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Brain Sciences
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Challenges and Perspectives of Neurological Disorders: Series II
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Challenges and Perspectives of Neurological Disorders: Series II

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

Deadline for manuscript submissions: 21 March 2025 | Viewed by 9538

Special Issue Editors

Dr. Woon-Man Kung

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Guest Editor
1. Department of Exercise and Health Promotion, College of Kinesiology and Health, Chinese Culture University, Taipei, Taiwan
2. Division of Neurosurgery, Department of Surgery, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan
Interests: neurosurgery; neurological disorders; brain; dementia; clinical neuroscience; neuroimaging; neuroinformatics; neuroepidemiology; neuropharmacology; evidence-based medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Dina Nur Anggraini Ningrum

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Guest Editor
Public Health Department, Faculty of Medicine, Universitas Negeri Semarang (UNNES), Semarang, Indonesia
Interests: biomedical informatics; health informatics; health information system; public health; epidemiology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neurological disorders are increasingly recognized as one of the most prevalent disorders worldwide, with a high burden on patients, families, and society as a whole. Neurological disorders can affect the brain, spinal cord, and other nerves throughout the body. The results can be upsetting or even detrimentally devastating. There are many different etiologies per se. Neurological disorders refer not only to common neurodegenerative diseases, such as neurovascular disorders, neuro-oncological diseases, neuroinflammation, and infection, as well as traumatic brain/spinal cord injuries, but also to a category of other less common pathogeneses.

The burden of neurological disorders has increased in the past and is likely to increase in the future, due to the aging population worldwide, thus placing an increasing demand on the already overstretched resources and services for patients with neurological disorders. There is an urgent need to improve the prevention and management of neurological disorders across the globe.

We are excited to announce the call for papers for the second series of our Special Issue “Challenges and Perspectives of Neurological Disorders: Series II”. This series continues our commitment to gather and showcase the latest research, innovations, and challenges in the field of neurological disorders. It facilitates a thorough understanding of neurological disorders, which builds upon the foundation and impact laid by the first series. In this second series, we are particularly interested in papers that build upon the work presented in the first series, offering new perspectives, addressing unresolved challenges, and presenting innovative solutions.

Dr. Woon-Man Kung
Dr. Dina Nur Anggraini Ningrum
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

  • nervous system disease
  • prevention and control
  • diagnosis and therapy
  • neurobiology
  • neuroinflammation
  • biomarker
  • neuroimaging
  • neuropharmacology
  • neurosurgery
  • neuroinformatics

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Published Papers (5 papers)

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22 pages, 5766 KiB  
Article
Machine-Learning-Based Depression Detection Model from Electroencephalograph (EEG) Data Obtained by Consumer-Grade EEG Device
by Kei Suzuki, Tipporn Laohakangvalvit and Midori Sugaya
Brain Sci. 2024, 14(11), 1107; https://doi.org/10.3390/brainsci14111107 - 30 Oct 2024
Viewed by 1349
Abstract
Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, [...] Read more.
Background/Objectives: There have been attempts to detect depression using medical-grade electroencephalograph (EEG) data based on a machine learning approach. EEG has garnered interest as a method for assessing brainwaves by attaching electrodes to the scalp to obtain electrical activity in the brain. Recently, machine learning has been applied to the EEG data to detect depression, with encouraging results. Specifically, studies using medical-grade EEG data have shown that depression can be accurately detected. However, there is a need to expand the range of applications by achieving a score with machine learning using simpler consumer-grade brain wave sensors. At present, a sufficient score has not been achieved.; Methods: To improve the score of depression detection, we quantified various EEG indices to train models such as power spectrum, asymmetry, complexity, and functional connectivity. In addition, feature selection was performed to ensure that the model learns only promising EEG indices for depression detection. The feature selection methods were Light Gradient Boosting Machine (LightGBM) feature importance, mutual information, ReliefF and ElasticNet coefficients. The selected EEG indices were learned by the LightGBM model, which is reported to be as accurate as the latest deep learning models. In cross-validation, the independence of test and training data was ensured to avoid excessively calculated score; Results: The results showed that the Macro F1 score was 91.59%, suggesting that a consumer-grade EEG can detect depression. In addition, analysis of the EEG indices selected by feature selection indicated that the Macro F1 score was about 80% for single EEG indices such as differential entropy in the frequency band β and functional connectivity in the left frontal region in the frequency band 1–128 Hz; Conclusions: Although the data were obtained from a consumer-grade EEG, the results suggest that these EEG indices are promising for detection depression. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Neurological Disorders: Series II)
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12 pages, 745 KiB  
Article
Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism
by Ronghua Ling, Min Wang, Jiaying Lu, Shaoyou Wu, Ping Wu, Jingjie Ge, Luyao Wang, Yingqian Liu, Juanjuan Jiang, Kuangyu Shi, Zhuangzhi Yan, Chuantao Zuo and Jiehui Jiang
Brain Sci. 2024, 14(7), 680; https://doi.org/10.3390/brainsci14070680 - 4 Jul 2024
Viewed by 1729
Abstract
The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 [...] Read more.
The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson’s disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Neurological Disorders: Series II)
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12 pages, 3440 KiB  
Article
Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer’s Disease Continuum
by Min Wang, Min Wei, Luyao Wang, Jun Song, Axel Rominger, Kuangyu Shi and Jiehui Jiang
Brain Sci. 2024, 14(6), 575; https://doi.org/10.3390/brainsci14060575 - 4 Jun 2024
Viewed by 1573
Abstract
Clinical cognitive advancement within the Alzheimer’s disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear [...] Read more.
Clinical cognitive advancement within the Alzheimer’s disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls (p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment (r = 0.726, p < 0.001), AD (r = 0.845, p < 0.001), and AD continuum (r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Neurological Disorders: Series II)
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9 pages, 487 KiB  
Article
The Effects of Traditional Chinese Herbal Dietary Formula on the Ability of Daily Life and Physical Function in Elderly Patients with Mild Cognitive Impairment
by Xiaofan Xu, Dan Shi, Yuchen Chen, Luyao Wang, Jiehui Jiang and Shuyun Xiao
Brain Sci. 2024, 14(4), 333; https://doi.org/10.3390/brainsci14040333 - 29 Mar 2024
Viewed by 1931
Abstract
We aimed to examine the association of traditional Chinese herbal dietary formulas with ability of daily life and physical function in elderly patients with mild cognitive impairment. The current study included 60 cases of elderly patients with mild cognitive impairment from Yueyang Hospital [...] Read more.
We aimed to examine the association of traditional Chinese herbal dietary formulas with ability of daily life and physical function in elderly patients with mild cognitive impairment. The current study included 60 cases of elderly patients with mild cognitive impairment from Yueyang Hospital of Integrated Traditional Chinese Medicine and Western Medicine, Shanghai University of Traditional Chinese Medicine and Hongkou District, Shanghai. The participants were randomly divided into two groups: group A (herbal dietary formula group, consisting of Alpiniae Oxyphyllae Fructus, Nelumbinis plumula, Chinese Yam, Poria cocos, and Jineijin), 30 cases, and group B (vitamin E), 30 cases, treatment for 3 months. Cognitive function was measured using the Montreal Cognitive Assessment (MOCA) and Mini-Mental State Examination (MMSE); body function was measured using the Chinese Simplified Physical Performance Test (CMPPT), including stand static balance, sitting-up timing, squat timing, and six-meter walk timing. Daily life based on ability was measured by grip strength and the Activity of Daily Living Scale (ADL). The lower the scores of the above items, the poorer the disease degree, except for ADL: the lower the score, the higher the self-care ability. After 3 months of treatment, the two-handed grip strength of both the herbal dietary formula group and vitamin E group increased; the ADL, sitting-up timing, squatting timing, and six-meter walking timing decreased after medication, being statistically significantly different (p < 0.05). The two-handed grip strength of group A increased significantly, and the ADL, sitting-up timing, squatting timing, and six-meter walking timing decreased distinctly compared with the vitamin E group. There was a statistically significant difference (p < 0.05). The scores of MMSE, MOCA, total CMPPT, and standing static balance of the herbal dietary formula group increased after medication. The difference was statistically significant (p < 0.05). The vitamin E group’s MMSE and MOCA scores, CMPPT total scores, and standing resting balance scores did not change significantly after medication (p > 0.05). In summary, a traditional Chinese herbal dietary formula can improve body and cognitive function in patients with MCI, and the curative effect is better than that of vitamin E. Traditional Chinese herbal dietary formulas can improve the daily life quality of MCI patients, which has clinical application value. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Neurological Disorders: Series II)
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14 pages, 1241 KiB  
Article
APOEε4 Carriers Exhibit Objective Cognitive Deficits: A Cross-Sectional Study in a Single Center Trial
by Yanfang Zeng, Wenying Du, Mingkai Zhang, Ariel Walker, Ying Han and Yuchuan Ding
Brain Sci. 2024, 14(3), 281; https://doi.org/10.3390/brainsci14030281 - 15 Mar 2024
Viewed by 2131
Abstract
Objective: To explore the association between the apolipoprotein E (APOE) genotype and objectively assessed cognitive function. Methods: In this cross-sectional study, 537 participants underwent a neuropsychological assessment for cognitive function and blood testing for APOE genotype. Based on cognitive test results, participants were [...] Read more.
Objective: To explore the association between the apolipoprotein E (APOE) genotype and objectively assessed cognitive function. Methods: In this cross-sectional study, 537 participants underwent a neuropsychological assessment for cognitive function and blood testing for APOE genotype. Based on cognitive test results, participants were stratified into two cohorts: Cognitively Unimpaired participants (CU) and Cognitively Impaired participants (CI). The CI group was further divided into Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Furthermore, we conducted age stratification, categorizing participants into three age groups: age 1: <65 years, age 2: 65–75 years, and age 3: >75 years. We assessed the disparities in cognitive function associated with ε4 carrier status across different age brackets. Plasma amyloid-β levels were measured in a cohort of 294 participants to investigate potential interactions involving ε4 carrier status, diagnosis, sex, or plasma markers. Results: The APOE genotypic distribution among the 537 participants was characterized as follows: ε2/ε2 (5 participants), ε2/ε3 (67), ε2/ε4 (13), ε3/ε3 (330), ε3/ε4 (113), and ε4/ε4 (9). Allele frequencies were: ε3 at 78.21%, ε4 at 13.41%, and ε2 at 8.38%. Notably, the ε4 carrier frequency was markedly elevated in the AD group at 81.8% when compared to MCI at 32.8% and CU at 21.3% (p < 0.05). Within the Cognitively Unimpaired (CU) cohort, the sole discernible contrast between ε4+ and ε4− emerged in STT-B (p < 0.05). Within the CI group, ε4 carriers showed statistically poorer scores as compared to non-ε4 carriers in several cognitive tests (p < 0.05). Age stratification result revealed that, among ε4 carriers, cognitive function scores within the age 3 group were significantly inferior to those of age 1 and age 2 groups (p < 0.05). Plasma amyloid-β detection was applied to the 294 participants. We tested plasma amyloid-β (Aβ42) and plasma amyloid-β (Aβ40) levels and calculated the Aβ42/Aβ40 ratio. We found that among female ε4 carriers, both Aβ42 and the Aβ42/Aβ40 ratio were notably lower than their male counterparts (p < 0.05). Conclusions: The ε3/ε3 was the most prevalent among participants, succeeded by ε3/ε4 and ε2/ε3. The least prevalent were ε2/ε4, ε4/ε4, and ε2/ε2 genotypes. The ε3 was predominant, followed by the ε4 and ε2. Individuals with the ε4 allele exhibited significant cognitive impairment, with an especially high prevalence in AD group at 81.8%. The study unveils a pronounced correlation between the ε4 allele and cognitive deficits, implying its potential role in the advancement and severity of cognitive disorders, notably Alzheimer’s disease. Cognitive function declines with age in individuals carrying the ε4, and women are more affected by ε4. Full article
(This article belongs to the Special Issue Challenges and Perspectives of Neurological Disorders: Series II)
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