Functional brain connectivity in clinical high-risk youth from the Kenya psychosis-risk outcomes study (KePROS):...

The clinical high-risk (CHR) state identifies individuals at elevated risk for psychosis, yet neurobiological markers of vulnerability remain incompletely characterized, particularly in African populations. Functional Network Area and Topography Analysis (FUNCATA) quantifies individualized network size and spatial organization, addressing limitations of group-average parcellations. Prior studies in schizophrenia identified dorsal attention network (DAN) enlargement and topographic abnormalities. We applied FUNCATA to a Kenyan CHR cohort to examine whether similar alterations are detectable. Twenty-four healthy controls and 63 CHR participants (ages 18-26) underwent resting-state fMRI in Nairobi. Individualized functional networks were derived using template matching. Network size and Topographic Abnormality Index (TAI) were compared between groups using ANCOVA. Associations with symptom dimensions (BPRS-derived principal components), global functioning, cognition, and longitudinal symptom trajectories were examined. CHR participants demonstrated modest left-lateralized DAN enlargement (d = 0.61) and reduced salience network (SAL) size (d = 0.50). More robust effects emerged for TAI, with significantly elevated spatial abnormalities across multiple networks, most prominently the DAN (d = 1.18), default mode network (d = 0.91) and cingulo-opercular network (d = 0.80). Larger left DAN size was associated with longitudinal worsening of general psychopathology, while SAL size was associated with unusual thought processing and role functioning. TAI measures showed limited associations with clinical outcomes after correction. These results suggest that functional network enlargement and spatial reorganization are detectable in CHR individuals within a non-Western cohort. DAN enlargement and widespread topographic abnormalities may represent early neurobiological features of psychosis vulnerability. TAI appears particularly sensitive to spatial deviations and may serve as a promising biomarker for risk stratification and longitudinal monitoring.