Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.6
2.8
Journals
Brain Sciences
Special Issues
Using Neuroimaging to Explore Neurodegenerative Diseases
share announcement

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 727

Special Issue Editor

Dr. Ting Shen

E-Mail Website
Guest 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

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

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

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
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

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 443
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop