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O'Connor SAJ, Watson EJR, Grech-Sollars M, Finnegan ME, Honeyfield L, Quest RA, Waldman AD, Vizcaychipi MP. Perioperative research into memory (PRiMe), part 2: Adult burns intensive care patients show altered structure and function of the default mode network. Burns 2024:S0305-4179(24)00142-6. [PMID: 38890052 DOI: 10.1016/j.burns.2024.05.008] [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: 05/26/2023] [Revised: 03/24/2024] [Accepted: 05/02/2024] [Indexed: 06/20/2024]
Abstract
BACKGROUND Long-term cognitive impairment (LTCI) is experienced by up to two thirds of patients discharged from burns intensive care units (BICUs), however little is known about its neurobiological basis. This study investigated if patients previously admitted to BICU showed structural and functional MRI changes of the Default Mode Network (DMN). METHODS Fifteen patients previously admitted to BICU with a significant burns injury, and 15 matched volunteers, underwent structural and functional MRI scans. Functional connectivity, fractional anisotropy and cortical thickness of the main DMN subdivisions (anterior DMN (aDMN), posterior DMN (pDMN) and right (rTPJ) and left (lTPJ) temporo-parietal junctions) were compared between patients and volunteers, with differences correlated against cognitive performance. RESULTS Functional connectivity between rTPJ and pDMN (t = 2.91, p = 0.011) and between rTPJ and lTPJ (t = 3.18, p = 0.008) was lower in patients compared to volunteers. Functional connectivity between rTPJ and pDMN correlated with cognitive performance (r2 =0.33, p < 0.001). Mean fractional anisotropy of rTPJ (t = 2.70, p = 0.008) and lTPJ (T = 2.39, p = 0.015) was lower in patients but there was no difference in cortical thickness. CONCLUSIONS Patients previously admitted to BICU show structural and functional disruption of the DMN. Since functional changes correlate with cognitive performance, this should direct further research into intensive-care-related cognitive impairment.
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Affiliation(s)
- Stuart A J O'Connor
- Magill Department of Anaesthesia, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK; Department of Academic Anaesthesia, Pain and Intensive Care Medicine (APMIC), Imperial College London, London, UK
| | - Edward J R Watson
- Magill Department of Anaesthesia, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK; Department of Academic Anaesthesia, Pain and Intensive Care Medicine (APMIC), Imperial College London, London, UK.
| | - Matthew Grech-Sollars
- Department of Computer Science, University College London, London, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
| | - Mary E Finnegan
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK; Department of Bioengineering, Imperial College London, London, UK
| | - Lesley Honeyfield
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Rebecca A Quest
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK; Department of Bioengineering, Imperial College London, London, UK
| | - Adam D Waldman
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK; Department of Brain Sciences, Imperial College London, London, UK
| | - Marcela P Vizcaychipi
- Magill Department of Anaesthesia, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK; Department of Academic Anaesthesia, Pain and Intensive Care Medicine (APMIC), Imperial College London, London, UK
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Riazi H, Nazari M, Raoufy MR, Mirnajafi-Zadeh J, Shojaei A. Olfactory Epithelium Stimulation Using Rhythmic Nasal Air-Puffs Improves the Cognitive Performance of Individuals with Acute Sleep Deprivation. Brain Sci 2024; 14:378. [PMID: 38672027 PMCID: PMC11048381 DOI: 10.3390/brainsci14040378] [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: 03/03/2024] [Revised: 04/08/2024] [Accepted: 04/11/2024] [Indexed: 04/28/2024] Open
Abstract
This study aimed to investigate the effects of intranasal air-puffing on cognitive impairments and brain cortical activity following one night of partial sleep deprivation (PSD) in adults. A total of 26 healthy adults underwent the numerical Stroop test (NST) and electroencephalography (EEG) before and after one night of PSD. Following PSD, subjects in the treatment group (n = 13) received nasal air-puffs (5 Hz, 3 min) before beginning the NST and EEG recording. Administration of nasal air-puffs in the treatment group restored the PSD-induced increase in error rate and decrease in reaction time and missing rate in the NST. Intranasal air-puffs recovered the PSD-induced augmentation of delta and theta power and the reduction of beta and gamma power in the EEG, particularly in the frontal lobes. Intranasal air-puffing also almost reversed the PSD-induced decrease in EEG signal complexity. Furthermore, it had a restorative effect on PSD-induced alteration in intra-default mode network functional connectivity in the beta and gamma frequency bands. Rhythmic nasal air-puffing can mitigate acute PSD-induced impairments in cognitive functions. It exerts part of its ameliorating effect by restoring neuronal activity in cortical brain areas involved in cognitive processing.
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Affiliation(s)
- Hanieh Riazi
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran 14117-13116, Iran; (H.R.); (M.R.R.); (J.M.-Z.)
| | - Milad Nazari
- Department of Molecular Biology and Genetics, Aarhus University, 8000 Aarhus, Denmark;
- Center for Proteins in Memory—PROMEMO, Danish National Research Foundation, 1057 København, Denmark
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran 14117-13116, Iran; (H.R.); (M.R.R.); (J.M.-Z.)
- Institute for Brain and Cognition, Tarbiat Modares University, Tehran 14117-13116, Iran
| | - Javad Mirnajafi-Zadeh
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran 14117-13116, Iran; (H.R.); (M.R.R.); (J.M.-Z.)
- Institute for Brain and Cognition, Tarbiat Modares University, Tehran 14117-13116, Iran
| | - Amir Shojaei
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran 14117-13116, Iran; (H.R.); (M.R.R.); (J.M.-Z.)
- Institute for Brain and Cognition, Tarbiat Modares University, Tehran 14117-13116, Iran
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3
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Arjmandi-Rad S, Vestergaard Nieland JD, Goozee KG, Vaseghi S. The effects of different acetylcholinesterase inhibitors on EEG patterns in patients with Alzheimer's disease: A systematic review. Neurol Sci 2024; 45:417-430. [PMID: 37843690 DOI: 10.1007/s10072-023-07114-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 10/01/2023] [Indexed: 10/17/2023]
Abstract
OBJECTIVE Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common type of dementia. The early diagnosis of AD is an important factor for the control of AD progression. Electroencephalography (EEG) can be used for early diagnosis of AD. Acetylcholinesterase inhibitors (AChEIs) are also used for the amelioration of AD symptoms. In this systematic review, we reviewed the effect of different AChEIs including donepezil, rivastigmine, tacrine, physostigmine, and galantamine on EEG patterns in patients with AD. METHODS PubMed electronic database was searched and 122 articles were found. After removal of unrelated articles, 24 articles were selected for the present study. RESULTS AChEIs can decrease beta, theta, and delta frequency bands in patients with AD. However, conflicting results were found for alpha band. Some studies have shown increased alpha frequency, while others have shown decreased alpha frequency following treatment with AChEIs. The only difference was the type of drug. CONCLUSIONS We found that studies reporting the decreased alpha frequency used donepezil and galantamine, while studies reporting the increased alpha frequency used rivastigmine and tacrine. It was suggested that future studies should focus on the effect of different AChEIs on EEG bands, especially alpha frequency in patients with AD, to compare their effects and find the reason for their different influence on EEG patterns. Also, differences between the effects of AChEIs on oligodendrocyte differentiation and myelination may be another important factor. This is the first article investigating the effect of different AChEIs on EEG patterns in patients with AD.
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Affiliation(s)
- Shirin Arjmandi-Rad
- Institute for Cognitive & Brain Sciences, Shahid Beheshti University, Tehran, Iran
| | | | - Kathryn G Goozee
- KaRa Institute of Neurological Diseases Pty Ltd, Macquarie, NSW, Australia
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Salar Vaseghi
- Cognitive Neuroscience Lab, Medicinal Plants Research Center, Institute of Medicinal Plants, ACECR, Karaj, Iran.
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Babiloni C, Lopez S, Noce G, Ferri R, Panerai S, Catania V, Soricelli A, Salvatore M, Nobili F, Arnaldi D, Famà F, Massa F, Buttinelli C, Giubilei F, Stocchi F, Vacca L, Marizzoni M, D'Antonio F, Bruno G, De Lena C, Güntekin B, Yıldırım E, Hanoğlu L, Yener G, Yerlikaya D, Taylor JP, Schumacher J, McKeith I, Bonanni L, Pantano P, Piervincenzi C, Petsas N, Frisoni GB, Del Percio C, Carducci F. Relationship between default mode network and resting-state electroencephalographic alpha rhythms in cognitively unimpaired seniors and patients with dementia due to Alzheimer's disease. Cereb Cortex 2023; 33:10514-10527. [PMID: 37615301 PMCID: PMC10588004 DOI: 10.1093/cercor/bhad300] [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: 09/15/2022] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/25/2023] Open
Abstract
Here we tested the hypothesis of a relationship between the cortical default mode network (DMN) structural integrity and the resting-state electroencephalographic (rsEEG) rhythms in patients with Alzheimer's disease with dementia (ADD). Clinical and instrumental datasets in 45 ADD patients and 40 normal elderly (Nold) persons originated from the PDWAVES Consortium (www.pdwaves.eu). Individual rsEEG delta, theta, alpha, and fixed beta and gamma bands were considered. Freeware platforms served to derive (1) the (gray matter) volume of the DMN, dorsal attention (DAN), and sensorimotor (SMN) cortical networks and (2) the rsEEG cortical eLORETA source activities. We found a significant positive association between the DMN gray matter volume, the rsEEG alpha source activity estimated in the posterior DMN nodes (parietal and posterior cingulate cortex), and the global cognitive status in the Nold and ADD participants. Compared with the Nold, the ADD group showed lower DMN gray matter, lower rsEEG alpha source activity in those nodes, and lower global cognitive status. This effect was not observed in the DAN and SMN. These results suggest that the DMN structural integrity and the rsEEG alpha source activities in the DMN posterior hubs may be related and predict the global cognitive status in ADD and Nold persons.
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Affiliation(s)
- Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
- Hospital San Raffaele Cassino, Cassino (FR), Italy
| | - Susanna Lopez
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
| | | | | | | | | | - Andrea Soricelli
- IRCCS Synlab SDN, Naples, Italy
- Department of Motor Sciences and Healthiness, University of Naples Parthenope, Naples, 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, 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, Italy
| | - Francesco Famà
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Federico Massa
- Clinica neurologica, IRCCS Ospedale Policlinico San Martino, Genova, 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
| | | | | | - Moira Marizzoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizia D'Antonio
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Giuseppe Bruno
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Carlo De Lena
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Bahar Güntekin
- Department of Biophysics, International School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Ebru Yıldırım
- Program of Electroneurophysiology, Vocational School, Istanbul Medipol University, Istanbul, Turkey
| | - Lutfu Hanoğlu
- Department of Neurology, School of Medicine, Istanbul Medipol University, Istanbul, Turkey
| | - Görsev Yener
- Izmir School of Economics, Faculty of Medicine, Izmir, Turkey
| | - Deniz Yerlikaya
- Health Sciences Institute, Department of Neurosciences, Dokuz Eylül University, Izmir, Turkey
| | - John Paul Taylor
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Julia Schumacher
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
- German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany
| | - Ian McKeith
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Laura Bonanni
- Department of Medicine and Aging Sciences, University “G. d'Annunzio” of Chieti-Pescara, Chieti, Italy
| | - Patrizia Pantano
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli (IS), Italy
| | | | - Nikolaos Petsas
- Scuola di Specializzazione in Statistica Medica e Biometria, Dipartimento di Sanità Pubblica e Malattie Infettive, Sapienza University of Rome, Rome, Italy
| | - Giovanni B Frisoni
- Laboratory of Alzheimer's Neuroimaging and Epidemiology, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
- Memory Clinic and LANVIE - Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland
| | - Claudio Del Percio
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
| | - Filippo Carducci
- Department of Physiology and Pharmacology “Vittorio Erspamer,” Sapienza University of Rome, Rome, Italy
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Nevado A, del Rio D, Pacios J, Maestú F. Neuropsychological networks in cognitively healthy older adults and dementia patients. NEUROPSYCHOLOGY, DEVELOPMENT, AND COGNITION. SECTION B, AGING, NEUROPSYCHOLOGY AND COGNITION 2022; 29:903-927. [PMID: 34415217 PMCID: PMC9485389 DOI: 10.1080/13825585.2021.1965951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 08/04/2021] [Indexed: 10/20/2022]
Abstract
Neuropsychological tests have commonly been used to determine the organization of cognitive functions by identifying latent variables. In contrast, an approach which has seldom been employed is network analysis. We characterize the network structure of a set of representative neuropsychological test scores in cognitively healthy older adults and MCI and dementia patients using network analysis. We employed the neuropsychological battery from the National Alzheimer's Coordinating Center which included healthy controls (n = 7623), mild cognitive impairment patients (n = 5981) and dementia patients (n = 2040), defined according to the Clinical Dementia Rating. The results showed that, according to several network analysis measures, the most central cognitive function is executive function followed by attention, language, and memory. At the test level, the most central test was the Trail Making Test B, which measures cognitive flexibility. Importantly, these results and most other network measures, such as the community organization and graph representation, were similar across the three diagnostic groups. Therefore, network analysis can help to establish a ranking of cognitive functions and tests based on network centrality and suggests that this organization is preserved in dementia. Central nodes might be particularly relevant both from a theoretical and clinical point of view, as they are more associated with other nodes, and their disruption is likely to have a larger effect on the overall network than peripheral nodes. The present analysis may provide a proof of principle for the application of network analysis to cognitive data.
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Affiliation(s)
- Angel Nevado
- Experimental Psychology Department, Complutense University of Madrid, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - David del Rio
- Experimental Psychology Department, Complutense University of Madrid, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Javier Pacios
- Experimental Psychology Department, Complutense University of Madrid, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
| | - Fernando Maestú
- Experimental Psychology Department, Complutense University of Madrid, Madrid, Spain
- Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain
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Lazarou I, Georgiadis K, Nikolopoulos S, Oikonomou VP, Stavropoulos TG, Tsolaki A, Kompatsiaris I, Tsolaki M. Exploring Network Properties Across Preclinical Stages of Alzheimer’s Disease Using a Visual Short-Term Memory and Attention Task with High-Density Electroencephalography: A Brain-Connectome Neurophysiological Study. J Alzheimers Dis 2022; 87:643-664. [DOI: 10.3233/jad-215421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Visual short-term memory (VSTMT) and visual attention (VAT) exhibit decline in the Alzheimer’s disease (AD) continuum; however, network disruption in preclinical stages is scarcely explored. Objective: To advance our knowledge about brain networks in AD and discover connectivity alterations during VSTMT and VAT. Methods: Twelve participants with AD, 23 with mild cognitive impairment (MCI), 17 with subjective cognitive decline (SCD), and 21 healthy controls (HC) were examined using a neuropsychological battery at baseline and follow-up (three years). At baseline, the subjects were examined using high density electroencephalography while performing a VSTMT and VAT. For exploring network organization, we constructed weighted undirected networks and examined clustering coefficient, strength, and betweenness centrality from occipito-parietal regions. Results: One-way ANOVA and pair-wise t-test comparisons showed statistically significant differences in HC compared to SCD (t (36) = 2.43, p = 0.026), MCI (t (42) = 2.34, p = 0.024), and AD group (t (31) = 3.58, p = 0.001) in Clustering Coefficient. Also with regards to Strength, higher values for HC compared to SCD (t (36) = 2.45, p = 0.019), MCI (t (42) = 2.41, p = 0.020), and AD group (t (31) = 3.58, p = 0.001) were found. Follow-up neuropsychological assessment revealed converge of 65% of the SCD group to MCI. Moreover, SCD who were converted to MCI showed significant lower values in all network metrics compared to the SCD that remained stable. Conclusion: The present findings reveal that SCD exhibits network disorganization during visual encoding and retrieval with intermediate values between MCI and HC.
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Affiliation(s)
- Ioulietta Lazarou
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
- 1 Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Greece
| | - Kostas Georgiadis
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
- Informatics Department, Aristotle University of Thessaloniki, Makedonia, Greece
| | - Spiros Nikolopoulos
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
| | - Vangelis P. Oikonomou
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
| | - Thanos G. Stavropoulos
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
| | - Anthoula Tsolaki
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
- Greek Association of Alzheimer’s Disease and Related Disorders, Thessaloniki, Makedonia, Greece
| | - Ioannis Kompatsiaris
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
| | - Magda Tsolaki
- Information Technologies Institute, Centre for Research and Technology Hellas (CERTH-ITI), Thessaloniki, Makedonia, Greece
- 1 Department of Neurology, G.H. “AHEPA”, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Makedonia, Greece
- Greek Association of Alzheimer’s Disease and Related Disorders, Thessaloniki, Makedonia, Greece
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Schoonhoven DN, Briels CT, Hillebrand A, Scheltens P, Stam CJ, Gouw AA. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease. Alzheimers Res Ther 2022; 14:38. [PMID: 35219327 PMCID: PMC8881826 DOI: 10.1186/s13195-022-00970-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/30/2022] [Indexed: 01/08/2023]
Abstract
Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-00970-4.
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Affiliation(s)
- Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. .,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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Svaldi DO, Goñi J, Abbas K, Amico E, Clark DG, Muralidharan C, Dzemidzic M, West JD, Risacher SL, Saykin AJ, Apostolova LG. Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease. Hum Brain Mapp 2021; 42:3500-3516. [PMID: 33949732 PMCID: PMC8249900 DOI: 10.1002/hbm.25448] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/07/2021] [Accepted: 04/06/2021] [Indexed: 12/29/2022] Open
Abstract
Functional connectivity, as estimated using resting state functional MRI, has shown potential in bridging the gap between pathophysiology and cognition. However, clinical use of functional connectivity biomarkers is impeded by unreliable estimates of individual functional connectomes and lack of generalizability of models predicting cognitive outcomes from connectivity. To address these issues, we combine the frameworks of connectome predictive modeling and differential identifiability. Using the combined framework, we show that enhancing the individual fingerprint of resting state functional connectomes leads to robust identification of functional networks associated to cognitive outcomes and also improves prediction of cognitive outcomes from functional connectomes. Using a comprehensive spectrum of cognitive outcomes associated to Alzheimer's disease (AD), we identify and characterize functional networks associated to specific cognitive deficits exhibited in AD. This combined framework is an important step in making individual level predictions of cognition from resting state functional connectomes and in understanding the relationship between cognition and connectivity.
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Affiliation(s)
| | - Joaquín Goñi
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
- Weldon School of Biomedical EngineeringPurdue UniversityWest LafayetteIndianaUSA
| | - Kausar Abbas
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - Enrico Amico
- School of Industrial EngineeringPurdue UniversityWest LafayetteIndianaUSA
- Purdue Institute for Integrative Neuroscience, Purdue UniversityWest LafayetteIndianaUSA
| | - David G. Clark
- Indiana University School of MedicineIndianapolisIndianaUSA
| | | | | | - John D. West
- Indiana University School of MedicineIndianapolisIndianaUSA
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9
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Magalhães TNC, Gerbelli CLB, Pimentel-Silva LR, de Campos BM, de Rezende TJR, Rizzi L, Joaquim HPG, Talib LL, Forlenza OV, Cendes F, Balthazar MLF. Differences in structural and functional default mode network connectivity in amyloid positive mild cognitive impairment: a longitudinal study. Neuroradiology 2021; 64:141-150. [PMID: 34278511 DOI: 10.1007/s00234-021-02760-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Accepted: 06/28/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Default mode network (DMN) has emerged as a potential biomarker of Alzheimer's disease (AD); however, it is not clear whether it can differentiate amnestic mild cognitive impairment with altered amyloid (aMCI-Aβ +) who will evolve to AD. We evaluated if structural and functional connectivity (FC), hippocampal volumes (HV), and cerebrospinal fluid biomarkers (CSF-Aβ42, p-Tau, and t-Tau) can differentiate aMCI-Aβ + converters from non-converters. METHODS Forty-eight individuals (18 normal controls and 30 aMCI subjects in the AD continuum - with altered Aβ42 in the CSF) were followed up for an average of 13 months. We used MultiAtlas, UF2C, and Freesurfer software to evaluate diffusion tensor imaging, FC, and HV, respectively, INNOTEST® kits to measure CSF proteins, and neuropsychological tests. Besides, we performed different MANOVAs with further univariate analyses to differentiate groups. RESULTS During follow-up, 8/30 aMCI-Aβ + converted (26.6%) to AD dementia. There were no differences in multivariate analysis between groups in CSF biomarkers (p = 0.092) or at DMN functional connectivity (p = 0.814). aMCI-Aβ + converters had smaller right HV than controls (p = 0.013), and greater right cingulum parahippocampal bundle radial diffusivity than controls (p < 0.001) and non-converters (p = 0.036). CONCLUSION In this exploratory study, structural, but not functional, DMN connectivity alterations may differentiate aMCI-Aβ + subjects who converted to AD dementia.
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Affiliation(s)
- Thamires Naela Cardoso Magalhães
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil.
| | - Christian Luiz Baptista Gerbelli
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | - Luciana Ramalho Pimentel-Silva
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | - Brunno Machado de Campos
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | - Thiago Junqueira Ribeiro de Rezende
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | - Liara Rizzi
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | | | - Leda Leme Talib
- Laboratory of Neurosciences, (LIM 27), Department and Institute of Psychiatry, University of São Paulo (USP), São Paulo, Brazil
| | - Orestes Vicente Forlenza
- Laboratory of Neurosciences, (LIM 27), Department and Institute of Psychiatry, University of São Paulo (USP), São Paulo, Brazil
| | - Fernando Cendes
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
| | - Marcio Luiz Figueredo Balthazar
- Laboratory of Neuroimaging, Department of Neurology - Medical Sciences School, University of Campinas (UNICAMP), Rua Tessália Vieira de Camargo 126, Campinas, SP, 13083-887, Brazil
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10
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The effects of lutein and zeaxanthin on resting state functional connectivity in older Caucasian adults: a randomized controlled trial. Brain Imaging Behav 2021; 14:668-681. [PMID: 30680611 DOI: 10.1007/s11682-018-00034-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
The carotenoids lutein (L) and zeaxanthin (Z) accumulate in retinal regions of the eye and have long been shown to benefit visual health. A growing literature suggests cognitive benefits as well, particularly in older adults. The present randomized controlled trial sought to investigate the effects of L and Z on brain function using resting state functional magnetic resonance imaging (fMRI). It was hypothesized that L and Z supplementation would (1) improve intra-network integrity of default mode network (DMN) and (2) reduce inter-network connectivity between DMN and other resting state networks. 48 community-dwelling older adults (mean age = 72 years) were randomly assigned to receive a daily L (10 mg) and Z (2 mg) supplement or a placebo for 1 year. Resting state fMRI data were acquired at baseline and post-intervention. A dictionary learning and sparse coding computational framework, based on machine learning principles, was used to investigate intervention-related changes in functional connectivity. DMN integrity was evaluated by calculating spatial overlap rate with a well-established DMN template provided in the neuroscience literature. Inter-network connectivity was evaluated via time series correlations between DMN and nine other resting state networks. Contrary to expectation, results indicated that L and Z significantly increased rather than decreased inter-network connectivity (Cohen's d = 0.89). A significant intra-network effect on DMN integrity was not observed. Rather than restoring what has been described in the available literature as a "youth-like" pattern of intrinsic brain activity, L and Z may facilitate the aging brain's capacity for compensation by enhancing integration between networks that tend to be functionally segregated earlier in the lifespan.
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11
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Changes in electrophysiological static and dynamic human brain functional architecture from childhood to late adulthood. Sci Rep 2020; 10:18986. [PMID: 33149179 PMCID: PMC7642359 DOI: 10.1038/s41598-020-75858-0] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 10/20/2020] [Indexed: 12/15/2022] Open
Abstract
This magnetoencephalography study aimed at characterizing age-related changes in resting-state functional brain organization from mid-childhood to late adulthood. We investigated neuromagnetic brain activity at rest in 105 participants divided into three age groups: children (6-9 years), young adults (18-34 years) and healthy elders (53-78 years). The effects of age on static resting-state functional brain integration were assessed using band-limited power envelope correlation, whereas those on transient functional brain dynamics were disclosed using hidden Markov modeling of power envelope activity. Brain development from childhood to adulthood came with (1) a strengthening of functional integration within and between resting-state networks and (2) an increased temporal stability of transient (100-300 ms lifetime) and recurrent states of network activation or deactivation mainly encompassing lateral or medial associative neocortical areas. Healthy aging was characterized by decreased static resting-state functional integration and dynamic stability within the primary visual network. These results based on electrophysiological measurements free of neurovascular biases suggest that functional brain integration mainly evolves during brain development, with limited changes in healthy aging. These novel electrophysiological insights into human brain functional architecture across the lifespan pave the way for future clinical studies investigating how brain disorders affect brain development or healthy aging.
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12
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Ramírez-Toraño F, Bruña R, de Frutos-Lucas J, Rodríguez-Rojo IC, Marcos de Pedro S, Delgado-Losada ML, Gómez-Ruiz N, Barabash A, Marcos A, López Higes R, Maestú F. Functional Connectivity Hypersynchronization in Relatives of Alzheimer’s Disease Patients: An Early E/I Balance Dysfunction? Cereb Cortex 2020; 31:1201-1210. [DOI: 10.1093/cercor/bhaa286] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 08/05/2020] [Accepted: 09/01/2020] [Indexed: 12/13/2022] Open
Abstract
Abstract
Alzheimer’s disease (AD) studies on animal models, and humans showed a tendency of the brain tissue to become hyperexcitable and hypersynchronized, causing neurodegeneration. However, we know little about either the onset of this phenomenon or its early effects on functional brain networks. We studied functional connectivity (FC) on 127 participants (92 middle-age relatives of AD patients and 35 age-matched nonrelatives) using magnetoencephalography. FC was estimated in the alpha band in areas known both for early amyloid accumulation and disrupted FC in MCI converters to AD. We found a frontoparietal network (anterior cingulate cortex, dorsal frontal, and precuneus) where relatives of AD patients showed hypersynchronization in high alpha (not modulated by APOE-ε4 genotype) in comparison to age-matched nonrelatives. These results represent the first evidence of neurophysiological events causing early network disruption in humans, opening a new perspective for intervention on the excitation/inhibition unbalance.
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Affiliation(s)
- F Ramírez-Toraño
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - R Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Comunidad de Madrid 28029, Spain
| | - J de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Biological and Health Psychology Department, Universidad Autonoma de Madrid, Madrid, Comunidad de Madrid 28049, Spain
| | - I C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Facultad de Psicología, Centro Universitario Villanueva, Madrid, Comunidad de Madrid 28034, Spain
| | - S Marcos de Pedro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Facultad de Educación y Salud, Universidad Camilo José Cela, Madrid, Comunidad de Madrid 28010, Spain
| | - M L Delgado-Losada
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - N Gómez-Ruiz
- Sección Neurorradiología, Servicio de Diagnóstico por Imagen, Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
| | - A Barabash
- Endocrinology and Nutrition Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Madrid, Comunidad de Madrid 28029, Spain
| | - A Marcos
- Neurology Department, Hospital Clinico San Carlos and Instituto de Investigación Sanitaria del Hospital Clínico San Carlos, Madrid, Comunidad de Madrid 28040, Spain
| | - R López Higes
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
| | - F Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Technical University of Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Comunidad de Madrid 28223, Spain
- Networking Research Center on Bioengineering, Biomaterials, and Nanomedicine (CIBER-BBN), Madrid, Comunidad de Madrid 28029, Spain
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13
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Yan T, Wang Y, Weng Z, Du W, Liu T, Chen D, Li X, Wu J, Han Y. Early-Stage Identification and Pathological Development of Alzheimer's Disease Using Multimodal MRI. J Alzheimers Dis 2020; 68:1013-1027. [PMID: 30958352 DOI: 10.3233/jad-181049] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Alzheimer's disease (AD) is one of the most common progressive and irreversible neurodegenerative diseases. The study of the pathological mechanism of AD and early-stage diagnosis is essential and important. Subjective cognitive decline (SCD), the first at-risk stage of AD occurring prior to amnestic mild cognitive impairment (aMCI), is of great research value and has gained our interest. To investigate the entire pathological development of AD pathology efficiently, we proposed a machine learning classification method based on a multimodal support vector machine (SVM) to investigate the structural and functional connectivity patterns of the three stages of AD (SCD, aMCI, and AD). Our experiments achieved an accuracy of 98.58% in the AD group, 97.76% in the aMCI group, and 80.24% in the SCD group. Moreover, in our experiments, we identified the most discriminating brain regions, which were mainly located in the default mode network and subcortical structures (SCS). Notably, with the development of AD pathology, SCS regions have become increasingly important, and structural connectivity has shown more discriminative power than functional connectivity. The current study may shed new light on the pathological mechanism of AD and suggests that whole-brain connectivity may provide potential effective biomarkers for the early-stage diagnosis of AD.
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Affiliation(s)
- Tianyi Yan
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Yonghao Wang
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Zizheng Weng
- Daniel Felix Ritchie School of Engineering and Computer Science, University of Denver, Denver, CO, USA
| | - Wenying Du
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China
| | - Tiantian Liu
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Duanduan Chen
- School of Life Science, Beijing Institute of Technology, Beijing, China
| | - Xuesong Li
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China
| | - Jinglong Wu
- Beijing Advanced Innovation Center for Intelligent Robots and Systems; Beijing Institute of Technology, Beijing, China
| | - Ying Han
- Department of Neurology, XuanWu Hospital of Capital Medical University, Beijing, China.,Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing, China.,Beijing Institute of Geriatrics, Beijing, China.,National Clinical Research Center for Geriatric Disorders, Beijing, China
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14
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Cui X, Xiao J, Guo H, Wang B, Li D, Niu Y, Xiang J, Chen J. Clustering of Brain Function Network Based on Attribute and Structural Information and Its Application in Brain Diseases. Front Neuroinform 2020; 13:79. [PMID: 32116624 PMCID: PMC7020566 DOI: 10.3389/fninf.2019.00079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 12/24/2019] [Indexed: 12/02/2022] Open
Abstract
At present, the diagnosis of brain disease is mainly based on the self-reported symptoms and clinical signs of the patient, which can easily lead to psychiatrists' bias. The purpose of this study is to develop a brain network clustering model to accurately identify brain diseases based on resting state functional magnetic resonance imaging (fMRI) in the absence of clinical information. We use cosine similarity and sub-network kernels to measure attribute similarity and structure similarity, respectively. By integrating the structure similarity and attribute similarity into one matrix, spectral clustering is used to achieve brain network clustering. Finally, we evaluate this method on three diseases: Alzheimer's disease, Bipolar disorder patients, and Schizophrenia. The performance of methods is evaluated by measuring clustering consistency. Clustering consistency is similar to clustering accuracy, which is used to evaluate the consistency between the clustering labels and clinical diagnostic labels of the subjects. The experimental results show that our proposed method can significantly improve clustering performance, with a consistency of 60.6% for Alzheimer's disease, with a consistency of 100% for Schizophrenia, with a consistency of 100% for Bipolar disorder patients.
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Affiliation(s)
| | | | | | | | | | | | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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15
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What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons. Neurobiol Aging 2020; 85:58-73. [DOI: 10.1016/j.neurobiolaging.2019.09.008] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Revised: 08/27/2019] [Accepted: 09/14/2019] [Indexed: 01/14/2023]
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16
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De Marco M, Ourselin S, Venneri A. Age and hippocampal volume predict distinct parts of default mode network activity. Sci Rep 2019; 9:16075. [PMID: 31690806 PMCID: PMC6831650 DOI: 10.1038/s41598-019-52488-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Accepted: 10/08/2019] [Indexed: 01/20/2023] Open
Abstract
Group comparison studies have established that activity in the posterior part of the default-mode network (DMN) is down-regulated by both normal ageing and Alzheimer’s disease (AD). In this study linear regression models were used to disentangle distinctive DMN activity patterns that are more profoundly associated with either normal ageing or a structural marker of neurodegeneration. 312 datasets inclusive of healthy adults and patients were analysed. Days of life at scan (DOL) and hippocampal volume were used as predictors. Group comparisons confirmed a significant association between functional connectivity in the posterior cingulate/retrosplenial cortex and precuneus and both ageing and AD. Fully-corrected regression models revealed that DOL significantly predicted DMN strength in these regions. No such effect, however, was predicted by hippocampal volume. A significant positive association was found between hippocampal volumes and DMN connectivity in the right temporo-parietal junction (TPJ). These results indicate that postero-medial DMN down-regulation may not be specific to neurodegenerative processes but may be more an indication of brain vulnerability to degeneration. The DMN-TPJ disconnection is instead linked to the volumetric properties of the hippocampus, may reflect early-stage regional accumulation of pathology and might be of aid in the clinical detection of abnormal ageing.
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Affiliation(s)
- Matteo De Marco
- Department of Neuroscience, Medical School, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, S10 2RX, Sheffield, UK
| | - Sebastien Ourselin
- Department of Imaging and Biomedical Engineering, King's College London, Strand, London, UK
| | - Annalena Venneri
- Department of Neuroscience, Medical School, University of Sheffield, Royal Hallamshire Hospital, Beech Hill Road, S10 2RX, Sheffield, UK.
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17
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Pusil S, López ME, Cuesta P, Bruña R, Pereda E, Maestú F. Hypersynchronization in mild cognitive impairment: the ‘X’ model. Brain 2019; 142:3936-3950. [DOI: 10.1093/brain/awz320] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 08/06/2019] [Accepted: 08/13/2019] [Indexed: 12/21/2022] Open
Abstract
Hypersynchronization has been considered as a biomarker of synaptic dysfunction along the Alzheimeŕs disease continuum. In a longitudinal MEG study, Pusil et al. reveal changes in functional connectivity upon progression from MCI to Alzheimer’s disease. They propose the ‘X’ model to explain their findings, and suggest that hypersynchronization predicts conversion.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering and IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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18
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Pusil S, Dimitriadis SI, López ME, Pereda E, Maestú F. Aberrant MEG multi-frequency phase temporal synchronization predicts conversion from mild cognitive impairment-to-Alzheimer's disease. NEUROIMAGE-CLINICAL 2019; 24:101972. [PMID: 31522127 PMCID: PMC6745514 DOI: 10.1016/j.nicl.2019.101972] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 07/19/2019] [Accepted: 08/03/2019] [Indexed: 11/15/2022]
Abstract
Many neuroimaging studies focus on a frequency-specific or a multi-frequency network analysis showing that functional brain networks are disrupted in patients with Alzheimer's disease (AD). Although those studies enriched our knowledge of the impact of AD in brain's functionality, our goal is to test the effectiveness of combining neuroimaging with network neuroscience to predict with high accuracy subjects with mild cognitive impairment (MCI) that will convert to AD. In this study, eyes-closed resting-state magnetoencephalography (MEG) recordings from 27 stable MCI (sMCI) and 27 progressive MCI (pMCI) from two scan sessions (baseline and follow-up after approximately 3 years) were projected via beamforming onto an atlas-based set of regions of interest (ROIs). Dynamic functional connectivity networks were constructed independently for the five classical frequency bands while a multivariate phase-based coupling metric was adopted. Thus, computing the distance between the fluctuation of functional strength of every pair of ROIs between the two conditions with dynamic time wrapping (DTW), a large set of features was extracted. A machine learning algorithm revealed 30 DTW-based features in the five frequency bands that can distinguish the sMCI from pMCI with absolute accuracy (100%). Further analysis of the selected links revealed that most of the connected ROIs were part of the default mode network (DMN), the cingulo-opercular (CO), the fronto-parietal and the sensorimotor network. Overall, our dynamic network multi-frequency analysis approach provides an effective framework of constructing a sensitive MEG-based connectome biomarker for the prediction of conversion from MCI to Alzheimer's disease. 49 features in the five frequency bands discriminated the two groups with 100% accuracy. The analysis revealed most of the links were part of important brain networks as the DMN. The alteration of these links, usually affected in AD, is related to synaptic loss. Our multi-frequency approach provided a MEG connectome biomarker to predict the conversion to AD.
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Affiliation(s)
- Sandra Pusil
- Laboratory of Neuropsychology, University of the Balearic Islands, Spain; Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain.
| | - Stavros I Dimitriadis
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom; Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom; School of Psychology, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, School of Medicine, Cardiff University, Cardiff, United Kingdom; MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff, United Kingdom
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Electrical Engineering and Bioengineering Lab, Department of Industrial Engineering, IUNE Universidad de La Laguna, Tenerife, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain; Department of Experimental Psychology, Universidad Complutense de Madrid, Madrid, Spain; Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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19
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Bells S, Lefebvre J, Longoni G, Narayanan S, Arnold DL, Yeh EA, Mabbott DJ. White matter plasticity and maturation in human cognition. Glia 2019; 67:2020-2037. [PMID: 31233643 DOI: 10.1002/glia.23661] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 05/21/2019] [Accepted: 05/29/2019] [Indexed: 12/17/2022]
Abstract
White matter plasticity likely plays a critical role in supporting cognitive development. However, few studies have used the imaging methods specific to white matter tissue structure or experimental designs sensitive to change in white matter necessary to elucidate these relations. Here we briefly review novel imaging approaches that provide more specific information regarding white matter microstructure. Furthermore, we highlight recent studies that provide greater clarity regarding the relations between changes in white matter and cognition maturation in both healthy children and adolescents and those with white matter insult. Finally, we examine the hypothesis that white matter is linked to cognitive function via its impact on neural synchronization. We test this hypothesis in a population of children and adolescents with recurrent demyelinating syndromes. Specifically, we evaluate group differences in white matter microstructure within the optic radiation; and neural phase synchrony in visual cortex during a visual task between 25 patients and 28 typically developing age-matched controls. Children and adolescents with demyelinating syndromes show evidence of myelin and axonal compromise and this compromise predicts reduced phase synchrony during a visual task compared to typically developing controls. We investigate one plausible mechanism at play in this relationship using a computational model of gamma generation in early visual cortical areas. Overall, our findings show a fundamental connection between white matter microstructure and neural synchronization that may be critical for cognitive processing. In the future, longitudinal or interventional studies can build upon our knowledge of these exciting relations between white matter, neural communication, and cognition.
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Affiliation(s)
- Sonya Bells
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Jérémie Lefebvre
- Krembil Research Institute, University Health Network, Toronto, Ontario, Canada.,Department of Mathematics, University of Toronto, Toronto, Ontario, Canada
| | - Giulia Longoni
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Sridar Narayanan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Douglas L Arnold
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Eleun Ann Yeh
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Neurology, The Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Paediatrics, University of Toronto, Toronto, Ontario, Canada
| | - Donald J Mabbott
- Neurosciences and Mental Health Program, Research Institute, Hospital for Sick Children, Toronto, Ontario, Canada.,Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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20
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Li K, Luo X, Zeng Q, Huang P, Shen Z, Xu X, Xu J, Wang C, Zhou J, Zhang M. Gray matter structural covariance networks changes along the Alzheimer's disease continuum. NEUROIMAGE-CLINICAL 2019; 23:101828. [PMID: 31029051 PMCID: PMC6484365 DOI: 10.1016/j.nicl.2019.101828] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 04/01/2019] [Accepted: 04/15/2019] [Indexed: 11/24/2022]
Abstract
Alzheimer's disease (AD) has a long neuropathological accumulation phase before the onset of dementia. Such AD neuropathological deposition between neurons impairs the synaptic communication, resulting in networks disorganization. Our study aimed to explore the evolution patterns of gray matter structural covariance networks (SCNs) along AD continuum. Based on the AT(N) (i.e., Amyloid/Tau/Neurodegeneration) pathological classification system, we classified subjects into four groups using cerebrospinal fluid amyloid-beta1–42 (A) and phosphorylated tau protein181 (T). We identified 101 subjects with normal AD biomarkers (A-T-), 40 subjects with Alzheimer's pathologic change (A + T−), 101 subjects with biological AD (A + T+) and 91 AD with dementia (demented subjects with A + T+). We used four regions of interest to anchor default mode network (DMN, medial temporal subsystem and midline core subsystem), salience network (SN) and executive control network (ECN). Finally, we used a multi-regression model-based linear-interaction analysis to assess the SCN changes. Along the disease progression, DMN and SN showed increased structural association at the early stage while decreased structural association at the late stage. Moreover, ECN showed progressively increased structural association as AD neuropathological profiles progress. In conclusion, this study found the dynamic trajectory of SCNs changes along the AD continuum and support the network disconnection hypothesis underlying AD neuropathological progression. Further, SCN may potentially serve as an effective AD biomarker. To explore the AD continuum accurately by using the latest ATN classification system (based on neuropathological biomarkers). Using SCNs analysis to reflect the brain network changes, which may further lead to cognition alternations in AD. Results supported network disconnection hypothesis and showed a dynamic trajectory of SCNs changes along the AD continuum.
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Affiliation(s)
- Kaicheng Li
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiao Luo
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Qingze Zeng
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Peiyu Huang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Zhujing Shen
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Xiaojun Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jingjing Xu
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Chao Wang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Jiong Zhou
- Department of Neurology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China
| | - Minming Zhang
- Department of Radiology, School of Medicine, 2nd Affiliated Hospital of Zhejiang University, China.
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21
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Electrophysiological assessment methodology of sensory processing dysfunction in schizophrenia and dementia of the Alzheimer type. Neurosci Biobehav Rev 2019; 97:70-84. [DOI: 10.1016/j.neubiorev.2018.09.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2017] [Revised: 09/04/2018] [Accepted: 09/05/2018] [Indexed: 12/26/2022]
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22
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Cui X, Xiang J, Wang B, Xiao J, Niu Y, Chen J. Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment. Front Neurosci 2018; 12:701. [PMID: 30349451 PMCID: PMC6186843 DOI: 10.3389/fnins.2018.00701] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/18/2018] [Indexed: 02/03/2023] Open
Abstract
Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI.
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Affiliation(s)
- Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jihai Xiao
- Center of Information Management and Development, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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23
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Ishii R, Canuet L, Aoki Y, Hata M, Iwase M, Ikeda S, Nishida K, Ikeda M. Healthy and Pathological Brain Aging: From the Perspective of Oscillations, Functional Connectivity, and Signal Complexity. Neuropsychobiology 2018; 75:151-161. [PMID: 29466802 DOI: 10.1159/000486870] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/06/2017] [Accepted: 01/14/2018] [Indexed: 12/24/2022]
Abstract
Healthy aging is associated with impairment in cognitive information processing. Several neuroimaging methods such as functional magnetic resonance imaging, positron emission tomography and near-infrared spectroscopy have been used to explore healthy and pathological aging by relying on hemodynamic or metabolic changes that occur in response to brain activity. Since electroencephalography (EEG) and magnetoencephalography (MEG) are able to measure neural activity directly with a high temporal resolution of milliseconds, these neurophysiological techniques are particularly important to investigate the dynamics of brain activity underlying neurocognitive aging. It is well known that age is a major risk factor for Alzheimer's disease (AD), and that synaptic dysfunction represents an early sign of this disease associated with hallmark neuropathological findings. However, the neurophysiological mechanisms underlying AD are not fully elucidated. This review addresses healthy and pathological brain aging from a neurophysiological perspective, focusing on oscillatory activity changes during the resting state, event-related potentials and stimulus-induced oscillatory responses during cognitive or motor tasks, functional connectivity between brain regions, and changes in signal complexity. We also highlight the accumulating evidence on age-related EEG/MEG changes and biological markers of brain neurodegeneration, including genetic factors, structural abnormalities on magnetic resonance images, and the biochemical changes associated with Aβ deposition and tau pathology.
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Affiliation(s)
- Ryouhei Ishii
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Palliative Care, Ashiya Municipal Hospital, Ashiya, Japan
| | - Leonides Canuet
- Department of Cognitive, Social and Organizational Psychology, La Laguna University, Tenerife, Spain
| | - Yasunori Aoki
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan.,Department of Psychiatry, Nissay Hospital, Osaka, Japan
| | - Masahiro Hata
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
| | - Masao Iwase
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
| | - Shunichiro Ikeda
- Department of Psychiatry, Kansai Medical University, Moriguchi, Japan
| | - Keiichiro Nishida
- Department of Psychiatry, Kansai Medical University, Moriguchi, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Osaka University Graduate School of Medicine, Suita, Japan
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24
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López-Sanz D, Serrano N, Maestú F. The Role of Magnetoencephalography in the Early Stages of Alzheimer's Disease. Front Neurosci 2018; 12:572. [PMID: 30158852 PMCID: PMC6104188 DOI: 10.3389/fnins.2018.00572] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Accepted: 07/30/2018] [Indexed: 01/01/2023] Open
Abstract
The ever increasing proportion of aged people in modern societies is leading to a substantial increase in the number of people affected by dementia, and Alzheimer’s Disease (AD) in particular, which is the most common cause for dementia. Throughout the course of the last decades several different compounds have been tested to stop or slow disease progression with limited success, which is giving rise to a strong interest toward the early stages of the disease. Alzheimer’s disease has an extended an insidious preclinical stage in which brain pathology accumulates slowly until clinical symptoms are observable in prodromal stages and in dementia. For this reason, the scientific community is focusing into investigating early signs of AD which could lead to the development of validated biomarkers. While some CSF and PET biomarkers have already been introduced in the clinical practice, the use of non-invasive measures of brain function as early biomarkers is still under investigation. However, the electrophysiological mechanisms and the early functional alterations underlying preclinical Alzheimer’s Disease is still scarcely studied. This work aims to briefly review the most relevant findings in the field of electrophysiological brain changes as measured by magnetoencephalography (MEG). MEG has proven its utility in some clinical areas. However, although its clinical relevance in dementia is still limited, a growing number of studies highlighted its sensitivity in these preclinical stages. Studies focusing on different analytical approaches will be reviewed. Furthermore, their potential applications to establish early diagnosis and determine subsequent progression to dementia are discussed.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Noelia Serrano
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain.,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain
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25
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Dimitriadis SI, López ME, Bruña R, Cuesta P, Marcos A, Maestú F, Pereda E. How to Build a Functional Connectomic Biomarker for Mild Cognitive Impairment From Source Reconstructed MEG Resting-State Activity: The Combination of ROI Representation and Connectivity Estimator Matters. Front Neurosci 2018; 12:306. [PMID: 29910704 PMCID: PMC5992286 DOI: 10.3389/fnins.2018.00306] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Accepted: 04/20/2018] [Indexed: 11/24/2022] Open
Abstract
Our work aimed to demonstrate the combination of machine learning and graph theory for the designing of a connectomic biomarker for mild cognitive impairment (MCI) subjects using eyes-closed neuromagnetic recordings. The whole analysis based on source-reconstructed neuromagnetic activity. As ROI representation, we employed the principal component analysis (PCA) and centroid approaches. As representative bi-variate connectivity estimators for the estimation of intra and cross-frequency interactions, we adopted the phase locking value (PLV), the imaginary part (iPLV) and the correlation of the envelope (CorrEnv). Both intra and cross-frequency interactions (CFC) have been estimated with the three connectivity estimators within the seven frequency bands (intra-frequency) and in pairs (CFC), correspondingly. We demonstrated how different versions of functional connectivity graphs single-layer (SL-FCG) and multi-layer (ML-FCG) can give us a different view of the functional interactions across the brain areas. Finally, we applied machine learning techniques with main scope to build a reliable connectomic biomarker by analyzing both SL-FCG and ML-FCG in two different options: as a whole unit using a tensorial extraction algorithm and as single pair-wise coupling estimations. We concluded that edge-weighed feature selection strategy outperformed the tensorial treatment of SL-FCG and ML-FCG. The highest classification performance was obtained with the centroid ROI representation and edge-weighted analysis of the SL-FCG reaching the 98% for the CorrEnv in α1:α2 and 94% for the iPLV in α2. Classification performance based on the multi-layer participation coefficient, a multiplexity index reached 52% for iPLV and 52% for CorrEnv. Selected functional connections that build the multivariate connectomic biomarker in the edge-weighted scenario are located in default-mode, fronto-parietal, and cingulo-opercular network. Our analysis supports the notion of analyzing FCG simultaneously in intra and cross-frequency whole brain interactions with various connectivity estimators in beamformed recordings.
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Affiliation(s)
- Stavros I. Dimitriadis
- Division of Psychological Medicine and Clinical Neurosciences, School of Medicine, Cardiff University, Cardiff, United Kingdom
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroinformatics Group, Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff, United Kingdom
- Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United Kingdom
| | - María E. López
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Ricardo Bruña
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
| | - Alberto Marcos
- Department of Neurology, San Carlos University Hospital, Madrid, Spain
| | - Fernando Maestú
- Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Madrid, Spain
- Electrical Engineering and Bioengineering Group, Department of Industrial Engineering and IUNE, Universidad de La Laguna, Tenerife, Spain
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26
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Ma HR, Sheng LQ, Pan PL, Wang GD, Luo R, Shi HC, Dai ZY, Zhong JG. Cerebral glucose metabolic prediction from amnestic mild cognitive impairment to Alzheimer's dementia: a meta-analysis. Transl Neurodegener 2018; 7:9. [PMID: 29713467 PMCID: PMC5911957 DOI: 10.1186/s40035-018-0114-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 04/03/2018] [Indexed: 12/14/2022] Open
Abstract
Brain 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) has been utilized to monitor disease conversion from amnestic mild cognitive impairment (aMCI) to Alzheimer’s dementia (AD). However, the conversion patterns of FDG-PET metabolism across studies are not conclusive. We conducted a voxel-wise meta-analysis using Seed-based d Mapping that included 10 baseline voxel-wise FDG-PET comparisons between 93 aMCI converters and 129 aMCI non-converters from nine longitudinal studies. The most robust and reliable metabolic alterations that predicted conversion from aMCI to AD were localized in the left posterior cingulate cortex (PCC)/precuneus. Furthermore, meta-regression analyses indicated that baseline mean age and severity of cognitive impairment, and follow-up duration were significant moderators for metabolic alterations in aMCI converters. Our study revealed hypometabolism in the left PCC/precuneus as an early feature in the development of AD. This finding has important implications in understanding the neural substrates for AD conversion and could serve as a potential imaging biomarker for early detection of AD as well as for tracking disease progression at the predementia stage.
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Affiliation(s)
- Hai Rong Ma
- 1Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, People's Republic of China
| | - Li Qin Sheng
- 1Department of Neurology, Traditional Chinese Medicine Hospital of Kunshan, Kunshan, People's Republic of China
| | - Ping Lei Pan
- 2Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
| | - Gen Di Wang
- 2Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
| | - Rong Luo
- 2Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
| | - Hai Cun Shi
- 2Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
| | - Zhen Yu Dai
- 3Department of Radiology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
| | - Jian Guo Zhong
- 2Department of Neurology, School of Medicine, Affiliated Yancheng Hospital, Southeast University, West Xindu Road 2#, Yancheng, Jiangsu Province 224001 People's Republic of China
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27
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De Marco M, Duzzi D, Meneghello F, Venneri A. Cognitive Efficiency in Alzheimer's Disease is Associated with Increased Occipital Connectivity. J Alzheimers Dis 2018; 57:541-556. [PMID: 28269781 DOI: 10.3233/jad-161164] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
There are cognitive domains which remain fully functional in a proportion of Alzheimer's disease (AD) patients. It is unknown, however, what distinctive mechanisms sustain such efficient processing. The concept of "cognitive efficiency" was investigated in these patients by operationalizing it as a function of the level of performance shown on the Letter Fluency test, on which, very often, patients in the early stages of AD show unimpaired performance. Forty-five individuals at the prodromal/early stage of AD (diagnosis supported by subsequent clinical follow-ups) and 45 healthy controls completed a battery of neuropsychological tests and an MRI protocol which included resting state acquisitions. The Letter Fluency test was the only task on which no between-group difference in performance was found. Participants were divided into "low-performing" and "high-performing" according to the global median. Dual-regression methods were implemented to compute six patterns of network connectivity. The diagnosis-by-level of performance interaction was inferred on each pattern to determine the network distinctiveness of efficient performance in AD. Significant interactions were found in the anterior default mode network, and in both left and right executive control networks. For all three circuits, high-performing patients showed increased connectivity within the ventral and dorsal part of BA19, as confirmed by post hoc t tests. Peristriate remapping is suggested to play a compensatory role. Since the occipital lobe is the neurophysiological source of long-range cortical connectivity, it is speculated that the physiological mechanisms of functional connectivity might sustain occipital functional remapping in early AD, particularly for those functions which are sustained by areas not excessively affected by the prodromal disease.
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Affiliation(s)
- Matteo De Marco
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Sheffield, UK.,IRCCS Fondazione Ospedale San Camillo, Venice Lido, Italy
| | - Davide Duzzi
- IRCCS Fondazione Ospedale San Camillo, Venice Lido, Italy
| | | | - Annalena Venneri
- Department of Neuroscience, University of Sheffield, Royal Hallamshire Hospital, Sheffield, UK.,IRCCS Fondazione Ospedale San Camillo, Venice Lido, Italy
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28
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Yao Z, Hu B, Chen X, Xie Y, Gutknecht J, Majoe D. Learning Metabolic Brain Networks in MCI and AD by Robustness and Leave-One-Out Analysis: An FDG-PET Study. Am J Alzheimers Dis Other Demen 2018; 33:42-54. [PMID: 28931302 PMCID: PMC10852436 DOI: 10.1177/1533317517731535] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2024]
Abstract
This study attempted to better understand the properties associated with the metabolic brain network in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Graph theory was employed to investigate the topological organization of metabolic brain network among 86 patients with MCI, 89 patients with AD, and 97 normal controls (NCs) using 18F fluoro-deoxy-glucose positron emission tomography (FDG-PET) data. The whole brain was divided into 82 areas by Brodmann atlas to construct networks. We found that MCI and AD showed a loss of small-world properties and topological aberrations, and MCI showed an intermediate measurement between NC and AD. The networks of MCI and AD were vulnerable to attacks resulting from the altered topological pattern. Furthermore, individual contributions were correlated with Mini-Mental State Examination and Clinical Dementia Rating. The present study indicated that the topological patterns of the metabolic networks were aberrant in patients with MCI and AD, which may be particularly helpful in uncovering the pathophysiology underlying the cognitive dysfunction in MCI and AD.
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Affiliation(s)
- Zhijun Yao
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Bin Hu
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Xuejiao Chen
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Yuanwei Xie
- School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, People’s Republic of China
| | - Jürg Gutknecht
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
| | - Dennis Majoe
- Computer Systems Institute, ETH Zürich, Zürich, Switzerland
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29
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Li Q, Wu X, Xie F, Chen K, Yao L, Zhang J, Guo X, Li R. Aberrant Connectivity in Mild Cognitive Impairment and Alzheimer Disease Revealed by Multimodal Neuroimaging Data. NEURODEGENER DIS 2018; 18:5-18. [DOI: 10.1159/000484248] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Accepted: 10/16/2017] [Indexed: 01/12/2023] Open
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30
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Li Q, Wu X, Xu L, Chen K, Yao L. Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Cognitively Unimpaired Individuals Using Multi-feature Kernel Discriminant Dictionary Learning. Front Comput Neurosci 2018; 11:117. [PMID: 29375356 PMCID: PMC5767247 DOI: 10.3389/fncom.2017.00117] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2017] [Accepted: 12/19/2017] [Indexed: 01/03/2023] Open
Abstract
Accurate classification of either patients with Alzheimer's disease (AD) or patients with mild cognitive impairment (MCI), the prodromal stage of AD, from cognitively unimpaired (CU) individuals is important for clinical diagnosis and adequate intervention. The current study focused on distinguishing AD or MCI from CU based on the multi-feature kernel supervised within-Class-similar discriminative dictionary learning algorithm (MKSCDDL), which we introduced in a previous study, demonstrating that MKSCDDL had superior performance in face recognition. Structural magnetic resonance imaging (sMRI), fluorodeoxyglucose (FDG) positron emission tomography (PET), and florbetapir-PET data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were all included for classification of AD vs. CU, MCI vs. CU, as well as AD vs. MCI (113 AD patients, 110 MCI patients, and 117 CU subjects). By adopting MKSCDDL, we achieved a classification accuracy of 98.18% for AD vs. CU, 78.50% for MCI vs. CU, and 74.47% for AD vs. MCI, which in each instance was superior to results obtained using several other state-of-the-art approaches (MKL, JRC, mSRC, and mSCDDL). In addition, testing time results outperformed other high quality methods. Therefore, the results suggested that the MKSCDDL procedure is a promising tool for assisting early diagnosis of diseases using neuroimaging data.
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Affiliation(s)
- Qing Li
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Xia Wu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Lele Xu
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Kewei Chen
- Banner Alzheimer's Institute and Banner Good Samaritan PET Center, Phoenix, AZ, United States
| | - Li Yao
- Department of Electronics, College of Information Science and Technology, Beijing Normal University, Beijing, China.,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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31
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Alderson T, Kehoe E, Maguire L, Farrell D, Lawlor B, Kenny RA, Lyons D, Bokde ALW, Coyle D. Disrupted Thalamus White Matter Anatomy and Posterior Default Mode Network Effective Connectivity in Amnestic Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:370. [PMID: 29167639 PMCID: PMC5682321 DOI: 10.3389/fnagi.2017.00370] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Accepted: 10/26/2017] [Indexed: 11/21/2022] Open
Abstract
Alzheimer’s disease (AD) and its prodromal state amnestic mild cognitive impairment (aMCI) are characterized by widespread abnormalities in inter-areal white matter fiber pathways and parallel disruption of default mode network (DMN) resting state functional and effective connectivity. In healthy subjects, DMN and task positive network interaction are modulated by the thalamus suggesting that abnormal task-based DMN deactivation in aMCI may be a consequence of impaired thalamo-cortical white matter circuitry. Thus, this article uses a multimodal approach to assess white matter integrity between thalamus and DMN components and associated effective connectivity in healthy controls (HCs) relative to aMCI patients. Twenty-six HC and 20 older adults with aMCI underwent structural, functional and diffusion MRI scanning using the high angular resolution diffusion-weighted acquisition protocol. The DMN of each subject was identified using independent component analysis (ICA) and resting state effective connectivity was calculated between thalamus and DMN nodes. White matter integrity changes between thalamus and DMN were investigated with constrained spherical deconvolution (CSD) tractography. Significant structural deficits in thalamic white matter projection fibers to posterior DMN components posterior cingulate cortex (PCC) and lateral inferior parietal lobe (IPL) were identified together with significantly reduced effective connectivity from left thalamus to left IPL. Crucially, impaired thalamo-cortical white matter circuitry correlated with memory performance. Disrupted thalamo-cortical structure was accompanied by significant reductions in IPL and PCC cortico-cortical effective connectivity. No structural deficits were found between DMN nodes. Abnormal posterior DMN activity may be driven by changes in thalamic white matter connectivity; a view supported by the close anatomical and functional association of thalamic nuclei effected by AD pathology and the posterior DMN nodes. We conclude that dysfunctional posterior DMN activity in aMCI is consistent with disrupted cortico-thalamo-cortical processing and thalamic-based dissemination of hippocampal disease agents to cortical hubs.
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Affiliation(s)
- Thomas Alderson
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
| | - Elizabeth Kehoe
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Liam Maguire
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
| | - Dervla Farrell
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Brian Lawlor
- Mercer's Institute for Research on Ageing, St. James's Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | - Rose A Kenny
- Mercer's Institute for Research on Ageing, St. James's Hospital, Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland
| | | | - Arun L W Bokde
- Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland
| | - Damien Coyle
- Intelligent Systems Research Centre, University Ulster, Derry, United Kingdom
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32
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Koelewijn L, Bompas A, Tales A, Brookes MJ, Muthukumaraswamy SD, Bayer A, Singh KD. Alzheimer's disease disrupts alpha and beta-band resting-state oscillatory network connectivity. Clin Neurophysiol 2017; 128:2347-2357. [PMID: 28571910 PMCID: PMC5674981 DOI: 10.1016/j.clinph.2017.04.018] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 02/17/2017] [Accepted: 04/17/2017] [Indexed: 11/16/2022]
Abstract
OBJECTIVE Neuroimaging studies in Alzheimer's disease (AD) yield conflicting results due to selective investigation. We conducted a comprehensive magnetoencephalography study of connectivity changes in AD and healthy ageing in the resting-state. METHODS We performed a whole-brain, source-space assessment of oscillatory neural signalling in multiple frequencies comparing AD patients, elderly and young controls. We compared eyes-open and closed group oscillatory envelope activity in networks obtained through temporal independent component analysis, and calculated whole-brain node-based amplitude and phase connectivity. RESULTS In bilateral parietotemporal areas, oscillatory envelope amplitude increased with healthy ageing, whereas both local amplitude and node-to-global connectivity decreased with AD. AD-related decreases were spatially specific and restricted to the alpha and beta bands. A significant proportion of the variance in areas of peak group difference was explained by cognitive integrity, in addition to group. None of the groups differed in phase connectivity. Results were highly similar for eyes-open and closed resting-state. CONCLUSIONS These results support the disconnection syndrome hypothesis and suggest that AD shows distinct and unique patterns of disrupted neural functioning, rather than accelerated healthy ageing. SIGNIFICANCE Whole-brain assessments show that disrupted regional oscillatory envelope amplitude and connectivity in the alpha and beta bands play a key role in AD.
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Affiliation(s)
- Loes Koelewijn
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
| | - Aline Bompas
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
| | - Andrea Tales
- Department of Psychology, College of Human and Health Sciences, Swansea University, Swansea, UK.
| | - Matthew J Brookes
- Sir Peter Mansfield Magnetic Resonance Centre, School of Physics and Astronomy, University of Nottingham, Nottingham, UK.
| | | | - Antony Bayer
- School of Medicine, Cardiff University, University Hospital Llandough, Cardiff, UK.
| | - Krish D Singh
- CUBRIC, School of Psychology, Cardiff University, Maindy Road, Cardiff, UK.
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33
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Coquelet N, Mary A, Peigneux P, Goldman S, Wens V, De Tiège X. The electrophysiological connectome is maintained in healthy elders: a power envelope correlation MEG study. Sci Rep 2017; 7:13984. [PMID: 29070789 PMCID: PMC5656690 DOI: 10.1038/s41598-017-13829-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2017] [Accepted: 09/25/2017] [Indexed: 12/21/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies report age-related changes in resting-state functional connectivity (rsFC), suggesting altered or reorganized connectivity patterns with age. However, age-related changes in neurovascular coupling might also partially account for altered connectivity patterns. Here, we used resting-state magnetoencephalography (MEG) and a connectome approach in carefully selected healthy young adults and elders. The MEG connectome was estimated as rsFC matrices involving forty nodes from six major resting-state networks. Source-level rsFC maps were computed in relevant frequency bands using leakage-corrected envelope correlations. Group differences were statistically assessed using non-parametric permutation tests. Our results failed to evidence significant age-related differences after correction for multiple comparisons in the α and the β bands both for static and dynamic rsFC, suggesting that the electrophysiological connectome is maintained in healthy ageing. Further studies should compare the evolution of the human brain connectome as estimated using fMRI and MEG in same healthy young and elder adults, as well as in ageing conditions associated with cognitive decline. At present, our results are in agreement with the brain maintenance theory for successful aging as they suggest that preserved intrinsic functional brain integration contributes to preserved cognitive functioning in healthy elders.
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Affiliation(s)
- N Coquelet
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
| | - A Mary
- Neuropsychology and Functional Imaging Research Group (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Normandie Univ, UNICAEN, PSL Research University, EPHE, INSERM, U1077, CHU de Caen, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France
| | - P Peigneux
- Neuropsychology and Functional Imaging Research Group (UR2NF), Centre for Research in Cognition and Neurosciences (CRCN), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium
| | - S Goldman
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of functional Neuroimaging, CUB-Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - V Wens
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of functional Neuroimaging, CUB-Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
| | - X De Tiège
- Laboratoire de Cartographie fonctionnelle du Cerveau (LCFC), UNI - ULB Neuroscience Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.,Department of functional Neuroimaging, CUB-Hôpital Erasme, Université libre de Bruxelles, Brussels, Belgium
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34
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Li M, Zheng G, Zheng Y, Xiong Z, Xia R, Zhou W, Wang Q, Liang S, Tao J, Chen L. Alterations in resting-state functional connectivity of the default mode network in amnestic mild cognitive impairment: an fMRI study. BMC Med Imaging 2017; 17:48. [PMID: 28814282 PMCID: PMC5559812 DOI: 10.1186/s12880-017-0221-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Accepted: 07/31/2017] [Indexed: 01/04/2023] Open
Abstract
Background Amnestic mild cognitive impairment (aMCI) is characterized by cognitive functional decline, especially in memory. Resting-state functional magnetic resonance imaging (fMRI) has been widely used in neuroimaging studies that explore alterations between patients and normal individuals to elucidate the pathological mechanisms of different diseases. The current study was performed to investigate alterations in the functional connectivity of the default mode network (DMN) in aMCI patients compared to healthy elderly controls, as well as further define the association between neurological alterations and memory function. Methods Twenty-five aMCI patients and 25 healthy individuals were recruited and underwent both fMRI and neuropsychological examinations. fMRI data was analyzed by independent component analysis. Results Compared to healthy individuals, aMCI patients exhibited a significant increase in functional connectivity between the DMN and right-middle and right-superior frontal gyri, left-middle occipital gyrus, and left-middle temporal gyrus, but reduced functional connectivity between the DMN and left-middle and left-inferior frontal gyri and left insula. These alterations were found to be associated with reduced memory function. Conclusions aMCI patients exhibited abnormal functional connectivity between the DMN and certain brain regions which is associated with changes in memory function associated with aMCI. Electronic supplementary material The online version of this article (doi:10.1186/s12880-017-0221-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Moyi Li
- Department of Rehabilitation, The First Affiliated Hospital of Nanchang University, Nanchang, 330006, China.,College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Guohua Zheng
- College of Health Information Technology and Management, Shanghai University of Medicine & Health Sciences, Shanghai, 201318, China.
| | - Yuhui Zheng
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Zhenyu Xiong
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Rui Xia
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Wenji Zhou
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Qin Wang
- The Second Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250001, China
| | - Shengxiang Liang
- Physical Science and Technology College, Zhengzhou University, Zhengzhou, 450001, China.,Division of Nuclear Technology and Applications, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing, 100049, China.,Beijing Engineering Research Center of Radiographic Techniques and Equipment, Beijing, 100049, China
| | - Jing Tao
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China
| | - Lidian Chen
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China. .,Fujian Key Laboratory of Rehabilitation Technology, Fuzhou, 350003, China.
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35
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Yang C, Zhong S, Zhou X, Wei L, Wang L, Nie S. The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:261. [PMID: 28824422 PMCID: PMC5545578 DOI: 10.3389/fnagi.2017.00261] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/24/2017] [Indexed: 12/20/2022] Open
Abstract
A large number of morphology-based studies have previously reported a variety of regional abnormalities in hemispheric asymmetry in Alzheimer’s disease (AD). Recently, neuroimaging studies have revealed changes in the topological organization of the structural network in AD. However, little is known about the alterations in topological asymmetries. In the present study, we used diffusion tensor image tractography to construct the hemispheric brain white matter networks of 25 AD patients, 95 mild cognitive impairment (MCI) patients, and 48 normal control (NC) subjects. Graph theoretical approaches were then employed to estimate hemispheric topological properties. Rightward asymmetry in both global and local network efficiencies were observed between the two hemispheres only in AD patients. The brain regions/nodes exhibiting increased rightward asymmetry in both AD and MCI patients were primarily located in the parahippocampal gyrus and cuneus. The observed rightward asymmetry was attributed to changes in the topological properties of the left hemisphere in AD patients. Finally, we found that the abnormal hemispheric asymmetries of brain network properties were significantly correlated with memory performance (Rey’s Auditory Verbal Learning Test). Our findings provide new insights into the lateralized nature of hemispheric disconnectivity and highlight the potential for using hemispheric asymmetry of brain network measures as biomarkers for AD.
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Affiliation(s)
- Cheng Yang
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Suyu Zhong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijing, China
| | - Xiaolong Zhou
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Long Wei
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China.,Laiwu Vocational and Technical CollegeShandong, China
| | - Lijia Wang
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
| | - Shengdong Nie
- Institute of Medical Imaging Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and TechnologyShanghai, China
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36
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Changes in White Matter Microstructure Impact Cognition by Disrupting the Ability of Neural Assemblies to Synchronize. J Neurosci 2017; 37:8227-8238. [PMID: 28743724 DOI: 10.1523/jneurosci.0560-17.2017] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2017] [Revised: 07/11/2017] [Accepted: 07/14/2017] [Indexed: 12/27/2022] Open
Abstract
Cognition is compromised by white matter (WM) injury but the neurophysiological alterations linking them remain unclear. We hypothesized that reduced neural synchronization caused by disruption of neural signal propagation is involved. To test this, we evaluated group differences in: diffusion tensor WM microstructure measures within the optic radiations, primary visual area (V1), and cuneus; neural phase synchrony to a visual attention cue during visual-motor task; and reaction time to a response cue during the same task between 26 pediatric patients (17/9: male/female) treated with cranial radiation treatment for a brain tumor (12.67 ± 2.76 years), and 26 healthy children (16/10: male/female; 12.01 ± 3.9 years). We corroborated our findings using a corticocortical computational model representing perturbed signal conduction from myelin. Patients show delayed reaction time, WM compromise, and reduced phase synchrony during visual attention compared with healthy children. Notably, using partial least-squares-path modeling we found that WM insult within the optic radiations, V1, and cuneus is a strong predictor of the slower reaction times via disruption of neural synchrony in visual cortex. Observed changes in synchronization were reproduced in a computational model of WM injury. These findings provide new evidence linking cognition with WM via the reliance of neural synchronization on propagation of neural signals.SIGNIFICANCE STATEMENT By comparing brain tumor patients to healthy children, we establish that changes in the microstructure of the optic radiations and neural synchrony during visual attention predict reaction time. Furthermore, by testing the directionality of these links through statistical modeling and verifying our findings with computational modeling, we infer a causal relationship, namely that changes in white matter microstructure impact cognition in part by disturbing the ability of neural assemblies to synchronize. Together, our human imaging data and computer simulations show a fundamental connection between WM microstructure and neural synchronization that is critical for cognitive processing.
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37
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Höller Y, Butz K, Thomschewski A, Schmid E, Uhl A, Bathke AC, Zimmermann G, Tomasi SO, Nardone R, Staffen W, Höller P, Leitinger M, Höfler J, Kalss G, Taylor AC, Kuchukhidze G, Trinka E. Reliability of EEG Interactions Differs between Measures and Is Specific for Neurological Diseases. Front Hum Neurosci 2017; 11:350. [PMID: 28725190 PMCID: PMC5496950 DOI: 10.3389/fnhum.2017.00350] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2016] [Accepted: 06/20/2017] [Indexed: 11/21/2022] Open
Abstract
Alterations of interaction (connectivity) of the EEG reflect pathological processes in patients with neurologic disorders. Nevertheless, it is questionable whether these patterns are reliable over time in different measures of interaction and whether this reliability of the measures is the same across different patient populations. In order to address this topic we examined 22 patients with mild cognitive impairment, five patients with subjective cognitive complaints, six patients with right-lateralized temporal lobe epilepsy, seven patients with left lateralized temporal lobe epilepsy, and 20 healthy controls. We calculated 14 measures of interaction from two EEG-recordings separated by 2 weeks. In order to characterize test-retest reliability, we correlated these measures for each group and compared the correlations between measures and between groups. We found that both measures of interaction as well as groups differed from each other in terms of reliability. The strongest correlation coefficients were found for spectrum, coherence, and full frequency directed transfer function (average rho > 0.9). In the delta (2–4 Hz) range, reliability was lower for mild cognitive impairment compared to healthy controls and left lateralized temporal lobe epilepsy. In the beta (13–30 Hz), gamma (31–80 Hz), and high gamma (81–125 Hz) frequency ranges we found decreased reliability in subjective cognitive complaints compared to mild cognitive impairment. In the gamma and high gamma range we found increased reliability in left lateralized temporal lobe epilepsy patients compared to healthy controls. Our results emphasize the importance of documenting reliability of measures of interaction, which may vary considerably between measures, but also between patient populations. We suggest that studies claiming clinical usefulness of measures of interaction should provide information on the reliability of the results. In addition, differences between patient groups in reliability of interactions in the EEG indicate the potential of reliability to serve as a new biomarker for pathological memory decline as well as for epilepsy. While the brain concert of information flow is generally variable, high reliability, and thus, low variability may reflect abnormal firing patterns.
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Affiliation(s)
- Yvonne Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Kevin Butz
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Aljoscha Thomschewski
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria
| | - Elisabeth Schmid
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria
| | - Andreas Uhl
- Department of Computer Sciences, Paris Lodron University of SalzburgSalzburg, Austria
| | - Arne C Bathke
- Department of Mathematics, Paris Lodron University of SalzburgSalzburg, Austria
| | - Georg Zimmermann
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria.,Department of Mathematics, Paris Lodron University of SalzburgSalzburg, Austria
| | - Santino O Tomasi
- Department of Neurosurgery, Christian Doppler Medical Centre, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Raffaele Nardone
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria.,Department of Neurology, Franz Tappeiner HospitalMerano, Italy
| | - Wolfgang Staffen
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Peter Höller
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria
| | - Markus Leitinger
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Julia Höfler
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Gudrun Kalss
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Alexandra C Taylor
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Giorgi Kuchukhidze
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria
| | - Eugen Trinka
- Department of Neurology, Christian Doppler Medical Centre and Centre for Cognitive Neuroscience, Paracelsus Medical University SalzburgSalzburg, Austria.,Spinal Cord Injury and Tissue Regeneration Center, Paracelsus Medical UniversitySalzburg, Austria
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38
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López ME, Engels MMA, van Straaten ECW, Bajo R, Delgado ML, Scheltens P, Hillebrand A, Stam CJ, Maestú F. MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment. Front Aging Neurosci 2017; 9:107. [PMID: 28487647 PMCID: PMC5403893 DOI: 10.3389/fnagi.2017.00107] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Accepted: 04/04/2017] [Indexed: 11/20/2022] Open
Abstract
Subjects with mild cognitive impairment (MCI) have an increased risk of developing Alzheimer’s disease (AD), and their functional brain networks are presumably already altered. To test this hypothesis, we compared magnetoencephalography (MEG) eyes-closed resting-state recordings from 29 MCI subjects and 29 healthy elderly subjects in the present exploratory study. Functional connectivity in different frequency bands was assessed with the phase lag index (PLI) in source space. Normalized weighted clustering coefficient (normalized Cw) and path length (normalized Lw), as well as network measures derived from the minimum spanning tree [MST; i.e., betweenness centrality (BC) and node degree], were calculated. First, we found altered PLI values in the lower and upper alpha bands in MCI patients compared to controls. Thereafter, we explored network differences in these frequency bands. Normalized Cw and Lw did not differ between the groups, whereas BC and node degree of the MST differed, although these differences did not survive correction for multiple testing using the False Discovery Rate (FDR). As an exploratory study, we may conclude that: (1) the increases and decreases observed in PLI values in lower and upper alpha bands in MCI patients may be interpreted as a dual pattern of disconnection and aberrant functioning; (2) network measures are in line with connectivity findings, indicating a lower efficiency of the brain networks in MCI patients; (3) the MST centrality measures are more sensitive to detect subtle differences in the functional brain networks in MCI than traditional graph theoretical metrics.
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Affiliation(s)
- Maria E López
- Laboratory of Neuropsychology, Universitat de les Illes BalearsPalma de Mallorca, Spain.,Networking Research Center on Bioengineering, Biomaterials and NanomedicineMadrid, Spain
| | - Marjolein M A Engels
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Elisabeth C W van Straaten
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands.,Nutricia Advanced Medical Nutrition, Nutricia ResearchUtrecht, Netherlands
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridMadrid, Spain
| | - María L Delgado
- Seniors Center of the District of ChamartínMadrid, Spain.,Department of Basic Psychology II, Complutense University of MadridMadrid, Spain
| | - Philip Scheltens
- Alzheimer Center and Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Neuroscience Campus Amsterdam, VU University Medical CenterAmsterdam, Netherlands
| | - Fernando Maestú
- Networking Research Center on Bioengineering, Biomaterials and NanomedicineMadrid, Spain.,Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridMadrid, Spain.,Department of Basic Psychology II, Complutense University of MadridMadrid, Spain
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39
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López-Sanz D, Bruña R, Garcés P, Martín-Buro MC, Walter S, Delgado ML, Montenegro M, López Higes R, Marcos A, Maestú F. Functional Connectivity Disruption in Subjective Cognitive Decline and Mild Cognitive Impairment: A Common Pattern of Alterations. Front Aging Neurosci 2017; 9:109. [PMID: 28484387 PMCID: PMC5399035 DOI: 10.3389/fnagi.2017.00109] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Accepted: 04/04/2017] [Indexed: 11/28/2022] Open
Abstract
Functional connectivity (FC) alterations represent a key feature in Alzheimer's Disease (AD) and provide a useful tool to characterize and predict the course of the disease. Those alterations have been also described in Mild Cognitive Impairment (MCI), a prodromal stage of AD. There is a growing interest in detecting AD pathology in the brain in the very early stages of the disorder. Subjective Cognitive Decline (SCD) could represent a preclinical asymptomatic stage of AD but very little is known about this population. In the present work we assessed whether FC disruptions are already present in this stage, and if they share any spatial distribution properties with MCI alterations (a condition known to be highly related to AD). To this end, we measured electromagnetic spontaneous activity with MEG in 39 healthy control elders, 41 elders with SCD and 51 MCI patients. The results showed FC alterations in both SCD and MCI compared to the healthy control group. Interestingly, both groups exhibited a very similar spatial pattern of altered links: a hyper-synchronized anterior network and a posterior network characterized by a decrease in FC. This decrease was more pronounced in the MCI group. These results highlight that elders with SCD present FC alterations. More importantly, those disruptions affected AD typically related areas and showed great overlap with the alterations exhibited by MCI patients. These results support the consideration of SCD as a preclinical stage of AD and may indicate that FC alterations appear very early in the course of the disease.
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Affiliation(s)
- David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain
| | - Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain
| | - María Carmen Martín-Buro
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Stefan Walter
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Centro de investigación biomédica, Getafe HospitalGetafe, Spain
| | - María Luisa Delgado
- Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Mercedes Montenegro
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de MadridMadrid, Spain
| | - Ramón López Higes
- Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
| | - Alberto Marcos
- Neurology Department, San Carlos Clinical HospitalMadrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of MadridPozuelo de Alarcón, Spain.,Department of Basic Psychology II, Complutense University of MadridPozuelo de Alarcón, Spain
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40
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López-Sanz D, Bruña R, Garcés P, Camara C, Serrano N, Rodríguez-Rojo IC, Delgado ML, Montenegro M, López-Higes R, Yus M, Maestú F. Alpha band disruption in the AD-continuum starts in the Subjective Cognitive Decline stage: a MEG study. Sci Rep 2016; 6:37685. [PMID: 27883082 PMCID: PMC5121589 DOI: 10.1038/srep37685] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Accepted: 11/01/2016] [Indexed: 11/09/2022] Open
Abstract
The consideration of Subjective Cognitive Decline (SCD) as a preclinical stage of AD remains still a matter of debate. Alpha band alterations represent one of the most significant changes in the electrophysiological profile of AD. In particular, AD patients exhibit reduced alpha relative power and frequency. We used alpha band activity measured with MEG to study whether SCD and MCI elders present these electrophysiological changes characteristic of AD, and to determine the evolution of the observed alterations across AD spectrum. The total sample consisted of 131 participants: 39 elders without SCD, 41 elders with SCD and 51 MCI patients. All of them underwent MEG and MRI scans and neuropsychological assessment. SCD and MCI patients exhibited a similar reduction in alpha band activity compared with the no SCD group. However, only MCI patients showed a slowing in their alpha peak frequency compared with both SCD and no SCD. These changes in alpha band were related to worse cognition. Our results suggest that AD-related alterations may start in the SCD stage, with a reduction in alpha relative power. It is later, in the MCI stage, where the slowing of the spectral profile takes place, giving rise to objective deficits in cognitive functioning.
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Affiliation(s)
- D López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - R Bruña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - P Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - C Camara
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain
| | - N Serrano
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - I C Rodríguez-Rojo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M L Delgado
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Montenegro
- Memory Decline Prevention Center Madrid Salud, Ayuntamiento de Madrid, Spain
| | - R López-Higes
- Department of Basic Psychology II, Complutense University of Madrid, Spain
| | - M Yus
- Radiology Department, San Carlos University Hospital, Madrid, Spain
| | - F Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Spain.,Department of Basic Psychology II, Complutense University of Madrid, Spain
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41
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Munro CE, Donovan NJ, Guercio BJ, Wigman SE, Schultz AP, Amariglio RE, Rentz DM, Johnson KA, Sperling RA, Marshall GA. Neuropsychiatric Symptoms and Functional Connectivity in Mild Cognitive Impairment. J Alzheimers Dis 2016; 46:727-35. [PMID: 25854929 DOI: 10.3233/jad-150017] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Neuropsychiatric symptoms (NPS), such as apathy and depression, commonly accompany cognitive and functional decline in early Alzheimer's disease (AD). Prior studies have shown associations between affective NPS and neurodegeneration of medial frontal and inferior temporal regions in mild cognitive impairment (MCI) and AD dementia. OBJECTIVE To investigate the association between functional connectivity in four brain networks and NPS in elderly with MCI. METHODS NPS were assessed using the Neuropsychiatric Inventory in 42 subjects with MCI. Resting-state functional connectivity in four networks (default mode network, fronto-parietal control network (FPCN), dorsal attention network, and ventral attention network) was assessed using seed-based magnetic resonance imaging. Factor analysis was used to identify two factors of NPS: Affective and Hyperactivity. Linear regression models were utilized with the neuropsychiatric factors as the dependent variable and the four networks as the predictors of interest. Covariates included age, gender, premorbid intelligence, processing speed, memory, head movement, and signal-to-noise ratio. These analyses were repeated with the individual items of the affective factor, using the same predictors. RESULTS There was a significant association between greater Affective factor symptoms and reduced FPCN connectivity (p = 0.03). There was no association between the Hyperactivity factor and any of the networks. Secondary analyses revealed an association between greater apathy and reduced FPCN connectivity (p = 0.005), but none in other networks. CONCLUSIONS Decreased connectivity in the FPCN may be associated with greater affective symptoms, particularly apathy, early in AD. These findings extend prior studies, using different functional imaging modalities in individuals with greater disease severity.
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Affiliation(s)
- Catherine E Munro
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Nancy J Donovan
- Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Sarah E Wigman
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Aaron P Schultz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Rebecca E Amariglio
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Dorene M Rentz
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Keith A Johnson
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Reisa A Sperling
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gad A Marshall
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA.,Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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42
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Klados MA, Styliadis C, Frantzidis CA, Paraskevopoulos E, Bamidis PD. Beta-Band Functional Connectivity is Reorganized in Mild Cognitive Impairment after Combined Computerized Physical and Cognitive Training. Front Neurosci 2016; 10:55. [PMID: 26973445 PMCID: PMC4770438 DOI: 10.3389/fnins.2016.00055] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 02/05/2016] [Indexed: 01/12/2023] Open
Abstract
Physical and cognitive idleness constitute significant risk factors for the clinical manifestation of age-related neurodegenerative diseases. In contrast, a physically and cognitively active lifestyle may restructure age-declined neuronal networks enhancing neuroplasticity. The present study, investigated the changes of brain's functional network in a group of elderly individuals at risk for dementia that were induced by a combined cognitive and physical intervention scheme. Fifty seniors meeting Petersen's criteria of Mild Cognitive Impairment were equally divided into an experimental (LLM), and an active control (AC) group. Resting state electroencephalogram (EEG) was measured before and after the intervention. Functional networks were estimated by computing the magnitude square coherence between the time series of all available cortical sources as computed by standardized low resolution brain electromagnetic tomography (sLORETA). A statistical model was used to form groups' characteristic weighted graphs. The introduced modulation was assessed by networks' density and nodes' strength. Results focused on the beta band (12-30 Hz) in which the difference of the two networks' density is maximum, indicating that the structure of the LLM cortical network changes significantly due to the intervention, in contrast to the network of AC. The node strength of LLM participants in the beta band presents a higher number of bilateral connections in the occipital, parietal, temporal and prefrontal regions after the intervention. Our results show that the combined training scheme reorganizes the beta-band functional connectivity of MCI patients. ClinicalTrials.gov Identifier: NCT02313935 https://clinicaltrials.gov/ct2/show/NCT02313935.
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Affiliation(s)
- Manousos A Klados
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of ThessalonikiThessaloniki, Greece; Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain SciencesLeipzig, Germany
| | - Charis Styliadis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Christos A Frantzidis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Evangelos Paraskevopoulos
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Panagiotis D Bamidis
- Medical Physics Laboratory, Faculty of Health Sciences, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
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43
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Tao Y, Liu B, Zhang X, Li J, Qin W, Yu C, Jiang T. The Structural Connectivity Pattern of the Default Mode Network and Its Association with Memory and Anxiety. Front Neuroanat 2015; 9:152. [PMID: 26635544 PMCID: PMC4659898 DOI: 10.3389/fnana.2015.00152] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 11/12/2015] [Indexed: 01/11/2023] Open
Abstract
The default mode network (DMN) is one of the most widely studied resting state functional networks. The structural basis for the DMN is of particular interest and has been studied by several researchers using diffusion tensor imaging (DTI). Most of these previous studies focused on a few regions or white matter tracts of the DMN so that the global structural connectivity pattern and network properties of the DMN remain unclear. Moreover, evidences indicate that the DMN is involved in both memory and emotion, but how the DMN regulates memory and anxiety from the perspective of the whole DMN structural network remains unknown. We used multimodal neuroimaging methods to investigate the structural connectivity pattern of the DMN and the association of its network properties with memory and anxiety in 205 young healthy subjects with age ranging from 18 to 29 years old. The Group ICA method was used to extract the DMN component from functional magnetic resonance imaging (fMRI) data and a probabilistic fiber tractography technique based on DTI data was applied to construct the global structural connectivity pattern of the DMN. Then we used the graph theory method to analyze the DMN structural network and found that memory quotient (MQ) score was significantly positively correlated with the global and local efficiency of the DMN whereas anxiety was found to be negatively correlated with the efficiency. The strong structural connectivity between multiple brain regions within DMN may reflect that the DMN has certain structural basis. Meanwhile, the results we found that the network efficiency of the DMN were related to memory and anxiety measures, indicated that the DMN may play a role in the memory and anxiety.
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Affiliation(s)
- Yan Tao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China
| | - Bing Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China
| | - Xiaolong Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China
| | - Jin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China
| | - Wen Qin
- Department of Radiology, Tianjin Medical University General Hospital Tianjin, China
| | - Chunshui Yu
- Department of Radiology, Tianjin Medical University General Hospital Tianjin, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences Beijing, China ; National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences Beijing, China ; Queensland Brain Institute, The University of Queensland Brisbane, QLD, Australia ; Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu, China
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44
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Network Disruption and Cerebrospinal Fluid Amyloid-Beta and Phospho-Tau Levels in Mild Cognitive Impairment. J Neurosci 2015; 35:10325-30. [PMID: 26180207 DOI: 10.1523/jneurosci.0704-15.2015] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
UNLABELLED Synaptic dysfunction is a core deficit in Alzheimer's disease, preceding hallmark pathological abnormalities. Resting-state magnetoencephalography (MEG) was used to assess whether functional connectivity patterns, as an index of synaptic dysfunction, are associated with CSF biomarkers [i.e., phospho-tau (p-tau) and amyloid beta (Aβ42) levels]. We studied 12 human subjects diagnosed with mild cognitive impairment due to Alzheimer's disease, comparing those with normal and abnormal CSF levels of the biomarkers. We also evaluated the association between aberrant functional connections and structural connectivity abnormalities, measured with diffusion tensor imaging, as well as the convergent impact of cognitive deficits and CSF variables on network disorganization. One-third of the patients converted to Alzheimer's disease during a follow-up period of 2.5 years. Patients with abnomal CSF p-tau and Aβ42 levels exhibited both reduced and increased functional connectivity affecting limbic structures such as the anterior/posterior cingulate cortex, orbitofrontal cortex, and medial temporal areas in different frequency bands. A reduction in posterior cingulate functional connectivity mediated by p-tau was associated with impaired axonal integrity of the hippocampal cingulum. We noted that several connectivity abnormalities were predicted by CSF biomarkers and cognitive scores. These preliminary results indicate that CSF markers of amyloid deposition and neuronal injury in early Alzheimer's disease associate with a dual pattern of cortical network disruption, affecting key regions of the default mode network and the temporal cortex. MEG is useful to detect early synaptic dysfunction associated with Alzheimer's disease brain pathology in terms of functional network organization. SIGNIFICANCE STATEMENT In this preliminary study, we used magnetoencephalography and an integrative approach to explore the impact of CSF biomarkers, neuropsychological scores, and white matter structural abnormalities on neural function in mild cognitive impairment. Disruption in functional connectivity between several pairs of cortical regions associated with abnormal levels of biomarkers, cognitive deficits, or with impaired axonal integrity of hippocampal tracts. Amyloid deposition and tau protein-related neuronal injury in early Alzheimer's disease are associated with synaptic dysfunction and a dual pattern of cortical network disorganization (i.e., desynchronization and hypersynchronization) that affects key regions of the default mode network and temporal areas.
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45
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Avila J, Perry G, Strange BA, Hernandez F. Alternative neural circuitry that might be impaired in the development of Alzheimer disease. Front Neurosci 2015; 9:145. [PMID: 25954151 PMCID: PMC4407584 DOI: 10.3389/fnins.2015.00145] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2015] [Accepted: 04/08/2015] [Indexed: 11/13/2022] Open
Abstract
It is well established that some individuals with normal cognitive capacity have abundant senile plaques in their brains. It has been proposed that those individuals are resilient or have compensation factors to prevent cognitive decline. In this comment, we explore an alternative mechanism through which cognitive capacity is maintained. This mechanism could involve the impairment of alternative neural circuitry. Also, the proportion of molecules such as Aβ or tau protein present in different areas of the brain could be important.
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Affiliation(s)
- Jesus Avila
- Neurobiology, Centro de Biologia Molecular Severo Ochoa (CSIC-UAM) Madrid, Spain ; Centro de Investigacion Biomedica en Red de Enfermedades Neurodegenerativas Madrid, Spain
| | - George Perry
- College of Sciences, The University of Texas at San Antonio San Antonio, TX, USA
| | - Bryan A Strange
- Department of Neuroimaging, Reina Sofia Foundation, Center for Alzheimer Research, FCIEN Madrid, Spain ; Laboratory for Clinical Neuroscience, CTB, Universidad Politecnica de Madrid Madrid, Spain
| | - Felix Hernandez
- Neurobiology, Centro de Biologia Molecular Severo Ochoa (CSIC-UAM) Madrid, Spain ; Centro de Investigacion Biomedica en Red de Enfermedades Neurodegenerativas Madrid, Spain
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46
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Neuroplastic effects of combined computerized physical and cognitive training in elderly individuals at risk for dementia: an eLORETA controlled study on resting states. Neural Plast 2015; 2015:172192. [PMID: 25945260 PMCID: PMC4405298 DOI: 10.1155/2015/172192] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2014] [Revised: 03/09/2015] [Accepted: 03/16/2015] [Indexed: 12/20/2022] Open
Abstract
The present study investigates whether a combined cognitive and physical training may induce changes in the cortical activity as measured via electroencephalogram (EEG) and whether this change may index a deceleration of pathological processes of brain aging. Seventy seniors meeting the clinical criteria of mild cognitive impairment (MCI) were equally divided into 5 groups: 3 experimental groups engaged in eight-week cognitive and/or physical training and 2 control groups: active and passive. A 5-minute long resting state EEG was measured before and after the intervention. Cortical EEG sources were modelled by exact low resolution brain electromagnetic tomography (eLORETA). Cognitive function was assessed before and after intervention using a battery of neuropsychological tests including the minimental state examination (MMSE). A significant training effect was identified only after the combined training scheme: a decrease in the post- compared to pre-training activity of precuneus/posterior cingulate cortex in delta, theta, and beta bands. This effect was correlated to improvements in cognitive capacity as evaluated by MMSE scores. Our results indicate that combined physical and cognitive training shows indices of a positive neuroplastic effect in MCI patients and that EEG may serve as a potential index of gains versus cognitive declines and neurodegeneration. This trial is registered with ClinicalTrials.gov Identifier NCT02313935.
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