Progressive Disruption of Dynamic Functional Network Connectivity in Patients With Hepatitis B Virus‐related cirrhosis

Lin, Shiu‐Ru; Li, Jing; Chen, Shengli; Lin, Xia; Ye, Min; Qiu, Yingwei

doi:10.1002/jmri.27740

Cited by 10 publications

(6 citation statements)

References 41 publications

(74 reference statements)

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“…The similarity between windowed functional connectivity matrices was calculated using the default distance measures (sqEuclidean distance). It has been proven to be an effective measure for high-dimensional data (Lin et al, 2021 ; Xu et al, 2022 ). Furthermore, we performed a cluster validity analysis to evaluate the optimal number of states based on three criteria (Silhouette, Davies-Bouldin values, and Calinski – Harabasz), and the maximum number of states allowed to estimate was set at 10.…”

Section: Methodsmentioning

confidence: 99%

The influence of childhood emotional neglect experience on brain dynamic functional connectivity in young adults

Jin,

Xu,

et al. 2023

European Journal of Psychotraumatology

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Background: Childhood emotional neglect (CEN) confers a great risk for developing multiple psychiatric disorders; however, the neural basis for this association remains unknown. Using a dynamic functional connectivity approach, this study aimed to examine the effects of CEN experience on functional brain networks in young adults. Method: In total, 21 healthy young adults with CEN experience and 26 without childhood trauma experience were recruited. The childhood trauma experience was assessed using the childhood trauma questionnaire (CTQ), and eligible participants underwent resting-state functional MRI. Sliding windows and k-means clustering were used to identify temporal features of large-scale functional connectivity states (frequency, mean dwell time, and transition numbers). Result: Dynamic analysis revealed two separate connection states: state 1 was more frequent and characterized by extensive weak connections between the brain regions. State 2 was relatively infrequent and characterized by extensive strong connections between the brain regions. Compared to the control group, the CEN group had a longer mean dwell time in state 1 and significantly decreased transition numbers between states 1 and 2. Conclusion: The CEN experience affects the temporal properties of young adults’ functional brain connectivity. Young adults with CEN experience tend to be stable in state 1 (extensive weak connections between the brain regions), reducing transitions between states, and reflecting impaired metastability or functional network flexibility.

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Section: Methodsmentioning

confidence: 99%

The influence of childhood emotional neglect experience on brain dynamic functional connectivity in young adults

Jin,

Xu,

et al. 2023

European Journal of Psychotraumatology

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“…The preprocessing of rs-fMRI data was performed using the Statistical Parametric Mapping 12 (SPM12) software package and the Data Processing Assistant for Resting-State fMRI (DPARSF) [ 35 ] as in our previous work [ 21 , 36 ]. The main steps were as follows: (1) we removed the first 10 time points to allow for signal equilibration; (2) we performed slice-timing correction and motion correction; and (3) we applied spatial normalization to the Montreal National Institute space (MNI) and smoothing using a 6 mm full-width at high maximum (FWHM) Gaussian kernel.…”

Section: Methodsmentioning

confidence: 99%

“…One can consider dFNC to be more specific than static FC because it unpacks temporal features otherwise averaged in static FC measures [ 24 – 26 ]. dFNC has been successfully used to investigate the dynamic functional modulations in patients with schizophrenia [ 20 ], patients with hepatic encephalopathy [ 21 ], and healthy aged subjects [ 22 ]. However, no study has been conducted to uncover the relationship between dFNC and suicidality in MDD.…”

Section: Introductionmentioning

confidence: 99%

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Zhang

et al. 2022

Transl Psychiatry

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Major depressive disorder (MDD) is a severe brain disease associated with a significant risk of suicide. Identification of suicidality is sometimes life-saving for MDD patients. We aimed to explore the use of dynamic functional network connectivity (dFNC) for suicidality detection in MDD patients. A total of 173 MDD patients, including 48 without suicide risk (NS), 74 with suicide ideation (SI), and 51 having attempted suicide (SA), participated in the present study. Thirty-eight healthy controls were also recruited for comparison. A sliding window approach was used to derive the dFNC, and the K-means clustering method was used to cluster the windowed dFNC. A linear support vector machine was used for classification, and leave-one-out cross-validation was performed for validation. Other machine learning methods were also used for comparison. MDD patients had widespread hypoconnectivity in both the strongly connected states (states 2 and 5) and the weakly connected state (state 4), while the dysfunctional connectivity within the weakly connected state (state 4) was mainly driven by suicidal attempts. Furthermore, dFNC matrices, especially the weakly connected state, could be used to distinguish MDD from healthy controls (area under curve [AUC] = 82), and even to identify suicidality in MDD patients (AUC = 78 for NS vs. SI, AUC = 88 for NS vs. SA, and AUC = 74 for SA vs. SI), with vision-related and default-related inter-network connectivity serving as important features. Thus, the dFNC abnormalities observed in this study might further improve our understanding of the neural substrates of suicidality in MDD patients.

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“…In the past decade, resting-state functional MRI (rs-fMRI) has been widely used to investigate macroscopic neural phenotypes of cognitive deterioration in HBV-RC patients from the NMHE stage to MHE stage. [7][8][9] Combining rs-fMRI and large-scale graph-theoretical methods, Jao et al 7 demonstrated that brain networks in HBV-RC patients were progressively more random, less clustered, and less modular as the disease advanced from NMHE to OHE, while the topological centrality and functional connections between nodes were progressively reorganized. Lin et al 8 focused on the functional connectivity (FC) between the hippocampus and other brain regions and found that the FC of the left hippocampus (LHP), the bilateral posterior cingulate gyri of the LHP and the left angular gyrus (LAG) and of the right hippocampus (RHP), and the bilateral medial frontal cortex (MFC) gradually decreased in the progression from NMHE to MHE.…”

mentioning

confidence: 99%

“…In the past decade, resting‐state functional MRI (rs‐fMRI) has been widely used to investigate macroscopic neural phenotypes of cognitive deterioration in HBV‐RC patients from the NMHE stage to MHE stage 7–9 . Combining rs‐fMRI and large‐scale graph‐theoretical methods, Jao et al 7 demonstrated that brain networks in HBV‐RC patients were progressively more random, less clustered, and less modular as the disease advanced from NMHE to OHE, while the topological centrality and functional connections between nodes were progressively reorganized.…”

mentioning

confidence: 99%

Association Between MRI‐Assessed Patterns of Connectome Gradient and Gene‐Expression Profiles in Two Independent Patient Cohorts With Hepatitis B Virus‐Related Cirrhosis

Lin

et al. 2023

Magnetic Resonance Imaging

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BackgroundPatients with hepatitis B virus‐related cirrhosis (HBV‐RC) exhibit progressive neurologic dysfunction from primary sensorimotor to high‐order cognition, as their disease advances. However, the exact neurobiologic mechanisms and the potential association with gene‐expression profiles are not fully understood.PurposeTo explore the hierarchical disorganization in the large‐scale functional connectomes in HBV‐RC patients and to investigate its potential underlying molecular basis.Study TypeProspective.PopulationFifty HBV‐RC patients and 40 controls (Cohort 1) and 30 HBV‐RC patients and 38 controls (Cohort 2).Field Strength/SequenceGradient‐echo echo‐planar and fast field echo sequences at 3.0 T (Cohort 1) and 1.5 T (Cohort 2).AssessmentData were processed with Dpabi and the BrainSpace package. Gradient scores were evaluated from global to voxel level. Cognitive measurement and patients grouping were based on psychometric hepatic encephalopathy scores. The whole‐brain microarray‐based gene‐expression data were obtained from the AIBS website.Statistical TestsOne‐way ANOVA, chi‐square test, two‐sample t‐test, Kruskal–Wallis test, Spearman's correlation coefficient (r), the gaussian random field correction, false discovery rate (FDR) correction and the Bonferroni correction. Significance level: P < 0.05.ResultsHBV‐RC patients exhibited a robust and replicable connectome gradient dysfunction, which was significantly associated with the gene‐expression profiles in both cohorts (r = 0.52 and r = 0.56, respectively). The most correlated genes were enriched in γ‐aminobutyric acid (GABA) and GABA‐related receptor genes (FDR q value <0.05). Moreover, the connectome gradient dysfunction at network level observed in HBV‐RC patients correlated with their poor cognitive performance (Cohort 2: visual network, r = −0.56; subcortical network, r = 0.66; frontoparietal network, r = 0.51).Data ConclusionHBV‐RC patients had hierarchical disorganization in the large‐scale functional connectomes, which may underly their cognitive impairment. In addition, we showed the potential molecular mechanism of the connectome gradient dysfunction, which suggested the importance of GABA and GABA‐related receptor genes.Evidence Level2Technical EfficacyStage 2

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Progressive Disruption of Dynamic Functional Network Connectivity in Patients With Hepatitis B Virus‐related cirrhosis

Cited by 10 publications

References 41 publications

The influence of childhood emotional neglect experience on brain dynamic functional connectivity in young adults

The influence of childhood emotional neglect experience on brain dynamic functional connectivity in young adults

Identification of suicidality in patients with major depressive disorder via dynamic functional network connectivity signatures and machine learning

Association Between MRI‐Assessed Patterns of Connectome Gradient and Gene‐Expression Profiles in Two Independent Patient Cohorts With Hepatitis B Virus‐Related Cirrhosis

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