Right hippocampal volume and attenuated psychotic symptoms distinguish subgroups of youth with common patterns of temporoparietal effective connectivity

From dysconnectivity to symptoms: large-scale resting-state networks relate to psychotic phenomenology in schizophrenia.

BACKGROUND Schizophrenia is increasingly conceptualized as a disorder of large-scale brain network integration, yet how specific symptom phenotypes relate to reproducible resting-state functional connectivity (rsFC) signatures remains unclear. METHODS Resting-state fMRI data from 386 patients with schizophrenia and 212 healthy controls were analyzed to characterize large-scale functional connectivity patterns. Group-level connectivity differences were first identified (uncorrected P < 0.05, for exploratory feature selection), followed by within-patient analyses examining associations between altered connectivity and symptom subitems while adjusting for demographic factors. Multivariate models were then used to evaluate whether connectivity patterns showed systematic associations with individual symptom profiles. RESULTS Group comparisons revealed a dysconnectivity pattern characterized by reduced cross-network coupling between the visual network and higher-order systems, alongside selective increases in frontoparietal circuits. Within patients, connectivity alterations showed distinct association patterns across delusion, hallucination, and negative-symptom subitems. Multivariate analyses further indicated modest associations with several hallucination (e.g., H8, R²≈0.09) and delusion (e.g., D3, R²≈0.07) subitems, whereas associations with negative symptoms were minimal and showed limited generalization. CONCLUSIONS These findings support a hierarchical dysconnectivity profile centered on impaired perceptual-cognitive integration and suggest that specific positive-symptom phenotypes may be associated with partially consistent rsFC signatures. Overall effect sizes were modest, indicating that rsFC captures only a limited component of symptom variability. CLINICAL TRIAL NUMBER Not applicable.

Functional connectivity predictors and mechanisms of symptom change in functional neurological disorder

Abstract Clinical trajectories in patients with functional neurological disorder (FND) are variable, and the neural mechanisms underlying this heterogeneity remain poorly understood. This longitudinal brain imaging study examined resting-state functional connectivity predictors and mechanisms of symptom change in FND. Thirty-two adults with FND (motor and/or seizure phenotypes) completed baseline questionnaires and functional MRI (fMRI), followed by naturalistic treatment for 6.8 ± 0.8 months. All participants completed follow-up questionnaires; 28 completed follow-up fMRI. At each timepoint, three graph-theory network metrics of resting-state functional connectivity were computed: whole-brain weighted-degree (centrality), cortical integration (between-network connectivity), and cortical segregation (within-network connectivity). All analyses adjusted for age, sex, antidepressants, head motion, time between sessions and baseline score of interest, with cluster-wise correction. Results were contextualized against 50 age-, sex-, and head motion-matched healthy controls (HCs). Based on patient-reported Clinical Global Impression of Improvement ratings, 59.4% improved, 31.3% were unchanged, and 9.3% worsened. Core FND symptom (i.e. Screening for Somatoform Symptoms-7 Subscale for Conversion Disorder) and non-core physical symptom (Patient Health Questionnaire-15) scores showed variable trajectories, with no group-level changes. For whole-brain weighted-degree analyses, baseline centrality in right middle frontal, precentral, and left cerebellar regions was positively associated with core FND symptom change; longitudinally, centrality decreases in right precentral, superior parietal, lateral occipital, and cerebellar regions were associated with symptom improvement. For cortical integration analyses, baseline between-network connectivity in ventral attention, frontoparietal, and default mode network regions was positively associated with core FND symptom change; longitudinally, decreases in between-network connectivity for regions of these same networks were associated with symptom improvement. For cortical segregation analyses, baseline within-network connectivity in frontoparietal network regions was positively associated with core FND symptom change; no regions showed longitudinal segregation changes associated with symptom change. The right anterior insula emerged as a convergent site across baseline and longitudinal integration analyses, with the most improved participants showing elevated baseline between-network connectivity relative to HCs that normalized at follow-up. More modest functional connectivity associations were observed with non-core physical symptom change, spanning baseline within-network connectivity in dorsal attention network regions and longitudinal between-network connectivity increases in visual network regions. Findings remained significant adjusting for FND phenotype, although several attenuated when accounting for baseline affective symptoms or trauma burden. In conclusion, this study identified baseline and longitudinal resting-state functional connectivity features linked to symptom change in FND, highlighting the potential of large-scale network interactions as prognostic markers and providing mechanistic insights that set the stage for novel, biologically informed interventions.

Disrupted Hierarchical Functional Brain Organization in Affective and Psychotic Disorders: Insights from Functional Brain Gradients

Patients with psychosis and depression show widespread alterations in brain resting-state functional connectivity (rs-FC), affecting both sensory and higher-order brain regions. In this study, we investigate disruptions in the hierarchical organization of brain functional networks in patients with psychotic and affective disorders. We derived functional brain gradients, low dimensional representations of rs-FC that capture cortical hierarchy, in a large patient sample including clinical high-risk for psychosis (CHR-P) patients, recent-onset psychosis (ROP) patients, recent-onset depression (ROD) patients, and healthy controls (HC). We examined regional alterations, network-level alterations and functional differentiation and their relationship to clinical symptoms. In addition, we linked case-control differences to receptor expression maps to explore underlying neurobiological mechanisms. All patient groups exhibited alterations in the visual-to-sensorimotor gradient, while only ROP patients showed alterations in the association-to-sensory gradient. CHR-P and ROP patients exhibited lower values in the ventral attention network. Additionally, patients combined showed higher values in the somatomotor network, a reduced gradient range and altered between-network dispersion. ROD showed reduced within-network dispersion in the attentional networks and a reduced range. Correlational analysis revealed weak associations of gradient measures with functioning, visual dysfunctions and cognition. Furthermore case-control differences showed associations to receptor expression maps, suggesting the involvement of neurotransmitter systems in these disruptions. Our findings reveal transdiagnostic and disease-specific alterations of hierarchical brain organization. These alterations indicate deficits in functional integration across psychiatric diseases, highlighting the role of attentional and sensory networks in disease processes.

Disrupted hierarchical functional brain organization in affective and psychotic disorders: insights from functional brain gradients

Individuals with psychosis and depression show widespread alterations in brain resting-state functional connectivity (rs-FC), affecting both sensory and higher-order brain regions. In this study, we investigate disruptions in the hierarchical organization of brain functional networks in individuals with psychotic and affective disorders. We derived functional brain gradients, low dimensional representations of rs-FC that capture cortical hierarchy, in a sample of 1071 (56.3% female) participants, including clinical high-risk for psychosis (CHR-P) individuals, recent-onset psychosis (ROP) patients, recent-onset depression (ROD) patients, and healthy controls (HC). We examined regional alterations, network-level alterations and functional differentiation and their relationship to clinical symptoms. In addition, we linked case-control differences to receptor expression maps to explore underlying neurobiological mechanisms. All clinical groups exhibited alterations in the visual-to-sensorimotor gradient, while only ROP patients showed alterations in the sensory-to-association gradient. CHR-P and ROP individuals exhibited lower values in the ventral attention network. Clinical groups combined showed higher values in the somatomotor network, a reduced gradient range and altered between-network dispersion. ROD patients showed reduced within-network dispersion in the attentional networks and a reduced range. Correlational analysis revealed weak associations of gradient measures with functioning, visual dysfunctions and cognition. Case-control differences showed associations to receptor expression maps, suggesting the involvement of neurotransmitter systems in these disruptions. Our findings reveal transdiagnostic and disease-specific alterations of hierarchical brain organization. These alterations indicate deficits in functional integration across psychiatric diseases, highlighting the role of attentional and sensory networks in disease processes.

Longitudinal inference of hippocampal volume and morphometric hippocampal-cortical covariance in psychosis.

Schizophrenia and related psychoses are characterized by reductions in hippocampal volume and morphometric hippocampal-cortical covariance. No study to date has inferred the temporal relationship between deviations in subfield and adjacent white matter volume and connectivity across psychosis. To explore this relationship, we sampled cross-sectional neuroimaging data from 174 patients and 120 non-clinical controls from two studies on first- and multi-episode psychosis. We applied Subtype and Stage Inference (SuStaIn), a machine learning algorithm combining clustering and disease progression modeling, to eight measures of hippocampal volume and morphometric hippocampal-cortical covariance (subfields and white matter regions per hemisphere) combined. We then performed in-depth SuStaIn analyses for 18 volumes (five subfields and four white matter regions per hemisphere) and 18 connectivity measures (graph-theory-based participation coefficient) separately. Some patients and controls showed no deviations (no increase or decrease) from control means in volume and covariance. For individuals who did show deviations, we inferred data-driven stages from deviations in volume toward covariance. In-depth SuStaIn analyses inferred three distinct volume patterns (intact volume, subfield-dominant, whiter matter-dominant) followed by one of four possible connectivity patterns (intact connectivity, whiter matter-dominant, subfield-dominant, subiculum-dominant). These subtypes showed significant differences in age, patient-control ratio and associations between data-driven stages and memory. In previous work, altered hippocampal volume and morphometric covariance distinctively precede cognitive and clinical symptoms of psychosis. While not all patients were assigned a staging pattern for hippocampal volume or connectivity, our findings might thus contribute to elucidating distinct hippocampal patterns underlying heterogeneous manifestations of psychosis for a subgroup of patients.

Longitudinal Brain Correlates of Cognitive Performance in Early Psychosis