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Arjmandi-Rad S, Vestergaard Nieland JD, Goozee KG, Vaseghi S. The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review. Neurol Sci 2024; 45:417-430. [PMID: 37843690 DOI: 10.1007/s10072-023-07114-y] [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: 06/21/2023] [Accepted: 10/01/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD. METHODS PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study. RESULTS AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug. CONCLUSIONS We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.
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Affiliation(s)
- Shirin Arjmandi-Rad
- Institute for Cognitive & Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | | | - Kathryn G Goozee
- KaRa Institute of Neurological Diseases Pty Ltd, Macquarie, NSW, Australia
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Salar Vaseghi
- Cognitive Neuroscience Lab, Medicinal Plants Research Center, Institute of Medicinal Plants, ACECR, Karaj, Iran.
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2
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Dong H, Yang X, Sun Z. How glutamatergic synapse loss affects the firing rhythm of DG-CA3 model related with Alzheimer's disease. Cogn Neurodyn 2022; 16:167-181. [PMID: 35126776 PMCID: PMC8807830 DOI: 10.1007/s11571-021-09705-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2021] [Revised: 06/21/2021] [Accepted: 07/20/2021] [Indexed: 02/03/2023] Open
Abstract
As well known that synapse loss is a significant pathological feature of Alzheimer's disease (AD), meanwhile, the hippocampus is one of brain regions to be first affected in the early stage of AD. Thus, this work employs a comprehensive DG-CA3 network model of the hippocampus so as to explore the neuronal correlation between glutamatergic synapse loss and abnormal firing rhythm associated with AD from the perspective of neurocomputation. The neuropathological condition of glutamatergic synapse loss caused by the reduction of Shank3 protein in AD patients is imitated by decreasing glutamatergic excitatory synapse strength between different neurons. By means of power spectral analysis and dynamics technique, the numerical results reveal that excitability of pyramidal neuron as well as oriens lacunosum-moleculare (O-LM) cell in CA3 region is strongly degraded by the decrease of NMDA or AMPA-type glutamatergic excitatory synapse strength. Moreover, the relative power together with the peak of relative power density within alpha band is also diminished by decreasing glutamatergic synapse strength. These findings accord with the electrophysiological experiment of EEG that there is a decrease of alpha rhythm for AD patients, on the same time, they could highlight the significance of glutamatergic synapse loss in the pathogenesis of AD.
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Affiliation(s)
- Han Dong
- School of Mathematics and Statistic, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - XiaoLi Yang
- School of Mathematics and Statistic, Shaanxi Normal University, Xi’an, 710062 People’s Republic of China
| | - ZhongKui Sun
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi’an, 710072 People’s Republic of China
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3
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Liu Y, Xu X, Zhou Y, Xu J, Dong X, Li X, Yin S, Wen D. Coupling feature extraction method of resting state EEG Signals from amnestic mild cognitive impairment with type 2 diabetes mellitus based on weight permutation conditional mutual information. Cogn Neurodyn 2021; 15:987-997. [PMID: 34790266 PMCID: PMC8572246 DOI: 10.1007/s11571-021-09682-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/28/2021] [Accepted: 04/19/2021] [Indexed: 01/06/2023] Open
Abstract
This study aimed to find a good coupling feature extraction method to effectively analyze resting state EEG signals (rsEEG) of amnestic mild cognitive impairment(aMCI) with type 2 diabetes mellitus(T2DM) and normal control (NC) with T2DM. A method of EEG signal coupling feature extraction based on weight permutation conditional mutual information (WPCMI) was proposed in this research. With the WPCMI method, coupling feature strength of two time series in Alpha1, Alpha2, Beta1, Beta2 and Gamma bands for aMCI with T2DM and NC with T2DM could be extracted respectively. Then selected three frequency bands coupling feature matrix with the help of multi-spectral image transformation method to map it as spectral image characteristics. And finally classified these characteristics through the convolution neural network method(CNN). For aMCI with T2DM and NC with T2DM, the highest classification accuracy of 96%, 95%, 95% could be achieved respectively in the combination of three frequency bands (Alpha1, Alpha2, Gamma), (Beta1, Beta2 and Gamma) and (Alpha2, Beta1, Beta2). This WPCMI method highlighted the coupling dynamic characteristics of EEG signals, and its classification performance was better than all previous methods in aMCI with T2DM diagnosis field. WPCMI method could be used as an effective biomarker to distinguish EEG signals of aMCI with T2DM and NC with T2DM. The coupling feature extraction method used in this paper provided a new perspective for the EEG analysis of aMCI with T2DM.
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Affiliation(s)
- Yijun Liu
- School of Science, Yanshan University, Qinhuangdao, China
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xiaodong Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Jian Xu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Xianling Dong
- Department of Biomedical Engineering, Chengde Medical University, Chengde, China
| | - Xiaoli Li
- The National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force Hospital of Chinese People’s Liberation Army, Beijing, China
| | - Dong Wen
- Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing, China
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4
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Yang X, Zhang R, Sun Z, Kurths J. Controlling Alzheimer's Disease Through the Deep Brain Stimulation to Thalamic Relay Cells. Front Comput Neurosci 2021; 15:636770. [PMID: 34819845 PMCID: PMC8606419 DOI: 10.3389/fncom.2021.636770] [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: 12/02/2020] [Accepted: 10/11/2021] [Indexed: 11/23/2022] Open
Abstract
Experimental and clinical studies have shown that the technique of deep brain stimulation (DBS) plays a potential role in the regulation of Alzheimer’s disease (AD), yet it still desires for ongoing studies including clinical trials, theoretical approach and action mechanism. In this work, we develop a modified thalamo-cortico-thalamic (TCT) model associated with AD to explore the therapeutic effects of DBS on AD from the perspective of neurocomputation. First, the neuropathological state of AD resulting from synapse loss is mimicked by decreasing the synaptic connectivity strength from the Inter-Neurons (IN) neuron population to the Thalamic Relay Cells (TRC) neuron population. Under such AD condition, a specific deep brain stimulation voltage is then implanted into the neural nucleus of TRC in this TCT model. The symptom of AD is found significantly relieved by means of power spectrum analysis and nonlinear dynamical analysis. Furthermore, the therapeutic effects of DBS on AD are systematically examined in different parameter space of DBS. The results demonstrate that the controlling effect of DBS on AD can be efficient by appropriately tuning the key parameters of DBS including amplitude A, period P and duration D. This work highlights the critical role of thalamus stimulation for brain disease, and provides a theoretical basis for future experimental and clinical studies in treating AD.
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Affiliation(s)
- XiaoLi Yang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - RuiXi Zhang
- School of Mathematics and Statistics, Shaanxi Normal University, Xi'an, China
| | - ZhongKui Sun
- Department of Applied Mathematics, Northwestern Polytechnical University, Xi'an, China
| | - Jürgen Kurths
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Department of Physics, Humboldt University of Berlin, Berlin, Germany.,Centre for Analysis of Complex Systems, World-Class Research Center "Digital Biodesign and Personalized Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
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Muthuraman M, Palotai M, Jávor-Duray B, Kelemen A, Koirala N, Halász L, Erőss L, Fekete G, Bognár L, Deuschl G, Tamás G. Frequency-specific network activity predicts bradykinesia severity in Parkinson's disease. Neuroimage Clin 2021; 32:102857. [PMID: 34662779 PMCID: PMC8526781 DOI: 10.1016/j.nicl.2021.102857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Revised: 09/15/2021] [Accepted: 10/12/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Bradykinesia has been associated with beta and gamma band interactions in the basal ganglia-thalamo-cortical circuit in Parkinson's disease. In this present cross-sectional study, we aimed to search for neural networks with electroencephalography whose frequency-specific actions may predict bradykinesia. METHODS Twenty Parkinsonian patients treated with bilateral subthalamic stimulation were first prescreened while we selected four levels of contralateral stimulation (0: OFF, 1-3: decreasing symptoms to ON state) individually, based on kinematics. In the screening period, we performed 64-channel electroencephalography measurements simultaneously with electromyography and motion detection during a resting state, finger tapping, hand grasping tasks, and pronation-supination of the arm, with the four levels of contralateral stimulation. We analyzed spectral power at the low (13-20 Hz) and high (21-30 Hz) beta frequency bands and low (31-60 Hz) and high (61-100 Hz) gamma frequency bands using the dynamic imaging of coherent sources. Structural equation modelling estimated causal relationships between the slope of changes in network beta and gamma activities and the slope of changes in bradykinesia measures. RESULTS Activity in different subnetworks, including predominantly the primary motor and premotor cortex, the subthalamic nucleus predicted the slopes in amplitude and speed while switching between stimulation levels. These subnetwork dynamics on their preferred frequencies predicted distinct types and parameters of the movement only on the contralateral side. DISCUSSION Concurrent subnetworks affected in bradykinesia and their activity changes in the different frequency bands are specific to the type and parameters of the movement; and the primary motor and premotor cortex are common nodes.
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Affiliation(s)
- Muthuraman Muthuraman
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Marcell Palotai
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | | | - Andrea Kelemen
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Nabin Koirala
- Movement Disorders, Imaging and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, Department of Neurology, University Medical Center of the Johannes Gutenberg University, Mainz, Germany; Haskins Laboratories, New Haven, USA
| | - László Halász
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Loránd Erőss
- National Institute of Clinical Neurosciences, Budapest, Hungary
| | - Gábor Fekete
- Department of Neurosurgery, University of Debrecen, Debrecen, Hungary
| | - László Bognár
- Department of Neurosurgery, University of Debrecen, Debrecen, Hungary
| | - Günther Deuschl
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
| | - Gertrúd Tamás
- Department of Neurology, Semmelweis University, Budapest, Hungary.
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6
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Urdanibia-Centelles O, Nielsen RM, Rostrup E, Vedel-Larsen E, Thomsen K, Nikolic M, Johnsen B, Møller K, Lauritzen M, Benedek K. Automatic continuous EEG signal analysis for diagnosis of delirium in patients with sepsis. Clin Neurophysiol 2021; 132:2075-2082. [PMID: 34284242 DOI: 10.1016/j.clinph.2021.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 04/12/2021] [Accepted: 05/06/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE In critical care, continuous EEG (cEEG) monitoring is useful for delirium diagnosis. Although visual cEEG analysis is most commonly used, automatic cEEG analysis has shown promising results in small samples. Here we aimed to compare visual versus automatic cEEG analysis for delirium diagnosis in septic patients. METHODS We obtained cEEG recordings from 102 septic patients who were scored for delirium six times daily. A total of 1252 cEEG blocks were visually analyzed, of which 805 blocks were also automatically analyzed. RESULTS Automatic cEEG analyses revealed that delirium was associated with 1) high mean global field power (p < 0.005), mainly driven by delta activity; 2) low average coherence across all electrode pairs and all frequencies (p < 0.01); 3) lack of intrahemispheric (fronto-temporal and temporo-occipital regions) and interhemispheric coherence (p < 0.05); and 4) lack of cEEG reactivity (p < 0.005). Classification accuracy was assessed by receiver operating characteristic (ROC) curve analysis, revealing a slightly higher area under the curve for visual analysis (0.88) than automatic analysis (0.74) (p < 0.05). CONCLUSIONS Automatic cEEG analysis is a useful supplement to visual analysis, and provides additional cEEG diagnostic classifiers. SIGNIFICANCE Automatic cEEG analysis provides useful information in septic patients.
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Affiliation(s)
- Olalla Urdanibia-Centelles
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Rikke M Nielsen
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark
| | - Egill Rostrup
- Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Mental Health Centre Glostrup, Copenhagen University Hospital, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Esben Vedel-Larsen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Kirsten Thomsen
- Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Miki Nikolic
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
| | - Birger Johnsen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Nørrebrogade 44, 8000 Aarhus C, Denmark.
| | - Kirsten Møller
- Department of Neuroanesthesiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100 Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200, Denmark.
| | - Martin Lauritzen
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark; Center for Healthy Aging and Department of Neuroscience, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark.
| | - Krisztina Benedek
- Department of Clinical Neurophysiology, The Neuroscience Centre, Rigshospitalet, University of Copenhagen, Valdemar Hansens Vej 1-23, Glostrup, Denmark.
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7
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Lejko N, Larabi DI, Herrmann CS, Aleman A, Ćurčić-Blake B. Alpha Power and Functional Connectivity in Cognitive Decline: A Systematic Review and Meta-Analysis. J Alzheimers Dis 2020; 78:1047-1088. [PMID: 33185607 PMCID: PMC7739973 DOI: 10.3233/jad-200962] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background: Mild cognitive impairment (MCI) is a stage between expected age-related cognitive decline and dementia. Dementias have been associated with changes in neural oscillations across the frequency spectrum, including the alpha range. Alpha is the most prominent rhythm in human EEG and is best detected during awake resting state (RS). Though several studies measured alpha power and synchronization in MCI, findings have not yet been integrated. Objective: To consolidate findings on power and synchronization of alpha oscillations across stages of cognitive decline. Methods: We included studies published until January 2020 that compared power or functional connectivity between 1) people with MCI and cognitively healthy older adults (OA) or people with a neurodegenerative dementia, and 2) people with progressive and stable MCI. Random-effects meta-analyses were performed when enough data was available. Results: Sixty-eight studies were included in the review. Global RS alpha power was lower in AD than in MCI (ES = –0.30; 95% CI = –0.51, –0.10; k = 6), and in MCI than in OA (ES = –1.49; 95% CI = –2.69, –0.29; k = 5). However, the latter meta-analysis should be interpreted cautiously due to high heterogeneity. The review showed lower RS alpha power in progressive than in stable MCI, and lower task-related alpha reactivity in MCI than in OA. People with MCI had both lower and higher functional connectivity than OA. Publications lacked consistency in MCI diagnosis and EEG measures. Conclusion: Research indicates that RS alpha power decreases with increasing impairment, and could—combined with measures from other frequency bands—become a biomarker of early cognitive decline.
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Affiliation(s)
- Nena Lejko
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Daouia I Larabi
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.,Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | | | - André Aleman
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
| | - Branislava Ćurčić-Blake
- University of Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, Groningen, The Netherlands
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Gutiérrez-de Pablo V, Gómez C, Poza J, Maturana-Candelas A, Martins S, Gomes I, Lopes AM, Pinto N, Hornero R. Relationship between the Presence of the ApoE ε4 Allele and EEG Complexity along the Alzheimer's Disease Continuum. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3849. [PMID: 32664228 PMCID: PMC7411888 DOI: 10.3390/s20143849] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2020] [Revised: 06/29/2020] [Accepted: 07/08/2020] [Indexed: 12/15/2022]
Abstract
Alzheimer's disease (AD) is the most prevalent cause of dementia, being considered a major health problem, especially in developed countries. Late-onset AD is the most common form of the disease, with symptoms appearing after 65 years old. Genetic determinants of AD risk are vastly unknown, though, ε 4 allele of the ApoE gene has been reported as the strongest genetic risk factor for AD. The objective of this study was to analyze the relationship between brain complexity and the presence of ApoE ε 4 alleles along the AD continuum. For this purpose, resting-state electroencephalography (EEG) activity was analyzed by computing Lempel-Ziv complexity (LZC) from 46 healthy control subjects, 49 mild cognitive impairment subjects, 45 mild AD patients, 44 moderate AD patients and 33 severe AD patients, subdivided by ApoE status. Subjects with one or more ApoE ε 4 alleles were included in the carriers subgroups, whereas the ApoE ε 4 non-carriers subgroups were formed by subjects without any ε 4 allele. Our results showed that AD continuum is characterized by a progressive complexity loss. No differences were observed between AD ApoE ε 4 carriers and non-carriers. However, brain activity from healthy subjects with ApoE ε 4 allele (carriers subgroup) is more complex than from non-carriers, mainly in left temporal, frontal and posterior regions (p-values < 0.05, FDR-corrected Mann-Whitney U-test). These results suggest that the presence of ApoE ε 4 allele could modify the EEG complexity patterns in different brain regions, as the temporal lobes. These alterations might be related to anatomical changes associated to neurodegeneration, increasing the risk of suffering dementia due to AD before its clinical onset. This interesting finding might help to advance in the development of new tools for early AD diagnosis.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Carlos Gómez
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
| | - Sandra Martins
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Iva Gomes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Alexandra M. Lopes
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
| | - Nádia Pinto
- Institute of Molecular Pathology and Immunology of the University of Porto (IPATIMUP), 4200-135 Porto, Portugal; (S.M.); (I.G.); (A.M.L.); (N.P.)
- Institute of Research and Innovation in Health (i3S), University of Porto, 4200-135 Porto, Portugal
- Center of Mathematics of the University of Porto (CMUP), 4169-007 Porto, Portugal
| | - Roberto Hornero
- Biomedical Engineering Group, E.T.S.I. de Telecomunicación, Universidad de Valladolid, 47011 Valladolid, Spain; (V.G.-d.P.); (J.P.); (A.M.-C.); (R.H.)
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina, (CIBER-BBN), 28029 Madrid, Spain
- Instituto de Investigación en Matemáticas (IMUVA), Universidad de Valladolid, 47011 Valladolid, Spain
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9
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Wei Q, Bai T, Brown EC, Xie W, Chen Y, Ji G, Ramasubbu R, Tian Y, Wang K. Thalamocortical connectivity in electroconvulsive therapy for major depressive disorder. J Affect Disord 2020; 264:163-171. [PMID: 32056746 DOI: 10.1016/j.jad.2019.11.120] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 09/28/2019] [Accepted: 11/28/2019] [Indexed: 12/18/2022]
Abstract
BACKGROUND Electroconvulsive therapy (ECT) can lead to rapid and effective responses in major depressive disorder (MDD). However, the precise neural mechanisms of ECT for MDD are still unclear. Previous work has confirmed that thalamocortical circuits play an important role in emotion and cognition. However, the relationship between mechanisms of ECT for MDD and thalamocortical connectivity has not yet been investigated. METHOD Thalamocortical functional connectivity analysis was performed on resting-state functional magnetic resonance imaging (fMRI) data collected from 28 MDD patients both pre- and post-ECT treatment, as well as 20 healthy controls. The cortex was parceled into six regions of interest (ROIs), which were used as seeds to assess the functional connectivity between the cortex and each voxel in the thalamus. Then, functional connectivity between the identified thalamic subregions and the rest of the brain was quantified to better localize thalamocortical connectivity related to ECT. Structural connectivity among the functionally abnormal regions was also determined using probabilistic tractography from diffusion tensor imaging (DTI) data. RESULTS There was decreased parietal cortex-left pulvinar and left pulvinar-bilateral precuneus functional connectivity in post-ECT MDD patients, compared to pre-ECT MDD patients. Furthermore, functional connectivity strength of parietal cortex-left pulvinar and left pulvinar-bilateral precuneus was negative correlation with verbal fluency test scores in post-ECT MDD patients. No significant change was found in structural connectivity analysis. LIMITATIONS The sample size of our study was not large. CONCLUSION Our findings implicate that the specific abnormalities in thalamocortical circuit may be associated with cognitive impairment induced by ECT.
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Affiliation(s)
- Qiang Wei
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Tongjian Bai
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Elliot C Brown
- Charité-Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Neuroscience Research Center, Berlin Institute of Health, Berlin, Germany; Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Wen Xie
- Anhui Mental Health Center, Hefei, China
| | - Yang Chen
- Anhui Mental Health Center, Hefei, China
| | - Gongjun Ji
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Rajamannar Ramasubbu
- Mathison Centre for Mental Health Research and Education, University of Calgary, Calgary, AB, Canada; Department of Psychiatry, University of Calgary, Calgary, AB, Canada; Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Yanghua Tian
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
| | - Kai Wang
- Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei 230022, China; Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.
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10
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The Role of Physical Fitness in Cognitive-Related Biomarkers in Persons at Genetic Risk of Familial Alzheimer's Disease. J Clin Med 2019; 8:jcm8101639. [PMID: 31591322 PMCID: PMC6832576 DOI: 10.3390/jcm8101639] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 12/28/2022] Open
Abstract
Introduction: Nondemented people with a family history of Alzheimer’s disease (ADFH) and the ApoE-4 allele have been demonstrated to show a trend for a higher probability of cognitive decline and aberrant levels of cognitive-related biomarkers. However, the potential interactive effects on physical fitness have not been investigated. Purpose: The primary purpose of this study was to determine whether ADFH individuals with the ApoE-4 genotype show deviant brain event-related neural oscillatory performance and cognitively-related molecular indices. A secondary purpose was to examine the interactive effects on physical fitness. Methods: Blood samples were provided from 110 individuals with ADFH to assess molecular biomarkers and the ApoE genotype for the purpose of dividing them into an ApoE-4 group (n = 16) and a non-ApoE-4 group (n = 16) in order for them to complete a visuospatial working memory task while simultaneously recording electroencephalographic signals. They also performed a senior functional physical fitness (SFPF) test. Results: While performing the cognitive task, the ApoE-4 relative to non-ApoE-4 group showed worse accuracy rates (ARs) and brain neural oscillatory performance. There were no significant between-group differences with regard to any molecular biomarkers (e.g., IL-1β, IL-6, IL-8, BDNF, Aβ1-40, Aβ1-42). VO2max was significantly correlated with the neuropsychological performance (i.e., ARs and RTs) in the 2-item and 4-item conditions in the ApoE-4 group and across the two groups. However, the electroencephalogram (EEG) oscillations during visuospatial working memory processing in the two conditions were not correlated with any SFPF scores or cardiorespiratory tests in the two groups. Conclusions: ADFH individuals with the ApoE-4 genotype only showed deviant neuropsychological (e.g., ARs) and neural oscillatory performance when performing the cognitive task with a higher visuospatial working memory load. Cardiorespiratory fitness potentially played an important role in neuropsychological impairment in this group.
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11
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Tait L, Stothart G, Coulthard E, Brown JT, Kazanina N, Goodfellow M. Network substrates of cognitive impairment in Alzheimer’s Disease. Clin Neurophysiol 2019; 130:1581-1595. [DOI: 10.1016/j.clinph.2019.05.027] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 03/26/2019] [Accepted: 05/17/2019] [Indexed: 12/28/2022]
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12
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Wen D, Jia P, Hsu SH, Zhou Y, Lan X, Cui D, Li G, Yin S, Wang L. Estimating coupling strength between multivariate neural series with multivariate permutation conditional mutual information. Neural Netw 2018; 110:159-169. [PMID: 30562649 DOI: 10.1016/j.neunet.2018.11.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 10/05/2018] [Accepted: 11/20/2018] [Indexed: 02/03/2023]
Abstract
Recently, coupling between groups of neurons or different brain regions has been widely studied to provide insights into underlying mechanisms of brain functions. To comprehensively understand the effect of such coupling, it is necessary to accurately extract the coupling strength information among multivariate neural signals from the whole brain. This study proposed a new method named multivariate permutation conditional mutual information (MPCMI) to quantitatively estimate the coupling strength of multivariate neural signals (MNS). The performance of the MPCMI method was validated on the simulated MNS generated by multi-channel neural mass model (MNMM). The coupling strength feature of simulated MNS extracted by MPCMI showed better performance compared with standard methods, such as permutation conditional mutual information (PCMI), multivariate Granger causality (MVGC), and Granger causality analysis (GCA). Furthermore, the MPCMI was applied to estimate the coupling strengths of two-channel resting-state electroencephalographic (rsEEG) signals from different brain regions of 19 patients with amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) and 20 normal control (NC) with T2DM in Alpha1 and Alpha2 frequency bands. Empirical results showed that the MPCMI could effectively extract the coupling strength features that were significantly different between the aMCI and the NC. Hence, the proposed MPCMI method could be an effective estimate of coupling strengths of MNS, and might be a viable biomarker for clinical applications.
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Affiliation(s)
- Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
| | - Peilei Jia
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Software Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Sheng-Hsiou Hsu
- Swartz Center for Computational Neuroscience, University of California San Diego, La Jolla, CA, 92093, United States
| | - Yanhong Zhou
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China.
| | - Xifa Lan
- Department of Neurology, First Hospital of Qinhuangdao, Qinhuangdao 066000, China
| | - Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China; The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao 066004, China
| | - Guolin Li
- School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
| | - Shimin Yin
- Department of Neurology, The Rocket Force General Hospital of Chinese People's Liberation Army, Beijing 100088, China
| | - Lei Wang
- Department of Neurology, The Rocket Force General Hospital of Chinese People's Liberation Army, Beijing 100088, China
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13
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Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy. ENTROPY 2018; 20:e20060419. [PMID: 33265509 PMCID: PMC7512937 DOI: 10.3390/e20060419] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Revised: 05/20/2018] [Accepted: 05/26/2018] [Indexed: 12/02/2022]
Abstract
Quantification of the complexity of signals recorded concurrently from multivariate systems, such as the brain, plays an important role in the study and characterization of their state and state transitions. Multivariate analysis of the electroencephalographic signals (EEG) over time is conceptually most promising in unveiling the global dynamics of dynamical brain disorders such as epilepsy. We employed a novel methodology to study the global complexity of the epileptic brain en route to seizures. The developed measures of complexity were based on Multivariate Matching Pursuit (MMP) decomposition of signals in terms of time–frequency Gabor functions (atoms) and Shannon entropy. The measures were first validated on simulation data (Lorenz system) and then applied to EEGs from preictal (before seizure onsets) periods, recorded by intracranial electrodes from eight patients with temporal lobe epilepsy and a total of 42 seizures, in search of global trends of complexity before seizures onset. Out of five Gabor measures of complexity we tested, we found that our newly defined measure, the normalized Gabor entropy (NGE), was able to detect statistically significant (p < 0.05) nonlinear trends of the mean global complexity across all patients over 1 h periods prior to seizures’ onset. These trends pointed to a slow decrease of the epileptic brain’s global complexity over time accompanied by an increase of the variance of complexity closer to seizure onsets. These results show that the global complexity of the epileptic brain decreases at least 1 h prior to seizures and imply that the employed methodology and measures could be useful in identifying different brain states, monitoring of seizure susceptibility over time, and potentially in seizure prediction.
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14
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Koelewijn L, Bompas A, Tales A, Brookes MJ, Muthukumaraswamy SD, Bayer A, Singh KD. Alzheimer's disease disrupts alpha and beta-band resting-state oscillatory network connectivity. Clin Neurophysiol 2017; 128:2347-2357. [PMID: 28571910 PMCID: PMC5674981 DOI: 10.1016/j.clinph.2017.04.018] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/17/2017] [Accepted: 04/17/2017] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Neuroimaging studies in Alzheimer's disease (AD) yield conflicting results due to selective investigation. We conducted a comprehensive magnetoencephalography study of connectivity changes in AD and healthy ageing in the resting-state. METHODS We performed a whole-brain, source-space assessment of oscillatory neural signalling in multiple frequencies comparing AD patients, elderly and young controls. We compared eyes-open and closed group oscillatory envelope activity in networks obtained through temporal independent component analysis, and calculated whole-brain node-based amplitude and phase connectivity. RESULTS In bilateral parietotemporal areas, oscillatory envelope amplitude increased with healthy ageing, whereas both local amplitude and node-to-global connectivity decreased with AD. AD-related decreases were spatially specific and restricted to the alpha and beta bands. A significant proportion of the variance in areas of peak group difference was explained by cognitive integrity, in addition to group. None of the groups differed in phase connectivity. Results were highly similar for eyes-open and closed resting-state. CONCLUSIONS These results support the disconnection syndrome hypothesis and suggest that AD shows distinct and unique patterns of disrupted neural functioning, rather than accelerated healthy ageing. SIGNIFICANCE Whole-brain assessments show that disrupted regional oscillatory envelope amplitude and connectivity in the alpha and beta bands play a key role in AD.
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Affiliation(s)
- Loes Koelewijn
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
| | - Aline Bompas
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
| | - Andrea Tales
- Department of Psychology, College of Human and Health Sciences, Swansea University, Swansea, UK.
| | - Matthew J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
| | | | - Antony Bayer
- School of Medicine, Cardiff University, University Hospital Llandough, Cardiff, UK.
| | - Krish D Singh
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
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15
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Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol 2017; 120:86-95. [DOI: 10.1016/j.ijpsycho.2017.07.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 06/10/2017] [Accepted: 07/11/2017] [Indexed: 11/21/2022]
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16
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Höller Y, Bathke AC, Uhl A, Strobl N, Lang A, Bergmann J, Nardone R, Rossini F, Zauner H, Kirschner M, Jahanbekam A, Trinka E, Staffen W. Combining SPECT and Quantitative EEG Analysis for the Automated Differential Diagnosis of Disorders with Amnestic Symptoms. Front Aging Neurosci 2017; 9:290. [PMID: 28936173 PMCID: PMC5594223 DOI: 10.3389/fnagi.2017.00290] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 08/23/2017] [Indexed: 12/17/2022] Open
Abstract
Single photon emission computed tomography (SPECT) and Electroencephalography (EEG) have become established tools in routine diagnostics of dementia. We aimed to increase the diagnostic power by combining quantitative markers from SPECT and EEG for differential diagnosis of disorders with amnestic symptoms. We hypothesize that the combination of SPECT with measures of interaction (connectivity) in the EEG yields higher diagnostic accuracy than the single modalities. We examined 39 patients with Alzheimer's dementia (AD), 69 patients with depressive cognitive impairment (DCI), 71 patients with amnestic mild cognitive impairment (aMCI), and 41 patients with amnestic subjective cognitive complaints (aSCC). We calculated 14 measures of interaction from a standard clinical EEG-recording and derived graph-theoretic network measures. From regional brain perfusion measured by 99mTc-hexamethyl-propylene-aminoxime (HMPAO)-SPECT in 46 regions, we calculated relative cerebral perfusion in these patients. Patient groups were classified pairwise with a linear support vector machine. Classification was conducted separately for each biomarker, and then again for each EEG- biomarker combined with SPECT. Combination of SPECT with EEG-biomarkers outperformed single use of SPECT or EEG when classifying aSCC vs. AD (90%), aMCI vs. AD (70%), and AD vs. DCI (100%), while a selection of EEG measures performed best when classifying aSCC vs. aMCI (82%) and aMCI vs. DCI (90%). Only the contrast between aSCC and DCI did not result in above-chance classification accuracy (60%). In general, accuracies were higher when measures of interaction (i.e., connectivity measures) were applied directly than when graph-theoretical measures were derived. We suggest that quantitative analysis of EEG and machine-learning techniques can support differentiating AD, aMCI, aSCC, and DCC, especially when being combined with imaging methods such as SPECT. Quantitative analysis of EEG connectivity could become an integral part for early differential diagnosis of cognitive impairment.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
| | - Arne C Bathke
- Department of Mathematics, Paris Lodron University of SalzburgSalzburg, Austria
| | - Andreas Uhl
- Multimedia Signal Processing and Security Lab, Department of Computer Sciences, Paris Lodron University of SalzburgSalzburg, Austria
| | - Nicolas Strobl
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
| | - Adelheid Lang
- Department of Psychology, Centre for Cognitive Neuroscience, Paris Lodron University of SalzburgSalzburg, Austria
| | - Jürgen Bergmann
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
| | - Raffaele Nardone
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of SalzburgSalzburg, Austria.,Department of Neurology, Franz Tappeiner HospitalMerano, Italy
| | - Fabio Rossini
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
| | - Harald Zauner
- Cardiovascular and Neurological Rehabilitation CenterGroßgmain, Austria
| | - Margarita Kirschner
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
| | | | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical University of SalzburgSalzburg, Austria
| | - Wolfgang Staffen
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University of SalzburgSalzburg, Austria
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17
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Nguyen LT, Mudar RA, Chiang HS, Schneider JM, Maguire MJ, Kraut MA, Hart J. Theta and Alpha Alterations in Amnestic Mild Cognitive Impairment in Semantic Go/NoGo Tasks. Front Aging Neurosci 2017; 9:160. [PMID: 28588479 PMCID: PMC5440918 DOI: 10.3389/fnagi.2017.00160] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 05/08/2017] [Indexed: 12/29/2022] Open
Abstract
Growing evidence suggests that cognitive control processes are impaired in amnestic mild cognitive impairment (aMCI); however the nature of these alterations needs further examination. The current study examined differences in electroencephalographic theta and alpha power related to cognitive control processes involving response execution and response inhibition in 22 individuals with aMCI and 22 age-, sex-, and education-matched cognitively normal controls. Two Go/NoGo tasks involving semantic categorization were used. In the basic categorization task, Go/NoGo responses were made based on exemplars of a single car (Go) and a single dog (NoGo). In the superordinate categorization task, responses were made based on multiple exemplars of objects (Go) and animals (NoGo). Behavioral data showed that the aMCI group had more false alarms during the NoGo trials compared to controls. The EEG data revealed between group differences related to response type in theta (4–7 Hz) and low-frequency alpha (8–10 Hz) power. In particular, the aMCI group differed from controls in theta power during the NoGo trials at frontal and parietal electrodes, and in low-frequency alpha power during Go trials at parietal electrodes. These results suggest that alterations in theta power converge with behavioral deterioration in response inhibition, whereas alterations in low-frequency alpha power appear to precede behavioral changes in response execution. Both behavioral and electrophysiological correlates combined provide a more comprehensive characterization of cognitive control deficits in aMCI.
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Affiliation(s)
- Lydia T Nguyen
- Neuroscience Program, University of Illinois at Urbana-ChampaignChampaign, IL, United States
| | - Raksha A Mudar
- Neuroscience Program, University of Illinois at Urbana-ChampaignChampaign, IL, United States.,Department of Speech and Hearing Science, University of Illinois at Urbana-ChampaignChampaign, IL, United States
| | - Hsueh-Sheng Chiang
- School of Behavioral and Brain Sciences, The University of Texas at DallasRichardson, TX, United States
| | - Julie M Schneider
- School of Behavioral and Brain Sciences, The University of Texas at DallasRichardson, TX, United States
| | - Mandy J Maguire
- School of Behavioral and Brain Sciences, The University of Texas at DallasRichardson, TX, United States
| | - Michael A Kraut
- Department of Radiology, The Johns Hopkins University School of MedicineBaltimore, MD, United States
| | - John Hart
- School of Behavioral and Brain Sciences, The University of Texas at DallasRichardson, TX, United States
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18
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Cui D, Pu W, Liu J, Bian Z, Li Q, Wang L, Gu G. A new EEG synchronization strength analysis method: S-estimator based normalized weighted-permutation mutual information. Neural Netw 2016; 82:30-8. [PMID: 27451314 DOI: 10.1016/j.neunet.2016.06.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2015] [Revised: 06/17/2016] [Accepted: 06/21/2016] [Indexed: 12/20/2022]
Abstract
Synchronization is an important mechanism for understanding information processing in normal or abnormal brains. In this paper, we propose a new method called normalized weighted-permutation mutual information (NWPMI) for double variable signal synchronization analysis and combine NWPMI with S-estimator measure to generate a new method named S-estimator based normalized weighted-permutation mutual information (SNWPMI) for analyzing multi-channel electroencephalographic (EEG) synchronization strength. The performances including the effects of time delay, embedding dimension, coupling coefficients, signal to noise ratios (SNRs) and data length of the NWPMI are evaluated by using Coupled Henon mapping model. The results show that the NWPMI is superior in describing the synchronization compared with the normalized permutation mutual information (NPMI). Furthermore, the proposed SNWPMI method is applied to analyze scalp EEG data from 26 amnestic mild cognitive impairment (aMCI) subjects and 20 age-matched controls with normal cognitive function, who both suffer from type 2 diabetes mellitus (T2DM). The proposed methods NWPMI and SNWPMI are suggested to be an effective index to estimate the synchronization strength.
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Affiliation(s)
- Dong Cui
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Weiting Pu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Jing Liu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
| | - Zhijie Bian
- Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China
| | - Qiuli Li
- Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China
| | - Lei Wang
- Department of Neurology, The Rocket Force General Hospital of PLA, Beijing, China
| | - Guanghua Gu
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China.
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19
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Resting-state EEG coupling analysis of amnestic mild cognitive impairment with type 2 diabetes mellitus by using permutation conditional mutual information. Clin Neurophysiol 2015; 127:335-348. [PMID: 26142876 DOI: 10.1016/j.clinph.2015.05.016] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2014] [Revised: 04/29/2015] [Accepted: 05/12/2015] [Indexed: 12/12/2022]
Abstract
OBJECTIVE This study was meant to explore whether the coupling strength and direction of resting-state electroencephalogram (rsEEG) could be used as an indicator to distinguish the patients of type 2 diabetes mellitus (T2DM) with or without amnestic mild cognitive impairment (aMCI). METHODS Permutation conditional mutual information (PCMI) was used to calculate the coupling strength and direction of rsEEG signals between different brain areas of 19 aMCI and 20 normal control (NC) with T2DM on 7 frequency bands: Delta, Theta, Alpha1, Alpha2, Beta1, Beta2 and Gamma. The difference in coupling strength or direction of rsEEG between two groups was calculated. The correlation between coupling strength or direction of rsEEG and score of different neuropsychology scales were also calculated. RESULTS We have demonstrated that PCMI can calculate effectively the coupling strength and directionality of EEG signals between different brain regions. The significant difference in coupling strength and directionality of EEG signals was found between the patients of aMCI and NC with T2DM on different brain regions. There also existed significant correlation between sex or age and coupling strength or coupling directionality of EEG signals between a few different brain regions from all subjects. CONCLUSIONS The coupling strength or directionality of EEG signals calculated by PCMI are significantly different between aMCI and NC with T2DM. SIGNIFICANCE These results showed that the coupling strength or directionality of EEG signals calculated by PCMI might be used as a biomarker in distinguishing the aMCI from NC with T2DM.
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20
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Wen D, Zhou Y, Li X. A critical review: coupling and synchronization analysis methods of EEG signal with mild cognitive impairment. Front Aging Neurosci 2015; 7:54. [PMID: 25941486 PMCID: PMC4403503 DOI: 10.3389/fnagi.2015.00054] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2015] [Accepted: 03/30/2015] [Indexed: 11/13/2022] Open
Abstract
At present, the clinical diagnosis of mild cognitive impairment (MCI) patients becomes the important approach of evaluating early Alzheimer's disease. The methods of EEG signal coupling and synchronization act as a key role in evaluating and diagnosing MCI patients. Recently, these coupling and synchronization methods were used to analyze the EEG signals of MCI patients according to different angles, and many important discoveries have been achieved. However, considering that every method is single-faceted in solving problems, these methods have various deficiencies when analyzing EEG signals of MCI patients. This paper reviewed in detail the coupling and synchronization analysis methods, analyzed their advantages and disadvantages, and proposed a few research questions needed to solve in the future. Also, the principles and best performances of these methods were described. It is expected that the performance analysis of these methods can provide the theoretical basis for the method selection of analyzing EEG signals of MCI patients and the future research directions.
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Affiliation(s)
- Dong Wen
- School of Information Science and Engineering, Yanshan University, Qinhuangdao, China
- The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province, Yanshan University, Qinhuangdao, China
| | - Yanhong Zhou
- Institute of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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21
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Cerebral and blood correlates of reduced functional connectivity in mild cognitive impairment. Brain Struct Funct 2014; 221:631-45. [PMID: 25366971 DOI: 10.1007/s00429-014-0930-6] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2014] [Accepted: 10/23/2014] [Indexed: 12/15/2022]
Abstract
Growing evidence suggests that decreased functional connectivity in cortical networks precedes clinical stages of Alzheimer's disease (AD), although our knowledge about cerebral and biological correlates of this phenomenon is limited. To shed light on this issue, we have investigated whether resting-state oscillatory connectivity patterns in healthy older (HO) and amnestic mild cognitive impairment (aMCI) subjects are related to anatomical grey matter (GM) and functional (2-[18F]fluoro-2-deoxy-D-glucose (FDG)-PET) changes of neuroelectric sources of alpha rhythms, and/or to changes in plasma amyloid-beta (Aβ) and serum lipid levels, blood markers tied to AD pathogenesis and aging-related cognitive decline. We found that aMCI subjects showed decreased levels of cortical connectivity, reduced FDG-PET intake of the precuneus, and GM atrophy of the thalamus, together with higher levels of Aβ and apolipoprotein B (ApoB) compared to HO. Interestingly, levels of high-density lipoprotein (HDL) cholesterol were positively correlated with the strength of neural-phase coupling in aMCI subjects, and increased triglycerides accompanied bilateral GM loss in the precuneus of aMCI subjects. Together, these findings provide peripheral blood correlates of reduced resting-state cortical connectivity in aMCI, supported by anatomo-functional changes in cerebral sources of alpha rhythms. This framework constitutes an integrated approach to assess functional changes in cortical networks through neuroimaging and peripheral blood markers during early stages of neurodegeneration.
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22
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Höller Y, Trinka E. What do temporal lobe epilepsy and progressive mild cognitive impairment have in common? Front Syst Neurosci 2014; 8:58. [PMID: 24795575 PMCID: PMC3997046 DOI: 10.3389/fnsys.2014.00058] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 03/25/2014] [Indexed: 12/27/2022] Open
Abstract
Temporal lobe epilepsy (TLE) and mild cognitive impairment (MCI) are both subject to intensive memory research. Memory problems are a core characteristic of both conditions and we wonder if there are analogies which would enrich the two distinct research communities. In this review we focus on memory decline in both conditions, that is, the most feared psychosocial effect. While it is clear that memory decline in MCI is highly likely and would lead to the more severe diagnosis of Alzheimer's disease, it is a debate if TLE is a dementing disease or not. As such, like for MCI, one can differentiate progressive from stable TLE subtypes, mainly depending on the age of onset. Neuroimaging techniques such as volumetric analysis of the hippocampus, entorhinal, and perirhinal cortex show evidence of pathological changes in TLE and are predictive for memory decline in MCI. Several studies emphasize that it is necessary to extend the region of interest—even whole-brain characteristics can be predictive for conversion from MCI to Alzheimer's disease. Electroencephalography is increasingly subject to computational neuroscience, revealing new approaches for analyzing frequency, spatial synchronization, and information content of the signals. These methods together with event-related designs that assess memory functions are highly promising for understanding the mechanisms of memory decline in both TLE and MCI populations. Finally, there is evidence that the potential of such markers for memory decline is far from being exhausted. Similar structural and neurophysiological characteristics are linked to memory decline in TLE and MCI. We raise the hope that interdisciplinary research and cross-talk between fields such as research on epilepsy and dementia, will shed further light on the dementing characteristics of the pathological basis of MCI and TLE and support the development of new memory enhancing treatment strategies.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University Salzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University Salzburg, Austria
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Impaired cortical oscillatory coupling in mild cognitive impairment: anatomical substrate and ApoE4 effects. Brain Struct Funct 2014; 220:1721-37. [PMID: 24682246 DOI: 10.1007/s00429-014-0757-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2013] [Accepted: 03/16/2014] [Indexed: 01/04/2023]
Abstract
Our current knowledge about the anatomical substrate of impaired resting-state cortical oscillatory coupling in mild cognitive impairment is still rudimentary. Here, we show that both resting-state oscillatory coupling and its anatomical correlates clearly distinguish healthy older (HO) adults from individuals with amnestic mild cognitive impairment (aMCI). aMCI showed failures in neural-phase coupling of resting-state electroencephalographic alpha activity mostly evident between fronto-temporal and parietal regions. As oligomers of amyloid-beta (Aβ) are linked to synaptic dysfunction in Alzheimer's disease (AD), we further investigated whether plasma concentrations of these oligomers (Aβ40 and Aβ42) accounted for impaired patterns of oscillatory coupling in aMCI. Results revealed that decreased plasma Aβ42 was associated with augmented coupling of parieto-temporal regions in HO subjects, but no relationship was found in aMCI. Oscillatory coupling of frontal regions was also significantly reduced in aMCI carriers of the ε4 allele of the Apolipoprotein E (ApoE) compared to ε4 noncarriers, although neither neuroanatomical nor plasma Aβ changes accounted for this difference. However, the abnormal pattern of oscillatory coupling in aMCI was negatively related to volume of the angular gyrus, and positively related to volume of the precuneus and the splenium of the corpus callosum. Previous evidence suggests that all these regions are neuropathological targets of AD. The current study takes that scenario one step further, suggesting that this anatomical damage could be responsible for disrupted cortical oscillatory coupling in aMCI. Together, these data shed light on how the MCI status modifies anatomo-functional relationships underlying coordination of large-scale cortical systems in the resting-state.
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Sen Bhattacharya B, Cakir Y, Serap-Sengor N, Maguire L, Coyle D. Model-based bifurcation and power spectral analyses of thalamocortical alpha rhythm slowing in Alzheimer's Disease. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2012.10.023] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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25
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Cognitive control of response inhibition and switching: Hemispheric lateralization and hand preference. Brain Cogn 2013; 82:283-90. [DOI: 10.1016/j.bandc.2013.04.013] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Revised: 04/16/2013] [Accepted: 04/30/2013] [Indexed: 11/20/2022]
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Gerdes L, Gerdes P, Lee SW, H Tegeler C. HIRREM™: a noninvasive, allostatic methodology for relaxation and auto-calibration of neural oscillations. Brain Behav 2013; 3:193-205. [PMID: 23532171 PMCID: PMC3607159 DOI: 10.1002/brb3.116] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2012] [Revised: 11/28/2012] [Accepted: 12/09/2012] [Indexed: 12/17/2022] Open
Abstract
Disturbances of neural oscillation patterns have been reported with many disease states. We introduce methodology for HIRREM™ (high-resolution, relational, resonance-based electroencephalic mirroring), also known as Brainwave Optimization™, a noninvasive technology to facilitate relaxation and auto-calibration of neural oscillations. HIRREM is a precision-guided technology for allostatic therapeutics, intended to help the brain calibrate its own functional set points to optimize fitness. HIRREM technology collects electroencephalic data through two-channel recordings and delivers a series of audible musical tones in near real time. Choices of tone pitch and timing are made by mathematical algorithms, principally informed by the dominant frequency in successive instants of time, to permit resonance between neural oscillatory frequencies and the musical tones. Relaxation of neural oscillations through HIRREM appears to permit auto-calibration toward greater hemispheric symmetry and more optimized proportionation of regional spectral power. To illustrate an application of HIRREM, we present data from a randomized clinical trial of HIRREM as an intervention for insomnia (n = 19). On average, there was reduction of right-dominant temporal lobe high-frequency (23-36 Hz) EEG asymmetry over the course of eight successive HIRREM sessions. There was a trend for correlation between reduction of right temporal lobe dominance and magnitude of insomnia symptom reduction. Disturbances of neural oscillation have implications for both neuropsychiatric health and downstream peripheral (somatic) physiology. The possibility of noninvasive optimization for neural oscillatory set points through HIRREM suggests potentially multitudinous roles for this technology. Research is currently ongoing to further explore its potential applications and mechanisms of action.
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Affiliation(s)
- Lee Gerdes
- Brain State Technologies LLC Scottsdale, Arizona, 85260
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Moretti DV, Paternicò D, Binetti G, Zanetti O, Frisoni GB. Analysis of grey matter in thalamus and basal ganglia based on EEG α3/α2 frequency ratio reveals specific changes in subjects with mild cognitive impairment. ASN Neuro 2012; 4:e00103. [PMID: 23126239 PMCID: PMC3522208 DOI: 10.1042/an20120058] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 10/29/2012] [Accepted: 11/05/2012] [Indexed: 11/24/2022] Open
Abstract
GM (grey matter) changes of thalamus and basal ganglia have been demonstrated to be involved in AD (Alzheimer's disease). Moreover, the increase of a specific EEG (electroencephalogram) marker, α3/α2, have been associated with AD-converters subjects with MCI (mild cognitive impairment). To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to detect biomarkers (morpho-physiological) early predictive of AD and non-AD dementia. Seventy-four adult subjects with MCI underwent EEG recording and high-resolution 3D MRI (three-dimensional magnetic resonance imaging). The α3/α2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of α3/α2 ratio. GM density differences between groups were investigated using a VBM (voxel-based morphometry) technique. Subjects with higher α3/α2 ratios when compared with subjects with lower and middle α3/α2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally) and of the pulvinar nuclei in the thalamus; The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.
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Key Words
- alzheimer's disease
- basal ganglia
- electroencephalogram (eeg)
- mild cognitive impairment
- thalamus
- voxel-based morphometry (vbm)
- ad, alzheimer's disease
- dartel, diffeomorphic anatomical registration using exponentiated lie
- eeg, electroencephalogram
- fmri, functional magnetic resonance imaging
- gm, grey matter
- iaf, individual α frequency
- mci, mild cognitive impairment
- mmse, mini-mental state examination
- pet, positron-emission tomography
- tf, transition frequency
- tiv, total intracranial volume
- vbm, voxel-based morphometry
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Sander MC, Lindenberger U, Werkle-Bergner M. Lifespan age differences in working memory: a two-component framework. Neurosci Biobehav Rev 2012; 36:2007-33. [PMID: 22771333 DOI: 10.1016/j.neubiorev.2012.06.004] [Citation(s) in RCA: 91] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2011] [Revised: 05/29/2012] [Accepted: 06/12/2012] [Indexed: 10/28/2022]
Abstract
We suggest that working memory (WM) performance can be conceptualized as the interplay of low-level feature binding processes and top-down control, relating to posterior and frontal brain regions and their interaction in a distributed neural network. We propose that due to age-differential trajectories of posterior and frontal brain regions top-down control processes are not fully mature until young adulthood and show marked decline with advancing age, whereas binding processes are relatively mature in children, but show senescent decline in older adults. A review of the literature spanning from middle childhood to old age shows that binding and top-down control processes undergo profound changes across the lifespan. We illustrate commonalities and dissimilarities between children, younger adults, and older adults reflecting the change in the two components' relative contribution to visual WM performance across the lifespan using results from our own lab. We conclude that an integrated account of visual WM lifespan changes combining research from behavioral neuroscience and cognitive psychology of child development as well as aging research opens avenues to advance our understanding of cognition in general.
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Affiliation(s)
- Myriam C Sander
- Max Planck Institute for Human Development, Center for Lifespan Psychology, Lentzeallee 94, 14195 Berlin, Germany.
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29
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Brain oscillatory complexity across the life span. Clin Neurophysiol 2012; 123:2154-62. [PMID: 22647457 DOI: 10.1016/j.clinph.2012.04.025] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2011] [Revised: 04/23/2012] [Accepted: 04/25/2012] [Indexed: 11/20/2022]
Abstract
OBJECTIVE Considering the increasing use of complexity estimates in neuropsychiatric populations, a normative study is critical to define the 'normal' behaviour of brain oscillatory complexity across the life span. METHOD This study examines changes in resting-state magnetoencephalogram (MEG) complexity - quantified with the Lempel-Ziv complexity (LZC) algorithm - due to age and gender in a large sample of 222 (100 males/122 females) healthy participants with ages ranging from 7 to 84 years. RESULTS A significant quadratic (curvilinear) relationship (p<0.05) between age and complexity was found, with LZC maxima being reached by the sixth decade of life. Once that peak was crossed, complexity values slowly decreased until late senescence. Females exhibited higher LZC values than males, with significant differences in the anterior, central and posterior regions (p<0.05). CONCLUSIONS These results suggest that the evolution of brain oscillatory complexity across the life span might be considered a new illustration of a 'normal' physiological rhythm. SIGNIFICANCE Previous and forthcoming clinical studies using complexity estimates might be interpreted from a more complete and dynamical perspective. Pathologies not only cause an 'abnormal' increase or decrease of complexity values but they actually 'break' the 'normal' pattern of oscillatory complexity evolution as a function of age.
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Werkle-Bergner M, Freunberger R, Sander MC, Lindenberger U, Klimesch W. Inter-individual performance differences in younger and older adults differentially relate to amplitude modulations and phase stability of oscillations controlling working memory contents. Neuroimage 2012; 60:71-82. [DOI: 10.1016/j.neuroimage.2011.11.071] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2011] [Revised: 11/22/2011] [Accepted: 11/23/2011] [Indexed: 10/14/2022] Open
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Sander MC, Werkle-Bergner M, Lindenberger U. Amplitude modulations and inter-trial phase stability of alpha-oscillations differentially reflect working memory constraints across the lifespan. Neuroimage 2012; 59:646-54. [DOI: 10.1016/j.neuroimage.2011.06.092] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2011] [Revised: 06/09/2011] [Accepted: 06/29/2011] [Indexed: 11/30/2022] Open
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Abuhassan K, Coyle D, Maguire LP. Investigating the neural correlates of pathological cortical networks in Alzheimer's disease using heterogeneous neuronal models. IEEE Trans Biomed Eng 2011; 59:890-6. [PMID: 22207633 DOI: 10.1109/tbme.2011.2181843] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper describes an investigation into the pathophysiological causes of abnormal cortical oscillations in Alzheimer's disease (AD) using two heterogeneous neuronal network models. The effect of excitatory circuit disruption on the beta band power (13-30 Hz) using a conductance-based network model of 200 neurons is assessed. Then, the neural correlates of abnormal cortical oscillations in different frequency bands based on a larger network model of 1000 neurons consisting of different types of cortical neurons are also analyzed. EEG studies in AD patients have shown that beta band power (13-30 Hz) decreased in the early stages of the disease with a parallel increase in theta band power (4-7 Hz). This abnormal change progresses with the later stages of the disease but with decreased power spectra in other fast frequency bands plus an increase in delta band power (1-3 Hz). Our results show that, despite the heterogeneity of the network models, the beta band power is significantly affected by excitatory neural and synaptic loss. Second, the results of modeling a functional impairment in the excitatory circuit shows that beta band power exhibits the most decrease compared with other bands. Previous biological experiments on different types of cultural excitatory neurons show that cortical neuronal death is mediated by dysfunctional ionic behavior that might specifically contribute to the pathogenesis of β-amyloid-peptide-induced neuronal death in AD. Our study also shows that beta band power was the first affected component when the modeled excitatory circuit begins to lose neurons and synapses.
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Affiliation(s)
- Kamal Abuhassan
- Intelligent Systems Research Centre, University of Ulster, Derry, UK.
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Moretti DV, Paternicò D, Binetti G, Zanetti O, Frisoni GB. EEG markers are associated to gray matter changes in thalamus and basal ganglia in subjects with mild cognitive impairment. Neuroimage 2011; 60:489-96. [PMID: 22166796 DOI: 10.1016/j.neuroimage.2011.11.086] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2011] [Revised: 11/14/2011] [Accepted: 11/21/2011] [Indexed: 10/14/2022] Open
Abstract
BACKGROUND Gray matter (GM) changes of thalamus and basal ganglia have been demonstrated to be involved in Alzheimer's disease (AD). Moreover, the increase of two EEG markers, alpha3/alpha2 and theta/gamma ratio, have been associated with, respectively, AD converter and non-AD converter subjects with mild cognitive impairment (MCI). OBJECTIVE To study the association of prognostic EEG markers with specific GM changes of thalamus and basal ganglia in subjects with MCI to identify different MCI populations. METHODS 74 adult subjects with mild cognitive impairment underwent EEG recording and high resolution 3D magnetic resonance imaging (MRI). The theta/gamma and alpha3/alpha2 ratio was computed for each subject. Three groups were obtained according to increasing tertile values of both alpha3/alpha2 and theta/gamma ratio. Gray matter density differences between groups were investigated using a voxel-based morphometry technique. RESULTS Subjects with higher a3/a2 ratios when compared to subjects with lower and middle a3/a2 ratios showed minor atrophy in the ventral stream of basal ganglia (head of caudate nuclei and accumbens nuclei bilaterally) and of the pulvinar nuclei in the thalamus; subjects with higher t/g ratio showed minor atrophy in putamina nuclei bilaterally than subjects with middle ratio. CONCLUSION The integrated analysis of EEG and morpho-structural markers could be useful in the comprehension of anatomo-physiological underpinning of the MCI entity.
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Affiliation(s)
- D V Moretti
- IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli di Brescia, Italy.
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34
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The correlation between white-matter microstructure and the complexity of spontaneous brain activity: A difussion tensor imaging-MEG study. Neuroimage 2011; 57:1300-7. [DOI: 10.1016/j.neuroimage.2011.05.079] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 04/18/2011] [Accepted: 05/30/2011] [Indexed: 01/02/2023] Open
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Bhattacharya BS, Coyle D, Maguire LP. A thalamo-cortico-thalamic neural mass model to study alpha rhythms in Alzheimer's disease. Neural Netw 2011; 24:631-45. [PMID: 21435838 DOI: 10.1016/j.neunet.2011.02.009] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2010] [Revised: 02/03/2011] [Accepted: 02/28/2011] [Indexed: 11/16/2022]
Abstract
We present a lumped computational model of the thalamo-cortico-thalamic circuitry. The model essentially consists of two modules: a thalamic module and a cortical module. The thalamic module circuitry is a modified version of a classic neural mass computational model of the thalamic circuitry to simulate cortical alpha rhythms and which we have used in previous research to study EEG abnormality associated with Alzheimer's Disease (AD). Here, we introduce a modified synaptic structure representing a neuronal population in the thalamic model. Furthermore, the synaptic organisation and connectivity parameter values in the model are based on experimental data reported from the dorsal Lateral Geniculate Nucleus of different species. The cortical module circuitry is based on a recent work studying cortical brain rhythms. We vary the synaptic connectivity parameters in the thalamic module of the model to simulate the effects of AD on brain synaptic circuitry and study power within the alpha frequency bands. The power and dominant frequencies of the model output are studied in three sub-bands within the alpha band: lower alpha (7-9 Hz), middle alpha (9-11 Hz) and upper alpha (11-13 Hz). Such an analytical method conforms to recent comparative EEG studies on young adults, healthy aged adults and MCI or early stage AD patients. The results show a remarkable role of the synaptic connectivities in the inhibitory thalamic cell populations on the alpha band power and frequency. Furthermore, the total number of active synapses in the thalamic cell populations produces the slowing of alpha rhythms and a simultaneous decrease of alpha band power in the brain as a result of AD.
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Affiliation(s)
- Basabdatta Sen Bhattacharya
- Intelligent Systems Research Centre, Magee Campus, University of Ulster, Northland Road, Derry BT48 7JL, Northern Ireland, United Kingdom.
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Pons AJ, Cantero JL, Atienza M, Garcia-Ojalvo J. Relating structural and functional anomalous connectivity in the aging brain via neural mass modeling. Neuroimage 2010; 52:848-61. [PMID: 20056154 DOI: 10.1016/j.neuroimage.2009.12.105] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2009] [Revised: 12/22/2009] [Accepted: 12/23/2009] [Indexed: 11/28/2022] Open
Abstract
The structural changes that arise as the brain ages influence its functionality. In many cases, the anatomical degradation simply leads to normal aging. In others, the neurodegeneration is large enough to cause neurological disorders (e.g. Alzheimer's disease). Structure and function can be both currently measured using noninvasive techniques, such as magnetic resonance imaging (MRI) and electroencephalography (EEG) respectively. However, a full theoretical scheme linking structural and functional degradation is still lacking. Here we present a neural mass model that aims to bridge both levels of description and that reproduces experimentally observed multichannel EEG recordings of alpha rhythm in young subjects, healthy elderly subjects, and patients with mild cognitive impairment. We focus our attention in the dominant frequency of the signals at different electrodes and in the correlation between specific electrode pairs, measured via the phase-lag index. Our model allows us to study the influence of different structural connectivity pathways, independently of each other, on the normal and aberrantly aging brain. In particular, we study in detail the effect of the thalamic input on specific cortical regions, the long-range connectivity between cortical regions, and the short-range coupling within the same cortical area. Once the influence of each type of connectivity is determined, we characterize the regions of parameter space compatible with the EEG recordings of the populations under study. Our results show that the different types of connectivity must be fine-tuned to maintain the brain in a healthy functioning state independently of its age and brain condition.
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Affiliation(s)
- A J Pons
- Departament de Física i Enginyeria Nuclear, Universitat Politècnica de Catalunya, Edifici GAIA, Rambla Sant Nebridi s/n, 08222 Terrassa, Spain
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