Polyconnectomic scoring of functional connectivity patterns across eight neuropsychiatric and three neurodegenerative disorders.

Abstract

Background: Neuropsychiatric and neurodegenerative disorders involve diverse changes in brain functional connectivity. As an alternative to approaches searching for specific mosaic patterns of affected connections and networks, we used polyconnectomic scoring to quantify disorder-related whole-brain connectivity signatures into interpretable, personalized scores.

Methods: The polyconnectomic score (PCS) measures the extent to which an individual's functional connectivity (FC) mirrors the whole-brain circuitry characteristics of a trait. We computed PCS for eight neuropsychiatric conditions (attention-deficit/hyperactivity disorder, anxiety-related disorders, autism spectrum disorder, obsessive-compulsive disorder, bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) and three neurodegenerative conditions (Alzheimer's disease, frontotemporal dementia, and Parkinson's disease) across 22 datasets with resting-state functional MRI of 10,667 individuals (5,325 patients, 5,342 controls). We further examined PCS in 26,673 individuals from the population-based UK Biobank cohort.

Results: PCS was consistently higher in out-of-sample patients across six of the eight neuropsychiatric and across all three investigated neurodegenerative disorders ([min, max]: AUC = [0.55, 0.73], p_FDR = [1.8 x 10^-16, 4.5 x 10^-2]). Individuals with elevated PCS levels for neuropsychiatric conditions exhibited higher neuroticism (p_FDR < 9.7 x 10^-5), lower cognitive performance (p_FDR < 5.3 x 10^-5), and lower general wellbeing (p_FDR < 9.7 x 10^-4).

Conclusions: Our findings reveal generalizable whole-brain connectivity alterations in brain disorders. PCS effectively aggregates disorder-related signatures across the entire brain into an interpretable, subject-specific metric. A toolbox is provided for PCS computation.