Towards precise resting-state fMRI biomarkers in psychiatry: synthesizing developments in transdiagnostic research, dimensional models of psychopathology, and normative neurodevelopment

TL;DR

This paper reviews recent advances in resting-state fMRI research in psychiatry, emphasizing transdiagnostic, dimensional, and developmental approaches to identify more specific and clinically useful biomarkers.

Contribution

It synthesizes developments across three research avenues and proposes a framework integrating machine learning and network science to improve biomarker specificity.

Findings

Transdiagnostic research compares multiple disorders to find common and distinct biomarkers.

Dimensional models map the full spectrum of symptoms beyond traditional categories.

Modeling individual connectomes across development enhances understanding of neurobiological variability.

Abstract

Searching for biomarkers has been a chief pursuit of the field of psychiatry. Toward this end, studies have catalogued candidate resting-state biomarkers in nearly all forms of mental disorder. However, it is becoming increasingly clear that these biomarkers lack specificity, limiting their capacity to yield clinical impact. We discuss three avenues of research that are overcoming this limitation: (i) the adoption of transdiagnostic research designs, which involve studying and explicitly comparing multiple disorders from distinct diagnostic axes of psychiatry; (ii) dimensional models of psychopathology that map the full spectrum of symptomatology and that cut across traditional disorder boundaries; and (iii) modeling individuals' unique functional connectomes throughout development. We provide a framework for tying these subfields together that draws on tools from machine learning and network science.