Functional Hubs in Mild Cognitive Impairment

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.

Download Full-text

Gaussian embedding-based functional brain connectomic analysis for amnestic mild cognitive impairment patients with cognitive training

10.1101/779744 ◽

2019 ◽

Author(s):

Mengjia Xu ◽

Zhijiang Wang ◽

Haifeng Zhang ◽

Dimitrios Pantazis ◽

Huali Wang ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Early Stage ◽

Brain Networks ◽

Disease Diagnosis ◽

Brain Regions ◽

Brain Network ◽

Amnestic Mild Cognitive Impairment ◽

Functional Brain ◽

Functional Brain Network

AbstractIdentifying heterogeneous cognitive impairment markers at an early stage is vital for Alzheimer’s disease diagnosis. However, due to complex and uncertain brain connectivity features in the cognitive domains, it remains challenging to quantify functional brain connectomic changes during non-pharmacological interventions for amnestic mild cognitive impairment (aMCI) patients. We present a new quantitative functional brain network analysis of fMRI data based on the multi-graph unsupervised Gaussian embedding method (MG2G). This neural network-based model can effectively learn low-dimensional Gaussian distributions from the original high-dimensional sparse functional brain networks, quantify uncertainties in link prediction, and discover the intrinsic dimensionality of brain networks. Using the Wasserstein distance to measure probabilistic changes, we discovered that brain regions in the default mode network and somatosensory/somatomotor hand, fronto-parietal task control, memory retrieval, and visual and dorsal attention systems had relatively large variations during non-pharmacological training, which might provide distinct biomarkers for fine-grained monitoring of aMCI cognitive alteration.

Download Full-text

Electroencephalography-based functional brain network analysis according to lesion location and motor function in stroke patients

10.21203/rs.2.20942/v1 ◽

2020 ◽

Author(s):

Da-Hye Kim ◽

Gyu Hyun Kwon ◽

Wanjoo Park ◽

Yun-Hee Kim ◽

Seong-Whan Lee ◽

...

Keyword(s):

Brain Networks ◽

Brain Network ◽

Control Group ◽

Stroke Patients ◽

Functional Brain ◽

Lesion Location ◽

Functional Brain Network ◽

Functional Brain Networks ◽

Stroke Lesion ◽

The Brain

Abstract Background. While numerous studies have investigated changes in brain activation after stroke, limited information exists on the association between functional brain networks and lesion location in stroke patients. Methods. We compared the characteristics of brain networks among patients with cortico-subcortical lesions (n = 5), subcortical lesions (n = 7), and age-matched healthy controls (n = 12) during the execution of hand movements. Functional brain networks were analyzed based on network parameters in beta frequency electroencephalography (EEG) bands. Results. Our results indicated that while the healthy control group had appropriate compensatory patterns on the brain network with an aging effect, the two stroke lesion groups exhibited different hyper-connected characteristics in the brain network within the sensorimotor regions, particularly the contralesional M1, during motor execution. In addition, the betweenness centrality on the contralesional motor area was identified as a promising biomarker for motor functional ability associated with stroke. Our findings further allowed us to identify the characteristics of the stroke lesion that could not be found with EEG power by using the EEG brain network on the cerebral cortex. Conclusions. We anticipate that our study will improve the understanding of the complex changes that occur in the brain network as a result of stroke, and support the development of more effective and efficient rehabilitation programs based on lesion location for stroke patients.

Download Full-text

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

Journal of Alzheimer s Disease ◽

10.3233/jad-215156 ◽

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.

Download Full-text

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

International Journal of Neural Systems ◽

10.1142/s0129065720500513 ◽

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.

Download Full-text

Relationship between Amyloid-β Deposition and the Coupling between Structural and Functional Brain Networks in Patients with Mild Cognitive Impairment and Alzheimer’s Disease

Brain Sciences ◽

10.3390/brainsci11111535 ◽

2021 ◽

Vol 11 (11) ◽

pp. 1535

Author(s):

Hui Zhang ◽

Edward S. Hui ◽

Peng Cao ◽

Henry K. F. Mak

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Networks ◽

Amyloid Β ◽

Potential Candidate ◽

Functional Brain ◽

Specific Effects ◽

Functional Brain Networks

Previous studies have demonstrated that the accumulation of amyloid-β (Aβ) pathologies has distinctive stage-specific effects on the structural and functional brain networks along the Alzheimer’s disease (AD) continuum. A more comprehensive account of both types of brain network may provide a better characterization of the stage-specific effects of Aβ pathologies. A potential candidate for this joint characterization is the coupling between the structural and functional brain networks (SC-FC coupling). We therefore investigated the effect of Aβ accumulation on global SC-FC coupling in patients with mild cognitive impairment (MCI), AD, and healthy controls. Patients with MCI were dichotomized according to their level of Aβ pathology seen in 18F-flutemetamol PET-CT scans—namely, Aβ-negative and Aβ-positive. Our results show that there was no difference in global SC-FC coupling between different cohorts. During the prodromal AD stage, there was a significant negative correlation between the level of Aβ pathology and the global SC-FC coupling of MCI patients with positive Aβ, but no significant correlation for MCI patients with negative Aβ. During the AD dementia stage, the correlation between Aβ pathology and global SC-FC coupling in patients with AD was positive. Our results suggest that Aβ pathology has distinctive stage-specific effects on global coupling between the structural and functional brain networks along the AD continuum.

Download Full-text

Longitudinal study of functional brain network reorganization in clinically isolated syndrome

Multiple Sclerosis Journal ◽

10.1177/1352458518813108 ◽

2018 ◽

Vol 26 (2) ◽

pp. 188-200 ◽

Cited By ~ 6

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.

Download Full-text