scholarly journals Associations of the Disrupted Functional Brain Network and Cognitive Function in End-Stage Renal Disease Patients on Maintenance Hemodialysis: A Graph Theory-Based Study of Resting-State Functional Magnetic Resonance Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Die Zhang ◽  
Yingying Chen ◽  
Hua Wu ◽  
Lin Lin ◽  
Qing Xie ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Function ◽  
Brain Network ◽  
Maintenance Hemodialysis ◽  
Functional Magnetic Resonance ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Stage Renal Disease ◽  
End Stage

Objective: Cognitive impairment (CI) is a common neurological complication in patients with end-stage renal disease undergoing maintenance hemodialysis (MHD). Brain network analysis based on graph theory is a promising tool for studying CI. Therefore, the purpose of this study was to analyze the changes of functional brain networks in patients on MHD with and without CI by using graph theory and further explore the underlying neuropathological mechanism of CI in these patients.Methods: A total of 39 patients on MHD (19 cases with CI and 20 without) and 25 healthy controls (HCs) matched for age, sex, and years of education were enrolled in the study. Resting-state functional magnetic resonance imaging (rs-fMRI) and T1-weighted high-resolution anatomical data were obtained, and functional brain networks for each subject were constructed. The brain network parameters at the global and regional levels were calculated, and a one-way analysis of covariance was used to compare the differences across the three groups. The associations between the changed graph-theory parameters and cognitive function scores in patients on MHD were evaluated using Spearman correlation analysis.Results: Compared with HCs, the global parameters [sigma, gamma, and local efficiency (Eloc)] in both patient groups decreased significantly (p < 0.05, Bonferroni corrected). The clustering coefficient (Cp) in patients with CI was significantly lower than that in the other two groups (p < 0.05, Bonferroni corrected). The regional parameters were significantly lower in the right superior frontal gyrus, dorsolateral (SFGdor) and gyrus rectus (REC) of patients with CI than those of patients without CI; however the nodal local efficiency in the left amygdala was significantly increased (all p < 0.05, Bonferroni corrected). The global Cp and regional parameters in the three brain regions (right SFGdor, REC, and left amygdala) were significantly correlated with the cognitive function scores (all FDR q < 0.05).Conclusion: This study confirmed that the topology of the functional brain network was disrupted in patients on MHD with and without CI and the disruption of brain network was more severe in patients with CI. The abnormal brain network parameters are closely related to cognitive function in patients on MHD.

Differences in Functional Brain Networks Between Subjective Cognitive Decline with and without Worry Groups: A Graph Theory Study from SILCODE

2021 ◽  
pp. 1-11
Author(s):  
Yi Liu ◽  
Zhuoyuan Li ◽  
Xueyan Jiang ◽  
Wenying Du ◽  
Xiaoqi Wang ◽  
...  
Keyword(s):  
Graph Theory ◽  
Cognitive Decline ◽  
Path Length ◽  
Brain Networks ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Subjective Cognitive Decline ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

Background: Evidence suggests that subjective cognitive decline (SCD) individuals with worry have a higher risk of cognitive decline. However, how SCD-related worry influences the functional brain network is still unknown. Objective: In this study, we aimed to explore the differences in functional brain networks between SCD subjects with and without worry. Methods: A total of 228 participants were enrolled from the Sino Longitudinal Study on Cognitive Decline (SILCODE), including 39 normal control (NC) subjects, 117 SCD subjects with worry, and 72 SCD subjects without worry. All subjects completed neuropsychological assessments, APOE genotyping, and resting-state functional magnetic resonance imaging (rs-fMRI). Graph theory was applied for functional brain network analysis based on both the whole brain and default mode network (DMN). Parameters including the clustering coefficient, shortest path length, local efficiency, and global efficiency were calculated. Two-sample T-tests and chi-square tests were used to analyze differences between two groups. In addition, a false discovery rate-corrected post hoc test was applied. Results: Our analysis showed that compared to the SCD without worry group, SCD with worry group had significantly increased functional connectivity and shortest path length (p = 0.002) and a decreased clustering coefficient (p = 0.013), global efficiency (p = 0.001), and local efficiency (p <  0.001). The above results appeared in both the whole brain and DMN. Conclusion: There were significant differences in functional brain networks between SCD individuals with and without worry. We speculated that worry might result in alterations of the functional brain network for SCD individuals and then result in a higher risk of cognitive decline.

scholarly journals Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

2021 ◽  
Vol 11 (8) ◽  
pp. 1066
Author(s):  
Han Li ◽  
Qizhong Zhang ◽  
Ziying Lin ◽  
Farong Gao
Keyword(s):  
Graph Theory ◽  
Brain Networks ◽  
Tensor Decomposition ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Functional Brain ◽  
Dynamic Methods ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Elm Classifier

Epilepsy is a chronic neurological disorder which can affect 65 million patients worldwide. Recently, network based analyses have been of great help in the investigation of seizures. Now graph theory is commonly applied to analyze functional brain networks, but functional brain networks are dynamic. Methods based on graph theory find it difficult to reflect the dynamic changes of functional brain network. In this paper, an approach to extracting features from brain functional networks is presented. Dynamic functional brain networks can be obtained by stacking multiple functional brain networks on the time axis. Then, a tensor decomposition method is used to extract features, and an ELM classifier is introduced to complete epilepsy prediction. In the prediction of epilepsy, the accuracy and F1 score of the feature extracted by tensor decomposition are higher than the degree and clustering coefficient. The features extracted from the dynamic functional brain network by tensor decomposition show better and more comprehensive performance than degree and clustering coefficient in epilepsy prediction.

scholarly journals Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

Dynamic Reorganization of the Cortical Functional Brain Network in Affective Processing and Cognitive Reappraisal

2020 ◽  
Vol 30 (10) ◽  
pp. 2050051
Author(s):  
Feng Fang ◽  
Thomas Potter ◽  
Thinh Nguyen ◽  
Yingchun Zhang
Keyword(s):  
Emotion Regulation ◽  
Large Scale ◽  
Brain Networks ◽  
Cognitive Reappraisal ◽  
Brain Network ◽  
Affective Processing ◽  
Functional Brain ◽  
Affective Stimuli ◽  
Functional Brain Network ◽  
Functional Brain Networks

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.

scholarly journals Abnormal rich club organization in end-stage renal disease patients before dialysis initiation and undergoing maintenance hemodialysis

2020 ◽  
Author(s):  
Shaohui Ma ◽  
Ming Zhang ◽  
Yang Liu ◽  
Dun Ding ◽  
Peng Li ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Renal Disease ◽  
Cystatin C ◽  
End Stage Renal Disease ◽  
Neuropsychological Tests ◽  
Maintenance Hemodialysis ◽  
Rich Club ◽  
Stage Renal Disease ◽  
End Stage ◽  
Esrd Patients

Abstract Background End-stage renal disease (ESRD) patients are at substantially higher risk for developing cognitive impairment compared with the healthy population. Dialysis is an essential way to maintain the life of ESRD patients. Based on previous research, there did not provide an uncontested result whether cognition was improved or worsened during dialysis. Methods To explore the impact of dialysis treatment on cognitive performance, we recruited healthy controls (HCs), ESRD patients before dialysis initiation (bESRD) and those undergoing maintenance hemodialysis (mESRD). All ESRD patients performed a serious of blood biochemistry tests (hemoglobin, urea, cystatin C, Na+, K + and parathyroid hormone). Neuropsychological tests were used to measure cognitive function. By using diffusion tensor imaging and graph-theory approaches, the topological organization of the whole-brain structural network was investigated. Generalized linear models (GLMs) were performed to investigate blood biochemistry predictors of the neuropsychological tests and the results of graph analyses in mESRD and bESRD groups. Results Neuropsychological analysis showed mESRD exhibited greater cognitive function than bESRD, but both were worse than HCs. Whole-brain graph analyses revealed that increased global efficiency and normalized shortest path length remained in the bESRD and mESRD than the HCs. Besides, lower normalized clustering coefficient was in bESRD relative to the HCs and mESRD. For the GLMs analysis, only the Cystatin C level was significantly associated with the average fiber length of rich club connections in bESRD. Conclusions Our study revealed that dialysis had a limited effect on cognitive improvement. Cystatin C may be a risk feature of cognitive decline of bESRD.

scholarly journals Abnormal rich club organization in end-stage renal disease patients before dialysis initiation and undergoing maintenance hemodialysis

2020 ◽  
Author(s):  
Shaohui Ma ◽  
Ming Zhang ◽  
Yang Liu ◽  
Dun Ding ◽  
Peng Li ◽  
...  
Keyword(s):  
Cognitive Function ◽  
Renal Disease ◽  
Cystatin C ◽  
End Stage Renal Disease ◽  
Neuropsychological Tests ◽  
Maintenance Hemodialysis ◽  
Rich Club ◽  
Stage Renal Disease ◽  
End Stage ◽  
Esrd Patients

Abstract Background: End-stage renal disease (ESRD) patients are at a substantially higher risk for developing cognitive impairment compared with the healthy population. Dialysis is an essential way to maintain the life of ESRD patients. Based on previous research, there isn’t an uncontested result whether cognition was improved or worsened during dialysis.Methods: To explore the impact of dialysis treatment on cognitive performance, we recruited healthy controls (HCs), predialysis ESRD patients (predialysis group), and maintenance hemodialysis ESRD patients (HD group). All ESRD patients performed six blood biochemistry tests (hemoglobin, urea, cystatin C, Na+, K+, and parathyroid hormone). Neuropsychological tests were used to measure cognitive function. By using diffusion tensor imaging and graph-theory approaches, the topological organization of the whole-brain structural network was investigated. Generalized linear models (GLMs) were performed to investigate blood biochemistry predictors of the neuropsychological tests and the results of graph analyses in the HD group and predialysis group.Results: Neuropsychological analysis showed the HD group exhibited better cognitive function than the predialysis group, but both were worse than HCs. Whole-brain graph analyses revealed that increased global efficiency and normalized shortest path length remained in the predialysis group and HD group than the HCs. Besides, a lower normalized clustering coefficient was found in the predialysis group relative to the HCs and HD group. For the GLM analysis, only the Cystatin C level was significantly associated with the average fiber length of rich club connections in the predialysis group.Conclusions: Our study revealed that dialysis had a limited effect on cognitive improvement.

Longitudinal study of functional brain network reorganization in clinically isolated syndrome

2018 ◽  
Vol 26 (2) ◽  
pp. 188-200 ◽  
Author(s):  
Ismail Koubiyr ◽  
Mathilde Deloire ◽  
Pierre Besson ◽  
Pierrick Coupé ◽  
Cécile Dulau ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Early Stage ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Early Stages ◽  
Global Efficiency ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Brain Network Reorganization

Background: There is a lack of longitudinal studies exploring the topological organization of functional brain networks at the early stages of multiple sclerosis (MS). Objective: This study aims to assess potential brain functional reorganization at rest in patients with CIS (PwCIS) after 1 year of evolution and to characterize the dynamics of functional brain networks at the early stage of the disease. Methods: We prospectively included 41 PwCIS and 19 matched healthy controls (HCs). They were scanned at baseline and after 1 year. Using graph theory, topological metrics were calculated for each region. Hub disruption index was computed for each metric. Results: Hub disruption indexes of degree and betweenness centrality were negative at baseline in patients ( p < 0.05), suggesting brain reorganization. After 1 year, hub disruption indexes for degree and betweenness centrality were still negative ( p < 0.00001), but such reorganization appeared more pronounced than at baseline. Different brain regions were driving these alterations. No global efficiency differences were observed between PwCIS and HCs either at baseline or at 1 year. Conclusion: Dynamic changes in functional brain networks appear at the early stages of MS and are associated with the maintenance of normal global efficiency in the brain, suggesting a compensatory effect.

scholarly journals Dynamic Functional Brain Reconfiguration During Sustained Pain

2021 ◽  
Author(s):  
Jae-Joong Lee ◽  
Sungwoo Lee ◽  
Dong Hee Lee ◽  
Choong-Wan Woo
Keyword(s):  
Early Stage ◽  
Dynamic Network ◽  
Brain Network ◽  
Dynamic Changes ◽  
Pain Experience ◽  
Functional Brain ◽  
Complex Interactions ◽  
Functional Brain Network ◽  
Functional Brain Networks ◽  
Over Time

Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain network representations are dynamically reconfigured over time while experiencing pain. Here, we investigated the dynamic changes in the functional brain networks during 20-min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from the other primary somatomotor cortices and integrated with subcortical and frontoparietal regions, constituting a brain-wide pain supersystem. As pain decreased over time, the subcortical and frontoparietal regions were separated from this pain supersystem and connected to multiple cerebellar regions. Machine-learning models based on these dynamic network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, potentially advancing our mechanistic understanding of chronic pain.

scholarly journals Functional Hubs in Mild Cognitive Impairment

2015 ◽  
Vol 25 (03) ◽  
pp. 1550034 ◽  
Author(s):  
Adrián Navas ◽  
David Papo ◽  
Stefano Boccaletti ◽  
F. Del-Pozo ◽  
Ricardo Bajo ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Short Term Memory ◽  
Brain Networks ◽  
Memory Task ◽  
Brain Network ◽  
Control Group ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Functional Brain Networks

We investigate how hubs of functional brain networks are modified as a result of mild cognitive impairment (MCI), a condition causing a slight but noticeable decline in cognitive abilities, which sometimes precedes the onset of Alzheimer's disease. We used magnetoencephalography (MEG) to investigate the functional brain networks of a group of patients suffering from MCI and a control group of healthy subjects, during the execution of a short-term memory task. Couplings between brain sites were evaluated using synchronization likelihood, from which a network of functional interdependencies was constructed and the centrality, i.e. importance, of their nodes was quantified. The results showed that, with respect to healthy controls, MCI patients were associated with decreases and increases in hub centrality respectively in occipital and central scalp regions, supporting the hypothesis that MCI modifies functional brain network topology, leading to more random structures.

scholarly journals Prediction of stimulus-independent and task-unrelated thought from functional brain networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Aaron Kucyi ◽  
Michael Esterman ◽  
James Capella ◽  
Allison Green ◽  
Mai Uchida ◽  
...  
Keyword(s):  
Resting State Fmri ◽  
Mind Wandering ◽  
Neural Substrates ◽  
Brain Network ◽  
Multiple Trait ◽  
Functional Brain ◽  
Hyperactivity Disorder ◽  
Functional Brain Network ◽  
Trait Level ◽  
Functional Brain Networks

AbstractNeural substrates of “mind wandering” have been widely reported, yet experiments have varied in their contexts and their definitions of this psychological phenomenon, limiting generalizability. We aimed to develop and test the generalizability, specificity, and clinical relevance of a functional brain network-based marker for a well-defined feature of mind wandering—stimulus-independent, task-unrelated thought (SITUT). Combining functional MRI (fMRI) with online experience sampling in healthy adults, we defined a connectome-wide model of inter-regional coupling—dominated by default-frontoparietal control subnetwork interactions—that predicted trial-by-trial SITUT fluctuations within novel individuals. Model predictions generalized in an independent sample of adults with attention-deficit/hyperactivity disorder (ADHD). In three additional resting-state fMRI studies (total n = 1115), including healthy individuals and individuals with ADHD, we demonstrated further prediction of SITUT (at modest effect sizes) defined using multiple trait-level and in-scanner measures. Our findings suggest that SITUT is represented within a common pattern of brain network interactions across time scales and contexts.

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