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Lydon EA, Panfil HB, Yako S, Mudar RA. Behavioral and neural measures of semantic conflict monitoring: Findings from a novel picture-word interference task. Brain Res 2024; 1834:148900. [PMID: 38555981 DOI: 10.1016/j.brainres.2024.148900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 03/23/2024] [Accepted: 03/28/2024] [Indexed: 04/02/2024]
Abstract
Conflict monitoring has been studied extensively using experimental paradigms that manipulate perceptual dimensions of stimuli and responses. The picture-word interference (PWI) task has historically been used to examine semantic conflict, but primarily for the purpose of examining lexical retrieval. In this study, we utilized two novel PWI tasks to assess conflict monitoring in the context of semantic conflict. Participants included nineteen young adults (14F, age = 20.79 ± 3.14) who completed two tasks: Animals and Objects. Task and conflict effects were assessed by examining behavioral (reaction time and accuracy) and neurophysiological (oscillations in theta, alpha, and beta band) measures. Results revealed conflict effects within both tasks, but the pattern of findings differed across the two semantic categories. Participants were slower to respond to unmatched versus matched trials on the Objects task only and were less accurate responding to matched versus unmatched trials in the Animals task only. We also observed task differences, with participants responding more accurately on conflict trials for Animals compared to Objects. Differences in neural oscillations were observed, including between-task differences in low beta oscillations and within-task differences in theta, alpha, and low beta. We also observed significant correlations between task performance and standard measures of cognitive control. This work provides new insights into conflict monitoring, highlighting the importance of examining conflict across different semantic categories, especially in the context of animacy. The findings serve as a benchmark to assess conflict monitoring using PWI tasks across populations of varying cognitive ability.
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Affiliation(s)
- Elizabeth A Lydon
- Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Holly B Panfil
- Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Sharbel Yako
- Molecular and Cellular Biology, University of Illinois Urbana-Champaign, Champaign, IL, USA
| | - Raksha A Mudar
- Speech and Hearing Science, University of Illinois Urbana-Champaign, Champaign, IL, USA.
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2
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Yang L, Xiao X, Yu L, Shen Z, Luo Y, Zhao G, Dou Z, Lin W, Yang J, Yang L, Yu S. Neural mechanisms of working memory dysfunction in patients with chronic insomnia disorder. Sleep Med 2023; 112:151-158. [PMID: 37865032 DOI: 10.1016/j.sleep.2023.10.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 09/17/2023] [Accepted: 10/11/2023] [Indexed: 10/23/2023]
Abstract
OBJECTIVE This study aimed to investigate the neural mechanisms underlying working memory impairment in patients with chronic insomnia disorder (CID) using event-related potentials (ERP) and resting-state functional connectivity (rsFC) approaches. METHODS Participants, including CID patients and healthy controls (HCs), completed clinical scales and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). EEG analysis compared reaction times, P3 amplitudes, event-related spectral perturbations (ERSP), and inter-trial phase synchronisation (ITPS) between CID patients and HCs. Subsequently, frontal regions (i.e., the Superior Frontal Gyrus [SFG] and Middle Frontal Gyrus [MFG]) corresponding to the EEG were selected as seeds for rsFC analysis. Correlation analyses were conducted to further investigate the relationship between functional connectivity abnormalities in brain regions and clinical symptom severity and P3 amplitude in CID patients. RESULTS Compared to HCs, CID patients exhibited slower reaction times across all working memory conditions, with the deficits becoming more pronounced as memory load increased. ERP analysis revealed increased P3 amplitude, theta wave power, and reduced inter-trial synchrony in CID patients. rsFC analysis showed decreased connectivity of SFG-posterior cingulated cortex (PCC), SFG-MFG, and MFG-frontal pole (FP), and increased connectivity of MFG- Middle Temporal Gyrus (MTG)in CID patients. Importantly, a significant correlation was found between the rsFC of SFG-MTG and P3 amplitude during 1-back. CONCLUSION This study confirms deficits in working memory capacity in patients with CID, specifically in the neural mechanisms of cognitive processing that vary depending on the level of cognitive load. Alterations in connectivity patterns within and between the frontal and temporal regions may be the neural basis of the cognitive impairment.
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Affiliation(s)
- Lu Yang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Xiangwen Xiao
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Liyong Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zhifu Shen
- Center of Interventional Medicine, Affiliated Hospital of North Sichuan Medical College, North Sichuan Medical College, Nanchong, China
| | - Yucai Luo
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Guangli Zhao
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Zeyang Dou
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Wenting Lin
- School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Jie Yang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Lili Yang
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
| | - Siyi Yu
- School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine, Chengdu, China; Acupuncture and Brain Science Research Center, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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Casagrande CC, Rempe MP, Springer SD, Wilson TW. Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer's disease using electrophysiological methods. Ageing Res Rev 2023; 88:101950. [PMID: 37156399 PMCID: PMC10261850 DOI: 10.1016/j.arr.2023.101950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/27/2023] [Accepted: 05/05/2023] [Indexed: 05/10/2023]
Abstract
With an aging population, cognitive decline and neurodegenerative disorders are an emerging public health crises with enormous, yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.
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Affiliation(s)
- Chloe C Casagrande
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA
| | - Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Seth D Springer
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE 68178, USA.
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López-Cuenca I, Nebreda A, García-Colomo A, Salobrar-García E, de Frutos-Lucas J, Bruña R, Ramírez AI, Ramirez-Toraño F, Salazar JJ, Barabash A, Gil P, Maestú F, Ramírez JM, de Hoz R. Early visual alterations in individuals at-risk of Alzheimer's disease: a multidisciplinary approach. Alzheimers Res Ther 2023; 15:19. [PMID: 36694201 DOI: 10.1186/s13195-023-01166-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 01/08/2023] [Indexed: 01/26/2023]
Abstract
BACKGROUND The earliest pathological features of Alzheimer's disease (AD) appear decades before the clinical symptoms. The pathology affects the brain and the eye, leading to retinal structural changes and functional visual alterations. Healthy individuals at high risk of developing AD present alterations in these ophthalmological measures, as well as in resting-state electrophysiological activity. However, it is unknown whether the ophthalmological alterations are related to the visual-related electrophysiological activity. Elucidating this relationship is paramount to understand the mechanisms underlying the early deterioration of the system and an important step in assessing the suitability of these measures as early biomarkers of disease. METHODS In total, 144 healthy subjects: 105 with family history of AD and 39 without, underwent ophthalmologic analysis, magnetoencephalography recording, and genotyping. A subdivision was made to compare groups with less demographic and more risk differences: 28 high-risk subjects (relatives/APOEɛ4 +) and 16 low-risk (non-relatives/APOEɛ4 -). Differences in visual acuity, contrast sensitivity, and macular thickness were evaluated. Correlations between each variable and visual-related electrophysiological measures (M100 latency and time-frequency power) were calculated for each group. RESULTS High-risk groups showed increased visual acuity. Visual acuity was also related to a lower M100 latency and a greater power time-frequency cluster in the high-risk group. Low-risk groups did not show this relationship. High-risk groups presented trends towards a greater contrast sensitivity that did not remain significant after correction for multiple comparisons. The highest-risk group showed trends towards the thinning of the inner plexiform and inner nuclear layers that did not remain significant after correction. The correlation between contrast sensitivity and macular thickness, and the electrophysiological measures were not significant after correction. The difference between the high- and low- risk groups correlations was no significant. CONCLUSIONS To our knowledge, this paper is the first of its kind, assessing the relationship between ophthalmological and electrophysiological measures in healthy subjects at distinct levels of risk of AD. The results are novel and unexpected, showing an increase in visual acuity among high-risk subjects, who also exhibit a relationship between this measure and visual-related electrophysiological activity. These results have not been previously explored and could constitute a useful object of research as biomarkers for early detection and the evaluation of potential interventions' effectiveness.
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Saunders TS, Gadd DA, Spires‐Jones TL, King D, Ritchie C, Muniz‐Terrera G. Associations between cerebrospinal fluid markers and cognition in ageing and dementia: A systematic review. Eur J Neurosci 2022; 56:5650-5713. [PMID: 35338546 PMCID: PMC9790745 DOI: 10.1111/ejn.15656] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/08/2022] [Accepted: 03/13/2022] [Indexed: 12/30/2022]
Abstract
A biomarker associated with cognition in neurodegenerative dementias would aid in the early detection of disease progression, complement clinical staging and act as a surrogate endpoint in clinical trials. The current systematic review evaluates the association between cerebrospinal fluid protein markers of synapse loss and neuronal injury and cognition. We performed a systematic search which revealed 67 studies reporting an association between cerebrospinal fluid markers of interest and neuropsychological performance. Despite the substantial heterogeneity between studies, we found some evidence for an association between neurofilament-light and worse cognition in Alzheimer's diseases, frontotemporal dementia and typical cognitive ageing. Moreover, there was an association between cerebrospinal fluid neurogranin and cognition in those with an Alzheimer's-like cerebrospinal fluid biomarker profile. Some evidence was found for cerebrospinal fluid neuronal pentraxin-2 as a correlate of cognition across dementia syndromes. Due to the substantial heterogeneity of the field, no firm conclusions can be drawn from this review. Future research should focus on improving standardization and reporting as well as establishing the importance of novel markers such as neuronal pentraxin-2 and whether such markers can predict longitudinal cognitive decline.
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Affiliation(s)
- Tyler S. Saunders
- UK Dementia Research InstituteThe University of EdinburghEdinburghUK,Center for Discovery Brain SciencesThe University of EdinburghEdinburghUK,Center for Clinical Brain SciencesThe University of EdinburghEdinburghUK,Center for Dementia PreventionThe University of EdinburghEdinburghUK
| | - Danni A. Gadd
- Center for Genomic and Experimental Medicine, Institute of Genetics and Molecular MedicineUniversity of EdinburghEdinburghUK
| | - Tara L. Spires‐Jones
- UK Dementia Research InstituteThe University of EdinburghEdinburghUK,Center for Discovery Brain SciencesThe University of EdinburghEdinburghUK
| | - Declan King
- UK Dementia Research InstituteThe University of EdinburghEdinburghUK,Center for Discovery Brain SciencesThe University of EdinburghEdinburghUK
| | - Craig Ritchie
- Center for Clinical Brain SciencesThe University of EdinburghEdinburghUK,Center for Dementia PreventionThe University of EdinburghEdinburghUK
| | - Graciela Muniz‐Terrera
- Center for Clinical Brain SciencesThe University of EdinburghEdinburghUK,Center for Dementia PreventionThe University of EdinburghEdinburghUK
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Vaghari D, Bruna R, Hughes LE, Nesbitt D, Tibon R, Rowe JB, Maestu F, Henson RN. A multi-site, multi-participant magnetoencephalography resting-state dataset to study dementia: The BioFIND dataset. Neuroimage 2022; 258:119344. [PMID: 35660461 PMCID: PMC7613066 DOI: 10.1016/j.neuroimage.2022.119344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 04/26/2022] [Accepted: 05/30/2022] [Indexed: 01/04/2023] Open
Abstract
Early detection of Alzheimer's Disease (AD) is vital to reduce the burden of dementia and for developing effective treatments. Neuroimaging can detect early brain changes, such as hippocampal atrophy in Mild Cognitive Impairment (MCI), a prodromal state of AD. However, selecting the most informative imaging features by machine-learning requires many cases. While large publically-available datasets of people with dementia or prodromal disease exist for Magnetic Resonance Imaging (MRI), comparable datasets are missing for Magnetoencephalography (MEG). MEG offers advantages in its millisecond resolution, revealing physiological changes in brain oscillations or connectivity before structural changes are evident with MRI. We introduce a MEG dataset with 324 individuals: patients with MCI and healthy controls. Their brain activity was recorded while resting with eyes closed, using a 306-channel MEG scanner at one of two sites (Madrid or Cambridge), enabling tests of generalization across sites. A T1-weighted MRI is provided to assist source localisation. The MEG and MRI data are formatted according to international BIDS standards and analysed freely on the DPUK platform (https://portal.dementiasplatform.uk/Apply). Here, we describe this dataset in detail, report some example (benchmark) analyses, and consider its limitations and future directions.
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Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Iran
| | - Ricardo Bruna
- Department of Experimental Psychology, Complutense University of Madrid, Spain; Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Spain
| | - Laura E Hughes
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - David Nesbitt
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - Roni Tibon
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK
| | - James B Rowe
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Cambridge University Hospitals NHS Trust and Department of Clinical Neurosciences, University of Cambridge, UK
| | - Fernando Maestu
- Department of Experimental Psychology, Complutense University of Madrid, Spain; Center for Biomedical Technology, Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Spain
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
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7
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Mousavi M, Manshadi MD, Soltani M, Kashkooli FM, Rahmim A, Mosavi A, Kvasnica M, Atkinson PM, Kovács L, Koltay A, Kiss N, Adeli H. Modeling the efficacy of different anti-angiogenic drugs on treatment of solid tumors using 3D computational modeling and machine learning. Comput Biol Med 2022; 146:105511. [DOI: 10.1016/j.compbiomed.2022.105511] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 12/11/2022]
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8
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Güntekin B, Aktürk T, Arakaki X, Bonanni L, Del Percio C, Edelmayer R, Farina F, Ferri R, Hanoğlu L, Kumar S, Lizio R, Lopez S, Murphy B, Noce G, Randall F, Sack AT, Stocchi F, Yener G, Yıldırım E, Babiloni C. Are there consistent abnormalities in event-related EEG oscillations in patients with Alzheimer's disease compared to other diseases belonging to dementia? Psychophysiology 2022; 59:e13934. [PMID: 34460957 DOI: 10.1111/psyp.13934] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Revised: 07/31/2021] [Accepted: 08/09/2021] [Indexed: 01/30/2023]
Abstract
Cerebrospinal and structural-molecular neuroimaging in-vivo biomarkers are recommended for diagnostic purposes in Alzheimer's disease (AD) and other dementias; however, they do not explain the effects of AD neuropathology on neurophysiological mechanisms underpinning cognitive processes. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association reviewed the field literature and reached consensus on the event-related electroencephalographic oscillations (EROs) that show consistent abnormalities in patients with significant cognitive deficits due to Alzheimer's, Parkinson's (PD), Lewy body (LBD), and cerebrovascular diseases. Converging evidence from oddball paradigms showed that, as compared to cognitively unimpaired (CU) older adults, AD patients had lower amplitude in widespread delta (>4 Hz) and theta (4-7 Hz) phase-locked EROs as a function of disease severity. Similar effects were also observed in PD, LBD, and/or cerebrovascular cognitive impairment patients. Non-phase-locked alpha (8-12 Hz) and beta (13-30 Hz) oscillations were abnormally reduced (event-related desynchronization, ERD) in AD patients relative to CU. However, studies on patients with other dementias remain lacking. Delta and theta phase-locked EROs during oddball tasks may be useful neurophysiological biomarkers of cognitive systems at work in heuristic and intervention clinical trials performed in AD patients, but more research is needed regarding their potential role for other dementias.
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Affiliation(s)
- Bahar Güntekin
- Research Institute for Health Sciences and Technologies (SABITA), Regenerative and Restorative Medicine Research Center (REMER), Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Tuba Aktürk
- Research Institute for Health Sciences and Technologies (SABITA), Regenerative and Restorative Medicine Research Center (REMER), Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
- Vocational School, Program of Electroneurophysiology, Istanbul Medipol University, Istanbul, Turkey
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | | | - Laura Bonanni
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University G d'Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | - Francesca Farina
- School of Psychology, Trinity College Dublin, Dublin, Ireland
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Lütfü Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Sanjeev Kumar
- Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | | | - Susanna Lopez
- Department of Physiology and Pharmacology "Vittorio Erspamer", Sapienza University of Rome, Rome, Italy
| | | | | | - Fiona Randall
- Vertex Pharmaceuticals Incorporated, Boston, Massachusetts, USA
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Pisana, Rome, Italy
| | - Görsev Yener
- Izmir Biomedicine and Genome Center, Dokuz Eylul University Health Campus, Izmir, Turkey
| | - Ebru Yıldırım
- Research Institute for Health Sciences and Technologies (SABITA), Regenerative and Restorative Medicine Research Center (REMER), Clinical Electrophysiology, Neuroimaging and Neuromodulation Lab, Istanbul Medipol University, Istanbul, Turkey
- Vocational School, Program of Electroneurophysiology, Istanbul Medipol University, Istanbul, Turkey
| | - Claudio Babiloni
- Alzheimer's Association, Chicago, Illinois, USA
- Institute for Research and Medical Care, Hospital San Raffaele of Cassino, Cassino, Italy
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Porcaro C, Vecchio F, Miraglia F, Zito G, Rossini PM. Dynamics of the "Cognitive" Brain Wave P3b at Rest for Alzheimer Dementia Prediction in Mild Cognitive Impairment. Int J Neural Syst 2022; 32:2250022. [PMID: 35435134 DOI: 10.1142/s0129065722500228] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Alzheimer's disease (AD) is the most common cause of dementia that involves a progressive and irrevocable decline in cognitive abilities and social behavior, thus annihilating the patient's autonomy. The theoretical assumption that disease-modifying drugs are most effective in the early stages hopefully in the prodromal stage called mild cognitive impairment (MCI) urgently pushes toward the identification of robust and individualized markers of cognitive decline to establish an early pharmacological intervention. This requires the combination of well-established neural mechanisms and the development of increasingly sensitive methodologies. Among the neurophysiological markers of attention and cognition, one of the sub-components of the 'cognitive brain wave' P300 recordable in an odd-ball paradigm -namely the P3b- is extensively regarded as a sensitive indicator of cognitive performance. Several studies have reliably shown that changes in the amplitude and latency of the P3b are strongly related to cognitive decline and aging both healthy and pathological. Here, we used a P3b spatial filter to enhance the electroencephalographic (EEG) characteristics underlying 175 subjects divided into 135 MCI subjects, 20 elderly controls (EC), and 20 young volunteers (Y). The Y group served to extract the P3b spatial filter from EEG data, which was later applied to the other groups during resting conditions with eyes open and without being asked to perform any task. The group of 135 MCI subjects could be divided into two subgroups at the end of a month follow-up: 75 with stable MCI (MCI-S, not converted to AD), 60 converted to AD (MCI-C). The P3b spatial filter was built by means of a signal processing method called Functional Source Separation (FSS), which increases signal-to-noise ratio by using a weighted sum of all EEG recording channels rather than relying on a single, or a small sub-set, of channels. A clear difference was observed for the P3b dynamics at rest between groups. Moreover, a machine learning approach showed that P3b at rest could correctly distinguish MCI from EC (80.6% accuracy) and MCI-S from MCI-C (74.1% accuracy), with an accuracy as high as 93.8% in discriminating between MCI-C and EC. Finally, a comparison of the Bayes factor revealed that the group differences among MCI-S and MCI-C were 138 times more likely to be detected using the P3b dynamics compared with the best performing single electrode (Pz) approach. In conclusion, we propose that P3b as measured through spatial filters can be safely regarded as a simple and sensitive marker to predict the conversion from an MCI to AD status eventually combined with other non-neurophysiological biomarkers for a more precise definition of dementia having neuropathological Alzheimer characteristics.
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Affiliation(s)
- Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy.,Institute of Cognitive Sciences and Technologies, (ISTC) - National Research Council (CNR), Rome, Italy.,Centre for Human Brain Health and School of Psychology, University of Birmingham, Birmingham, UK
| | - Fabrizio Vecchio
- Brain Connectivity Laboratory, Department of Neurosciences & Neurorehabilitation, IRCCS San Raffaele-Roma, Rome, Italy.,Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy
| | - Francesca Miraglia
- Department of Theoretical and Applied Sciences, eCampus University, Novedrate (Como), Italy.,Department of Neurology, Neurovascular Treatment Unit, San Camillo de Lellis Hospital, Rieti, Italy
| | - Giancarlo Zito
- Brain Connectivity Laboratory, Department of Neurosciences & Neurorehabilitation, IRCCS San Raffaele-Roma, Rome, Italy.,Department of Neurology, Neurovascular Treatment Unit, San Camillo de Lellis Hospital, Rieti, Italy
| | - Paolo Maria Rossini
- Brain Connectivity Laboratory, Department of Neurosciences & Neurorehabilitation, IRCCS San Raffaele-Roma, Rome, Italy
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Parker AF, Ohlhauser L, Scarapicchia V, Smart CM, Szoeke C, Gawryluk JR. A Systematic Review of Neuroimaging Studies Comparing Individuals with Subjective Cognitive Decline to Healthy Controls. J Alzheimers Dis 2022; 86:1545-1567. [DOI: 10.3233/jad-215249] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Background: Individuals with subjective cognitive decline (SCD) are hypothesized to be the earliest along the cognitive continuum between healthy aging and Alzheimer’s disease (AD), although more research is needed on this topic. Given that treatment approaches may be most effective pre-clinically, a primary objective of emerging research is to identify biological markers of SCD using neuroimaging methods. Objective: The current review aimed to comprehensively present the neuroimaging studies on SCD to date. Methods: PubMed and PsycINFO databases were searched for neuroimaging studies of individuals with SCD. Quality assessments were completed using the Appraisal tool for Cross-Sectional Studies. Results: In total, 62 neuroimaging studies investigating differences between participants with SCD and healthy controls were identified. Specifically, the number of studies were as follows: 36 MRI, 6 PET, 8 MRI/PET, 4 EEG, 7 MEG, and 1 SPECT. Across neuroimaging modalities, 48 of the 62 included studies revealed significant differences in brain structure and/or function between groups. Conclusion: Neuroimaging methods can identify differences between healthy controls and individuals with SCD. However, inconsistent results were found within and between neuroimaging modalities. Discrepancies across studies may be best accounted for by methodological differences, notably variable criteria for SCD, and differences in participant characteristics and risk factors for AD. Clinic based recruitment and cross-sectional study design were common and may bias the literature. Future neuroimaging investigations of SCD should consistently incorporate the standardized research criteria for SCD (as recommended by the SCD-Initiative), include more details of their SCD sample and their symptoms, and examine groups longitudinally.
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Affiliation(s)
- Ashleigh F. Parker
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Lisa Ohlhauser
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Vanessa Scarapicchia
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Colette M. Smart
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
| | - Cassandra Szoeke
- Department of Medicine, University of Melbourne, Melbourne, Australia
| | - Jodie R. Gawryluk
- Department of Psychology, University of Victoria, BC, Canada
- Institute on Aging and Lifelong Health, University of Victoria, BC, Canada
- Division of Medical Sciences, University of Victoria, BC, Canada
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Singh P, Wa Torek M, Ceglarek A, Fąfrowicz M, Lewandowska K, Marek T, Sikora-Wachowicz B, Oświȩcimka P. Analysis of fMRI Signals from Working Memory Tasks and Resting-State of Brain: Neutrosophic-Entropy-Based Clustering Algorithm. Int J Neural Syst 2022; 32:2250012. [PMID: 35179104 DOI: 10.1142/s0129065722500125] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This study applies a neutrosophic-entropy-based clustering algorithm (NEBCA) to analyze the fMRI signals. We consider the data obtained from four different working memory tasks and the brain's resting state for the experimental purpose. Three non-overlapping clusters of data related to temporal brain activity are determined and statistically analyzed. Moreover, we used the Uniform Manifold Approximation and Projection (UMAP) method to reduce system dimensionality and present the effectiveness of NEBCA. The results show that using NEBCA, we are able to distinguish between different working memory tasks and resting-state and identify subtle differences in the related activity of brain regions. By analyzing the statistical properties of the entropy inside the clusters, the various regions of interest (ROIs), according to Automated Anatomical Labeling (AAL) atlas crucial for clustering procedure, are determined. The inferior occipital gyrus is established as an important brain region in distinguishing the resting state from the tasks. Moreover, the inferior occipital gyrus and superior parietal lobule are identified as necessary to correct the data discrimination related to the different memory tasks. We verified the statistical significance of the results through the two-sample t-test and analysis of surrogates performed by randomization of the cluster elements. The presented methodology is also appropriate to determine the influence of time of day on brain activity patterns. The differences between working memory tasks and resting-state in the morning are related to a lower index of small-worldness and sleep inertia in the first hours after waking. We also compared the performance of NEBCA to two existing algorithms, KMCA and FKMCA. We showed the advantage of the NEBCA over these algorithms that could not effectively accumulate fMRI signals with higher variability.
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Affiliation(s)
- Pritpal Singh
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland
| | - Marcin Wa Torek
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland.,Faculty of Computer Science and Telecommunications, Cracow University of Technology, Kraków 31-155, Poland
| | - Anna Ceglarek
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Magdalena Fąfrowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Koryna Lewandowska
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Tadeusz Marek
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Barbara Sikora-Wachowicz
- Department of Cognitive Neuroscience and Neuroergonomics, Jagiellonian University, Kraków 30-348, Poland
| | - Paweł Oświȩcimka
- Institute of Theoretical Physics, Jagiellonian University, Kraków 30-348, Poland.,Complex Systems Theory Department, Institute of Nuclear Physics, Polish Academy of Sciences, Kraków 31-342, Poland
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12
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Johnson EL, Arciniega H, Jones KT, Kilgore-Gomez A, Berryhill ME. Individual predictors and electrophysiological signatures of working memory enhancement in aging. Neuroimage 2022; 250:118939. [PMID: 35104647 PMCID: PMC8923157 DOI: 10.1016/j.neuroimage.2022.118939] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 01/25/2022] [Indexed: 12/21/2022] Open
Abstract
A primary goal of translational neuroscience is to identify the neural mechanisms of age-related cognitive decline and develop protocols to maximally improve cognition. Here, we demonstrate how interventions that apply noninvasive neurostimulation to older adults improve working memory (WM). We found that one session of sham-controlled transcranial direct current stimulation (tDCS) selectively improved WM in older adults with more education, extending earlier work and underscoring the importance of identifying individual predictors of tDCS responsivity. Improvements in WM were associated with two distinct electrophysiological signatures. First, a broad enhancement of theta network synchrony tracked improvements in behavioral accuracy, with tDCS effects moderated by education level. Further analysis revealed that accuracy dynamics reflected an anterior-posterior network distribution regardless of cathode placement. Second, specific enhancements of theta-gamma phase-amplitude coupling (PAC) reflecting tDCS current flow tracked improvements in reaction time (RT). RT dynamics further explained inter-individual variability in WM improvement independent of education. These findings illuminate theta network synchrony and theta-gamma PAC as distinct but complementary mechanisms supporting WM in aging. Both mechanisms are amenable to intervention, the effectiveness of which can be predicted by individual demographic factors.
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Affiliation(s)
- Elizabeth L Johnson
- Northwestern University, Departments of Medical Social Sciences and Pediatrics, Chicago, IL, 60611.
| | - Hector Arciniega
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02215
| | - Kevin T Jones
- University of California-San Francisco, Department of Neurology, Neuroscape, San Francisco, CA, 94158
| | - Alexandrea Kilgore-Gomez
- Department of Psychology, Program in Cognitive and Brain Sciences, Program in Integrative Neuroscience, University of Nevada, Reno, 89557
| | - Marian E Berryhill
- Department of Psychology, Program in Cognitive and Brain Sciences, Program in Integrative Neuroscience, University of Nevada, Reno, 89557.
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13
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Abstract
Color has an important role in object recognition and visual working memory (VWM). Decoding color VWM in the human brain is helpful to understand the mechanism of visual cognitive process and evaluate memory ability. Recently, several studies showed that color could be decoded from scalp electroencephalogram (EEG) signals during the encoding stage of VWM, which process visible information with strong neural coding. Whether color could be decoded from other VWM processing stages, especially the maintaining stage which processes invisible information, is still unknown. Here, we constructed an EEG color graph convolutional network model (ECo-GCN) to decode colors during different VWM stages. Based on graph convolutional networks, ECo-GCN considers the graph structure of EEG signals and may be more efficient in color decoding. We found that (1) decoding accuracies for colors during the encoding, early, and late maintaining stages were 81.58%, 79.36%, and 77.06%, respectively, exceeding those during the pre-stimuli stage (67.34%), and (2) the decoding accuracy during maintaining stage could predict participants' memory performance. The results suggest that EEG signals during the maintaining stage may be more sensitive than behavioral measurement to predict the VWM performance of human, and ECo-GCN provides an effective approach to explore human cognitive function.
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Affiliation(s)
- Xiaowei Che
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Yuanjie Zheng
- Key Laboratory of Intelligent Computing & Information, Security in Universities of Shandong Shandong Provincial, Key Laboratory for Novel Distributed Computer Software, Technology Shandong Key Laboratory of Medical, Physics and Image Processing School of Information, Science and Engineering Institute of Biomedical Sciences, Shandong Normal University, Jinan 250358, P. R. China
| | - Xin Chen
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Sutao Song
- School of Information Science and Engineering, Shandong Normal University, Jinan 250358, P. R. China
| | - Shouxin Li
- Department of Psychology, Shandong Normal University, Jinan, P. R. China
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14
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Yaqub MA, Hong KS, Zafar A, Kim CS. Control of Transcranial Direct Current Stimulation Duration by Assessing Functional Connectivity of Near-Infrared Spectroscopy Signals. Int J Neural Syst 2021; 32:2150050. [PMID: 34609264 DOI: 10.1142/s0129065721500507] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Transcranial direct current stimulation (tDCS) has been shown to create neuroplasticity in healthy and diseased populations. The control of stimulation duration by providing real-time brain state feedback using neuroimaging is a topic of great interest. This study presents the feasibility of a closed-loop modulation for the targeted functional network in the prefrontal cortex. We hypothesize that we cannot improve the brain state further after reaching a specific state during a stimulation therapy session. A high-definition tDCS of 1[Formula: see text]mA arranged in a ring configuration was applied at the targeted right prefrontal cortex of 15 healthy male subjects for 10[Formula: see text]min. Functional near-infrared spectroscopy was used to monitor hemoglobin chromophores during the stimulation period continuously. The correlation matrices obtained from filtered oxyhemoglobin were binarized to form subnetworks of short- and long-range connections. The connectivity in all subnetworks was analyzed individually using a new quantification measure of connectivity percentage based on the correlation matrix. The short-range network in the stimulated hemisphere showed increased connectivity in the initial stimulation phase. However, the increase in connection density reduced significantly after 6[Formula: see text]min of stimulation. The short-range network of the left hemisphere and the long-range network gradually increased throughout the stimulation period. The connectivity percentage measure showed a similar response with network theory parameters. The connectivity percentage and network theory metrics represent the brain state during the stimulation therapy. The results from the network theory metrics, including degree centrality, efficiency, and connection density, support our hypothesis and provide a guideline for feedback on the brain state. The proposed neuro-feedback scheme is feasible to control the stimulation duration to avoid overdosage.
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Affiliation(s)
- M Atif Yaqub
- ICFO-Institut de Ciències Fotòniques, The Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain
| | - Keum-Shik Hong
- School of Mechanical Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
| | - Amad Zafar
- Department of Electrical Engineering, University of Lahore, Sihala Zone V, Islamabad, Pakistan
| | - Chang-Seok Kim
- Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Geumjeong-gu, Busan 46241, Korea
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15
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Tzimourta KD, Christou V, Tzallas AT, Giannakeas N, Astrakas LG, Angelidis P, Tsalikakis D, Tsipouras MG. Machine Learning Algorithms and Statistical Approaches for Alzheimer's Disease Analysis Based on Resting-State EEG Recordings: A Systematic Review. Int J Neural Syst 2021; 31:2130002. [PMID: 33588710 DOI: 10.1142/s0129065721300023] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Alzheimer's Disease (AD) is a neurodegenerative disorder and the most common type of dementia with a great prevalence in western countries. The diagnosis of AD and its progression is performed through a variety of clinical procedures including neuropsychological and physical examination, Electroencephalographic (EEG) recording, brain imaging and blood analysis. During the last decades, analysis of the electrophysiological dynamics in AD patients has gained great research interest, as an alternative and cost-effective approach. This paper summarizes recent publications focusing on (a) AD detection and (b) the correlation of quantitative EEG features with AD progression, as it is estimated by Mini Mental State Examination (MMSE) score. A total of 49 experimental studies published from 2009 until 2020, which apply machine learning algorithms on resting state EEG recordings from AD patients, are reviewed. Results of each experimental study are presented and compared. The majority of the studies focus on AD detection incorporating Support Vector Machines, while deep learning techniques have not yet been applied on large EEG datasets. Promising conclusions for future studies are presented.
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Affiliation(s)
- Katerina D Tzimourta
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GR50100, Greece.,Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Vasileios Christou
- Q Base R&D, Science & Technology Park of Epirus, University of Ioannina Campus, Ioannina GR45110, Greece.,Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Alexandros T Tzallas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, School of Informatics and Telecommunications, University of Ioannina, Arta GR47100, Greece
| | - Loukas G Astrakas
- Department of Medical Physics, Medical School, University of Ioannina, Ioannina GR45110, Greece
| | - Pantelis Angelidis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Dimitrios Tsalikakis
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
| | - Markos G Tsipouras
- Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani GR50100, Greece
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16
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Amezquita-Sanchez JP, Mammone N, Morabito FC, Adeli H. A New dispersion entropy and fuzzy logic system methodology for automated classification of dementia stages using electroencephalograms. Clin Neurol Neurosurg 2020; 201:106446. [PMID: 33383465 DOI: 10.1016/j.clineuro.2020.106446] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 01/09/2023]
Abstract
A new EEG-based methodology is presented for differential diagnosis of the Alzheimer's disease (AD), Mild Cognitive Impairment (MCI), and healthy subjects employing the discrete wavelet transform (DWT), dispersion entropy index (DEI), a recently-proposed nonlinear measurement, and a fuzzy logic-based classification algorithm. The effectiveness and usefulness of the proposed methodology are evaluated by employing a database of measured EEG data acquired from 135 subjects, 45 MCI, 45 AD and 45 healthy subjects. The proposed methodology differentiates MCI and AD patients from HC subjects with an accuracy of 82.6-86.9%, sensitivity of 91 %, and specificity of 87 %.
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Affiliation(s)
- Juan P Amezquita-Sanchez
- Autonomous University of Queretaro (UAQ), Faculty of Engineering, Departments Biomedical and Electromechanical, Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, C. P. 76807, San Juan del Río, Qro., Mexico
| | - Nadia Mammone
- Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy
| | - Francesco C Morabito
- Department DICEAM of the Mediterranean University of Reggio Calabria, 89060, Reggio Calabria, Italy
| | - Hojjat Adeli
- Departments of Biomedical Informatics and Neuroscience, The Ohio State University, 470 Hitchcock Hall, 2070 Neil Avenue, Columbus, OH, 43220, USA.
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