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Brain Sciences
Special Issues
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: 29 September 2024 | Viewed by 1704

Special Issue Editors

Dr. Mukesh Dhamala

E-Mail Website
Guest Editor
Director of NeuroPhysics and Systems Neuroscience Laboratory, Department of Physics and Astronomy, Neuroscience Institute, Georgia State University, Atlanta, GA 30303, USA
Interests: computational neuroscience; systems neuroscience; multisensory decision-making; neural network oscillations; brain dynamics; network activity analysis
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

Published Papers (2 papers)

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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
Viewed by 665
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
Viewed by 849
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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