scholarly journals Topological Disruption of Structural Brain Networks in Patients With Cognitive Impairment Following Cerebellar Infarction

2019 ◽  
Vol 10 ◽  
Author(s):  
Duohao Wang ◽  
Qun Yao ◽  
Miao Yu ◽  
Chaoyong Xiao ◽  
Lin Fan ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Brain Networks ◽  
Cerebellar Infarction ◽  
Structural Brain

scholarly journals A Comparative Study of Structural and Metabolic Brain Networks in Patients With Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Cuibai Wei ◽  
Shuting Gong ◽  
Qi Zou ◽  
Wei Zhang ◽  
Xuechun Kang ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Path Length ◽  
Brain Networks ◽  
Brain Regions ◽  
Clustering Coefficient ◽  
Characteristic Path Length ◽  
Glucose Intake ◽  
Item Scores ◽  
Structural Brain

Background: Changes in the metabolic and structural brain networks in mild cognitive impairment (MCI) have been widely researched. However, few studies have compared the differences in the topological properties of the metabolic and structural brain networks in patients with MCI.Methods: We analyzedmagnetic resonance imaging (MRI) and fluoro-deoxyglucose positron emission tomography (FDG-PET) data of 137 patients with MCI and 80 healthy controls (HCs). The HC group data comes from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The permutation test was used to compare the network parameters (characteristic path length, clustering coefficient, local efficiency, and global efficiency) between the two groups. Partial Pearson’s correlation analysis was used to calculate the correlations of the changes in gray matter volume and glucose intake in the key brain regions in MCI with the Alzheimer’s Disease Assessment Scale-Cognitive (ADAS-cog) sub-item scores.Results: Significant changes in the brain network parameters (longer characteristic path length, larger clustering coefficient, and lower local efficiency and global efficiency) were greater in the structural network than in the metabolic network (longer characteristic path length) in MCI patients than in HCs. We obtained the key brain regions (left globus pallidus, right calcarine fissure and its surrounding cortex, left lingual gyrus) by scanning the hubs. The volume of gray matter atrophy in the left globus pallidus was significantly positively correlated with comprehension of spoken language (p = 0.024) and word-finding difficulty in spontaneous speech item scores (p = 0.007) in the ADAS-cog. Glucose intake in the three key brain regions was significantly negatively correlated with remembering test instructions items in ADAS-cog (p = 0.020, p = 0.014, and p = 0.008, respectively).Conclusion: Structural brain networks showed more changes than metabolic brain networks in patients with MCI. Some brain regions with significant changes in betweenness centrality in both structural and metabolic networks were associated with MCI.

scholarly journals Degeneration of structural brain networks is associated with cognitive decline after ischaemic stroke

2020 ◽  
Author(s):  
Michele Veldsman ◽  
Hsiao-ju Cheng ◽  
Fang Ji ◽  
Emilio Werden ◽  
Mohamed Khlif ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Brain Networks ◽  
Higher Order ◽  
Cognitive Networks ◽  
Stroke Patients ◽  
Domain Specific ◽  
Subacute Stroke ◽  
Structural Brain

Abstract One third of ischemic stroke patients develop cognitive impairment. It is not known whether topographical secondary neurodegeneration within distributed brain structural covariance networks (SCNs) underlies this cognitive decline. We examined longitudinal changes in SCNs and their relationship to domain-specific cognitive decline in 73 ischemic stroke patients. Patients were scanned with magnetic resonance imaging (MRI) and assessed on five cognitive domains at subacute (3-months) and chronic (1-year) timepoints. Individual-level SCN scores of major cognitive networks were derived from MRI data at each timepoint. We found that distributed degeneration in higher-order cognitive networks was associated with cognitive impairment in subacute stroke. Importantly, faster degradation in these major cognitive SCNs over time was associated with greater decline in attention, memory, and language domains. Our findings suggest that subacute ischemic stroke is associated with degeneration of higher-order structural brain networks and degradation of these networks contribute to individual trajectories of longitudinal domain-specific cognitive dysfunction.

scholarly journals Intergenerational transmission of the patterns of functional and structural brain networks

iScience ◽  
2021 ◽  
pp. 102708
Author(s):  
Yu Takagi ◽  
Naohiro Okada ◽  
Shuntaro Ando ◽  
Noriaki Yahata ◽  
Kentaro Morita ◽  
...  
Keyword(s):  
Intergenerational Transmission ◽  
Brain Networks ◽  
Structural Brain

scholarly journals Association between age of cannabis initiation and gray matter covariance networks in recent onset psychosis

2021 ◽  
Author(s):  
Nora Penzel ◽  
◽  
Linda A. Antonucci ◽  
Linda T. Betz ◽  
Rachele Sanfelici ◽  
...  
Keyword(s):  
Gray Matter ◽  
Inferior Frontal Gyrus ◽  
Brain Networks ◽  
Developmental Trajectory ◽  
Cannabis Use ◽  
Brain Maturation ◽  
Confounding Factors ◽  
Recent Onset ◽  
Increased Risk ◽  
Structural Brain

AbstractCannabis use during adolescence is associated with an increased risk of developing psychosis. According to a current hypothesis, this results from detrimental effects of early cannabis use on brain maturation during this vulnerable period. However, studies investigating the interaction between early cannabis use and brain structural alterations hitherto reported inconclusive findings. We investigated effects of age of cannabis initiation on psychosis using data from the multicentric Personalized Prognostic Tools for Early Psychosis Management (PRONIA) and the Cannabis Induced Psychosis (CIP) studies, yielding a total sample of 102 clinically-relevant cannabis users with recent onset psychosis. GM covariance underlies shared maturational processes. Therefore, we performed source-based morphometry analysis with spatial constraints on structural brain networks showing significant alterations in schizophrenia in a previous multisite study, thus testing associations of these networks with the age of cannabis initiation and with confounding factors. Earlier cannabis initiation was associated with more severe positive symptoms in our cohort. Greater gray matter volume (GMV) in the previously identified cerebellar schizophrenia-related network had a significant association with early cannabis use, independent of several possibly confounding factors. Moreover, GMV in the cerebellar network was associated with lower volume in another network previously associated with schizophrenia, comprising the insula, superior temporal, and inferior frontal gyrus. These findings are in line with previous investigations in healthy cannabis users, and suggest that early initiation of cannabis perturbs the developmental trajectory of certain structural brain networks in a manner imparting risk for psychosis later in life.

scholarly journals Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Yifei Zhang ◽  
Xiaodan Chen ◽  
Xinyuan Liang ◽  
Zhijiang Wang ◽  
Teng Xie ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Human Brain ◽  
Cognitive Performance ◽  
Power Law ◽  
Resting State ◽  
Brain Networks ◽  
Functional Networks ◽  
Whole Brain ◽  
Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

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