Using Neuroimaging to Explore Neurodegenerative Diseases

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurodegenerative Diseases".

Deadline for manuscript submissions: 25 December 2025 | Viewed by 572

Special Issue Editor

Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Interests: neurodegenerative diseases; multi-omics research; neuroimaging; imaging-omics study; data-driven subtyping and disease tracking

Special Issue Information

Dear Colleagues,

Neuroimaging advancements have revolutionized our understanding of neurodegenerative diseases, offering unprecedented insights into pathophysiology, early diagnosis, and therapeutic innovation. This Special Issue, “Using Neuroimaging to Explore Neurodegenerative Diseases” aims to compile cutting-edge research and interdisciplinary perspectives on the application of neuroimaging to unravel the complexities of neurodegeneration. We welcome contributions addressing: (1) emerging technologies; (2) novel biomarkers for diagnosing and disease tracking; (3) multimodal integration; (4) AI-driven analytics for subtype classification or prognostic modeling; (5) clinical translation of imaging findings; and (6) cross-disciplinary synergies with genomics, proteomics, and digital health tools. Submissions may include original research articles, reviews articles, systematic reviews, short communications that bridge technical advancements with clinical or mechanistic insights.

Dr. Ting Shen
Guest Editor

Manuscript Submission Information

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Keywords

  • neuroimaging
  • neurodegenerative diseases
  • multidisciplinary research
  • imaging-omics study
  • multimodal integration
  • AI-driven analytics

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Published Papers (1 paper)

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Research

20 pages, 3404 KiB  
Article
Dynamic Synergy Network Analysis Reveals Stage-Specific Regional Dysfunction in Alzheimer’s Disease
by Xiaoyan Zhang, Chao Han, Jingbo Xia, Lingli Deng and Jiyang Dong
Brain Sci. 2025, 15(6), 636; https://doi.org/10.3390/brainsci15060636 - 12 Jun 2025
Viewed by 311
Abstract
Background: Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by progressive neurodegeneration and connectivity deterioration. While resting-state functional magnetic resonance imaging (fMRI) provides critical insights into brain network abnormalities, traditional mutual information-based methods exhibit inherent limitations in characterizing the dynamic synergistic mechanisms [...] Read more.
Background: Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder characterized by progressive neurodegeneration and connectivity deterioration. While resting-state functional magnetic resonance imaging (fMRI) provides critical insights into brain network abnormalities, traditional mutual information-based methods exhibit inherent limitations in characterizing the dynamic synergistic mechanisms between cerebral regions. Method: This study pioneered the application of an Integrated Information Decomposition (ΦID) framework in AD brain network analysis, constructing single-sample network models based on ΦID-derived synergy metrics to systematically compare their differences with mutual information-based methods in pathological sensitivity, computational robustness, and network representation capability, while detecting brain regions with declining dynamic synergy during AD progression through intergroup t-tests. Result: The key finding are as follows: (1) synergy metrics exhibited lower intra-group coefficient of variation than mutual information metrics, indicating higher computational stability; (2) single-sample reconstruction significantly enhanced the statistical power in intergroup difference detection; (3) synergy metrics captured brain network features that are undetectable by traditional mutual information methods, with more pronounced differences between networks; (4) key node analysis demonstrated spatiotemporal degradation patterns progressing from initial dysfunction in orbitofrontal–striatal–temporoparietal pathways accompanied by multi-regional impairments during prodromal stages, through moderate-phase decline located in the right middle frontal and postcentral gyri, to advanced-stage degeneration of the right supramarginal gyrus and left inferior parietal lobule. ΦID-driven dynamic synergy network analysis provides novel information integration theory-based biomarkers for AD progression diagnosis and potentially lays the foundation for pathological understanding and subsequent targeted therapy development. Full article
(This article belongs to the Special Issue Using Neuroimaging to Explore Neurodegenerative Diseases)
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