Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks

Piguet, Camille; Karahanoğlu, Fikret Işık; Saccaro, Luigi Francesco; Ville, Dimitri Van De; Vuilleumier, Patrik

doi:10.1016/j.nicl.2021.102833

Cited by 33 publications

(52 citation statements)

References 133 publications

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“…The 13 extracted spatial maps represented in each iCAP correspond to well-known resting state networks and were reminiscent of common task-related and cognitive networks typically observed in fMRI studies (Van Den Heuvel and Pol, 2010). They also correspond to iCAP networks identified in previous studies (Bommarito et al, 2021; Piguet et al, 2021; Siffredi et al, 2021; Zöller et al, 2019). Specifically, the obtained networks included sensory-related networks, i.e., sensori-motor/auditory, primary and secondary visual; higher level cognitive network, i.e., the fronto-parietal, frontal and fronto-temporal left and right networks; and default-mode network (DMN) decomposed into precuneus/posterior DMN and posterior DMN.…”

Section: Resultssupporting

confidence: 84%

Section: Participant Characteristicssupporting

confidence: 87%

“…The iCAPs framework has previously being found to provide relevant makers of psychiatric symptomatology in adults and children, such as depression, anxiety and psychotic symptoms (Piguet et al, 2021; Zöller et al, 2019). In the current study, despite comparable dynamic features of resting state networks between VPT and full-term controls, different patterns of association with socio-emotional outcomes were observed across the two groups.…”

Section: Discussionmentioning

confidence: 99%

“…For rs-fMRI data, the mean framewise displacement for each frame was computed to quantify the extent of head motion from volume to volume for each participant (Power et al, 2012;Power et al, 2014). Following Power and colleagues' recommendations, volume censoring ("scrubbing") for motion correction were applied to frames with a mean framewise displacement above 0.5 mm, as well as one frame before and two after.…”

Section: Head Motionmentioning

confidence: 99%

“…Using a whole-brain data-driven approach, this framework not only allows to extract spatial and temporal characteristics of large scale brain networks but also temporal overlaps between these different networks. Using the iCAPs framework, recent studies conducted in children, adolescents and adults have found significant association between large scale brain dynamics and psychiatric symptoms including depression, anxiety and psychotic symptoms (Piguet et al, 2021; Zöller et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Large scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes

Siffredi

Liverani

Freitas

et al. 2022

Preprint

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Children born very preterm (VPT; < 32 completed weeks of gestation) are at high risk of neurodevelopmental and neurobehavioral difficulties associated with atypical brain maturation. The analysis of large-scale brain network dynamics during rest allows to investigate brain functional connectivity and their association with behavioural outcomes. Of interest, prematurity has been associated with atypical socio-emotional development with significant implications for the forming of peer relationships, adaptive functioning, academic achievement and mental health. In this study, we extracted dynamic functional connectivity by using the innovation-driven co-activation patterns (iCAPs) framework in preterm and full-term children aged 6 to 9 to explore changes in spatial organisation, laterality and temporal dynamics of spontaneous large-scale brain activity. Multivariate pattern analysis was used to explore potential biomarkers for socio-emotional difficulties in preterm children. Results show a spatial organisation of 13 networks retrieved comparable to full-term controls. Dynamic features and lateralisation of network brain activity were also comparable across groups for all large-scale brain networks. Despite apparent similarities in terms of dynamical functional connectivity parameters, multivariate pattern analysis unveiled group differences in their associations with socio-emotional abilities. While a pattern of decreased engagement in certain brain networks were associated with better socio-emotional abilities in full-term controls; in the VPT group, better socio-emotional abilities were associated with coordination of activity across different networks, i.e., coupling duration between different pairs of networks. It is possible that group differences reflect reduced degree of maturation of functional architecture in the VPT group for socio-emotional abilities.

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Section: Resultssupporting

confidence: 84%

Section: Participant Characteristicssupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Head Motionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Large scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes

Siffredi

Liverani

Freitas

et al. 2022

Preprint

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Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

et al. 2023

Self Cite

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Objectives : Bipolar disorder (BD) is a severe, chronic, affective disorder characterized by recurrent switching between mood states, psychomotor and cognitive symptoms, which can linger in euthymic states as residual symptoms. Hippocampal alterations may play a key role in the neural processing of BD symptoms. However, its dynamic functional connectivity (dFC) remains unclear. Therefore, the present study explores hippocampal dFC in relation to BD symptoms. Methods : We assessed hippocampus‐based dFC coactivation patterns (CAPs) on resting‐state fMRI data of 25 euthymic BD patients and 25 age‐ and sex‐matched healthy controls (HC). Results : Bilateral hippocampal dFC with somatomotor networks (SMN) was reduced in BD, compared to HC, while at the same time dFC between the left hippocampus and midcingulo‐insular salience system (SN) was higher in BD. Correlational analysis between CAPs and clinical scores revealed that dFC between the bilateral hippocampus and the default‐like network (DMN) correlated with depression scores in BD. Furthermore, pathological hyperconnectivity between the default mode network (DMN) and SMN and the frontoparietal network (FPN) was modulated by the same depression scores in BD. Conclusions : Overall, we observed alterations of large‐scale functional brain networks associated with decreased flexibility in cognitive control, salience detection, and emotion processing in BD. Additionally, the present study provides new insights on the neural architecture underlying a self‐centered perspective on the environment in BD patients. dFC markers may improve detection, treatment, and follow‐up of BD patients and of disabling residual depressive symptoms in particular.

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Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

Yang,

Yao,

Zhang

et al. 2023

Brain Struct Funct

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Brain states can be characterized by recurring coactivation patterns (CAPs).Traditional CAP analysis is performed at the group-level, while the human brain is individualized and the functional connectome has shown the uniqueness as fingerprints.Whether stable individual CAPs could be obtained from a single fMRI scan and could individual CAPs improve the identification is unclear. An open dataset, the midnight scan club was used in this study to answer these questions. Four CAP states were identified at three distinct levels (group-, subject-and scan-level) separately, and the CAPs were then reconstructed for each scan. Identification rate and differential identifiability were used to evaluate the subject identification performance. Our results demonstrated that the individual CAPs were unstable when using a single scan. By maintaining high intra-subject similarity and inter-subject differences, subject-level CAPs achieved the best identification performance. Particularly, brain regions that contributed to the identifiability were mainly located in higher-order networks (e.g., frontal-parietal network). Besides, head motion reduced the intra-subject similarity, while its impact on identification rate was non-significant. Finally, a pipeline was developed to depict brain-behavior associations in datasets with few samples and dense sampling, and individualized CAP dynamics showed an above-chance level correlation with IQ.

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Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks

Cited by 33 publications

References 133 publications

Large scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes

Large scale brain network dynamics in very preterm children and relationship with socio-emotional outcomes

Dynamic functional hippocampal markers of residual depressive symptoms in euthymic bipolar disorder

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

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