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Brain Sciences
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Biomarkers Identification for Neurological Diseases and Neurorehabilitation
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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: 24 February 2025 | Viewed by 15466

Special Issue Editors

Dr. Cota Navin Gupta

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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

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Keywords

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

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Published Papers (7 papers)

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Research

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24 pages, 4391 KiB  
Article
A Computational Model of Deep Brain Stimulation for Parkinson’s Disease Tremor and Bradykinesia
by Sandeep Sathyanandan Nair and Srinivasa Chakravarthy
Brain Sci. 2024, 14(6), 620; https://doi.org/10.3390/brainsci14060620 - 20 Jun 2024
Viewed by 1745
Abstract
Parkinson’s disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions, and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) [...] Read more.
Parkinson’s disease (PD) is a progressive neurological disorder that is typically characterized by a range of motor dysfunctions, and its impact extends beyond physical abnormalities into emotional well-being and cognitive symptoms. The loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) leads to an array of dysfunctions in the functioning of the basal ganglia (BG) circuitry that manifests into PD. While active research is being carried out to find the root cause of SNc cell death, various therapeutic techniques are used to manage the symptoms of PD. The most common approach in managing the symptoms is replenishing the lost dopamine in the form of taking dopaminergic medications such as levodopa, despite its long-term complications. Another commonly used intervention for PD is deep brain stimulation (DBS). DBS is most commonly used when levodopa medication efficacy is reduced, and, in combination with levodopa medication, it helps reduce the required dosage of medication, prolonging the therapeutic effect. DBS is also a first choice option when motor complications such as dyskinesia emerge as a side effect of medication. Several studies have also reported that though DBS is found to be effective in suppressing severe motor symptoms such as tremors and rigidity, it has an adverse effect on cognitive capabilities. Henceforth, it is important to understand the exact mechanism of DBS in alleviating motor symptoms. A computational model of DBS stimulation for motor symptoms will offer great insights into understanding the mechanisms underlying DBS, and, along this line, in our current study, we modeled a cortico-basal ganglia circuitry of arm reaching, where we simulated healthy control (HC) and PD symptoms as well as the DBS effect on PD tremor and bradykinesia. Our modeling results reveal that PD tremors are more correlated with the theta band, while bradykinesia is more correlated with the beta band of the frequency spectrum of the local field potential (LFP) of the subthalamic nucleus (STN) neurons. With a DBS current of 220 pA, 130 Hz, and a 100 microsecond pulse-width, we could found the maximum therapeutic effect for the pathological dynamics simulated using our model using a set of parameter values. However, the exact DBS characteristics vary from patient to patient, and this can be further studied by exploring the model parameter space. This model can be extended to study different DBS targets and accommodate cognitive dynamics in the future to study the impact of DBS on cognitive symptoms and thereby optimize the parameters to produce optimal performance effects across modalities. Combining DBS with rehabilitation is another frontier where DBS can reduce symptoms such as tremors and rigidity, enabling patients to participate in their therapy. With DBS providing instant relief to patients, a combination of DBS and rehabilitation can enhance neural plasticity. One of the key motivations behind combining DBS with rehabilitation is to expect comparable results in motor performance even with milder DBS currents. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
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13 pages, 1253 KiB  
Article
Prefrontal Cortex Responses to Social Video Stimuli in Young Children with and without Autism Spectrum Disorder
by Candida Barreto, Adrian Curtin, Yigit Topoglu, Jessica Day-Watkins, Brigid Garvin, Grant Foster, Zuhal Ormanoglu, Elisabeth Sheridan, James Connell, David Bennett, Karen Heffler and Hasan Ayaz
Brain Sci. 2024, 14(5), 503; https://doi.org/10.3390/brainsci14050503 - 16 May 2024
Viewed by 1939
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social [...] Read more.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
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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://doi.org/10.3390/brainsci14030239 - 29 Feb 2024
Viewed by 1595
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)
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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://doi.org/10.3390/brainsci13091297 - 8 Sep 2023
Cited by 3 | Viewed by 1880
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)
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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://doi.org/10.3390/brainsci12111439 - 26 Oct 2022
Cited by 3 | Viewed by 1546
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)
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Review

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27 pages, 2902 KiB  
Review
Alzheimer’s Disease: Understanding Motor Impairments
by Jesús Andrade-Guerrero, Humberto Martínez-Orozco, Marcos M. Villegas-Rojas, Alberto Santiago-Balmaseda, Karen M. Delgado-Minjares, Isaac Pérez-Segura, Mauricio T. Baéz-Cortés, Miguel A. Del Toro-Colin, Magdalena Guerra-Crespo, Oscar Arias-Carrión, Sofía Diaz-Cintra and Luis O. Soto-Rojas
Brain Sci. 2024, 14(11), 1054; https://doi.org/10.3390/brainsci14111054 - 24 Oct 2024
Viewed by 827
Abstract
Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder and the leading cause of dementia worldwide, profoundly impacts health and quality of life. While cognitive impairments—such as memory loss, attention deficits, and disorientation—predominate in AD, motor symptoms, though common, remain underexplored. These motor symptoms, [...] Read more.
Alzheimer’s disease (AD), the most prevalent neurodegenerative disorder and the leading cause of dementia worldwide, profoundly impacts health and quality of life. While cognitive impairments—such as memory loss, attention deficits, and disorientation—predominate in AD, motor symptoms, though common, remain underexplored. These motor symptoms, including gait disturbances, reduced cardiorespiratory fitness, muscle weakness, sarcopenia, and impaired balance, are often associated with advanced stages of AD and contribute to increased mortality. Emerging evidence, however, suggests that motor symptoms may be present in earlier stages and can serve as predictive markers for AD in older adults. Despite a limited understanding of the underlying mechanisms driving these motor symptoms, several key pathways have been identified, offering avenues for further investigation. This review provides an in-depth analysis of motor symptoms in AD, discussing its progression, potential mechanisms, and therapeutic strategies. Addressing motor symptoms alongside cognitive decline may enhance patient functionality, improve quality of life, and support more comprehensive disease management strategies. Full article
(This article belongs to the Special Issue Biomarkers Identification for Neurological Diseases and Neurorehabilitation)
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Other

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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://doi.org/10.3390/brainsci14030194 - 21 Feb 2024
Cited by 6 | Viewed by 4352
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)
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