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Brain Sciences
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Clinical Application of Neuroimaging in Cerebral Vascular Diseases
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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: closed (25 April 2024) | Viewed by 4145

Special Issue Editors

Dr. Xiaofei Hu

E-Mail Website
Guest Editor
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

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

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

Published Papers (4 papers)

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Research

11 pages, 1741 KiB  
Article
Changes in Structural Neural Networks in the Recovery Process of Motor Paralysis after Stroke
by Ikuo Kimura, Atsushi Senoo and Masahiro Abo
Brain Sci. 2024, 14(3), 197; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14030197 - 21 Feb 2024
Viewed by 755
Abstract
In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received [...] Read more.
In recent years, neurorehabilitation has been actively used to treat motor paralysis after stroke. However, the impacts of rehabilitation on neural networks in the brain remain largely unknown. Therefore, we investigated changes in structural neural networks after rehabilitation therapy in patients who received a combination of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) and intensive occupational therapy (intensive-OT) as neurorehabilitation. Fugl-Meyer assessment (FMA) for upper extremity (FMA-UE) and Action Research Arm Test (ARAT), both of which reflected upper limb motor function, were conducted before and after rehabilitation therapy. At the same time, diffusion tensor imaging (DTI) and three-dimensional T1-weighted imaging (3D T1WI) were performed. After analyzing the structural connectome based on DTI data, measures related to connectivity in neural networks were calculated using graph theory. Rehabilitation therapy prompted a significant increase in connectivity with the isthmus of the cingulate gyrus in the ipsilesional hemisphere (p < 0.05) in patients with left-sided paralysis, as well as a significant decrease in connectivity with the ipsilesional postcentral gyrus (p < 0.05). These results indicate that LF-rTMS combined with intensive-OT may facilitate motor function recovery by enhancing the functional roles of networks in motor-related areas of the ipsilesional cerebral hemisphere. Full article
(This article belongs to the Special Issue Clinical Application of Neuroimaging in Cerebral Vascular Diseases)
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10 pages, 239 KiB  
Article
Correlation between Morphological and Hemodynamic Parameters of Carotid Arteries and Cerebral Vasomotor Reactivity
by Stefan Stoisavljevic, Milica Stojanovic, Mirjana Zdraljevic, Vuk Aleksic, Tatjana Pekmezovic and Milija Mijajlovic
Brain Sci. 2024, 14(2), 167; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14020167 - 07 Feb 2024
Viewed by 839
Abstract
The function of cerebral small vessels can be assessed using cerebral vasomotor reactivity (VMR). Our aim in this retrospective cross-sectional study was to investigate a correlation between carotid artery stenosis measured through ultrasonographic morphological and hemodynamic parameters and cerebral VMR. A total of [...] Read more.
The function of cerebral small vessels can be assessed using cerebral vasomotor reactivity (VMR). Our aim in this retrospective cross-sectional study was to investigate a correlation between carotid artery stenosis measured through ultrasonographic morphological and hemodynamic parameters and cerebral VMR. A total of 285 patients (125 males; mean age 54) were included. The breath-holding index (BHI) was used to evaluate cerebral VMR. Ultrasonographic carotid artery parameters were collected: the presence and characteristics of carotid plaques, the degree of carotid diameter stenosis, intima–media thickness (IMT), peak systolic velocity (PSV), and end diastolic velocity (EDV). Additionally, hemodynamic parameters of the middle cerebral artery (MCA) were evaluated, including the mean flow velocity (MFV) and pulsatility index (PI). The following was collected from patients’ medical histories: age, gender, and vascular risk factors. A negative correlation between the BHI and age (r = −0.242, p < 0.01), BHI and the presence of carotid plaques, BHI and IMT (r = −0.203, p < 0.01), and BHI and the PI of MCA on both sides (r = −0.268, p < 0.01) was found. We found a positive correlation between the BHI in the left MCA and EDV in the left internal carotid artery (r = 0.121, p < 0.05). This study shows the correlation between cerebral VMR and carotid stenosis but indicates a higher influence of morphological parameters on VMR values. Full article
(This article belongs to the Special Issue Clinical Application of Neuroimaging in Cerebral Vascular Diseases)
17 pages, 9655 KiB  
Article
Mortality Prediction of Patients with Subarachnoid Hemorrhage Using a Deep Learning Model Based on an Initial Brain CT Scan
by Sergio García-García, Santiago Cepeda, Dominik Müller, Alejandra Mosteiro, Ramón Torné, Silvia Agudo, Natalia de la Torre, Ignacio Arrese and Rosario Sarabia
Brain Sci. 2024, 14(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14010010 - 22 Dec 2023
Cited by 1 | Viewed by 918
Abstract
Background: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. Methods: [...] Read more.
Background: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. Methods: We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model’s architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Results: Images from 219 patients were processed; 175 for training and validation and 44 for the model’s evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). Conclusion: Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge. Full article
(This article belongs to the Special Issue Clinical Application of Neuroimaging in Cerebral Vascular Diseases)
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19 pages, 3840 KiB  
Article
Disrupted Gray Matter Networks Associated with Cognitive Dysfunction in Cerebral Small Vessel Disease
by Yian Gao, Shengpei Wang, Haotian Xin, Mengmeng Feng, Qihao Zhang, Chaofan Sui, Lingfei Guo, Changhu Liang and Hongwei Wen
Brain Sci. 2023, 13(10), 1359; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13101359 - 22 Sep 2023
Viewed by 1013
Abstract
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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