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Požar R, Martin T, Giordani B, Kavcic V. Enhanced functional brain network integration in mild cognitive impairment during cognitive task performance: A compensatory mechanism or a result of neural disinhibition? Eur J Neurosci 2024. [PMID: 39180174 DOI: 10.1111/ejn.16511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2024] [Revised: 07/16/2024] [Accepted: 08/06/2024] [Indexed: 08/26/2024]
Abstract
Although previous studies have observed increased global network integration during tasks in persons with mild cognitive impairment (MCI), the association between this integration and actual task performance has remained unexplored. Understanding this link is crucial for uncovering the underlying mechanism behind these changes in network integration and their potential role in MCI. Here, to find such a link, we investigated brain network integration derived from electroencephalography recordings during a visual motion discrimination task in older adults with MCI and those with normal cognition. We focused on a critical period just before stimulus presentation, which is known to be important for task performance. Our results revealed that during this period, MCI patients exhibited increased network integration compared to controls. Interestingly, increased integration was associated with worse task performance in the MCI group, suggesting it was not beneficial. No such association was found in the control group. Notably, this difference existed despite similar overall task performance between the groups. This suboptimal integration pattern during the cognitive task might reflect network de-differentiation due to disinhibition in MCI patients. Collectively, our study highlights the potential of analysing network integration during tasks to identify cognitive impairment and suggest a distinct role for network integration in MCI patients compared with healthy controls.
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Affiliation(s)
- Rok Požar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia
- Andrej Marušič Institute, University of Primorska, Koper, Slovenia
- Physics and Mechanics, Institute of Mathematics, Ljubljana, Slovenia
| | - Tim Martin
- Kennesaw State University, Kennesaw, Georgia, USA
| | - Bruno Giordani
- Michigan Alzheimer's Disease Research Center, Ann Arbor, Michigan, USA
- University of Michigan, Ann Arbor, Michigan, USA
| | - Voyko Kavcic
- Wayne State University, Institute of Gerontology, Detroit, Michigan, USA
- International Institute of Applied Gerontology, Ljubljana, Slovenia
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2
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Holston EC. An Integrative Review about Electrophysiological Biomarkers of Cognitive Impairment in Alzheimer's Disease: A Developing Relationship. Issues Ment Health Nurs 2024; 45:746-757. [PMID: 38954497 DOI: 10.1080/01612840.2024.2352011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations.
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Affiliation(s)
- Ezra C Holston
- Orvis School of Nursing, University of Nevada Reno, Reno, Nevada, USA
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Deng J, Sun B, Kavcic V, Liu M, Giordani B, Li T. Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG. Alzheimers Dement 2024; 20:145-158. [PMID: 37496373 PMCID: PMC10811294 DOI: 10.1002/alz.13411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 07/05/2023] [Accepted: 07/06/2023] [Indexed: 07/28/2023]
Abstract
BACKGROUND Early discrimination and prediction of cognitive decline are crucial for the study of neurodegenerative mechanisms and interventions to promote cognitive resiliency. METHODS Our research is based on resting-state electroencephalography (EEG) and the current dataset includes 137 consensus-diagnosed, community-dwelling Black Americans (ages 60-90 years, 84 healthy controls [HC]; 53 mild cognitive impairment [MCI]) recruited through Wayne State University and Michigan Alzheimer's Disease Research Center. We conducted multiscale analysis on time-varying brain functional connectivity and developed an innovative soft discrimination model in which each decision on HC or MCI also comes with a connectivity-based score. RESULTS The leave-one-out cross-validation accuracy is 91.97% and 3-fold accuracy is 91.17%. The 9 to 18 months' progression trend prediction accuracy over an availability-limited subset sample is 84.61%. CONCLUSION The EEG-based soft discrimination model demonstrates high sensitivity and reliability for MCI detection and shows promising capability in proactive prediction of people at risk of MCI before clinical symptoms may occur.
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Affiliation(s)
- Jinxian Deng
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
| | - Boxin Sun
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
| | - Voyko Kavcic
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- International Institute of Applied GerontologyLjubljanaSlovenia
| | - Mingyan Liu
- Department of Electrical Engineering and Computer ScienceUniversity of MichiganAnn ArborMichiganUSA
| | - Bruno Giordani
- Departments of PsychiatryNeurologyPsychology and School of NursingUniversity of MichiganAnn ArborMichiganUSA
- Michigan Alzheimer's Disease Research CenterAnn ArborMichiganUSA
| | - Tongtong Li
- Department of Electrical and Computer EngineeringMichigan State UniversityEast LansingMichiganUSA
- Michigan Alzheimer's Disease Research CenterAnn ArborMichiganUSA
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Buzi G, Fornari C, Perinelli A, Mazza V. Functional connectivity changes in mild cognitive impairment: A meta-analysis of M/EEG studies. Clin Neurophysiol 2023; 156:183-195. [PMID: 37967512 DOI: 10.1016/j.clinph.2023.10.011] [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: 04/19/2023] [Revised: 08/31/2023] [Accepted: 10/22/2023] [Indexed: 11/17/2023]
Abstract
OBJECTIVE Early synchrony alterations have been observed through electrophysiological techniques in Mild Cognitive Impairment (MCI), which is considered the intermediate phase between healthy aging (HC) and Alzheimer's disease (AD). However, the documented direction (hyper/hypo-synchronization), regions and frequency bands affected are inconsistent. This meta-analysis intended to elucidate existing evidence linked to potential neurophysiological biomarkers of AD. METHODS We conducted a random-effects meta-analysis that entailed the unbiased inclusion of Non-statistically Significant Unreported Effect Sizes ("MetaNSUE") of electroencephalogram (EEG) and magnetoencephalogram (MEG) studies investigating functional connectivity changes at rest along the healthy-pathological aging continuum, searched through PubMed, Scopus, Web of Science and PsycINFO databases until June 2023. RESULTS Of the 3852 articles extracted, we analyzed 12 papers, and we found an alpha synchrony decrease in MCI compared to HC, specifically between temporal-parietal (d = -0.26) and frontal-parietal areas (d = -0.25). CONCLUSIONS Alterations of alpha synchrony are present even at MCI stage. SIGNIFICANCE Synchrony measures may be promising for the detection of the first hallmarks of connectivity alterations, even at the prodromal stages of the AD, before clinical symptoms occur.
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Affiliation(s)
- Giulia Buzi
- U1077 INSERM-EPHE-UNICAEN, Caen 14000, France
| | - Chiara Fornari
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
| | - Alessio Perinelli
- Department of Physics, University of Trento, Trento, Italy; INFN-TIFPA, Trento, Italy
| | - Veronica Mazza
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Italy.
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Ghanbari M, Li G, Hsu L, Yap P. Accumulation of network redundancy marks the early stage of Alzheimer's disease. Hum Brain Mapp 2023; 44:2993-3006. [PMID: 36896755 PMCID: PMC10171535 DOI: 10.1002/hbm.26257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/15/2023] [Accepted: 02/18/2023] [Indexed: 03/11/2023] Open
Abstract
Brain wiring redundancy counteracts aging-related cognitive decline by reserving additional communication channels as a neuroprotective mechanism. Such a mechanism plays a potentially important role in maintaining cognitive function during the early stages of neurodegenerative disorders such as Alzheimer's disease (AD). AD is characterized by severe cognitive decline and involves a long prodromal stage of mild cognitive impairment (MCI). Since MCI subjects are at high risk of converting to AD, identifying MCI individuals is essential for early intervention. To delineate the redundancy profile during AD progression and enable better MCI diagnosis, we define a metric that reflects redundant disjoint connections between brain regions and extract redundancy features in three high-order brain networks-medial frontal, frontoparietal, and default mode networks-based on dynamic functional connectivity (dFC) captured by resting-state functional magnetic resonance imaging (rs-fMRI). We show that redundancy increases significantly from normal control (NC) to MCI individuals and decreases slightly from MCI to AD individuals. We further demonstrate that statistical features of redundancy are highly discriminative and yield state-of-the-art accuracy of up to 96.8 ± 1.0% in support vector machine (SVM) classification between NC and MCI individuals. This study provides evidence supporting the notion that redundancy serves as a crucial neuroprotective mechanism in MCI.
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Affiliation(s)
- Maryam Ghanbari
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Guoshi Li
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Li‐Ming Hsu
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
| | - Pew‐Thian Yap
- Department of RadiologyUniversity of North CarolinaChapel HillNorth CarolinaUSA
- Biomedical Research Imaging CenterUniversity of North CarolinaChapel HillNorth CarolinaUSA
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Adebisi AT, Veluvolu KC. Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review. Front Aging Neurosci 2023; 15:1039496. [PMID: 36936496 PMCID: PMC10020520 DOI: 10.3389/fnagi.2023.1039496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Background Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification. Objective With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. Methods In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy. Results Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics. Significance This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.
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Affiliation(s)
- Abdulyekeen T. Adebisi
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Kalyana C. Veluvolu
- School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
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Požar R, Kero K, Martin T, Giordani B, Kavcic V. Task aftereffect reorganization of resting state functional brain networks in healthy aging and mild cognitive impairment. Front Aging Neurosci 2023; 14:1061254. [PMID: 36711212 PMCID: PMC9876535 DOI: 10.3389/fnagi.2022.1061254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 12/05/2022] [Indexed: 01/12/2023] Open
Abstract
The view of the human brain as a complex network has led to considerable advances in understanding the brain's network organization during rest and task, in both health and disease. Here, we propose that examining brain networks within the task aftereffect model, in which we compare resting-state networks immediately before and after a cognitive engagement task, may enhance differentiation between those with normal cognition and those with increased risk for cognitive decline. We validated this model by comparing the pre- and post-task resting-state functional network organization of neurologically intact elderly and those with mild cognitive impairment (MCI) derived from electroencephalography recordings. We have demonstrated that a cognitive task among MCI patients induced, compared to healthy controls, a significantly higher increment in global network integration with an increased number of vertices taking a more central role within the network from the pre- to post-task resting state. Such modified network organization may aid cognitive performance by increasing the flow of information through the most central vertices among MCI patients who seem to require more communication and recruitment across brain areas to maintain or improve task performance. This could indicate that MCI patients are engaged in compensatory activation, especially as both groups did not differ in their task performance. In addition, no significant group differences were observed in network topology during the pre-task resting state. Our findings thus emphasize that the task aftereffect model is relevant for enhancing the identification of network topology abnormalities related to cognitive decline, and also for improving our understanding of inherent differences in brain network organization for MCI patients, and could therefore represent a valid marker of cortical capacity and/or cortical health.
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Affiliation(s)
- Rok Požar
- Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia,Andrej Marušič Institute, University of Primorska, Koper, Slovenia,Institute of Mathematics, Physics and Mechanics, Ljubljana, Slovenia,*Correspondence: Rok Požar, ✉
| | - Katherine Kero
- Institute of Gerontology, Wayne State University, Detroit, MI, United States
| | - Tim Martin
- Department of Psychological Science, Kennesaw State University, Kennesaw, GA, United States
| | - Bruno Giordani
- Michigan Alzheimer’s Disease Research Center, University of Michigan, Ann Arbor, MI, United States
| | - Voyko Kavcic
- Institute of Gerontology, Wayne State University, Detroit, MI, United States,International Institute of Applied Gerontology, Ljubljana, Slovenia
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Blomsma N, de Rooy B, Gerritse F, van der Spek R, Tewarie P, Hillebrand A, Otte WM, Stam CJ, van Dellen E. Minimum spanning tree analysis of brain networks: A systematic review
of network size effects, sensitivity for neuropsychiatric pathology and disorder
specificity. Netw Neurosci 2022; 6:301-319. [PMID: 35733422 PMCID: PMC9207994 DOI: 10.1162/netn_a_00245] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 03/10/2022] [Indexed: 11/05/2022] Open
Abstract
Brain network characteristics’ potential to serve as a neurological and psychiatric pathology biomarker has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. It is yet unknown whether this approach leads to more consistent findings across studies and converging outcomes of either disease-specific biomarkers or transdiagnostic effects. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies (N = 43) to study consistency of MST metrics between different network sizes and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. Analysis of data from control groups (12 studies) showed that MST leaf fraction but not diameter decreased with increasing network size. Studies showed a broad range in metric values, suggesting that specific processing pipelines affect MST topology. Contradicting findings remain in the inconclusive literature of MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders across pathologies, and is associated with symptom severity and disease progression; (2) neurophysiological studies in epilepsy show frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology in alpha band is found across disorders associated with attention impairments. The potential of brain network characteristics to serve as biomarker of neurological and psychiatric pathology has been hampered by the so-called thresholding problem. The minimum spanning tree (MST) is increasingly applied to overcome this problem. We performed a systematic review on MST analysis in neurophysiological and neuroimaging studies and assessed disease specificity and transdiagnostic sensitivity of MST metrics for neurological and psychiatric conditions. MST leaf fraction but not diameter decreased with increasing network size. Contradicting findings remain in the literature on MST brain network studies, but some trends were seen: (1) a more linelike organization characterizes neurodegenerative disorders; (2) in epilepsy there are frequency band specific MST alterations that normalize after successful treatment; and (3) less efficient MST topology is found across disorders associated with attention impairments.
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Affiliation(s)
- Nicky Blomsma
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Bart de Rooy
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Frank Gerritse
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Rick van der Spek
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Prejaas Tewarie
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Wim M. Otte
- University Medical Center Utrecht, Department of Child Neurology, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
| | - Cornelis Jan Stam
- Amsterdam UMC, Vrije Universiteit Amsterdam, Department of Neurology and Department of Clinical Neurophysiology and MEG center, Amsterdam Neuroscience, De Boelelaan 1117, Amsterdam, The Netherlands
| | - Edwin van Dellen
- University Medical Center Utrecht, Department of Psychiatry, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
- University Medical Center Utrecht, Department of Intensive Care Medicine, Brain Center, Heidelberglaan 100, Utrecht, the Netherlands
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9
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Wen D, Xu J, Wu Z, Liu Y, Zhou Y, Li J, Wang S, Dong X, Saripan MI, Song H. The Effective Cognitive Assessment and Training Methods for COVID-19 Patients With Cognitive Impairment. Front Aging Neurosci 2022; 13:827273. [PMID: 35087399 PMCID: PMC8787269 DOI: 10.3389/fnagi.2021.827273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 12/16/2021] [Indexed: 11/13/2022] Open
Affiliation(s)
- Dong Wen
- Brain Computer Intelligence and Intelligent Health Institution, Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
| | - Jian Xu
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Zhonglin Wu
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yijun Liu
- Department of Statistics, School of Science, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- Department of Computer Science and Technology, School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
- *Correspondence: Yanhong Zhou
| | - Jingjing Li
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Shaochang Wang
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianlin Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - M. Iqbal Saripan
- Department of Computer and Communication Systems Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Malaysia
| | - Haiqing Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing, China
- Haiqing Song
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Youssef N, Xiao S, Liu M, Lian H, Li R, Chen X, Zhang W, Zheng X, Li Y, Li Y. Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals. Front Comput Neurosci 2021; 15:698386. [PMID: 34776913 PMCID: PMC8579961 DOI: 10.3389/fncom.2021.698386] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 09/29/2021] [Indexed: 11/13/2022] Open
Abstract
The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group; the functional connectivity patterns were different in the left hemisphere between two groups; the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (-3.89%); (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.
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Affiliation(s)
- Nadia Youssef
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Shasha Xiao
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Meng Liu
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Haipeng Lian
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Renren Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Xi Chen
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Wei Zhang
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Xiaoran Zheng
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yunxia Li
- Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Yingjie Li
- School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,School of Life Sciences, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
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11
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Kavcic V, Daugherty AM, Giordani B. Post-task modulation of resting state EEG differentiates MCI patients from controls. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12153. [PMID: 33665343 PMCID: PMC7896632 DOI: 10.1002/dad2.12153] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 12/08/2020] [Accepted: 12/10/2020] [Indexed: 01/16/2023]
Abstract
Background: Early identification of cognitive decline is critical for identifying individuals for inclusion in clinical trials and for eventual care planning. Methods: A sample (ages 60-90 years) of consensus-diagnosed, community-dwelling Blacks (61 cognitively typical [HC], 28 amnestic mild cognitive impairment [aMCI], and 14 nonamnestic MCI [naMCI]) were recruited from the Michigan Alzheimer's Disease Research Center and the Wayne State University Institute of Gerontology. Participants received two resting state electroencephalograms (rsEEG, eyes closed) between which they engaged in a visual motion direction discrimination task. rsEEG %change current source densities across all frequency bands and regions of interest were calculated. Results: EEG current density was not different across groups for pre-task resting state. However, compared to HC, aMCI showed significantly greater declines at temporal and central cortical sites, while naMCI showed significant parietal declines. Conclusion: This novel approach of post-pre/cognitive challenge rsEEG successfully discriminated older persons with MCI from those without was sensitive to cognitive decline.
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Affiliation(s)
- Voyko Kavcic
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- International Institute of Applied GerontologyLjubljanaSlovenia
| | - Ana M. Daugherty
- Institute of GerontologyWayne State UniversityDetroitMichiganUSA
- Department of PsychologyDepartment of Psychiatry and Behavioral NeurosciencesWayne State UniversityDetroitMichiganUSA
| | - Bruno Giordani
- Departments of PsychiatryNeurology, and Psychology and School of NursingUniversity of MichiganAnn ArborMichiganUSA
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12
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Foy JG, Foy MR. Dynamic Changes in EEG Power Spectral Densities During NIH-Toolbox Flanker, Dimensional Change Card Sort Test and Episodic Memory Tests in Young Adults. Front Hum Neurosci 2020; 14:158. [PMID: 32508607 PMCID: PMC7248326 DOI: 10.3389/fnhum.2020.00158] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 04/14/2020] [Indexed: 11/13/2022] Open
Abstract
Much is known about electroencephalograph (EEG) patterns during sleep, but until recently, it was difficult to study EEG patterns during conscious, awake behavior. Technological advances such as powerful wireless EEG systems have led to a renewed interest in EEG as a clinical and research tool for studying real-time changes in the brain. We report here the first normative study of EEG activity while healthy young adults completed a series of cognitive tests recently published by the National Institutes of Health Toolbox Cognitive Battery (NIH-TCB), a commonly-used standardized measure of cognition primarily used in clinical populations. In this preliminary study using a wireless EEG system, we examined power spectral density (PSD) in four EEG frequency bands. During baseline and cognitive testing, PSD activity for the lower frequency bands (theta and alpha) was greater, relative to the higher frequency bands (beta and gamma), suggesting participants were relaxed and mentally alert. Alpha, beta and gamma activity was increased during a memory test compared to two other, less demanding executive function tests. Gamma activity was also inversely correlated with performance on the memory test, consistent with the neural efficiency hypothesis which proposes that better cognitive performance may link with lower cortical energy consumption. In summary, our study suggests that cognitive performance is related to the dynamics of EEG activity in a normative young adult population.
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Affiliation(s)
- Judith G. Foy
- Department of Psychology, Loyola Marymount University, Los Angeles, CA, United States
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