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The Increase of Theta Power and Decrease of Alpha/Theta Ratio as a Manifestation of Cognitive Impairment in Parkinson's Disease. J Clin Med 2023; 12:jcm12041569. [PMID: 36836103 PMCID: PMC9965386 DOI: 10.3390/jcm12041569] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 02/06/2023] [Accepted: 02/13/2023] [Indexed: 02/18/2023] Open
Abstract
In this study, we aim to assess and examine cognitive functions in Parkinson's Disease patients using EEG recordings, with a central focus on characteristics associated with a cognitive decline. Based on neuropsychological evaluation using Mini-Mental State Examination, Montreal Cognitive Assessment, and Addenbrooke's Cognitive Examination-III, 98 participants were divided into three cognitive groups. All the particpants of the study underwent EEG recordings with spectral analysis. The results revealed an increase in the absolute theta power in patients with Parkinson's disease dementia (PD-D) compared to cognitively normal status (PD-CogN, p=0.00997) and a decrease in global relative beta power in PD-D compared to PD-CogN (p=0.0413). An increase in theta relative power in the left temporal region (p=0.0262), left occipital region (p=0.0109), and right occipital region (p=0.0221) were observed in PD-D compared to PD-N. The global alpha/theta ratio and global power spectral ratio significantly decreased in PD-D compared to PD-N (p = 0.001). In conclusion, the increase in relative theta power and the decrease in relative beta power are characteristic changes in EEG recordings in PD patients with cognitive impairment. Identifying these changes can be a useful biomarker and a complementary tool in the neuropsychological diagnosis of cognitive impairment in Parkinson's Disease.
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Kopańska M, Ochojska D, Mytych W, Lis MW, Banaś-Ząbczyk A. Development of a brain wave model based on the quantitative analysis of EEG and EEG biofeedback therapy in patients with panic attacks during the COVID-19 pandemic. Sci Rep 2022; 12:14908. [PMID: 36050377 PMCID: PMC9436169 DOI: 10.1038/s41598-022-19068-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Accepted: 08/24/2022] [Indexed: 11/24/2022] Open
Abstract
The current global crisis facing the world is the COVID-19 pandemic. Infection from the SARS-CoV-2 virus leads to serious health complications and even death. As it turns out, COVID-19 not only physically assails the health of those infected, but also leads to serious mental illness regardless of the presence of the disease. Social isolation, fear, concern for oneself and one's loved ones, all of this occurs when a pandemic overloads people. People exhibit numerous neurological disorders that have never happened to them before. Patients are diagnosed with frequent panic attacks, the result of which can be seen in their Quantitative Electroencephalogram results. This test may be one of the main diagnostic tools of the COVID-19 pandemic. From the results obtained, it is possible to compare and draw conclusions. This method of testing effectively allows EEG biofeedback training and observes its effect on brain activity. The feedback received in this way gives us the opportunity to properly tailor a protocol for the patient and their conditions. Numerous studies support the effectiveness of EEG biofeedback for panic attacks and other psychiatric disorders. The purpose of our study was to show the effectiveness of EEG biofeedback with a Quantitative Electroencephalogram of the brainwave pattern after having COVID-19 and what symptoms may result.
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Affiliation(s)
- Marta Kopańska
- Department of Pathophysiology, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszow, Poland.
| | - Danuta Ochojska
- Department of Psychology, Institute of Pedagogy, College of Social Sciences, University of Rzeszow, 35-959, Rzeszow, Poland
| | - Wiktoria Mytych
- Students Science Club "Reh-Tech", University of Rzeszow, Rzeszow, Poland
| | - Marcin W Lis
- Department of Zoology and Animal Welfare, Faculty of Animal Science, University of Agriculture in Cracow, 30-059, Cracow, Poland
| | - Agnieszka Banaś-Ząbczyk
- Department of Biology, Institute of Medical Sciences, Medical College of Rzeszow University, Rzeszow, Poland
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García Pretelt FJ, Suárez Relevo JX, Aguillón D, Lopera F, Ochoa JF, Tobón Quintero CA. Automatic Classification of Subjects of the PSEN1-E280A Family at Risk of Developing Alzheimer’s Disease Using Machine Learning and Resting State Electroencephalography. J Alzheimers Dis 2022; 87:817-832. [DOI: 10.3233/jad-210148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The study of genetic variant carriers provides an opportunity to identify neurophysiological changes in preclinical stages. Electroencephalography (EEG) is a low-cost and minimally invasive technique which, together with machine learning, provide the possibility to construct systems that classify subjects that might develop Alzheimer’s disease (AD). Objective: The aim of this paper is to evaluate the capacity of the machine learning techniques to classify healthy Non-Carriers (NonCr) from Asymptomatic Carriers (ACr) of PSEN1-E280A variant for autosomal dominant Alzheimer’s disease (ADAD), using spectral features from EEG channels and brain-related independent components (ICs) obtained using independent component analysis (ICA). Methods: EEG was recorded in 27 ACr and 33 NonCr. Statistical significance analysis was applied to spectral information from channels and group ICA (gICA), standardized low-resolution tomography (sLORETA) analysis was applied over the IC as well. Strategies for feature selection and classification like Chi-square, mutual informationm and support vector machines (SVM) were evaluated over the dataset. Results: A test accuracy up to 83% was obtained by implementing a SVM with spectral features derived from gICA. The main findings are related to theta and beta rhythms, generated in the parietal and occipital regions, like the precuneus and superior parietal lobule. Conclusion: Promising models for classification of preclinical AD due to PSEN-1-E280A variant can be trained using spectral features, and the importance of the beta band and precuneus region is highlighted in asymptomatic stages, opening up the possibility of its use as a screening methodology.
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Affiliation(s)
- Francisco J. García Pretelt
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Jazmín X. Suárez Relevo
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - David Aguillón
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Francisco Lopera
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - John Fredy Ochoa
- Bioinstrumentation and Clinical Engineering Research Group (GIBIC), Bioengineering Program, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
| | - Carlos A. Tobón Quintero
- Neuroscience Group of Antioquia (GNA), Medical School, Universidad de Antioquia, Medellín, Colombia
- Neuropsychology and Behavior Group (GRUNECO), Medical School, Universidad de Antioquia, Medellín, Colombia
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Riganello F, Vatrano M, Carozzo S, Russo M, Lucca LF, Ursino M, Ruggiero V, Cerasa A, Porcaro C. The Timecourse of Electrophysiological Brain-Heart Interaction in DoC Patients. Brain Sci 2021; 11:750. [PMID: 34198911 PMCID: PMC8228557 DOI: 10.3390/brainsci11060750] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 05/31/2021] [Accepted: 06/03/2021] [Indexed: 01/09/2023] Open
Abstract
Disorders of Consciousness (DOC) are a spectrum of pathologies affecting one's ability to interact with the external world. Two possible conditions of patients with DOC are Unresponsive Wakefulness Syndrome/Vegetative State (UWS/VS) and Minimally Conscious State (MCS). Analysis of spontaneous EEG activity and the Heart Rate Variability (HRV) are effective techniques in exploring and evaluating patients with DOC. This study aims to observe fluctuations in EEG and HRV parameters in the morning/afternoon resting-state recording. The study enrolled 13 voluntary Healthy Control (HC) subjects and 12 DOC patients (7 MCS, 5 UWS/VS). EEG and EKG were recorded. PSDalpha, PSDtheta powerband, alpha-blocking, alpha/theta of the EEG, Complexity Index (CI) and SDNN of EKG were analyzed. Higher values of PSDalpha, alpha-blocking, alpha/theta and CI values and lower values of PSD theta characterized HC individuals in the morning with respect to DOC patients. In the afternoon, we detected a significant difference between groups in the CI, PSDalpha, PSDtheta, alpha/theta and SDNN, with lower PSDtheta value for HC. CRS-R scores showed a strong correlation with recorded parameters mainly during evaluations in the morning. Our finding put in evidence the importance of the assessment, as the stimulation of DOC patients in research for behavioural response, in the morning.
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Affiliation(s)
- Francesco Riganello
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Martina Vatrano
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Simone Carozzo
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Miriam Russo
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Lucia Francesca Lucca
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Maria Ursino
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Valentina Ruggiero
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
| | - Antonio Cerasa
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
- Institute for Biomedical Research and Innovation (IRIB)—National Research Council of Italy (CNR), 87050 Mangone, Italy
| | - Camillo Porcaro
- S.Anna Institute—Research in Advanced Neurorehabilitation, 88900 Crotone, Italy; (M.V.); (S.C.); (M.R.); (L.F.L.); (M.U.); (V.R.); (A.C.); (C.P.)
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham B15 2TT, UK
- Institute of Cognitive Sciences and Technologies (ISTC) - National Research Council (CNR), 00185 Rome, Italy
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Trammell JP, MacRae PG, Davis G, Bergstedt D, Anderson AE. The Relationship of Cognitive Performance and the Theta-Alpha Power Ratio Is Age-Dependent: An EEG Study of Short Term Memory and Reasoning during Task and Resting-State in Healthy Young and Old Adults. Front Aging Neurosci 2017; 9:364. [PMID: 29163144 PMCID: PMC5682032 DOI: 10.3389/fnagi.2017.00364] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 10/23/2017] [Indexed: 01/25/2023] Open
Abstract
Objective: The Theta-Alpha ratio (TAR) is known to differ based upon age and cognitive ability, with pathological electroencephalography (EEG) patterns routinely found within neurodegenerative disorders of older adults. We hypothesized that cognitive ability would predict EEG metrics differently within healthy young and old adults, and that healthy old adults not showing age-expected EEG activity may be more likely to demonstrate cognitive deficits relative to old adults showing these expected changes. Methods: In 216 EEG blocks collected in 16 young and 20 old adults during rest (eyes open, eyes closed) and cognitive tasks (short-term memory [STM]; matrix reasoning [RM; Raven's matrices]), models assessed the contributing roles of cognitive ability, age, and task in predicting the TAR. A general linear mixed-effects regression model was used to model this relationship, including interaction effects to test whether increased cognitive ability predicted TAR differently for young and old adults at rest and during cognitive tasks. Results: The relationship between cognitive ability and the TAR across all blocks showed age-dependency, and cognitive performance at the CZ midline location predicted the TAR measure when accounting for the effect of age (p < 0.05, chi-square test of nested models). Age significantly interacted with STM performance in predicting the TAR (p < 0.05); increases in STM were associated with increased TAR in young adults, but not in old adults. RM showed similar interaction effects with aging and TAR (p < 0.10). Conclusion: EEG correlates of cognitive ability are age-dependent. Adults who did not show age-related EEG changes were more likely to exhibit cognitive deficits than those who showed age-related changes. This suggests that healthy aging should produce moderate changes in Alpha and TAR measures, and the absence of such changes signals impaired cognitive functioning.
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Affiliation(s)
- Janet P Trammell
- Division of Social Sciences and Natural Sciences, Seaver College, Pepperdine University, Malibu, CA, United States
| | - Priscilla G MacRae
- Division of Social Sciences and Natural Sciences, Seaver College, Pepperdine University, Malibu, CA, United States
| | - Greta Davis
- Division of Social Sciences and Natural Sciences, Seaver College, Pepperdine University, Malibu, CA, United States
| | - Dylan Bergstedt
- Division of Social Sciences and Natural Sciences, Seaver College, Pepperdine University, Malibu, CA, United States
| | - Ariana E Anderson
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States.,Department of Statistics, University of California, Los Angeles, Los Angeles, CA, United States
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Correlation of Visuospatial Ability and EEG Slowing in Patients with Parkinson's Disease. PARKINSON'S DISEASE 2017; 2017:3659784. [PMID: 28348918 PMCID: PMC5350347 DOI: 10.1155/2017/3659784] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 02/05/2017] [Indexed: 01/22/2023]
Abstract
Background. Visuospatial dysfunction is among the first cognitive symptoms in Parkinson's disease (PD) and is often predictive for PD-dementia. Furthermore, cognitive status in PD-patients correlates with quantitative EEG. This cross-sectional study aimed to investigate the correlation between EEG slowing and visuospatial ability in nondemented PD-patients. Methods. Fifty-seven nondemented PD-patients (17 females/40 males) were evaluated with a comprehensive neuropsychological test battery and a high-resolution 256-channel EEG was recorded. A median split was performed for each cognitive test dividing the patients sample into either a normal or lower performance group. The electrodes were split into five areas: frontal, central, temporal, parietal, and occipital. A linear mixed effects model (LME) was used for correlational analyses and to control for confounding factors. Results. Subsequently, for the lower performance, LME analysis showed a significant positive correlation between ROCF score and parietal alpha/theta ratio (b = .59, p = .012) and occipital alpha/theta ratio (b = 0.50, p = .030). No correlations were found in the group of patients with normal visuospatial abilities. Conclusion. We conclude that a reduction of the parietal alpha/theta ratio is related to visuospatial impairments in PD-patients. These findings indicate that visuospatial impairment in PD-patients could be influenced by parietal dysfunction.
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