate automation of network-based analyses, dynamic connectivity, and deep learning. Another key advance includes multimodal data fusion approaches which can provide convergent and complementary evidence pointing to possible mechanisms as well as increase predictive accuracy.SummaryThe search for clinically relevant neuroimaging biomarkers for neurological and psychiatric disorders is rapidly accelerating. Here, we highlight some of these aspects, provide recent examples from studies in our group, and link to other ongoing work in the field. It is critical that access and use of these advanced approaches becomes mainstream, this will help propel the community forward and facilitate the production of robust and replicable neuroimaging biomarkers.