Computational Modelling of Brain Network Dynamics in Psychotic and Affective Disorders

This dissertation explores the role of dynamic functional connectivity (dFC) as an intermediate phenotype linking neurobiological characteristics and clinical outcomes in psychotic and affective disorders. The thesis aims to reveal alterations in dFC in psychotic and affective patients, study the impact of neurobiology on static and dynamic FC patterns, and identify neurobiological processes which might contribute to static and dynamic FC changes in psychotic and affective disorder. Study I, which compared dFC patterns of patients with recent-onset psychosis (ROP), patients with recent-onset depression (ROD), individuals with a clinical high risk for psychosis (CHR), and healthy individuals, found diagnosis-specific alterations in ROP and ROD patients as well as transdiagnostic alterations exhibited by all patient groups. We also identified a dFC pattern which was significantly correlated with psychosis symptom severity across the patient groups. Study II investigated the relationship between neurobiological characteristics and static and dynamic FC using brain network modelling, identifying FC correlates of global coupling and excitatory synaptic coupling, as well as model fits. In addition, this study investigated the effect of altering regional model parameters on global FC, showing that distinct small subsets of regions produced outsized effects on static and dynamic FC and regional effects were correlated with network structure. Study III employed brain network modelling of static and dynamic FC to reveal an increase in regional recurrent excitation in CHR individuals and ROD patients compared to healthy controls and ROP patients. Integrating the findings from these three studies, this dissertation contributes to the understanding of the role of dFC in psychotic and affective disorders, providing evidence for neurobiological underpinnings and clinical consequences of alterations to dFC

Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders

Background Psychosis and depression patients exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC), allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders. Methods In the present study, we report the analysis of dFC in a large patient sample including 127 clinical high-risk patients (CHR), 142 recent-onset psychosis (ROP) patients, 134 recent-onset depression (ROD) patients, and 256 healthy controls (HC). A sliding window-based technique was used to calculate the time-dependent FC in resting-state MRI data, followed by clustering to reveal recurrent FC states in each diagnostic group. Results We identified five unique FC states, which could be identified in all groups with high consistency (rmean = 0.889, sd = 0.116). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly-connected FC state in ROD patients (p < 0.0005) compared to most other groups, and a common increase in the lifetime of a FC state characterised by high sensorimotor and cingulo-opercular connectivities in all patient groups compared to the HC group (p < 0.0002). Canonical correlation analysis revealed a mode which exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < 0.0029), which was associated with positive psychosis symptom severity and several dFC parameters. Conclusions Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression, psychosis and clinical risk states

Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders

Background: Patients with psychosis and patients with depression exhibit widespread neurobiological abnormalities. The analysis of dynamic functional connectivity (dFC) allows for the detection of changes in complex brain activity patterns, providing insights into common and unique processes underlying these disorders. Methods: We report the analysis of dFC in a large sample including 127 patients at clinical high risk for psychosis, 142 patients with recent-onset psychosis, 134 patients with recent-onset depression, and 256 healthy control participants. A sliding window-based technique was used to calculate the time-dependent FC in resting-state magnetic resonance imaging data, followed by clustering to reveal recurrent FC states in each diagnostic group. Results: We identified 5 unique FC states, which could be identified in all groups with high consistency (mean r = 0.889 [SD = 0.116]). Analysis of dynamic parameters of these states showed a characteristic increase in the lifetime and frequency of a weakly connected FC state in patients with recent-onset depression (p < .0005) compared with the other groups and a common increase in the lifetime of an FC state characterized by high sensorimotor and cingulo-opercular connectivities in all patient groups compared with the healthy control group (p < .0002). Canonical correlation analysis revealed a mode that exhibited significant correlations between dFC parameters and clinical variables (r = 0.617, p < .0029), which was associated with positive psychosis symptom severity and several dFC parameters. Conclusions: Our findings indicate diagnosis-specific alterations of dFC and underline the potential of dynamic analysis to characterize disorders such as depression and psychosis and clinical risk states