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Idiopathic psychosis shows considerable biological heterogeneity across cases. B-SNIP used psychosis-relevant biomarkers to identity psychosis Biotypes, which will aid etiological and targeted treatment investigations. Psychosis probands from the B-SNIP consortium (n = 1907), their first-degree biological relatives (n = 705), and healthy participants (n = 895) completed a biomarker battery composed of cognition, saccades, and auditory EEG measurements. ERP quantifications were substantially modified from previous iterations of this approach. Multivariate integration reduced multiple biomarker outcomes to 11 "bio-factors". Twenty-four different approaches indicated bio-factor data among probands were best distributed as three subgroups. Numerical taxonomy with k-means constructed psychosis Biotypes, and rand indices evaluated consistency of Biotype assignments. Psychosis subgroups, their non-psychotic first-degree relatives, and healthy individuals were compared across bio-factors. The three psychosis Biotypes differed significantly on all 11 bio-factors, especially prominent for general cognition, antisaccades, ERP magnitude, and intrinsic neural activity. Rand indices showed excellent consistency of clustering membership when samples included at least 1100 subjects. Canonical discriminant analysis described composite bio-factors that simplified group comparisons and captured neural dysregulation, neural vigor, and stimulus salience variates. Neural dysregulation captured Biotype-2, low neural vigor captured Biotype-1, and deviations of stimulus salience captured Biotype-3. First-degree relatives showed similar patterns as their Biotyped proband relatives on general cognition, antisaccades, ERP magnitudes, and intrinsic brain activity. Results extend previous efforts by the B-SNIP consortium to characterize biologically distinct psychosis Biotypes. They also show that at least 1100 observations are necessary to achieve consistent outcomes. First-degree relative data implicate specific bio-factor deviations to the subtype of their proband and may inform studies of genetic risk.
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Background: Understanding how treatments change neurobiology is critical to developing predictors of treatment response. This is especially true for anxiety disorders-the most common psychiatric disorders across the lifespan. With this in mind, we examined neurofunctional predictors of treatment response and neurofunctional changes associated with treatment across anxiety disorders. Methods: PubMed/Medline was searched for prospective treatment studies that included parallel examinations of functional activation or connectivity (both task-based and resting state) in adults and youth with panic disorder and generalized, separation, and/or social anxiety disorders published before April 30, 2021. All studies examining baseline predictors or changes related to pharmacologic and psychotherapeutic treatment of DSM-TV and DSM-5 anxiety disorders were included. Demographic, clinical, and treatment data as well as neurofunctional outcomes were extracted and summarized. Results: Twenty-nine studies examined changes in functional activation and/or connectivity (56 treatment arms) related to treatment and twenty-three examined neurofunctional predictors of treatment response. Predictors of treatment response and treatment-related neurofunctional changes were frequently observed within amygdala-prefrontal circuits. However, immense heterogeneity and few replication studies preclude a cohesive neurofunctional treatment response model across anxiety disorders. Conclusions: The extant literature describing neurofunctional aspects of treatment response in anxiety disorders is best viewed as a partially constructed scaffold on which to build a clinically translatable set of robust neuroimaging biomarkers that can be used to guide treatment and to select from available treatment. The construction of this understanding will require harmonization of analytic and task approaches, larger samples, and replication of component studies.
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Objectives: Placebo response is one of the most significant barriers to detecting treatment effects in pediatric (and adult) clinical trials focusing on affective and anxiety disorders. We sought to identify neurofunctional predictors of placebo response in adolescents with generalized anxiety disorder (GAD) by examining dynamic and static functional brain connectivity. Methods: Before randomization to blinded placebo, adolescents, aged 12-17 years, with GAD (N = 25) underwent resting state functional magnetic resonance imaging. Whole brain voxelwise correlation analyses were used to determine the relationship between change in anxiety symptoms from baseline to week 8 and seed-based dynamic and static functional connectivity maps of regions in the salience and ventral attention networks (amygdala, dorsal anterior cingulate cortex [dACC], and ventrolateral prefrontal cortex [VLPFC]). Results: Greater dynamic functional connectivity variability in amygdala, dACC, VLPFC, and regions within salience, default mode, and frontoparietal networks was associated with greater placebo response. Lower static functional connectivity between amygdala and dorsolateral prefrontal cortex, amygdala and medial prefrontal cortex, dACC and posterior cingulate cortex and greater static functional connectivity between VLPFC and inferior parietal lobule were associated with greater placebo response. Conclusion: Placebo response is associated with a distinct dynamic and static connectivity fingerprint characterized by "variable" dynamic but "weak" static connectivity in the salience, default mode, frontoparietal, and ventral attention networks. These data provide granular evidence of how circuit-based biotypes mechanistically relate to placebo response. Finding biosignatures that predict placebo response is critically important in clinical psychopharmacology and to improve our ability to detect medication-placebo differences in clinical trials.
