Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.9
3.3
Journals
Brain Sciences
Special Issues
Biomarkers Identification for Neurological Diseases and Neurorehabilitation
share announcement

Biomarkers Identification for Neurological Diseases and Neurorehabilitation

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

Deadline for manuscript submissions: 21 August 2024 | Viewed by 5722

Special Issue Editors

Dr. Cota Navin Gupta

E-Mail Website
Guest Editor
Neural Engineering Lab, Department of Biosciences and Bioengineering (BSBE), Institute of Technology Guwahati, Guwahati 781039, Assam, India
Interests: brain computer interfaces and imaging genetics
Dr. Shyamanta M. Hazarika

E-Mail Website
Guest Editor
Biomimetic Robotics and Artificial Intelligence Lab, Department of Mechanical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India
Interests: robotic neurorehabilitation; cognitive systems; knowledge representation and reasoning

Special Issue Information

Dear Colleagues,

Biomarkers refer to a quantifiable biological parameter that is measured as an indicator of normal biological or pathogenic processes and holds promise for translational research. There is growing interest in biomarker identification. This research topic’s goal is two-fold, namely "Biomarkers Identification for Neurological Diseases and Neurorehabilitation".

The identification of biomarkers in psychiatry is essential for the diagnosis and treatment of each patient. When compared to other diseases, mental illnesses present the peculiarity to be classified by diagnostic categories with a broad and variable list of symptoms. Comprehensive understanding of neuropsychiatric disorders from multiple scales and dimensions is now possible with neuroimaging modalities like high spatial resolution MRI, high temporal resolution EEG/MEG and ecologically valid near-infrared spectroscopy (NIRS). These provide reliable, data-driven methods for exploring brain structure and function. In this respect, the identification of biomarkers in psychiatry allows clinicians to stratify healthy and diseased individuals as well as groups within disease (i.e., subgrouping), which in turn may lead to more focused treatment options. We welcome original and review articles related to novel data-driven methodologies and innovative investigation paradigms into a multi-scale, multidimensional characterization of the neuropsychiatric diseases/disorders (like Parkinson’s disease, depression, anxiety disorder, obsessive-compulsive disorder, schizophrenia, Alzheimer’s disease, etc.), as well as their potential clinical applications. Brain biomarkers include the assessment of cognitive workload in healthy human beings during various tasks. Digital biomarkers of mental health, created using data extracted from everyday technologies like smartphones and wearable devices are also of interest.

Delivering assistance-as-required remains one of the major challenges for neurorehabilitation. Robot-assisted interventions hold promise not only for objective and quantifiable assessment of motor performance but also retention of residual skills. Driven by advances in non-invasive signal processing and insights into neuroscience, parameters linked to brain plasticity as well as neurophysiological parameters linked to motor learning are increasingly being explored as biomarkers for neurorehabilitation. We welcome original and review articles related to biomarkers for robotic neurorehabilitation. We are particularly interested in biomarkers that are being investigated with examples of their current and/or potential applications in robotic neurorehabilitation.

Dr. Cota Navin Gupta
Dr. Shyamanta M. Hazarika
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

  • biomarkers
  • neurological diseases
  • psychiatry
  • Parkinson’s disease
  • Alzheimer’s disease
  • neurorehabilitation

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

14 pages, 1043 KiB  
Article
The Prognostic Role of Candidate Serum Biomarkers in the Post-Acute and Chronic Phases of Disorder of Consciousness: A Preliminary Study
by Rita Formisano, Mariagrazia D’Ippolito, Marco Giustini, Sheila Catani, Stefania Mondello, Iliana Piccolino, Filomena Iannuzzi, Kevin K. Wang and Ronald L. Hayes
Brain Sci. 2024, 14(3), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14030239 - 29 Feb 2024
Viewed by 850
Abstract
Introduction: Serum biomarkers, such as Neurofilament Light (NF-L), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase (UCH-L1), and Total-tau (T-Tau) have been proposed for outcome prediction in the acute phase of severe traumatic brain injury, but they have been less investigated in patients [...] Read more.
Introduction: Serum biomarkers, such as Neurofilament Light (NF-L), Glial Fibrillary Acidic Protein (GFAP), Ubiquitin C-terminal Hydrolase (UCH-L1), and Total-tau (T-Tau) have been proposed for outcome prediction in the acute phase of severe traumatic brain injury, but they have been less investigated in patients with prolonged DoC (p-DoC). Methods: We enrolled 25 p-DoC patients according to the Coma Recovery Scale-Revised (CRS-R). We identified different time points: injury onset (t0), first blood sampling at admission in Neurorehabilitation (t1), and second blood sampling at discharge (t2). Patients were split into improved (improved level of consciousness from t1 to t2) and not-improved (unchanged or worsened level of consciousness from t1 to t2). Results: All biomarker levels decreased over time, even though each biomarker reveals typical features. Serum GFAP showed a weak correlation between t1 and t2 (p = 0.001), while no correlation was observed for serum NF-L (p = 0.955), UCH-L1 (p = 0.693), and T-Tau (p = 0.535) between t1 and t2. Improved patients showed a significant decrease in the level of NF-L (p = 0.0001), UCH-L1 (p = 0.001), and T-Tau (p = 0.002), but not for serum GFAP (p = 0.283). No significant statistical differences were observed in the not-improved group. Conclusions: A significant correlation was found between the level of consciousness improvement and decreased NF-L, UCH-L1, and T-Tau levels. Future studies on the association of serum biomarkers with neurophysiological and neuroimaging prognostic indicators are recommended. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
►▼ Show Figures

Figure 1

15 pages, 4621 KiB  
Article
Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals Using Structural MRI
by Tanmayee Samantaray, Utsav Gupta, Jitender Saini and Cota Navin Gupta
Brain Sci. 2023, 13(9), 1297; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci13091297 - 08 Sep 2023
Viewed by 1330
Abstract
We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson’s disease (PD) patients and 70 healthy controls (HC) from the National Institute [...] Read more.
We propose a novel algorithm called Unique Brain Network Identification Number (UBNIN) for encoding the brain networks of individual subjects. To realize this objective, we employed structural MRI on 180 Parkinson’s disease (PD) patients and 70 healthy controls (HC) from the National Institute of Mental Health and Neurosciences, India. We parcellated each subject’s brain volume and constructed an individual adjacency matrix using the correlation between the gray matter volumes of every pair of regions. The unique code is derived from values representing connections for every node (i), weighted by a factor of 2−(i−1). The numerical representation (UBNIN) was observed to be distinct for each individual brain network, which may also be applied to other neuroimaging modalities. UBNIN ranges observed for PD were 15,360 to 17,768,936,615,460,608, and HC ranges were 12,288 to 17,733,751,438,064,640. This model may be implemented as a neural signature of a person’s unique brain connectivity, thereby making it useful for brainprinting applications. Additionally, we segregated the above datasets into five age cohorts: A: ≤32 years (n1 = 4, n2 = 5), B: 33–42 years (n1 = 18, n2 = 14), C: 43–52 years (n1 = 42, n2 = 23), D: 53–62 years (n1 = 69, n2 = 22), and E: ≥63 years (n1 = 46, n2 = 6), where n1 and n2 are the number of individuals in PD and HC, respectively, to study the variation in network topology over age. Sparsity was adopted as the threshold estimate to binarize each age-based correlation matrix. Connectivity metrics were obtained using Brain Connectivity toolbox (Version 2019-03-03)-based MATLAB (R2020a) functions. For each age cohort, a decreasing trend was observed in the mean clustering coefficient with increasing sparsity. Significantly different clustering coefficients were noted in PD between age-cohort B and C (sparsity: 0.63, 0.66), C and E (sparsity: 0.66, 0.69), and in HC between E and B (sparsity: 0.75 and above 0.81), E and C (sparsity above 0.78), E and D (sparsity above 0.84), and C and D (sparsity: 0.9). Our findings suggest network connectivity patterns change with age, indicating network disruption may be due to the underlying neuropathology. Varying clustering coefficients for different cohorts indicate that information transfer between neighboring nodes changes with age. This provides evidence of age-related brain shrinkage and network degeneration. We also discuss limitations and provide an open-access link to software codes and a help file for the entire study. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
►▼ Show Figures

Graphical abstract

8 pages, 650 KiB  
Article
Metalloproteinases and Tissue Inhibitors in Generalized Myasthenia Gravis. A Preliminary Study
by Vincenzo Di Stefano, Chiara Tubiolo, Andrea Gagliardo, Rosalia Lo Presti, Maria Montana, Massimiliano Todisco, Antonino Lupica, Gregorio Caimi, Cristina Tassorelli, Brigida Fierro, Filippo Brighina and Giuseppe Cosentino
Brain Sci. 2022, 12(11), 1439; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci12111439 - 26 Oct 2022
Cited by 3 | Viewed by 1203
Abstract
Introduction: Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) have recently been proposed as promising biomarkers in different immune-mediated disorders. We evaluated the plasma levels of MMP-9 and MMP-2 and their tissue inhibitors TIMP-1 and TIMP-2 in a patients’ cohort with generalized [...] Read more.
Introduction: Matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) have recently been proposed as promising biomarkers in different immune-mediated disorders. We evaluated the plasma levels of MMP-9 and MMP-2 and their tissue inhibitors TIMP-1 and TIMP-2 in a patients’ cohort with generalized myasthenia gravis (MG). Methods: Plasma concentrations of MMP-9, MMP-2, TIMP-1 and TIMP-2 were evaluated in 14 patients with generalized MG and 13 age- and sex-matched healthy controls. The severity of disease was assessed by the modified Osserman classification. Results: Compared to the healthy subjects, MG patients had increased plasma concentrations of MMP-9, but reduced plasma levels of MMP-2 and TIMP-1. MG patients also showed a positive correlation between MMP-2 concentrations and disease severity. An increase in MMP-9 levels and MMP-9/TIMP-1 ratio and a decrease in MMP-2 levels and MMP-2/TIMP-2 ratio were detected in patients with generalized MG. Higher levels of MMP-2 correlated with greater disease severity. Discussion: Our preliminary findings suggest that MMPs and TIMPs could play a role in the pathogenesis of MG and might be associated with the risk of clinical deterioration. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
►▼ Show Figures

Figure 1

Other

Jump to: Research

20 pages, 823 KiB  
Systematic Review
Effects and Mechanisms of Exercise on Brain-Derived Neurotrophic Factor (BDNF) Levels and Clinical Outcomes in People with Parkinson’s Disease: A Systematic Review and Meta-Analysis
by Daan G. M. Kaagman, Erwin E. H. van Wegen, Natalie Cignetti, Emily Rothermel, Tim Vanbellingen and Mark A. Hirsch
Brain Sci. 2024, 14(3), 194; https://0-doi-org.brum.beds.ac.uk/10.3390/brainsci14030194 - 21 Feb 2024
Viewed by 1313
Abstract
Introduction: Exercise therapy may increase brain-derived neurotrophic factor (BDNF) levels and improve clinical outcomes in people living with Parkinson’s disease (PD). This systematic review was performed to investigate the effect of exercise therapy on BDNF levels and clinical outcomes in human PD and [...] Read more.
Introduction: Exercise therapy may increase brain-derived neurotrophic factor (BDNF) levels and improve clinical outcomes in people living with Parkinson’s disease (PD). This systematic review was performed to investigate the effect of exercise therapy on BDNF levels and clinical outcomes in human PD and to discuss mechanisms proposed by authors. Method: A search on the literature was performed on PubMed up to December 2023 using the following key words: Parkinson’s disease AND exercise, exercise therapy, neurological rehabilitation AND brain-derived neurotrophic factor, brain-derived neurotrophic factor/blood, brain-derived neurotrophic factor/cerebrospinal fluid AND randomized clinical trial, intervention study. Only randomized clinical trials comparing an exercise intervention to treatment as usual, usual care (UC), sham intervention, or no intervention were included. Results: A meta-analysis of BDNF outcomes with pooled data from five trials (N = 216 participants) resulted in a significant standardized mean difference (SMD) of 1.20 [95% CI 0.53 to 1.87; Z = 3.52, p = 0.0004, I2 = 77%], favoring exercise using motorized treadmill, Speedflex machine, rowing machine, and non-specified exercise. Significant improvements were found in Unified Parkinson’s Disease Rating Scale (UPDRS), UPDRS-III, 6 Minute Walk Test (6MWT), and Berg Balance Scale (BBS). Methodological quality of trials was categorized as “good” in three trials, “fair” in one trial, and “poor” in one trial. Conclusion: Key results of this systematic review are that exercise therapy is effective in raising serum BDNF levels and seems effective in alleviating PD motor symptoms. Exercise therapy confers neuroplastic effects on Parkinson brain, mediated, in part, by BDNF. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop