Special Issue "Clinical Application of Neuroimaging in Cerebral Vascular Diseases"

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

Deadline for manuscript submissions: 25 April 2024 | Viewed by 1025

Special Issue Editors

Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing 400047, China
Interests: fMRI/dMRI/MRI; quantitative imaging; artificial intelligence; radiomics; network neuroscience; cognitive neuroscience; genetic neuroimaging; biomarkers; Parkinson's disease; stroke; cerebral small vessel disease; neuro-oncology
Prof. Dr. Xiaohuan Xia
E-Mail Website
Co-Guest Editor
West Campus, Tongji University, Shanghai 200331, China
Interests: the pathogenesis and novel therapeutic strategies of neurological diseases including neurodegenerative diseases, cerebrovascular disease, acute brain injury, and neuropsychiatric disorders

Special Issue Information

Dear Colleagues,

Cerebral small vessel disease (CSVD) is a cluster of cerebrovascular diseases affecting the small vessels, arteries and veins. Diagnosis of CSVD can be challenging due to its complex clinical manifestations. Advanced neuroimaging techniques have shown great potential in deepening our understanding of the pathophysiological mechanisms of CSVD.

This Special Issue aims to advance our comprehensive understanding of the physiology and pathology of CSVD and related diseases, including neurological mechanisms, potential biomarkers and diagnostic techniques, using neuroimaging methods.

This collection solicits original research, reviews, mini-reviews and perspectives focusing on CSVD and related diseases. The topics of interest may include, but are not limited to:

1) Understanding the neural mechanisms of CSVD and its relationship with stroke, cognitive impairment, gait and balance disorders.

2) The structural and functional changes of CSVD, particularly blood–brain barrier dysfunction, neurotransmission abnormalities and neuroinflammation.

3) Developing imaging markers to improve early and differential diagnoses.

4) Neuroimaging assessment and prediction of CSVD progression and severity.

5) Evaluating the pharmacological/non-pharmacological interventions and therapeutic efficacy using neuroimaging approaches.

6) Improving neuroimaging techniques and data analysis, such as multimodal approaches and deep learning-based approaches.

Dr. Xiaofei Hu
Prof. Dr. Xiaohuan Xia
Guest Editors

Manuscript Submission Information

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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.


  • CSVD
  • cerebrovascular diseases
  • neuroimaging
  • mechanisms
  • diagnosis
  • therapy

Published Papers (1 paper)

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19 pages, 3840 KiB  
Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease
Brain Sci. 2023, 13(10), 1359; https://doi.org/10.3390/brainsci13101359 - 22 Sep 2023
Viewed by 610
This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 [...] Read more.
This study aims to investigate the disrupted topological organization of gray matter (GM) structural networks in cerebral small vessel disease (CSVD) patients with cerebral microbleeds (CMBs). Subject-wise structural networks were constructed from GM volumetric features of 49 CSVD patients with CMBs (CSVD-c), 121 CSVD patients without CMBs (CSVD-n), and 74 healthy controls. The study used graph theory to analyze the global and regional properties of the network and their correlation with cognitive performance. We found that both the control and CSVD groups exhibited efficient small-world organization in GM networks. However, compared to controls, CSVD-c and CSVD-n patients exhibited increased global and local efficiency (Eglob/Eloc) and decreased shortest path lengths (Lp), indicating increased global integration and local specialization in structural networks. Although there was no significant global topology change, partially reorganized hub distributions were found between CSVD-c and CSVD-n patients. Importantly, regional topology in nonhub regions was significantly altered between CSVD-c and CSVD-n patients, including the bilateral anterior cingulate gyrus, left superior parietal gyrus, dorsolateral superior frontal gyrus, and right MTG, which are involved in the default mode network (DMN) and sensorimotor functional modules. Intriguingly, the global metrics (Eglob, Eloc, and Lp) were significantly correlated with MoCA, AVLT, and SCWT scores in the control group but not in the CSVD-c and CSVD-n groups. In contrast, the global metrics were significantly correlated with the SDMT score in the CSVD-s and CSVD-n groups but not in the control group. Patients with CSVD show a disrupted balance between local specialization and global integration in their GM structural networks. The altered regional topology between CSVD-c and CSVD-n patients may be due to different etiological contributions, which may offer a novel understanding of the neurobiological processes involved in CSVD with CMBs. Full article
(This article belongs to the Special Issue Clinical Application of Neuroimaging in Cerebral Vascular Diseases)
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