Identification of minimal hepatic encephalopathy based on dynamic functional connectivity

2021 ◽  
Author(s):  
Yue Cheng ◽  
Gaoyan Zhang ◽  
Xiaodong Zhang ◽  
Yuexuan Li ◽  
Jingli Li ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Time Window ◽  
Brain Connectivity ◽  
Minimal Hepatic Encephalopathy ◽  
Classification Performance ◽  
Support Vector ◽  
Dynamic Features ◽  
Dynamic Functional Connectivity ◽  
Window Approach

Abstract To investigate whether dynamic functional connectivity (DFC) metrics can better identify minimal hepatic encephalopathy (MHE) patients from cirrhotic patients without any hepatic encephalopathy (noHE) and healthy controls (HCs). Resting-state functional MRI data were acquired from 62 patients with cirrhosis (MHE, n=30; noHE, n=32) and 41 HCs. We used the sliding time window approach and functional connectivity analysis to extract the time-varying properties of brain connectivity. Three DFC characteristics (i.e., strength, stability, and variability) were calculated. For comparison, we also calculated the static functional connectivity (SFC). A linear support vector machine was used to differentiate MHE patients from noHE and HCs using DFC and SFC metrics as classification features. The leave-one-out cross-validation method was used to estimate the classification performance. The strength of DFC (DFC-Dstrength) achieved the best accuracy (MHE vs. noHE, 72.5%; MHE vs. HCs, 84%; and noHE vs. HCs, 88%) compared to the other dynamic features. Compared to static features, the classification accuracies of the DFC-Dstrength feature were improved by 10.5%, 8%, and 14% for MHE vs. noHE, MHE vs. HC, and noHE vs. HCs, respectively. Based on the DFC-Dstrength, seven nodes were identified as the most discriminant features to classify MHE from noHE, including left inferior parietal lobule, left supramarginal gyrus, left calcarine, left superior frontal gyrus, left cerebellum, right postcentral gyrus, and right insula. In summary , DFC characteristics have a higher classification accuracy in identifying MHE from cirrhosis patients. Our findings suggest the usefulness of DFC in capturing neural processes and identifying disease-related biomarkers important for MHE identification.

scholarly journals Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation

2021 ◽  
Vol 15 ◽  
Author(s):  
Jiaming Lu ◽  
Qian Chen ◽  
Danyan Li ◽  
Wen Zhang ◽  
Siyan Xing ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Sensitivity And Specificity ◽  
Premature Ejaculation ◽  
Brain Activity ◽  
Clinical Severity ◽  
Support Vector ◽  
Dynamic Functional Connectivity ◽  
Network Connection ◽  
Window Approach ◽  
State 1

Purpose: Neuroimaging has demonstrated altered static functional connectivity in patients with premature ejaculation (PE), while studies examining dynamic changes in spontaneous brain activity in PE patients are still lacking. We aimed to explore the reconfiguration of dynamic functional connectivity (DFC) states in lifelong PE (LPE) patients and to distinguish LPE patients from normal controls (NCs) using a machine learning method based on DFC state features.Methods: Thirty-six LPE patients and 23 NCs were recruited. Resting-state functional magnetic resonance imaging (fMRI) data, the clinical rating scores on the Chinese Index of PE (CIPE), and intravaginal ejaculatory latency time (IELT) were collected from each participant. DFC was calculated by the sliding window approach. Finally, the Lagrangian support vector machine (LSVM) classifier was applied to distinguish LPE patients from NCs using the DFC parameters. Two DFC state metrics (reoccurrence times and transition frequencies) were introduced and we assessed the correlations between DFC state metrics and clinical variables, and the accuracy, sensitivity, and specificity of the LSVM classifier.Results: By k-means clustering, four distinct DFC states were identified. The LPE patients showed an increase in the reoccurrence times for state 3 (p < 0.05, Bonferroni corrected) but a decrease for state 1 (p < 0.05, Bonferroni corrected) compared to the NCs. Moreover, the LPE patients had significantly less frequent transitions between state 1 and state 4 (p < 0.05, uncorrected) while more frequent transitions between state 3 and state 4 (p < 0.05, uncorrected) than the NCs. The reoccurrence times and transition frequencies showed significant associations with the CIPE scores and IELTs. The accuracy, sensitivity, and specificity of the LSVM classifier were 90.35, 87.59, and 85.59%, respectively.Conclusion: LPE patients were more inclined to be in DFC states reinforced intra-network and inter-network connection. These features correlated with clinical syndromes and can classify the LPE patients from NCs. Our results of reconfiguration of DFC states may provide novel insights for the understanding of central etiology underlying LPE, indicate neuroimaging biomarkers for the evaluation of clinical severity of LPE.

Dynamic Changes in the Mental Rotation Network Revealed by Pattern Recognition Analysis of fMRI Data

2009 ◽  
Vol 21 (5) ◽  
pp. 890-904 ◽  
Author(s):  
Janaina Mourao-Miranda ◽  
Christine Ecker ◽  
Joao R. Sato ◽  
Michael Brammer
Keyword(s):  
Pattern Recognition ◽  
Functional Connectivity ◽  
Mental Rotation ◽  
Temporal Dynamics ◽  
Parietal Lobe ◽  
Time Window ◽  
Support Vector ◽  
Dynamic Changes ◽  
Angular Disparity ◽  
Spatio Temporal

We investigated the temporal dynamics and changes in connectivity in the mental rotation network through the application of spatio-temporal support vector machines (SVMs). The spatio-temporal SVM [Mourao-Miranda, J., Friston, K. J., et al. (2007). Dynamic discrimination analysis: A spatial-temporal SVM. Neuroimage, 36, 88–99] is a pattern recognition approach that is suitable for investigating dynamic changes in the brain network during a complex mental task. It does not require a model describing each component of the task and the precise shape of the BOLD impulse response. By defining a time window including a cognitive event, one can use spatio-temporal fMRI observations from two cognitive states to train the SVM. During the training, the SVM finds the discriminating pattern between the two states and produces a discriminating weight vector encompassing both voxels and time (i.e., spatio-temporal maps). We showed that by applying spatio-temporal SVM to an event-related mental rotation experiment, it is possible to discriminate between different degrees of angular disparity (0° vs. 20°, 0° vs. 60°, and 0° vs. 100°), and the discrimination accuracy is correlated with the difference in angular disparity between the conditions. For the comparison with highest accuracy (0° vs. 100°), we evaluated how the most discriminating areas (visual regions, parietal regions, supplementary, and premotor areas) change their behavior over time. The frontal premotor regions became highly discriminating earlier than the superior parietal cortex. There seems to be a parcellation of the parietal regions with an earlier discrimination of the inferior parietal lobe in the mental rotation in relation to the superior parietal. The SVM also identified a network of regions that had a decrease in BOLD responses during the 100° condition in relation to the 0° condition (posterior cingulate, frontal, and superior temporal gyrus). This network was also highly discriminating between the two conditions. In addition, we investigated changes in functional connectivity between the most discriminating areas identified by the spatio-temporal SVM. We observed an increase in functional connectivity between almost all areas activated during the 100° condition (bilateral inferior and superior parietal lobe, bilateral premotor area, and SMA) but not between the areas that showed a decrease in BOLD response during the 100° condition.

scholarly journals Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

Hippocampal atrophy and functional connectivity disruption in cirrhotic patients with minimal hepatic encephalopathy

2019 ◽  
Vol 34 (6) ◽  
pp. 1519-1529 ◽  
Author(s):  
Weiwen Lin ◽  
Xuhui Chen ◽  
Yong-Qing Gao ◽  
Zhe-Ting Yang ◽  
Weizhu Yang ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Minimal Hepatic Encephalopathy ◽  
Hippocampal Atrophy ◽  
Cirrhotic Patients

Subnetwork mining on functional connectivity network for classification of minimal hepatic encephalopathy

2017 ◽  
Vol 12 (3) ◽  
pp. 901-911 ◽  
Author(s):  
Daoqiang Zhang ◽  
Liyang Tu ◽  
Long-Jiang Zhang ◽  
Biao Jie ◽  
Guang-Ming Lu
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Minimal Hepatic Encephalopathy

scholarly journals Altered Modular Organization of Functional Connectivity Networks in Cirrhotic Patients without Overt Hepatic Encephalopathy

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Gang Zheng ◽  
Liping Zhang ◽  
Long Jiang Zhang ◽  
Qiang Li ◽  
Zhiying Pan ◽  
...  
Keyword(s):  
Hepatic Encephalopathy ◽  
Functional Connectivity ◽  
Neuropsychological Test ◽  
Minimal Hepatic Encephalopathy ◽  
Ammonia Level ◽  
Modular Organization ◽  
Healthy Controls ◽  
Overt Hepatic Encephalopathy ◽  
Cirrhotic Patients ◽  
Subcortical Regions

Minimal hepatic encephalopathy (MHE) is associated with changes in functional connectivity. To investigate the patterns of modular changes of the functional connectivity in the progression of MHE, resting-state functional magnetic resonance imaging was acquired in 24 MHE patients, 31 cirrhotic patients without minimal hepatic encephalopathy (non-HE), and 38 healthy controls. Newman’s metric, the modularityQvalue, was maximized and compared in three groups. Topological roles with the progression of MHE were illustrated by intra- and intermodular connectivity changes. Results showed that theQvalue of MHE patients was significantly lower than that of controlsP<0.01rather than that of non-HE patientsP>0.05, which was correlated with neuropsychological test scores rather than the ammonia level and Child-Pugh score. Less intrasubcortical connections and more isolated subcortical modules were found with the progression of MHE. The non-HE patients had the same numbers of connect nodes as controls and had more hubs compared with MHE patients and healthy controls. Our findings supported that both intra- and intermodular connectivity, especially those related to subcortical regions, were continuously impaired in cirrhotic patients. The adjustments of hubs and connector nodes in non-HE patients could be a compensation for the decreased modularity in their functional connectivity networks.

scholarly journals Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Zhuqing Jiao ◽  
Peng Gao ◽  
Yixin Ji ◽  
Haifeng Shi
Keyword(s):  
Graph Theory ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Functional Connectivity ◽  
Time Window ◽  
Clustering Coefficient ◽  
Dynamic Functional Connectivity ◽  
Disease Evolution ◽  
Functional Connections ◽  
Window Method

Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Researchers tend to discuss its early state (early MCI, eMCI) due to its high conversion rate of dementia and poor treatment effect in the middle and late stages. Currently, the research on the disease evolution of the brain functional networks of patients with MCI has gradually become a research hotspot. In this study, we compare the differences in dynamic functional connectivity among eMCI, late MCI (lMCI), and normal control (NC) groups, and their graph theory indicators reveal the integration and segregation of functional connectivity states. Firstly, dynamic functional network windows were constructed based on the sliding time window method, and then these window samples were clustered by k-means to extract the functional connectivity states. The differences in the three groups were compared by analyzing the graph theory indicators, such as the participation coefficient, module degree distribution, clustering coefficient, global efficiency, and local efficiency, which distinguish the functional connectivity states. The results reveal that the NC group has the strongest integration and segregation, followed by the eMCI group, and the lMCI group has the weakest integration and segregation. We conclude that with the aggravation of MCI, the integration and segregation of dynamic functional connectivity states tend to decline. The results also reflect that the lMCI group has significantly more brain functional connections in some states, such as IPL.L-MTG.R and DCG.R-SMG.L, than the eMCI group, while the lMCI group has significantly less OLF.L-SPG.L than the NC group.

scholarly journals Alterations in Dynamic Functional Connectivity in Individuals With Subjective Cognitive Decline

2021 ◽  
Vol 13 ◽  
Author(s):  
Qian Chen ◽  
Jiaming Lu ◽  
Xin Zhang ◽  
Yi Sun ◽  
Wenqian Chen ◽  
...  
Keyword(s):  
Graph Theory ◽  
Functional Connectivity ◽  
Cognitive Decline ◽  
Cognitive Performance ◽  
Dwell Time ◽  
Subjective Cognitive Decline ◽  
Control Group ◽  
Dynamic Functional Connectivity ◽  
Neuropsychological Evaluations ◽  
Window Approach

Purpose: To investigate the dynamic functional connectivity (DFC) and static parameters of graph theory in individuals with subjective cognitive decline (SCD) and the associations of DFC and topological properties with cognitive performance.Methods: Thirty-three control subjects and 32 SCD individuals were enrolled in this study, and neuropsychological evaluations and resting-state functional magnetic resonance imaging scanning were performed. Thirty-three components were selected by group independent component analysis to construct 7 functional networks. Based on the sliding window approach and k-means clustering, distinct DFC states were identified. We calculated the temporal properties of fractional windows in each state, the mean dwell time in each state, and the number of transitions between each pair of DFC states. The global and local static parameters were assessed by graph theory analysis. The differences in DFC and topological metrics, and the associations of the altered neuroimaging measures with cognitive performance were assessed.Results: The whole cohort demonstrated 4 distinct connectivity states. Compared to the control group, the SCD group showed increased fractional windows and an increased mean dwell time in state 4, characterized by hypoconnectivity both within and between networks. The SCD group also showed decreased fractional windows and a decreased mean dwell time in state 2, dominated by hyperconnectivity within and between the auditory, visual and somatomotor networks. The number of transitions between state 1 and state 2, between state 2 and state 3, and between state 2 and state 4 was significantly reduced in the SCD group compared to the control group. No significant differences in global or local topological metrics were observed. The altered DFC properties showed significant correlations with cognitive performance.Conclusion: Our findings indicated DFC network reconfiguration in the SCD stage, which may underlie the early cognitive decline in SCD subjects and serve as sensitive neuroimaging biomarkers for the preclinical detection of individuals with incipient Alzheimer's disease.

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