Special Issue "Biomarkers in the Clinical Diagnostic of Central Nervous System Diseases"

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Clinical Neurology".

Deadline for manuscript submissions: 20 June 2023 | Viewed by 994

Special Issue Editor

IRCCS Centro Neurolesi Bonino Pulejo, Messina, Italy
Interests: voxel based morphometric (VBM); funcitional magnetic resonance imaging (fMRI); diffusion tensor imaging (DTI); high density electroencephalography (HD-EEG); near infrared spectroscopy (NIRS); neuropsychology; mild cognitive impairment (MCI); Alzheimer disease (AD); disorders of consciousness (DOC)
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Special Issue Information

Dear Colleagues,

Tools for the diagnosis of neurological disease used in current clinical practice, such as cognitive psychometric tests, clinical scores and morphological/structural examinations of the brain, do not always unequivocally discriminate early-stage pathological scenarios from normal aging and do not have the same accuracy as the analysis of hierarchically more complex aspects such as acquired brain connectivity by neuroimaging techniques.

The identification of sensitive instrumental markers for neurological disease is a goal of the greatest importance.

This Special Issue is a timely collection of papers that review and propose new techniques to detect and predict neurological disease (mild cognitive impairment (MCI), Alzheimer disease (AD), disorders of consciousness (DOCs)) and to evaluate the efficacy of therapies. We aim to discuss the key options for neurological diseases and state-of-the-art treatment. Moreover, there is still the urgent need to find measures to prevent neurons from injury or degeneration, and ideally to keep neuronal and glial damage under the threshold of symptom manifestation.

We will further discuss the most promising and intriguing future treatment options with a focus on methodology and neurorehabilitation strategies.

Dr. Lilla Bonanno
Guest Editor

Manuscript Submission Information

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Keywords

  • neurological diseases
  • neurorehabilitation
  • mathematical modeling
  • EEG-HD
  • fMRI
  • NIRS
  • VBM
  • algorithm

Published Papers (1 paper)

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Research

Article
Identification of Functional Cortical Plasticity in Children with Cerebral Palsy Associated to Robotic-Assisted Gait Training: An fNIRS Study
J. Clin. Med. 2022, 11(22), 6790; https://doi.org/10.3390/jcm11226790 - 16 Nov 2022
Cited by 3 | Viewed by 740
Abstract
Cerebral palsy (CP) is a non-progressive neurologic condition that causes gait limitations, spasticity, and impaired balance and coordination. Robotic-assisted gait training (RAGT) has become a common rehabilitation tool employed to improve the gait pattern of people with neurological impairments. However, few studies have [...] Read more.
Cerebral palsy (CP) is a non-progressive neurologic condition that causes gait limitations, spasticity, and impaired balance and coordination. Robotic-assisted gait training (RAGT) has become a common rehabilitation tool employed to improve the gait pattern of people with neurological impairments. However, few studies have demonstrated the effectiveness of RAGT in children with CP and its neurological effects through portable neuroimaging techniques, such as functional near-infrared spectroscopy (fNIRS). The aim of the study is to evaluate the neurophysiological processes elicited by RAGT in children with CP through fNIRS, which was acquired during three sessions in one month. The repeated measure ANOVA was applied to the β-values delivered by the General Linear Model (GLM) analysis used for fNIRS data analysis, showing significant differences in the activation of both prefrontal cortex (F (1.652, 6.606) = 7.638; p = 0.022), and sensorimotor cortex (F (1.294, 5.175) = 11.92; p = 0.014) during the different RAGT sessions. In addition, a cross-validated Machine Learning (ML) framework was implemented to estimate the gross motor function measure (GMFM-88) from the GLM β-values, obtaining an estimation with a correlation coefficient r = 0.78. This approach can be used to tailor clinical treatment to each child, improving the effectiveness of rehabilitation for children with CP. Full article
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