A Framework for Advancing Mechanistic Neurobehavioral Biomarkers in Psychiatry

Neuropsychiatry has yet to surmount the fundamental challenge of mapping behavioral pathology to its underlying neural pathology. This gap limits the development of treatments for specific circuit pathologies, despite the great potential of neuroimaging measures. We show that the field may be moving toward refining limited statistical frameworks, while clinically translatable solutions could emerge with broader considerations. We posit that the failure to operate within a formalism that defines falsifiable parameters might have hindered progress. Here, we propose a provisional formalism with the intention of bringing elements into focus that seem necessary to advance the development of precision treatments in psychiatry. Specifically, we propose that this formalism should consider 3 defining axes: 1) type of mechanism, 2) severity of mechanism, and 3) time. These 3 axes define a 3-dimensional dynamic mechanism complexity space (MCS). In turn, we posit that at any point in this MCS, there are embedded neurobehavioral subspaces or geometries for neural-to-symptom variation, which themselves are multidimensional and dynamic. This formalism provides for the mapping of the MCS to a neurobehavioral subspace. Furthermore, we articulate how this formalism accommodates integration of spectra from genetics and systems biology (transcriptomics, epigenomics, etc.) to neural mechanisms, symptom variance, and ultimately taxa of mental illness (i.e., categories). Finally, we argue that this formalism creates an opportunity to evaluate different types of treatment development that map onto dynamically evolving mechanisms of illness that are likely a hallmark feature of neuropsychiatric illness.