Understanding the Functioning of Brain Networks in Health and Disease

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

Deadline for manuscript submissions: 25 September 2025 | Viewed by 451

Special Issue Editor


E-Mail Website
Guest Editor
1. Leicester School of Allied Health Sciences, De Montfort University, Leicester LE1 9BH, UK
2. Leicester Institute for Pharmaceutical, Health and Social Care Innovations (LIPHSCI), De Montfort University, Leicester LE1 9BH, UK
Interests: artificial intelligence; brain-computer interfaces; complexity; connectivity; deep learning; EEG; entropy; fMRI; neurofeedback; neuroimaging

Special Issue Information

Dear Colleagues,

Understanding brain networks in health and disease provides insights into how the brain’s interconnected regions collaborate for cognitive, emotional, and motor functions. In healthy brains, networks such as the Default Mode Network (DMN), Central Executive Network (CEN), and Salience Network dynamically interact based on cognitive demands, displaying adaptability and plasticity. These networks allow for efficient information processing and adaptation to new experiences.

In disease states, disruptions in functional connectivity and signal complexity within and between networks are common. For example, in Alzheimer’s Disease, there is reduced DMN connectivity, correlating with memory loss. Schizophrenia shows altered connectivity in networks related to thought and perception, while depression involves abnormal DMN and limbic system interactions, contributing to emotional dysregulation. Neurodevelopmental disorders like Autism exhibit atypical social and emotional network functioning, affecting communication skills.

Techniques like functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), and diffusion tensor imaging (DTI) help map brain network activity, revealing patterns of disruption in various conditions. These insights have led to clinical applications like neurofeedback and personalized medicine, allowing for targeted therapies and interventions. Understanding brain networks is crucial for developing more effective treatments for neurological and psychiatric disorders, fostering better outcomes in mental health and cognitive rehabilitation.

Dr. Moses O. Sokunbi
Guest Editor

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 networks
  • cognitive dysfunction
  • diffusion tensor imaging (DTI)
  • electroencephalography (EEG)
  • functional connectivity
  • functional magnetic resonance imaging (fMRI)
  • magnetoencephalography (MEG)
  • neurological disorders
  • neuroplasticity
  • psychiatric disorders
  • artificial intelligence
  • deep learning
  • neural networks
  • signal complexity

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

16 pages, 4737 KiB  
Article
Co-Community Network Analysis Reveals Alterations in Brain Networks in Alzheimer’s Disease
by Xiaodong Wang, Zhaokai Zhang, Lingli Deng and Jiyang Dong
Brain Sci. 2025, 15(5), 517; https://doi.org/10.3390/brainsci15050517 - 18 May 2025
Viewed by 80
Abstract
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. [...] Read more.
Background: Alzheimer’s disease (AD) is a common neurodegenerative disease. Functional magnetic resonance imaging (fMRI) can be used to measure the temporal correlation of blood-oxygen-level-dependent (BOLD) signals in the brain to assess the brain’s intrinsic connectivity and capture dynamic changes in the brain. In this study, our research goal is to investigate how the brain network structure, as measured by resting-state fMRI, differs across distinct physiological states. Method: With the research goal of addressing the limitations of BOLD signal-based brain networks constructed using Pearson correlation coefficients, individual brain networks and community detection are used to study the brain networks based on co-community probability matrices (CCPMs). We used CCPMs and enrichment analysis to compare differences in brain network topological characteristics among three typical brain states. Result: The experimental results indicate that AD patients with increasing disease severity levels will experience the isolation of brain networks and alterations in the topological characteristics of brain networks, such as the Somatomotor Network (SMN), dorsal attention network (DAN), and Default Mode Network (DMN). Conclusion: This work suggests that using different data-driven methods based on CCPMs to study alterations in the topological characteristics of brain networks would provide better information complementarity, which can provide a novel analytical perspective for AD progression and a new direction for the extraction of neuro-biomarkers in the early diagnosis of AD. Full article
(This article belongs to the Special Issue Understanding the Functioning of Brain Networks in Health and Disease)
Show Figures

Figure 1

Back to TopTop