Another Step Toward the Prediction of Antipsychotic Treatment Response Using Functional Connectivity

Incremental predictive value of genetic risk and functional brain connectivity in determining antipsychotic response in schizophrenia

We aimed to assess the incremental value of schizophrenia polygenic risk score (PgRS) and resting-state functional brain connectivity (rsFC) when added to clinical data in predicting the six-week response to oral risperidone (Risperdal) in schizophrenia. Fifty-seven, 54, and 43 individuals in a group of never-before-treated first-episode schizophrenia had good quality whole-genome sequencing (10x), rsFC, and both genomic and rsFC data, respectively, at baseline. Symptom severity ratings were obtained at baseline and six-weeks of oral risperidone (Risperdal) treatment. The primary outcome was the percentage change in the Positive and Negative Syndrome Scale Total scores after risperidone (Risperdal) treatment. Clinical, PgRS, and rsFC determinants of treatment response were first evaluated independently. Subsequently, three blocks of hierarchical multiple regression analyses with leave-one-out cross-validation (n = 43), were implemented to study clinical, clinical + PgRS and clinical + PgRS + rsFC determinants of treatment response. While the combined clinical variables did not show a statistically significant prediction of treatment response, adding PgRS (9 % R2 change) and rsFC (26 % R2 change) in hierarchical steps, significantly improved the overall proportion of variance explained in treatment response. This proof-of-concept investigation underscores the incremental benefits offered by genetic and neuroimaging metrics over clinical measures in determining prospectively-ascertained short-term treatment response in first-episode schizophrenia.

Diagnostically distinct resting state fMRI energy distributions: A subject-specific maximum entropy modeling study

Objective

Existing neuroimaging studies of psychotic and mood disorders have reported brain activation differences (first-order properties) and altered pairwise correlation-based functional connectivity (second-order properties). However, both approaches have certain limitations that can be overcome by integrating them in a pairwise maximum entropy model (MEM) that better represents a comprehensive picture of fMRI signal patterns and provides a system-wide summary measure called energy. This study examines the applicability of individual-level MEM for psychiatry and identifies image-derived model coefficients related to model parameters.

Method

MEMs are fit to resting state fMRI data from each individual with schizophrenia/schizoaffective disorder, bipolar disorder, and major depression (n=132) and demographically matched healthy controls (n=132) from the UK Biobank to different subsets of the default mode network (DMN) regions.

Results

The model satisfactorily explained observed brain energy state occurrence probabilities across all participants, and model parameters were significantly correlated with image-derived coefficients for all groups. Within clinical groups, averaged energy level distributions were higher in schizophrenia/schizoaffective disorder but lower in bipolar disorder compared to controls for both bilateral and unilateral DMN. Major depression energy distributions were higher compared to controls only in the right hemisphere DMN.

Conclusions

Diagnostically distinct energy states suggest that probability distributions of temporal changes in synchronously active nodes may underlie each diagnostic entity. Subject-specific MEMs allow for factoring in the individual variations compared to traditional group-level inferences, offering an improved measure of biologically meaningful correlates of brain activity that may have potential clinical utility.