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| Home > Publications database > Alterations of Functional Connectivity Dynamics in Affective and Psychotic Disorders |
| Journal Article | FZJ-2024-02679 |
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2024
Elsevier Inc.
Amsterdam [u.a.]
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Please use a persistent id in citations: doi:10.1016/j.bpsc.2024.02.013 doi:10.34734/FZJ-2024-02679
Abstract: 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.MethodsIn 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.ResultsWe 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.ConclusionsOur 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.IntroductionPsychotic and affective disorders are both prevalent and highly disruptive to patients’ quality of life, making them some of the most important contributors to global disease burden [1]. Understanding the pathophysiology underlying these disorders through neuroimaging might facilitate the development of tools for early diagnosis or the identification of novel interventions [2,3]. The analysis of connectivity between brain regions, particularly dynamic functional connectivity (dFC), has proven an effective method of characterizing brain alterations in health and disease [4,5]. Studying dFC abnormalities in patients with psychiatric disorders could reveal important information on brain changes associated with psychiatric symptoms, and provide indications of their mechanisms.The discovery of the behaviourally meaningful network structure of brain functional connectivity at rest [6,7] spurred numerous investigations of FC in patients with a range of brain disorders, including psychosis and depression [8,9]. Several more recent studies have also examined changes in FC in patients at clinical high risk for psychosis (CHR) [10, 11, 12], a prodromal stage that often precedes a full psychotic disorder. This population is particularly interesting because pathophysiological processes can be investigated before potential effects of treatment, hospitalisation and disability are consolidated [13]. Since there is significant clinical overlap between depression, psychosis and CHR patients [2,14], comparing brain changes between these groups might provide insights into diagnosis-specific disease processes.Studies of static, or time-averaged, functional brain connectivity indicate robust alterations in patients with depression [15,16] and psychosis [17,18]. Aberrant connectivity patterns particularly in the default mode (DMN), central executive (CEN) and salience networks (SN) have been identified in both affective and psychotic disorders, but the specific patterns of abnormalities differ between diagnoses [19,20]. While psychosis patients exhibit reduced FC both within the DMN and between the DMN and SN [21], studies show an increase in these FCs, and a decrease in connectivity between the DMN and CEN, in depression [16]. Communication between cortical and subcortical areas is also disturbed in both disorders, with alterations in FC commonly found between subcortical structures such as the striatum and areas in the prefrontal cortex [22,23]. The same brain networks are likewise affected in CHR patients [24], although a differentiation between psychotic, affective and CHR-specific changes is lacking.However, analyses of static FC are limited as they neglect the time-dependent variability of brain network connectivity. These techniques cannot uncover alterations in FC in patients with psychiatric disorders that occur only in the temporal domain. Since research suggests that dynamic properties of FC change in depressive and psychotic disorders [25, 26, 27, 28], their examination might reveal symptom-related and transdiagnostic brain abnormalities. One powerful approach to detecting temporal alterations of brain connectivity is based on computing FC within sliding windows. This allows for the identification of recurrent FC states, which are characterised by specific patterns of correlated activity between brain regions or brain networks [29,30]. The characteristics of such FC states are promising potential biomarkers of psychotic and affective disorders, and reveal information about changes in transient brain activity and mechanisms that cannot be gained from static FC alone [5,31].The analysis of dynamic connectivity has so far been limited to studies with small sample size and provided heterogenous findings [5,32,4,33]. Some initial findings suggest an overall decrease in temporal variability in depression [34], with patients spending longer in a weakly-connected state [4]. In contrast, patients with psychosis spend less time in states characterised by high connectivity within and between sensory areas, and more time in states with high connectivity within the DMN [25]. Moreover, other studies indicate that psychosis is associated with temporal disconnectivity [35,36]. The limited data on dynamic functional connectivity (dFC) changes in CHR patients available indicates some overlap of abnormalities with psychosis patients in the connectivity pattern of a dominant FC state, but also variations specific to the prodromal state [28]. It is still unclear, however, to what extent those findings are related to psychotic symptoms, rather than to a general burden of disease or affective symptoms that both CHR and psychosis patients commonly experience. Due to the wide variety of methodologies employed in dFC analyses [29], comparing results across studies remains challenging, which makes it particularly important to contrast the CHR, psychotic and affective patients with each other as well as with healthy control participants.In the present work, we provide first results from a large-scale neuroimaging study of CHR, recent-onset psychosis patients (ROP), patients with recent-onset depression (ROD) and healthy control individuals (HC). We investigated dFC changes by combining the sliding window method with a consensus clustering approach to identify a set of FC states. We then compared the dFC features of these states, specifically lifetimes, frequencies and transition frequencies, across diagnostic groups. We investigated their relationship with clinical variables such as symptom severity, level of functioning, and cognitive scores with the aim of identifying specific and transdiagnostic alterations in dFC.
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