Mapping Brain-Behavior Space Relationships Along the Psychosis Spectrum

Abstract

Difficulties in advancing effective patient-specific therapies for psychiatric disorders highlights a need to develop a neurobiologically-grounded, quantitatively stable mapping between neural and symptom variation. This gap is particularly acute for psychosis-spectrum disorders (PSD). Here, in a sample of 436 cross-diagnostic PSD patients, we derived and replicated a data-driven dimensionality-reduced symptom space across hallmark psychopathology symptoms and cognitive deficits, which was predictive at the single patient level. In turn, these data-reduced symptom axes mapped onto distinct and replicable univariate brain maps. Critically, we found that multivariate brain-behavior mapping techniques (e.g. canonical correlation analysis) did not show stable results. Instead, we show that a univariate brain-behavioral space (BBS) mapping can resolve stable individualized prediction. Finally, we show a proof-of-principle framework for relating personalized BBS metrics with molecular targets via serotonin and glutamate receptor manipulations and gene expression maps. Collectively, these results highlight a stable and data-driven BBS mapping across PSD, which offers an actionable quantitative path that can be iteratively optimized for personalized clinical biomarker endpoints.

Variability in sampling of cortex-wide neural dynamics explains individual differences in functional connectivity and behavioral phenotype

“…1I; r=-0.46 p=0.033 permutation test, n=20 mice). These results are consistent with the spectrum of behavior and functional connectivity seen in humans [2][3][4][5] , highlighting the face validity of the VPA model.…”

“…Understanding how individual differences in behavior relate to differences in neural activity is critical for developing individualized treatments of neuropsychiatric and neurodevelopmental disorders. Considerable work has linked behavioral variability to differences in how correlated neural activity is between brain areas, termed ‘functional connectivity.’ For example, patterns in functional connectivity vary across individuals 2 and are disrupted in autism 3 , schizophrenia 4 , and depression 5 . However, the changes in neural activity that underlie this altered functional connectivity remain unclear.…”

Reducing the dimensions of psychotic illness

“…This new symptom structure could aid in elucidating the underlying biopsychosocial causes of symptoms, informing and improving clinical practice. In fact, recent neuroimaging research has shown that PCA components were superior for identifying robust correlates of brain connectivity compared to traditional scale sub-scores 33 . Our framework could also inform computational understanding of reality monitoring.…”

Uncovering the Genetic Profiles Underlying the Intrinsic Organization of the Human Cerebellum

“…To evaluate the relationship between these genes and human behaviors, we leveraged the stable brain-behavior space strategy proposed by Ji et al [27], which could be briefly divided into the following three sections. These procedures were called Behavioral-FC-Gene mapping analysis to facilitate a better understanding and fluency of the paper.…”

“…One promising approach is imaging-transcriptomics analysis [24–26], which allows the brain-wide spatial analysis of microscopic transcriptome data combined with macroscopic neuroimaging phenotypes [9]. Moreover, it also offers the opportunity to link the transcriptome data with the behavior variations via the neuroimaging [27]. These cross-scale analyses could provide a better understanding of the interaction between microscale gene expression and macroscale functional network, and ultimately involved in the individual behaviors, and importantly, their putative multiscale interactions in cerebellar related diseases [28].…”