Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders

Skåtun, Kristina Cecilie; Kaufmann, Tobias; Tønnesen, Siren; Biele, Guido; Melle, Ingrid; Agartz, Ingrid; Alnæs, Dag; Andreassen, Ole A.; Westlye, Lars T.

doi:10.1503/jpn.150159

Cited by 54 publications

(30 citation statements)

References 52 publications

(49 reference statements)

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“…FMRIB'S ICA-based Xnoiseifier (FIX) (29,30) was used to automatically classify noise components and regress them out from the main signal, with a threshold of 60. FIX has been shown to substantially improve the temporal signal to noise ratio (tSNR) (31,32), which was computed before and after FIX (33). In line with previous studies (31,32,34), denoising substantially increased tSNR (t = 36.177, p < .001, Cohen's d = 1.86) and none of the scans were deemed to have insufficient quality after denoising.…”

Section: Image Processingsupporting

confidence: 61%

“…FIX has been shown to substantially improve the temporal signal to noise ratio (tSNR) (31,32), which was computed before and after FIX (33). In line with previous studies (31,32,34), denoising substantially increased tSNR (t = 36.177, p < .001, Cohen's d = 1.86) and none of the scans were deemed to have insufficient quality after denoising. T1-weighted volumes were skull-stripped using FreeSurfer 5.3 (35) and used for standard space (MNI-152) registration with FLIRT, refining the process with boundary-based registration (BBR) (36) and FNIRT.…”

Section: Image Processingsupporting

confidence: 61%

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Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

Maglanoc

Landrø

Jonassen

et al. 2019

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Section: Image Processingsupporting

confidence: 61%

Section: Image Processingsupporting

confidence: 61%

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

Maglanoc

Landrø

Jonassen

et al. 2019

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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“…These impaired interactions between brain regions support the notion of a disconnectivity syndrome in schizophrenia, which suggests dysfunctional coordination or synchronization of neuronal oscillations in large-scale networks (Uhlhaas 2013). Alterations of neuronal synchrony parameters of these brain regions have also been demonstrated to be implicated in the pathophysiological processes of the disorder (Uhlhaas et al 2008;Roach et al 2013;Skåtun et al 2016). …”

Section: Introductionsupporting

confidence: 65%

Differences of temporal dynamics and signal complexity of gamma band oscillations in first-episode psychosis during a working memory task

et al. 2017

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Gamma band oscillations participate in the temporal binding needed to synchronize cortical networks, involved in early sensory and short term memory processes. In earlier studies, alterations of these neurophysiological parameters have been found in psychotic disorders. To date no study has explored the temporal dynamics and signal complexity of gamma band oscillations in first episode psychosis (FEP). To address this issue, gamma band analysis was performed in 15 FEP patients and 18 healthy controls who successfully performed an adapted 2-back working memory task. Multiple linear and logistic regression models were computed to explore the relationship between the cognitive status and gamma oscillation changes over time. Based on regression model results, phase diagrams were constructed and their complexity was estimated using fractal dimension, a mathematical tool that describes shapes as numeric values. When adjusted for gamma values at time lags -3 to -4 ms and -15 to -16 ms, FEP patients displayed significantly higher time-dependent changes than controls, independently of the nature of the task. The present results are consistent with a discoordination of the activity of cortical generators engaged by the stimulus apparition in FEP patients, leading to a global binding deficit. In addition, fractal analysis showing higher complexity of gamma signal, confirmed this deficit. Our results provide evidence for recruitment of supplementary cortical generators as compensating mechanisms and yield further understanding for the pathophysiology cognitive impairments in FEP.

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“…FMRIB'S ICA-based Xnoiseifier (FIX) 28,29 was used to automatically classify noise components and regress them out from the main signal, with a threshold of 60. FIX has been shown to substantially improve the temporal signal to noise ratio (tSNR), 30,31 which was computed before and after FIX. 32 T1-weighted volumes were skull-stripped using FreeSurfer 5.3 33 and used for standard space (MNI-152) registration with FLIRT, refining the process with boundary-based registration (BBR) 34 and FNIRT.…”

Section: Image Processingmentioning

confidence: 99%

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

Maglanoc

Landrø

Jonassen

et al. 2018

Preprint

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Background: Depression is a complex disorder with large inter-individual variability in symptom profiles that often occur alongside symptoms of other psychiatric domains such as anxiety. A dimensional and symptom-based approach may help refine the characterization and classification of depressive and anxiety disorders and thus aid in establishing robust biomarkers. We assess the brain functional connectivity correlates of a symptom-based clustering of individuals using functional brain imaging data. Methods: We assessed symptoms of depression and anxiety using Beck's Depression andBeck's Anxiety inventories in individuals with or without a history of depression, and high dimensional data clustering to form subgroups based on symptom profiles. To assess the biological relevance of this subtyping, we compared functional magnetic resonance imagingbased dynamic and static functional connectivity between subgroups in a subset of the total sample. Results:We identified five subgroups with distinct symptom profiles, cutting across diagnostic boundaries and differing in terms of total severity, symptom patterns and centrality. For instance, inability to relax, fear of the worst, and feelings of guilt were among the most severe symptoms in subgroup 1, 2 and 3, respectively. These subgroups showed evidence of differential static brain connectivity patterns, in particular comprising a frontotemporal network. In contrast, we found no significant associations with clinical sum scores, dynamic functional connectivity or global connectivity measures. Conclusion:Adding to the ongoing pursuit of individual-based treatment, the results show subtyping based on a dimensional conceptualization and unique constellations of anxiety and depression symptoms is supported by distinct brain static functional connectivity patterns.

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Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders

Cited by 54 publications

References 52 publications

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

Data-Driven Clustering Reveals a Link Between Symptoms and Functional Brain Connectivity in Depression

Differences of temporal dynamics and signal complexity of gamma band oscillations in first-episode psychosis during a working memory task

Data-driven clustering reveals a link between symptoms and functional brain connectivity in depression

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