scholarly journals Age- and sex-related topological organisation of human brain functional networks and their relationship to cognition

2021 ◽  
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest C Koch ◽  
Karen Mather ◽  
...  
Keyword(s):  
Cognitive Performance ◽  
Resting State ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Networks ◽  
State Graph ◽  
Uk Biobank ◽  
Age Related ◽  
Functional Brain Network ◽  
Age And Sex

Age and sex have been associated with changes in functional brain network topology, which may in turn affect cognition in older adults. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 UK Biobank participants. Age was associated with an overall decrease in the effectiveness of network communication (i.e. integration) and loss of functional specialisation (i.e. segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks which were less segregated than in men. Age-related changes were also more apparent in men than women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition. This may imply that individual measures may be inadequate to capture much of the variance in neural activity or its output and need further refinement.

scholarly journals Age- and Sex-Related Topological Organization of Human Brain Functional Networks and Their Relationship to Cognition

2021 ◽  
Vol 13 ◽  
Author(s):  
Heidi Foo ◽  
Anbupalam Thalamuthu ◽  
Jiyang Jiang ◽  
Forrest Koch ◽  
Karen A. Mather ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Performance ◽  
Large Population ◽  
Brain Regions ◽  
Brain Network ◽  
State Graph ◽  
Functional Brain ◽  
Age Related ◽  
Functional Brain Network ◽  
Age And Sex

Age and sex associated with changes in the functional brain network topology and cognition in large population of older adults have been poorly understood. We explored this question further by examining differences in 11 resting-state graph theory measures with respect to age, sex, and their relationships with cognitive performance in 17,127 United Kingdom Biobank participants (mean = 62.83 ± 7.41 years). Age was associated with an overall decrease in the effectiveness of network communication (i.e., integration) and loss of functional specialization (i.e., segregation) of specific brain regions. Sex differences were also observed, with women showing more efficient networks, which were less segregated than in men (FDR adjusted p < 0.05). The age-related changes were also more apparent in men than in women, which suggests that men may be more vulnerable to cognitive decline with age. Interestingly, while network segregation and strength of limbic network were only nominally associated with cognitive performance, the network measures collectively were significantly associated with cognition (FDR adjusted p ≤ 0.002). This may imply that individual measures may be inadequate to capture much of the variance in the neural activity or its output and need further refinement. The complexity of the organization of the functional brain may be shaped by the age and sex of an individual, which ultimately may influence the cognitive performance of older adults. Age and sex stratification may be used to inform clinical neuroscience research to identify older adults at risk of cognitive dysfunction.

scholarly journals Age-related changes in spatial and temporal features of resting state fMRI

2017 ◽  
Author(s):  
Shruti G. Vij ◽  
Jason S. Nomi ◽  
Dina R. Dajani ◽  
Lucina Q. Uddin
Keyword(s):  
Resting State ◽  
Large Scale ◽  
Spectral Composition ◽  
Brain Network ◽  
Functional Networks ◽  
Functional Changes ◽  
Age Related ◽  
Temporal Features ◽  
Functional Brain Network ◽  
Age Related Changes

AbstractDevelopment and aging are associated with functional changes in the brain across the lifespan. These changes can be detected in spatial and temporal features of resting state functional MRI (rs-fMRI) data. Independent vector analysis (IVA) is a whole-brain multivariate approach that can be used to comprehensively assess these changes in spatial and temporal features. We present a multi-dimensional approach to assessing age-related changes in spatial and temporal features of statistically independent components identified by IVA in a cross-sectional lifespan sample (ages 6-85 years). We show that while large-scale brain network configurations remain consistent throughout the lifespan, changes continue to occur in both local organization and in the spectral composition of these functional networks. We show that the spatial extent of functional networks decreases with age, but with no significant change in the peak functional loci of these networks. Additionally, we show differential age-related patterns across the frequency spectrum; lower frequency correlations decrease across the lifespan whereas higher-frequency correlations increase. These changes indicate an increasing stability of networks with age. In addition to replicating results from previous studies, the current results uncover new aspects of functional brain network changes across the lifespan that are frequency band-dependent.

scholarly journals Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects

2020 ◽  
Vol 4 (1) ◽  
pp. 89-114 ◽  
Author(s):  
Tania S. Kong ◽  
Caterina Gratton ◽  
Kathy A. Low ◽  
Chin Hong Tan ◽  
Antonio M. Chiarelli ◽  
...  
Keyword(s):  
White Matter ◽  
Cognitive Performance ◽  
Cognitive Aging ◽  
Optical Tomography ◽  
Brain Network ◽  
Cortical Atrophy ◽  
Resting State Functional Connectivity ◽  
Functional Brain ◽  
Age Related ◽  
Functional Brain Network

Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting-state functional connectivity and gray and white matter status. Recently we have shown that the elasticity of cerebral arteries also explains some of the variance in cognitive and brain health in aging. Here, we investigated how network segregation, cerebral arterial elasticity (measured with pulse-DOT—the arterial pulse based on diffuse optical tomography) and gray and white matter status jointly account for age-related differences in cognitive performance. We hypothesized that at least some of the variance in brain and cognitive aging is linked to reduced cerebrovascular elasticity, leading to increased cortical atrophy and white matter abnormalities, which, in turn, are linked to reduced network segregation and decreases in cognitive performance. Pairwise comparisons between these variables are consistent with an exploratory hierarchical model linking them, especially when focusing on association network segregation (compared with segregation in sensorimotor networks). These findings suggest that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging.

scholarly journals Discovering dynamic functional networks in the human neonatal brain with electric source imaging

2019 ◽  
Author(s):  
Steve Mehrkanoon
Keyword(s):  
Human Brain ◽  
Limbic System ◽  
Resting State ◽  
Large Scale ◽  
Temporal Dynamics ◽  
Brain Network ◽  
Functional Networks ◽  
Source Imaging ◽  
Functional Brain Network ◽  
Macro Scale

AbstractWhen the human brain manifests the birth of organised communication among local and large-scale neuronal populations activity remains undescribed. We report, in resting-state EEG source-estimates of 100 infants at term age, the existence of macro-scale dynamic functional connectivity, which have rich topological organisations, distinct spectral fingerprints and scale-invariance temporal dynamics. These functional networks encompass the default mode, primary sensory-limbic system, thalamo-frontal, thalamo-sensorimotor and visual-limbic system confined in the delta and low-alpha frequency intervals (1-8 Hz). The temporal dynamics of these networks not only are nested within much slower timescale (¡ 0.1 Hz) but also correlated in a hierarchical leading-following organisation. We show that the anatomically constrained richly organised spatial topologies, spectral contents and temporal fluctuations of resting-state networks reflect an established intrinsic dynamic functional connectome in the human brain at term age. The graph theoretical analysis of the spatial architectures of the networks revealed small-world topology and distinct rich-club organisations of interconnected cortical hubs that exhibit rich synchronous dynamics at multiple timescales. The approach opens new avenues to advance our understanding about the early configuration organisation of dynamic networks in the human brain and offers a novel monitoring platform to investigate functional brain network development in sick preterm infants.

scholarly journals Resting‐State EEG Microstates Parallel Age‐Related Differences in Allocentric Spatial Working Memory Performance

2021 ◽  
Author(s):  
Adeline Jabès ◽  
Giuliana Klencklen ◽  
Paolo Ruggeri ◽  
Christoph M. Michel ◽  
Pamela Banta Lavenex ◽  
...  
Keyword(s):  
Older Adults ◽  
Working Memory ◽  
Resting State ◽  
Cognitive Aging ◽  
Temporal Dynamics ◽  
Spatial Working Memory ◽  
Brain Activity ◽  
Memory Performance ◽  
Brain Regions ◽  
Age Related

AbstractAlterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25–30-year-olds and twenty-five 64–75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C′ and D, but especially in microstate maps C and C′. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C′. Moreover, although there was a higher probability to transition from any map towards maps C, C′ and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.

scholarly journals Psychological resilience correlates with EEG source-space brain network flexibility

2019 ◽  
Vol 3 (2) ◽  
pp. 539-550 ◽  
Author(s):  
Véronique Paban ◽  
Julien Modolo ◽  
Ahmad Mheich ◽  
Mahmoud Hassan
Keyword(s):  
Brain Networks ◽  
Brain Regions ◽  
Brain Network ◽  
Personality Testing ◽  
Functional Networks ◽  
Psychological Resilience ◽  
Full Spectrum ◽  
Sliding Windows ◽  
Resilience Scale ◽  
Network Flexibility

We aimed at identifying the potential relationship between the dynamical properties of the human functional network at rest and one of the most prominent traits of personality, namely resilience. To tackle this issue, we used resting-state EEG data recorded from 45 healthy subjects. Resilience was quantified using the 10-item Connor-Davidson Resilience Scale (CD-RISC). By using a sliding windows approach, brain networks in each EEG frequency band (delta, theta, alpha, and beta) were constructed using the EEG source-space connectivity method. Brain networks dynamics were evaluated using the network flexibility, linked with the tendency of a given node to change its modular affiliation over time. The results revealed a negative correlation between the psychological resilience and the brain network flexibility for a limited number of brain regions within the delta, alpha, and beta bands. This study provides evidence that network flexibility, a metric of dynamic functional networks, is strongly correlated with psychological resilience as assessed from personality testing. Beyond this proof-of-principle that reliable EEG-based quantities representative of personality traits can be identified, this motivates further investigation regarding the full spectrum of personality aspects and their relationship with functional networks.

scholarly journals Depression Disorder Classification of fMRI Data Using Sparse Low-Rank Functional Brain Network and Graph-Based Features

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Wang ◽  
Yanshuang Ren ◽  
Wensheng Zhang
Keyword(s):  
Pearson Correlation ◽  
Brain Regions ◽  
Brain Network ◽  
Classification Performance ◽  
Low Rank ◽  
Fmri Data ◽  
Healthy Controls ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Depression Disorder

Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.

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.

scholarly journals Functional brain network modeling in sub-acute stroke patients and healthy controls during rest and continuous attentive tracking

2019 ◽  
Author(s):  
Erlend S. Dørum ◽  
Tobias Kaufmann ◽  
Dag Alnæs ◽  
Geneviève Richard ◽  
Knut K. Kolskår ◽  
...  
Keyword(s):  
Brain Function ◽  
Repeated Measures ◽  
High Sensitivity ◽  
Brain Regions ◽  
Brain Network ◽  
System Level ◽  
Cerebral Stroke ◽  
Significant Group ◽  
Functional Brain Network ◽  
Regularized Linear Discriminant Analysis

AbstractA cerebral stroke is characterized by compromised brain function due to an interruption in cerebrovascular blood supply. Although stroke incurs focal damage determined by the vascular territory affected, clinical symptoms commonly involve multiple functions and cognitive faculties that are insufficiently explained by the focal damage alone. Functional connectivity (FC) refers to the synchronous activity between spatially remote brain regions organized in a network of interconnected brain regions. Functional magnetic resonance imaging (fMRI) has advanced this system-level understanding of brain function, elucidating the complexity of stroke outcomes, as well as providing information useful for prognostic and rehabilitation purposes.We tested for differences in brain network connectivity between a group of patients with minor ischemic strokes in sub-acute phase (n=44) and matched controls (n=100). As neural network configuration is dependent on cognitive effort, we obtained fMRI data during rest and two load levels of a multiple object tacking (MOT) task. Network nodes and time-series were estimated using independent component analysis (ICA) and dual regression, with network edges defined as the partial temporal correlations between node pairs. The full set of edgewise FC went into a cross-validated regularized linear discriminant analysis (rLDA) to classify groups and cognitive load.MOT task performance and cognitive tests revealed no significant group differences. While multivariate machine learning revealed high sensitivity to experimental condition, with classification accuracies between rest and attentive tracking approaching 100%, group classification was at chance level, with negligible differences between conditions. Repeated measures ANOVA showed significantly stronger synchronization between a temporal node and a sensorimotor node in patients across conditions. Overall, the results revealed high sensitivity of FC indices to task conditions, and suggest relatively small brain network-level disturbances after clinically mild strokes.

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