Bridging the Gap: Strategies to Make Psychiatric Neuroimaging Clinically Relevant.

Abstract

How do we make psychiatric neuroimaging relevant to clinical care? Decades of neuroimaging studies have identified a large and growing canon of correlations between biological and clinical measures in psychiatric disorders. Despite this accumulation of knowledge, neuroimaging has yet to change how care is delivered in the clinic. Furthermore, for most common disorders, such as depression and schizophrenia, neuroimaging has not generated useful models of pathophysiology—that is, models that have been validated and that have subsequently given rise to increasingly sophisticatedmodels.How can neuroimagingmove beyond this roadblock? A commonly suggested solution involves the use of increasingly large sample sizes combined with increasing the volume of data collected per individual. In this Disruptive Innovation, we argue that this incremental approach is unlikely to succeed on its own. We argue that a critical next step to make psychiatric imaging relevant is to perturb imaging–phenotype relationships to identify causal relationships.We provide an example of how this approach is used to identify a brain circuit responsible for medication-refractory symptoms in schizophrenia. We then provide a roadmap for how this strategy can be applied broadly and, in so doing, advance therapeutics directly informed by neuroimaging. Broadly speaking, the long-standing promise of neuroimaging in psychiatric disorders is usually conceptualized in two forms: first, that imaging will clarify diagnosis/prognosis and second, that imaging will directly inform new approaches to intervention, specifically by localizing pathophysiology. Regarding diagnosis/prognosis, while neuroimaging has clear utility in diagnosing and treating neurologic disorders—such as multiple sclerosis, where imaging complements the physical examination—this is not the case in psychiatry. Psychiatric clinics have seen little clinical utility for neuroimaging for several reasons. Existing diagnostic neuroimaging studies generally compare binary outcomes (e.g., differentiating schizophrenia from bipolar disorder) at the group level. This approach does not reflect, however, a realistic clinical scenario where imaging would guide treatment. Rather, we should take an individualized approach to understanding psychopathology. For example, a more realistic scenario would be symptom driven, such as asking if auditory hallucinations are the product of a psychotic disorder, posttraumatic stress disorder, personality disorder, or more than one of these entities. The field has failed to consider the continuum of illness present in psychiatric disorders and failed to acknowledge the gradient of severity within individual patients. To address this, wemust identify the systems responsible for symptom severity, including through the use of continuous measures of psychopathology rather than categorical assessments that are unlikely to reflect the underlying biology. A proposed solution to the irrelevance of psychiatric neuroimaging principally involves collecting more data by accruing larger samples with longer MRI scans and analysis using novel analytic pipelines. Supporters of this approach suggest that intersubject heterogeneity and the low reliability of standard fMRI techniques at the individual level are inhibiting the translation of neuroimaging findings into clinical application. In order to bridge this gap, supporters propose collecting larger quantities of fMRI data in single individuals as opposed to smaller quantities across larger groups, such as by increasing fMRI scan time to >100 minutes in order to reliably image deep structures, such as the basal ganglia and thalamus. An example of this approach includes the Midnight Scan Club, which imaged >20 hours of fMRI data in ten different individuals. It has been proposed that increasing longitudinal scan data would also resolve differences in scanning conditions. An elaboration of this idea is the conjunction of complementary biological data—that is, combining multimodal imaging with gene expression data from the Allen brain atlas or with polygenic risk scores. There is no indication, however, that we are approaching quantities of observational/correlational data that will be sufficient for informing individual-level clinical questions. Furthermore, the increasing complexity of analytic methods can move toward an extreme, hyperdimensional space, where variables lose their direct clinical relevance. For example, graph theoretical measures based on connectome From Harvard Medical School (Drs. Ward and Brady); Department of Psychiatry, Brigham&Women’s Hospital, Boston, MA (Dr.Ward); Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA (Dr. Brady); Mclean Hospital, Belmont, MA (Drs. Brady and Halko).