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Shahzad MN, Ali H. Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data. Psychiatry Res Neuroimaging 2024; 343:111845. [PMID: 38908302 DOI: 10.1016/j.pscychresns.2024.111845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 05/26/2024] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
BACKGROUND The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpose, the present study aims to classify individuals with PTSD versus healthy control. METHODS The resting-state functional MRI (rs-fMRI) scans of 19 PTSD and 24 healthy control male subjects have been used to identify the activation pattern in most affected brain regions using group-level independent component analysis (ICA) and t-test. To classify PTSD-affected subjects from healthy control six machine learning techniques including random forest, Naive Bayes, support vector machine, decision tree, K-nearest neighbor, linear discriminant analysis, and deep learning three-dimensional 3D-CNN have been performed on the data and compared. RESULTS The rs-fMRI scans of the most commonly investigated 11 regions of trauma-exposed and healthy brains are analyzed to observe their level of activation. Amygdala and insula regions are determined as the most activated regions from the regions-of-interest in the brain of PTSD subjects. In addition, machine learning techniques have been applied to the components extracted from ICA but the models provided low classification accuracy. The ICA components are also fed into the 3D-CNN model, which is trained with a 5-fold cross-validation method. The 3D-CNN model demonstrated high accuracies, such as 98.12%, 98.25 %, and 98.00 % on average with training, validation, and testing datasets, respectively. CONCLUSION The findings indicate that 3D-CNN is a surpassing method than the other six considered techniques and it helps to recognize PTSD patients accurately.
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Affiliation(s)
| | - Haider Ali
- Department of Statistics, University of Gujrat, Gujrat, Pakistan
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2
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Bacon EJ, He D, Achi NAD, Wang L, Li H, Yao-Digba PDZ, Monkam P, Qi S. Neuroimage analysis using artificial intelligence approaches: a systematic review. Med Biol Eng Comput 2024; 62:2599-2627. [PMID: 38664348 DOI: 10.1007/s11517-024-03097-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 04/14/2024] [Indexed: 08/18/2024]
Abstract
In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.
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Affiliation(s)
- Eric Jacob Bacon
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China
| | - Dianning He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | | | - Lanbo Wang
- Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China
| | - Han Li
- Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China
| | | | - Patrice Monkam
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
| | - Shouliang Qi
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.
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3
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Yamasaki T, Zhao Z. Editorial: Novel brain imaging methods for the aid of neurological and neuropsychiatric disorders. Front Neurosci 2024; 18:1468794. [PMID: 39170674 PMCID: PMC11336413 DOI: 10.3389/fnins.2024.1468794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 07/26/2024] [Indexed: 08/23/2024] Open
Affiliation(s)
- Takao Yamasaki
- Department of Neurology, Minkodo Minohara Hospital, Fukuoka, Japan
- Kumagai Institute of Health Policy, Fukuoka, Japan
| | - Zhiyong Zhao
- Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
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Praharsha CH, Poulose A. CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture. Comput Biol Med 2024; 180:108945. [PMID: 39094328 DOI: 10.1016/j.compbiomed.2024.108945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 07/03/2024] [Accepted: 07/24/2024] [Indexed: 08/04/2024]
Abstract
Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.
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Affiliation(s)
- Chittathuru Himala Praharsha
- School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Thiruvananthapuram 695551, Kerala, India.
| | - Alwin Poulose
- School of Data Science, Indian Institute of Science Education and Research Thiruvananthapuram (IISER TVM), Vithura, Thiruvananthapuram 695551, Kerala, India.
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5
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Iammarino E, Marcantoni I, Sbrollini A, Mortada MHDJ, Morettini M, Burattini L. Scalp Electroencephalogram-Derived Involvement Indexes during a Working Memory Task Performed by Patients with Epilepsy. SENSORS (BASEL, SWITZERLAND) 2024; 24:4679. [PMID: 39066076 PMCID: PMC11280559 DOI: 10.3390/s24144679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/11/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
Electroencephalography (EEG) wearable devices are particularly suitable for monitoring a subject's engagement while performing daily cognitive tasks. EEG information provided by wearable devices varies with the location of the electrodes, the suitable location of which can be obtained using standard multi-channel EEG recorders. Cognitive engagement can be assessed during working memory (WM) tasks, testing the mental ability to process information over a short period of time. WM could be impaired in patients with epilepsy. This study aims to evaluate the cognitive engagement of nine patients with epilepsy, coming from a public dataset by Boran et al., during a verbal WM task and to identify the most suitable location of the electrodes for this purpose. Cognitive engagement was evaluated by computing 37 engagement indexes based on the ratio of two or more EEG rhythms assessed by their spectral power. Results show that involvement index trends follow changes in cognitive engagement elicited by the WM task, and, overall, most changes appear most pronounced in the frontal regions, as observed in healthy subjects. Therefore, involvement indexes can reflect cognitive status changes, and frontal regions seem to be the ones to focus on when designing a wearable mental involvement monitoring EEG system, both in physiological and epileptic conditions.
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Affiliation(s)
| | | | | | | | | | - Laura Burattini
- Department of Information Engineering, Engineering Faculty, Università Politecnica delle Marche, 60131 Ancona, Italy; (E.I.); (I.M.); (A.S.); (M.J.M.); (M.M.)
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El-Tallawy SN, Pergolizzi JV, Vasiliu-Feltes I, Ahmed RS, LeQuang JK, El-Tallawy HN, Varrassi G, Nagiub MS. Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review. Pain Ther 2024; 13:293-317. [PMID: 38430433 PMCID: PMC11111436 DOI: 10.1007/s40122-024-00584-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 02/08/2024] [Indexed: 03/03/2024] Open
Abstract
Pain is a significant health issue, and pain assessment is essential for proper diagnosis, follow-up, and effective management of pain. The conventional methods of pain assessment often suffer from subjectivity and variability. The main issue is to understand better how people experience pain. In recent years, artificial intelligence (AI) has been playing a growing role in improving clinical diagnosis and decision-making. The application of AI offers promising opportunities to improve the accuracy and efficiency of pain assessment. This review article provides an overview of the current state of AI in pain assessment and explores its potential for improving accuracy, efficiency, and personalized care. By examining the existing literature, research gaps, and future directions, this article aims to guide further advancements in the field of pain management. An online database search was conducted via multiple websites to identify the relevant articles. The inclusion criteria were English articles published between January 2014 and January 2024). Articles that were available as full text clinical trials, observational studies, review articles, systemic reviews, and meta-analyses were included in this review. The exclusion criteria were articles that were not in the English language, not available as free full text, those involving pediatric patients, case reports, and editorials. A total of (47) articles were included in this review. In conclusion, the application of AI in pain management could present promising solutions for pain assessment. AI can potentially increase the accuracy, precision, and efficiency of objective pain assessment.
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Affiliation(s)
- Salah N El-Tallawy
- Anesthesia and Pain Department, College of Medicine, King Khalid University Hospital, King Saud University, Riyadh, Saudi Arabia.
- Anesthesia and Pain Department, Faculty of Medicine, Minia University & NCI, Cairo University, Giza, Egypt.
| | | | - Ingrid Vasiliu-Feltes
- Science, Entrepreneurship and Investments Institute, University of Miami, Miami, USA
| | - Rania S Ahmed
- College of Medicine, Alfaisal University, Riyadh, Saudi Arabia
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Valdez-Gaxiola CA, Rosales-Leycegui F, Gaxiola-Rubio A, Moreno-Ortiz JM, Figuera LE. Early- and Late-Onset Alzheimer's Disease: Two Sides of the Same Coin? Diseases 2024; 12:110. [PMID: 38920542 PMCID: PMC11202866 DOI: 10.3390/diseases12060110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/04/2024] [Accepted: 05/18/2024] [Indexed: 06/27/2024] Open
Abstract
Early-onset Alzheimer's disease (EOAD), defined as Alzheimer's disease onset before 65 years of age, has been significantly less studied than the "classic" late-onset form (LOAD), although EOAD often presents with a more aggressive disease course, caused by variants in the APP, PSEN1, and PSEN2 genes. EOAD has significant differences from LOAD, including encompassing diverse phenotypic manifestations, increased genetic predisposition, and variations in neuropathological burden and distribution. Phenotypically, EOAD can be manifested with non-amnestic variants, sparing the hippocampi with increased tau burden. The aim of this article is to review the different genetic bases, risk factors, pathological mechanisms, and diagnostic approaches between EOAD and LOAD and to suggest steps to further our understanding. The comprehension of the monogenic form of the disease can provide valuable insights that may serve as a roadmap for understanding the common form of the disease.
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Affiliation(s)
- César A. Valdez-Gaxiola
- División de Genética, Centro de Investigación Biomédica de Occidente, IMSS, Guadalajara 44340, Jalisco, Mexico; (C.A.V.-G.); (F.R.-L.)
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Frida Rosales-Leycegui
- División de Genética, Centro de Investigación Biomédica de Occidente, IMSS, Guadalajara 44340, Jalisco, Mexico; (C.A.V.-G.); (F.R.-L.)
- Maestría en Ciencias del Comportamiento, Instituto de Neurociencias, Centro Universitario de Ciencias Biológicas y Agropecuarias, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Abigail Gaxiola-Rubio
- Instituto de Investigación en Ciencias Biomédicas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico;
- Facultad de Medicina, Universidad Autónoma de Guadalajara, Zapopan 45129, Jalisco, Mexico
| | - José Miguel Moreno-Ortiz
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
- Instituto de Genética Humana “Dr. Enrique Corona Rivera”, Departamento de Biología Molecular y Genómica, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
| | - Luis E. Figuera
- División de Genética, Centro de Investigación Biomédica de Occidente, IMSS, Guadalajara 44340, Jalisco, Mexico; (C.A.V.-G.); (F.R.-L.)
- Doctorado en Genética Humana, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara 44340, Jalisco, Mexico
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Pérez-Ramos A, Romero-López-Alberca C, Hidalgo-Figueroa M, Berrocoso E, Pérez-Revuelta JI. A systematic review of the biomarkers associated with cognition and mood state in bipolar disorder. Int J Bipolar Disord 2024; 12:18. [PMID: 38758506 PMCID: PMC11101403 DOI: 10.1186/s40345-024-00340-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 05/02/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Bipolar disorder (BD) is a severe psychiatric disorder characterized by changes in mood that alternate between (hypo) mania or depression and mixed states, often associated with functional impairment and cognitive dysfunction. But little is known about biomarkers that contribute to the development and sustainment of cognitive deficits. The aim of this study was to review the association between neurocognition and biomarkers across different mood states. METHOD Search databases were Web of Science, Scopus and PubMed. A systematic review was carried out following the PRISMA guidelines. Risk of bias was assessed with the Newcastle-Ottawa Scale. Studies were selected that focused on the correlation between neuroimaging, physiological, genetic or peripheral biomarkers and cognition in at least two phases of BD: depression, (hypo)mania, euthymia or mixed. PROSPERO Registration No.: CRD42023410782. RESULTS A total of 1824 references were screened, identifying 1023 published articles, of which 336 were considered eligible. Only 16 provided information on the association between biomarkers and cognition in the different affective states of BD. The included studies found: (i) Differences in levels of total cholesterol and C reactive protein depending on mood state; (ii) There is no association found between cognition and peripheral biomarkers; (iii) Neuroimaging biomarkers highlighted hypoactivation of frontal areas as distinctive of acute state of BD; (iv) A deactivation failure has been reported in the ventromedial prefrontal cortex (vmPFC), potentially serving as a trait marker of BD. CONCLUSION Only a few recent articles have investigated biomarker-cognition associations in BD mood phases. Our findings underline that there appear to be central regions involved in BD that are observed in all mood states. However, there appear to be underlying mechanisms of cognitive dysfunction that may vary across different mood states in BD. This review highlights the importance of standardizing the data and the assessment of cognition, as well as the need for biomarkers to help prevent acute symptomatic phases of the disease, and the associated functional and cognitive impairment.
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Affiliation(s)
- Anaid Pérez-Ramos
- Barcelona Clinic Schizophrenia Unit, Hospital Clinic of Barcelona, Neuroscience Institute, Barcelona, Spain
- Centre for Biomedical Research in Mental Health (CIBERSAM), ISCI-III, Madrid, Spain
- Neuropsychopharmacology and Psychobiology Research Group, Department of Neuroscience, Faculty of Medicine, University of Cadiz, Cadiz, Spain
| | - Cristina Romero-López-Alberca
- Centre for Biomedical Research in Mental Health (CIBERSAM), ISCI-III, Madrid, Spain.
- Personality, Evaluation and Psychological Treatment Area, Department of Psychology, University of Cadiz, Cadiz, Spain.
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit, Puerta del Mar University Hospital, Cadiz, Spain.
| | - Maria Hidalgo-Figueroa
- Centre for Biomedical Research in Mental Health (CIBERSAM), ISCI-III, Madrid, Spain
- Neuropsychopharmacology and Psychobiology Research Group, Psychobiology Area, Department of Psychology, University of Cadiz, Cadiz, Spain
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit, Puerta del Mar University Hospital, Cadiz, Spain
| | - Esther Berrocoso
- Centre for Biomedical Research in Mental Health (CIBERSAM), ISCI-III, Madrid, Spain
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit, Puerta del Mar University Hospital, Cadiz, Spain
- Neuropsychopharmacology and Psychobiology Research Group, Department of Neuroscience, Faculty of Medicine, University of Cadiz, Cadiz, Spain
| | - Jose I Pérez-Revuelta
- Centre for Biomedical Research in Mental Health (CIBERSAM), ISCI-III, Madrid, Spain
- Clinical Management of Mental Health Unit, University Hospital of Jerez, Andalusian Health Service, Cadiz, Spain
- Biomedical Research and Innovation Institute of Cadiz (INiBICA), Research Unit, Puerta del Mar University Hospital, Cadiz, Spain
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Tanaka M, Battaglia S, Giménez-Llort L, Chen C, Hepsomali P, Avenanti A, Vécsei L. Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry. Cells 2024; 13:790. [PMID: 38786014 PMCID: PMC11120114 DOI: 10.3390/cells13100790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 04/28/2024] [Indexed: 05/25/2024] Open
Abstract
Translational research in neurological and psychiatric diseases is a rapidly advancing field that promises to redefine our approach to these complex conditions [...].
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Affiliation(s)
- Masaru Tanaka
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Tisza Lajos krt. 113, H-6725 Szeged, Hungary;
| | - Simone Battaglia
- Center for Studies and Research in Cognitive Neuroscience, Department of Psychology “Renzo Canestrari”, Cesena Campus, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy;
- Department of Psychology, University of Turin, 10124 Turin, Italy
| | - Lydia Giménez-Llort
- Institut de Neurociències, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain;
- Department of Psychiatry & Forensic Medicine, Faculty of Medicine, Campus Bellaterra, Universitat Autònoma de Barcelona, Cerdanyola del Vallès, 08193 Barcelona, Spain
| | - Chong Chen
- Division of Neuropsychiatry, Department of Neuroscience, Yamaguchi University Graduate School of Medicine, Yamaguchi 755-8505, Japan;
| | - Piril Hepsomali
- School of Psychology and Clinical Language Sciences, University of Reading, Reading RG6 6ET, UK;
| | - Alessio Avenanti
- Center for Studies and Research in Cognitive Neuroscience, Department of Psychology “Renzo Canestrari”, Cesena Campus, Alma Mater Studiorum Università di Bologna, 47521 Cesena, Italy;
- Neuropsychology and Cognitive Neuroscience Research Center (CINPSI Neurocog), Universidad Católica del Maule, Talca 3460000, Chile
| | - László Vécsei
- HUN-REN-SZTE Neuroscience Research Group, Hungarian Research Network, University of Szeged (HUN-REN-SZTE), Danube Neuroscience Research Laboratory, Tisza Lajos krt. 113, H-6725 Szeged, Hungary;
- Department of Neurology, Albert Szent-Györgyi Medical School, University of Szeged, Semmelweis u. 6, H-6725 Szeged, Hungary
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Tesfaye E, Getnet M, Anmut Bitew D, Adugna DG, Maru L. Brain functional connectivity in hyperthyroid patients: systematic review. Front Neurosci 2024; 18:1383355. [PMID: 38726033 PMCID: PMC11080614 DOI: 10.3389/fnins.2024.1383355] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/05/2024] [Indexed: 05/12/2024] Open
Abstract
Introduction Functional connectivity (FC) is the correlation between brain regions' activities, studied through neuroimaging techniques like fMRI. It helps researchers understand brain function, organization, and dysfunction. Hyperthyroidism, characterized by high serum levels of free thyroxin and suppressed thyroid stimulating hormone, can lead to mood disturbance, cognitive impairment, and psychiatric symptoms. Excessive thyroid hormone exposure can enhance neuronal death and decrease brain volume, affecting memory, attention, emotion, vision, and motor planning. Methods We conducted thorough searches across Google Scholar, PubMed, Hinari, and Science Direct to locate pertinent articles containing original data investigating FC measures in individuals diagnosed with hyperthyroidism. Results The systematic review identified 762 articles, excluding duplicates and non-matching titles and abstracts. Four full-text articles were included in this review. In conclusion, a strong bilateral hippocampal connection in hyperthyroid individuals suggests a possible neurobiological influence on brain networks that may affect cognitive and emotional processing. Systematic Review Registration PROSPERO, CRD42024516216.
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Affiliation(s)
- Ephrem Tesfaye
- Department of Biomedical Sciences, Madda Walabu University Goba Referral Hospital, Bale-Robe, Ethiopia
| | - Mihret Getnet
- Department of Human Physiology, School of Medicine, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
- Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Desalegn Anmut Bitew
- Department of Reproductive Health, Institute of Public Health, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Dagnew Getnet Adugna
- Department of Anatomy, School of Medicine, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
| | - Lemlemu Maru
- Department of Human Physiology, School of Medicine, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia
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11
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Zuo Q, Li R, Shi B, Hong J, Zhu Y, Chen X, Wu Y, Guo J. U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis. Front Comput Neurosci 2024; 18:1387004. [PMID: 38694950 PMCID: PMC11061376 DOI: 10.3389/fncom.2024.1387004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Accepted: 04/02/2024] [Indexed: 05/04/2024] Open
Abstract
Introduction The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data. Methods In this paper, a novel U-shaped convolutional transformer GAN (UCT-GAN) model is proposed to restore the missing brain functional time-series data. The proposed model leverages the power of generative adversarial networks (GANs) while incorporating a U-shaped architecture to effectively capture hierarchical features in the restoration process. Besides, the multi-level temporal-correlated attention and the convolutional sampling in the transformer-based generator are devised to capture the global and local temporal features for the missing time series and associate their long-range relationship with the other brain regions. Furthermore, by introducing multi-resolution consistency loss, the proposed model can promote the learning of diverse temporal patterns and maintain consistency across different temporal resolutions, thus effectively restoring complex brain functional dynamics. Results We theoretically tested our model on the public Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, and our experiments demonstrate that the proposed model outperforms existing methods in terms of both quantitative metrics and qualitative assessments. The model's ability to preserve the underlying topological structure of the brain functional networks during restoration is a particularly notable achievement. Conclusion Overall, the proposed model offers a promising solution for restoring brain functional time-series and contributes to the advancement of neuroscience research by providing enhanced tools for disease analysis and interpretation.
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Affiliation(s)
- Qiankun Zuo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
| | - Ruiheng Li
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Binghua Shi
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
| | - Jin Hong
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Yanfei Zhu
- School of Foreign Languages, Sun Yat-sen University, Guangzhou, China
| | - Xuhang Chen
- Faculty of Science and Technology, University of Macau, Taipa, Macao SAR, China
| | - Yixian Wu
- School of Mechanical Engineering, Beijing Institute of Petrochemical Technology, Beijing, China
| | - Jia Guo
- Hubei Key Laboratory of Digital Finance Innovation, Hubei University of Economics, Wuhan, Hubei, China
- School of Information Engineering, Hubei University of Economics, Wuhan, Hubei, China
- Hubei Internet Finance Information Engineering Technology Research Center, Hubei University of Economics, Wuhan, Hubei, China
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12
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Tiefenbach J, Shannon L, Lobosky M, Johnson S, Chan HH, Byram N, Machado AG, Androjna C, Baker KB. A novel restrainer device for acquistion of brain images in awake rats. Neuroimage 2024; 289:120556. [PMID: 38423263 PMCID: PMC10935597 DOI: 10.1016/j.neuroimage.2024.120556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/20/2024] [Accepted: 02/26/2024] [Indexed: 03/02/2024] Open
Abstract
Functional neuroimaging methods like fMRI and PET are vital in neuroscience research, but require that subjects remain still throughout the scan. In animal research, anesthetic agents are typically applied to facilitate the acquisition of high-quality data with minimal motion artifact. However, anesthesia can have profound effects on brain metabolism, selectively altering dynamic neural networks and confounding the acquired data. To overcome the challenge, we have developed a novel head fixation device designed to support awake rat brain imaging. A validation experiment demonstrated that the device effectively minimizes animal motion throughout the scan, with mean absolute displacement and mean relative displacement of 0.0256 (SD: 0.001) and 0.009 (SD: 0.002), across eight evaluated subjects throughout fMRI image acquisition (total scanning time per subject: 31 min, 12 s). Furthermore, the awake scans did not induce discernable stress to the animals, with stable physiological parameters throughout the scan (Mean HR: 344, Mean RR: 56, Mean SpO2: 94 %) and unaltered serum corticosterone levels (p = 0.159). In conclusion, the device presented in this paper offers an effective and safe method of acquiring functional brain images in rats, allowing researchers to minimize the confounding effects of anesthetic use.
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Affiliation(s)
- Jakov Tiefenbach
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, OH 44195, USA.
| | - Logan Shannon
- Engineering Core, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Mark Lobosky
- Small Animal Imaging Core, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Sadie Johnson
- Engineering Core, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Hugh H Chan
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Nicole Byram
- Cleveland Clinic Innovations, Cleveland Clinic, OH 44195, USA
| | - Andre G Machado
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Charlie Androjna
- Engineering Core, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
| | - Kenneth B Baker
- Department of Neuroscience, Lerner Research Institute, Cleveland Clinic, OH 44195, USA
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13
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Rodríguez-Serrano LM, Wöbbeking-Sánchez M, De La Torre L, Pérez-Elvira R, Chávez-Hernández ME. Changes in EEG Activity and Cognition Related to Physical Activity in Older Adults: A Systematic Review. Life (Basel) 2024; 14:440. [PMID: 38672711 PMCID: PMC11051307 DOI: 10.3390/life14040440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 03/13/2024] [Accepted: 03/23/2024] [Indexed: 04/28/2024] Open
Abstract
Aging is generally associated with a decline in important cognitive functions that can be observed in EEG. Physical activity in older adults should be considered one of the main strategies to promote health and prevent disease in the elderly. The present study aimed to systematically review studies of EEG activity and cognitive function changes associated with physical activity in older adults. Records from PubMed, Scopus, and EBSCO databases were searched and, following the PRISMA guidelines, nine studies were included in the present systematic review. A risk of bias assessment was performed using the National Institute of Health Quality Assessment Tool for Case-control Studies instrument. The studies analyzed used two main strategies to determine the effects of physical activity on cognition and EEG: (1) multiscale entropy and power frequencies; and (2) event-related potentials. In terms of EEG activity, it can be concluded that exercise-induced neuroplasticity underlies improvements in cognitive function in healthy older adults.
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Affiliation(s)
- Luis Miguel Rodríguez-Serrano
- Facultad de Psicología, Universidad Anáhuac México, Universidad Anáhuac Avenue 46, Lomas Anáhuac, Huixquilucan 52786, Mexico; (L.M.R.-S.); (M.E.C.-H.)
| | - Marina Wöbbeking-Sánchez
- Facultad de Psicología, Universidad de Salamanca, Avenida de la Merced 109, 37005 Salamanca, Spain
| | - Lizbeth De La Torre
- Facultad de Psicología, Universidad Pontificia de Salamanca, Calle de la Compañía 5, 37002 Salamanca, Spain;
| | - Ruben Pérez-Elvira
- Laboratorio de Neuropsicofisiología, NEPSA Rehabilitación Neurológica, Facultad de Psicología, Universidad Pontificia de Salamanca, Calle de la Compañía 5, 37002 Salamanca, Spain
| | - María Elena Chávez-Hernández
- Facultad de Psicología, Universidad Anáhuac México, Universidad Anáhuac Avenue 46, Lomas Anáhuac, Huixquilucan 52786, Mexico; (L.M.R.-S.); (M.E.C.-H.)
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14
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Du L, Roy S, Wang P, Li Z, Qiu X, Zhang Y, Yuan J, Guo B. Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders. Ageing Res Rev 2024; 95:102230. [PMID: 38364912 DOI: 10.1016/j.arr.2024.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.
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Affiliation(s)
- Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China.
| | - Shubham Roy
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Pan Wang
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Zhigang Li
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Xiaoting Qiu
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Yinghe Zhang
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Jianpeng Yuan
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China.
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15
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Chiang MC, Yang YP, Nicol CJB, Wang CJ. Gold Nanoparticles in Neurological Diseases: A Review of Neuroprotection. Int J Mol Sci 2024; 25:2360. [PMID: 38397037 PMCID: PMC10888679 DOI: 10.3390/ijms25042360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
This review explores the diverse applications of gold nanoparticles (AuNPs) in neurological diseases, with a specific focus on Alzheimer's disease (AD), Parkinson's disease (PD), and stroke. The introduction highlights the pivotal role of neuroinflammation in these disorders and introduces the unique properties of AuNPs. The review's core examines the mechanisms by which AuNPs exert neuroprotection and anti-neuro-inflammatory effects, elucidating various pathways through which they manifest these properties. The potential therapeutic applications of AuNPs in AD are discussed, shedding light on promising avenues for therapy. This review also explores the prospects of utilizing AuNPs in PD interventions, presenting a hopeful outlook for future treatments. Additionally, the review delves into the potential of AuNPs in providing neuroprotection after strokes, emphasizing their significance in mitigating cerebrovascular accidents' aftermath. Experimental findings from cellular and animal models are consolidated to provide a comprehensive overview of AuNPs' effectiveness, offering insights into their impact at both the cellular and in vivo levels. This review enhances our understanding of AuNPs' applications in neurological diseases and lays the groundwork for innovative therapeutic strategies in neurology.
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Affiliation(s)
- Ming-Chang Chiang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
| | - Yu-Ping Yang
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 33136, USA;
- Department of Biochemistry and Molecular Biology, Miller School of Medicine, University of Miami, Miami, FL 33136, USA
| | - Christopher J. B. Nicol
- Departments of Pathology & Molecular Medicine and Biomedical & Molecular Sciences, Cancer Biology and Genetics Division, Cancer Research Institute, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Chieh-Ju Wang
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City 242, Taiwan
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16
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Prokai-Tatrai K, Prokai L. The impact of 17β-estradiol on the estrogen-deficient female brain: from mechanisms to therapy with hot flushes as target symptoms. Front Endocrinol (Lausanne) 2024; 14:1310432. [PMID: 38260155 PMCID: PMC10800853 DOI: 10.3389/fendo.2023.1310432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024] Open
Abstract
Sex steroids are essential for whole body development and functions. Among these steroids, 17β-estradiol (E2) has been known as the principal "female" hormone. However, E2's actions are not restricted to reproduction, as it plays a myriad of important roles throughout the body including the brain. In fact, this hormone also has profound effects on the female brain throughout the life span. The brain receives this gonadal hormone from the circulation, and local formation of E2 from testosterone via aromatase has been shown. Therefore, the brain appears to be not only a target but also a producer of this steroid. The beneficial broad actions of the hormone in the brain are the end result of well-orchestrated delayed genomic and rapid non-genomic responses. A drastic and steady decline in circulating E2 in a female occurs naturally over an extended period of time starting with the perimenopausal transition, as ovarian functions are gradually declining until the complete cessation of the menstrual cycle. The waning of endogenous E2 in the blood leads to an estrogen-deficient brain. This adversely impacts neural and behavioral functions and may lead to a constellation of maladies such as vasomotor symptoms with varying severity among women and, also, over time within an individual. Vasomotor symptoms triggered apparently by estrogen deficiency are related to abnormal changes in the hypothalamus particularly involving its preoptic and anterior areas. However, conventional hormone therapies to "re-estrogenize" the brain carry risks due to multiple confounding factors including unwanted hormonal exposure of the periphery. In this review, we focus on hot flushes as the archetypic manifestation of estrogen deprivation in the brain. Beyond our current mechanistic understanding of the symptoms, we highlight the arduous process and various obstacles of developing effective and safe therapies for hot flushes using E2. We discuss our preclinical efforts to constrain E2's beneficial actions to the brain by the DHED prodrug our laboratory developed to treat maladies associated with the hypoestrogenic brain.
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Affiliation(s)
- Katalin Prokai-Tatrai
- Department of Pharmacology & Neuroscience, University of North Texas Health Science Center, Fort Worth, TX, United States
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17
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Arora R, Baldi A. Revolutionizing Neurological Disorder Treatment: Integrating Innovations in Pharmaceutical Interventions and Advanced Therapeutic Technologies. Curr Pharm Des 2024; 30:1459-1471. [PMID: 38616755 DOI: 10.2174/0113816128284824240328071911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 01/31/2024] [Accepted: 02/12/2024] [Indexed: 04/16/2024]
Abstract
Neurological disorders impose a significant burden on individuals, leading to disabilities and a reduced quality of life. However, recent years have witnessed remarkable advancements in pharmaceutical interventions aimed at treating these disorders. This review article aims to provide an overview of the latest innovations and breakthroughs in neurological disorder treatment, with a specific focus on key therapeutic areas such as Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, and stroke. This review explores emerging trends in drug development, including the identification of novel therapeutic targets, the development of innovative drug delivery systems, and the application of personalized medicine approaches. Furthermore, it highlights the integration of advanced therapeutic technologies such as gene therapy, optogenetics, and neurostimulation techniques. These technologies hold promise for precise modulation of neural circuits, restoration of neuronal function, and even disease modification. While these advancements offer hopeful prospects for more effective and tailored treatments, challenges such as the need for improved diagnostic tools, identification of new targets for intervention, and optimization of drug delivery methods will remain. By addressing these challenges and continuing to invest in research and collaboration, we can revolutionize the treatment of neurological disorders and significantly enhance the lives of those affected by these conditions.
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Affiliation(s)
- Rimpi Arora
- Pharma Innovation Lab., Department of Pharmaceutical Sciences & Technology, Maharaja Ranjit Singh Punjab Technical University, Bathinda 151001, India
| | - Ashish Baldi
- Pharma Innovation Lab., Department of Pharmaceutical Sciences & Technology, Maharaja Ranjit Singh Punjab Technical University, Bathinda 151001, India
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18
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Javvaji CK, Vagha JD, Meshram RJ, Taksande A. Assessment Scales in Cerebral Palsy: A Comprehensive Review of Tools and Applications. Cureus 2023; 15:e47939. [PMID: 38034189 PMCID: PMC10685081 DOI: 10.7759/cureus.47939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 10/28/2023] [Indexed: 12/02/2023] Open
Abstract
Cerebral palsy (CP) is a complex neurological condition characterized by motor dysfunction affecting millions worldwide. This comprehensive review delves into the critical role of assessment in managing CP. Beginning with exploring its definition and background, we elucidate the diverse objectives of CP assessment, ranging from diagnosis and goal setting to research and epidemiology. We examine standard assessment scales and tools, discuss the challenges inherent in CP assessment, and highlight emerging trends, including integrating technology, personalized medicine, and neuroimaging. The applications of CP assessment in clinical diagnosis, treatment planning, research, and education are underscored. Recommendations for the future encompass standardization, interdisciplinary collaboration, research priorities, and professional training. In conclusion, we emphasize the importance of assessment as a compass guiding the care of individuals with CP, issuing a call to action for improved assessment practices to shape a brighter future for those affected by this condition.
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Affiliation(s)
- Chaitanya Kumar Javvaji
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Jayant D Vagha
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Revat J Meshram
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Amar Taksande
- Pediatrics, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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19
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Clemente-Suárez VJ, Redondo-Flórez L, Beltrán-Velasco AI, Ramos-Campo DJ, Belinchón-deMiguel P, Martinez-Guardado I, Dalamitros AA, Yáñez-Sepúlveda R, Martín-Rodríguez A, Tornero-Aguilera JF. Mitochondria and Brain Disease: A Comprehensive Review of Pathological Mechanisms and Therapeutic Opportunities. Biomedicines 2023; 11:2488. [PMID: 37760929 PMCID: PMC10526226 DOI: 10.3390/biomedicines11092488] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 09/02/2023] [Accepted: 09/04/2023] [Indexed: 09/29/2023] Open
Abstract
Mitochondria play a vital role in maintaining cellular energy homeostasis, regulating apoptosis, and controlling redox signaling. Dysfunction of mitochondria has been implicated in the pathogenesis of various brain diseases, including neurodegenerative disorders, stroke, and psychiatric illnesses. This review paper provides a comprehensive overview of the intricate relationship between mitochondria and brain disease, focusing on the underlying pathological mechanisms and exploring potential therapeutic opportunities. The review covers key topics such as mitochondrial DNA mutations, impaired oxidative phosphorylation, mitochondrial dynamics, calcium dysregulation, and reactive oxygen species generation in the context of brain disease. Additionally, it discusses emerging strategies targeting mitochondrial dysfunction, including mitochondrial protective agents, metabolic modulators, and gene therapy approaches. By critically analysing the existing literature and recent advancements, this review aims to enhance our understanding of the multifaceted role of mitochondria in brain disease and shed light on novel therapeutic interventions.
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Affiliation(s)
- Vicente Javier Clemente-Suárez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain; (V.J.C.-S.); (J.F.T.-A.)
- Group de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla 080002, Colombia
| | - Laura Redondo-Flórez
- Department of Health Sciences, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, C/Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain
| | - Ana Isabel Beltrán-Velasco
- Psychology Department, Facultad de Ciencias de la Vida y la Naturaleza, Universidad Antonio de Nebrija, 28240 Madrid, Spain
| | - Domingo Jesús Ramos-Campo
- LFE Research Group, Department of Health and Human Performance, Faculty of Physical Activity and Sport Science-INEF, Universidad Politécnica de Madrid, 28040 Madrid, Spain
| | - Pedro Belinchón-deMiguel
- Department of Nursing and Nutrition, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, 28670 Villaviciosa de Odón, Spain;
| | | | - Athanasios A. Dalamitros
- Laboratory of Evaluation of Human Biological Performance, School of Physical Education and Sport Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Rodrigo Yáñez-Sepúlveda
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar 2520000, Chile;
| | - Alexandra Martín-Rodríguez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain; (V.J.C.-S.); (J.F.T.-A.)
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