Multivariate Functional Connectivity Features Linked to Psychopathology in Early Psychosis
Poster No:
421
Submission Type:
Abstract Submission
Authors:
Haley Wang1, Zhen-Qi Liu2, Jason Nomi1, Charles Schleifer3, Carrie Bearden3, Bratislav Misic2, Lucina Uddin4, Katherine Karlsgodt1
Institutions:
1University of California, Los Angeles, Los Angeles, CA, 2Montreal Neurological Institute, Montreal, Quebec, 3University of California, Los Angeles, Los Angeles, Los Angeles, CA, 4Department of Psychology, University of California Los Angeles, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Early psychosis (EP) research often focuses on diagnostic categories, despite overlapping symptoms and shared etiologies. Aberrant synchronization of brain activity between regions, indicative of resting-state functional connectivity (RSFC) alterations, reflects disruptions in neurotransmitter signaling and synaptic plasticity, crucial for higher-order cognitive functions. Meta-analyses reveal widespread RSFC reductions in psychosis, especially in the default mode, salience, and central executive networks. However, the relationship between RSFC and various dimensions of psychopathology remains unclear due to heterogeneity in symptoms and RSFC distributions across studies. Given symptom overlap across disorders, investigating brain-symptom relationships transdiagnostically, especially in adolescence and EP, is vital. Identifying shared neurobiological underpinnings of symptom profiles, independent of diagnosis, is essential for understanding core neuropathological mechanisms and developing targeted treatments for youth.
Methods:
Partial least squares (PLS) correlation, a statistical learning method was employed to derive latent components (LCs) that optimize covarying patterns between brain and clinical features. Significant LCs were determined via permutation tests with 5,000 iterations and false discovery rate (FDR) correction (q<.05). Projecting raw data onto LCs yielded composite scores for RSFC and clinical features. Loadings, as Pearson's correlations, reflected variable contributions to LCs. Confidence intervals were determined through cross-validation and bootstrapping (10,000 repetitions). Data from the Human Connectome Project-Early Psychosis Release 1.1 (HCP-EP) included 124 patients aged 16-35 years (38.7% female). Patients were diagnosed with schizophrenia, schizoaffective disorder, and psychotic mood disorders within the first three years of onset. Sensitivity and post-hoc analyses examined potential confounders (e.g., age, sex, IQ, head motion, diagnoses, duration of illness, substance use, antipsychotic medication dosage).
Results:
A single significant LC (p<.001) captured 41.8% of the covariance between RSFC variables and clinical symptoms (r=0.43, p<.001), indicating its potential relevance to EP pathophysiology. Examination of the LC loadings revealed a pattern of hyperconnectivity in RSFC associated with specific symptomatology across early psychosis diagnoses. Greater RSFC composite scores were linked to increased between-network RSFC features across the brain. Sensory-motor (visual and somatomotor) and default mode (A and B) networks exhibited greater RSFC with the control B network and with subcortical regions, including the amygdala, nucleus accumbens, posterior thalamus, and putamen. The RSFC signature was particularly linked to symptoms related to an inflexible cognitive pattern, including elevated rigidity, anxiety, hostility, and tension. No significant differences in RSFC or clinical composite scores were observed across diagnostic groups after FDR correction (q>.05), suggesting a transdiagnostic pattern of brain-behavior associations. These findings were also not influenced by confounds.
Conclusions:
We identified a stable and replicable neurobiological signature of RSFC alterations in EP across diagnoses, characterized by inflexibility and tension, associated with distinct whole-brain RSFC patterns across EP disorders. This LC implicated hyperconnectivity of the somatomotor and default networks with subcortical and control networks. The brain-behavior association might index intermediate neurobiological processes and potentially serve as transdiagnostic phenotypes, providing a more comprehensive characterization of individuals' clinical variability during EP. These suggest the clinical utility of data-driven methods to reveal symptom domains sharing neurobiological underpinnings, potentially guiding the refinement and development of targeted treatments tailored to specific symptom clusters or neurobiological profiles.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotional Perception
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Multivariate Approaches 2
Keywords:
Affective Disorders
Computational Neuroscience
DISORDERS
FUNCTIONAL MRI
Multivariate
Psychiatric Disorders
Schizophrenia
1|2Indicates the priority used for review
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