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Brain Sciences
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Understanding the Functioning of Brain Networks in Health and Disease
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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 4015

Special Issue Editor

Dr. Moses O. Sokunbi

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

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Published Papers (4 papers)

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Research

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16 pages, 610 KiB  
Article
Wired Differently? Brain Temporal Complexity and Intelligence in Autism Spectrum Disorder
by Moses O. Sokunbi, Oumayma Soula, Bertha Ochieng and Roger T. Staff
Brain Sci. 2025, 15(8), 796; https://doi.org/10.3390/brainsci15080796 - 26 Jul 2025
Viewed by 1018
Abstract
Background: Autism spectrum disorder (ASD) is characterised by atypical behavioural and cognitive diversity, yet the neural underpinnings linking brain activity and individual presentations remain poorly understood. In this study, we investigated the relationship between resting-state functional magnetic resonance imaging (fMRI) signal complexity and [...] Read more.
Background: Autism spectrum disorder (ASD) is characterised by atypical behavioural and cognitive diversity, yet the neural underpinnings linking brain activity and individual presentations remain poorly understood. In this study, we investigated the relationship between resting-state functional magnetic resonance imaging (fMRI) signal complexity and intelligence (full-scale intelligence quotient (FIQ); verbal intelligence quotient (VIQ); and performance intelligence quotient (PIQ)) in male adults with ASD (n = 14) and matched neurotypical controls (n = 15). Methods: We used three complexity-based metrics: Hurst exponent (H), fuzzy approximate entropy (fApEn), and fuzzy sample entropy (fSampEn) to characterise resting-state fMRI signal dynamics, and correlated these measures with standardised intelligence scores. Results: Using a whole-brain measure, ASD participants showed significant negative correlations between PIQ and both fApEn and fSampEn, suggesting that increased neural irregularity may relate to reduced cognitive–perceptual performance in autistic individuals. No significant associations between entropy (fApEn and fSampEn) and PIQ were found in the control group. Group differences in brain–behaviour associations were confirmed through formal interaction testing using Fisher’s r-to-z transformation, which showed significantly stronger correlations in the ASD group. Complementary regression analyses with interaction terms further demonstrated that the entropy (fApEn and fSampEn) and PIQ relationship was significantly moderated by group, reinforcing evidence for autism-specific neural mechanisms underlying cognitive function. Conclusions: These findings provide insight into how cognitive functions in autism may not only reflect deficits but also an alternative neural strategy, suggesting that distinct temporal patterns may be associated with intelligence in ASD. These preliminary findings could inform clinical practice and influence health and social care policies, particularly in autism diagnosis and personalised support planning. Full article
(This article belongs to the Special Issue Understanding the Functioning of Brain Networks in Health and Disease)
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23 pages, 16941 KiB  
Article
Functional Importance Backbones of the Brain at Rest, Wakefulness, and Sleep
by Klaus Lehnertz and Timo Bröhl
Brain Sci. 2025, 15(7), 772; https://doi.org/10.3390/brainsci15070772 - 20 Jul 2025
Viewed by 500
Abstract
Background: The brain is never truly at rest. Even in the absence of external tasks, it remains active, continuously organizing itself into large-scale resting-state networks involved in shaping our internal thoughts and experiences. Understanding the networks’ structure and dynamics is key to [...] Read more.
Background: The brain is never truly at rest. Even in the absence of external tasks, it remains active, continuously organizing itself into large-scale resting-state networks involved in shaping our internal thoughts and experiences. Understanding the networks’ structure and dynamics is key to uncovering how the brain functions as a whole. While previous studies have mapped resting-state networks and explored the roles of individual brain regions (network vertices), the relevance of the time-dependent functional interactions (network edges) between them remains largely unexplored. Methods: Here, we assess this relevance by elucidating the time-evolving importance of both brain regions and their interactions, associated with the networks’ constituents, using the fundamental concept of centrality. We investigate long-term electrophysiological recordings of brain dynamics from more than 100 participants and reveal new insights into how resting-state networks are organized over longer times. Results: Our findings reveal that the functional architecture of brain networks in a resting state is critically shaped by the dynamic interplay between brain regions. We identified functional importance backbones–core sets of dynamically central vertices and edges–whose configuration varies significantly between subgroups and further varies with different brain states, including wakefulness and sleep. Notably, regions associated with the default mode network exhibited adaptable patterns of centrality, challenging the notion of static network cores. Conclusions: By considering the temporal evolution of both vertices and edges, we provide a more comprehensive understanding of intrinsic brain activity and its functional relevance. This dynamic perspective reveals how the brain’s intrinsic activity is coordinated across space and time, highlighting the existence of functional importance backbones that adapt to different brain states. Full article
(This article belongs to the Special Issue Understanding the Functioning of Brain Networks in Health and Disease)
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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 642
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)
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Review

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45 pages, 6622 KiB  
Review
Evolutionary Trajectories of Consciousness: From Biological Foundations to Technological Horizons
by Evgenii Gusev, Alexey Sarapultsev and Maria Komelkova
Brain Sci. 2025, 15(7), 734; https://doi.org/10.3390/brainsci15070734 - 9 Jul 2025
Viewed by 1348
Abstract
Consciousness remains one of the most critical yet least understood functions of the brain, not only in humans but also in certain highly organized animal species. In this review, we propose treating consciousness as an emergent, goal-directed informational system organized by the subjective [...] Read more.
Consciousness remains one of the most critical yet least understood functions of the brain, not only in humans but also in certain highly organized animal species. In this review, we propose treating consciousness as an emergent, goal-directed informational system organized by the subjective “self” as an active system-forming factor. We present an integrative theoretical–systems framework in which subjectivity functions as system-forming factor of consciousness (SFF) throughout biological evolution. Beginning with proto-conscious invertebrates, we trace progressive elaborations of working and long-term memory, the refinement of behavioral programs, and the emergence of an internal arbiter capable of resolving competing drives. In endothermic vertebrates, subjectivity acquires distinct functional features—sensory filtering, causal reasoning, and adaptive arbitration—underpinned by increasingly complex neural architectures. This evolutionary trajectory culminates in humans, where subjectivity attains its highest level of organization through culturally mediated networks. Although the framework does not assume any specific neural substrate, it provides a testable roadmap linking evolutionary biology, information theory, and quantitative modeling. By clarifying why consciousness arose and how subjectivity shapes complex networks, this perspective also lays the groundwork for exploring possible nonbiological extensions of subjectivity. Full article
(This article belongs to the Special Issue Understanding the Functioning of Brain Networks in Health and Disease)
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