Altered dynamic functional connectivity across mood states in bipolar disorder

Liu, Min; Wang, Yuchen; Zhang, Aixia; Yang, Chunxia; Liu, Penghong; Wang, Junyan; Zhang, Kerang; Wang, Yanfang; Sun, Ning

doi:10.1016/j.brainres.2020.147143

Cited by 24 publications

(24 citation statements)

References 68 publications

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“…In our current study, decreased FcHo was revealed in the left MTG. This finding was strongly supported by a growing body of prior neuroimaging studies, which have broadly revealed that BD participants had both morphological and functional abnormalities of left MTG (Chai et al, 2020;Chrobak et al, 2021;Hibar et al, 2018;Liu et al, 2021;Wang et al, 2016). The MTG involves in the processing of numerous complex emotions, cognitions, and behaviors (Xu et al, 2019).…”

Section: Discussionmentioning

confidence: 58%

Alterations in resting‐state whole‐brain functional connectivity pattern similarity in bipolar disorder patients

et al. 2022

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Background Previous neuroimaging studies have extensively demonstrated many signs of functionally spontaneous local neural activity abnormalities in bipolar disorder (BD) patients using resting‐state functional magnetic resonance imaging (rs‐fMRI). However, how to identify the changes of voxel‐wise whole‐brain functional connectivity pattern and its corresponding functional connectivity changes remain largely unclear in BD patients. The current study aimed to investigate the voxel‐wise changes of functional connectivity patterns in BD patients using publicly available data from the UCLA CNP LA5c Study. Methods A total of 45 BD patients and 115 healthy control subjects were finally included and whole‐brain functional connectivity homogeneity (FcHo) was calculated from their rs‐fMRI. Moreover, the alterations of corresponding functional connectivity were subsequently identified using seed‐based resting‐state functional connectivity analysis. Results Individuals with BD exhibited significantly lower FcHo values in the left middle temporal gyrus (MTG) when compared with controls. Functional connectivity findings further indicated decreased functional connectivities between left MTG and cluster 1 (left superior temporal gyrus, extend to middle temporal gyrus, rolandic operculum), cluster 2 (right postcentral, extend to right precentral) in BD patients. The mean FcHo values of left MTG were positively correlated with insomnia, middle scores and appetite increase scores. The mean functional connectivities of left MTG to cluster 1 were negatively correlated with grandiose delusions scores. While the functional connections between left MTG with cluster 2 were negatively correlated with delusions of reference and positively correlated with insomnia, middle scores in BD patients. Conclusions Our findings suggested that abnormal FcHo and functional connections in those areas of the brain involving DMN and SMN networks might play a crucial role in the neuropathology of BD.

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Section: Discussionmentioning

confidence: 58%

Alterations in resting‐state whole‐brain functional connectivity pattern similarity in bipolar disorder patients

et al. 2022

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“…This means that the strength of association between the two regions may change over time. This concept is called Dynamic Functional Connectivity (DFC), and is shown to be increasingly important in understanding cognitive processing (Chen et al, 2020b;Kinany et al, 2020;Liu et al, 2020;Premi et al, 2020), and mental disorders including ADHD (Kaboodvand et al, 2020), and ASD (Mash et al, 2019;Rabany et al, 2019). This dynamic behavior is usually detected using a sliding window framework.…”

Section: Dynamic Functional Connectivitymentioning

confidence: 99%

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Eslami

Almuqhim

Raiker

et al. 2021

Front. Neuroinform.

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Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.

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“…Ruminators tend to have heightened risk of developing depressive episodes ( Nolen-Hoeksema, 2000 ), making rumination a common symptom of disorders characterized by affective dysregulation ( Kovács et al, 2020 , Nolen-Hoeksema, 2000 ). Particularly, although a profile of aberrant brain structure and functional network organization has been found in mood disorders (for a review, see Bi and He, 2014 , Gong and He, 2015 , Liu et al, 2021 ), the specific underlying neural mechanisms of rumination remains elusive to date.…”

Section: Introductionmentioning

confidence: 99%

Aberrant functional metastability and structural connectivity are associated with rumination in individuals with major depressive disorder

Zhang

Tam

Wong

et al. 2022

NeuroImage: Clinical

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Highlights Higher synchrony and lower metastability in adults with higher levels of rumination. Prefrontal white matter integrity deficits are also associated with rumination. Most prominent aberrations were found in the genu of the corpus callosum. Structural connectivity as the basis between dynamic connectivity and rumination. New outlook on altered structural integrity and metastability subserving rumination.

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Altered dynamic functional connectivity across mood states in bipolar disorder

Cited by 24 publications

References 68 publications

Alterations in resting‐state whole‐brain functional connectivity pattern similarity in bipolar disorder patients

Alterations in resting‐state whole‐brain functional connectivity pattern similarity in bipolar disorder patients

Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Aberrant functional metastability and structural connectivity are associated with rumination in individuals with major depressive disorder

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