Associations between Neighborhood SES and Functional Brain Network Development

Abstract Higher socioeconomic status (SES) in childhood is associated with stronger cognitive abilities, higher academic achievement, and lower incidence of mental illness later in development. While prior work has mapped the associations between neighborhood SES and brain structure, little is known about the relationship between SES and intrinsic neural dynamics. Here, we capitalize upon a large cross-sectional community-based sample (Philadelphia Neurodevelopmental Cohort, ages 8–22 years, n = 1012) to examine associations between age, SES, and functional brain network topology. We characterize this topology using a local measure of network segregation known as the clustering coefficient and find that it accounts for a greater degree of SES-associated variance than mesoscale segregation captured by modularity. High-SES youth displayed stronger positive associations between age and clustering than low-SES youth, and this effect was most pronounced for regions in the limbic, somatomotor, and ventral attention systems. The moderating effect of SES on positive associations between age and clustering was strongest for connections of intermediate length and was consistent with a stronger negative relationship between age and local connectivity in these regions in low-SES youth. Our findings suggest that, in late childhood and adolescence, neighborhood SES is associated with variation in the development of functional network structure in the human brain.

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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.

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Exploring Functional Brain Network Modularity in Educational Contexts

10.1101/2022.01.06.475275 ◽

2022 ◽

Author(s):

Adam B Weinberger ◽

Robert A Cortes ◽

Richard F Betzel ◽

Adam E Green

Keyword(s):

Resting State ◽

Cognitive Abilities ◽

Spatial Scales ◽

Brain Network ◽

Academic Learning ◽

Functional Brain ◽

Functional Brain Network ◽

Network Modularity ◽

Task Conditions ◽

And Task

The brain's modular functional organization facilitates adaptability. Modularity has been linked with a wide range of cognitive abilities such as intelligence, memory, and learning. However, much of this work has (1) considered modularity while a participant is at rest rather than during tasks conditions and/or (2) relied primarily on lab-based cognitive assessments. Thus, the extent to which modularity can provide information about real-word behavior remains largely unknown. Here, we investigated whether functional modularity during resting-state and task-based fMRI was associated with academic learning (measured by GPA) and ability (measured by PSAT) in a large sample of high school students. Additional questions concerned the extent to which modularity differs between rest and task conditions, and across spatial scales. Results indicated that whole-brain modularity during task conditions was significantly associated with academic learning. In contrast to prior work, no such associations were observed for resting-state modularity. We further showed that differences in modularity between task conditions and resting-state varied across spatial scales. Taken together, the present findings inform how functional brain network modularity - during task conditions and while at rest - relate to a range of cognitive abilities.

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Functional brain network organization measured with magnetoencephalography predicts cognitive decline in multiple sclerosis

Multiple Sclerosis Journal ◽

10.1177/1352458520977160 ◽

2020 ◽

pp. 135245852097716

Author(s):

Ilse M Nauta ◽

Shanna D Kulik ◽

Lucas C Breedt ◽

Anand JC Eijlers ◽

Eva MM Strijbis ◽

...

Keyword(s):

Cognitive Decline ◽

Structural Damage ◽

Minimum Spanning Tree ◽

Brain Network ◽

Network Organization ◽

Network Integration ◽

Beta Band ◽

Cross Sectional ◽

Functional Brain ◽

Functional Brain Network

Background: Cognitive decline remains difficult to predict as structural brain damage cannot fully explain the extensive heterogeneity found between MS patients. Objective: To investigate whether functional brain network organization measured with magnetoencephalography (MEG) predicts cognitive decline in MS patients after 5 years and to explore its value beyond structural pathology. Methods: Resting-state MEG recordings, structural MRI, and neuropsychological assessments were analyzed of 146 MS patients, and 100 patients had a 5-year follow-up neuropsychological assessment. Network properties of the minimum spanning tree (i.e. backbone of the functional brain network) indicating network integration and overload were related to baseline and longitudinal cognition, correcting for structural damage. Results: A more integrated beta band network (i.e. smaller diameter) and a less integrated delta band network (i.e. lower leaf fraction) predicted cognitive decline after 5 years ([Formula: see text]), independent of structural damage. Cross-sectional analyses showed that a less integrated network (e.g. lower tree hierarchy) related to worse cognition, independent of frequency band. Conclusions: The level of functional brain network integration was an independent predictive marker of cognitive decline, in addition to the severity of structural damage. This work thereby indicates the promise of MEG-derived network measures in predicting disease progression in MS.

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The Insula Is a Hub for Functional Brain Network in Patients With Anti-N-Methyl-D-Aspartate Receptor Encephalitis

Frontiers in Neuroscience ◽

10.3389/fnins.2021.642390 ◽

2021 ◽

Vol 15 ◽

Author(s):

Chunyan Li ◽

Xiaomin Pang ◽

Ke Shi ◽

Qijia Long ◽

Jinping Liu ◽

...

Keyword(s):

Parietal Lobe ◽

Brain Network ◽

Clustering Coefficient ◽

Hamilton Depression Scale ◽

Healthy Controls ◽

Functional Brain ◽

Aspartate Receptor ◽

Nmdar Encephalitis ◽

Functional Brain Network ◽

Left Insula

BackgroundIn recent years, imaging technologies have been rapidly evolving, with an emphasis on the characterization of brain structure changes and functional imaging in patients with autoimmune encephalitis. However, the neural basis of anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and its linked cognitive decline is unclear. Our research aimed to assess changes in the functional brain network in patients with anti-NMDAR encephalitis and whether these changes lead to cognitive impairment.MethodsTwenty-one anti-NMDAR encephalitis patients and 22 age-, gender-, and education status-matched healthy controls were assessed using resting functional magnetic resonance imaging (fMRI) scanning and neuropsychological tests, including the Hamilton Depression Scale (HAMD24), the Montreal Cognitive Assessment (MoCA), and the Hamilton Anxiety Scale (HAMA). A functional brain network was constructed using fMRI, and the topology of the network parameters was analyzed using graph theory. Next, we extracted the aberrant topological parameters of the functional network as seeds and compared causal connectivity with the whole brain. Lastly, we explored the correlation of aberrant topological structures with deficits in cognitive performance.ResultsRelative to healthy controls, anti-NMDAR encephalitis patients exhibited decreased MoCA scores and increased HAMA and HAMD24 scores (p < 0.05). The nodal clustering coefficient and nodal local efficiency of the left insula (Insula_L) were significantly decreased in anti-NMDAR encephalitis patients (p < 0.05 following Bonferroni correction). Moreover, anti-NMDAR encephalitis patients showed a weakened causal connectivity from the left insula to the left inferior parietal lobe (Parietal_Inf_L) compared to healthy controls. Conversely, the left superior parietal lobe (Parietal_sup_L) exhibited an enhanced causal connectivity to the left insula in anti-NMDAR encephalitis patients compared to controls. Unexpectedly, these alterations were not correlated with any neuropsychological test scores.ConclusionThis research describes topological abnormalities in the functional brain network in anti-NMDAR encephalitis. These results will be conducive to understand the structure and function of the brain network of patients with anti-NMDAR encephalitis and further explore the neuropathophysiological mechanisms.

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Automated Extraction of Human Functional Brain Network Properties Associated with Working Memory Load through a Machine Learning-Based Feature Selection Algorithm

Computational Intelligence and Neuroscience ◽

10.1155/2018/4835676 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Satoru Hiwa ◽

Shogo Obuchi ◽

Tomoyuki Hiroyasu

Keyword(s):

Working Memory ◽

Functional Connectivity ◽

Brain Regions ◽

Brain Network ◽

Clustering Coefficient ◽

Support Vector ◽

Neural Basis ◽

Functional Brain ◽

Functional Brain Network ◽

Network Properties

Working memory (WM) load-dependent changes of functional connectivity networks have previously been investigated by graph theoretical analysis. However, the extraordinary number of nodes represented within the complex network of the human brain has hindered the identification of functional regions and their network properties. In this paper, we propose a novel method for automatically extracting characteristic brain regions and their graph theoretical properties that reflect load-dependent changes in functional connectivity using a support vector machine classification and genetic algorithm optimization. The proposed method classified brain states during 2- and 3-back test conditions based upon each of the three regional graph theoretical metrics (degree, clustering coefficient, and betweenness centrality) and automatically identified those brain regions that were used for classification. The experimental results demonstrated that our method achieved a >90% of classification accuracy using each of the three graph metrics, whereas the accuracy of the conventional manual approach of assigning brain regions was only 80.4%. It has been revealed that the proposed framework can extract meaningful features of a functional brain network that is associated with WM load from a large number of nodal graph theoretical metrics without prior knowledge of the neural basis of WM.

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Brain network connectivity differs in early-onset neurodegenerative dementia

Neurology ◽

10.1212/wnl.0000000000004577 ◽

2017 ◽

Vol 89 (17) ◽

pp. 1764-1772 ◽

Cited By ~ 32

Author(s):

Massimo Filippi ◽

Silvia Basaia ◽

Elisa Canu ◽

Francesca Imperiale ◽

Alessandro Meani ◽

...

Keyword(s):

Functional Connectivity ◽

Early Onset ◽

Network Architecture ◽

Parietal Lobe ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Network ◽

Graph Analysis ◽

Functional Brain ◽

Functional Brain Network

Objective:To investigate functional brain network architecture in early-onset Alzheimer disease (EOAD) and behavioral variant frontotemporal dementia (bvFTD).Methods:Thirty-eight patients with bvFTD, 37 patients with EOAD, and 32 age-matched healthy controls underwent 3D T1-weighted and resting-state fMRI. Graph analysis and connectomics assessed global and local functional topologic network properties, regional functional connectivity, and intrahemispheric and interhemispheric between-lobe connectivity.Results:Despite similarly extensive cognitive impairment relative to controls, patients with EOAD showed severe global functional network alterations (lower mean nodal strength, local efficiency, clustering coefficient, and longer path length), while patients with bvFTD showed relatively preserved global functional brain architecture. Patients with bvFTD demonstrated reduced nodal strength in the frontoinsular lobe and a relatively focal altered functional connectivity of frontoinsular and temporal regions. Functional connectivity breakdown in the posterior brain nodes, particularly in the parietal lobe, differentiated patients with EOAD from those with bvFTD. While EOAD was associated with widespread loss of both intrahemispheric and interhemispheric functional correlations, bvFTD showed a preferential disruption of the intrahemispheric connectivity.Conclusions:Disease-specific patterns of functional network topology and connectivity alterations were observed in patients with EOAD and bvFTD. Graph analysis and connectomics may aid clinical diagnosis and help elucidate pathophysiologic differences between neurodegenerative dementias.

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Topological Abnormalities of Functional Brain Network in Early-Stage Parkinson’s Disease Patients With Mild Cognitive Impairment

Frontiers in Neuroscience ◽

10.3389/fnins.2020.616872 ◽

2020 ◽

Vol 14 ◽

Author(s):

Xiangbin Chen ◽

Mengting Liu ◽

Zhibing Wu ◽

Hao Cheng

Keyword(s):

Parkinson’S Disease ◽

Parkinson's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Information Exchange ◽

Early Stage ◽

Brain Network ◽

Clustering Coefficient ◽

Functional Brain ◽

Functional Brain Network

Recent studies have demonstrated structural and functional alterations in Parkinson’s disease (PD) with mild cognitive impairment (MCI). However, the topological patterns of functional brain networks in newly diagnosed PD patients with MCI are unclear so far. In this study, we used functional magnetic resonance imaging (fMRI) and graph theory approaches to explore the functional brain network in 45 PD patients with MCI (PD-MCI), 22 PD patients without MCI (PD-nMCI), and 18 healthy controls (HC). We found that the PD-MCI, PD-nMCI, and HC groups exhibited a small-world architecture in the functional brain network. However, early-stage PD-MCI patients had decreased clustering coefficient, increased characteristic path length, and changed nodal centrality in the default mode network (DMN), control network (CN), somatomotor network (SMN), and visual network (VN), which might contribute to factors for MCI symptoms in PD patients. Our results demonstrated that PD-MCI patients were associated with disrupted topological organization in the functional network, thus providing a topological network insight into the role of information exchange in the underlying development of MCI symptoms in PD patients.

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Prediction of Epilepsy Based on Tensor Decomposition and Functional Brain Network

Brain Sciences ◽

10.3390/brainsci11081066 ◽

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.

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The research of EEG feature extraction and classification for subjects with different organizational commitment

MATEC Web of Conferences ◽

10.1051/matecconf/202235503042 ◽

2022 ◽

Vol 355 ◽

pp. 03042

Author(s):

Rui Zhang ◽

Ziyang Wang ◽

Yu Liu

Keyword(s):

Organizational Commitment ◽

Personality Trait ◽

Brain Network ◽

Clustering Coefficient ◽

Permutation Entropy ◽

Future Research ◽

Big Five Personality ◽

Functional Brain ◽

Functional Brain Network ◽

Validation Experiment

With the development of EEG analysis technology, researchers have gradually explored the correlation between personality trait (such as Big Five personality) and EEG. However, there are still many challenges in model construction. In this paper, we tried to classify the people with different organizational commitment personality trait through EEG. Firstly, we organized the participants to complete the organizational commitment questionnaire and recorded their resting state EEG. We divided 10 subjects into two classes (positive and negative) according to the questionnaire scores. Then, various EEG features including power spectral density, microstate, functional brain network and nonlinear features from segmented EEG sample were extracted as the input of different machine learning classifiers. Next, several evaluation metrics were used to evaluate the results of the cross-validation experiment. Finally, the results show that the EEG power in α band, the weighted clustering coefficient of functional brain network and the Permutation Entropy of EEG are relatively good features for this classification task. Furthermore, the highest classification accuracy rate can reach 79.9% with 0.87 AUC (the area under the ROC). The attempts in this paper may serve as the basis for our future research.

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