Background: Previous studies analyzed brain functional connectivity (FC) based on resting-state fMRI (RS-fMRI) data to reveal the neuropathology of bipolar disorder (BD) and suggested that their FC alterations are at widespread network-level. However, few studies have analyzed the dynamic functional network connectivity (dFNC) in BD. Thus, we aimed to reveal the dFNC properties of BD in this study.
Methods: The RS-fMRI data were collected from 51 unmedicated depressed BD II patients and 50 healthy controls. We analyzed the dFNC properties by using an independent component analysis, sliding window correlation, k-means clustering, and graph theory methods.
Results: The intrinsic brain FNC could be clustered into three configuration states, one with sparse connections between all functional networks (State 1), another with negative correlations between the salience network, cerebellum, basal ganglia and the sensory networks (State 2), and a third with negative correlations between the default mode network and the other functional networks (State 3). The BD patients had increased time in State 2, decreased time in State 3, and increased transition number between states. And the time spent in State 2 was positively correlated with the HDRS24 score in the BD patients. In addition, the BD patients had increased dynamic variance in the small-world properties of FNC.
Limitations: This study did not examine data from BD patients in other episodes and other BD types.
Conclusions: This study detected abnormal dFNC properties in BD, which indicated their FNC unstability and provided new insights into the neuropathology of their affective and cognitive deficits.
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http://dx.doi.org/10.1016/j.jad.2019.04.103 | DOI Listing |
Neuroimage Clin
September 2024
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Henan Province 450052, China. Electronic address:
Background: Internal capsule strokes often result in multidomain cognitive impairments across memory, attention, and executive function, typically due to disruptions in brain network connectivity. Our study examines these impairments by analyzing interactions within the triple-network model, focusing on both static and dynamic aspects.
Methods: We collected resting-state fMRI data from 62 left (CI_L) and 56 right (CI_R) internal capsule stroke patients, along with 57 healthy controls (HC).
Eur Arch Psychiatry Clin Neurosci
July 2024
Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, 550002, Guizhou, China.
Dynamic functional network connectivity (dFNC) is an expansion of static FNC (sFNC) that reflects connectivity variations among brain networks. This study aimed to investigate changes in sFNC and dFNC strength and temporal properties in individuals with subthreshold depression (StD). Forty-two individuals with subthreshold depression and 38 healthy controls (HCs) were included in this study.
View Article and Find Full Text PDFBrain Connect
August 2024
Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China.
Previous research has focused on static functional connectivity in gait disorders caused by cerebral small vessel disease (CSVD), neglecting dynamic functional connections and network attribution. This study aims to investigate alterations in dynamic functional network connectivity (dFNC) and topological organization variance in CSVD-related gait disorders. A total of 85 patients with CSVD, including 41 patients with CSVD and gait disorders (CSVD-GD), 44 patients with CSVD and non-gait disorders (CSVD-NGD), and 32 healthy controls (HC), were enrolled in this study.
View Article and Find Full Text PDFNeurobiol Dis
June 2024
Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Ji'nan, China; Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Ji'nan, China; Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Ji'nan, China. Electronic address:
Background: The clinical symptoms of progressive supranuclear palsy (PSP) may be mediated by aberrant dynamic functional network connectivity (dFNC). While earlier research has found altered functional network connections in PSP patients, the majority of those studies have concentrated on static functional connectivity. Nevertheless, in this study, we sought to evaluate the modifications in dynamic characteristics and establish the correlation between these disease-related changes and clinical variables.
View Article and Find Full Text PDFbioRxiv
June 2024
Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia, USA.
Dynamic functional network connectivity (dFNC) analysis is a widely used approach for studying brain function and offering insight into how brain networks evolve over time. Typically, dFNC studies utilized fixed spatial maps and evaluate transient changes in coupling among time courses estimated from independent component analysis (ICA). This manuscript presents a complementary approach that relaxes this assumption by spatially reordering the components dynamically at each timepoint to optimize for a smooth gradient in the FNC (i.
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