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Hasoon J, Hamilton CA, Schumacher J, Colloby S, Donaghy PC, Thomas AJ, Taylor JP. EEG Functional Connectivity Differences Predict Future Conversion to Dementia in Mild Cognitive Impairment With Lewy Body or Alzheimer Disease. Int J Geriatr Psychiatry 2024; 39:e6138. [PMID: 39261275 DOI: 10.1002/gps.6138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 08/04/2024] [Accepted: 08/13/2024] [Indexed: 09/13/2024]
Abstract
BACKGROUND Predicting which individuals may convert to dementia from mild cognitive impairment (MCI) remains difficult in clinical practice. Electroencephalography (EEG) is a widely available investigation but there is limited research exploring EEG connectivity differences in patients with MCI who convert to dementia. METHODS Participants with a diagnosis of MCI due to Alzheimer's disease (MCI-AD) or Lewy body disease (MCI-LB) underwent resting state EEG recording. They were followed up annually with a review of the clinical diagnosis (n = 66). Participants with a diagnosis of dementia at year 1 or year 2 follow up were classed as converters (n = 23) and those with a diagnosis of MCI at year 2 were classed as stable (n = 43). We used phase lag index (PLI) to estimate functional connectivity as well as analysing dominant frequency (DF) and relative band power. The Network-based statistic (NBS) toolbox was used to assess differences in network topology. RESULTS The converting group had reduced DF (U = 285.5, p = 0.005) and increased relative pre-alpha power (U = 702, p = 0.005) consistent with previous findings. PLI showed reduced average beta band synchrony in the converting group (U = 311, p = 0.014) as well as significant differences in alpha and beta network topology. Logistic regression models using regional beta PLI values revealed that right central to right lateral (Sens = 56.5%, Spec = 86.0%, -2LL = 72.48, p = 0.017) and left central to right lateral (Sens = 47.8%, Spec = 81.4%, -2LL = 71.37, p = 0.012) had the best classification accuracy and fit when adjusted for age and MMSE score. CONCLUSION Patients with MCI who convert to dementia have significant differences in EEG frequency, average connectivity and network topology prior to the onset of dementia. The MCI group is clinically heterogeneous and have underlying physiological differences that may be driving the progression of cognitive symptoms. EEG connectivity could be useful to predict which patients with MCI-AD and MCI-LB convert to dementia, regardless of the neurodegenerative aetiology.
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Affiliation(s)
- Jahfer Hasoon
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Calum A Hamilton
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Julia Schumacher
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
- Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock-Greifswald, Rostock, Germany
- Department of Neurology, University Medical Center Rostock, Rostock, Germany
| | - Sean Colloby
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Paul C Donaghy
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Alan J Thomas
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | - John-Paul Taylor
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
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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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Csukly G, Tombor L, Hidasi Z, Csibri E, Fullajtár M, Huszár Z, Koszovácz V, Lányi O, Vass E, Koleszár B, Kóbor I, Farkas K, Rosenfeld V, Berente DB, Bolla G, Kiss M, Kamondi A, Horvath AA. Low Functional network integrity in cognitively unimpaired and MCI subjects with depressive symptoms: results from a multi-center fMRI study. Transl Psychiatry 2024; 14:179. [PMID: 38580625 PMCID: PMC10997664 DOI: 10.1038/s41398-024-02891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/19/2024] [Accepted: 03/22/2024] [Indexed: 04/07/2024] Open
Abstract
Evidence suggests that depressive symptomatology is a consequence of network dysfunction rather than lesion pathology. We studied whole-brain functional connectivity using a Minimum Spanning Tree as a graph-theoretical approach. Furthermore, we examined functional connectivity in the Default Mode Network, the Frontolimbic Network (FLN), the Salience Network, and the Cognitive Control Network. All 183 elderly subjects underwent a comprehensive neuropsychological evaluation and a 3 Tesla brain MRI scan. To assess the potential presence of depressive symptoms, the 13-item version of the Beck Depression Inventory (BDI) or the Geriatric Depression Scale (GDS) was utilized. Participants were assigned into three groups based on their cognitive status: amnestic mild cognitive impairment (MCI), non-amnestic MCI, and healthy controls. Regarding affective symptoms, subjects were categorized into depressed and non-depressed groups. An increased mean eccentricity and network diameter were found in patients with depressive symptoms relative to non-depressed ones, and both measures showed correlations with depressive symptom severity. In patients with depressive symptoms, a functional hypoconnectivity was detected between the Anterior Cingulate Cortex (ACC) and the right amygdala in the FLN, which impairment correlated with depressive symptom severity. While no structural difference was found in subjects with depressive symptoms, the volume of the hippocampus and the thickness of the precuneus and the entorhinal cortex were decreased in subjects with MCI, especially in amnestic MCI. The increase in eccentricity and diameter indicates a more path-like functional network configuration that may lead to an impaired functional integration in depression, a possible cause of depressive symptomatology in the elderly.
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Affiliation(s)
- Gabor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary.
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary.
| | - László Tombor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zoltan Hidasi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Eva Csibri
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Máté Fullajtár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Zsolt Huszár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Vanda Koszovácz
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Orsolya Lányi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Edit Vass
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - Boróka Koleszár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary
| | - István Kóbor
- Medical Imaging Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Farkas
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Viktoria Rosenfeld
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Dalida Borbála Berente
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
| | - Gergo Bolla
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Measurement and Information Systems, University of Technology and Economics, Budapest, Hungary
| | - Mate Kiss
- Siemens Healthcare, Budapest, Hungary
| | - Anita Kamondi
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Andras Attila Horvath
- Neurocognitive Research Center, Budapest, National Institute of Mental Health, Neurology, and Neurosurgery, Budapest, Hungary
- Department of Anatomy Histology and Embryology, Semmelweis University, Budapest, Hungary
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Stam CJ. Hub overload and failure as a final common pathway in neurological brain network disorders. Netw Neurosci 2024; 8:1-23. [PMID: 38562292 PMCID: PMC10861166 DOI: 10.1162/netn_a_00339] [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: 06/21/2023] [Accepted: 09/26/2023] [Indexed: 04/04/2024] Open
Abstract
Understanding the concept of network hubs and their role in brain disease is now rapidly becoming important for clinical neurology. Hub nodes in brain networks are areas highly connected to the rest of the brain, which handle a large part of all the network traffic. They also show high levels of neural activity and metabolism, which makes them vulnerable to many different types of pathology. The present review examines recent evidence for the prevalence and nature of hub involvement in a variety of neurological disorders, emphasizing common themes across different types of pathology. In focal epilepsy, pathological hubs may play a role in spreading of seizure activity, and removal of such hub nodes is associated with improved outcome. In stroke, damage to hubs is associated with impaired cognitive recovery. Breakdown of optimal brain network organization in multiple sclerosis is accompanied by cognitive dysfunction. In Alzheimer's disease, hyperactive hub nodes are directly associated with amyloid-beta and tau pathology. Early and reliable detection of hub pathology and disturbed connectivity in Alzheimer's disease with imaging and neurophysiological techniques opens up opportunities to detect patients with a network hyperexcitability profile, who could benefit from treatment with anti-epileptic drugs.
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Affiliation(s)
- Cornelis Jan Stam
- Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Narmashiri A, Akbari F, Sohrabi A, Hatami J. Conspiracy beliefs are associated with a reduction in frontal beta power and biases in categorizing ambiguous stimuli. Heliyon 2023; 9:e20249. [PMID: 37810845 PMCID: PMC10550632 DOI: 10.1016/j.heliyon.2023.e20249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 09/14/2023] [Accepted: 09/14/2023] [Indexed: 10/10/2023] Open
Abstract
Prior beliefs, such as conspiracy beliefs, significantly influence our perception of the natural world. However, the brain activity associated with perceptual decision-making in conspiracy beliefs is not well understood. To shed light on this topic, we conducted a study examining the EEG activity of believers, and skeptics during resting state with perceptual decision-making task. Our study shows that conspiracy beliefs are related to the reduced power of beta frequency band. Furthermore, skeptics tended to misclassify ambiguous face stimuli as houses more frequently than believers. These results help to explain the differences in brain activity between believers and skeptics, especially in how conspiracy beliefs impact the categorization of ambiguous stimuli.
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Affiliation(s)
- Abdolvahed Narmashiri
- School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Bio-intelligence Research Unit, Sharif Brain Center, Electrical Engineering Department, Sharif University of Technology, Tehran, Iran
- Shahid Beheshti University, Tehran, Iran
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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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Hadiyoso S, Ong PA, Zakaria H, Rajab TLE. EEG-Based Spectral Dynamic in Characterization of Poststroke Patients with Cognitive Impairment for Early Detection of Vascular Dementia. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:5666229. [PMID: 36444210 PMCID: PMC9701122 DOI: 10.1155/2022/5666229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 09/08/2022] [Accepted: 11/03/2022] [Indexed: 10/17/2023]
Abstract
One common type of vascular dementia (VaD) is poststroke dementia (PSD). Vascular dementia can occur in one-third of stroke patients. The worsening of cognitive function can occur quickly if not detected and treated early. One of the potential medical modalities for observing this disorder by considering costs and safety factors is electroencephalogram (EEG). It is thought that there are differences in the spectral dynamics of the EEG signal between the normal group and stroke patients with cognitive impairment so that it can be used in detection. Therefore, this study proposes an EEG signal characterization method using EEG spectral power complexity measurements to obtain features of poststroke patients with cognitive impairment and normal subjects. Working memory EEGs were collected and analyzed from forty-two participants, consisting of sixteen normal subjects, fifteen poststroke patients with mild cognitive impairment, and eleven poststroke patients with dementia. From the analysis results, it was found that there were differences in the dynamics of the power spectral in each group, where the spectral power of the cognitively impaired group was more regular than the normal group. Notably, (1) significant differences in spectral entropy (SpecEn) with a p value <0.05 were found for all electrodes, (2) there was a relationship between SpecEn values and the severity of dementia (SpecEnDem < SpecEnMCI < SpecEnNormal), and (3) a post hoc multiple comparison test showed significant differences between groups at the F7 electrode. This study shows that spectral complexity analysis can discriminate between normal and poststroke patients with cognitive impairment. For further studies, it is necessary to simulate performance validation so that the proposed approach can be used in the early detection of poststroke dementia and monitoring the development of dementia.
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Affiliation(s)
- Sugondo Hadiyoso
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
- School of Applied Science, Telkom University, Bandung, Indonesia
| | - Paulus Anam Ong
- Departement of Neurology, Faculty of Medicine, Padjadjaran University, Dr. Hasan Sadikin General Hospital, Bandung, Indonesia
| | - Hasballah Zakaria
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
| | - Tati Latifah E. Rajab
- School of Electrical Engineering and Informatics, Bandung Institute of Technology, Bandung, Indonesia
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Aydın S, Akın B. Machine learning classification of maladaptive rumination and cognitive distraction in terms of frequency specific complexity. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103740] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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