Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 20375

Special Issue Editors

1. Department of Radiology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China
2. Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China
Interests: artificial intelligence; imaging genetics; neuroimaging; exposome
Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, No. 569, Xinsi Road, Baqiao District, Xi'an 710038, China
Interests: multimodal imaging analysis; quantitative MRI; medical imaging; precision medicine
School of Artificial Intelligence, Hebei University of Technology, No. 5340, Xiping Road, Beichen District, Tianjin 300401, China
Interests: machine learning; pattern recognition; multimodal neuroimaging analysis; imaging genetics
Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University (SYSU), No. 600, Tianhe Road, Guangzhou 510630, China
Interests: application of deep learning in medical imaging; resting-state fMRI; computer programming

Special Issue Information

Dear Colleagues,

Neuropsychiatric disorders have an enormous impact on human health in modern society. Brain imaging provides powerful tools to examine the structural, functional and metabolic brain alterations underlying these disorders. However, traditional neuroimaging analytic approaches may not meet the accuracy and efficiency requirements of clinical practice and neuroscience research. In recent years, brain imaging combined with state-of-the-art technologies such as artificial intelligence has yielded promising results for personalized medicine. This Special Issue aims to summarize the most recent advances of brain imaging methods for advancing our understanding of neuropsychiatric disorders towards precision medicine.

This Special Issue invites research articles focused on developing and optimizing image processing methods in brain imaging, including but not limited to neuropsychiatric disorder diagnosis and prognosis, as well as neuroimaging data registration and segmentation.

This Special Issue will cover important methodological questions and novel applications in neuroimaging data analysis for fetal, infant, adolescent, and adult brain images. It will benefit neuroscientists and clinicians by introducing advanced techniques that will elevate the accuracy and efficiency of neuroimaging data processing. As a result, this will advance the understanding of the pathophysiological mechanisms and find biomarkers that would be valuable for clinical diagnosis, monitoring disease progression, and evaluating treatment effects.

Dr. Feng Liu
Dr. Yang Yang
Dr. Xiaoke Hao
Dr. Chao Li
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. Journal of Personalized Medicine 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 2600 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

  • neuropsychiatric disorders
  • personalized medicine
  • brain imaging
  • image classification
  • image segmentation
  • image registration

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

11 pages, 1081 KiB  
Article
White Matter Hyperintensities in Young Patients from a Neurological Outpatient Clinic: Prevalence, Risk Factors, and Correlation with Enlarged Perivascular Spaces
by Qiaoqiao Zou, Mingliang Wang, Danni Zhang, Xiaoer Wei and Wenbin Li
J. Pers. Med. 2023, 13(3), 525; https://doi.org/10.3390/jpm13030525 - 15 Mar 2023
Cited by 2 | Viewed by 13463
Abstract
(1) Background: to investigate the prevalence of white matter hyperintensities (WMH), risk factors, and correlation with enlarged perivascular spaces (ePVS) among young patients (age, 16–45 years) in a neurological outpatient clinic. (2) Methods: a total of 887 young patients who underwent a head [...] Read more.
(1) Background: to investigate the prevalence of white matter hyperintensities (WMH), risk factors, and correlation with enlarged perivascular spaces (ePVS) among young patients (age, 16–45 years) in a neurological outpatient clinic. (2) Methods: a total of 887 young patients who underwent a head magnetic resonance imaging (MRI)examination between 1 June 2021, and 30 November 2021, were included in this study. Paraventricular WMH (PWMH), deep WMH (DWMH), ePVS in the centrum semiovale (CSO-ePVS), and basal ganglia (BG-ePVS) were rated. Logistic regression analysis was used to identify the best predictors for the presence of WMH and, for the association of the severity of ePVS with the presence of WMH. Goodman–Kruskal gamma test was used to assess the correlation between the severity of ePVS and WMH. (3) Results: the prevalence of WMH was 37.0%, with low severity. Age, hypertension (p < 0.001), headache (p = 0.031), syncope (p = 0.012), and sleep disturbance (p = 0.003) were associated with the presence of DWMH. Age, sex (p = 0.032), hypertension (p = 0.004) and sleep disturbance (p < 0.001) were associated with the presence of PWMH. The severity of CSO-ePVS was associated with the presence and the severities of DWMH. The severity of BG-ePVS was associated with the presence and severities of DWMH and PWMH. (4) Conclusions: the prevalence of WMH was 37% and mild in young patients without specific causes. Older age, female, hypertension, headache, syncope, and sleep disturbance were associated with WMH. The severity of ePVS had an impact on the presence and severity of WMH in the corresponding brain regions. Full article
(This article belongs to the Special Issue Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders)
Show Figures

Figure 1

17 pages, 2402 KiB  
Article
Fusing Multiview Functional Brain Networks by Joint Embedding for Brain Disease Identification
by Chengcheng Wang, Limei Zhang, Jinshan Zhang, Lishan Qiao and Mingxia Liu
J. Pers. Med. 2023, 13(2), 251; https://doi.org/10.3390/jpm13020251 - 29 Jan 2023
Cited by 1 | Viewed by 1452
Abstract
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the [...] Read more.
Background: Functional brain networks (FBNs) derived from resting-state functional MRI (rs-fMRI) have shown great potential in identifying brain disorders, such as autistic spectrum disorder (ASD). Therefore, many FBN estimation methods have been proposed in recent years. Most existing methods only model the functional connections between brain regions of interest (ROIs) from a single view (e.g., by estimating FBNs through a specific strategy), failing to capture the complex interactions among ROIs in the brain. Methods: To address this problem, we propose fusion of multiview FBNs through joint embedding, which can make full use of the common information of multiview FBNs estimated by different strategies. More specifically, we first stack the adjacency matrices of FBNs estimated by different methods into a tensor and use tensor factorization to learn the joint embedding (i.e., a common factor of all FBNs) for each ROI. Then, we use Pearson’s correlation to calculate the connections between each embedded ROI in order to reconstruct a new FBN. Results: Experimental results obtained on the public ABIDE dataset with rs-fMRI data reveal that our method is superior to several state-of-the-art methods in automated ASD diagnosis. Moreover, by exploring FBN “features” that contributed most to ASD identification, we discovered potential biomarkers for ASD diagnosis. The proposed framework achieves an accuracy of 74.46%, which is generally better than the compared individual FBN methods. In addition, our method achieves the best performance compared to other multinetwork methods, i.e., an accuracy improvement of at least 2.72%. Conclusions: We present a multiview FBN fusion strategy through joint embedding for fMRI-based ASD identification. The proposed fusion method has an elegant theoretical explanation from the perspective of eigenvector centrality. Full article
(This article belongs to the Special Issue Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders)
Show Figures

Figure 1

13 pages, 1597 KiB  
Article
Predicting a Favorable (mRS 0–2) or Unfavorable (mRS 3–6) Stroke Outcome by Arterial Spin Labeling and Amide Proton Transfer Imaging in Post-Thrombolysis Stroke Patients
by Qinmeng He, Guomin Li, Meien Jiang, Qianling Zhou, Yunyu Gao and Jianhao Yan
J. Pers. Med. 2023, 13(2), 248; https://doi.org/10.3390/jpm13020248 - 29 Jan 2023
Cited by 3 | Viewed by 1467
Abstract
(1) Background: The objective of this study was to determine whether arterial spin labeling (ASL), amide proton transfer (APT), or their combination could distinguish between patients with a low and high modified Rankin Scale (mRS) and forecast the effectiveness of the therapy; (2) [...] Read more.
(1) Background: The objective of this study was to determine whether arterial spin labeling (ASL), amide proton transfer (APT), or their combination could distinguish between patients with a low and high modified Rankin Scale (mRS) and forecast the effectiveness of the therapy; (2) Methods: Fifty-eight patients with subacute phase ischemic stroke were included in this study. Based on cerebral blood flow (CBF) and asymmetry magnetic transfer ratio (MTRasym) images, histogram analysis was performed on the ischemic area to acquire imaging biomarkers, and the contralateral area was used as a control. Imaging biomarkers were compared between the low (mRS 0–2) and high (mRS 3–6) mRS score groups using the Mann–Whitney U test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the potential biomarkers in differentiating between the two groups; (3) Results: The rAPT 50th had an area under the ROC curve (AUC) of 0.728, with a sensitivity of 91.67% and a specificity of 61.76% for differentiating between patients with low and high mRS scores. Moreover, the AUC, sensitivity, and specificity of the rASL max were 0.926, 100%, and 82.4%, respectively. Combining the parameters with logistic regression could further improve the performance in predicting prognosis, leading to an AUC of 0.968, a sensitivity of 100%, and a specificity of 91.2%; (4) Conclusions: The combination of APT and ASL may be a potential imaging biomarker to reflect the effectiveness of thrombolytic therapy for stroke patients, assisting in guiding treatment approaches and identifying high-risk patients such as those with severe disability, paralysis, and cognitive impairment. Full article
(This article belongs to the Special Issue Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders)
Show Figures

Figure 1

15 pages, 2766 KiB  
Article
Altered White Matter Network Topology in Panic Disorder
by Molin Jiang, Ping Zhang, Xiangyun Yang, Aihong Yu, Jie Zhang, Xiaoyu Xu and Zhanjiang Li
J. Pers. Med. 2023, 13(2), 227; https://doi.org/10.3390/jpm13020227 - 27 Jan 2023
Cited by 1 | Viewed by 1387
Abstract
Panic disorder (PD) is an anxiety disorder that impairs life quality and social function and is associated with distributed brain regions. However, the alteration of the structural network remains unclear in PD patients. This study explored the specific characteristics of the structural brain [...] Read more.
Panic disorder (PD) is an anxiety disorder that impairs life quality and social function and is associated with distributed brain regions. However, the alteration of the structural network remains unclear in PD patients. This study explored the specific characteristics of the structural brain network in patients with PD by graph theory analysis of diffusion tensor images (DTI). A total of 81 PD patients and 48 matched healthy controls were recruited for this study. The structural networks were constructed, and the network topological properties for individuals were estimated. At the global level, the network efficiency was higher, while the shortest path length and clustering coefficient were lower in the PD group compared to the healthy control (HC) group. At the nodal level, the PD group showed a widespread higher nodal efficiency and lower average shortest path length in the prefrontal, sensorimotor, limbic, insula, and cerebellum regions. Overall, the current results showed that the alteration of information processing in the fear network might play a role in the pathophysiology of PD. Full article
(This article belongs to the Special Issue Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders)
Show Figures

Figure 1

12 pages, 1636 KiB  
Article
Altered Cerebral Blood Flow in the Progression of Chronic Kidney Disease
by Weizhao Lin, Mengchen Liu, Xixin Wu, Shandong Meng, Kanghui Yu, Huanhuan Su, Quanhai Liang, Feng Chen, Jincheng Li, Wenqin Xiao, Huangsheng Ling, Yunfan Wu and Guihua Jiang
J. Pers. Med. 2023, 13(1), 142; https://doi.org/10.3390/jpm13010142 - 11 Jan 2023
Cited by 2 | Viewed by 1412
Abstract
Background: In chronic kidney disease (CKD), cognitive impairment is a definite complication. However, the mechanisms of how CKD leads to cognitive impairment are not clearly known. Methods: Cerebral blood flow (CBF) information was collected from 37 patients with CKD (18 in stage 3; [...] Read more.
Background: In chronic kidney disease (CKD), cognitive impairment is a definite complication. However, the mechanisms of how CKD leads to cognitive impairment are not clearly known. Methods: Cerebral blood flow (CBF) information was collected from 37 patients with CKD (18 in stage 3; 19 in stage 4) and 31 healthy controls (HCs). For CKD patients, we also obtained laboratory results as well as neuropsychological tests. We conducted brain perfusion imaging studies using arterial spin labeling and calculated the relationship between regional CBF changes and various clinical indicators and neuropsychological tests. We also generated receiver operator characteristic (ROC) curves to explore whether CBF value changes in certain brain regions can be used to identify CKD. Results: Compared with HCs, CBF decreased in the right insula and increased in the left hippocampus in the CKD4 group; through partial correlation analysis, we found that CBF in the right insula was negatively correlated with the number connection test A (NCT-A) (r = −0.544, p = 0.024); CBF in the left hippocampus was positively correlated with blood urea nitrogen (r = 0.649, p = 0.005) and negatively correlated with serum calcium level (r = −0.646, p = 0.005). By comparing the ROC curve area, it demonstrated that altered CBF values in the right insula (AUC = 0.861, p < 0.01) and left hippocampus (AUC = 0.862, p < 0.01) have a good ability to identify CKD. Conclusions: Our study found that CBF alterations in the left hippocampus and the right insula brain of adult patients with stage 4 CKD were correlated with disease severity or laboratory indicators. These findings provide further insight into the relationship between altered cerebral perfusion and cognitive impairment in patients with non-end-stage CKD as well as, additional information the underlying neuropathophysiological mechanisms. Full article
(This article belongs to the Special Issue Brain Imaging and Personalized Medicine in Neuropsychiatric Disorders)
Show Figures

Figure 1

Back to TopTop