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Behavioral Sciences
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Molecular and Multi-Modals Biomarkers in Neurological Disorders: Relationship to Intervention
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Molecular and Multi-Modals Biomarkers in Neurological Disorders: Relationship to Intervention

A special issue of Behavioral Sciences (ISSN 2076-328X). This special issue belongs to the section "Experimental and Clinical Neurosciences".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 9712

Special Issue Editor

Prof. Dr. Maria de los Angeles Robinson Agramonte

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Guest Editor
International Center for Neurological Restoration, Havana, Cuba
Interests: neuroimmunology; neurodegenerations; demyelinating diseases; Multiple Sclerosis; COVID-19 pandemic; HcoV; SARS-CoV-2; virus neuroinvasion; stem cells; neuroinflammation; neurodevelopment disorders

Special Issue Information

Dear Colleagues,

This Special Issue aims to resume the main topics on biomarkers in neurodevelopment and neurodegenaratve disorders and others involving neuronflammation in their pathology. Topics include, biomarkers and interventions in diseases like multiple sclerosis, Parkinson disease and autism, neuropsychological markers as paraclinical tool to measure efficacy of intervention in these diseases as well as to express evidences of drug resistance from brain perfussion patterns. Clinical and paraclinical analysis arguing the immunological disturbs underlying brain damage and neural functional connectivity in chronic neurological diseases are also relevant to be proposed.

Prof. Dr. Maria de los Angeles Robinson Agramonte
Guest Editor

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Keywords

  • neurodevelopmental disorders, autism
  • demyelinating diseases
  • epilepsy
  • parkinson disease
  • neuropsychological markers
  • neuroimaging
  • multiple sclerosis
  • ataxy

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Published Papers (3 papers)

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Research

14 pages, 966 KiB  
Article
Clinical Phenotypes and Mortality Biomarkers: A Study Focused on COVID-19 Patients with Neurological Diseases in Intensive Care Units
by Lilia María Morales Chacón, Lídice Galán García, Tania Margarita Cruz Hernández, Nancy Pavón Fuentes, Carlos Maragoto Rizo, Ileana Morales Suarez, Odalys Morales Chacón, Elianne Abad Molina and Luisa Rocha Arrieta
Behav. Sci. 2022, 12(7), 234; https://doi.org/10.3390/bs12070234 - 15 Jul 2022
Cited by 2 | Viewed by 1842
Abstract
Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January [...] Read more.
Purpose: To identify clinical phenotypes and biomarkers for best mortality prediction considering age, symptoms and comorbidities in COVID-19 patients with chronic neurological diseases in intensive care units (ICUs). Subjects and Methods: Data included 1252 COVID-19 patients admitted to ICUs in Cuba between January and August 2021. A k-means algorithm based on unsupervised learning was used to identify clinical patterns related to symptoms, comorbidities and age. The Stable Sparse Classifiers procedure (SSC) was employed for predicting mortality. The classification performance was assessed using the area under the receiver operating curve (AUC). Results: Six phenotypes using a modified v-fold cross validation for the k-means algorithm were identified: phenotype class 1, mean age 72.3 years (ys)—hypertension and coronary artery disease, alongside typical COVID-19 symptoms; class 2, mean age 63 ys—asthma, cough and fever; class 3, mean age 74.5 ys—hypertension, diabetes and cough; class 4, mean age 67.8 ys—hypertension and no symptoms; class 5, mean age 53 ys—cough and no comorbidities; class 6, mean age 60 ys—without symptoms or comorbidities. The chronic neurological disease (CND) percentage was distributed in the six phenotypes, predominantly in phenotypes of classes 3 (24.72%) and 4 (35,39%); χ² (5) 11.0129 p = 0.051134. The cerebrovascular disease was concentrated in classes 3 and 4; χ² (5) = 36.63, p = 0.000001. The mortality rate totaled 325 (25.79%), of which 56 (17.23%) had chronic neurological diseases. The highest in-hospital mortality rates were found in phenotypes 1 (37.22%) and 3 (33.98%). The SSC revealed that a neurological symptom (ageusia), together with two neurological diseases (cerebrovascular disease and Parkinson’s disease), and in addition to ICU days, age and specific symptoms (fever, cough, dyspnea and chilliness) as well as particular comorbidities (hypertension, diabetes and asthma) indicated the best prediction performance (AUC = 0.67). Conclusions: The identification of clinical phenotypes and mortality biomarkers using practical variables and robust statistical methodologies make several noteworthy contributions to basic and experimental investigations for distinguishing the COVID-19 clinical spectrum and predicting mortality. Full article
(This article belongs to the Special Issue Molecular and Multi-Modals Biomarkers in Neurological Disorders: Relationship to Intervention)
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16 pages, 1557 KiB  
Article
Relation of Brain Perfusion Patterns to Sudden Unexpected Death Risk Stratification: A Study in Drug Resistant Focal Epilepsy
by Lilia Morales Chacon, Lidice Galan Garcia, Jorge Bosch-Bayard, Karla Batista García-Ramo, Margarita Minou Báez Martin, Maydelin Alfonso Alfonso, Sheyla Berrillo Batista, Tania de la Paz Bermudez, Judith González González and Abel Sánchez Coroneux
Behav. Sci. 2022, 12(7), 207; https://doi.org/10.3390/bs12070207 - 24 Jun 2022
Viewed by 2051
Abstract
To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology [...] Read more.
To explore the role of the interictal and ictal SPECT to identity functional neuroimaging biomarkers for SUDEP risk stratification in patients with drug-resistant focal epilepsy (DRFE). Twenty-nine interictal-ictal Single photon emission computed tomography (SPECT) scans were obtained from nine DRFE patients. A methodology for the relative quantification of cerebral blood flow of 74 cortical and sub-cortical structures was employed. The optimal number of clusters (K) was estimated using a modified v-fold cross-validation for the use of K means algorithm. The two regions of interest (ROIs) that represent the hypoperfused and hyperperfused areas were identified. To select the structures related to the SUDEP-7 inventory score, a data mining method that computes an automatic feature selection was used. During the interictal and ictal state, the hyperperfused ROIs in the largest part of patients were the bilateral rectus gyrus, putamen as well as globus pallidus ipsilateral to the seizure onset zone. The hypoperfused ROIs included the red nucleus, substantia nigra, medulla, and entorhinal area. The findings indicated that the nearly invariability in the perfusion pattern during the interictal to ictal transition observed in the ipsi-lateral putamen F = 12.60, p = 0.03, entorhinal area F = 25.80, p = 0.01, and temporal middle gyrus F = 12.60, p = 0.03 is a potential biomarker of SUDEP risk. The results presented in this paper allowed identifying hypo- and hyperperfused brain regions during the ictal and interictal state potentially related to SUDEP risk stratification. Full article
(This article belongs to the Special Issue Molecular and Multi-Modals Biomarkers in Neurological Disorders: Relationship to Intervention)
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18 pages, 5078 KiB  
Article
Sequential Semiology of Seizures and Brain Perfusion Patterns in Patients with Drug-Resistant Focal Epilepsies: A Perspective from Neural Networks
by Jorge L. Arocha Pérez, Lilia M. Morales Chacón, Karla Batista García Ramo and Lídice Galán García
Behav. Sci. 2022, 12(4), 107; https://doi.org/10.3390/bs12040107 - 14 Apr 2022
Cited by 3 | Viewed by 5327
Abstract
Ictal semiology and brain single-photon emission computed tomography have been performed in approaching the epileptogenic zone in drug-resistant focal epilepsies. The authors aim to describe the brain structures involved in the ictal and interictal epileptogenic network from sequential semiology and brain perfusion quantitative [...] Read more.
Ictal semiology and brain single-photon emission computed tomography have been performed in approaching the epileptogenic zone in drug-resistant focal epilepsies. The authors aim to describe the brain structures involved in the ictal and interictal epileptogenic network from sequential semiology and brain perfusion quantitative patterns analysis. A sequential representation of seizures was performed (n = 15). A two-level analysis (individual and global) was carried out for the analysis of brain perfusion quantification and estimating network structures from the perfusion indexes. Most of the subjects started with focal seizures without impaired consciousness, followed by staring, automatisms, language impairments and evolution to a bilateral tonic-clonic seizure (temporal lobe and posterior quadrant epilepsy). Frontal lobe epilepsy seizures continued with upper limb clonus and evolution to bilateral tonic-clonic. The perfusion index of the epileptogenic zone ranged between 0.439–1.362 (mesial and lateral structures), 0.826–1.266 in dorsolateral frontal structures and 0.678–1.507 in the occipital gyrus. The interictal epileptogenic network proposed involved the brainstem and other subcortical structures. For the ictal state, it included the rectus gyrus, putamen and cuneus. The proposed methodology provides information about the brain structures in the neural networks in patients with drug-resistant focal epilepsies. Full article
(This article belongs to the Special Issue Molecular and Multi-Modals Biomarkers in Neurological Disorders: Relationship to Intervention)
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