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Gutiérrez-de Pablo V, Poza J, Maturana-Candelas A, Rodríguez-González V, Tola-Arribas MÁ, Cano M, Hoshi H, Shigihara Y, Hornero R, Gómez C. Exploring the disruptions of the neurophysiological organization in Alzheimer's disease: An integrative approach. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 250:108197. [PMID: 38688139 DOI: 10.1016/j.cmpb.2024.108197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/20/2023] [Accepted: 04/21/2024] [Indexed: 05/02/2024]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's disease (AD) is a neurological disorder that impairs brain functions associated with cognition, memory, and behavior. Noninvasive neurophysiological techniques like magnetoencephalography (MEG) and electroencephalography (EEG) have shown promise in reflecting brain changes related to AD. These techniques are usually assessed at two levels: local activation (spectral, nonlinear, and dynamic properties) and global synchronization (functional connectivity, frequency-dependent network, and multiplex network organization characteristics). Nonetheless, the understanding of the organization formed by the existing relationships between these levels, henceforth named neurophysiological organization, remains unexplored. This work aims to assess the alterations AD causes in the resting-state neurophysiological organization. METHODS To that end, three datasets from healthy controls (HC) and patients with dementia due to AD were considered: MEG database (55 HC and 87 patients with AD), EEG1 database (51 HC and 100 patients with AD), and EEG2 database (45 HC and 82 patients with AD). To explore the alterations induced by AD in the relationships between several features extracted from M/EEG data, association networks (ANs) were computed. ANs are graphs, useful to quantify and visualize the intricate relationships between multiple features. RESULTS Our results suggested a disruption in the neurophysiological organization of patients with AD, exhibiting a greater inclination towards the local activation level; and a significant decrease in the complexity and diversity of the ANs (p-value ¡ 0.05, Mann-Whitney U-test, Bonferroni correction). This effect might be due to a shift of the neurophysiological organization towards more regular configurations, which may increase its vulnerability. Moreover, our findings support the crucial role played by the local activation level in maintaining the stability of the neurophysiological organization. Classification performance exhibited accuracy values of 83.91%, 73.68%, and 72.65% for MEG, EEG1, and EEG2 databases, respectively. CONCLUSION This study introduces a novel, valuable methodology able to integrate parameters characterize different properties of the brain activity and to explore the intricate organization of the neurophysiological organization at different levels. It was noted that AD increases susceptibility to changes in functional neural organization, suggesting a greater ease in the development of severe impairments. Therefore, ANs could facilitate a deeper comprehension of the complex interactions in brain function from a global standpoint.
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Affiliation(s)
- Víctor Gutiérrez-de Pablo
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain.
| | - Jesús Poza
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Aarón Maturana-Candelas
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Víctor Rodríguez-González
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
| | - Miguel Ángel Tola-Arribas
- CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; Department of Neurology, Río Hortega University Hospital, Valladolid, Spain
| | - Mónica Cano
- Department of Clinical Neurophysiology, Río Hortega University Hospital, Valladolid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain; IMUVA, Instituto de Investigación en Matemáticas, University of Valladolid, Spain
| | - Carlos Gómez
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain; CIBER-BBN, Centro de Investigación Biomédica en Red - Bioingeniería, Biomateriales y Nanomedicina, Spain
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Aghdam MA, Bozdag S, Saeed F. PVTAD: ALZHEIMER'S DISEASE DIAGNOSIS USING PYRAMID VISION TRANSFORMER APPLIED TO WHITE MATTER OF T1-WEIGHTED STRUCTURAL MRI DATA. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.17.567617. [PMID: 38045324 PMCID: PMC10690181 DOI: 10.1101/2023.11.17.567617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder, and timely diagnosis is crucial for early interventions. AD is known to have disruptive local and global brain neural connections that may be instrumental in understanding and extracting specific biomarkers. Previous machine-learning approaches are mostly based on convolutional neural network (CNN) and standard vision transformer (ViT) models which may not sufficiently capture the multidimensional local and global patterns that may be indicative of AD. Therefore, in this paper, we propose a novel approach called PVTAD to classify AD and cognitively normal (CN) cases using pretrained pyramid vision transformer (PVT) and white matter (WM) of T1-weighted structural MRI (sMRI) data. Our approach combines the advantages of CNN and standard ViT to extract both local and global features indicative of AD from the WM coronal middle slices. We performed experiments on subjects with T1-weighed MPRAGE sMRI scans from the ADNI dataset. Our results demonstrate that the PVTAD achieves an average accuracy of 97.7% and F1-score of 97.6%, outperforming the single and parallel CNN and standard ViT architectures based on sMRI data for AD vs. CN classification.
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Affiliation(s)
- Maryam Akhavan Aghdam
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
| | - Serdar Bozdag
- Department of Computer Science and Engineering, University of North Texas, Denton, TX, United States
- Department of Mathematics, University of North Texas, Denton, TX, United States
- BioDiscovery Institute, University of North Texas, Denton, TX, United States
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, FL, United States
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3
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Kim SE, Shin C, Yim J, Seo K, Ryu H, Choi H, Park J, Min BK. Resting-state electroencephalographic characteristics related to mild cognitive impairments. Front Psychiatry 2023; 14:1231861. [PMID: 37779609 PMCID: PMC10539934 DOI: 10.3389/fpsyt.2023.1231861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/28/2023] [Indexed: 10/03/2023] Open
Abstract
Alzheimer's disease (AD) causes a rapid deterioration in cognitive and physical functions, including problem-solving, memory, language, and daily activities. Mild cognitive impairment (MCI) is considered a risk factor for AD, and early diagnosis and treatment of MCI may help slow the progression of AD. Electroencephalography (EEG) analysis has become an increasingly popular tool for developing biomarkers for MCI and AD diagnosis. Compared with healthy elderly, patients with AD showed very clear differences in EEG patterns, but it is inconclusive for MCI. This study aimed to investigate the resting-state EEG features of individuals with MCI (n = 12) and cognitively healthy controls (HC) (n = 13) with their eyes closed. EEG data were analyzed using spectral power, complexity, functional connectivity, and graph analysis. The results revealed no significant difference in EEG spectral power between the HC and MCI groups. However, we observed significant changes in brain complexity and networks in individuals with MCI compared with HC. Patients with MCI exhibited lower complexity in the middle temporal lobe, lower global efficiency in theta and alpha bands, higher local efficiency in the beta band, lower nodal efficiency in the frontal theta band, and less small-world network topology compared to the HC group. These observed differences may be related to underlying neuropathological alterations associated with MCI progression. The findings highlight the potential of network analysis as a promising tool for the diagnosis of MCI.
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Affiliation(s)
- Seong-Eun Kim
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Chanwoo Shin
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Junyeop Yim
- Department of Applied Mathematics, Kongju National University, Gongju-si, Republic of Korea
| | - Kyoungwon Seo
- Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul, Republic of Korea
| | - Hokyoung Ryu
- Graduate School of Technology and Innovation Management, Hanyang University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Jinseok Park
- Department of Neurology, College of Medicine, Hanyang University, Seoul, Republic of Korea
| | - Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
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4
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Taleei T, Nazem-Zadeh MR, Amiri M, Keliris GA. EEG-based functional connectivity for tactile roughness discrimination. Cogn Neurodyn 2023; 17:921-940. [PMID: 37522039 PMCID: PMC10374498 DOI: 10.1007/s11571-022-09876-1] [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: 02/03/2022] [Revised: 07/26/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022] Open
Abstract
Tactile sensation and perception involve cooperation between different parts of the brain. Roughness discrimination is an important phase of texture recognition. In this study, we investigated how different roughness levels would influence the brain network characteristics. We recorded EEG signals from nine right-handed healthy subjects who underwent touching three surfaces with different levels of roughness. The experiment was separately repeated in 108 trials for each hand for both static and dynamic touch. For estimation of the functional connectivity between brain regions, the phase lag index method was employed. Frequency-specific connectivity patterns were observed in the ipsilateral and contralateral hemispheres to the hand of interest, for delta, theta, alpha, and beta frequency bands under the study. A number of connections were identified to be in charge of discrimination between surfaces in both alpha and beta frequency bands for the left hand in static touch and for the right hand in dynamic touch. In addition, common connections were determined in both hands for all three roughness in alpha band for static touch and in theta band for dynamic touch. The common connections were identified for the smooth surface in beta band for static touch and in delta and alpha bands for dynamic touch. As observed for static touch in alpha band and for dynamic touch in theta band, the number of common connections between the two hands was decreased by increasing the surface roughness. The results of this research would extend the current knowledge about tactile information processing in the brain. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09876-1.
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Affiliation(s)
- Tahereh Taleei
- Medical Biology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
| | - Mohammad-Reza Nazem-Zadeh
- Research Center for Molecular and Cellular Imaging, Advanced Medical Technologies and Equipment Institute (AMTEI), Tehran University of Medical Sciences (TUMS), Tehran, Iran
- Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences (TUMS), Tehran, Iran
| | - Mahmood Amiri
- Medical Technology Research Center, Institute of Health Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran
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5
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Tăuƫan AM, Casula EP, Pellicciari MC, Borghi I, Maiella M, Bonni S, Minei M, Assogna M, Palmisano A, Smeralda C, Romanella SM, Ionescu B, Koch G, Santarnecchi E. TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification. Sci Rep 2023; 13:7667. [PMID: 37169900 PMCID: PMC10175269 DOI: 10.1038/s41598-022-22978-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 10/21/2022] [Indexed: 05/13/2023] Open
Abstract
The combination of TMS and EEG has the potential to capture relevant features of Alzheimer's disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45-80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients' classification as well as longitudinal disease tracking.
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Affiliation(s)
- Alexandra-Maria Tăuƫan
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- AI Multimedia Lab, Research Center CAMPUS, University Politehnica of Bucharest, 061344, Bucharest, Romania
| | - Elias P Casula
- Santa Lucia Foundation, 00179, Rome, Italy
- Department of Psychology, La Sapienza University, Via dei Marsi 78, 00185, Rome, Italy
| | | | | | | | | | | | | | - Annalisa Palmisano
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Education, Psychology and Communication, University of Bari Aldo Moro, Bari, Italy
| | - Carmelo Smeralda
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy
| | - Sara M Romanella
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Siena Brain Investigation & Neuromodulation Lab (Si-BIN Lab), Department of Medicine, Surgery, Neurology and Clinical Neurophysiology Section, University of Siena, Siena, Italy
| | - Bogdan Ionescu
- AI Multimedia Lab, Research Center CAMPUS, University Politehnica of Bucharest, 061344, Bucharest, Romania
| | - Giacomo Koch
- Department of Neuroscience and Rehabilitation, Section of Human Physiology, University of Ferrara, 44121, Ferrara, Italy
- Santa Lucia Foundation, 00179, Rome, Italy
| | - Emiliano Santarnecchi
- Precision Neuroscience and Neuromodulation Program & Network Control Laboratory, Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
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6
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Adebisi AT, Veluvolu KC. Brain network analysis for the discrimination of dementia disorders using electrophysiology signals: A systematic review. Front Aging Neurosci 2023; 15:1039496. [PMID: 36936496 PMCID: PMC10020520 DOI: 10.3389/fnagi.2023.1039496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 03/06/2023] Open
Abstract
Background Dementia-related disorders have been an age-long challenge to the research and healthcare communities as their various forms are expressed with similar clinical symptoms. These disorders are usually irreversible at their late onset, hence their lack of validated and approved cure. Since their prodromal stages usually lurk for a long period of time before the expression of noticeable clinical symptoms, a secondary prevention which has to do with treating the early onsets has been suggested as the possible solution. Connectivity analysis of electrophysiology signals has played significant roles in the diagnosis of various dementia disorders through early onset identification. Objective With the various applications of electrophysiology signals, the purpose of this study is to systematically review the step-by-step procedures of connectivity analysis frameworks for dementia disorders. This study aims at identifying the methodological issues involved in such frameworks and also suggests approaches to solve such issues. Methods In this study, ProQuest, PubMed, IEEE Xplore, Springer Link, and Science Direct databases are employed for exploring the evolution and advancement of connectivity analysis of electrophysiology signals of dementia-related disorders between January 2016 to December 2022. The quality of assessment of the studied articles was done using Cochrane guidelines for the systematic review of diagnostic test accuracy. Results Out of a total of 4,638 articles found to have been published on the review scope between January 2016 to December 2022, a total of 51 peer-review articles were identified to completely satisfy the review criteria. An increasing trend of research in this domain is identified within the considered time frame. The ratio of MEG and EEG utilization found within the reviewed articles is 1:8. Most of the reviewed articles employed graph theory metrics for their analysis with clustering coefficient (CC), global efficiency (GE), and characteristic path length (CPL) appearing more frequently compared to other metrics. Significance This study provides general insight into how to employ connectivity measures for the analysis of electrophysiology signals of dementia-related disorders in order to better understand their underlying mechanism and their differential diagnosis.
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Affiliation(s)
- Abdulyekeen T. Adebisi
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Kalyana C. Veluvolu
- School of Electronics Engineering, Kyungpook National University, Daegu, Republic of Korea
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7
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Recognition of odor and pleasantness based on olfactory EEG combined with functional brain network model. INT J MACH LEARN CYB 2023. [DOI: 10.1007/s13042-023-01797-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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8
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Lopez S, Del Percio C, Lizio R, Noce G, Padovani A, Nobili F, Arnaldi D, Famà F, Moretti DV, Cagnin A, Koch G, Benussi A, Onofrj M, Borroni B, Soricelli A, Ferri R, Buttinelli C, Giubilei F, Güntekin B, Yener G, Stocchi F, Vacca L, Bonanni L, Babiloni C. Patients with Alzheimer's disease dementia show partially preserved parietal 'hubs' modeled from resting-state alpha electroencephalographic rhythms. Front Aging Neurosci 2023; 15:780014. [PMID: 36776437 PMCID: PMC9908964 DOI: 10.3389/fnagi.2023.780014] [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: 09/20/2021] [Accepted: 01/05/2023] [Indexed: 01/28/2023] Open
Abstract
Introduction Graph theory models a network by its nodes (the fundamental unit by which graphs are formed) and connections. 'Degree' hubs reflect node centrality (the connection rate), while 'connector' hubs are those linked to several clusters of nodes (mainly long-range connections). Methods Here, we compared hubs modeled from measures of interdependencies of between-electrode resting-state eyes-closed electroencephalography (rsEEG) rhythms in normal elderly (Nold) and Alzheimer's disease dementia (ADD) participants. At least 5 min of rsEEG was recorded and analyzed. As ADD is considered a 'network disease' and is typically associated with abnormal rsEEG delta (<4 Hz) and alpha rhythms (8-12 Hz) over associative posterior areas, we tested the hypothesis of abnormal posterior hubs from measures of interdependencies of rsEEG rhythms from delta to gamma bands (2-40 Hz) using eLORETA bivariate and multivariate-directional techniques in ADD participants versus Nold participants. Three different definitions of 'connector' hub were used. Results Convergent results showed that in both the Nold and ADD groups there were significant parietal 'degree' and 'connector' hubs derived from alpha rhythms. These hubs had a prominent outward 'directionality' in the two groups, but that 'directionality' was lower in ADD participants than in Nold participants. Discussion In conclusion, independent methodologies and hub definitions suggest that ADD patients may be characterized by low outward 'directionality' of partially preserved parietal 'degree' and 'connector' hubs derived from rsEEG alpha rhythms.
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Affiliation(s)
- Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | - Roberta Lizio
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Flavio Nobili
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Dario Arnaldi
- Clinica Neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Francesco Famà
- Dipartimento di Neuroscienze, Oftalmologia, Genetica, Riabilitazione e Scienze Materno-infantili (DiNOGMI), Università di Genova, Genova, Italy
| | - Davide V. Moretti
- Alzheimer’s Disease Rehabilitation Unit, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | | | - Giacomo Koch
- Non-Invasive Brain Stimulation Unit/Department of Behavioral and Clinical Neurology, Santa Lucia Foundation IRCCS, Rome, Italy
- Stroke Unit, Department of Neuroscience, Tor Vergata Policlinic, Rome, Italy
| | - Alberto Benussi
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Marco Onofrj
- Department of Neuroscience Imaging and Clinical Sciences and CESI, University “G. D’Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, Italy
| | | | - Carla Buttinelli
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Franco Giubilei
- Department of Neuroscience, Mental Health and Sensory Organs, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, School of Medicine, Istanbul Medipol University, Istanbul, Türkiye
- Research Institute for Health Sciences and Technologies (SABITA), Istanbul Medipol University, Istanbul, Türkiye
| | - Görsev Yener
- Department of Neurology, Dokuz Eylül University Medical School, Izmir, Türkiye
- Faculty of Medicine, Izmir University of Economics, Izmir, Türkiye
| | - Fabrizio Stocchi
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
- Telematic University San Raffaele, Rome, Italy
| | - Laura Vacca
- Institute for Research and Medical Care, IRCCS San Raffaele Roma, Rome, Italy
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University G. D’Annunzio of Chieti-Pescara, Chieti, Italy
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, Sapienza University of Rome, Rome, Italy
- San Raffaele of Cassino, Cassino, Italy
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9
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Parreño Torres A, Roncero-Parra C, Borja AL, Mateo-Sotos J. Inter-Hospital Advanced and Mild Alzheimer's Disease Classification Based on Electroencephalogram Measurements via Classical Machine Learning Algorithms. J Alzheimers Dis 2023; 95:1667-1683. [PMID: 37718814 DOI: 10.3233/jad-230525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
BACKGROUND In pursuit of diagnostic tools capable of targeting distinct stages of Alzheimer's disease (AD), this study explores the potential of electroencephalography (EEG) combined with machine learning (ML) algorithms to identify patients with mild or moderate AD (ADM) and advanced AD (ADA). OBJECTIVE This study aims to assess the classification accuracy of six classical ML algorithms using a dataset of 668 patients from multiple hospitals. METHODS The dataset comprised measurements obtained from 668 patients, distributed among control, ADM, and ADA groups, collected from five distinct hospitals between 2011 and 2022. For classification purposes, six classical ML algorithms were employed: support vector machine, Bayesian linear discriminant analysis, decision tree, Gaussian Naïve Bayes, K-nearest neighbor and random forest. RESULTS The RF algorithm exhibited outstanding performance, achieving a remarkable balanced accuracy of 93.55% for ADA classification and 93.25% for ADM classification. The consistent reliability in distinguishing ADA and ADM patients underscores the potential of the EEG-based approach for AD diagnosis. CONCLUSIONS By leveraging a dataset sourced from multiple hospitals and encompassing a substantial patient cohort, coupled with the straightforwardness of the implemented models, it is feasible to attain notably robust results in AD classification.
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Affiliation(s)
| | | | - Alejandro L Borja
- School of Industrial Engineering, University of Castilla-La Mancha, Albacete, Spain
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10
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Identifying Patients with Epilepsy Having Depression/Anxiety Disorder Using Common Spatial Patterns of Functional EEG Networks. J Med Biol Eng 2022. [DOI: 10.1007/s40846-022-00726-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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11
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Xue J, Yao R, Cui X, Wang B, Wei J, Wu X, Sun J, Yang Y, Xiang J, Liu Y. Abnormal information interaction in multilayer directed network based on cross-frequency integration of mild cognitive impairment and Alzheimer’s disease. Cereb Cortex 2022; 33:4230-4247. [PMID: 36104855 DOI: 10.1093/cercor/bhac339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/14/2022] [Accepted: 08/01/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
Mild cognitive impairment (MCI) and Alzheimer’s disease (AD) have been reported to result in abnormal cross-frequency integration. However, previous studies have failed to consider specific abnormalities in receiving and outputting information among frequency bands during integration. Here, we investigated heterogeneity in receiving and outputting information during cross-frequency integration in patients. The results showed that during cross-frequency integration, information interaction first increased and then decreased, manifesting in the heterogeneous distribution of inter-frequency nodes for receiving information. A possible explanation was that due to damage to some inter-frequency hub nodes, intra-frequency nodes gradually became new inter-frequency nodes, whereas original inter-frequency nodes gradually became new inter-frequency hub nodes. Notably, damage to the brain regions that receive information between layers was often accompanied by a strengthened ability to output information and the emergence of hub nodes for outputting information. Moreover, an important compensatory mechanism assisted in the reception of information in the cingulo-opercular and auditory networks and in the outputting of information in the visual network. This study revealed specific abnormalities in information interaction and compensatory mechanism during cross-frequency integration, providing important evidence for understanding cross-frequency integration in patients with MCI and AD.
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Affiliation(s)
- Jiayue Xue
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Rong Yao
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jing Wei
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Xubin Wu
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Sun
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yanli Yang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology , No. 209, University Street, Jinzhong, Shanxi, 030600 , China
| | - Yi Liu
- Department of Anesthesiology, Shanxi Province Cancer Hospital , No. 3, Workers New Street, Taiyuan, Shanxi, 030013 , China
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12
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Qin Y, Hu Z, Chen Y, Liu J, Jiang L, Che Y, Han C. Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1093. [PMID: 36010760 PMCID: PMC9407608 DOI: 10.3390/e24081093] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 08/06/2022] [Accepted: 08/06/2022] [Indexed: 06/15/2023]
Abstract
Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.
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13
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Nigro S, Filardi M, Tafuri B, De Blasi R, Cedola A, Gigli G, Logroscino G. The Role of Graph Theory in Evaluating Brain Network Alterations in Frontotemporal Dementia. Front Neurol 2022; 13:910054. [PMID: 35837233 PMCID: PMC9275562 DOI: 10.3389/fneur.2022.910054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 06/02/2022] [Indexed: 11/21/2022] Open
Abstract
Frontotemporal dementia (FTD) is a spectrum of clinical syndromes that affects personality, behavior, language, and cognition. The current diagnostic criteria recognize three main clinical subtypes: the behavioral variant of FTD (bvFTD), the semantic variant of primary progressive aphasia (svPPA), and the non-fluent/agrammatic variant of PPA (nfvPPA). Patients with FTD display heterogeneous clinical and neuropsychological features that highly overlap with those presented by psychiatric syndromes and other types of dementia. Moreover, up to now there are no reliable disease biomarkers, which makes the diagnosis of FTD particularly challenging. To overcome this issue, different studies have adopted metrics derived from magnetic resonance imaging (MRI) to characterize structural and functional brain abnormalities. Within this field, a growing body of scientific literature has shown that graph theory analysis applied to MRI data displays unique potentialities in unveiling brain network abnormalities of FTD subtypes. Here, we provide a critical overview of studies that adopted graph theory to examine the topological changes of large-scale brain networks in FTD. Moreover, we also discuss the possible role of information arising from brain network organization in the diagnostic algorithm of FTD-spectrum disorders and in investigating the neural correlates of clinical symptoms and cognitive deficits experienced by patients.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Salvatore Nigro
| | - Marco Filardi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
| | - Roberto De Blasi
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, Tricase, Lecce, Italy
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, Lecce, Italy
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari Aldo Moro, “Pia Fondazione Cardinale G. Panico”, Tricase, Italy
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari Aldo Moro, Bari, Italy
- *Correspondence: Giancarlo Logroscino
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14
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Das S, Puthankattil SD. Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease. Front Comput Neurosci 2022; 16:877912. [PMID: 35733555 PMCID: PMC9207343 DOI: 10.3389/fncom.2022.877912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 04/26/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundFunctional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.MethodFunctional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.ResultsReal EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.ConclusionTaken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.
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15
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Brain Connectivity and Graph Theory Analysis in Alzheimer’s and Parkinson’s Disease: The Contribution of Electrophysiological Techniques. Brain Sci 2022; 12:brainsci12030402. [PMID: 35326358 PMCID: PMC8946843 DOI: 10.3390/brainsci12030402] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/08/2022] [Accepted: 03/16/2022] [Indexed: 12/31/2022] Open
Abstract
In recent years, applications of the network science to electrophysiological data have increased as electrophysiological techniques are not only relatively low cost, largely available on the territory and non-invasive, but also potential tools for large population screening. One of the emergent methods for the study of functional connectivity in electrophysiological recordings is graph theory: it allows to describe the brain through a mathematic model, the graph, and provides a simple representation of a complex system. As Alzheimer’s and Parkinson’s disease are associated with synaptic disruptions and changes in the strength of functional connectivity, they can be well described by functional connectivity analysis computed via graph theory. The aim of the present review is to provide an overview of the most recent applications of the graph theory to electrophysiological data in the two by far most frequent neurodegenerative disorders, Alzheimer’s and Parkinson’s diseases.
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16
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Zarei AA, Jensen W, Faghani Jadidi A, Lontis R, Atashzar SF. Gamma-band Enhancement of Functional Brain Connectivity Following Transcutaneous Electrical Nerve Stimulation. J Neural Eng 2022; 19. [PMID: 35234662 DOI: 10.1088/1741-2552/ac59a1] [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: 11/30/2021] [Accepted: 03/01/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Transcutaneous electrical nerve stimulation (TENS) has been suggested as a possible non-invasive pain treatment. However, the underlying mechanism of the analgesic effect of TENS and how brain network functional connectivity is affected following the use of TENS is not yet fully understood. The purpose of this study was to investigate the effect of high-frequency TENS on the alternation of functional brain network connectivity and the corresponding topographical changes, besides perceived sensations. APPROACH Forty healthy subjects participated in this study. EEG data and sensory profiles were recorded before and up to an hour following high-frequency TENS (100 Hz) in sham and intervention groups. Brain source activity from EEG data was estimated using the LORETA algorithm. In order to generate the brain connectivity network, the Phase lag index was calculated for all pair-wise connections of eight selected brain areas over six different frequency bands (i.e., δ, θ, α, β, γ, and 0.5-90 Hz). MAIN RESULTS The results suggested that the functional connectivity between the primary somatosensory cortex (SI) and the anterior cingulate cortex (ACC), in addition to functional connectivity between S1 and the medial prefrontal cortex (mPFC), were significantly increased in the gamma-band, following the TENS intervention. Additionally, using graph theory, several significant changes were observed in global and local characteristics of functional brain connectivity in gamma-band. SIGNIFICANCE Our observations in this paper open a neuropsychological window of understanding the underlying mechanism of TENS and the corresponding changes in functional brain connectivity, simultaneously with alternation in sensory perception.
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Affiliation(s)
- Ali Asghar Zarei
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg Universitet, Fredrik Bajers Vej 7 D3, Aalborg, 9220, DENMARK
| | - Winnie Jensen
- Center for Sensory-Motor Interaction Department of Health Science and Technology, Aalborg University, Fredrik Bajers Vej 7, 9220 Aalborg, Aalborg, 9220, DENMARK
| | - Armita Faghani Jadidi
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg Universitet, Fredrik Bajers Vej 7 D3, Aalborg, 9220, DENMARK
| | - Romulus Lontis
- Center for Neuroplasticity and Pain (CNAP), Department of Health Science and Technology, Aalborg Universitet, Fredrik Bajers Vej 7 D3, Aalborg, 9220, DENMARK
| | - S Farokh Atashzar
- Departments of Electrical and Computer Engineering, and Mechanical and Aerospace Engineering, New York University, 5 MetroTech Center #266D Brooklyn, NY 11201, New York, New York, NY 11201, UNITED STATES
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17
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Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline. Bioengineering (Basel) 2022; 9:bioengineering9020062. [PMID: 35200415 PMCID: PMC8869328 DOI: 10.3390/bioengineering9020062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/26/2022] [Accepted: 01/29/2022] [Indexed: 11/25/2022] Open
Abstract
This study addresses brain network analysis over different clinical severity stages of cognitive dysfunction using electroencephalography (EEG). We exploit EEG data of subjective cognitive impairment (SCI) patients, mild cognitive impairment (MCI) patients and Alzheimer’s disease (AD) patients. We propose a new framework to study the topological networks with a spatiotemporal entropy measure for estimating the connectivity. Our results show that functional connectivity and graph analysis are frequency-band dependent, and alterations start at the MCI stage. In delta, the SCI group exhibited a decrease of clustering coefficient and an increase of path length compared to MCI and AD. In alpha, the opposite behavior appeared, suggesting a rapid and high efficiency in information transmission across the SCI network. Modularity analysis showed that electrodes of the same brain region were distributed over several modules, and some obtained modules in SCI were extended from anterior to posterior regions. These results demonstrate that the SCI network was more resilient to neuronal damage compared to that of MCI and even more compared to that of AD. Finally, we confirm that MCI is a transitional stage between SCI and AD, with a predominance of high-strength intrinsic connectivity, which may reflect the compensatory response to the neuronal damage occurring early in the disease process.
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18
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Li Z, Han Y, Jiang J. Different brain functional networks between subjective cognitive decline and health control based on graph theory. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5752-5755. [PMID: 34892426 DOI: 10.1109/embc46164.2021.9630421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Subjective cognitive decline (SCD) is a preclinical stage before cognitive impairment, which has a high conversion risk into Alzheimer's disease. However, it is still unknown on the brain functional differences between SCD and healthy controls (HC) subjects. This study therefore proposed a complex brain network analysis based on graph theory. In this study, we selected functional magnetic resonance imaging (fMRI) scans from Xuanwu Hospital of Capital Medical University, including 27 SCD and 42 HC subjects. First, we constructed brain functional connectivity network to obtain brain network topology parameters, including clustering parameters, shortest path length, global efficiency, local efficiency, small world attributes, and modularity. Then, we compared differences on the parameters between two groups. As a result, both SCD and HC groups showed the characteristics of small world. Both global efficiency and local efficiency of HC groups were higher than those of the SCD group. In addition, we found that the global modularity of the SCD group (6 modules) was higher than the HC group (7 modules). Our findings indicated that there were differences in brain functional networks between SCD and HC groups. Graph theory analysis may be useful and helpful to discriminate SCD and HC subjects.
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19
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Guo L, Man R, Wu Y, Yu H, Xu G. Anti-injury function of complex spiking neural networks under targeted attack. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.07.092] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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20
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Wang C, Wang Z, Xie B, Shi X, Yang P, Liu L, Qu T, Qin Q, Xing Y, Zhu W, Teipel SJ, Jia J, Zhao G, Li L, Tang Y. Binaural processing deficit and cognitive impairment in Alzheimer's disease. Alzheimers Dement 2021; 18:1085-1099. [PMID: 34569690 DOI: 10.1002/alz.12464] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 07/07/2021] [Accepted: 08/05/2021] [Indexed: 01/08/2023]
Abstract
Speech comprehension in noisy environments depends on central auditory functions, which are vulnerable in Alzheimer's disease (AD). Binaural processing exploits two ear sounds to optimally process degraded sound information; its characteristics are poorly understood in AD. We studied behavioral and electrophysiological alterations in binaural processing among 121 participants (AD = 27; amnestic mild cognitive impairment [aMCI] = 33; subjective cognitive decline [SCD] = 30; cognitively normal [CN] = 31). We observed impairment of binaural processing in AD and aMCI, and detected a U-shaped curve change in phase synchrony (declining from CN to SCD and to aMCI, but increasing from aMCI to AD). This improvement in phase synchrony accompanying more severe cognitive stages could reflect neural adaptation for binaural processing. Moreover, increased phase synchrony is associated with worse memory during the stages when neural adaptation apparently occurs. These findings support a hypothesis that neural adaptation for binaural processing deficit may exacerbate cognitive impairment, which could help identify biomarkers and therapeutic targets in AD.
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Affiliation(s)
- Changming Wang
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Zhibin Wang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Beijia Xie
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Xinrui Shi
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Pengcheng Yang
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Speech and Hearing Research Center, Peking University, Beijing, China
| | - Lei Liu
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Speech and Hearing Research Center, Peking University, Beijing, China
| | - Tianshu Qu
- Speech and Hearing Research Center, Peking University, Beijing, China.,Key Laboratory on Machine Perception (Ministry of Education), Peking University, Beijing, China
| | - Qi Qin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Yi Xing
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
| | - Wei Zhu
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Stefan J Teipel
- Department of Psychosomatic Medicine, University Medicine Rostock, Rostock, Germany.,DZNE, German Center for Neurodegenerative Diseases, Rostock, Germany
| | - Jianping Jia
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Key Laboratory of Geriatric Cognitive Disorders, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
| | - Guoguang Zhao
- Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China
| | - Liang Li
- School of Psychological and Cognitive Sciences, Peking University, Beijing, China.,Speech and Hearing Research Center, Peking University, Beijing, China.,Key Laboratory on Machine Perception (Ministry of Education), Peking University, Beijing, China.,Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China.,Beijing Institute for Brain Disorders, Beijing, China
| | - Yi Tang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, National Center for Neurological Disorders, Beijing, China.,Key Laboratory of Neurodegenerative Diseases, Ministry of Education of the People's Republic of China, Beijing, China
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21
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Rodrigues PM, Bispo BC, Garrett C, Alves D, Teixeira JP, Freitas D. Lacsogram: A New EEG Tool to Diagnose Alzheimer's Disease. IEEE J Biomed Health Inform 2021; 25:3384-3395. [PMID: 33784628 DOI: 10.1109/jbhi.2021.3069789] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
This work proposes the application of a new electroencephalogram (EEG) signal processing tool - the lacsogram - to characterize the Alzheimer's disease (AD) activity and to assist on its diagnosis at different stages: Mild Cognitive Impairment (MCI), Mild and Moderate AD (ADM) and Advanced AD (ADA). Statistical analyzes are performed to lacstral distances between conventional EEG subbands to find measures capable of discriminating AD in all stages and characterizing the AD activity in each electrode. Cepstral distances are used for comparison. Comparing all AD stages and Controls (C), the most important significances are the lacstral distances between subbands θ and α ( p = 0.0014 0.05). The topographic maps show significant differences in parietal, temporal and frontal regions as AD progresses. Machine learning models with a leave-one-out cross-validation process are applied to lacstral/cepstral distances to develop an automatic method for diagnosing AD. The following classification accuracies are obtained with an artificial neural network: 95.55% for All vs All, 98.06% for C vs MCI, 95.99% for C vs ADM, 93.85% for MCI vs ADM-ADA. In C vs MCI, C vs ADM and MCI vs ADM-ADA, the proposed method outperforms the state-of-art methods by 5%, 1%, and 2%, respectively. In All vs All, it outperforms the state-of-art EEG and non-EEG methods by 6% and 2%, respectively. These results indicate that the proposed method represents an improvement in diagnosing AD.
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22
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Ji J, Chen Z, Yang C. Convolutional Neural Network with Sparse Strategies to Classify Dynamic Functional Connectivity. IEEE J Biomed Health Inform 2021; 26:1219-1228. [PMID: 34314368 DOI: 10.1109/jbhi.2021.3100559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Classification of dynamic functional connectivity (DFC) is becoming a promising approach for diagnosing various neurodegenerative diseases. However, the existing methods generally face the problem of overfitting. To solve it, this paper proposes a convolutional neural network with three sparse strategies named SCNN to classify DFC. Firstly, an element-wise filter is designed to impose sparse constraints on the DFC matrix by replacing the redundant elements with zeroes, where the DFC matrix is specially constructed to quantify the spatial and temporal variation of DFC. Secondly, a 11 convolutional filter is adopted to reduce the dimensionality of the sparse DFC matrix, and remove meaningless features resulted from zero elements in the subsequent convolution process. Finally, an extra sparse optimization classifier is employed to optimize the parameters of the above two filters, which can effectively improve the ability of SCNN to extract discriminative features. Experimental results on multiple resting-state fMRI datasets demonstrate that the proposed model provides a better classification performance of DFC compared with several state-of-the-art methods, and can identify the abnormal brain functional connectivity.
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23
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Yang D, Zhu X, Yan C, Peng Z, Bagonis M, Laurienti PJ, Styner M, Wu G. Joint hub identification for brain networks by multivariate graph inference. Med Image Anal 2021; 73:102162. [PMID: 34274691 DOI: 10.1016/j.media.2021.102162] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 07/01/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022]
Abstract
Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.
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Affiliation(s)
- Defu Yang
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China; Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Xiaofeng Zhu
- School of Natural and Computational Science, Massey University, Auckland, New Zealand
| | - Chenggang Yan
- Intelligent Information Processing Laboratory, Hangzhou Dianzi University, Hangzhou, China
| | - Ziwen Peng
- Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen, China
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | | | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Computer Science, University of North Carolina at Chapel Hill, USA.
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24
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Nigro S, Tafuri B, Urso D, De Blasi R, Frisullo ME, Barulli MR, Capozzo R, Cedola A, Gigli G, Logroscino G. Brain Structural Covariance Networks in Behavioral Variant of Frontotemporal Dementia. Brain Sci 2021; 11:brainsci11020192. [PMID: 33557411 PMCID: PMC7915789 DOI: 10.3390/brainsci11020192] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/29/2021] [Accepted: 02/01/2021] [Indexed: 11/17/2022] Open
Abstract
Recent research on behavioral variant frontotemporal dementia (bvFTD) has shown that personality changes and executive dysfunctions are accompanied by a disease-specific anatomical pattern of cortical and subcortical atrophy. We investigated the structural topological network changes in patients with bvFTD in comparison to healthy controls. In particular, 25 bvFTD patients and 20 healthy controls underwent structural 3T MRI. Next, bilaterally averaged values of 34 cortical surface areas, 34 cortical thickness values, and six subcortical volumes were used to capture single-subject anatomical connectivity and investigate network organization using a graph theory approach. Relative to controls, bvFTD patients showed altered small-world properties and decreased global efficiency, suggesting a reduced ability to combine specialized information from distributed brain regions. At a local level, patients with bvFTD displayed lower values of local efficiency in the cortical thickness of the caudal and rostral middle frontal gyrus, rostral anterior cingulate, and precuneus, cuneus, and transverse temporal gyrus. A significant correlation was also found between the efficiency of caudal anterior cingulate thickness and Mini-Mental State Examination (MMSE) scores in bvFTD patients. Taken together, these findings confirm the selective disruption in structural brain networks of bvFTD patients, providing new insights on the association between cognitive decline and graph properties.
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Affiliation(s)
- Salvatore Nigro
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Benedetta Tafuri
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Daniele Urso
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Neurosciences, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, De Crespigny Park, London SE5 8AF, UK
| | - Roberto De Blasi
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Radiology, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy
| | - Maria Elisa Frisullo
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Maria Rosaria Barulli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Rosa Capozzo
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
| | - Alessia Cedola
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
| | - Giuseppe Gigli
- Institute of Nanotechnology (NANOTEC), National Research Council, 73100 Lecce, Italy; (S.N.); (A.C.); (G.G.)
- Department of Mathematics and Physics “Ennio De Giorgi”, University of Salento, Campus Ecotekne, 73100 Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology, University of Bari ‘Aldo Moro, “Pia Fondazione Cardinale G. Panico”, 73039 Tricase, Italy; (B.T.); (D.U.); (R.D.B.); (M.E.F.); (M.R.B.); (R.C.)
- Department of Basic Medicine, Neuroscience, and Sense Organs, University of Bari ‘Aldo Moro’, 70124 Bari, Italy
- Correspondence: or giancarlo.; Tel.: +39-0833/773904
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Ruiz-Gómez SJ, Hornero R, Poza J, Santamaría-Vázquez E, Rodríguez-González V, Maturana-Candelas A, Gomez C. A new method to build multiplex networks using Canonical Correlation Analysis for the characterization of the Alzheimer's disease continuum. J Neural Eng 2021; 18. [PMID: 33395667 DOI: 10.1088/1741-2552/abd82c] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 01/04/2021] [Indexed: 01/21/2023]
Abstract
OBJECTIVE The aim of this study was to solve one of the current limitations for the characterization of the brain network in the Alzheimer's disease (AD) continuum. Nowadays, frequency-dependent approaches have reached contradictory results depending on the frequency band under study, tangling the possible clinical interpretations. APPROACH To overcome this issue, we proposed a new method to build multiplex networks based on canonical correlation analysis (CCA). Our method determines two basis vectors using the source and electrodelevel frequency-specific network parameters for a reference group, and then project the results for the rest of the groups into these hyperplanes to make them comparable. It was applied to: (i) synthetic signals generated with a Kuramoto-based model; and (ii) a resting-state EEG database formed by recordings from 51 cognitively healthy controls, 51 mild cognitive impairment subjects, 51 mild AD patients, 50 moderate AD patients, and 50 severe AD patients. MAIN RESULTS Our results using synthetic signals showed that the interpretation of the proposed CCA-based multiplex parameters (multiplex average node degree, multiplex characteristic path length and multiplex clustering coefficient) can be analogous to their frequency-specific counterparts, as they displayed similar behaviors in terms of average connectivity, integration, and segregation. Findings using real EEG recordings revealed that dementia due to AD is characterized by a significant increase in average connectivity, and by a loss of integration and segregation. SIGNIFICANCE We can conclude that CCA can be used to build multiplex networks based from frequency-specific results, summarizing all the available information and avoiding the limitations of possible frequency-specific conflicts. Additionally, our method supposes a novel approach for the construction and analysis of multiplex networks during AD continuum.
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Affiliation(s)
- Saúl J Ruiz-Gómez
- Teoría de la señal y comunicaciones e Ingeniería telemática, Universidad de Valladolid, Paseo de Belén 15, Valladolid, Valladolid, 47011, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Eduardo Santamaría-Vázquez
- Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Universidad de Valladolid, Plaza de Santa Cruz, 8, Valladolid, Valladolid, 47002, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, Paseo Belen 15, Valladolid, 47011, SPAIN
| | | | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
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Guo L, Kan E, Wu Y, Lv H, Xu G. Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise. PLoS One 2021; 15:e0244683. [PMID: 33382788 PMCID: PMC7774963 DOI: 10.1371/journal.pone.0244683] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/14/2020] [Indexed: 11/24/2022] Open
Abstract
With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.
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Affiliation(s)
- Lei Guo
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, China
- * E-mail:
| | - Enyu Kan
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, China
| | - Youxi Wu
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Huan Lv
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Hebei University of Technology, Tianjin, China
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Jamaloo F, Mikaeili M, Noroozian M. Multi metric functional connectivity analysis based on continuous hidden Markov model with application in early diagnosis of Alzheimer’s disease. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Ruiz-Gomez SJ, Gomez C, Poza J, Revilla-Vallejo M, Gutierrez-de-Pablo V, Rodriguez-Gonzalez V, Maturana-Candelas A, Hornero R. Volume Conduction Effects on Connectivity Metrics: Application of Network Parameters to Characterize Alzheimer's Disease Continuum. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:30-33. [PMID: 33017923 DOI: 10.1109/embc44109.2020.9176398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This study had two main objectives: (i) to study the effects of volume conduction on different connectivity metrics (Amplitude Envelope Correlation AEC, Phase Lag Index PLI, and Magnitude Squared Coherence MSCOH), comparing the coupling patterns at electrode- and sensor-level; and (ii) to characterize spontaneous EEG activity during different stages of Alzheimer's disease (AD) continuum by means of three complementary network parameters: node degree (k), characteristic path length (L), and clustering coefficient (C). Our results revealed that PLI and AEC are weakly influenced by volume conduction compared to MSCOH, but they are not immune to it. Furthermore, network parameters obtained from PLI showed that AD continuum is characterized by an increase in L and C in low frequency bands, suggesting lower integration and higher segregation as the disease progresses. These network changes reflect the abnormalities during AD continuum and are mainly due to neuronal alterations, because PLI is slightly affected by volume conduction effects.
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Human brain connectivity: Clinical applications for clinical neurophysiology. Clin Neurophysiol 2020; 131:1621-1651. [DOI: 10.1016/j.clinph.2020.03.031] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 12/12/2022]
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Das S, Puthankattil SD. Complex network analysis of MCI-AD EEG signals under cognitive and resting state. Brain Res 2020; 1735:146743. [PMID: 32114060 DOI: 10.1016/j.brainres.2020.146743] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2019] [Revised: 01/09/2020] [Accepted: 02/26/2020] [Indexed: 12/17/2022]
Abstract
OBJECTIVE The purpose of this study is to characterize functional connectivity changes in mild cognitive impaired Alzheimer's disease (MCI-AD) under resting and cognitive task conditions. METHOD EEG signals were recorded under resting states (Eyes closed (EC) and Eyes open (EO)) and cognitive states (Mental Arithmetic Eyes closed (MAEC) and Mental Arithmetic Eyes open (MAEO)) conditions. Functional connectivity metrics were calculated using weighted phase lag index (WPLI). Topological features of the functional connectivity network were analyzed through minimum spanning tree (MST) algorithm. Betweenness centrality was estimated in five different regions of the brain to study hub importance. RESULTS An increase in values of eccentricity and diameter were observed in patient group in five frequency bands of delta, theta, alpha1, alpha 2 and beta bands under resting and cognitive states. A reduction in leaf fraction was observed in alpha 1 band of EO condition. The results indicated a reduction in functional integration in the brain network of MCI-AD patients. Analysis of MST parameters revealed a higher disintegrated network for the alpha band under EO protocol. The study of hub status in the network displayed an elevated hub status in the central region for the patient group under cognitive task. The study also observed increased integration in gamma band in MCI - AD subjects than healthy controls under cognitive load. CONCLUSION Disintegration of functional network in alpha band under eyes open protocol and elevated hub strength in central region during cognitive task could be distinguishing features that could aid early detection of AD.
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Affiliation(s)
- Surya Das
- Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India.
| | - Subha D Puthankattil
- Department of Electrical Engineering, National Institute of Technology, Calicut 673601, Kerala, India.
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Wang F, Tian YC, Zhang X, Hu F. Detecting Disorders of Consciousness in Brain Injuries From EEG Connectivity Through Machine Learning. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2020. [DOI: 10.1109/tetci.2020.3032662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Liu J, Ji J, Jia X, Zhang A. Learning Brain Effective Connectivity Network Structure Using Ant Colony Optimization Combining With Voxel Activation Information. IEEE J Biomed Health Inform 2019; 24:2028-2040. [PMID: 31603829 DOI: 10.1109/jbhi.2019.2946676] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Learning brain effective connectivity (EC) networks from functional magnetic resonance imaging (fMRI) data has become a new hot topic in the neuroinformatics field. However, how to accurately and efficiently learn brain EC networks is still a challenging problem. In this paper, we propose a new algorithm to learn the brain EC network structure using ant colony optimization (ACO) algorithm combining with voxel activation information, named as VACOEC. First, VACOEC uses the voxel activation information to measure the independence between each pair of brain regions and effectively restricts the space of candidate solutions, which makes many unnecessary searches of ants be avoided. Then, by combining the global score increase of a solution with the voxel activation information, a new heuristic function is designed to guide the process of ACO to search for the optimal solution. The experimental results on simulated datasets show that the proposed method can accurately and efficiently identify the directions of the brain EC networks. Moreover, the experimental results on real-world data show that patients with Alzheimers disease (AD) exhibit decreased effective connectivity not only in the intra-network within the default mode network (DMN) and salience network (SN), but also in the inter-network between DMN and SN, compared with normal control (NC) subjects. The experimental results demonstrate that VACOEC is promising for practical applications in the neuroimaging studies of geriatric subjects and neurological patients.
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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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Is brain connectome research the future frontier for subjective cognitive decline? A systematic review. Clin Neurophysiol 2019; 130:1762-1780. [PMID: 31401485 DOI: 10.1016/j.clinph.2019.07.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 06/26/2019] [Accepted: 07/07/2019] [Indexed: 11/24/2022]
Abstract
OBJECTIVE We performed a systematic literature review on Subjective Cognitive Decline (SCD) in order to examine whether the resemblance of brain connectome and functional connectivity (FC) alterations in SCD with respect to MCI, AD and HC can help us draw conclusions on the progression of SCD to more advanced stages of dementia. METHODS We searched for studies that used any neuroimaging tool to investigate potential differences/similarities of brain connectome in SCD with respect to HC, MCI, and AD. RESULTS Sixteen studies were finally included in the review. Apparent FC connections and disruptions were observed in the white matter, default mode and gray matter networks in SCD with regards to HC, MCI, and AD. Interestingly, more apparent connections in SCD were located over the posterior regions, while an increase of FC over anterior regions was observed as the disease progressed. CONCLUSIONS Elders with SCD display a significant disruption of the brain network, which in most of the cases is worse than HC across multiple network parameters. SIGNIFICANCE The present review provides comprehensive and balanced coverage of a timely target research activity around SCD with the intention to identify similarities/differences across patient groups on the basis of brain connectome properties.
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Pei Q, Zhuo Z, Jing B, Meng Q, Ma X, Mo X, Liu H, Liang W, Ni J, Li H. The effects of repetitive transcranial magnetic stimulation on the whole-brain functional network of postherpetic neuralgia patients. Medicine (Baltimore) 2019; 98:e16105. [PMID: 31232955 PMCID: PMC6636965 DOI: 10.1097/md.0000000000016105] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
The effects of repetitive transcranial magnetic stimulation (rTMS), the clinical treatment for postherpetic neuralgia (PHN), on whole-brain functional network of PHN patients is not fully understood.To explore the effects of rTMS on the whole-brain functional network of PHN patients.10 PHN patients (male/female: 5/5 Age: 63-79 years old) who received rTMS treatment were recruited in this study. High-resolution T1-weighted and functional Magnetic Resonance Imaging (fMRI) were acquired before and after 10 consecutive rTMS sessions. The whole-brain functional connectivity networks were constructed by Pearson correlation. Global and node-level network parameters, which can reflect the topological organization of the brain network, were calculated to investigate the characteristics of whole-brain functional networks. Non-parametric paired signed rank tests were performed for the above network parameters with sex and age as covariates. P < .05 (with FDR correction for multi-comparison analysis) indicated a statistically significant difference. Correlation analysis was performed between the network parameters and clinical variables.The rTMS showed significant increase in characteristic path length and decrease of clustering coefficient, global, and local efficiency derived from the networks at some specific network sparsity, but it showed no significant difference for small-worldness. rTMS treatment showed significant differences in the brain regions related to sensory-motor, emotion, cognition, affection, and memory, as observed by changes in node degree, node betweenness, and node efficiency. Besides, node-level network parameters in some brain areas showed significant correlations with clinical variables including visual analog scales (VAS) and pain duration.rTMS has significant effects on the whole-brain functional network of PHN patients with a potential for suppression of sensory-motor function and improvement of emotion, cognition, affection, and memory functions.
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Affiliation(s)
- Qian Pei
- Beijing Jishuitan Hospital
- Department of Pain Management, Xuanwu Hospital Capital Medical University
| | - Zhizheng Zhuo
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Qianqian Meng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Xiangyu Ma
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Xiao Mo
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Han Liu
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | | | - Jiaxiang Ni
- Department of Pain Management, Xuanwu Hospital Capital Medical University
| | - Haiyun Li
- School of Biomedical Engineering, Capital Medical University, Beijing, China
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van Montfort SJT, van Dellen E, Stam CJ, Ahmad AH, Mentink LJ, Kraan CW, Zalesky A, Slooter AJC. Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis. NEUROIMAGE-CLINICAL 2019; 23:101809. [PMID: 30981940 PMCID: PMC6461601 DOI: 10.1016/j.nicl.2019.101809] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 03/25/2019] [Accepted: 03/31/2019] [Indexed: 01/05/2023]
Abstract
Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome. Delirium is consistently associated with functional network impairments. Risk factors are associated with lower structural connectivity strength. Risk factors are associated with a less efficient structural network organization. Structural impairments make the functional network more vulnerable to deterioration. Functional network disintegration seems to be a final common pathway for delirium.
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Affiliation(s)
- S J T van Montfort
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands.
| | - E van Dellen
- Department of Psychiatry and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - C J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical Center, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands
| | - A H Ahmad
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Psychology, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands
| | - L J Mentink
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - C W Kraan
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands; Faculty of Science and Technology, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
| | - A Zalesky
- Melbourne Neuropsychiatry Center, Department of Psychiatry, Level 3, Alan Gilbert Building, 161 Barry Street, Carlton South, 3053 Victoria, University of Melbourne and Melbourne Health, Australia
| | - A J C Slooter
- Department of Intensive Care Medicine and Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584 CX Utrecht, The Netherlands
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Ota M, Matsuo J, Sato N, Teraishi T, Hori H, Hattori K, Kamio Y, Maikusa N, Matsuda H, Kunugi H. Relationship between Autistic Spectrum Trait and Regional Cerebral Blood Flow in Healthy Male Subjects. Psychiatry Investig 2018; 15:956-961. [PMID: 30205670 PMCID: PMC6212697 DOI: 10.30773/pi.2018.07.27] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Accepted: 07/27/2018] [Indexed: 12/27/2022] Open
Abstract
OBJECTIVE Autistic spectrum traits are postulated to lie on a continuum that extends between individuals with autism and individuals with typical development. The present study was carried out to investigate functional and network abnormalities associated with autistic spectrum trait in healthy male subjects. METHODS Subjects were 41 healthy male subjects who underwent the social responsiveness scale-adult (SRS-A) and magnetic resonance imaging. RESULTS There was significant positive correlation between the total score of SRS-A and the regional cerebral blood flow (CBF) in posterior cingulate cortex (PCC). Also, there were changes in functional network such as in cingulate corti, insula and fusiform cortex. Further, we also found the significant difference of functional networks between the healthy male subjects with high or low autistic spectrum trait, and these points were congruent with the previous perceptions derived from autistic-spectrum disorders. CONCLUSION These findings suggest a biological basis for the autistic spectrum trait and may be useful for the imaging marker of autism symptomatology.
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Affiliation(s)
- Miho Ota
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Junko Matsuo
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Noriko Sato
- Department of Radiology, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Toshiya Teraishi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroaki Hori
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Kotaro Hattori
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Yoko Kamio
- Department of Child and Adolescent Mental Health, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Norihide Maikusa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroshi Matsuda
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Tokyo, Japan
| | - Hiroshi Kunugi
- Department of Mental Disorder Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Tokyo, Japan
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Network biology: Describing biological systems by complex networks. Phys Life Rev 2018; 24:159-161. [DOI: 10.1016/j.plrev.2017.12.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Accepted: 12/15/2017] [Indexed: 11/21/2022]
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Gosak M, Markovič R, Dolenšek J, Rupnik MS, Marhl M, Stožer A, Perc M. Loosening the shackles of scientific disciplines with network science. Phys Life Rev 2018; 24:162-167. [DOI: 10.1016/j.plrev.2018.01.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Accepted: 01/19/2018] [Indexed: 02/09/2023]
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Kabbara A, Eid H, El Falou W, Khalil M, Wendling F, Hassan M. Reduced integration and improved segregation of functional brain networks in Alzheimer’s disease. J Neural Eng 2018; 15:026023. [DOI: 10.1088/1741-2552/aaaa76] [Citation(s) in RCA: 54] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Murphy AC, Bassett DS. A Network Neuroscience of Neurofeedback for Clinical Translation. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2017; 1:63-70. [PMID: 29057385 DOI: 10.1016/j.cobme.2017.03.003] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
In the emerging field of network neuroscience, the brain is represented as a network of discrete yet functionally and structurally interconnected areas. Mathematical and computational tools to characterize the organization of this network can provide insights into the principles guiding brain structure and function, and can pinpoint differences between healthy individuals and individuals suffering from psychiatric disease or neurological disorders. The field is now faced with the question of how to devise clinical interventions that target these network alterations. Potential solutions to this question include the combination of emerging theories of network control with cutting-edge interventions such as neurofeedback. Each of these techniques may now be mature enough to combine to obtain a theoretically-motivated framework informing viable neuropsychiatric therapies.
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Affiliation(s)
- Andrew C Murphy
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA.,Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA
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42
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Jalili M. Graph theoretical analysis of Alzheimer's disease: Discrimination of AD patients from healthy subjects. Inf Sci (N Y) 2017. [DOI: 10.1016/j.ins.2016.08.047] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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43
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Son SJ, Kim J, Park H. Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer's disease patients. PLoS One 2017; 12:e0173426. [PMID: 28333946 PMCID: PMC5363868 DOI: 10.1371/journal.pone.0173426] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 02/19/2017] [Indexed: 02/04/2023] Open
Abstract
Regional volume atrophy and functional degeneration are key imaging hallmarks of Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI), respectively. We jointly explored regional volume atrophy and functional connectivity to better characterize neuroimaging data of AD and mild cognitive impairment (MCI). All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared regional volume atrophy and functional connectivity in 10 subcortical regions using structural MRI and resting-state functional MRI (rs-fMRI). Neuroimaging data of normal controls (NC) (n = 35), MCI (n = 40), and AD (n = 30) were compared. Significant differences of regional volumes and functional connectivity measures between groups were assessed using permutation tests in 10 regions. The regional volume atrophy and functional connectivity of identified regions were used as features for the random forest classifier to distinguish among three groups. The features of the identified regions were also regarded as connectional fingerprints that could distinctively separate a given group from the others. We identified a few regions with distinctive regional atrophy and functional connectivity patterns for NC, MCI, and AD groups. A three label classifier using the information of regional volume atrophy and functional connectivity of identified regions achieved classification accuracy of 53.33% to distinguish among NC, MCI, and AD. We identified distinctive regional atrophy and functional connectivity patterns that could be regarded as a connectional fingerprint.
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Affiliation(s)
- Seong-Jin Son
- Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Jonghoon Kim
- Department of Electronic, Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science (IBS), Suwon, Korea
- School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
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44
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Sotero RC. Topology, Cross-Frequency, and Same-Frequency Band Interactions Shape the Generation of Phase-Amplitude Coupling in a Neural Mass Model of a Cortical Column. PLoS Comput Biol 2016; 12:e1005180. [PMID: 27802274 PMCID: PMC5089773 DOI: 10.1371/journal.pcbi.1005180] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 09/29/2016] [Indexed: 11/19/2022] Open
Abstract
Phase-amplitude coupling (PAC), a type of cross-frequency coupling (CFC) where the phase of a low-frequency rhythm modulates the amplitude of a higher frequency, is becoming an important indicator of information transmission in the brain. However, the neurobiological mechanisms underlying its generation remain undetermined. A realistic, yet tractable computational model of the phenomenon is thus needed. Here we analyze a neural mass model of a cortical column, comprising fourteen neuronal populations distributed across four layers (L2/3, L4, L5 and L6). A control analysis showed that the conditional transfer entropy (cTE) measure is able to correctly estimate the flow of information between neuronal populations. Then, we computed cTE from the phases to the amplitudes of the oscillations generated in the cortical column. This approach provides information regarding directionality by distinguishing PAC from APC (amplitude-phase coupling), i.e. the information transfer from amplitudes to phases, and can be used to estimate other types of CFC such as amplitude-amplitude coupling (AAC) and phase-phase coupling (PPC). While experiments often only focus on one or two PAC combinations (e.g., theta-gamma or alpha-gamma), we found that a cortical column can simultaneously generate almost all possible PAC combinations, depending on connectivity parameters, time constants, and external inputs. PAC interactions with and without an anatomical equivalent (direct and indirect interactions, respectively) were analyzed. We found that the strength of PAC between two populations was strongly correlated with the strength of the effective connections between the populations and, on average, did not depend on whether the PAC connection was direct or indirect. When considering a cortical column circuit as a complex network, we found that neuronal populations making indirect PAC connections had, on average, higher local clustering coefficient, efficiency, and betweenness centrality than populations making direct connections and populations not involved in PAC connections. This suggests that their interactions were more effective when transmitting information. Since approximately 60% of the obtained interactions represented indirect connections, our results highlight the importance of the topology of cortical circuits for the generation of the PAC phenomenon. Finally, our results demonstrated that indirect PAC interactions can be explained by a cascade of direct CFC and same-frequency band interactions, suggesting that PAC analysis of experimental data should be accompanied by the estimation of other types of frequency interactions for an integrative understanding of the phenomenon.
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Affiliation(s)
- Roberto C. Sotero
- Hotchkiss Brain Institute, Department of Radiology, University of Calgary, Calgary, AB, Canada
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Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter? Sci Rep 2016; 6:29780. [PMID: 27417262 PMCID: PMC4945914 DOI: 10.1038/srep29780] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 06/23/2016] [Indexed: 11/08/2022] Open
Abstract
The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
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