  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.
