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Jin H, Ranasinghe KG, Prabhu P, Dale C, Gao Y, Kudo K, Vossel K, Raj A, Nagarajan SS, Jiang F. Dynamic functional connectivity MEG features of Alzheimer's disease. Neuroimage 2023; 281:120358. [PMID: 37699440 PMCID: PMC10865998 DOI: 10.1016/j.neuroimage.2023.120358] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 08/14/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes time-varying fluctuations of functional brain network activity. While many studies have investigated static functional connectivity, it has been unclear whether features of dynamic functional connectivity are associated with neurodegenerative diseases. Popular sliding-window and clustering methods for extracting dynamic RSFC have various limitations that prevent extracting reliable features to address this question. Here, we use a novel and robust time-varying dynamic network (TVDN) approach to extract the dynamic RSFC features from high resolution magnetoencephalography (MEG) data of participants with Alzheimer's disease (AD) and matched controls. The TVDN algorithm automatically and adaptively learns the low-dimensional spatiotemporal manifold of dynamic RSFC and detects dynamic state transitions in data. We show that amongst all the functional features we investigated, the dynamic manifold features are the most predictive of AD. These include: the temporal complexity of the brain network, given by the number of state transitions and their dwell times, and the spatial complexity of the brain network, given by the number of eigenmodes. These dynamic features have higher sensitivity and specificity in distinguishing AD from healthy subjects than the existing benchmarks do. Intriguingly, we found that AD patients generally have higher spatial complexity but lower temporal complexity compared with healthy controls. We also show that graph theoretic metrics of dynamic component of TVDN are significantly different in AD versus controls, while static graph metrics are not statistically different. These results indicate that dynamic RSFC features are impacted in neurodegenerative disease like Alzheimer's disease, and may be crucial to understanding the pathophysiological trajectory of these diseases.
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Affiliation(s)
- Huaqing Jin
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kamalini G Ranasinghe
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Memory and Aging Center, University of California San Francisco, San Francisco, CA, USA
| | - Pooja Prabhu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Corby Dale
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Kiwamu Kudo
- Medical Imaging Business Center, Ricoh Company, Ltd., Kanazawa, 920-0177, Japan
| | - Keith Vossel
- Department of Neurology, University of California Los Angeles, Los Angeles, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Fei Jiang
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA.
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2
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Fuscà M, Siebenhühner F, Wang SH, Myrov V, Arnulfo G, Nobili L, Palva JM, Palva S. Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data. Nat Commun 2023; 14:4736. [PMID: 37550300 PMCID: PMC10406818 DOI: 10.1038/s41467-023-40056-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 07/10/2023] [Indexed: 08/09/2023] Open
Abstract
Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.
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Affiliation(s)
- Marco Fuscà
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
| | - Felix Siebenhühner
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki, Aalto University, and Helsinki University Hospital, Helsinki, Finland
| | - Sheng H Wang
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
- CEA, NeuroSpin, Gif-sur-Yvette, France
- MIND team, Inria, Université Paris-Saclay, Bures-sur-Yvette, France
| | - Vladislav Myrov
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Gabriele Arnulfo
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Dept. of Informatics, Bioengineering, Robotics and System engineering, University of Genoa, Genoa, Italy
| | - Lino Nobili
- Child Neuropsychiatry Unit, IRCCS, Istituto G. Gaslini, Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
- "Claudio Munari" Epilepsy Surgery Centre, Niguarda Hospital, Milan, Italy
| | - J Matias Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Satu Palva
- Centre for Cognitive Neuroimaging, School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
- Neuroscience Center, HiLIFE-Helsinki Institute of Life Science, University of Helsinki, Helsinki, Finland.
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3
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Ouyang J, Zhao Q, Adeli E, Zaharchuk G, Pohl KM. Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2558-2569. [PMID: 35404811 PMCID: PMC9578549 DOI: 10.1109/tmi.2022.3166131] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The continuous progression of neurological diseases are often categorized into conditions according to their severity. To relate the severity to changes in brain morphometry, there is a growing interest in replacing these categories with a continuous severity scale that longitudinal MRIs are mapped onto via deep learning algorithms. However, existing methods based on supervised learning require large numbers of samples and those that do not, such as self-supervised models, fail to clearly separate the disease effect from normal aging. Here, we propose to explicitly disentangle those two factors via weak-supervision. In other words, training is based on longitudinal MRIs being labelled either normal or diseased so that the training data can be augmented with samples from disease categories that are not of primary interest to the analysis. We do so by encouraging trajectories of controls to be fully encoded by the direction associated with brain aging. Furthermore, an orthogonal direction linked to disease severity captures the residual component from normal aging in the diseased cohort. Hence, the proposed method quantifies disease severity and its progression speed in individuals without knowing their condition. We apply the proposed method on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N =632 ). We then show that the model properly disentangled normal aging from the severity of cognitive impairment by plotting the resulting disentangled factors of each subject and generating simulated MRIs for a given chronological age and condition. Moreover, our representation obtains higher balanced accuracy when used for two downstream classification tasks compared to other pre-training approaches. The code for our weak-supervised approach is available at https://github.com/ouyangjiahong/longitudinal-direction-disentangle.
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4
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Chino B, Cuesta P, Pacios J, de Frutos-Lucas J, Torres-Simón L, Doval S, Marcos A, Bruña R, Maestú F. Episodic memory dysfunction and hypersynchrony in brain functional networks in cognitively intact subjects and MCI: a study of 379 individuals. GeroScience 2022; 45:477-489. [PMID: 36109436 PMCID: PMC9886758 DOI: 10.1007/s11357-022-00656-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 09/01/2022] [Indexed: 02/03/2023] Open
Abstract
Delayed recall (DR) impairment is one of the most significant predictive factors in defining the progression to Alzheimer's disease (AD). Changes in brain functional connectivity (FC) could accompany this decline in the DR performance even in a resting state condition from the preclinical stages to the diagnosis of AD itself, so the characterization of the relationship between the two phenomena has attracted increasing interest. Another aspect to contemplate is the potential moderator role of the APOE genotype in this association, considering the evidence about their implication for the disease. 379 subjects (118 mild cognitive impairment (MCI) and 261 cognitively intact (CI) individuals) underwent an extensive evaluation, including MEG recording. Applying cluster-based permutation test, we identified a cluster of differences in FC and studied which connections drove such an effect in DR. The moderation effect of APOE genotype between FC results and delayed recall was evaluated too. Higher FC in beta band in the right occipital region is associated with lower DR scores in both groups. A significant anteroposterior link emerged in the seed-based analysis with higher values in MCI. Moreover, APOE genotype appeared as a moderator between beta FC and DR performance only in the CI group. An increased beta FC in the anteroposterior brain region appears to be associated with lower memory performance in MCI. This finding could help discriminate the pattern of the progression of healthy aging to MCI and the relation between resting state and memory performance.
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Affiliation(s)
- Brenda Chino
- Institute of Neuroscience, Autonomous University of Barcelona, Barcelona, Spain. .,Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain.
| | - Pablo Cuesta
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Javier Pacios
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Jaisalmer de Frutos-Lucas
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain ,Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027 Australia ,Centro de Investigación Nebrija en Cognición (CINC), Universidad de Nebrija, Madrid, Spain
| | - Lucía Torres-Simón
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Sandra Doval
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
| | - Alberto Marcos
- Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Neurology Department, Hospital Clinico San Carlos, Madrid, Spain
| | - Ricardo Bruña
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Department of Radiology, Rehabilitation, and Physiotherapy, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain
| | - Fernando Maestú
- Center for Cognitive and Computational Neuroscience, Complutense University of Madrid, Madrid, Spain ,Instituto de Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain ,Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain
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5
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Arakaki X, Hung SM, Rochart R, Fonteh AN, Harrington MG. Alpha desynchronization during Stroop test unmasks cognitively healthy individuals with abnormal CSF Amyloid/Tau. Neurobiol Aging 2022; 112:87-101. [PMID: 35066324 PMCID: PMC8976735 DOI: 10.1016/j.neurobiolaging.2021.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 11/27/2021] [Accepted: 11/30/2021] [Indexed: 01/15/2023]
Abstract
Synaptic dysfunctions precede cognitive decline in Alzheimer's disease by decades, affect executive functions, and can be detected by quantitative electroencephalography (qEEG). We used quantitative electroencephalography combined with Stroop testing to identify changes of inhibitory controls in cognitively healthy individuals with an abnormal versus normal ratio of cerebrospinal fluid (CSF) amyloid/total-tau. We studied two groups of participants (60-94 years) with either normal (CH-NAT or controls, n = 20) or abnormal (CH-PAT, n = 21) CSF amyloid/tau ratio. We compared: alpha event-related desynchronization (ERD), alpha spectral entropy (SE), and their relationships with estimated cognitive reserve. CH-PATs had more negative occipital alpha ERD, and higher frontal and occipital alpha SE during low load congruent trials, indicating hyperactivity. CH-PATs demonstrated fewer frontal SE changes with higher load, incongruent Stroop testing. Correlations of alpha ERD with estimated cognitive reserve were significant in CH-PATs but not in CH-NATs. These results suggested compensatory hyperactivity in CH-PATs compared to CH-NATs. We did not find differences in alpha ERD comparisons with individual CSF amyloid(A), p-tau(T), total-tau(N) biomarkers.
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6
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Bruña R, Maestú F, López-Sanz D, Bagic A, Cohen AD, Chang YF, Cheng Y, Doman J, Huppert T, Kim T, Roush RE, Snitz BE, Becker JT. Sex Differences in Magnetoencephalography-Identified Functional Connectivity in the Human Connectome Project Connectomics of Brain Aging and Dementia Cohort. Brain Connect 2021; 12:561-570. [PMID: 34726478 PMCID: PMC9419974 DOI: 10.1089/brain.2021.0059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Introduction: The human brain shows modest traits of sexual dimorphism, with the female brain, on average, 10% smaller than the male brain. These differences do not imply a lowered cognitive performance, but suggest a more optimal brain organization in women. Here we evaluate the patterns of functional connectivity (FC) in women and men from the Connectomics of Brain Aging and Dementia sample. Methods: We used phase locking values to calculate FC from the magnetoencephalography time series in a sample of 138 old adults (87 females and 51 males). We compared the FC patterns between sexes, with the intention of detecting regions with different levels of connectivity. Results: We found a frontal cluster, involving anterior cingulate and the medial frontal lobe, where women showed higher FC values than men. Involved connections included the following: (1) medial parietal areas, such as posterior cingulate cortices and precunei; (2) right insula; and (3) medium cingulate and paracingulate cortices. Moreover, these differences persisted when considering only cognitively intact individuals, but not when considering only cognitively impaired individuals. Discussion: Increased anteroposterior FC has been identified as a biomarker for increased risk of developing cognitive impairment or dementia. In our study, cognitively intact women showed higher levels of FC than their male counterparts. This result suggests that neurodegenerative processes could be taking place in these women, but the changes are undetected by current diagnosis tools. FC, as measured here, might be valuable for early identification of this neurodegeneration.
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Affiliation(s)
- Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Experimental Psychology, Universidad Complutense de Madrid, Pozuelo de Alarcón, Madrid, Spain.,Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Center for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain.,Department of Psychobiology, Universidad Complutense de Madrid, Madrid, Spain
| | - Anto Bagic
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ann D Cohen
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yue-Fang Chang
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Yu Cheng
- Department of Statistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Biostatistics, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jack Doman
- Department of Neurosurgery, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ted Huppert
- Department of Electrical Engineering, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Tae Kim
- Department of Radiology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Rebecca E Roush
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Beth E Snitz
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James T Becker
- Department of Psychiatry, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Neurology, and The University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Psychology, The University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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7
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Roascio M, Canessa A, Trò RD, Mattioli P, Famà F, Giorgetti L, Girtler N, Orso B, Morbelli S, Nobili FM, Arnaldi D, Arnulfo G. Phase and amplitude EEG correlations change with disease progression in people with idiopathic rapid eye-movement sleep behavior disorder. Sleep 2021; 45:6374127. [PMID: 34551110 PMCID: PMC8754497 DOI: 10.1093/sleep/zsab232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/31/2021] [Indexed: 11/21/2022] Open
Abstract
Study Objectives Increased phase synchronization in electroencephalography (EEG) bands might reflect the activation of compensatory mechanisms of cognitive decline in people with neurodegenerative diseases. Here, we investigated whether altered large-scale couplings of brain oscillations could be linked to the balancing of cognitive decline in a longitudinal cohort of people with idiopathic rapid eye-movement sleep behavior disorder (iRBD). Methods We analyzed 18 patients (17 males, 69.7 ± 7.5 years) with iRBD undergoing high-density EEG (HD-EEG), presynaptic dopaminergic imaging, and clinical and neuropsychological (NPS) assessments at two time points (time interval 24.2 ± 5.9 months). We thus quantified the HD-EEG power distribution, orthogonalized amplitude correlation, and weighted phase-lag index at both time points and correlated them with clinical, NPS, and imaging data. Results Four patients phenoconverted at follow-up (three cases of parkinsonism and one of dementia). At the group level, NPS scores decreased over time, without reaching statistical significance. However, alpha phase synchronization increased and delta amplitude correlations decreased significantly at follow-up compared to baseline. Both large-scale network connectivity metrics were significantly correlated with NPS scores but not with sleep quality indices or presynaptic dopaminergic imaging data. Conclusions These results suggest that increased alpha phase synchronization and reduced delta amplitude correlation may be considered electrophysiological signs of an active compensatory mechanism of cognitive impairment in people with iRBD. Large-scale functional modifications may be helpful biomarkers in the characterization of prodromal stages of alpha-synucleinopathies.
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Affiliation(s)
- M Roascio
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - A Canessa
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - R D Trò
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - P Mattioli
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - F Famà
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - L Giorgetti
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - N Girtler
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - B Orso
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy
| | - S Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Nuclear Medicine, Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - F M Nobili
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - D Arnaldi
- Clinical Neurology, Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - G Arnulfo
- Department of Informatics, Bioengineering, Robotics and System engineering (DIBRIS), University of Genoa, Genoa, Italy.,Neuroscience Center, Helsinki Institute of Life Science (HiLife), University of Helsinki, Helsinki, Finland
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8
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Tang F, Zhu D, Ma W, Yao Q, Li Q, Shi J. Differences Changes in Cerebellar Functional Connectivity Between Mild Cognitive Impairment and Alzheimer's Disease: A Seed-Based Approach. Front Neurol 2021; 12:645171. [PMID: 34220669 PMCID: PMC8248670 DOI: 10.3389/fneur.2021.645171] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 05/24/2021] [Indexed: 11/13/2022] Open
Abstract
Background: Recent studies have discovered that functional connections are impaired among patients with Alzheimer's disease (AD), even at the preclinical stage. The cerebellum has been implicated as playing a role in cognitive processes. However, functional connectivity (FC) among cognitive sub-regions of the cerebellum in patients with AD and mild cognitive impairment (MCI) remains to be further elucidated. Objective: Our study aims to investigate the FC changes of the cerebellum among patients with AD and MCI, compared to healthy controls (HC). Additionally, we explored the role of cerebellum FC changes in the cognitive performance of all subjects. Materials: Resting-state functional magnetic resonance imaging (rs-fMRI) data from three different groups (28 AD patients, 26 MCI patients, and 30 HC) was collected. We defined cerebellar crus II and lobule IX as seed regions to assess the intragroup differences of cortico-cerebellar connectivity. Bias correlational analysis was performed to investigate the relationship between changes in FC and neuropsychological performance. Results: Compared to HC, AD patients had decreased FC within the caudate, limbic lobe, medial frontal gyrus (MFG), middle temporal gyrus, superior frontal gyrus, parietal lobe/precuneus, inferior temporal gyrus, and posterior cingulate gyrus. Interestingly, MCI patients demonstrated increased FC within inferior parietal lobe, and MFG, while they had decreased FC in the thalamus, inferior frontal gyrus, and superior frontal gyrus. Further analysis indicated that FC changes between the left crus II and the right thalamus, as well as between left lobule IX and the right parietal lobe, were both associated with cognitive decline in AD. Disrupted FC between left crus II and right thalamus, as well as between left lobule IX and right parietal lobe, was associated with attention deficit among subjects with MCI. Conclusion: These findings indicate that cortico-cerebellar FC in MCI and AD patients was significantly disrupted with different distributions, particularly in the default mode networks (DMN) and fronto-parietal networks (FPN) region. Increased activity within the fronto-parietal areas of MCI patients indicated a possible compensatory role for the cerebellum in cognitive impairment. Therefore, alterations in the cortico-cerebellar FC represent a novel approach for early diagnosis and a potential therapeutic target for early intervention.
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Affiliation(s)
- Fanyu Tang
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Donglin Zhu
- Department of Neurology, Affiliated to Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Nanjing Medical University, Nanjing, China
| | - Qun Yao
- Department of Neurology, Affiliated to Nanjing Medical University, Nanjing, China
| | - Qian Li
- Nanjing Medical University, Nanjing, China
| | - Jingping Shi
- Department of Neurology, Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
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9
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Maestú F, Fernández A. Role of Magnetoencephalography in the Early Stages of Alzheimer Disease. Neuroimaging Clin N Am 2021; 30:217-227. [PMID: 32336408 DOI: 10.1016/j.nic.2020.01.003] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
As synaptic dysfunction is an early manifestation of Alzheimer disease (AD) pathology, magnetoencephalography (MEG) is capable of detecting disruptions by assessing the synchronized oscillatory activity of thousands of neurons that rely on the integrity of neural connections. MEG findings include slowness of the oscillatory activity, accompanied by a reduction of the alpha band power, and dysfunction of the functional networks. These findings are associated with the neuropathology of the disease and cognitive impairment. These neurophysiological biomarkers predict which patients with mild cognitive impairment will develop dementia. MEG has demonstrated its utility as a noninvasive biomarker for early detection of AD.
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Affiliation(s)
- Fernando Maestú
- Department of Experimental Psychology, Complutense University of Madrid, Madrid, Spain; Centro de Tecnología Biomédica, Campus de Montegancedo de la UPM, Pozuelo de Alarcón, Madrid 28223, Spain.
| | - Alberto Fernández
- Centro de Tecnología Biomédica, Campus de Montegancedo de la UPM, Pozuelo de Alarcón, Madrid 28223, Spain; Department of Legal Medicine, Psychiatry and Pathology, Complutense University of Madrid, Madrid, Spain
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10
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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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11
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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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12
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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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13
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Badji A, Sabra D, Bherer L, Cohen-Adad J, Girouard H, Gauthier CJ. Arterial stiffness and brain integrity: A review of MRI findings. Ageing Res Rev 2019; 53:100907. [PMID: 31063866 DOI: 10.1016/j.arr.2019.05.001] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 03/30/2019] [Accepted: 05/02/2019] [Indexed: 12/21/2022]
Abstract
BACKGROUND Given the increasing incidence of vascular diseases and dementia, a better understanding of the cerebrovascular changes induced by arterial stiffness is important for early identification of white and gray matter abnormalities that might antedate the appearance of clinical cognitive symptoms. Here, we review the evidence from neuroimaging demonstrating the impact of arterial stiffness on the aging brain. METHOD This review presents findings from recent studies examining the association between arterial stiffness, cognitive function, cerebral hypoperfusion, and markers of neuronal fiber integrity using a variety of MRI techniques. RESULTS Overall, changes associated with arterial stiffness indicates that the corpus callosum, the internal capsule and the corona radiata may be the most vulnerable regions to microvascular damage. In addition, the microstructural integrity of these regions appears to be associated with cognitive performance. Changes in gray matter structure have also been found to be associated with arterial stiffness and are present as early as the 5th decade. Moreover, low cerebral perfusion has been associated with arterial stiffness as well as lower cognitive performance in age-sensitive tasks such as executive function. CONCLUSION Considering the established relationship between arterial stiffness, brain and cognition, this review highlights the need for future studies of brain structure and function in aging to implement measurements of arterial stiffness in parallel with quantitative imaging.
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Affiliation(s)
- Atef Badji
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montréal, QC, Canada; Neuroimaging Functional Unit (UNF), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada; Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Dalia Sabra
- Neuroimaging Functional Unit (UNF), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada; Department of Biomedical Science, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Louis Bherer
- Neuroimaging Functional Unit (UNF), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada; Research Center, Montreal Heart Institute, Montréal, QC, Canada; Department of Medicine, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montréal, QC, Canada; Neuroimaging Functional Unit (UNF), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada; Department of Neurosciences, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Hélène Girouard
- Neuroimaging Functional Unit (UNF), Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal (CRIUGM), Montréal, QC, Canada; Department of Pharmacology and Physiology, Faculty of Medicine, Université de Montréal, Montréal, QC, Canada
| | - Claudine J Gauthier
- Physics Department, Concordia University, Montréal, QC, Canada; PERFORM Centre, Concordia University, Montréal, QC, Canada; Research Center, Montreal Heart Institute, Montréal, QC, Canada.
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14
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Hughes LE, Henson RN, Pereda E, Bruña R, López-Sanz D, Quinn AJ, Woolrich MW, Nobre AC, Rowe JB, Maestú F. Biomagnetic biomarkers for dementia: A pilot multicentre study with a recommended methodological framework for magnetoencephalography. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:450-462. [PMID: 31431918 PMCID: PMC6579903 DOI: 10.1016/j.dadm.2019.04.009] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Introduction An increasing number of studies are using magnetoencephalography (MEG) to study dementia. Here we define a common methodological framework for MEG resting-state acquisition and analysis to facilitate the pooling of data from different sites. Methods Two groups of patients with mild cognitive impairment (MCI, n = 84) and healthy controls (n = 84) were combined from three sites, and site and group differences inspected in terms of power spectra and functional connectivity. Classification accuracy for MCI versus controls was compared across three different types of MEG analyses, and compared with classification based on structural MRI. Results The spectral analyses confirmed frequency-specific differences in patients with MCI, both in power and connectivity patterns, with highest classification accuracy from connectivity. Critically, site acquisition differences did not dominate the results. Discussion This work provides detailed protocols and analyses that are sensitive to cognitive impairment, and that will enable standardized data sharing to facilitate large-scale collaborative projects.
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Affiliation(s)
- Laura E Hughes
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.,Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Ernesto Pereda
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Department of Industrial Engineering, Instituto Universitario de Neurociencia, Universidad de La Laguna, Tenerife, Spain
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
| | - Andrew J Quinn
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, UK.,Wellcome Centre for Integrative Neuroscience, University of Oxford, Oxford, UK.,Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology, Madrid, Spain.,Biomedical Research Networking Center in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain.,Department of Experimental Psychology, Faculty of Psychology, Universidad Complutense de Madrid, Madrid, Spain
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15
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López-Sanz D, Bruña R, de Frutos-Lucas J, Maestú F. Magnetoencephalography applied to the study of Alzheimer's disease. PROGRESS IN MOLECULAR BIOLOGY AND TRANSLATIONAL SCIENCE 2019; 165:25-61. [PMID: 31481165 DOI: 10.1016/bs.pmbts.2019.04.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Magnetoencephalography (MEG) is a relatively modern neuroimaging technique able to study normal and pathological brain functioning with temporal resolution in the order of milliseconds and adequate spatial resolution. Although its clinical applications are still relatively limited, great advances have been made in recent years in the field of dementia and Alzheimer's disease (AD) in particular. In this chapter, we briefly describe the physiological phenomena underlying MEG brain signals and the different metrics that can be computed from these data in order to study the alterations disrupting brain activity not only in demented patients, but also in the preclinical and prodromal stages of the disease. Changes in non-linear brain dynamics, power spectral properties, functional connectivity and network topological changes observed in AD are narratively summarized in the context of the pathophysiology of the disease. Furthermore, the potential of MEG as a potential biomarker to identify AD pathology before dementia onset is discussed in the light of current knowledge and the relationship between potential MEG biomarkers and current established hallmarks of the disease is also reviewed. To this aim, findings from different approaches such as resting state or during the performance of different cognitive paradigms are discussed.Lastly, there is an increasing interest in current scientific literature in promoting interventions aimed at modifying certain lifestyles, such as nutrition or physical activity among others, thought to reduce or delay AD risk. We discuss the utility of MEG as a potential marker of the success of such interventions from the available literature.
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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 (UCM), Madrid, Spain
| | - Ricardo Bruña
- 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 (UCM), Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain
| | - Jaisalmer de Frutos-Lucas
- Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Technical University of Madrid (UPM), Madrid, Spain; Biological and Health Psychology Department, Universidad Autonoma de Madrid, Madrid, Spain; School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia
| | - 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 (UCM), Madrid, Spain; Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Zaragoza, Spain.
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16
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Maestú F, Cuesta P, Hasan O, Fernandéz A, Funke M, Schulz PE. The Importance of the Validation of M/EEG With Current Biomarkers in Alzheimer's Disease. Front Hum Neurosci 2019; 13:17. [PMID: 30792632 PMCID: PMC6374629 DOI: 10.3389/fnhum.2019.00017] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2018] [Accepted: 01/15/2019] [Indexed: 12/22/2022] Open
Abstract
Current biomarkers used in research and in clinical practice in Alzheimer's Disease (AD) are the analysis of cerebral spinal fluid (CSF) to detect levels of Aβ42 and phosphorylated-tau, amyloid and FDG-PET, and MRI volumetry. Some of these procedures are still invasive for patients or expensive. Electroencephalography (EEG) and Magnetoencephalography (MEG) are two non-invasive techniques able to detect the early synaptic dysfunction and track the course of the disease. However, in spite of its added value they are not part of the standard of care in clinical practice in dementia. In this paper we review what these neurophysiological techniques can add to the early diagnosis of AD, whether results in both modalities are related to each other or not, as well as the need of its validation against current biomarkers. We discuss their potential implications for the better understanding of the pathophysiological mechanisms of the disease as well as the need of performing simultaneous M/EEG recordings to better understand discrepancies between these two techniques. Finally, more studies are needed studying M/EEG with amyloid and Tau biomarkers.
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Affiliation(s)
- 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
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - 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 & IUNE Universidad de La Laguna, Tenerife, Spain
| | - Omar Hasan
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
| | - Alberto Fernandéz
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Universidad Complutense and Universidad Politécnica de Madrid, Madrid, Spain
- Department of Legal Medicine, Psychiatry, and Pathology, Universidad Complutense de Madrid, Madrid, Spain
| | - Michael Funke
- Magnetic Source Imaging Unit, Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center, Houston, TX, United States
| | - Paul E. Schulz
- McGovern Medical School University of Texas Health Science Center, Houston, TX, United States
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17
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Arakaki X, Lee R, King KS, Fonteh AN, Harrington MG. Alpha desynchronization during simple working memory unmasks pathological aging in cognitively healthy individuals. PLoS One 2019; 14:e0208517. [PMID: 30601822 PMCID: PMC6314588 DOI: 10.1371/journal.pone.0208517] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2018] [Accepted: 11/19/2018] [Indexed: 12/22/2022] Open
Abstract
Our aim is to explore if cognitive challenge combined with objective physiology can reveal abnormal frontal alpha event-related desynchronization (ERD), in early Alzheimer's disease (AD). We used quantitative electroencephalography (qEEG) to investigate brain activities during N-back working memory (WM) processing at two different load conditions (N = 0 or 2) in an aging cohort. We studied 60-100 year old participants, with normal cognition, and who fits one of two subgroups from cerebrospinal fluid (CSF) proteins: cognitively healthy (CH) with normal amyloid/tau ratio (CH-NAT, n = 10) or pathological amyloid/tau ratio (CH-PAT, n = 14). We recorded behavioral performances, and analyzed alpha power and alpha spectral entropy (SE) at three occasions: during the resting state, and at event-related desynchronization (ERD) [250 ~ 750 ms] during 0-back and 2-back. During 0-back WM testing, the behavioral performance was similar between the two groups, however, qEEG notably differentiated CH-PATs from CH-NATs on the simple, 0-back testing: Alpha ERD decreased from baseline only in the parietal region in CH-NATs, while it decreased in all brain regions in CH-PATs. Alpha SE did not change in CH-NATs, but was increased from baseline in the CH-PATs in frontal and left lateral regions (p<0.01), and was higher in the frontal region (p<0.01) of CH-PATs compared to CH-NATs. The alpha ERD and SE analyses suggest there is frontal lobe dysfunction during WM processing in the CH-PAT stage. Additional power and correlations with behavioral performance were also explored. This study provide pilot information to further evaluate whether this biomarker has clinical significance.
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Affiliation(s)
- Xianghong Arakaki
- Neurosciences, Huntington Medical Research Institutes, Pasadena, California, United States of America
| | - Ryan Lee
- Neurosciences, Huntington Medical Research Institutes, Pasadena, California, United States of America
| | - Kevin S. King
- Imaging Research, Huntington Medical Research Institutes, Pasadena, California, United States of America
| | - Alfred N. Fonteh
- Neurosciences, Huntington Medical Research Institutes, Pasadena, California, United States of America
| | - Michael G. Harrington
- Neurosciences, Huntington Medical Research Institutes, Pasadena, California, United States of America
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18
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Mandal PK, Banerjee A, Tripathi M, Sharma A. A Comprehensive Review of Magnetoencephalography (MEG) Studies for Brain Functionality in Healthy Aging and Alzheimer's Disease (AD). Front Comput Neurosci 2018; 12:60. [PMID: 30190674 PMCID: PMC6115612 DOI: 10.3389/fncom.2018.00060] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 07/09/2018] [Indexed: 12/16/2022] Open
Abstract
Neural oscillations were established with their association with neurophysiological activities and the altered rhythmic patterns are believed to be linked directly to the progression of cognitive decline. Magnetoencephalography (MEG) is a non-invasive technique to record such neuronal activity due to excellent temporal and fair amount of spatial resolution. Single channel, connectivity as well as brain network analysis using MEG data in resting state and task-based experiments were analyzed from existing literature. Single channel analysis studies reported a less complex, more regular and predictable oscillations in Alzheimer's disease (AD) primarily in the left parietal, temporal and occipital regions. Investigations on both functional connectivity (FC) and effective (EC) connectivity analysis demonstrated a loss of connectivity in AD compared to healthy control (HC) subjects found in higher frequency bands. It has been reported from multiplex network of MEG study in AD in the affected regions of hippocampus, posterior default mode network (DMN) and occipital areas, however, conclusions cannot be drawn due to limited availability of clinical literature. Potential utilization of high spatial resolution in MEG likely to provide information related to in-depth brain functioning and underlying factors responsible for changes in neuronal waves in AD. This review is a comprehensive report to investigate diagnostic biomarkers for AD may be identified by from MEG data. It is also important to note that MEG data can also be utilized for the same pursuit in combination with other imaging modalities.
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Affiliation(s)
- Pravat K. Mandal
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
- Department of Neurodegeneration, Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, Australia
| | - Anwesha Banerjee
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
| | - Manjari Tripathi
- Department of Neurology, All Indian Institute of Medical Sciences, New Delhi, India
| | - Ankita Sharma
- Neuroimaging and Neurospectroscopy Lab, National Brain Research Centre, Gurgaon, India
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19
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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: 39] [Impact Index Per Article: 6.5] [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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20
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Classification of mild cognitive impairment EEG using combined recurrence and cross recurrence quantification analysis. Int J Psychophysiol 2017; 120:86-95. [DOI: 10.1016/j.ijpsycho.2017.07.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 06/10/2017] [Accepted: 07/11/2017] [Indexed: 11/21/2022]
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21
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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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22
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Uhlhaas PJ, Liddle P, Linden DEJ, Nobre AC, Singh KD, Gross J. Magnetoencephalography as a Tool in Psychiatric Research: Current Status and Perspective. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2017; 2:235-244. [PMID: 28424797 PMCID: PMC5387180 DOI: 10.1016/j.bpsc.2017.01.005] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2016] [Revised: 01/03/2017] [Accepted: 01/18/2017] [Indexed: 11/05/2022]
Abstract
The application of neuroimaging to provide mechanistic insights into circuit dysfunctions in major psychiatric conditions and the development of biomarkers are core challenges in current psychiatric research. We propose that recent technological and analytic advances in magnetoencephalography (MEG), a technique that allows measurement of neuronal events directly and noninvasively with millisecond resolution, provides novel opportunities to address these fundamental questions. Because of its potential in delineating normal and abnormal brain dynamics, we propose that MEG provides a crucial tool to advance our understanding of pathophysiological mechanisms of major neuropsychiatric conditions, such as schizophrenia, autism spectrum disorders, and the dementias. We summarize the mechanisms underlying the generation of MEG signals and the tools available to reconstruct generators and underlying networks using advanced source-reconstruction techniques. We then surveyed recent studies that have used MEG to examine aberrant rhythmic activity in neuropsychiatric disorders. This was followed by links with preclinical research that has highlighted possible neurobiological mechanisms, such as disturbances in excitation/inhibition parameters, that could account for measured changes in neural oscillations. Finally, we discuss challenges as well as novel methodological developments that could pave the way for widespread application of MEG in translational research with the aim of developing biomarkers for early detection and diagnosis.
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Affiliation(s)
- Peter J Uhlhaas
- Institute for Neuroscience and Psychology, University of Glasgow, Glasgow.
| | - Peter Liddle
- Division of Psychiatry and Applied Psychology, University of Nottingham, Nottingham
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Cardiff; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, University of Oxford, Oxford, United Kingdom; Department of Experimental Psychology, University of Oxford, Oxford, United Kingdom
| | - Krish D Singh
- Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, Cardiff
| | - Joachim Gross
- Institute for Neuroscience and Psychology, University of Glasgow, Glasgow
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23
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Wang Z, Dai Z, Shu H, Liao X, Yue C, Liu D, Guo Q, He Y, Zhang Z. APOE Genotype Effects on Intrinsic Brain Network Connectivity in Patients with Amnestic Mild Cognitive Impairment. Sci Rep 2017; 7:397. [PMID: 28341847 PMCID: PMC5428452 DOI: 10.1038/s41598-017-00432-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2016] [Accepted: 02/20/2017] [Indexed: 12/03/2022] Open
Abstract
Whether and how the apolipoprotein E (APOE) ε4 genotype specifically modulates brain network connectivity in patients with amnestic mild cognitive impairment (aMCI) remain largely unknown. Here, we employed resting-state (‘task-free’) functional MRI and network centrality approaches to investigate local (degree centrality, DC) and global (eigenvector centrality, EC) functional integrity in the whole-brain connectome in 156 older adults, including 66 aMCI patients (27 ε4-carriers and 39 non-carriers) and 90 healthy controls (45 ε4-carriers and 45 non-carriers). We observed diagnosis-by-genotype interactions on DC in the left superior/middle frontal gyrus, right middle temporal gyrus and cerebellum, with higher values in the ε4-carriers than non-carriers in the aMCI group. We further observed diagnosis-by-genotype interactions on EC, with higher values in the right middle temporal gyrus but lower values in the medial parts of default-mode network in the ε4-carriers than non-carriers in the aMCI group. Notably, these genotype differences in DC or EC were absent in the control group. Finally, the network connectivity DC values were negatively correlated with cognitive performance in the aMCI ε4-carriers. Our findings suggest that the APOE genotype selectively modulates the functional integration of brain networks in patients with aMCI, thus providing important insight into the gene-connectome interaction in this disease.
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Affiliation(s)
- Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510006, China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Chunxian Yue
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Duan Liu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China
| | - Qihao Guo
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210009, China.
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24
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Nieboer D, Douw L, van Dijk BW, Heymans MW, Stam CJ, Twisk JWR. Relation between carotid stiffness, cognitive performance and brain connectivity in a healthy middle-aged population: an observational neurophysiological cohort study with magnetoencephalography. BMJ Open 2016; 6:e013441. [PMID: 27979838 PMCID: PMC5168642 DOI: 10.1136/bmjopen-2016-013441] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
OBJECTIVE Impaired blood flow of the carotid artery can result in cognitive impairment, but how these vascular impairments lead to global cognitive disturbances is largely unknown. Problems in functional connectivity between brain areas may be responsible for these widespread effects. Therefore, the aim of this study was to examine the association between carotid stiffness, functional connectivity and cognitive performance in relatively young and healthy adults before clinical vascular pathology occurs. DESIGN The Amsterdam Growth and Health Longitudinal Study: an observational study. SETTING Participants were included by attending 1 of the 2 selected secondary schools in The Netherlands. PARTICIPANTS Men (n=110) and women (n=120) aged 41-44 years (42±0.7). PRIMARY AND SECONDARY OUTCOME MEASURES Data were obtained with regard to local carotid stiffness captured measured with the Young's elastic modulus (YEM). All participants underwent a commonly used Dutch intelligence test and resting-state eyes-closed magnetoencephalography (MEG). Five artefact-free epochs were analysed. The phase lag index (PLI) was used as a measure of functional connectivity between all sensors and was assessed in six frequency bands (δ-γ). RESULTS Carotid stiffness was significantly associated with increased functional connectivity in the α2 band in men (β: 0.287; p=0.008). The same results were found for women in the β band (β: 0.216; p=0.040). Furthermore, carotid stiffness was associated with superior cognitive function in men (β: 0.238; p=0.007). In addition, there was neither a significant association nor a consistent pattern between cognitive function and functional connectivity. CONCLUSIONS The increased connectivity might be a maladaptive phenomenon caused by disinhibition of neurons which may explain the direction of the results. This study suggests that detection of increased (local) carotid stiffness may be promising to identify a disturbance in the organisation of the functional brain network, even before clinical vascular pathology occurs.
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Affiliation(s)
- Dagmar Nieboer
- Department of Methodology and Applied Biostatistics, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands
| | - Linda Douw
- Department of Anatomy and Neurosciences, VU University Medical Center, Amsterdam, The Netherlands
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA
| | - Bob W van Dijk
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
- Department of Physics and Medical Technology, VU University Medical Center, Amsterdam, The Netherlands
| | - Martijn W Heymans
- Department of Methodology and Applied Biostatistics, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam, The Netherlands
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, VU University Medical Center, Amsterdam, The Netherlands
| | - Jos W R Twisk
- Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
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25
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Amezquita-Sanchez JP, Adeli A, Adeli H. A new methodology for automated diagnosis of mild cognitive impairment (MCI) using magnetoencephalography (MEG). Behav Brain Res 2016; 305:174-80. [DOI: 10.1016/j.bbr.2016.02.035] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2016] [Revised: 02/24/2016] [Accepted: 02/26/2016] [Indexed: 10/22/2022]
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26
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Dimitriadis SI, Laskaris NA, Bitzidou MP, Tarnanas I, Tsolaki MN. A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front Neurosci 2015; 9:350. [PMID: 26539070 PMCID: PMC4611062 DOI: 10.3389/fnins.2015.00350] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2015] [Accepted: 09/14/2015] [Indexed: 11/13/2022] Open
Abstract
The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.
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Affiliation(s)
- Stavros I Dimitriadis
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece ; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Nikolaos A Laskaris
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece ; Neuroinformatics Group, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Malamati P Bitzidou
- Artificial Intelligence Information Analysis Lab, Department of Informatics, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Ioannis Tarnanas
- Health-IS Lab, Chair of Information Management, ETH Zurich Zurich, Switzerland ; 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
| | - Magda N Tsolaki
- 3rd Department of Neurology, Medical School, Aristotle University of Thessaloniki Thessaloniki, Greece
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27
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Maestú F, Peña JM, Garcés P, González S, Bajo R, Bagic A, Cuesta P, Funke M, Mäkelä JP, Menasalvas E, Nakamura A, Parkkonen L, López ME, Del Pozo F, Sudre G, Zamrini E, Pekkonen E, Henson RN, Becker JT. A multicenter study of the early detection of synaptic dysfunction in Mild Cognitive Impairment using Magnetoencephalography-derived functional connectivity. NEUROIMAGE-CLINICAL 2015; 9:103-9. [PMID: 26448910 PMCID: PMC4552812 DOI: 10.1016/j.nicl.2015.07.011] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Synaptic disruption is an early pathological sign of the neurodegeneration of Dementia of the Alzheimer's type (DAT). The changes in network synchronization are evident in patients with Mild Cognitive Impairment (MCI) at the group level, but there are very few Magnetoencephalography (MEG) studies regarding discrimination at the individual level. In an international multicenter study, we used MEG and functional connectivity metrics to discriminate MCI from normal aging at the individual person level. A labeled sample of features (links) that distinguished MCI patients from controls in a training dataset was used to classify MCI subjects in two testing datasets from four other MEG centers. We identified a pattern of neuronal hypersynchronization in MCI, in which the features that best discriminated MCI were fronto-parietal and interhemispheric links. The hypersynchronization pattern found in the MCI patients was stable across the five different centers, and may be considered an early sign of synaptic disruption and a possible preclinical biomarker for MCI/DAT. Across centers reliable abnormalities in the neuronal network organization of MCI patients These findings are consistent with the view that AD may, in its earliest stages, represent a disconnection syndrome. A high rate of classification accuracy in a blind study, especially for individuals who were cognitively normal All these suggest that MEG may be a useful marker of preclinical synaptic disruption. The hypersynchronization found in MCI patients may represent a compensatory response.
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Affiliation(s)
- Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Jose-Maria Peña
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Santiago González
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Ricardo Bajo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Anto Bagic
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Michael Funke
- Department of Pediatrics, University of Texas Health Science Center, Houston, USA
| | - Jyrki P Mäkelä
- BioMag Laboratory, HUS Medical Imaging Center, Helsinki University Central Hospital, Hensinki, Finland
| | - Ernestina Menasalvas
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Akinori Nakamura
- Department of Clinical and Experimental Neuroimaging, National Center for Geriatrics and Gerontology, Obu, Japan
| | - Lauri Parkkonen
- Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Aalto, Espoo, Finland ; Elekta Oy, Helsinki, Finland
| | - Maria E López
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Francisco Del Pozo
- Laboratory of Cognitive and Computational Neuroscience, Center for Biomedical Technology, Complutense University of Madrid and Technical University of Madrid, Madrid, Spain
| | - Gustavo Sudre
- Human Genome Research Institute, National Institutes of Health, Bethesda, USA
| | - Edward Zamrini
- Department of Neurology, University of Utah, Salt Lake City, USA
| | - Eero Pekkonen
- Department of Neurology, University of Helsinki, Finland
| | - Richard N Henson
- Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK
| | - James T Becker
- Department of Neurology, University of Pittsburgh, Pittsburgh, USA ; Department of Psychiatry, University of Pittsburgh, Pittsburgh, USA ; Department of Psychology, University of Pittsburgh, Pittsburgh, USA
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28
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López ME, Bruña R, Aurtenetxe S, Pineda-Pardo JÁ, Marcos A, Arrazola J, Reinoso AI, Montejo P, Bajo R, Maestú F. Alpha-band hypersynchronization in progressive mild cognitive impairment: a magnetoencephalography study. J Neurosci 2014; 34:14551-9. [PMID: 25355209 PMCID: PMC6608420 DOI: 10.1523/jneurosci.0964-14.2014] [Citation(s) in RCA: 75] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2014] [Revised: 07/30/2014] [Accepted: 08/02/2014] [Indexed: 12/23/2022] Open
Abstract
People with mild cognitive impairment (MCI) show a high risk to develop Alzheimer's disease (AD; Petersen et al., 2001). Nonetheless, there is a lack of studies about how functional connectivity patterns may distinguish between progressive (pMCI) and stable (sMCI) MCI patients. To examine whether there were differences in functional connectivity between groups, MEG eyes-closed recordings from 30 sMCI and 19 pMCI subjects were compared. The average conversion time of pMCI was 1 year, so they were considered as fast converters. To this end, functional connectivity in different frequency bands was assessed with phase locking value in source space. Then the significant differences between both groups were correlated with neuropsychological scores and entorhinal, parahippocampal, and hippocampal volumes. Both groups did not differ in age, gender, or educational level. pMCI patients obtained lower scores in episodic and semantic memory and also in executive functioning. At the structural level, there were no differences in hippocampal volume, although some were found in left entorhinal volume between both groups. Additionally, pMCI patients exhibit a higher synchronization in the alpha band between the right anterior cingulate and temporo-occipital regions than sMCI subjects. This hypersynchronization was inversely correlated with cognitive performance, both hippocampal volumes, and left entorhinal volume. The increase in phase synchronization between the right anterior cingulate and temporo-occipital areas may be predictive of conversion from MCI to AD.
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Affiliation(s)
- María Eugenía López
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain,
| | - Ricardo Bruña
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and
| | - Sara Aurtenetxe
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain
| | - José Ángel Pineda-Pardo
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Laboratory of Neuroimaging (Universidad Politécnica de Madrid) (National Pedagogic University), Centre for Biomedical Technology (CTB), 28223 Madrid, Spain
| | | | - Juan Arrazola
- Radiology, San Carlos University Hospital, 28040 Madrid, Spain
| | - Ana Isabel Reinoso
- Centre for Prevention of Cognitive Impairment, Madrid Health, 28006, Madrid, Spain, and
| | - Pedro Montejo
- Centre for Prevention of Cognitive Impairment, Madrid Health, 28006, Madrid, Spain, and
| | - Ricardo Bajo
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Mathematics, International University of La Rioja (UNIR), 26006 Logroño, Spain
| | - Fernando Maestú
- Laboratories of Cognitive and Computational Neuroscience (Complutense University of Madrid-Universidad Politécnica of Madrid) and Department of Basic Psychology II, Complutense University of Madrid (UCM), 28040 Madrid, Spain
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29
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Pineda-Pardo JA, Bruña R, Woolrich M, Marcos A, Nobre AC, Maestú F, Vidaurre D. Guiding functional connectivity estimation by structural connectivity in MEG: an application to discrimination of conditions of mild cognitive impairment. Neuroimage 2014; 101:765-77. [PMID: 25111472 PMCID: PMC4312351 DOI: 10.1016/j.neuroimage.2014.08.002] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2014] [Revised: 07/30/2014] [Accepted: 08/01/2014] [Indexed: 01/18/2023] Open
Abstract
Whole brain resting state connectivity is a promising biomarker that might help to obtain an early diagnosis in many neurological diseases, such as dementia. Inferring resting-state connectivity is often based on correlations, which are sensitive to indirect connections, leading to an inaccurate representation of the real backbone of the network. The precision matrix is a better representation for whole brain connectivity, as it considers only direct connections. The network structure can be estimated using the graphical lasso (GL), which achieves sparsity through l1-regularization on the precision matrix. In this paper, we propose a structural connectivity adaptive version of the GL, where weaker anatomical connections are represented as stronger penalties on the corresponding functional connections. We applied beamformer source reconstruction to the resting state MEG recordings of 81 subjects, where 29 were healthy controls, 22 were single-domain amnestic Mild Cognitive Impaired (MCI), and 30 were multiple-domain amnestic MCI. An atlas-based anatomical parcellation of 66 regions was obtained for each subject, and time series were assigned to each of the regions. The fiber densities between the regions, obtained with deterministic tractography from diffusion-weighted MRI, were used to define the anatomical connectivity. Precision matrices were obtained with the region specific time series in five different frequency bands. We compared our method with the traditional GL and a functional adaptive version of the GL, in terms of log-likelihood and classification accuracies between the three groups. We conclude that introducing an anatomical prior improves the expressivity of the model and, in most cases, leads to a better classification between groups. We propose an anatomy-driven method for functional connectivity estimation in MEG. Structural prior contributes to a better representation of the functional connectivity. The proposed method is shown to be useful as a biomarker for classification of MCI.
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Affiliation(s)
- José Angel Pineda-Pardo
- Laboratory of Neuroimaging, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain; Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Ricardo Bruña
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Mark Woolrich
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
| | - Alberto Marcos
- The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom; Department of Neurology, Hospital Clínico San Carlos, Madrid, Spain.
| | - Anna C Nobre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom.
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain.
| | - Diego Vidaurre
- Oxford Center for Human Brain Activity (OHBA), University of Oxford, Oxford, United Kingdom; The Oxford Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom.
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30
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López ME, Garcés P, Cuesta P, Castellanos NP, Aurtenetxe S, Bajo R, Marcos A, Montenegro M, Yubero R, del Pozo F, Sancho M, Maestú F. Synchronization during an internally directed cognitive state in healthy aging and mild cognitive impairment: a MEG study. AGE (DORDRECHT, NETHERLANDS) 2014; 36:9643. [PMID: 24658709 PMCID: PMC4082567 DOI: 10.1007/s11357-014-9643-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 03/10/2014] [Indexed: 06/03/2023]
Abstract
Mild cognitive impairment (MCI) is a stage between healthy aging and dementia. It is known that in this condition the connectivity patterns are altered in the resting state and during cognitive tasks, where an extra effort seems to be necessary to overcome cognitive decline. We aimed to determine the functional connectivity pattern required to deal with an internally directed cognitive state (IDICS) in healthy aging and MCI. This task differs from the most commonly employed ones in neurophysiology, since inhibition from external stimuli is needed, allowing the study of this control mechanism. To this end, magnetoencephalographic (MEG) signals were acquired from 32 healthy individuals and 38 MCI patients, both in resting state and while performing a subtraction task of two levels of difficulty. Functional connectivity was assessed with phase locking value (PLV) in five frequency bands. Compared to controls, MCIs showed higher PLV values in delta, theta, and gamma bands and an opposite pattern in alpha, beta, and gamma bands in resting state. These changes were associated with poorer neuropsychological performance. During the task, this group exhibited a hypersynchronization in delta, theta, beta, and gamma bands, which was also related to a lower cognitive performance, suggesting an abnormal functioning in this group. Contrary to controls, MCIs presented a lack of synchronization in the alpha band which may denote an inhibition deficit. Additionally, the magnitude of connectivity changes rose with the task difficulty in controls but not in MCIs, in line with the compensation-related utilization of neural circuits hypothesis (CRUNCH) model.
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Affiliation(s)
- María Eugenia López
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
- />Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
| | - Pilar Garcés
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
- />CEI Campus Moncloa, UCM-UPM, Madrid, Spain
- />Departamento de Física Aplicada III, Facultad de Física, Complutense University of Madrid, 28040 Madrid, Spain
| | - Pablo Cuesta
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Nazareth P. Castellanos
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Sara Aurtenetxe
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
- />Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
| | - Ricardo Bajo
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
- />Department of Mathematics, Universidad Internacional de La Rioja (UNIR), Logroño, La Rioja Spain
| | - Alberto Marcos
- />Neurology Department, San Carlos University Hospital, c/Martín Lagos s/n, 28040 Madrid, Spain
| | - Mercedes Montenegro
- />Memory Decline Prevention Center, Madrid Salud, Ayuntamiento de Madrid, c/ Montesa, 22, 28006 Madrid, Spain
| | - Raquel Yubero
- />Geriatric Department, San Carlos University Hospital, c/Martín Lagos s/n, 28040 Madrid, Spain
| | - Francisco del Pozo
- />Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
| | - Miguel Sancho
- />Departamento de Física Aplicada III, Facultad de Física, Complutense University of Madrid, 28040 Madrid, Spain
| | - Fernando Maestú
- />Laboratory of Cognitive and Computational Neuroscience (UCM-UPM), Centre for Biomedical Technology (CTB), Campus de Montegancedo s/n, Pozuelo de Alarcón, 28223 Madrid, Spain
- />Department of Basic Psychology II, Complutense University of Madrid, Madrid, Spain
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Pineda-Pardo JA, Garcés P, López ME, Aurtenetxe S, Cuesta P, Marcos A, Montejo P, Yus M, Hernández-Tamames JA, del Pozo F, Becker JT, Maestú F. White matter damage disorganizes brain functional networks in amnestic mild cognitive impairment. Brain Connect 2014; 4:312-22. [PMID: 24617580 PMCID: PMC4064724 DOI: 10.1089/brain.2013.0208] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Although progressive functional brain network disruption has been one of the hallmarks of Alzheimer's Disease, little is known about the origin of this functional impairment that underlies cognitive symptoms. We investigated how the loss of white matter (WM) integrity disrupts the organization of the functional networks at different frequency bands. The analyses were performed in a sample of healthy elders and mild cognitive impairment (MCI) subjects. Spontaneous brain magnetic activity (measured with magnetoencephalography) was characterized with phase synchronization analysis, and graph theory was applied to the functional networks. We identified WM areas (using diffusion weighted magnetic resonance imaging) that showed a statistical dependence between the fractional anisotropy and the graph metrics. These regions are part of an episodic memory network and were also related to cognitive functions. Our data support the hypothesis that disruption of the anatomical networks influences the organization at the functional level resulting in the prodromal dementia syndrome of MCI.
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Affiliation(s)
- José Angel Pineda-Pardo
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
- Laboratory of Neuroimaging, Fundación CIEN–Fundación Reina Sofía, Madrid, Spain
| | - Pilar Garcés
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - María Eugenia López
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Sara Aurtenetxe
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Pablo Cuesta
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - Alberto Marcos
- Department of Neurology, Hospital Clínico San Carlos, Madrid, Spain
| | - Pedro Montejo
- Center for the Prevention of Cognitive Impairment of the City of Madrid, Madrisalud, Madrid, Spain
| | - Miguel Yus
- Department of Radiology, Hospital Clínico San Carlos, Madrid, Spain
| | - Juan Antonio Hernández-Tamames
- Laboratory of Neuroimaging, Fundación CIEN–Fundación Reina Sofía, Madrid, Spain
- Department of Electronics Technology, Universidad Rey Juan Carlos, Móstoles, Spain
| | - Francisco del Pozo
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
| | - James T. Becker
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Department of Psychology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Center of Biomedical Technology, Universidad Politécnica de Madrid (UPM), Madrid, Spain
- Department of Basic Psychology, Universidad Complutense de Madrid, Madrid, Spain
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Ahmadlou M, Adeli A, Bajo R, Adeli H. Complexity of functional connectivity networks in mild cognitive impairment subjects during a working memory task. Clin Neurophysiol 2014; 125:694-702. [DOI: 10.1016/j.clinph.2013.08.033] [Citation(s) in RCA: 104] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 07/30/2013] [Accepted: 08/06/2013] [Indexed: 01/25/2023]
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Coullaut-Valera R, Arbaiza I, Bajo R, Arrúe R, López ME, Coullaut-Valera J, Correas A, López-Sanz D, Maestu F, Papo D. Drug polyconsumption is associated with increased synchronization of brain electrical-activity at rest and in a counting task. Int J Neural Syst 2013; 24:1450005. [PMID: 24344693 DOI: 10.1142/s0129065714500051] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Drug abusers typically consume not just one but several types of drugs, starting from alcohol and marijuana consumption, and then dramatically lapsing into addiction to harder drugs, such as cocaine, heroin, or amphetamine. The brain of drug abusers presents various structural and neurophysiological abnormalities, some of which may predate drug consumption onset. However, how these changes translate into modifications in functional brain connectivity is still poorly understood. To characterize functional connectivity patterns, we recorded Electroencephalogram (EEG) activity from 21 detoxified drug abusers and 20 age-matched control subjects performing a simple counting task and at rest activity. To evaluate the cortical brain connectivity network we applied the Synchronization Likelihood algorithm. The results showed that drug abusers had higher synchronization levels at low frequencies, mainly in the θ band (4-8 Hz) between frontal and posterior cortical regions. During the counting task, patients showed increased synchronization in the β (14-35 Hz), and γ (35-45 Hz) frequency bands, in fronto-posterior and interhemispheric temporal regions. Taken together 'slow-down' at rest and task-related 'over-exertion' could indicate that the brain of drug abusers is suffering from a premature form of ageing. Future studies will clarify whether this condition can be reversed following prolonged periods of abstinence.
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Affiliation(s)
- R Coullaut-Valera
- Instituto Rafael Coullaut de Psiquiatría, C/José Abascal 3, 28003 Madrid, Spain
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Aurtenetxe S, Castellanos NP, Moratti S, Bajo R, Gil P, Beitia G, del-Pozo F, Maestú F. Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task. Int J Psychophysiol 2013. [DOI: 10.1016/j.ijpsycho.2012.11.008] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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