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Brain Sciences
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Brain Network Connectivity Analysis in Neuroscience
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Brain Network Connectivity Analysis in Neuroscience

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 15873

Special Issue Editors

Dr. Mukesh Dhamala

E-Mail Website
Guest Editor
Director of NeuroPhysics and Systems Neuroscience Laboratory, Georgia State University, Atlanta, GA 30303, USA
Interests: cognitive neuroscience; computational neuroscience; neurophysics; neuroimaging techniques; brain data analysis methods
Special Issues, Collections and Topics in MDPI journals
Dr. Sahil Bajaj

E-Mail Website
Guest Editor
Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
Interests: neuroimaging; diffusion-weighted imaging; brain morphometry; brain connectivity; machine learning

Special Issue Information

Dear Colleagues,

Over the last few decades, brain network connectivity analysis has emerged as a powerful neuroscience field of research allowing us to delve into the structural and functional organization of the brain at various time and spatial scales from electrophysiological and neuroimaging recordings. By quantifying brain connectivity parameters, researchers can map out the networks that underlie specific cognitive functions, behaviors and pathological states. This further enables exploring how different brain areas are interconnected and how these connections contribute to the overall workings of the brain in wellness and sickness. The historical roots of this field can be traced back to the initial attempts at understanding neural pathways, which have since evolved into sophisticated analyses of structural and functional networks, contributing to our comprehension of cognitive functions, neural development and pathological states.

This Special Issue aims to capture the dynamic and innovative research occurring within the realm of brain network connectivity analysis. We welcome submissions that expand our understanding of neural connectivity, encompassing various methodologies, including but not limited to, functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), electroencephalography (EEG), magnetoencephalography (MEG), intracranial electroencephalography (iEEG) and functional near-infrared spectroscopy (fNIRS). The scope of the issue will cover topics such as the identification of connectivity biomarkers for brain disorders, the development of new computational methods for network analysis, and the integration of multimodal and multiparametric connectivity data with genetic and clinical information. This includes the development of new analytical frameworks, the application of machine learning and the exploration of the brain's network properties in health and disease.

We invite original research papers, reviews and brief communications that mainly address the following topics: (i) novel methods for analyzing brain network connectivity, (ii) longitudinal and cross-sectional studies on the evolution of brain networks, (iii) cross-modal integration of connectivity data, (iv) network-based approaches to understanding neurological and psychiatric disorders, (v) the relationship between brain connectivity and cognitive functions, (vi) the impact of genetics and environment on brain networks and (vii) translational research utilizing brain connectivity analysis.

All submissions will undergo a rigorous peer review process, ensuring the highest standards of research quality and scientific rigor. We look forward to your contributions to this exciting field and advancing our collective knowledge on brain network connectivity.

Dr. Mukesh Dhamala
Dr. Sahil Bajaj
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • brain connectivity
  • brain networks
  • brain dynamics
  • brain network interactions
  • brain organization
  • fMRI
  • DTI
  • EEG
  • MEG
  • iEEG
  • fNIRS
  • cognitive functions
  • brain dysfunctions
  • brain disorders
  • whole-brain connectivity

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Published Papers (9 papers)

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Research

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23 pages, 13995 KiB  
Article
The Effect of Dopaminergic Therapy in Parkinson’s Disease: A Graph Theory Analysis
by Karthik Siva, Palanisamy Ponnusamy, Vishal Chavda and Nicola Montemurro
Brain Sci. 2025, 15(4), 370; https://doi.org/10.3390/brainsci15040370 - 2 Apr 2025
Viewed by 413
Abstract
Background: Dopaminergic therapy (DT) is the gold standard pharmacological treatment for Parkinson’s disease (PD). Currently, understanding the neuromodulation effect in the brain of PD after DT is important for doctors to optimize doses and identify the adverse effects of medication. The objective [...] Read more.
Background: Dopaminergic therapy (DT) is the gold standard pharmacological treatment for Parkinson’s disease (PD). Currently, understanding the neuromodulation effect in the brain of PD after DT is important for doctors to optimize doses and identify the adverse effects of medication. The objective of this study is to investigate the brain connectivity alteration with and without DT in PD using resting-state EEG. Methods: Graph theory (GT) is an efficient technique for analyzing brain connectivity alteration in healthy and patient groups. We applied GT analyses on three groups, namely healthy control (HC), Parkinson with medication OFF (PD-OFF), and Parkinson with medication ON (PD-ON). Results: Using the clustering coefficient (CC), participation coefficient (PC), and small-worldness (SW) properties of GT, we showed that PD-ON patients’ brain connectivity normalized towards healthy group brain connectivity due to DT. This normalization effect appeared in the brain connectivity of all EEG frequency bands, such as theta, alpha, beta-1, beta-2, and gamma except the delta band. We also analyzed region-wise brain connectivity between 10 regions of interest (ROIs) (right and left frontal, right and left temporal, right and left parietal, right and left occipital, upper and lower midline regions) at the scalp level and compared across conditions. During PD-ON, we observed a significant decrease in alpha band connectivity between right frontal and left parietal (p-value 0.0432) and right frontal and left occipital (p-value 0.008) as well as right frontal and right temporal (p-value 0.041). Conclusion: These findings offer new insights into how dopaminergic therapy modulates brain connectivity across frequency bands and highlight the continuous elevation of both the segregation and small-worldness of the delta band even after medication as a potential biomarker for adverse effects due to medication. Additionally, reduced frontal alpha band connectivity is associated with cognitive impairment and levodopa-induced dyskinesia, highlighting its potential role in Parkinson’s disease progression. This study underscores the need for personalized treatments that address both motor and non-motor symptoms in PD patients. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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15 pages, 3501 KiB  
Article
Short-Term Restriction of Physical and Social Activities Effects on Brain Structure and Connectivity
by Yajuan Zhang, Lianghu Guo, Zhuoyang Gu, Qing Yang, Siyan Han and Han Zhang
Brain Sci. 2025, 15(1), 7; https://doi.org/10.3390/brainsci15010007 - 25 Dec 2024
Viewed by 872
Abstract
Background: Prolonged confinement in enclosed environments has raised concerns about its effects on both physical and mental health. Although increased rates of depression or anxiety during COVID-19 lockdowns have been reported, the effects of short-term restrictions on social activities and physical on brain [...] Read more.
Background: Prolonged confinement in enclosed environments has raised concerns about its effects on both physical and mental health. Although increased rates of depression or anxiety during COVID-19 lockdowns have been reported, the effects of short-term restrictions on social activities and physical on brain function and structure remain poorly known. Methods: This study explored longitudinal changes in brain gray matter volume (GMV) and functional connectivity (FC) immediately after and four months following a short-term lockdown in comparison to pre-lockdown conditions. MRI data were collected from 20 participants before the lockdown, from 29 participants (14 original, 15 new) two months post-lockdown, and from 27 out of the 29 participants four months post-lifting of the lockdown. Results: Results showed significant GMV reductions in the right gyrus rectus and cuneus post-lockdown, with further reductions observed four months after lifting the restrictions, affecting additional brain regions. Longitudinal FC trajectories revealed decreased connectivity between the default mode network (DMN) and sensorimotor/attention networks post-lockdown, and recovery after four months post-lifting of the lockdown. Conclusions: The observed plasticity in brain FC indicates substantial recovery potential with the potential long-term effect of structural changes. Our findings offer insights into the effects of isolation on the human brain, potentially informing rehabilitation mechanisms and interventions for individuals in similar conditions. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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19 pages, 3654 KiB  
Article
Fatigue Characterization of EEG Brain Networks Under Mixed Reality Stereo Vision
by Yan Wu, Chunguang Tao and Qi Li
Brain Sci. 2024, 14(11), 1126; https://doi.org/10.3390/brainsci14111126 - 7 Nov 2024
Viewed by 1374
Abstract
Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple [...] Read more.
Mixed Reality (MR) technology possesses profound and extensive potential across a multitude of domains, including, but not limited to industry, healthcare, and education. However, prolonged use of MR devices to watch stereoscopic content may lead to visual fatigue. Since visual fatigue involves multiple brain regions, our study aims to explore the topological characteristics of brain networks derived from electroencephalogram (EEG) data. Because the Phase-Locked Value (PLV) is capable of effectively measuring the phase synchronization relationship between brain regions, it was calculated between all pairs of channels in both comfort and fatigue states. Subsequently, a sparse brain network was constructed based on PLV by applying an appropriate threshold. The node properties (betweenness centrality, clustering coefficient, node efficiency) and edge properties (characteristic path length) were calculated based on the corresponding brain network within specific frequency bands for both comfort and fatigue states. In analyzing the PLV of brain connectivity in comfort and fatigue states, a notable enhancement in brain connectivity is observed within the alpha, theta, and delta frequency bands during fatigue status. By analyzing the node and edge properties of brain networks, it is evident that the mean values of these properties in the fatigue state were higher than those in the comfort state. By analyzing the node and edge properties at a local level, the average difference in betweenness centrality, clustering coefficients, and nodal efficiency across the three EEG frequency bands was computed to find significant brain regions. The main findings are as follows: Betweenness centrality primarily differs in frontal and parietal regions, with minor involvement in temporal and central regions. The clustering Coefficient mainly varies in the frontal region, with slight differences being seen in the temporal and occipital regions. Nodal efficiency primarily varies in the frontal, temporal, and central regions, with minor differences being seen in the parietal and occipital regions. Edge property analysis indicates that there is a higher occurrence of long-distance connections among brain regions during the fatigue state, which reflects a loss of synaptic transmission efficiency on a global level. Our study plays a crucial role in understanding the neural mechanisms underlying visual fatigue, potentially providing insights that could be applied to high-demand cognitive fields where prolonged use of MR devices leads to visual fatigue. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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13 pages, 1285 KiB  
Article
Association Between Glaucoma and Brain Structural Connectivity Based on Diffusion Tensor Tractography: A Bidirectional Mendelian Randomization Study
by Lian Shu, Xiaoxiao Chen and Xinghuai Sun
Brain Sci. 2024, 14(10), 1030; https://doi.org/10.3390/brainsci14101030 - 17 Oct 2024
Viewed by 1426
Abstract
Background: Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. Methods: To explore [...] Read more.
Background: Glaucoma is a neurodegenerative ocular disease that is accompanied by cerebral damage extending beyond the visual system. Recent studies based on diffusion tensor tractography have suggested an association between glaucoma and brain structural connectivity but have not clarified causality. Methods: To explore the causal associations between glaucoma and brain structural connectivity, a bidirectional Mendelian randomization (MR) study was conducted involving glaucoma and 206 diffusion tensor tractography traits. Highly associated genetic variations were applied as instrumental variables and statistical data were sourced from the database of FinnGen and UK Biobank. The inverse-variance weighted method was applied to assess causal relationships. Additional sensitivity analyses were also performed. Results: Glaucoma was potentially causally associated with alterations in three brain structural connectivities (from the SN to the thalamus, from the DAN to the putamen, and within the LN network) in the forward MR analysis, whereas the inverse MR results identified thirteen brain structural connectivity traits with a potential causal relationship to the risk of glaucoma. Both forward and reverse MR analyses satisfied the sensitivity test with no significant horizontal pleiotropy or heterogeneity. Conclusions: This study offered suggestive evidence for the potential causality between the risk of glaucoma and brain structural connectivity. Our findings also provided novel insights into the neurodegenerative mechanism of glaucoma. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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12 pages, 824 KiB  
Article
Depression Severity, Slow- versus Fast-Wave Neural Activity, and Symptoms of Melancholia
by Christopher F. Sharpley, Vicki Bitsika, Ian D. Evans, Kirstan A. Vessey, Emmanuel Jesulola and Linda L. Agnew
Brain Sci. 2024, 14(6), 607; https://doi.org/10.3390/brainsci14060607 - 15 Jun 2024
Viewed by 2032
Abstract
Melancholia is a major and severe subtype of depression, with only limited data regarding its association with neurological phenomena. To extend the current understanding of how particular aspects of melancholia are correlated with brain activity, electroencephalographic data were collected from 100 adults (44 [...] Read more.
Melancholia is a major and severe subtype of depression, with only limited data regarding its association with neurological phenomena. To extend the current understanding of how particular aspects of melancholia are correlated with brain activity, electroencephalographic data were collected from 100 adults (44 males and 56 females, all aged 18 y or more) and investigated for the association between symptoms of melancholia and the ratios of alpha/beta activity and theta/beta activity at parietal–occipital EEG sites PO1 and PO2. The results indicate differences in these associations according to the depressive status of participants and the particular symptom of melancholia. Depressed participants exhibited meaningfully direct correlations between alpha/beta and theta/beta activity and the feeling that “Others would be better off if I was dead” at PO1, whereas non-depressed participants had significant inverse correlations between theta/beta activity and “Feeling useless and not needed” and “I find it hard to make decisions” at PO1. The results are discussed in terms of the relative levels of fast-wave (beta) versus slow-wave (alpha, theta) activity exhibited by depressed and non-depressed participants in the parietal–occipital region and the cognitive activities that are relevant to that region. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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13 pages, 3045 KiB  
Article
From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder
by Chunyu Pan, Ying Ma, Lifei Wang, Yan Zhang, Fei Wang and Xizhe Zhang
Brain Sci. 2024, 14(5), 509; https://doi.org/10.3390/brainsci14050509 - 17 May 2024
Cited by 1 | Viewed by 2162
Abstract
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint [...] Read more.
Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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27 pages, 8185 KiB  
Article
Hierarchical Principal Components for Data-Driven Multiresolution fMRI Analyses
by Korey P. Wylie, Thao Vu, Kristina T. Legget and Jason R. Tregellas
Brain Sci. 2024, 14(4), 325; https://doi.org/10.3390/brainsci14040325 - 28 Mar 2024
Cited by 1 | Viewed by 1898
Abstract
Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot [...] Read more.
Understanding the organization of neural processing is a fundamental goal of neuroscience. Recent work suggests that these systems are organized as a multiscale hierarchy, with increasingly specialized subsystems nested inside general processing systems. Current neuroimaging methods, such as independent component analysis (ICA), cannot fully capture this hierarchy since they are limited to a single spatial scale. In this manuscript, we introduce multiresolution hierarchical principal components analysis (hPCA) and compare it to ICA using simulated fMRI datasets. Furthermore, we describe a parametric statistical filtering method developed to focus analyses on biologically relevant features. Lastly, we apply hPCA to the Human Connectome Project (HCP) to demonstrate its ability to estimate a hierarchy from real fMRI data. hPCA accurately estimated spatial maps and time series from networks with diverse hierarchical structures. Simulated hierarchies varied in the degree of branching, such as two-way or three-way subdivisions, and the total number of levels, with varying equal or unequal subdivision sizes at each branch. In each case, as well as in the HCP, hPCA was able to reconstruct a known hierarchy of networks. Our results suggest that hPCA can facilitate more detailed and comprehensive analyses of the brain’s network of networks and the multiscale regional specializations underlying neural processing and cognition. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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17 pages, 2712 KiB  
Article
Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes
by Jilong Shi, Fatima A. Nasrallah, Xuechen Mao, Qin Huang, Jun Pan and Anmin Li
Brain Sci. 2024, 14(3), 222; https://doi.org/10.3390/brainsci14030222 - 27 Feb 2024
Cited by 1 | Viewed by 1954
Abstract
Background: Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis [...] Read more.
Background: Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. Methods: A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. Results: The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes’ brain. Conclusions: Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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Review

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24 pages, 1440 KiB  
Review
Graph Neural Networks in Brain Connectivity Studies: Methods, Challenges, and Future Directions
by Hamed Mohammadi and Waldemar Karwowski
Brain Sci. 2025, 15(1), 17; https://doi.org/10.3390/brainsci15010017 - 27 Dec 2024
Cited by 1 | Viewed by 3099
Abstract
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain [...] Read more.
Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics of the human brain, providing insights into cognitive functions, behaviors, and neurological disorders. Traditional graph-theoretical methods, while foundational, often fall short in capturing the high-dimensional and dynamic nature of brain connectivity. Graph Neural Networks (GNNs) have recently emerged as a powerful approach for this purpose, with the potential to improve diagnostics, prognostics, and personalized interventions. This review examines recent studies leveraging GNNs in brain connectivity analysis, focusing on key methodological advancements in multimodal data integration, dynamic connectivity, and interpretability across various imaging modalities, including fMRI, MRI, DTI, PET, and EEG. Findings reveal that GNNs excel in modeling complex, non-linear connectivity patterns and enable the integration of multiple neuroimaging modalities to provide richer insights into both healthy and pathological brain networks. However, challenges remain, particularly in interpretability, data scarcity, and multimodal integration, limiting the full clinical utility of GNNs. Addressing these limitations through enhanced interpretability, optimized multimodal techniques, and expanded labeled datasets is crucial to fully harness the potential of GNNs for neuroscience research and clinical applications. Full article
(This article belongs to the Special Issue Brain Network Connectivity Analysis in Neuroscience)
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