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Cacciotti A, Pappalettera C, Miraglia F, Carrarini C, Pecchioli C, Rossini PM, Vecchio F. From data to decisions: AI and functional connectivity for diagnosis, prognosis, and recovery prediction in stroke. GeroScience 2024:10.1007/s11357-024-01301-1. [PMID: 39090502 DOI: 10.1007/s11357-024-01301-1] [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: 04/12/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024] Open
Abstract
Stroke is a severe medical condition which may lead to permanent disability conditions. The initial 8 weeks following a stroke are crucial for rehabilitation, as most recovery occurs during this period. Personalized approaches and predictive biomarkers are needed for tailored rehabilitation. In this context, EEG brain connectivity and Artificial Intelligence (AI) can play a crucial role in diagnosing and predicting stroke outcomes efficiently. In the present study, 127 patients with subacute ischemic lesions and 90 age- and gender-matched healthy controls were enrolled. EEG recordings were obtained from each participant within 15 days of stroke onset. Clinical evaluations were performed at baseline and at 40-days follow-up using the National Institutes of Health Stroke Scale (NIHSS). Functional connectivity analysis was conducted using Total Coherence (TotCoh) and Small Word (SW). Quadratic support vector machines (SVM) algorithms were implemented to classify healthy subjects compared to stroke patients (Healthy vs Stroke), determine the affected hemisphere (Left vs Right Hemisphere), and predict functional recovery (Functional Recovery Prediction). In the classification for Functional Recovery Prediction, an accuracy of 94.75%, sensitivity of 96.27% specificity of 92.33%, and AUC of 0.95 were achieved; for Healthy vs Stroke, an accuracy of 99.09%, sensitivity of 100%, specificity of 98.46%, and AUC of 0.99 were achieved. For Left vs Right Hemisphere classification, accuracy was 86.77%, sensitivity was 91.44%, specificity was 80.33%, and AUC was 0.87. These findings highlight the potential of utilizing functional connectivity measures based on EEG in combination with AI algorithms to improve patient outcomes by targeted rehabilitation interventions.
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Affiliation(s)
- Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Claudia Carrarini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
| | - Cristiano Pecchioli
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
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Lombardo FL, Lorenzini P, Mayer F, Massari M, Piscopo P, Bacigalupo I, Ancidoni A, Sciancalepore F, Locuratolo N, Remoli G, Salemme S, Cappa S, Perani D, Spadin P, Tagliavini F, Redolfi A, Cotelli M, Marra C, Caraglia N, Vecchio F, Miraglia F, Rossini PM, Vanacore N. Development of a prediction model of conversion to Alzheimer's disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project. Diagn Progn Res 2024; 8:11. [PMID: 39049042 PMCID: PMC11271065 DOI: 10.1186/s41512-024-00172-6] [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: 12/07/2023] [Accepted: 06/13/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND In recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer's disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future. METHODS The ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50-85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aβ1-42, Aβ1-42/1-40 ratio, Aβ1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram. DISCUSSION This paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions. TRIAL REGISTRATION ClinicalTrials.gov NCT03834402. Registered on February 8, 2019.
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Affiliation(s)
- Flavia L Lombardo
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy.
| | - Patrizia Lorenzini
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Flavia Mayer
- National Center for Drug Research and Evaluation, Italian National Institute of Health, Rome, Italy
| | - Marco Massari
- National Center for Drug Research and Evaluation, Italian National Institute of Health, Rome, Italy
| | - Paola Piscopo
- Department of Neuroscience, Italian National Institute of Health, Rome, Italy
| | - Ilaria Bacigalupo
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Antonio Ancidoni
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Francesco Sciancalepore
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Nicoletta Locuratolo
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
| | - Giulia Remoli
- Neurology Department and Brain Health Service, Fondazione IRCCS San Gerardo Dei Tintori, Monza, Italy
| | - Simone Salemme
- Department of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy
| | - Stefano Cappa
- University Institute of Advanced Studies IUSS Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Daniela Perani
- Vita-Salute San Raffaele University, Nuclear Medicine Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Patrizia Spadin
- "Associazione Italiana Malattia di Alzheimer" - AIMA, Milan, Italy
| | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni Di Dio Fatebenefratelli, Brescia, Italy
| | - Camillo Marra
- Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy
- Memory Clinic, Foundation Policlinico Agostino Gemelli IRCCS, Rome, Italy
| | - Naike Caraglia
- Memory Clinic, Foundation Policlinico Agostino Gemelli IRCCS, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Memory Clinic, Foundation Policlinico Agostino Gemelli IRCCS, Rome, Italy
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele, Rome, Italy
| | - Nicola Vanacore
- National Centre for Disease Prevention and Health Promotion, Italian National Institute of Health, Rome, Italy
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Nucci L, Miraglia F, Pappalettera C, Rossini PM, Vecchio F. Exploring the complexity of EEG patterns in Parkinson's disease. GeroScience 2024:10.1007/s11357-024-01277-y. [PMID: 38997574 DOI: 10.1007/s11357-024-01277-y] [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: 11/15/2023] [Accepted: 07/02/2024] [Indexed: 07/14/2024] Open
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily associated with motor dysfunctions. By the time of definitive diagnosis, about 60% of dopaminergic neurons have already been lost; moreover, even if dopaminergic drugs are highly effective in symptoms control, they only help maintaining a near-healthy condition when started as soon as possible. Therefore, interest in identifying early biomarkers of PD has grown in recent years, especially using neurophysiological techniques such as electroencephalography (EEG). This study aims to investigate brain complexity differences in PD patients compared to healthy controls, focusing on the beta band using approximate entropy (ApEn) analysis of resting-state EEG recordings. Sixty participants were recruited, including 25 PD patients and 35 healthy elderly subjects, matched for age and gender. EEG were recorded for each participant and ApEn values were computed in the beta 1 (13-20 Hz) and beta 2 (20-30 Hz) frequency bands for each EEG-channel and for ROIs. PD patients showed statistically lower ApEn values compared to controls in both beta 1 and beta 2 bands. Regarding electrodes analysis, beta 1 band alterations were found in frontocentral areas, while beta 2 band alterations were observed in centroparietal and frontocentral areas. Considering ROIs, statistically lower ApEn values for PD patients has been reported in central and parietal ROIs in the beta 2 band. Complexity reduction in these areas may underlie beta oscillatory activity dysfunction, reflecting impaired cortical mechanisms associated with motor dysfunction in PD. The results suggest that ApEn analysis of resting EEG activity may serve as a potential tool for early PD detection. Further studies are necessary to validate this approach in PD diagnosis and rehabilitation planning.
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Affiliation(s)
- Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, 00166, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
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Ungvari A, Gulej R, Csik B, Mukli P, Negri S, Tarantini S, Yabluchanskiy A, Benyo Z, Csiszar A, Ungvari Z. The Role of Methionine-Rich Diet in Unhealthy Cerebrovascular and Brain Aging: Mechanisms and Implications for Cognitive Impairment. Nutrients 2023; 15:4662. [PMID: 37960316 PMCID: PMC10650229 DOI: 10.3390/nu15214662] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/15/2023] Open
Abstract
As aging societies in the western world face a growing prevalence of vascular cognitive impairment and Alzheimer's disease (AD), understanding their underlying causes and associated risk factors becomes increasingly critical. A salient concern in the western dietary context is the high consumption of methionine-rich foods such as red meat. The present review delves into the impact of this methionine-heavy diet and the resultant hyperhomocysteinemia on accelerated cerebrovascular and brain aging, emphasizing their potential roles in cognitive impairment. Through a comprehensive exploration of existing evidence, a link between high methionine intake and hyperhomocysteinemia and oxidative stress, mitochondrial dysfunction, inflammation, and accelerated epigenetic aging is drawn. Moreover, the microvascular determinants of cognitive deterioration, including endothelial dysfunction, reduced cerebral blood flow, microvascular rarefaction, impaired neurovascular coupling, and blood-brain barrier (BBB) disruption, are explored. The mechanisms by which excessive methionine consumption and hyperhomocysteinemia might drive cerebromicrovascular and brain aging processes are elucidated. By presenting an intricate understanding of the relationships among methionine-rich diets, hyperhomocysteinemia, cerebrovascular and brain aging, and cognitive impairment, avenues for future research and potential therapeutic interventions are suggested.
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Affiliation(s)
- Anna Ungvari
- Department of Public Health, Semmelweis University, 1089 Budapest, Hungary
| | - Rafal Gulej
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Boglarka Csik
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Peter Mukli
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Sharon Negri
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Stefano Tarantini
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Andriy Yabluchanskiy
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
| | - Zoltan Benyo
- Institute of Translational Medicine, Semmelweis University, 1094 Budapest, Hungary;
- Cerebrovascular and Neurocognitive Disorders Research Group, Eötvös Loránd Research Network, Semmelweis University, 1094 Budapest, Hungary
| | - Anna Csiszar
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Translational Medicine, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
| | - Zoltan Ungvari
- Vascular Cognitive Impairment, Neurodegeneration and Healthy Brain Aging Program, Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA; (R.G.); (B.C.); (P.M.); (S.N.); (S.T.); (A.Y.); (A.C.); (Z.U.)
- Oklahoma Center for Geroscience and Healthy Brain Aging, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
- International Training Program in Geroscience, Department of Public Health, Doctoral School of Basic and Translational Medicine, Semmelweis University, 1089 Budapest, Hungary
- Stephenson Cancer Center, University of Oklahoma, Oklahoma City, OK 73104, USA
- Department of Health Promotion Sciences, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK 73104, USA
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Shetty SJ, Shetty S, Shettigar D, Pagilla V, Maiya GA. Effect of transcranial photobiomodulation on electrophysiological activity of brain in healthy individuals: A scoping review. J Clin Neurosci 2023; 117:156-167. [PMID: 37826867 DOI: 10.1016/j.jocn.2023.09.029] [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: 05/30/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/14/2023]
Abstract
BACKGROUND OBJECTIVE Transcranial photobiomodulation (tPBM) is a safe and non-invasive treatment that has recently emerged as an effective technique to apply near-infrared or red light to activate neural tissues. The objective is to review the literature on the effect of tPBM on electrophysiological activity in healthy individuals. METHODS Literature was searched through PubMed, Scopus, Web of Science, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Embase, and Ovid for transcranial photobiomodulation therapy in healthy individuals age group 18-80 years of either gender having electroencephalography as an outcome. Critical appraisal of included Randomized Controlled Trials and non-randomized experimental studies was done using Joanna Briggs Institute (JBI) critical appraisal tool. RESULTS A database search yielded a total of 4156 results. After eliminating 2626 duplicates, 1530 records were left. 32 articles were considered for full-text screening after 1498 records were excluded through title and abstract screening. 10 articles were included in this review. tPBM has been found to increase the higher electrophysiological oscillations and there is inconclusive evidence targeting the lower oscillatory electrophysiological frequencies. CONCLUSION Transcranial photobiomodulation can have promising effects on the electrophysiological activity of the brain in healthy individuals.
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Affiliation(s)
- Shrija Jaya Shetty
- Centre for Diabetic Foot Care and Research, Department of Physiotherapy, Manipal College of Health Professions (MCHP), Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India
| | - Saidan Shetty
- Department of Basic Medical Sciences, Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India
| | - Deeksha Shettigar
- Centre for Diabetic Foot Care and Research, Department of Physiotherapy, Manipal College of Health Professions (MCHP), Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India
| | - Vidyasagar Pagilla
- Department of Basic Medical Sciences, Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India
| | - G Arun Maiya
- Centre for Diabetic Foot Care and Research, Department of Physiotherapy, Manipal College of Health Professions (MCHP), Manipal Academy of Higher Education (MAHE), Manipal 576104, Karnataka, India.
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Miraglia F, Pappalettera C, Guglielmi V, Cacciotti A, Manenti R, Judica E, Vecchio F, Rossini PM. The combination of hyperventilation test and graph theory parameters to characterize EEG changes in mild cognitive impairment (MCI) condition. GeroScience 2023:10.1007/s11357-023-00733-5. [PMID: 36692591 PMCID: PMC10400506 DOI: 10.1007/s11357-023-00733-5] [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: 09/09/2022] [Accepted: 01/10/2023] [Indexed: 01/25/2023] Open
Abstract
Hyperventilation (HV) is a voluntary activity that causes changes in the neuronal firing characteristics noticeable in the electroencephalogram (EEG) signals. HV-related changes have been scribed to modulation of pO2/pCO2 blood contents. Therefore, an HV test is routinely used for highlighting brain abnormalities including those depending to neurobiological mechanisms at the basis of neurodegenerative disorders. The main aim of the present paper is to study the effectiveness of HV test in modifying the functional connectivity from the EEG signals that can be typical of a prodromal state of Alzheimer's disease (AD), the Mild Cognitive Impairment prodromal to Alzheimer condition. MCI subjects and a group of age-matched healthy elderly (Ctrl) were enrolled and subjected to EEG recording during HV, eyes-closed (EC), and eyes-open (EO) conditions. Since the cognitive decline in MCI seems to be a progressive disconnection syndrome, the approach we used in the present study is the graph theory, which allows to describe brain networks with a series of different parameters. Small world (SW), modularity (M), and global efficiency (GE) indexes were computed among the EC, EO, and HV conditions comparing the MCI group to the Ctrl one. All the three graph parameters, computed in the typical EEG frequency bands, showed significant changes among the three conditions, and more interestingly, a significant difference in the GE values between the MCI group and the Ctrl one was obtained, suggesting that the combination of HV test and graph theory parameters should be a powerful tool for the detection of possible cerebral dysfunction and alteration.
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Affiliation(s)
- Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy.
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy.
| | - Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Valeria Guglielmi
- Dipartimento Neuroscienze, Organi di Senso e Torace, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Rosa Manenti
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Elda Judica
- Department of Neurorehabilitation Sciences, Casa di Cura IGEA, Milano, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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Pappalettera C, Cacciotti A, Nucci L, Miraglia F, Rossini PM, Vecchio F. Approximate entropy analysis across electroencephalographic rhythmic frequency bands during physiological aging of human brain. GeroScience 2022; 45:1131-1145. [PMID: 36538178 PMCID: PMC9886767 DOI: 10.1007/s11357-022-00710-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022] Open
Abstract
Aging is the inevitable biological process that results in a progressive structural and functional decline associated with alterations in the resting/task-related brain activity, morphology, plasticity, and functionality. In the present study, we analyzed the effects of physiological aging on the human brain through entropy measures of electroencephalographic (EEG) signals. One hundred sixty-one participants were recruited and divided according to their age into young (n = 72) and elderly (n = 89) groups. Approximate entropy (ApEn) values were calculated in each participant for each EEG recording channel and both for the total EEG spectrum and for each of the main EEG frequency rhythms: delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-11 Hz), alpha 2 (11-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-45 Hz), to identify eventual statistical differences between young and elderly. To demonstrate that the ApEn represents the age-related brain changes, the computed ApEn values were used as features in an age-related classification of subjects (young vs elderly), through linear, quadratic, and cubic support vector machine (SVM). Topographic maps of the statistical results showed statistically significant difference between the ApEn values of the two groups found in the total spectrum and in delta, theta, beta 2, and gamma. The classifiers (linear, quadratic, and cubic SVMs) revealed high levels of accuracy (respectively 93.20 ± 0.37, 93.16 ± 0.30, 90.62 ± 0.62) and area under the curve (respectively 0.95, 0.94, 0.93). ApEn seems to be a powerful, very sensitive-specific measure for the study of cognitive decline and global cortical alteration/degeneration in the elderly EEG activity.
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Affiliation(s)
- Chiara Pappalettera
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Alessia Cacciotti
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Lorenzo Nucci
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy ,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166 Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Via Val Cannuta, 247, 00166, Rome, Italy. .,Department of Theoretical and Applied Sciences, eCampus University, Novedrate, Como, Italy.
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Analysis of complexity in the EEG activity of Parkinson's disease patients by means of approximate entropy. GeroScience 2022; 44:1599-1607. [PMID: 35344121 DOI: 10.1007/s11357-022-00552-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/19/2022] [Indexed: 11/04/2022] Open
Abstract
The objective of the present study is to explore the brain resting state differences between Parkinson's disease (PD) patients and age- and gender-matched healthy controls (elderly) in terms of complexity of electroencephalographic (EEG) signals. One non-linear approach to determine the complexity of EEG is the entropy. In this pilot study, 28 resting state EEGs were analyzed from 13 PD patients and 15 elderly subjects, applying approximate entropy (ApEn) analysis to EEGs in ten regions of interest (ROIs), five for each brain hemisphere (frontal, central, parietal, occipital, temporal). Results showed that PD patients presented statistically higher ApEn values than elderly confirming the hypothesis that PD is characterized by a remarkable modification of brain complexity and globally modifies the underlying organization of the brain. The higher-than-normal entropy of PD patients may describe a condition of low order and consequently low information flow due to an alteration of cortical functioning and processing of information. Understanding the dynamics of brain applying ApEn could be a useful tool to help in diagnosis, follow the progression of Parkinson's disease, and set up personalized rehabilitation programs.
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9
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Neuronavigated Magnetic Stimulation combined with cognitive training for Alzheimer's patients: an EEG graph study. GeroScience 2021; 44:159-172. [PMID: 34970718 DOI: 10.1007/s11357-021-00508-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/21/2021] [Indexed: 10/19/2022] Open
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disorder in elderly subjects. Recent studies verified the effects of cognitive training combined with repetitive transcranial magnetic stimulation (rTMS-COG) in AD patients. Here, we analyzed neuropsychological and neurophysiological data, derived from electroencephalography (EEG), to evaluate the effects of a 6-week protocol of rTMS-COG in 72 AD. We designed a randomized, double-blind, sham-controlled trial to evaluate efficacy of rTMS on 6 brain regions obtained by an individual MRI combined with COG related to brain areas to stimulate (i.e., syntax and grammar tasks, comprehension of lexical meaning and categorization tasks, action naming, object naming, spatial memory, spatial attention). Patients underwent neuropsychological and EEG examination before (T0), after treatment (T1), and after 40 weeks (T2), to evaluate the effects of rehabilitation therapy. "Small World" (SW) graph approach was introduced allowing us to model the architecture of brain connectivity in order to correlate it with cognitive improvements. We found that following 6 weeks of intensive daily treatment the immediate results showed an improvement in cognitive scales among AD patients. SW present no differences before and after the treatment, whereas a crucial SW modulation emerges at 40-week follow-up, emphasizing the importance of rTMS-COG rehabilitation treatment for AD. Additional results demonstrated that the delta and alpha1 SW seem to be diagnostic biomarkers of AD, whereas alpha2 SW might represent a prognostic biomarker of cognitive recovery. Derived EEG parameters can be awarded the role of diagnostic and predictive biomarkers of AD progression, and rTMS-COG can be regarded as a potentially useful treatment for AD.
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10
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Entropy as Measure of Brain Networks’ Complexity in Eyes Open and Closed Conditions. Symmetry (Basel) 2021. [DOI: 10.3390/sym13112178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Brain complexity can be revealed even through a comparison between two trivial conditions, such as eyes open and eyes closed (EO and EC respectively) during resting. Electroencephalogram (EEG) has been widely used to investigate brain networks, and several non-linear approaches have been applied to investigate EO and EC signals modulation, both symmetric and not. Entropy is one of the approaches used to evaluate the system disorder. This study explores the differences in the EO and EC awake brain dynamics by measuring entropy. In particular, an approximate entropy (ApEn) was measured, focusing on the specific cerebral areas (frontal, central, parietal, occipital, temporal) on EEG data of 37 adult healthy subjects while resting. Each participant was submitted to an EO and an EC resting EEG recording in two separate sessions. The results showed that in the EO condition the cerebral networks of the subjects are characterized by higher values of entropy than in the EC condition. All the cerebral regions are subjected to this chaotic behavior, symmetrically in both hemispheres, proving the complexity of networks dynamics dependence from the subject brain state. Remarkable dynamics regarding cerebral networks during simple resting and awake brain states are shown by entropy. The application of this parameter can be also extended to neurological conditions, to establish and monitor personalized rehabilitation treatments.
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11
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Vecchio F. Aging and brain connectivity by graph theory. Aging (Albany NY) 2021; 13:23874-23875. [PMID: 34731087 PMCID: PMC8610121 DOI: 10.18632/aging.203680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 10/25/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
- eCampus University, Novedrate (Co), Rome, Italy
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12
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Santana JERS, Baptista AF, Lucena R, Lopes TDS, do Rosário RS, Xavier MR, Fonseca A, Miranda JGV. Altered Dynamic Brain Connectivity in Individuals With Sickle Cell Disease and Chronic Pain Secondary to Hip Osteonecrosis. Clin EEG Neurosci 2021; 54:333-342. [PMID: 34779267 DOI: 10.1177/15500594211054297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Individuals with sickle cell disease (SCD) exhibit changes in static brain connectivity in rest. However, little known as chronic pain associated with hip osteonecrosis affects dynamic brain connectivity during rest and the motor imagery task. The aim of this study was to investigate the characteristics of the dynamic functional brain connectivity of individuals with SCD and chronic pain secondary to hip osteonecrosis. This is a cross-sectional study comparing the dynamic brain connectivity of healthy individuals (n = 18) with the dynamic brain connectivity of individuals with SCD and chronic pain (n = 22). Individuals with SCD and chronic pain were stratified into high- or low-intensity pain groups based on pain intensity at the time of assessment. Dynamic brain connectivity was assessed through electroencephalography in 3 stages, resting state with eyes closed, and during hip (painful for the SCD individuals) and hand (control, nonpainful) motor imagery. Average weight of the edges and full synchronization time (FST)-time required for 95% of the possible edges to appear over time during a given task-were evaluated. Regarding the average weight of the edges, individuals with SCD and high-intensity pain presented higher edge weight during hip motor imagery. The average weight of the edges correlated positively with pain intensity and depression symptoms. Individuals with SCD and chronic pain complete the cerebral network at rest more quickly (lower FST). Individuals with SCD and chronic pain/hip osteonecrosis have impaired dynamic brain network with shorter FST in rest network and more pronounced diffuse connectivity in individuals with high-intensity pain. The dynamic brain network evaluated by time-varying graphs and motif synchronization was able to identify differences between groups.
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Affiliation(s)
- Jamille Evelyn R S Santana
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Postgraduate Program in Medicine and Health, 28111Federal University of Bahia, Salvador, Brazil
| | - Abrahão F Baptista
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Postgraduate Program in Medicine and Health, 28111Federal University of Bahia, Salvador, Brazil.,Center for Mathematics, Computation and Cognition, 488583Federal University of ABC, Santo Andre, Brazil
| | - Rita Lucena
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Postgraduate Program in Medicine and Health, 28111Federal University of Bahia, Salvador, Brazil.,Medical School of Bahia, 28111Federal University of Bahia, Salvador, Brazil
| | - Tiago da S Lopes
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Postgraduate Program in Medicine and Health, 28111Federal University of Bahia, Salvador, Brazil.,Adventist Neuromodulation and Neuroscience Laboratory, Bahia Adventist College, Cachoeira, Brazil
| | - Raphael S do Rosário
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Institute of Physics, Federal University of Bahia, Salvador, Brazil
| | - Marjorie R Xavier
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil
| | - André Fonseca
- Center for Mathematics, Computation and Cognition, 488583Federal University of ABC, Santo Andre, Brazil
| | - José Garcia V Miranda
- Health and Functionality Study Group, 28111Federal University of Bahia, Salvador, Brazil.,Institute of Physics, Federal University of Bahia, Salvador, Brazil
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13
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Graph Theory on Brain Cortical Sources in Parkinson's Disease: The Analysis of 'Small World' Organization from EEG. SENSORS 2021; 21:s21217266. [PMID: 34770573 PMCID: PMC8587014 DOI: 10.3390/s21217266] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/17/2022]
Abstract
Parkinson’s disease (PD) is the second most common neurodegenerative disease in the elderly population. Similarly to other neurodegenerative diseases, the early diagnosis of PD is quite difficult. The current pilot study aimed to explore the differences in brain connectivity between PD and NOrmal eLDerly (Nold) subjects to evaluate whether connectivity analysis may speed up and support early diagnosis. A total of 26 resting state EEGs were analyzed from 13 PD patients and 13 age-matched Nold subjects, applying to cortical reconstructions the graph theory analyses, a mathematical representation of brain architecture. Results showed that PD patients presented a more ordered structure at slow-frequency EEG rhythms (lower value of SW) than Nold subjects, particularly in the theta band, whereas in the high-frequency alpha, PD patients presented more random organization (higher SW) than Nold subjects. The current results suggest that PD could globally modulate the cortical connectivity of the brain, modifying the functional network organization and resulting in motor and non-motor signs. Future studies could validate whether such an approach, based on a low-cost and non-invasive technique, could be useful for early diagnosis, for the follow-up of PD progression, as well as for evaluating pharmacological and neurorehabilitation treatments.
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14
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Büchel D, Lehmann T, Sandbakk Ø, Baumeister J. EEG-derived brain graphs are reliable measures for exploring exercise-induced changes in brain networks. Sci Rep 2021; 11:20803. [PMID: 34675312 PMCID: PMC8531386 DOI: 10.1038/s41598-021-00371-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022] Open
Abstract
The interaction of acute exercise and the central nervous system evokes increasing interest in interdisciplinary research fields of neuroscience. Novel approaches allow to monitor large-scale brain networks from mobile electroencephalography (EEG) applying graph theory, but it is yet uncertain whether brain graphs extracted after exercise are reliable. We therefore aimed to investigate brain graph reliability extracted from resting state EEG data before and after submaximal exercise twice within one week in male participants. To obtain graph measures, we extracted global small-world-index (SWI), clustering coefficient (CC) and characteristic path length (PL) based on weighted phase leg index (wPLI) and spectral coherence (Coh) calculation. For reliability analysis, Intraclass-Correlation-Coefficient (ICC) and Coefficient of Variation (CoV) were computed for graph measures before (REST) and after POST) exercise. Overall results revealed poor to excellent measures at PRE and good to excellent ICCs at POST in the theta, alpha-1 and alpha-2, beta-1 and beta-2 frequency band. Based on bootstrap-analysis, a positive effect of exercise on reliability of wPLI based measures was observed, while exercise induced a negative effect on reliability of Coh-based graph measures. Findings indicate that brain graphs are a reliable tool to analyze brain networks in exercise contexts, which might be related to the neuroregulating effect of exercise inducing functional connections within the connectome. Relative and absolute reliability demonstrated good to excellent reliability after exercise. Chosen graph measures may not only allow analysis of acute, but also longitudinal studies in exercise-scientific contexts.
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Affiliation(s)
- Daniel Büchel
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.
| | - Tim Lehmann
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Øyvind Sandbakk
- Department of Neuromedicine and Movement Science, Centre for Elite Sports Research, Norwegian University of Science and Technology, Trondheim, Norway
| | - Jochen Baumeister
- Department Sport & Health, Exercise Science & Neuroscience Unit, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
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15
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Wu Z, Sabel BA. Spacetime in the brain: rapid brain network reorganization in visual processing and recovery. Sci Rep 2021; 11:17940. [PMID: 34504129 PMCID: PMC8429559 DOI: 10.1038/s41598-021-96971-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 08/13/2021] [Indexed: 11/14/2022] Open
Abstract
Functional connectivity networks (FCN) are the physiological basis of brain synchronization to integrating neural activity. They are not rigid but can reorganize under pathological conditions or during mental or behavioral states. However, because mental acts can be very fast, like the blink of an eye, we now used the visual system as a model to explore rapid FCN reorganization and its functional impact in normal, abnormal and post treatment vision. EEG-recordings were time-locked to visual stimulus presentation; graph analysis of neurophysiological oscillations were used to characterize millisecond FCN dynamics in healthy subjects and in patients with optic nerve damage before and after neuromodulation with alternating currents stimulation and were correlated with visual performance. We showed that rapid and transient FCN synchronization patterns in humans can evolve and dissolve in millisecond speed during visual processing. This rapid FCN reorganization is functionally relevant because disruption and recovery after treatment in optic nerve patients correlated with impaired and recovered visual performance, respectively. Because FCN hub and node interactions can evolve and dissolve in millisecond speed to manage spatial and temporal neural synchronization during visual processing and recovery, we propose “Brain Spacetime” as a fundamental principle of the human mind not only in visual cognition but also in vision restoration.
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Affiliation(s)
- Zheng Wu
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.,Data and Knowledge Engineering Group, Faculty of Computer Science, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany
| | - Bernhard A Sabel
- Institute of Medical Psychology, Medical Faculty, Otto-von-Guericke University of Magdeburg, Magdeburg, Germany.
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16
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Vecchio F, Miraglia F, Alú F, Orticoni A, Judica E, Cotelli M, Rossini PM. Contribution of Graph Theory Applied to EEG Data Analysis for Alzheimer's Disease Versus Vascular Dementia Diagnosis. J Alzheimers Dis 2021; 82:871-879. [PMID: 34092648 DOI: 10.3233/jad-210394] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Most common progressive brain diseases in the elderly are Alzheimer's disease (AD) and vascular dementia (VaD). They present with relatively similar clinical symptoms of cognitive decline, but the underlying pathophysiological mechanisms are different. OBJECTIVE The aim is to explore the brain connectivity differences between AD and VaD patients compared to mild cognitive impairment (MCI) and normal elderly (Nold) subjects applying graph theory, in particular the Small World (SW) analysis. METHODS 274 resting state EEGs were analyzed in 100 AD, 80 MCI, 40 VaD, and 54 Nold subjects. Graph theory analyses were applied to undirected and weighted networks obtained by lagged linear coherence evaluated by eLORETA tool. RESULTS VaD and AD patients presented more ordered low frequency structure (lower value of SW) than Nold and MCI subjects, and more random organization (higher value of SW) in low and high frequency alpha rhythms. Differences between patients have been found in high frequency alpha rhythms in VaD (higher value of SW) with respect to AD, and in theta band with a trend which is more similar to MCI and Nold than to AD. MCI subjects presented a network organization which is intermediate, in low frequency bands, between Nold and patients. CONCLUSION Graph theory applied to EEG data has proved very useful in identifying differences in brain network patterns in subjects with dementia, proving to be a valid tool for differential diagnosis. Future studies will aim to validate this method to diagnose especially in the early stages of the disease and at single subject level.
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Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Francesca Alú
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Alessandro Orticoni
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
| | - Elda Judica
- Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Roma, Rome, Italy
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17
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Veldman MP, Maurits NM, Mantini D, Hortobágyi T. Age-dependent modulation of motor network connectivity for skill acquisition, consolidation and interlimb transfer after motor practice. Clin Neurophysiol 2021; 132:1790-1801. [PMID: 34130247 DOI: 10.1016/j.clinph.2021.03.051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 02/19/2021] [Accepted: 03/22/2021] [Indexed: 11/19/2022]
Abstract
OBJECTIVE Age-related differences in neural strategies for motor learning are not fully understood. We determined the effects of age on the relationship between motor network connectivity and motor skill acquisition, consolidation, and interlimb transfer using dynamic imaging of coherent sources. METHODS Healthy younger (n = 24, 18-24 y) and older (n = 24, 65-87 y) adults unilaterally practiced a visuomotor task and resting-state electroencephalographic data was acquired before and after practice as well as at retention. RESULTS The results showed that right-hand skill acquisition and consolidation did not differ between age groups. However, age affected the ability to transfer the newly acquired motor skill to the non-practiced limb. Moreover, strengthened left- and right-primary motor cortex-related beta connectivity was negatively and positively associated with right-hand skill acquisition and left-hand skill consolidation in older adults, respectively. CONCLUSION Age-dependent modulations of bilateral resting-state motor network connectivity indicate age-specific strategies for the acquisition, consolidation, and interlimb transfer of novel motor tasks. SIGNIFICANCE The present results provide insights into the mechanisms underlying motor learning that are important for the development of interventions for patients with unilateral injuries.
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Affiliation(s)
- M P Veldman
- KU Leuven, Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium; University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, the Netherlands; KU Leuven, Leuven Brain Institute (LBI), Leuven, Belgium.
| | - N M Maurits
- University of Groningen, University Medical Center Groningen, Department of Neurology, Groningen, the Netherlands
| | - D Mantini
- KU Leuven, Department of Movement Sciences, Movement Control and Neuroplasticity Research Group, Leuven, Belgium; Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, Venice, Italy
| | - T Hortobágyi
- University of Groningen, University Medical Center Groningen, Center for Human Movement Sciences, Groningen, the Netherlands; Institute of Sport Sciences and Physical Education, Faculty of Sciences, University of Pécs, Pécs, Hungary; Somogy County Kaposi Mór Teaching Hospital, Kaposvár, Hungary
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18
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Vecchio F, Miraglia F, Alù F, Judica E, Cotelli M, Pellicciari MC, Rossini PM. Human brain networks in physiological and pathological aging: reproducibility of EEG graph theoretical analysis in cortical connectivity. Brain Connect 2021; 12:41-51. [PMID: 33797981 DOI: 10.1089/brain.2020.0824] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Physiological and pathological brain aging plays a central role in brain networks modulation. The aim of the present paper was to assess the stability of a proposed method for the evaluation of Small World (SW) characteristics for the study of Human Connectome. METHODS 80 subjects were recruited: 36 young healthy controls, 32 elderly healthy controls, and 12 patients affected by Alzheimer's disease. Electroencephalograms (EEG) were recorded during six separate sessions (480 recordings) at an average inter-session interval of 3.8±0.2 days. Graph theory functions were applied to the undirected and weighted networks obtained by the lagged linear coherence evaluated by exact Low Resolution Electromagnetic Tomography (eLORETA). Were explored the following frequency bands: delta (2-4Hz), theta (4-8Hz), alpha1 (8-10.5Hz), alpha2 (10.5-13Hz), beta1 (13-20Hz), beta2 (20-30Hz) and gamma (30-40Hz). RESULTS The proposed method for the evaluation of Small World (SW) characteristics showed good reproducibility and stability. Furthermore, the results showed the pattern Young>Elderly>AD in low frequency delta and theta bands and vice versa in the higher alpha band. Finally, the correlation with age was confirmed in healthy subjects showing that older the age higher the SW values for alpha2. DISCUSSION Evidences from the present study confirm the stability of the Small World index and suggest that graph theory can support the analysis of connectivity patterns estimated from EEG. The proposed method for the evaluation of the characteristics of the Small World (SW) has shown good reproducibility and stability and applied to patient data, this technique could provide more information on the pathophysiological processes underlying the age-related brain disconnection, as well as on the administration of rehabilitation treatments at the right time that could allow to avoid unnecessary interventions.
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Affiliation(s)
- Fabrizio Vecchio
- IRCCS San Raffaele Pisana, 46729, Via di Val Cannuta, 247, 00166 Roma RM, Roma, Italy, 00163;
| | | | - Francesca Alù
- IRCCS San Raffaele Pisana, 46729, Roma, Lazio, Italy;
| | - Elda Judica
- Casa di Cura del Policlinico SpA, 390725, Milano, Lombardia, Italy;
| | - Maria Cotelli
- IRCCS Centro San Giovanni di Dio Fatebenefratelli, 18518, Brescia, Lombardia, Italy;
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19
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Alù F, Orticoni A, Judica E, Cotelli M, Rossini PM, Miraglia F, Vecchio F. Entropy modulation of electroencephalographic signals in physiological aging. Mech Ageing Dev 2021; 196:111472. [PMID: 33766746 DOI: 10.1016/j.mad.2021.111472] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 03/18/2021] [Accepted: 03/19/2021] [Indexed: 01/22/2023]
Abstract
Aging is a multifactorial physiological process characterized by the accumulation of degenerative processes impacting on different brain functions, including the cognitive one. A tool largely employed in the investigation of brain networks is the electroencephalogram (EEG). Given the cerebral complexity and dynamism, many non-linear approaches have been applied to explore age-related brain electrical activity modulation detected by the EEG: one of them is the entropy, which measures the disorder of a system. The present study had the aim to investigate aging influence on brain dynamics applying Approximate Entropy (ApEn) parameter to resting state EEG data of 68 healthy adult participants, divided with respect to their age in two groups, focusing on several specialized brain regions. Results showed that elderly participants present higher ApEn values than younger participants in the central, parietal and occipital areas, confirming the hypothesis that aging is characterized by an evolution of brain dynamics. Such findings may reflect a reduced synchronization of the neural networks cyclic activity, due to the reduction of cerebral connections typically found in aging process. Understanding the dynamics of brain networks by applying the entropy parameter could be useful for developing appropriate and personalized rehabilitation programs and for future studies on neurodegenerative diseases.
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Affiliation(s)
- Francesca Alù
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Alessandro Orticoni
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Elda Judica
- Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience and Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy.
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20
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Kaposzta Z, Stylianou O, Mukli P, Eke A, Racz FS. Decreased connection density and modularity of functional brain networks during n-back working memory paradigm. Brain Behav 2021; 11:e01932. [PMID: 33185986 PMCID: PMC7821619 DOI: 10.1002/brb3.1932] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/05/2020] [Accepted: 10/18/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Investigating how the brain adapts to increased mental workload through large-scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low-density setting. METHODS In this study, we utilized the n-back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n-times earlier. Electroencephalography recordings were performed under a control (0-back) and two task conditions of varying difficulty (2- and 3-back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures. RESULTS We found a substantial decrease in FC when transitioning from the 0- to the 2- or 3-back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands. CONCLUSION The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.
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Affiliation(s)
- Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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21
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Prajapati R, Emerson IA. Construction and analysis of brain networks from different neuroimaging techniques. Int J Neurosci 2020; 132:745-766. [DOI: 10.1080/00207454.2020.1837802] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Rutvi Prajapati
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Isaac Arnold Emerson
- Bioinformatics Programming Laboratory, Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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22
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Racz FS, Stylianou O, Mukli P, Eke A. Multifractal and Entropy-Based Analysis of Delta Band Neural Activity Reveals Altered Functional Connectivity Dynamics in Schizophrenia. Front Syst Neurosci 2020; 14:49. [PMID: 32792917 PMCID: PMC7394222 DOI: 10.3389/fnsys.2020.00049] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/29/2020] [Indexed: 12/14/2022] Open
Abstract
Dynamic functional connectivity (DFC) was established in the past decade as a potent approach to reveal non-trivial, time-varying properties of neural interactions – such as their multifractality or information content –, that otherwise remain hidden from conventional static methods. Several neuropsychiatric disorders were shown to be associated with altered DFC, with schizophrenia (SZ) being one of the most intensely studied among such conditions. Here we analyzed resting-state electroencephalography recordings of 14 SZ patients and 14 age- and gender-matched healthy controls (HC). We reconstructed dynamic functional networks from delta band (0.5–4 Hz) neural activity and captured their spatiotemporal dynamics in various global network topological measures. The acquired network measure time series were made subject to dynamic analyses including multifractal analysis and entropy estimation. Besides group-level comparisons, we built a classifier to explore the potential of DFC features in classifying individual cases. We found stronger delta-band connectivity, as well as increased variance of DFC in SZ patients. Surrogate data testing verified the true multifractal nature of DFC in SZ, with patients expressing stronger long-range autocorrelation and degree of multifractality when compared to controls. Entropy analysis indicated reduced temporal complexity of DFC in SZ. When using these indices as features, an overall cross-validation accuracy surpassing 89% could be achieved in classifying individual cases. Our results imply that dynamic features of DFC such as its multifractal properties and entropy are potent markers of altered neural dynamics in SZ and carry significant potential not only in better understanding its pathophysiology but also in improving its diagnosis. The proposed framework is readily applicable for neuropsychiatric disorders other than schizophrenia.
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Affiliation(s)
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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Vecchio F, Miraglia F, Alù F, Menna M, Judica E, Cotelli M, Rossini PM. Classification of Alzheimer’s Disease with Respect to Physiological Aging with Innovative EEG Biomarkers in a Machine Learning Implementation. J Alzheimers Dis 2020; 75:1253-1261. [DOI: 10.3233/jad-200171] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Francesca Alù
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Matteo Menna
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
| | - Elda Judica
- Department of Neurorehabilitation Sciences, Casa Cura Policlinico, Milano, Italy
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, Brescia, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, Rome, Italy
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24
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Rossini PM, Miraglia F, Alù F, Cotelli M, Ferreri F, Di Iorio R, Iodice F, Vecchio F. Neurophysiological Hallmarks of Neurodegenerative Cognitive Decline: The Study of Brain Connectivity as A Biomarker of Early Dementia. J Pers Med 2020; 10:E34. [PMID: 32365890 PMCID: PMC7354555 DOI: 10.3390/jpm10020034] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 04/26/2020] [Accepted: 04/27/2020] [Indexed: 02/07/2023] Open
Abstract
Neurodegenerative processes of various types of dementia start years before symptoms, but the presence of a "neural reserve", which continuously feeds and supports neuroplastic mechanisms, helps the aging brain to preserve most of its functions within the "normality" frame. Mild cognitive impairment (MCI) is an intermediate stage between dementia and normal brain aging. About 50% of MCI subjects are already in a stage that is prodromal-to-dementia and during the following 3 to 5 years will develop clinically evident symptoms, while the other 50% remains at MCI or returns to normal. If the risk factors favoring degenerative mechanisms are modified during early stages (i.e., in the prodromal), the degenerative process and the loss of abilities in daily living activities will be delayed. It is therefore extremely important to have biomarkers able to identify-in association with neuropsychological tests-prodromal-to-dementia MCI subjects as early as possible. MCI is a large (i.e., several million in EU) and substantially healthy population; therefore, biomarkers should be financially affordable, largely available and non-invasive, but still accurate in their diagnostic prediction. Neurodegeneration initially affects synaptic transmission and brain connectivity; methods exploring them would represent a 1st line screening. Neurophysiological techniques able to evaluate mechanisms of synaptic function and brain connectivity are attracting general interest and are described here. Results are quite encouraging and suggest that by the application of artificial intelligence (i.e., learning-machine), neurophysiological techniques represent valid biomarkers for screening campaigns of the MCI population.
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Affiliation(s)
- Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Francesca Miraglia
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Francesca Alù
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
| | - Maria Cotelli
- Neuropsychology Unit, IRCCS Istituto Centro San Giovanni di DioFatebenefratelli, 25125 Brescia, Italy;
| | - Florinda Ferreri
- Department of Neuroscience, Unit of Neurology and Neurophysiology, University of Padua, 35100 Padua, Italy;
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, 70100 Kuopio, Finland
| | - Riccardo Di Iorio
- Neurology Unit, IRCCS Polyclinic A. Gemelli Foundation, 00168 Rome, Italy;
| | - Francesco Iodice
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
- Neurology Unit, IRCCS Polyclinic A. Gemelli Foundation, 00168 Rome, Italy;
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neuroscience & Neurorehabilitation, IRCCS San Raffaele Pisana, 00167 Rome, Italy; (F.M.); (F.A.); (F.I.); (F.V.)
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