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Trubshaw M, Gohil C, Yoganathan K, Kohl O, Edmond E, Proudfoot M, Thompson AG, Talbot K, Stagg CJ, Nobre AC, Woolrich M, Turner MR. The cortical neurophysiological signature of amyotrophic lateral sclerosis. Brain Commun 2024; 6:fcae164. [PMID: 38779353 PMCID: PMC11109820 DOI: 10.1093/braincomms/fcae164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 03/11/2024] [Accepted: 03/09/2024] [Indexed: 05/25/2024] Open
Abstract
The progressive loss of motor function characteristic of amyotrophic lateral sclerosis is associated with widespread cortical pathology extending beyond primary motor regions. Increasing muscle weakness reflects a dynamic, variably compensated brain network disorder. In the quest for biomarkers to accelerate therapeutic assessment, the high temporal resolution of magnetoencephalography is uniquely able to non-invasively capture micro-magnetic fields generated by neuronal activity across the entire cortex simultaneously. This study examined task-free magnetoencephalography to characterize the cortical oscillatory signature of amyotrophic lateral sclerosis for having potential as a pharmacodynamic biomarker. Eight to ten minutes of magnetoencephalography in the task-free, eyes-open state was recorded in amyotrophic lateral sclerosis (n = 36) and healthy age-matched controls (n = 51), followed by a structural MRI scan for co-registration. Extracted magnetoencephalography metrics from the delta, theta, alpha, beta, low-gamma, high-gamma frequency bands included oscillatory power (regional activity), 1/f exponent (complexity) and amplitude envelope correlation (connectivity). Groups were compared using a permutation-based general linear model with correction for multiple comparisons and confounders. To test whether the extracted metrics could predict disease severity, a random forest regression model was trained and evaluated using nested leave-one-out cross-validation. Amyotrophic lateral sclerosis was characterized by reduced sensorimotor beta band and increased high-gamma band power. Within the premotor cortex, increased disability was associated with a reduced 1/f exponent. Increased disability was more widely associated with increased global connectivity in the delta, theta and high-gamma bands. Intra-hemispherically, increased disability scores were particularly associated with increases in temporal connectivity and inter-hemispherically with increases in frontal and occipital connectivity. The random forest model achieved a coefficient of determination (R2) of 0.24. The combined reduction in cortical sensorimotor beta and rise in gamma power is compatible with the established hypothesis of loss of inhibitory, GABAergic interneuronal circuits in pathogenesis. A lower 1/f exponent potentially reflects a more excitable cortex and a pathology unique to amyotrophic lateral sclerosis when considered with the findings published in other neurodegenerative disorders. Power and complexity changes corroborate with the results from paired-pulse transcranial magnetic stimulation. Increased magnetoencephalography connectivity in worsening disability is thought to represent compensatory responses to a failing motor system. Restoration of cortical beta and gamma band power has significant potential to be tested in an experimental medicine setting. Magnetoencephalography-based measures have potential as sensitive outcome measures of therapeutic benefit in drug trials and may have a wider diagnostic value with further study, including as predictive markers in asymptomatic carriers of disease-causing genetic variants.
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Affiliation(s)
- Michael Trubshaw
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Chetan Gohil
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Katie Yoganathan
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Oliver Kohl
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Evan Edmond
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Malcolm Proudfoot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Alexander G Thompson
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Kevin Talbot
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Charlotte J Stagg
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
| | - Anna C Nobre
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Mark Woolrich
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, UK
| | - Martin R Turner
- Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 7JX, UK
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, UK
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Wiesman AI, Gallego-Rudolf J, Villeneuve S, Baillet S, Wilson TW. Alignments between cortical neurochemical systems, proteinopathy and neurophysiological alterations along the Alzheimer's disease continuum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.13.24305551. [PMID: 38645027 PMCID: PMC11030470 DOI: 10.1101/2024.04.13.24305551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
Two neuropathological hallmarks of Alzheimer's disease (AD) are the accumulation of amyloid-β (Aβ) proteins and alterations in cortical neurophysiological signaling. Despite parallel research indicating disruption of multiple neurotransmitter systems in AD, it has been unclear whether these two phenomena are related to the neurochemical organization of the cortex. We leveraged task-free magnetoencephalography and positron emission tomography, with a cortical atlas of 19 neurotransmitters to study the alignment and interactions between alterations of neurophysiological signaling, Aβ deposition, and the neurochemical gradients of the human cortex. In patients with amnestic mild cognitive impairment (N = 18) and probable AD (N = 20), we found that changes in rhythmic, but not arrhythmic, cortical neurophysiological signaling relative to healthy controls (N = 20) are topographically aligned with cholinergic, serotonergic, and dopaminergic neurochemical systems. These neuro-physio-chemical alignments are related to the severity of cognitive and behavioral impairments. We also found that cortical Aβ plaques are preferentially deposited along neurochemical boundaries, and mediate how beta-band rhythmic cortical activity maps align with muscarinic acetylcholine receptors. Finally, we show in an independent dataset that many of these alignments manifest in the asymptomatic stages of cortical Aβ accumulation (N = 33; N = 71 healthy controls), particularly the Aβ-neurochemical alignments (57.1%) and neuro-physio-chemical alignments in the alpha frequency band (62.5%). Overall, the present study demonstrates that the expression of pathology in pre-clinical and clinical AD aligns topographically with the cortical distribution of chemical neuromodulator systems, scaling with clinical severity and with implications for potential pharmacotherapeutic pathways.
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van Nifterick AM, Scheijbeler EP, Gouw AA, de Haan W, Stam CJ. Local signal variability and functional connectivity: Sensitive measures of the excitation-inhibition ratio? Cogn Neurodyn 2024; 18:519-537. [PMID: 38699618 PMCID: PMC11061092 DOI: 10.1007/s11571-023-10003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/08/2023] [Accepted: 08/13/2023] [Indexed: 05/05/2024] Open
Abstract
A novel network version of permutation entropy, the inverted joint permutation entropy (JPEinv), holds potential as non-invasive biomarker of abnormal excitation-inhibition (E-I) ratio in Alzheimer's disease (AD). In this computational modelling study, we test the hypotheses that this metric, and related measures of signal variability and functional connectivity, are sensitive to altered E-I ratios. The E-I ratio in each neural mass of a whole-brain computational network model was systematically varied. We evaluated whether JPEinv, local signal variability (by permutation entropy) and functional connectivity (by weighted symbolic mutual information (wsMI)) were related to E-I ratio, on whole-brain and regional level. The hub disruption index can identify regions primarily affected in terms of functional connectivity strength (or: degree) by the altered E-I ratios. Analyses were performed for a range of coupling strengths, filter and time-delay settings. On whole-brain level, higher E-I ratios were associated with higher functional connectivity (by JPEinv and wsMI) and lower local signal variability. These relationships were nonlinear and depended on the coupling strength, filter and time-delay settings. On regional level, hub-like regions showed a selective decrease in functional degree (by JPEinv and wsMI) upon a lower E-I ratio, and non-hub-like regions showed a selective increase in degree upon a higher E-I ratio. These results suggest that abnormal functional connectivity and signal variability, as previously reported in patients across the AD continuum, can inform us about altered E-I ratios. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-023-10003-x.
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Affiliation(s)
- Anne M. van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J. Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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Verma P, Ranasinghe K, Prasad J, Cai C, Xie X, Lerner H, Mizuiri D, Miller B, Rankin K, Vossel K, Cheung SW, Nagarajan SS, Raj A. Impaired long-range excitatory time scale predicts abnormal neural oscillations and cognitive deficits in Alzheimer's disease. Alzheimers Res Ther 2024; 16:62. [PMID: 38504361 PMCID: PMC10953266 DOI: 10.1186/s13195-024-01426-7] [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: 12/07/2023] [Accepted: 03/04/2024] [Indexed: 03/21/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common form of dementia, progressively impairing cognitive abilities. While neuroimaging studies have revealed functional abnormalities in AD, how these relate to aberrant neuronal circuit mechanisms remains unclear. Using magnetoencephalography imaging we documented abnormal local neural synchrony patterns in patients with AD. To identify global abnormal biophysical mechanisms underlying the spatial and spectral electrophysiological patterns in AD, we estimated the parameters of a biophysical spectral graph model (SGM). METHODS SGM is an analytic neural mass model that describes how long-range fiber projections in the brain mediate the excitatory and inhibitory activity of local neuronal subpopulations. Unlike other coupled neuronal mass models, the SGM is linear, available in closed-form, and parameterized by a small set of biophysical interpretable global parameters. This facilitates their rapid and unambiguous inference which we performed here on a well-characterized clinical population of patients with AD (N = 88, age = 62.73 +/- 8.64 years) and a cohort of age-matched controls (N = 88, age = 65.07 +/- 9.92 years). RESULTS Patients with AD showed significantly elevated long-range excitatory neuronal time scales, local excitatory neuronal time scales and local inhibitory neural synaptic strength. The long-range excitatory time scale had a larger effect size, compared to local excitatory time scale and inhibitory synaptic strength and contributed highest for the accurate classification of patients with AD from controls. Furthermore, increased long-range time scale was associated with greater deficits in global cognition. CONCLUSIONS These results demonstrate that long-range excitatory time scale of neuronal activity, despite being a global measure, is a key determinant in the local spectral signatures and cognition in the human brain, and how it might be a parsimonious factor underlying altered neuronal activity in AD. Our findings provide new insights into mechanistic links between abnormal local spectral signatures and global connectivity measures in AD.
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Affiliation(s)
- Parul Verma
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Kamalini Ranasinghe
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | | | - Chang Cai
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Hannah Lerner
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Danielle Mizuiri
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Bruce Miller
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Katherine Rankin
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Keith Vossel
- Memory and Aging Center, Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Mary S. Easton Center for Alzheimer's Research and Care, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Steven W Cheung
- Department of Otolaryngology-Head and Neck Surgery, University of California San Francisco, San Francisco, CA, USA
- Surgical Services, Veterans Affairs, San Francisco, USA
| | - Srikantan S Nagarajan
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Hoshi H, Hirata Y, Fukasawa K, Kobayashi M, Shigihara Y. Oscillatory characteristics of resting-state magnetoencephalography reflect pathological and symptomatic conditions of cognitive impairment. Front Aging Neurosci 2024; 16:1273738. [PMID: 38352236 PMCID: PMC10861731 DOI: 10.3389/fnagi.2024.1273738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 01/12/2024] [Indexed: 02/16/2024] Open
Abstract
Background Dementia and mild cognitive impairment are characterised by symptoms of cognitive decline, which are typically assessed using neuropsychological assessments (NPAs), such as the Mini-Mental State Examination (MMSE) and Frontal Assessment Battery (FAB). Magnetoencephalography (MEG) is a novel clinical assessment technique that measures brain activities (summarised as oscillatory parameters), which are associated with symptoms of cognitive impairment. However, the relevance of MEG and regional cerebral blood flow (rCBF) data obtained using single-photon emission computed tomography (SPECT) has not been examined using clinical datasets. Therefore, this study aimed to investigate the relationships among MEG oscillatory parameters, clinically validated biomarkers computed from rCBF, and NPAs using outpatient data retrieved from hospital records. Methods Clinical data from 64 individuals with mixed pathological backgrounds were retrieved and analysed. MEG oscillatory parameters, including relative power (RP) from delta to high gamma bands, mean frequency, individual alpha frequency, and Shannon's spectral entropy, were computed for each cortical region. For SPECT data, three pathological parameters-'severity', 'extent', and 'ratio'-were computed using an easy z-score imaging system (eZIS). As for NPAs, the MMSE and FAB scores were retrieved. Results MEG oscillatory parameters were correlated with eZIS parameters. The eZIS parameters associated with Alzheimer's disease pathology were reflected in theta power augmentation and slower shift of the alpha peak. Moreover, MEG oscillatory parameters were found to reflect NPAs. Global slowing and loss of diversity in neural oscillatory components correlated with MMSE and FAB scores, whereas the associations between eZIS parameters and NPAs were sparse. Conclusion MEG oscillatory parameters correlated with both SPECT (i.e. eZIS) parameters and NPAs, supporting the clinical validity of MEG oscillatory parameters as pathological and symptomatic indicators. The findings indicate that various components of MEG oscillatory characteristics can provide valuable pathological and symptomatic information, making MEG data a rich resource for clinical examinations of patients with cognitive impairments. SPECT (i.e. eZIS) parameters showed no correlations with NPAs. The results contributed to a better understanding of the characteristics of electrophysiological and pathological examinations for patients with cognitive impairments, which will help to facilitate their co-use in clinical application, thereby improving patient care.
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Affiliation(s)
- Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | - Yoko Hirata
- Department of Neurosurgery, Kumagaya General Hospital, Kumagaya, Japan
| | | | - Momoko Kobayashi
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
| | - Yoshihito Shigihara
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
- Precision Medicine Centre, Kumagaya General Hospital, Kumagaya, Japan
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Wiesman AI, da Silva Castanheira J, Degroot C, Fon EA, Baillet S, Network QP. Adverse and compensatory neurophysiological slowing in Parkinson's disease. Prog Neurobiol 2023; 231:102538. [PMID: 37832713 PMCID: PMC10872886 DOI: 10.1016/j.pneurobio.2023.102538] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 09/27/2023] [Accepted: 10/09/2023] [Indexed: 10/15/2023]
Abstract
Patients with Parkinson's disease (PD) exhibit multifaceted changes in neurophysiological brain activity, hypothesized to represent a global cortical slowing effect. Using task-free magnetoencephalography and extensive clinical assessments, we found that neurophysiological slowing in PD is differentially associated with motor and non-motor symptoms along a sagittal gradient over the cortical anatomy. In superior parietal regions, neurophysiological slowing reflects an adverse effect and scales with cognitive and motor impairments, while across the inferior frontal cortex, neurophysiological slowing is compatible with a compensatory role. This adverse-to-compensatory gradient is sensitive to individual clinical profiles, such as drug regimens and laterality of symptoms; it is also aligned with the topography of neurotransmitter and transporter systems relevant to PD. We conclude that neurophysiological slowing in patients with PD signals both deleterious and protective mechanisms of the disease, from posterior to anterior regions across the cortex, respectively, with functional and clinical relevance to motor and cognitive symptoms.
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Affiliation(s)
- Alex I Wiesman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | | | - Clotilde Degroot
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Edward A Fon
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Sylvain Baillet
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
| | - Quebec Parkinson Network
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada
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Ladisich B, Rampp S, Trinka E, Weisz N, Schwartz C, Kraus T, Sherif C, Marhold F, Demarchi G. Network topology in brain tumor patients with and without structural epilepsy: a prospective MEG study. Ther Adv Neurol Disord 2023; 16:17562864231190298. [PMID: 37655227 PMCID: PMC10467269 DOI: 10.1177/17562864231190298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 07/07/2023] [Indexed: 09/02/2023] Open
Abstract
Background It was proposed that network topology is altered in brain tumor patients. However, there is no consensus on the pattern of these changes and evidence on potential drivers is lacking. Objectives We aimed to characterize neurooncological patients' network topology by analyzing glial brain tumors (GBTs) and brain metastases (BMs) with respect to the presence of structural epilepsy. Methods Network topology derived from resting state magnetoencephalography was compared between (1) patients and controls, (2) GBTs and BMs, and (3) patients with (PSEs) and without structural epilepsy (PNSEs). Eligible patients were investigated from February 2019 to March 2021. We calculated whole brain (WB) connectivity in six frequency bands, network topological parameters (node degree, average shortest path length, local clustering coefficient) and performed a stratification, where differences in power were identified. For data analysis, we used Fieldtrip, Brain Connectivity MATLAB toolboxes, and in-house built scripts. Results We included 41 patients (21 men), with a mean age of 60.1 years (range 23-82), of those were: GBTs (n = 23), BMs (n = 14), and other histologies (n = 4). Statistical analysis revealed a significantly decreased WB node degree in patients versus controls in every frequency range at the corrected level (p1-30Hz = 0.002, pγ = 0.002, pβ = 0.002, pα = 0.002, pθ = 0.024, and pδ = 0.002). At the descriptive level, we found a significant augmentation for WB local clustering coefficient (p1-30Hz = 0.031, pδ = 0.013) in patients compared to controls, which did not persist the false discovery rate correction. No differences regarding networks of GBTs compared to BMs were identified. However, we found a significant increase in WB local clustering coefficient (pθ = 0.048) and decrease in WB node degree (pα = 0.039) in PSEs versus PNSEs at the uncorrected level. Conclusion Our data suggest that network topology is altered in brain tumor patients. Histology per se might not, however, tumor-related epilepsy seems to influence the brain's functional network. Longitudinal studies and analysis of possible confounders are required to substantiate these findings.
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Affiliation(s)
- Barbara Ladisich
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Department of Neurosurgery, University Hospital St. Poelten, Dunant-Platz 1, St Polten 3100 Austria
- Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Stefan Rampp
- Department of Neurosurgery, Department of Neuroradiology, University Hospital Erlangen, Germany
- Department of Neurosurgery, University Hospital Halle (Saale), Germany
| | - Eugen Trinka
- Department of Neurology, Center for Cognitive Neuroscience Salzburg, Member of the European Reference Network, EpiCARE, Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
- Karl Landsteiner Institute of Neurorehabilitation and Space Neurology, Salzburg, Austria
| | - Nathan Weisz
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
| | - Christoph Schwartz
- Department of Neurosurgery, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
| | - Theo Kraus
- Institute of Pathology, University Hospital Salzburg, Paracelsus Medical University, Salzburg, Austria
| | - Camillo Sherif
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Franz Marhold
- Department of Neurosurgery, University Hospital St. Poelten, Karl Landsteiner University of Health Sciences, Krems an der Donau, Austria
| | - Gianpaolo Demarchi
- Neuroscience Institute, Christian Doppler University Hospital, Salzburg, Austria
- Center for Cognitive Neuroscience & Department of Psychology, Paris Lodron University, Salzburg, Austria
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van Heusden FC, van Nifterick AM, Souza BC, França ASC, Nauta IM, Stam CJ, Scheltens P, Smit AB, Gouw AA, van Kesteren RE. Neurophysiological alterations in mice and humans carrying mutations in APP and PSEN1 genes. Alzheimers Res Ther 2023; 15:142. [PMID: 37608393 PMCID: PMC10464047 DOI: 10.1186/s13195-023-01287-6] [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: 03/10/2023] [Accepted: 08/11/2023] [Indexed: 08/24/2023]
Abstract
BACKGROUND Studies in animal models of Alzheimer's disease (AD) have provided valuable insights into the molecular and cellular processes underlying neuronal network dysfunction. Whether and how AD-related neurophysiological alterations translate between mice and humans remains however uncertain. METHODS We characterized neurophysiological alterations in mice and humans carrying AD mutations in the APP and/or PSEN1 genes, focusing on early pre-symptomatic changes. Longitudinal local field potential recordings were performed in APP/PS1 mice and cross-sectional magnetoencephalography recordings in human APP and/or PSEN1 mutation carriers. All recordings were acquired in the left frontal cortex, parietal cortex, and hippocampus. Spectral power and functional connectivity were analyzed and compared with wildtype control mice and healthy age-matched human subjects. RESULTS APP/PS1 mice showed increased absolute power, especially at higher frequencies (beta and gamma) and predominantly between 3 and 6 moa. Relative power showed an overall shift from lower to higher frequencies over almost the entire recording period and across all three brain regions. Human mutation carriers, on the other hand, did not show changes in power except for an increase in relative theta power in the hippocampus. Mouse parietal cortex and hippocampal power spectra showed a characteristic peak at around 8 Hz which was not significantly altered in transgenic mice. Human power spectra showed a characteristic peak at around 9 Hz, the frequency of which was significantly reduced in mutation carriers. Significant alterations in functional connectivity were detected in theta, alpha, beta, and gamma frequency bands, but the exact frequency range and direction of change differed for APP/PS1 mice and human mutation carriers. CONCLUSIONS Both mice and humans carrying APP and/or PSEN1 mutations show abnormal neurophysiological activity, but several measures do not translate one-to-one between species. Alterations in absolute and relative power in mice should be interpreted with care and may be due to overexpression of amyloid in combination with the absence of tau pathology and cholinergic degeneration. Future studies should explore whether changes in brain activity in other AD mouse models, for instance, those also including tau pathology, provide better translation to the human AD continuum.
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Affiliation(s)
- Fran C van Heusden
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands
| | - Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Bryan C Souza
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, 6525AJ, The Netherlands
| | - Arthur S C França
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, 6525AJ, The Netherlands
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Amsterdam, 1105 BA, The Netherlands
| | - Ilse M Nauta
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - August B Smit
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, 1081HV, The Netherlands
| | - Ronald E van Kesteren
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, 1081HV, The Netherlands.
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Casagrande CC, Rempe MP, Springer SD, Wilson TW. Comprehensive review of task-based neuroimaging studies of cognitive deficits in Alzheimer's disease using electrophysiological methods. Ageing Res Rev 2023; 88:101950. [PMID: 37156399 PMCID: PMC10261850 DOI: 10.1016/j.arr.2023.101950] [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: 12/16/2022] [Revised: 03/27/2023] [Accepted: 05/05/2023] [Indexed: 05/10/2023]
Abstract
With an aging population, cognitive decline and neurodegenerative disorders are an emerging public health crises with enormous, yet still under-recognized burdens. Alzheimer's disease (AD) is the most common type of dementia, and the number of cases is expected to dramatically rise in the upcoming decades. Substantial efforts have been placed into understanding the disease. One of the primary avenues of research is neuroimaging, and while positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) are most common, crucial recent advancements in electrophysiological methods such as magnetoencephalography (MEG) and electroencephalography (EEG) have provided novel insight into the aberrant neural dynamics at play in AD pathology. In this review, we outline task-based M/EEG studies published since 2010 using paradigms probing the cognitive domains most affected by AD, including memory, attention, and executive functioning. Furthermore, we provide important recommendations for adapting cognitive tasks for optimal use in this population and adjusting recruitment efforts to improve and expand future neuroimaging work.
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Affiliation(s)
- Chloe C Casagrande
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA
| | - Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Seth D Springer
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE 68010, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE 68178, USA.
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10
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Rempe MP, Wiesman AI, Murman DL, May PE, Christopher-Hayes NJ, Wolfson SL, Johnson CM, Wilson TW. Sleep quality differentially modulates neural oscillations and proteinopathy in Alzheimer's disease. EBioMedicine 2023; 92:104610. [PMID: 37182265 PMCID: PMC10200835 DOI: 10.1016/j.ebiom.2023.104610] [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: 01/23/2023] [Revised: 04/21/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
BACKGROUND Alterations in resting-state neural activity have been reported in people with sleep disruptions and in patients with Alzheimer's disease, but the direct impact of sleep quality on Alzheimer's disease-related neurophysiological aberrations is unclear. METHODS We collected cross-sectional resting-state magnetoencephalography and extensive neuropsychological and clinical data from 38 biomarker-confirmed patients on the Alzheimer's disease spectrum and 20 cognitively normal older control participants. Sleep efficiency was quantified using the Pittsburgh Sleep Quality Index. FINDINGS Neural activity in the delta frequency range was differentially affected by poor sleep in patients on the Alzheimer's disease spectrum. Such neural changes were related to processing speed abilities and regional amyloid accumulation, and these associations were mediated and moderated, respectively, by sleep quality. INTERPRETATION Together, our results point to a mechanistic role for sleep disturbances in the widely reported neurophysiological aberrations seen in patients on the Alzheimer's disease spectrum, with implications for basic research and clinical intervention. FUNDING National Institutes of Health, USA.
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Affiliation(s)
- Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA; University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA
| | - Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, Quebec, H3A 0G4, Canada.
| | - Daniel L Murman
- University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA
| | - Pamela E May
- University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA
| | - Nicholas J Christopher-Hayes
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA; Center for Mind and Brain, University of California, Davis, CA, 95618, USA
| | - Sara L Wolfson
- University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA
| | - Craig M Johnson
- University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, 68010, USA; University of Nebraska Medical Center (UNMC), College of Medicine, Omaha, NE, 68198, USA; Department of Pharmacology and Neuroscience, Creighton University, Omaha, NE, 68178 USA
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11
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van Nifterick AM, Mulder D, Duineveld DJ, Diachenko M, Scheltens P, Stam CJ, van Kesteren RE, Linkenkaer-Hansen K, Hillebrand A, Gouw AA. Resting-state oscillations reveal disturbed excitation-inhibition ratio in Alzheimer's disease patients. Sci Rep 2023; 13:7419. [PMID: 37150756 PMCID: PMC10164744 DOI: 10.1038/s41598-023-33973-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023] Open
Abstract
An early disruption of neuronal excitation-inhibition (E-I) balance in preclinical animal models of Alzheimer's disease (AD) has been frequently reported, but is difficult to measure directly and non-invasively in humans. Here, we examined known and novel neurophysiological measures sensitive to E-I in patients across the AD continuum. Resting-state magnetoencephalography (MEG) data of 86 amyloid-biomarker-confirmed subjects across the AD continuum (17 patients diagnosed with subjective cognitive decline, 18 with mild cognitive impairment (MCI) and 51 with dementia due to probable AD (AD dementia)), 46 healthy elderly and 20 young control subjects were reconstructed to source-space. E-I balance was investigated by detrended fluctuation analysis (DFA), a functional E/I (fE/I) algorithm, and the aperiodic exponent of the power spectrum. We found a disrupted E-I ratio in AD dementia patients specifically, by a lower DFA, and a shift towards higher excitation, by a higher fE/I and a lower aperiodic exponent. Healthy subjects showed lower fE/I ratios (< 1.0) than reported in previous literature, not explained by age or choice of an arbitrary threshold parameter, which warrants caution in interpretation of fE/I results. Correlation analyses showed that a lower DFA (E-I imbalance) and a lower aperiodic exponent (more excitation) was associated with a worse cognitive score in AD dementia patients. In contrast, a higher DFA in the hippocampi of MCI patients was associated with a worse cognitive score. This MEG-study showed E-I imbalance, likely due to increased excitation, in AD dementia, but not in early stage AD patients. To accurately determine the direction of shift in E-I balance, validations of the currently used markers and additional in vivo markers of E-I are required.
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Affiliation(s)
- Anne M van Nifterick
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands.
| | - Danique Mulder
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Denise J Duineveld
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Marina Diachenko
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Ronald E van Kesteren
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
- Department of Molecular and Cellular Neurobiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Klaus Linkenkaer-Hansen
- Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Systems and Network Neurosciences, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Clinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
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12
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Brown J, Camporesi E, Lantero-Rodriguez J, Olsson M, Wang A, Medem B, Zetterberg H, Blennow K, Karikari TK, Wall M, Hill E. Tau in cerebrospinal fluid induces neuronal hyperexcitability and alters hippocampal theta oscillations. Acta Neuropathol Commun 2023; 11:67. [PMID: 37095572 PMCID: PMC10127378 DOI: 10.1186/s40478-023-01562-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 04/03/2023] [Indexed: 04/26/2023] Open
Abstract
Alzheimer's disease (AD) and other tauopathies are characterized by the aggregation of tau into soluble and insoluble forms (including tangles and neuropil threads). In humans, a fraction of both phosphorylated and non-phosphorylated N-terminal to mid-domain tau species, are secreted into cerebrospinal fluid (CSF). Some of these CSF tau species can be measured as diagnostic and prognostic biomarkers, starting from early stages of disease. While in animal models of AD pathology, soluble tau aggregates have been shown to disrupt neuronal function, it is unclear whether the tau species present in CSF will modulate neural activity. Here, we have developed and applied a novel approach to examine the electrophysiological effects of CSF from patients with a tau-positive biomarker profile. The method involves incubation of acutely-isolated wild-type mouse hippocampal brain slices with small volumes of diluted human CSF, followed by a suite of electrophysiological recording methods to evaluate their effects on neuronal function, from single cells through to the network level. Comparison of the toxicity profiles of the same CSF samples, with and without immuno-depletion for tau, has enabled a pioneering demonstration that CSF-tau potently modulates neuronal function. We demonstrate that CSF-tau mediates an increase in neuronal excitability in single cells. We then observed, at the network level, increased input-output responses and enhanced paired-pulse facilitation as well as an increase in long-term potentiation. Finally, we show that CSF-tau modifies the generation and maintenance of hippocampal theta oscillations, which have important roles in learning and memory and are known to be altered in AD patients. Together, we describe a novel method for screening human CSF-tau to understand functional effects on neuron and network activity, which could have far-reaching benefits in understanding tau pathology, thus allowing for the development of better targeted treatments for tauopathies in the future.
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Affiliation(s)
- Jessica Brown
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PL, UK
| | - Elena Camporesi
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
| | - Juan Lantero-Rodriguez
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
| | - Maria Olsson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
| | - Alice Wang
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Blanca Medem
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, WC1E 6BT, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, WC1E 6BT, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin, Madison, WI, 53792, USA
- School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, 43180, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 43180, Mölndal, Sweden
- Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Mark Wall
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK
| | - Emily Hill
- School of Life Sciences, University of Warwick, Coventry, CV4 7AL, UK.
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13
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Prado P, Moguilner S, Mejía JA, Sainz-Ballesteros A, Otero M, Birba A, Santamaria-Garcia H, Legaz A, Fittipaldi S, Cruzat J, Tagliazucchi E, Parra M, Herzog R, Ibáñez A. Source space connectomics of neurodegeneration: One-metric approach does not fit all. Neurobiol Dis 2023; 179:106047. [PMID: 36841423 PMCID: PMC11170467 DOI: 10.1016/j.nbd.2023.106047] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 02/05/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023] Open
Abstract
Brain functional connectivity in dementia has been assessed with dissimilar EEG connectivity metrics and estimation procedures, thereby increasing results' heterogeneity. In this scenario, joint analyses integrating information from different metrics may allow for a more comprehensive characterization of brain functional interactions in different dementia subtypes. To test this hypothesis, resting-state electroencephalogram (rsEEG) was recorded in individuals with Alzheimer's Disease (AD), behavioral variant frontotemporal dementia (bvFTD), and healthy controls (HCs). Whole-brain functional connectivity was estimated in the EEG source space using 101 different types of functional connectivity, capturing linear and nonlinear interactions in both time and frequency-domains. Multivariate machine learning and progressive feature elimination was run to discriminate AD from HCs, and bvFTD from HCs, based on joint analyses of i) EEG frequency bands, ii) complementary frequency-domain metrics (e.g., instantaneous, lagged, and total connectivity), and iii) time-domain metrics with different linearity assumption (e.g., Pearson correlation coefficient and mutual information). <10% of all possible connections were responsible for the differences between patients and controls, and atypical connectivity was never captured by >1/4 of all possible connectivity measures. Joint analyses revealed patterns of hypoconnectivity (patientsHCs) in both groups was mainly identified in frontotemporal regions. These atypicalities were differently captured by frequency- and time-domain connectivity metrics, in a bandwidth-specific fashion. The multi-metric representation of source space whole-brain functional connectivity evidenced the inadequacy of single-metric approaches, and resulted in a valid alternative for the selection problem in EEG connectivity. These joint analyses reveal patterns of brain functional interdependence that are overlooked with single metrics approaches, contributing to a more reliable and interpretable description of atypical functional connectivity in neurodegeneration.
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Affiliation(s)
- Pavel Prado
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Escuela de Fonoaudiología, Facultad de Odontología y Ciencias de la Rehabilitación, Universidad San Sebastián, Santiago, Chile
| | - Sebastian Moguilner
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Jhony A Mejía
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Ingeniería Biomédica, Universidad de Los Andes, Bogotá, Colombia
| | | | - Mónica Otero
- Facultad de Ingeniería, Arquitectura y Diseño, Universidad San Sebastián, Santiago, Chile; Centro BASAL Ciencia & Vida, Universidad San Sebastián, Santiago, Chile
| | - Agustina Birba
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina
| | - Hernando Santamaria-Garcia
- PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Global Brain Health Institute, University of California San Francisco, San Francisco, California; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland
| | - Agustina Legaz
- Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Sol Fittipaldi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; Global Brain Health Institute, Trinity College Dublin, Dublin, Ireland; National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Josephine Cruzat
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
| | - Enzo Tagliazucchi
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Departamento de Física, Universidad de Buenos Aires and Instituto de Física de Buenos Aires (IFIBA -CONICET), Buenos Aires, Argentina
| | - Mario Parra
- School of Psychological Sciences and Health, University of Strathclyde, Glasgow, United Kingdom
| | - Rubén Herzog
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Fundación para el Estudio de la Conciencia Humana (EcoH), Chile
| | - Agustín Ibáñez
- Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile; Cognitive Neuroscience Center (CNC), Universidad de San Andrés & CONICET, Buenos Aires, Argentina; PhD Neuroscience Program, Physiology and Psychiatry Departments, Pontificia Universidad Javeriana, Bogotá, Colombia; Memory and Cognition Center Intellectus, Hospital Universitario San Ignacio, Bogotá, Colombia; Trinity College Dublin (TCD), Dublin, Ireland.
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14
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van Nifterick AM, Gouw AA, van Kesteren RE, Scheltens P, Stam CJ, de Haan W. A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing. Alzheimers Res Ther 2022; 14:101. [PMID: 35879779 PMCID: PMC9310500 DOI: 10.1186/s13195-022-01041-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 07/02/2022] [Indexed: 01/30/2023]
Abstract
Background Neuronal hyperexcitability and inhibitory interneuron dysfunction are frequently observed in preclinical animal models of Alzheimer’s disease (AD). This study investigates whether these microscale abnormalities explain characteristic large-scale magnetoencephalography (MEG) activity in human early-stage AD patients. Methods To simulate spontaneous electrophysiological activity, we used a whole-brain computational network model comprised of 78 neural masses coupled according to human structural brain topology. We modified relevant model parameters to simulate six literature-based cellular scenarios of AD and compare them to one healthy and six contrast (non-AD-like) scenarios. The parameters include excitability, postsynaptic potentials, and coupling strength of excitatory and inhibitory neuronal populations. Whole-brain spike density and spectral power analyses of the simulated data reveal mechanisms of neuronal hyperactivity that lead to oscillatory changes similar to those observed in MEG data of 18 human prodromal AD patients compared to 18 age-matched subjects with subjective cognitive decline. Results All but one of the AD-like scenarios showed higher spike density levels, and all but one of these scenarios had a lower peak frequency, higher spectral power in slower (theta, 4–8Hz) frequencies, and greater total power. Non-AD-like scenarios showed opposite patterns mainly, including reduced spike density and faster oscillatory activity. Human AD patients showed oscillatory slowing (i.e., higher relative power in the theta band mainly), a trend for lower peak frequency and higher total power compared to controls. Combining model and human data, the findings indicate that neuronal hyperactivity can lead to oscillatory slowing, likely due to hyperexcitation (by hyperexcitability of pyramidal neurons or greater long-range excitatory coupling) and/or disinhibition (by reduced excitability of inhibitory interneurons or weaker local inhibitory coupling strength) in early AD. Conclusions Using a computational brain network model, we link findings from different scales and models and support the hypothesis of early-stage neuronal hyperactivity underlying E/I imbalance and whole-brain network dysfunction in prodromal AD. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-01041-4.
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15
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Rempe MP, Lew BJ, Embury CM, Christopher-Hayes NJ, Schantell M, Wilson TW. Spontaneous sensorimotor beta power and cortical thickness uniquely predict motor function in healthy aging. Neuroimage 2022; 263:119651. [PMID: 36206940 PMCID: PMC10071137 DOI: 10.1016/j.neuroimage.2022.119651] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Accepted: 09/23/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Spontaneous beta activity in the primary motor cortices has been shown to increase in amplitude with advancing age, and that such increases are tightly coupled to stronger motor-related beta oscillations during movement planning. However, the relationship between these age-related changes in spontaneous beta in the motor cortices, local cortical thickness, and overall motor function remains unclear. METHODS We collected resting-state magnetoencephalography (MEG), high-resolution structural MRI, and motor function scores using a neuropsychological battery from 126 healthy adults (56 female; age range = 22-72 years). MEG data were source-imaged and a whole-brain vertex-wise regression model was used to assess age-related differences in spontaneous beta power across the cortex. Cortical thickness was computed from the structural MRI data and local beta power and cortical thickness values were extracted from the sensorimotor cortices. To determine the unique contribution of age, spontaneous beta power, and cortical thickness to the prediction of motor function, a hierarchical regression approach was used. RESULTS There was an increase in spontaneous beta power with age across the cortex, with the strongest increase being centered on the sensorimotor cortices. Sensorimotor cortical thickness was not related to spontaneous beta power, above and beyond age. Interestingly, both cortical thickness and spontaneous beta power in sensorimotor regions each uniquely contributed to the prediction of motor function when controlling for age. DISCUSSION This multimodal study showed that cortical thickness and spontaneous beta activity in the sensorimotor cortices have dissociable contributions to motor function across the adult lifespan. These findings highlight the complexity of interactions between structure and function and the importance of understanding these interactions in order to advance our understanding of healthy aging and disease.
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Affiliation(s)
- Maggie P Rempe
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Brandon J Lew
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Christine M Embury
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Department of Psychology, University of Nebraska - Omaha (UNO), Omaha, NE, USA
| | - Nicholas J Christopher-Hayes
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; Center for Mind and Brain, University of California - Davis, Davis, CA, USA
| | - Mikki Schantell
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center (UNMC), Omaha, NE, USA; Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA.
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16
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Scheijbeler EP, van Nifterick AM, Stam CJ, Hillebrand A, Gouw AA, de Haan W. Network-level permutation entropy of resting-state MEG recordings: A novel biomarker for early-stage Alzheimer's disease? Netw Neurosci 2022; 6:382-400. [PMID: 35733433 PMCID: PMC9208018 DOI: 10.1162/netn_a_00224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/15/2021] [Indexed: 11/24/2022] Open
Abstract
Increasing evidence suggests that measures of signal variability and complexity could present promising biomarkers for Alzheimer's disease (AD). Earlier studies have however been limited to the characterization of local activity. Here, we investigate whether a network version of permutation entropy could serve as a novel biomarker for early-stage AD. Resting-state source-space magnetoencephalography was recorded in 18 subjects with subjective cognitive decline (SCD) and 18 subjects with mild cognitive impairment (MCI). Local activity was characterized by permutation entropy (PE). Network-level interactions were studied using the inverted joint permutation entropy (JPEinv), corrected for volume conduction. The JPEinv showed a reduction of nonlinear connectivity in MCI subjects in the theta and alpha band. Local PE showed increased theta band entropy. Between-group differences were widespread across brain regions. Receiver operating characteristic (ROC) analysis of classification of MCI versus SCD subjects revealed that a logistic regression model trained on JPEinv features (78.4% [62.5-93.3%]) slightly outperformed PE (76.9% [60.3-93.4%]) and relative theta power-based models (76.9% [60.4-93.3%]). Classification performance of theta JPEinv was at least as good as the relative theta power benchmark. The JPEinv is therefore a potential biomarker for early-stage AD that should be explored in larger studies.
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Affiliation(s)
- Elliz P. Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Anne M. van Nifterick
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Cornelis J. Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Alida A. Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Willem de Haan
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Vaghari D, Kabir E, Henson RN. Late combination shows that MEG adds to MRI in classifying MCI versus controls. Neuroimage 2022; 252:119054. [PMID: 35247546 PMCID: PMC8987738 DOI: 10.1016/j.neuroimage.2022.119054] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2021] [Revised: 02/20/2022] [Accepted: 03/01/2022] [Indexed: 12/12/2022] Open
Abstract
Early detection of Alzheimer's disease (AD) is essential for developing effective treatments. Neuroimaging techniques like Magnetic Resonance Imaging (MRI) have the potential to detect brain changes before symptoms emerge. Structural MRI can detect atrophy related to AD, but it is possible that functional changes are observed even earlier. We therefore examined the potential of Magnetoencephalography (MEG) to detect differences in functional brain activity in people with Mild Cognitive Impairment (MCI) - a state at risk of early AD. We introduce a framework for multimodal combination to ask whether MEG data from a resting-state provides complementary information beyond structural MRI data in the classification of MCI versus controls. More specifically, we used multi-kernel learning of support vector machines to classify 163 MCI cases versus 144 healthy elderly controls from the BioFIND dataset. When using the covariance of planar gradiometer data in the low Gamma range (30-48 Hz), we found that adding a MEG kernel improved classification accuracy above kernels that captured several potential confounds (e.g., age, education, time-of-day, head motion). However, accuracy using MEG alone (68%) was worse than MRI alone (71%). When simply concatenating (normalized) features from MEG and MRI into one kernel (Early combination), there was no advantage of combining MEG with MRI versus MRI alone. When combining kernels of modality-specific features (Intermediate combination), there was an improvement in multimodal classification to 74%. The biggest multimodal improvement however occurred when we combined kernels from the predictions of modality-specific classifiers (Late combination), which achieved 77% accuracy (a reliable improvement in terms of permutation testing). We also explored other MEG features, such as the variance versus covariance of magnetometer versus planar gradiometer data within each of 6 frequency bands (delta, theta, alpha, beta, low gamma, or high gamma), and found that they generally provided complementary information for classification above MRI. We conclude that MEG can improve on the MRI-based classification of MCI.
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Affiliation(s)
- Delshad Vaghari
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsanollah Kabir
- Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
| | - Richard N Henson
- MRC Cognition and Brain Sciences Unit, University of Cambridge, UK; Department of Psychiatry, University of Cambridge, UK.
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18
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Trevarrow MP, Reelfs A, Ott LR, Penhale SH, Lew BJ, Goeller J, Wilson TW, Kurz MJ. Altered spontaneous cortical activity predicts pain perception in individuals with cerebral palsy. Brain Commun 2022; 4:fcac087. [PMID: 35441137 PMCID: PMC9014448 DOI: 10.1093/braincomms/fcac087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 01/13/2022] [Accepted: 03/31/2022] [Indexed: 12/04/2022] Open
Abstract
Cerebral palsy is the most common paediatric neurological disorder and results in extensive impairment to the sensorimotor system. However, these individuals also experience increased pain perception, resulting in decreased quality of life. In the present study, we utilized magnetoencephalographic brain imaging to examine whether alterations in spontaneous neural activity predict the level of pain experienced in a cohort of 38 individuals with spastic diplegic cerebral palsy and 67 neurotypical controls. Participants completed 5 min of an eyes closed resting-state paradigm while undergoing a magnetoencephalography recording. The magnetoencephalographic data were then source imaged, and the power within the delta (2–4 Hz), theta (5–7 Hz), alpha (8–12 Hz), beta (15–29 Hz), low gamma (30–59 Hz) and high gamma (60–90 Hz) frequency bands were computed. The resulting power spectral density maps were analysed vertex-wise to identify differences in spontaneous activity between groups. Our findings indicated that spontaneous cortical activity was altered in the participants with cerebral palsy in the delta, alpha, beta, low gamma and high gamma bands across the occipital, frontal and secondary somatosensory cortical areas (all pFWE < 0.05). Furthermore, we also found that the altered beta band spontaneous activity in the secondary somatosensory cortices predicted heightened pain perception in the individuals with cerebral palsy (P = 0.039). Overall, these results demonstrate that spontaneous cortical activity within individuals with cerebral palsy is altered in comparison to their neurotypical peers and may predict increased pain perception in this patient population. Potentially, changes in spontaneous resting-state activity may be utilized to measure the effectiveness of current treatment approaches that are directed at reducing the pain experienced by individuals with cerebral palsy.
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Affiliation(s)
- Michael P. Trevarrow
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | - Anna Reelfs
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | - Lauren R. Ott
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | - Samantha H. Penhale
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | - Brandon J. Lew
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | - Jessica Goeller
- Department of Anesthesiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Tony W. Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
| | - Max J. Kurz
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
- Department of Pharmacology & Neuroscience, Creighton University, Omaha, NE, USA
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19
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Schoonhoven DN, Briels CT, Hillebrand A, Scheltens P, Stam CJ, Gouw AA. Sensitive and reproducible MEG resting-state metrics of functional connectivity in Alzheimer's disease. Alzheimers Res Ther 2022; 14:38. [PMID: 35219327 PMCID: PMC8881826 DOI: 10.1186/s13195-022-00970-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 01/30/2022] [Indexed: 01/08/2023]
Abstract
Background Analysis of functional brain networks in Alzheimer’s disease (AD) has been hampered by a lack of reproducible, yet valid metrics of functional connectivity (FC). This study aimed to assess both the sensitivity and reproducibility of the corrected amplitude envelope correlation (AEC-c) and phase lag index (PLI), two metrics of FC that are insensitive to the effects of volume conduction and field spread, in two separate cohorts of patients with dementia due to AD versus healthy elderly controls. Methods Subjects with a clinical diagnosis of AD dementia with biomarker proof, and a control group of subjective cognitive decline (SCD), underwent two 5-min resting-state MEG recordings. Data consisted of a test (AD = 28; SCD = 29) and validation (AD = 29; SCD = 27) cohort. Time-series were estimated for 90 regions of interest (ROIs) in the automated anatomical labelling (AAL) atlas. For each of five canonical frequency bands, the AEC-c and PLI were calculated between all 90 ROIs, and connections were averaged per ROI. General linear models were constructed to compare the global FC differences between the groups, assess the reproducibility, and evaluate the effects of age and relative power. Reproducibility of the regional FC differences was assessed using the Mann-Whitney U tests, with correction for multiple testing using the false discovery rate (FDR). Results The AEC-c showed significantly and reproducibly lower global FC for the AD group compared to SCD, in the alpha (8–13 Hz) and beta (13–30 Hz) bands, while the PLI revealed reproducibly lower FC for the AD group in the delta (0.5–4 Hz) band and higher FC for the theta (4–8 Hz) band. Regionally, the beta band AEC-c showed reproducibility for almost all ROIs (except for 13 ROIs in the frontal and temporal lobes). For the other bands, the AEC-c and PLI did not show regional reproducibility after FDR correction. The theta band PLI was susceptible to the effect of relative power. Conclusion For MEG, the AEC-c is a sensitive and reproducible metric, able to distinguish FC differences between patients with AD dementia and cognitively healthy controls. These two measures likely reflect different aspects of neural activity and show differential sensitivity to changes in neural dynamics. Supplementary Information The online version contains supplementary material available at 10.1186/s13195-022-00970-4.
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Affiliation(s)
- Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. .,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Casper T Briels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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20
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Xing J, Jia J, Wu X, Kuang L. A Spatiotemporal Brain Network Analysis of Alzheimer's Disease Based on Persistent Homology. Front Aging Neurosci 2022; 14:788571. [PMID: 35221988 PMCID: PMC8864674 DOI: 10.3389/fnagi.2022.788571] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/10/2022] [Indexed: 11/15/2022] Open
Abstract
Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer's disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer's Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.
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Affiliation(s)
- Jiacheng Xing
- School of Data Science and Technology, North University of China, Taiyuan, China
- Department of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Jiaying Jia
- School of Data Science and Technology, North University of China, Taiyuan, China
| | - Xin Wu
- Department of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Liqun Kuang
- School of Data Science and Technology, North University of China, Taiyuan, China
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21
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Núñez P, Gomez C, Rodríguez-González V, Hillebrand A, Tewarie P, Gomez-Pilar J, Molina V, Hornero R, Poza J. Schizophrenia induces abnormal frequency-dependent patterns of dynamic brain network reconfiguration during an auditory oddball task. J Neural Eng 2022; 19. [PMID: 35108688 DOI: 10.1088/1741-2552/ac514e] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 02/02/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Schizophrenia is a psychiatric disorder that has been shown to disturb the dynamic top-down processing of sensory information. Various imaging techniques have revealed abnormalities in brain activity associated with this disorder, both locally and between cerebral regions. However, there is increasing interest in investigating dynamic network response to novel and relevant events at the network level during an attention-demanding task with high-temporal-resolution techniques. The aim of the work was: (i) to test the capacity of a novel algorithm to detect recurrent brain meta-states from auditory oddball task recordings; and (ii) to evaluate how the dynamic activation and behavior of the aforementioned meta-states were altered in schizophrenia, since it has been shown to impair top-down processing of sensory information. APPROACH A novel unsupervised method for the detection of brain meta-states based on recurrence plots and community detection algorithms, previously tested on resting-state data, was used on auditory oddball task recordings. Brain meta-states and several properties related to their activation during target trials in the task were extracted from electroencephalography (EEG) data from patients with schizophrenia and cognitively healthy controls. MAIN RESULTS The methodology successfully detected meta-states during an auditory oddball task, and they appeared to show both frequency-dependent time-locked and non-time-locked activity with respect to the stimulus onset. Moreover, patients with schizophrenia displayed higher network diversity, and showed more sluggish meta-state transitions, reflected in increased dwell times, less complex meta-state sequences, decreased meta-state space speed, and abnormal ratio of negative meta-state correlations. SIGNIFICANCE Abnormal cognition in schizophrenia is also reflected in decreased brain flexibility at the dynamic network level, which may hamper top-down processing, possibly indicating impaired decision-making linked to dysfunctional predictive coding. Moreover, the results showed the ability of the methodology to find meaningful and task-relevant changes in dynamic connectivity and pathology-related group differences.
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Affiliation(s)
- Pablo Núñez
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
| | - Carlos Gomez
- Grupo de Ingeniería Biomédica, Universidad de Valladolid, E. T. S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo Belén, 15, Valladolid, Valladolid, 47011, SPAIN
| | - Víctor Rodríguez-González
- Teoría de la señal y comunicaciones e ingeniería telemática, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47011, SPAIN
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, 1081 HV, NETHERLANDS
| | - Prejaas Tewarie
- Department of Clinical Neurophysiology and MEG Centre, VU University Medical Centre Amsterdam, VU University Medical Centre, 1081 HV Amsterdam, Netherlands, Amsterdam, Noord-Holland, 1081 HV, NETHERLANDS
| | - Javier Gomez-Pilar
- Communications and Signal Theory, Universidad de Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, Valladolid, 47011, SPAIN
| | - Vicente Molina
- Universidad de Valladolid, School of Medicine, University of Valladolid, 47005 - Valladolid, Valladolid, 47002, SPAIN
| | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, ETSI Telecomunicacion, Paseo Belen 15, Valladolid, 47011, SPAIN
| | - Jesus Poza
- Communications and Signal Theory, University of Valladolid, E.T.S. Ingenieros de Telecomunicacion, Paseo de Belen 15, 47011 - Valladolid, Valladolid, 47002, SPAIN
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22
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Wiesman AI, Murman DL, Losh RA, Schantell M, Christopher-Hayes NJ, Johnson HJ, Willett MP, Wolfson SL, Losh KL, Johnson CM, May PE, Wilson TW. Spatially resolved neural slowing predicts impairment and amyloid burden in Alzheimer's disease. Brain 2022; 145:2177-2189. [PMID: 35088842 PMCID: PMC9246709 DOI: 10.1093/brain/awab430] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 10/05/2021] [Accepted: 10/24/2021] [Indexed: 11/28/2022] Open
Abstract
An extensive electrophysiological literature has proposed a pathological ‘slowing’ of neuronal activity in patients on the Alzheimer’s disease spectrum. Supported by numerous studies reporting increases in low-frequency and decreases in high-frequency neural oscillations, this pattern has been suggested as a stable biomarker with potential clinical utility. However, no spatially resolved metric of such slowing exists, stymieing efforts to understand its relation to proteinopathy and clinical outcomes. Further, the assumption that this slowing is occurring in spatially overlapping populations of neurons has not been empirically validated. In the current study, we collected cross-sectional resting state measures of neuronal activity using magnetoencephalography from 38 biomarker-confirmed patients on the Alzheimer’s disease spectrum and 20 cognitively normal biomarker-negative older adults. From these data, we compute and validate a new metric of spatially resolved oscillatory deviations from healthy ageing for each patient on the Alzheimer’s disease spectrum. Using this Pathological Oscillatory Slowing Index, we show that patients on the Alzheimer’s disease spectrum exhibit robust neuronal slowing across a network of temporal, parietal, cerebellar and prefrontal cortices. This slowing effect is shown to be directly relevant to clinical outcomes, as oscillatory slowing in temporal and parietal cortices significantly predicted both general (i.e. Montreal Cognitive Assessment scores) and domain-specific (i.e. attention, language and processing speed) cognitive function. Further, regional amyloid-β accumulation, as measured by quantitative 18F florbetapir PET, robustly predicted the magnitude of this pathological neural slowing effect, and the strength of this relationship between amyloid-β burden and neural slowing also predicted attentional impairments across patients. These findings provide empirical support for a spatially overlapping effect of oscillatory neural slowing in biomarker-confirmed patients on the Alzheimer’s disease spectrum, and link this effect to both regional proteinopathy and cognitive outcomes in a spatially resolved manner. The Pathological Oscillatory Slowing Index also represents a novel metric that is of potentially high utility across a number of clinical neuroimaging applications, as oscillatory slowing has also been extensively documented in other patient populations, most notably Parkinson’s disease, with divergent spectral and spatial features.
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Affiliation(s)
- Alex I Wiesman
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada.,Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA.,Memory Disorders & Behavioral Neurology Program, UNMC, Omaha, NE, USA
| | - Rebecca A Losh
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
| | - Mikki Schantell
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
| | | | - Hallie J Johnson
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
| | - Madelyn P Willett
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
| | | | - Kathryn L Losh
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
| | | | - Pamela E May
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience,Boys Town National Research Hospital, Omaha, NE, USA
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23
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Differential associations between neocortical tau pathology and blood flow with cognitive deficits in early-onset vs late-onset Alzheimer's disease. Eur J Nucl Med Mol Imaging 2022; 49:1951-1963. [PMID: 34997294 PMCID: PMC9016024 DOI: 10.1007/s00259-021-05669-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 12/20/2021] [Indexed: 12/23/2022]
Abstract
Purpose Early-onset Alzheimer’s disease (EOAD) and late-onset Alzheimer’s disease (LOAD) differ in neuropathological burden and type of cognitive deficits. Assessing tau pathology and relative cerebral blood flow (rCBF) measured with [18F]flortaucipir PET in relation to cognition may help explain these differences between EOAD and LOAD. Methods Seventy-nine amyloid-positive individuals with a clinical diagnosis of AD (EOAD: n = 35, age-at-PET = 59 ± 5, MMSE = 23 ± 4; LOAD: n = 44, age-at-PET = 71 ± 5, MMSE = 23 ± 4) underwent a 130-min dynamic [18F]flortaucipir PET scan and extensive neuropsychological assessment. We extracted binding potentials (BPND) and R1 (proxy of rCBF) from parametric images using receptor parametric mapping, in medial and lateral temporal, parietal, occipital, and frontal regions-of-interest and used nine neuropsychological tests covering memory, attention, language, and executive functioning. We first examined differences between EOAD and LOAD in BPND or R1 using ANOVA (region-of-interest analysis) and voxel-wise contrasts. Next, we performed linear regression models to test for potential interaction effects between age-at-onset and BPND/R1 on cognition. Results Both region-of-interest and voxel-wise contrasts showed higher [18F]flortaucipir BPND values across all neocortical regions in EOAD. By contrast, LOAD patients had lower R1 values (indicative of more reduced rCBF) in medial temporal regions. For both tau and flow in lateral temporal, and occipitoparietal regions, associations with cognitive impairment were stronger in EOAD than in LOAD (EOAD BPND − 0.76 ≤ stβ ≤ − 0.48 vs LOAD − 0.18 ≤ stβ ≤ − 0.02; EOAD R1 0.37 ≤ stβ ≤ 0.84 vs LOAD − 0.25 ≤ stβ ≤ 0.16). Conclusions Compared to LOAD, the degree of lateral temporal and occipitoparietal tau pathology and relative cerebral blood-flow is more strongly associated with cognition in EOAD. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05669-6.
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24
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Griffa A, Legdeur N, Badissi M, van den Heuvel MP, Stam CJ, Visser PJ, Hillebrand A. Magnetoencephalography Brain Signatures Relate to Cognition and Cognitive Reserve in the Oldest-Old: The EMIF-AD 90 + Study. Front Aging Neurosci 2021; 13:746373. [PMID: 34899269 PMCID: PMC8656941 DOI: 10.3389/fnagi.2021.746373] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 11/01/2021] [Indexed: 11/25/2022] Open
Abstract
The oldest-old subjects represent the fastest growing segment of society and are at high risk for dementia with a prevalence of up to 40%. Lifestyle factors, such as lifelong participation in cognitive and leisure activities, may contribute to individual cognitive reserve and reduce the risk for cognitive impairments. However, the neural bases underlying cognitive functioning and cognitive reserve in this age range are still poorly understood. Here, we investigate spectral and functional connectivity features obtained from resting-state MEG recordings in a cohort of 35 cognitively normal (92.2 ± 1.8 years old, 19 women) and 11 cognitively impaired (90.9 ± 1.9 years old, 1 woman) oldest-old participants, in relation to cognitive traits and cognitive reserve. The latter was approximated with a self-reported scale on lifelong engagement in cognitively demanding activities. Cognitively impaired oldest-old participants had slower cortical rhythms in frontal, parietal and default mode network regions compared to the cognitively normal subjects. These alterations mainly concerned the theta and beta band and partially explained inter-subject variability of episodic memory scores. Moreover, a distinct spectral pattern characterized by higher relative power in the alpha band was specifically associated with higher cognitive reserve while taking into account the effect of age and education level. Finally, stronger functional connectivity in the alpha and beta band were weakly associated with better cognitive performances in the whole group of subjects, although functional connectivity effects were less prominent than the spectral ones. Our results shed new light on the neural underpinnings of cognitive functioning in the oldest-old population and indicate that cognitive performance and cognitive reserve may have distinct spectral electrophysiological substrates.
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Affiliation(s)
- Alessandra Griffa
- Division of Neurology, Department of Clinical Neurosciences, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland.,Center of Neuroprosthetics, Institute of Bioengineering, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland.,Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Nienke Legdeur
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Maryam Badissi
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Martijn P van den Heuvel
- Dutch Connectome Lab, Department of Complex Trait Genetics, Center for Neuroscience and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Pieter Jelle Visser
- Department of Neurology, Amsterdam Neuroscience, Alzheimer Center Amsterdam, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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25
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Scheijbeler EP, Schoonhoven DN, Engels MMA, Scheltens P, Stam CJ, Gouw AA, Hillebrand A. Generating diagnostic profiles of cognitive decline and dementia using magnetoencephalography. Neurobiol Aging 2021; 111:82-94. [PMID: 34906377 DOI: 10.1016/j.neurobiolaging.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 10/11/2021] [Accepted: 11/04/2021] [Indexed: 10/19/2022]
Abstract
Accurate identification of the underlying cause(s) of cognitive decline and dementia is challenging due to significant symptomatic overlap between subtypes. This study presents a multi-class classification framework for subjects with subjective cognitive decline, mild cognitive impairment, Alzheimer's disease, dementia with Lewy bodies, fronto-temporal dementia and cognitive decline due to psychiatric illness, trained on source-localized resting-state magnetoencephalography data. Diagnostic profiles, describing probability estimates for each of the 6 diagnoses, were assigned to individual subjects. A balanced accuracy rate of 41% and multi-class area under the curve value of 0.75 were obtained for 6-class classification. Classification primarily depended on posterior relative delta, theta and beta power and amplitude-based functional connectivity in the beta and gamma frequency band. Dementia with Lewy bodies (sensitivity: 100%, precision: 20%) and Alzheimer's disease subjects (sensitivity: 51%, precision: 90%) could be classified most accurately. Fronto-temporal dementia subjects (sensitivity: 11%, precision: 3%) were most frequently misclassified. Magnetoencephalography biomarkers hold promise to increase diagnostic accuracy in a noninvasive manner. Diagnostic profiles could provide an intuitive tool to clinicians and may facilitate implementation of the classifier in the memory clinic.
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Affiliation(s)
- Elliz P Scheijbeler
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marjolein M A Engels
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Cornelis J Stam
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands; Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrij Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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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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27
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Wiesman AI, Mundorf VM, Casagrande CC, Wolfson SL, Johnson CM, May PE, Murman DL, Wilson TW. Somatosensory dysfunction is masked by variable cognitive deficits across patients on the Alzheimer's disease spectrum. EBioMedicine 2021; 73:103638. [PMID: 34689085 PMCID: PMC8550984 DOI: 10.1016/j.ebiom.2021.103638] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/04/2021] [Accepted: 10/06/2021] [Indexed: 11/09/2022] Open
Abstract
Background Alzheimer's disease (AD) is generally thought to spare primary sensory function; however, such interpretations have drawn from a literature that has rarely taken into account the variable cognitive declines seen in patients with AD. As these cognitive domains are now known to modulate cortical somatosensory processing, it remains possible that abnormalities in somatosensory function in patients with AD have been suppressed by neuropsychological variability in previous research. Methods In this study, we combine magnetoencephalographic (MEG) brain imaging during a paired-pulse somatosensory gating task with an extensive battery of neuropsychological tests to investigate the influence of cognitive variability on estimated differences in somatosensory function between biomarker-confirmed patients on the AD spectrum and cognitively-normal older adults. Findings We show that patients on the AD spectrum exhibit largely non-significant differences in somatosensory function when cognitive variability is not considered (p-value range: .020–.842). However, once attention and processing speed abilities are considered, robust differences in gamma-frequency somatosensory response amplitude (p < .001) and gating (p = .004) emerge, accompanied by significant statistical suppression effects. Interpretation These findings suggest that patients with AD exhibit insults to functional somatosensory processing in primary sensory cortices, but these effects are masked by variability in cognitive decline across individuals. Funding National Institutes of Health, USA; Fremont Area Alzheimer's Fund, USA
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Affiliation(s)
- Alex I Wiesman
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 845 Sherbrooke St W, Montreal, QC H3A 0G4, Canada; Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA.
| | - Victoria M Mundorf
- Center for Brain, Biology, and Behavior, University of Nebraska - Lincoln, Lincoln, NE, USA
| | - Chloe C Casagrande
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
| | | | | | - Pamela E May
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center (UNMC), Omaha, NE, USA; Memory Disorders and Behavioral Neurology Program, UNMC, Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA
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28
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Lew BJ, Fitzgerald EE, Ott LR, Penhale SH, Wilson TW. Three-year reliability of MEG resting-state oscillatory power. Neuroimage 2021; 243:118516. [PMID: 34454042 PMCID: PMC8590732 DOI: 10.1016/j.neuroimage.2021.118516] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 07/27/2021] [Accepted: 08/25/2021] [Indexed: 12/03/2022] Open
Abstract
Introduction: Resting-state oscillatory activity has been extensively studied across a wide array of disorders. Establishing which spectrally- and spatially-specific oscillatory components exhibit test-retest reliability is essential to move the field forward. While studies have shown short-term reliability of MEG resting-state activity, no studies have examined test-retest reliability across an extended period of time to establish the stability of these signals which is critical for reproducibility. Methods: We examined 18 healthy adults age 23 – 61 who completed three visits across three years. For each visit participants completed both a resting state MEG and structural MRI scan. MEG data were source imaged, and the cortical power in canonical frequency bands (delta, theta, alpha, beta, low gamma, high gamma) was computed Intra-class correlation coefficients (ICC) were then calculated across the cortex for each frequency band. Results: Over three years, power in the alpha and beta bands displayed the highest reliability estimates, while gamma showed the lowest estimates of three-year reliability. Spatially, delta, alpha, and beta all showed the highest degrees of reliability in the parietal cortex. Interestingly, the peak signal for each of these frequency bands was located outside of the parietal cortex, suggesting that reliability estimates were not solely dependent on the signal-to-noise ratio. Conclusion: Oscillatory resting-state power in parietal delta, posterior beta, and alpha across most of the cortex are reliable across three years and future MEEG studies may focus on these measures for the development of specific markers.
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Affiliation(s)
- Brandon J Lew
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Emily E Fitzgerald
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Lauren R Ott
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Samantha H Penhale
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA
| | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Boys Town, NE, USA; College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA.
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29
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Tok S, Ahnaou A, Drinkenburg W. Functional Neurophysiological Biomarkers of Early-Stage Alzheimer's Disease: A Perspective of Network Hyperexcitability in Disease Progression. J Alzheimers Dis 2021; 88:809-836. [PMID: 34420957 PMCID: PMC9484128 DOI: 10.3233/jad-210397] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Network hyperexcitability (NH) has recently been suggested as a potential neurophysiological indicator of Alzheimer’s disease (AD), as new, more accurate biomarkers of AD are sought. NH has generated interest as a potential indicator of certain stages in the disease trajectory and even as a disease mechanism by which network dysfunction could be modulated. NH has been demonstrated in several animal models of AD pathology and multiple lines of evidence point to the existence of NH in patients with AD, strongly supporting the physiological and clinical relevance of this readout. Several hypotheses have been put forward to explain the prevalence of NH in animal models through neurophysiological, biochemical, and imaging techniques. However, some of these hypotheses have been built on animal models with limitations and caveats that may have derived NH through other mechanisms or mechanisms without translational validity to sporadic AD patients, potentially leading to an erroneous conclusion of the underlying cause of NH occurring in patients with AD. In this review, we discuss the substantiation for NH in animal models of AD pathology and in human patients, as well as some of the hypotheses considering recently developed animal models that challenge existing hypotheses and mechanisms of NH. In addition, we provide a preclinical perspective on how the development of animal models incorporating AD-specific NH could provide physiologically relevant translational experimental data that may potentially aid the discovery and development of novel therapies for AD.
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Affiliation(s)
- Sean Tok
- Department of Neuroscience, Janssen Research & Development, Janssen Pharmaceutica NV, Beerse, Belgium.,Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, The Netherlands
| | - Abdallah Ahnaou
- Department of Neuroscience, Janssen Research & Development, Janssen Pharmaceutica NV, Beerse, Belgium
| | - Wilhelmus Drinkenburg
- Department of Neuroscience, Janssen Research & Development, Janssen Pharmaceutica NV, Beerse, Belgium.,Groningen Institute for Evolutionary Life Sciences, Faculty of Science and Engineering, University of Groningen, The Netherlands
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30
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Wiesman AI, Murman DL, May PE, Schantell M, Wolfson SL, Johnson CM, Wilson TW. Visuospatial alpha and gamma oscillations scale with the severity of cognitive dysfunction in patients on the Alzheimer's disease spectrum. ALZHEIMERS RESEARCH & THERAPY 2021; 13:139. [PMID: 34404472 PMCID: PMC8369319 DOI: 10.1186/s13195-021-00881-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 07/28/2021] [Indexed: 11/12/2022]
Abstract
Background Entrainment of neural oscillations in occipital cortices by external rhythmic visual stimuli has been proposed as a novel therapy for patients with Alzheimer’s disease (AD). Despite this increased interest in visual neural oscillations in AD, little is known regarding their role in AD-related cognitive impairment and in particular during visuospatial processing. Methods We used source-imaged magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 35 biomarker-confirmed patients on the AD spectrum and 20 biomarker-negative older adults. Neuronal oscillatory responses were imaged to the level of the cortex, and group classifications and neurocognitive relationships were modeled using logistic and linear regression, respectively. Results Visuospatial neuronal oscillations in the theta, alpha, and gamma ranges significantly predicted the classification of patients on the AD spectrum. Importantly, the direction of these effects differed by response frequency, such that patients on the AD spectrum exhibited weaker alpha-frequency responses in lateral occipital regions, and stronger gamma-frequency responses in the primary visual cortex, as compared to biomarker-negative older adults. In addition, alpha and gamma, but not theta, oscillations robustly predicted cognitive status (i.e., MoCA and MMSE scores), such that patients with neural responses that deviated more from those of healthy older adults exhibited poorer cognitive performance. Conclusions We find that the multi-spectral neural dynamics supporting visuospatial processing differentiate patients on the AD spectrum from cognitively normal, biomarker-negative older adults. Oscillations in the alpha and gamma bands also relate to cognitive status in ways that are informative for emerging clinical interventions.
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Affiliation(s)
- Alex I Wiesman
- Montreal Neurological Institute, McGill University, 845 Sherbrooke St W, Montreal, QC, H3A 0G4, Canada. .,Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA.
| | - Daniel L Murman
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA.,Memory Disorders & Behavioral Neurology Program, UNMC, Omaha, NE, USA
| | - Pamela E May
- Department of Neurological Sciences, University of Nebraska Medical Center, Omaha, NE, USA
| | - Mikki Schantell
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
| | | | | | - Tony W Wilson
- Institute for Human Neuroscience, Boys Town National Research Hospital, Omaha, NE, USA
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31
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Chen PY, Hsu HY, Chao YP, Nouchi R, Wang PN, Cheng CH. Altered mismatch response of inferior parietal lobule in amnestic mild cognitive impairment: A magnetoencephalographic study. CNS Neurosci Ther 2021; 27:1136-1145. [PMID: 34347358 PMCID: PMC8446215 DOI: 10.1111/cns.13691] [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: 01/11/2021] [Revised: 05/16/2021] [Accepted: 05/20/2021] [Indexed: 12/31/2022] Open
Abstract
Background Mismatch negativity (MMN) reflects the functional integrity of sensory memory function. With the advantages of independence of individual's focused attention and behavioral cooperation, this neurophysiological signal is particularly suitable for investigating elderly with cognitive decline such as amnestic mild cognitive impairment (aMCI). However, the existing results remain substantially inconsistent whether these patients show deficits of MMN. In order to reconcile the previous disputes, the present study used magnetoencephalography combined with distributed source imaging methods to determine the source‐level magnetic mismatch negativity (MMNm) in aMCI. Methods A total of 26 healthy controls (HC) and 26 patients with aMCI underwent an auditory oddball paradigm during the MEG recordings. MMNm amplitudes and latencies in the bilateral superior temporal gyrus, inferior frontal gyrus, and inferior parietal lobule (IPL) were compared between HC and aMCI groups. The correlations of MMNm responses with performance of auditory/verbal memory tests were examined. Finally, MMNm and its combination with verbal/auditory memory tests were submitted to receiver operating characteristic (ROC) curve analysis. Results Compared to HC, patients with aMCI showed significantly delayed MMNm latencies in the IPL. Among the patients with aMCI, longer MMNm latencies of left IPL were associated with lower scores of Chinese Version Verbal Learning Test (CVVLT). The ROC curve analysis revealed that the combination of MMNm latencies of left IPL and CVVLT scores yielded a moderate accuracy in the discrimination of aMCI from HC at an individual level. Conclusions Our data suggest dysfunctional MMNm in patients with aMCI, particularly in the IPL.
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Affiliation(s)
- Pin-Yu Chen
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan.,Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan
| | - Hui-Yun Hsu
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan.,Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Graduate Institute of Biomedical Engineering, Chang Gung University, Taoyuan, Taiwan.,Department of Neurology, Chang Gung Memorial Hospital, Linkou, Taiwan
| | - Rui Nouchi
- Institute of Development, Aging and Cancer (IDAC), Tohoku University, Sendai, Japan.,Smart Aging Research Center (S.A.R.C), Tohoku University, Sendai, Japan
| | - Pei-Ning Wang
- Division of General Neurology, Department of Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Neurology, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chia-Hsiung Cheng
- Department of Occupational Therapy and Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan, Taiwan.,Laboratory of Brain Imaging and Neural Dynamics (BIND Lab), Chang Gung University, Taoyuan, Taiwan.,Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan.,Department of Psychiatry, Chang Gung Memorial Hospital, Linkou, Taiwan
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32
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Maestú F, de Haan W, Busche MA, DeFelipe J. Neuronal excitation/inhibition imbalance: core element of a translational perspective on Alzheimer pathophysiology. Ageing Res Rev 2021; 69:101372. [PMID: 34029743 DOI: 10.1016/j.arr.2021.101372] [Citation(s) in RCA: 63] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Revised: 04/16/2021] [Accepted: 05/19/2021] [Indexed: 02/08/2023]
Abstract
Our incomplete understanding of the link between Alzheimer's Disease pathology and symptomatology is a crucial obstacle for therapeutic success. Recently, translational studies have begun to connect the dots between protein alterations and deposition, brain network dysfunction and cognitive deficits. Disturbance of neuronal activity, and in particular an imbalance in underlying excitation/inhibition (E/I), appears early in AD, and can be regarded as forming a central link between structural brain pathology and cognitive dysfunction. While there are emerging (non-)pharmacological options to influence this imbalance, the complexity of human brain dynamics has hindered identification of an optimal approach. We suggest that focusing on the integration of neurophysiological aspects of AD at the micro-, meso- and macroscale, with the support of computational network modeling, can unite fundamental and clinical knowledge, provide a general framework, and suggest rational therapeutic targets.
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33
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Rodríguez-González V, Gómez C, Hoshi H, Shigihara Y, Hornero R, Poza J. Exploring the Interactions Between Neurophysiology and Cognitive and Behavioral Changes Induced by a Non-pharmacological Treatment: A Network Approach. Front Aging Neurosci 2021; 13:696174. [PMID: 34393759 PMCID: PMC8358307 DOI: 10.3389/fnagi.2021.696174] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/13/2021] [Indexed: 11/24/2022] Open
Abstract
Dementia due to Alzheimer's disease (AD) is a neurological syndrome which has an increasing impact on society, provoking behavioral, cognitive, and functional impairments. AD lacks an effective pharmacological intervention; thereby, non-pharmacological treatments (NPTs) play an important role, as they have been proven to ameliorate AD symptoms. Nevertheless, results associated with NPTs are patient-dependent, and new tools are needed to predict their outcome and to improve their effectiveness. In the present study, 19 patients with AD underwent an NPT for 83.1 ± 38.9 days (mean ± standard deviation). The NPT was a personalized intervention with physical, cognitive, and memory stimulation. The magnetoencephalographic activity was recorded at the beginning and at the end of the NPT to evaluate the neurophysiological state of each patient. Additionally, the cognitive (assessed by means of the Mini-Mental State Examination, MMSE) and behavioral (assessed in terms of the Dementia Behavior Disturbance Scale, DBD-13) status were collected before and after the NPT. We analyzed the interactions between cognitive, behavioral, and neurophysiological data by generating diverse association networks, able to intuitively characterize the relationships between variables of a different nature. Our results suggest that the NPT remarkably changed the structure of the association network, reinforcing the interactions between the DBD-13 and the neurophysiological parameters. We also found that the changes in cognition and behavior are related to the changes in spectral-based neurophysiological parameters. Furthermore, our results support the idea that MEG-derived parameters can predict NPT outcome; specifically, a lesser degree of AD neurophysiological alterations (i.e., neural oscillatory slowing, decreased variety of spectral components, and increased neural signal regularity) predicts a better NPT prognosis. This study provides deeper insights into the relationships between neurophysiology and both, cognitive and behavioral status, proving the potential of network-based methodology as a tool to further understand the complex interactions elicited by NPTs.
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Affiliation(s)
| | - Carlos Gómez
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Hideyuki Hoshi
- Precision Medicine Centre, Hokuto Hospital, Obihiro, Japan
| | | | - Roberto Hornero
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain
| | - Jesús Poza
- Biomedical Engineering Group, Universidad de Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
- IMUVA, Instituto de Investigación en Matemáticas, Universidad de Valladolid, Valladolid, Spain
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Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
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Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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35
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Sorrentino P, Rucco R, Lardone A, Liparoti M, Troisi Lopez E, Cavaliere C, Soricelli A, Jirsa V, Sorrentino G, Amico E. Clinical connectome fingerprints of cognitive decline. Neuroimage 2021; 238:118253. [PMID: 34116156 DOI: 10.1016/j.neuroimage.2021.118253] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 05/29/2021] [Accepted: 06/07/2021] [Indexed: 12/29/2022] Open
Abstract
Brain connectome fingerprinting is rapidly rising as a novel influential field in brain network analysis. Yet, it is still unclear whether connectivity fingerprints could be effectively used for mapping and predicting disease progression from human brain data. We hypothesize that dysregulation of brain activity in disease would reflect in worse subject identification. We propose a novel framework, Clinical Connectome Fingerprinting, to detect individual connectome features from clinical populations. We show that "clinical fingerprints" can map individual variations between elderly healthy subjects and patients with mild cognitive impairment in functional connectomes extracted from magnetoencephalography data. We find that identifiability is reduced in patients as compared to controls, and show that these connectivity features are predictive of the individual Mini-Mental State Examination (MMSE) score in patients. We hope that the proposed methodology can help in bridging the gap between connectivity features and biomarkers of brain dysfunction in large-scale brain networks.
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Affiliation(s)
- Pierpaolo Sorrentino
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Rosaria Rucco
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | - Anna Lardone
- Department of Social and Developmental Psychology, University of Rome "Sapienza, Italy
| | - Marianna Liparoti
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy
| | | | | | - Andrea Soricelli
- Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; IRCCS SDN, Naples, Italy
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - Giuseppe Sorrentino
- Institute of Applied Sciences and Intelligent Systems, CNR, Pozzuoli, Italy; Department of Motor Sciences and Wellness, University of Naples "Parthenope", Italy; Hermitage Capodimonte Clinic, Naples, Italy.
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, EPFL, Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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Zhang H, Geng X, Wang Y, Guo Y, Gao Y, Zhang S, Du W, Liu L, Sun M, Jiao F, Yi F, Li X, Wang L. The Significance of EEG Alpha Oscillation Spectral Power and Beta Oscillation Phase Synchronization for Diagnosing Probable Alzheimer Disease. Front Aging Neurosci 2021; 13:631587. [PMID: 34163348 PMCID: PMC8215164 DOI: 10.3389/fnagi.2021.631587] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 05/11/2021] [Indexed: 11/30/2022] Open
Abstract
Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups’ EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: δ oscillation (1–4 Hz), θ oscillation (4–8 Hz), α oscillation (8–13 Hz), and β oscillation (13–30 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased α oscillation power and increased θ oscillation power; (2) decreased spectral entropy in α oscillation and elevated spectral entropy in β oscillation; and (3) decrease phase synchronization index in δ, θ, and β oscillation. We also found that α oscillation spectral power and β oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients.
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Affiliation(s)
- Haifeng Zhang
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Xinling Geng
- School of Biomedical Engineering, Capital Medical University, Beijing, China
| | - Yuanyuan Wang
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yanjun Guo
- Department of Neurology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Ya Gao
- Department of Geriatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Shouzi Zhang
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Wenjin Du
- Department of Neurology, Air Force Medical Center, Chinese People's Liberation Army, Beijing, China
| | - Lixin Liu
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Mingyan Sun
- Ninth Health Care Department of the Second Medical Center of PLA General Hospital, Beijing, China
| | - Fubin Jiao
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Fang Yi
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Department of Neurology, Lishilu Outpatient, Jingzhong Medical District, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Luning Wang
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
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Wiesman AI, Murman DL, May PE, Schantell M, Losh RA, Johnson HJ, Willet MP, Eastman JA, Christopher‐Hayes NJ, Knott NL, Houseman LL, Wolfson SL, Losh KL, Johnson CM, Wilson TW. Spatio-spectral relationships between pathological neural dynamics and cognitive impairment along the Alzheimer's disease spectrum. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2021; 13:e12200. [PMID: 34095434 PMCID: PMC8165730 DOI: 10.1002/dad2.12200] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/21/2021] [Accepted: 04/21/2021] [Indexed: 01/25/2023]
Abstract
INTRODUCTION Numerous studies have described aberrant patterns of rhythmic neural activity in patients along the Alzheimer's disease (AD) spectrum, yet the relationships between these pathological features and cognitive decline are uncertain. METHODS We acquired magnetoencephalography (MEG) data from 38 amyloid-PET biomarker-confirmed patients on the AD spectrum and a comparison group of biomarker-negative cognitively normal (CN) healthy adults, alongside an extensive neuropsychological battery. RESULTS By modeling whole-brain rhythmic neural activity with an extensive neuropsychological profile in patients on the AD spectrum, we show that the spectral and spatial features of deviations from healthy adults in neural population-level activity inform their relevance to domain-specific neurocognitive declines. DISCUSSION Regional oscillatory activity represents a sensitive metric of neuronal pathology in patients on the AD spectrum. By considering not only the spatial, but also the spectral, definitions of cortical neuronal activity, we show that domain-specific cognitive declines can be better modeled in these individuals.
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Affiliation(s)
- Alex I. Wiesman
- Montreal Neurological InstituteMcGill UniversityMontrealQuebecCanada
- Department of Neurological SciencesUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Daniel L. Murman
- Department of Neurological SciencesUniversity of Nebraska Medical CenterOmahaNebraskaUSA
- Memory Disorders & Behavioral Neurology ProgramUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Pamela E. May
- Department of Neurological SciencesUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Mikki Schantell
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Rebecca A. Losh
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Hallie J. Johnson
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Madelyn P. Willet
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Jacob A. Eastman
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | | | - Nichole L. Knott
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Lisa L. Houseman
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Sara L. Wolfson
- Geriatrics Medicine ClinicUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Kathryn L. Losh
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
| | - Craig M. Johnson
- Department of RadiologyUniversity of Nebraska Medical CenterOmahaNebraskaUSA
| | - Tony W. Wilson
- Institute for Human NeuroscienceBoys Town National Research HospitalOmahaNebraskaUSA
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38
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Xu M, Sanz DL, Garces P, Maestu F, Li Q, Pantazis D. A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression With MEG Brain Networks. IEEE Trans Biomed Eng 2021; 68:1579-1588. [PMID: 33400645 PMCID: PMC8162933 DOI: 10.1109/tbme.2021.3049199] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Characterizing the subtle changes of functional brain networks associated with the pathological cascade of Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression prior to clinical symptoms. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G), which can learn highly informative network features by mapping high-dimensional resting-state brain networks into a low-dimensional latent space. These latent distribution-based embeddings enable a quantitative characterization of subtle and heterogeneous brain connectivity patterns at different regions, and can be used as input to traditional classifiers for various downstream graph analytic tasks, such as AD early stage prediction, and statistical evaluation of between-group significant alterations across brain regions. We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
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Resveratrol, Metabolic Dysregulation, and Alzheimer's Disease: Considerations for Neurogenerative Disease. Int J Mol Sci 2021; 22:ijms22094628. [PMID: 33924876 PMCID: PMC8125227 DOI: 10.3390/ijms22094628] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/20/2021] [Accepted: 04/26/2021] [Indexed: 12/16/2022] Open
Abstract
Alzheimer’s disease (AD) has traditionally been discussed as a disease where serious cognitive decline is a result of Aβ-plaque accumulation, tau tangle formation, and neurodegeneration. Recently, it has been shown that metabolic dysregulation observed with insulin resistance and type-2 diabetes actively contributes to the progression of AD. One of the pathologies linking metabolic disease to AD is the release of inflammatory cytokines that contribute to the development of brain neuroinflammation and mitochondrial dysfunction, ultimately resulting in amyloid-beta peptide production and accumulation. Improving these metabolic impairments has been shown to be effective at reducing AD progression and improving cognitive function. The polyphenol resveratrol (RSV) improves peripheral metabolic disorders and may provide similar benefits centrally in the brain. RSV reduces inflammatory cytokine release, improves mitochondrial energetic function, and improves Aβ-peptide clearance by activating SIRT1 and AMPK. RSV has also been linked to improved cognitive function; however, the mechanisms of action are less defined. However, there is evidence to suggest that chronic RSV-driven AMPK activation may be detrimental to synaptic function and growth, which would directly impact cognition. This review will discuss the benefits and adverse effects of RSV on the brain, highlighting the major signaling pathways and some of the gaps surrounding the use of RSV as a treatment for AD.
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40
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Coomans EM, Schoonhoven DN, Tuncel H, Verfaillie SCJ, Wolters EE, Boellaard R, Ossenkoppele R, den Braber A, Scheper W, Schober P, Sweeney SP, Ryan JM, Schuit RC, Windhorst AD, Barkhof F, Scheltens P, Golla SSV, Hillebrand A, Gouw AA, van Berckel BNM. In vivo tau pathology is associated with synaptic loss and altered synaptic function. Alzheimers Res Ther 2021; 13:35. [PMID: 33546722 PMCID: PMC7866464 DOI: 10.1186/s13195-021-00772-0] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/11/2021] [Indexed: 01/08/2023]
Abstract
BACKGROUND The mechanism of synaptic loss in Alzheimer's disease is poorly understood and may be associated with tau pathology. In this combined positron emission tomography (PET) and magnetoencephalography (MEG) study, we aimed to investigate spatial associations between regional tau pathology ([18F]flortaucipir PET), synaptic density (synaptic vesicle 2A [11C]UCB-J PET) and synaptic function (MEG) in Alzheimer's disease. METHODS Seven amyloid-positive Alzheimer's disease subjects from the Amsterdam Dementia Cohort underwent dynamic 130-min [18F]flortaucipir PET, dynamic 60-min [11C]UCB-J PET with arterial sampling and 2 × 5-min resting-state MEG measurement. [18F]flortaucipir- and [11C]UCB-J-specific binding (binding potential, BPND) and MEG spectral measures (relative delta, theta and alpha power; broadband power; and peak frequency) were assessed in cortical brain regions of interest. Associations between regional [18F]flortaucipir BPND, [11C]UCB-J BPND and MEG spectral measures were assessed using Spearman correlations and generalized estimating equation models. RESULTS Across subjects, higher regional [18F]flortaucipir uptake was associated with lower [11C]UCB-J uptake. Within subjects, the association between [11C]UCB-J and [18F]flortaucipir depended on within-subject neocortical tau load; negative associations were observed when neocortical tau load was high, gradually changing into opposite patterns with decreasing neocortical tau burden. Both higher [18F]flortaucipir and lower [11C]UCB-J uptake were associated with altered synaptic function, indicative of slowing of oscillatory activity, most pronounced in the occipital lobe. CONCLUSIONS These results indicate that in Alzheimer's disease, tau pathology is closely associated with reduced synaptic density and synaptic dysfunction.
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Affiliation(s)
- Emma M Coomans
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
| | - Deborah N Schoonhoven
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Hayel Tuncel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sander C J Verfaillie
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Emma E Wolters
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Ronald Boellaard
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiep Scheper
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, The Netherlands
- Center for Neurogenomics and Cognitive Research, Department of Functional Genomics, Faculty of Science, Vrije Universiteit, Amsterdam, The Netherlands
| | - Patrick Schober
- Department of Anaesthesiology, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | | | | | - Robert C Schuit
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Albert D Windhorst
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- UCL Institutes of Neurology and Healthcare Engineering, London, UK
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sandeep S V Golla
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Arjan Hillebrand
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Alida A Gouw
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Clinical Neurophysiology and MEG Center, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Bart N M van Berckel
- Department of Radiology & Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
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High-dimensional brain-wide functional connectivity mapping in magnetoencephalography. J Neurosci Methods 2020; 348:108991. [PMID: 33181166 DOI: 10.1016/j.jneumeth.2020.108991] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 09/06/2020] [Accepted: 10/22/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Brain functional connectivity (FC) analyses based on magneto/electroencephalography (M/EEG) signals have yet to exploit the intrinsic high-dimensional information. Typically, these analyses are constrained to regions of interest to avoid the curse of dimensionality, with the latter leading to conservative hypothesis testing. NEW METHOD We removed such constraint by estimating high-dimensional source-based M/EEG-FC using cluster-permutation statistic (CPS) and demonstrated the feasibility of this approach by identifying resting-state changes in mild cognitive impairment (MCI), a prodromal stage of Alzheimer's disease. Particularly, we proposed a unified framework for CPS analysis together with a novel neighbourhood measure to estimate more compact and neurophysiological plausible neural communication. As clusters could more confidently reveal interregional communication, we proposed and tested a cluster-strength index to demonstrate other advantages of CPS analysis. RESULTS We found clusters of increased communication or hypersynchronization in MCI compared to healthy controls in delta (1-4 Hz) and higher-theta (6-8 Hz) bands oscillations. These mainly consisted of interactions between occipitofrontal and occipitotemporal regions in the left hemisphere, which may be critically affected in the early stages of Alzheimer's disease. CONCLUSIONS Our approach could be important to create high-resolution FC maps from neuroimaging studies in general, allowing the multimodal analysis of neural communication across multiple spatial scales. Particularly, FC clusters more robustly represent the interregional communication by identifying dense bundles of connections that are less sensitive to inter-individual anatomical and functional variability. Overall, this approach could help to better understand neural information processing in healthy and disease conditions as needed for developing biomarker research.
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Rodríguez-González V, Gómez C, Shigihara Y, Hoshi H, Revilla-Vallejo M, Hornero R, Poza J. Consistency of local activation parameters at sensor- and source-level in neural signals. J Neural Eng 2020; 17:056020. [PMID: 33055364 DOI: 10.1088/1741-2552/abb582] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Although magnetoencephalography and electroencephalography (M/EEG) signals at sensor level are robust and reliable, they suffer from different degrees of distortion due to changes in brain tissue conductivities, known as field spread and volume conduction effects. To estimate original neural generators from M/EEG activity acquired at sensor level, diverse source localisation algorithms have been proposed; however, they are not exempt from limitations and usually involve time-consuming procedures. Connectivity and network-based M/EEG analyses have been found to be affected by field spread and volume conduction effects; nevertheless, the influence of the aforementioned effects on widely used local activation parameters has not been assessed yet. The goal of this study is to evaluate the consistency of various local activation parameters when they are computed at sensor- and source-level. APPROACH Six spectral (relative power, median frequency, and individual alpha frequency) and non-linear parameters (Lempel-Ziv complexity, sample entropy, and central tendency measure) are computed from M/EEG signals at sensor- and source-level using four source inversion methods: weighted minimum norm estimate (wMNE), standardised low-resolution brain electromagnetic tomography (sLORETA), linear constrained minimum variance (LCMV), and dynamical statistical parametric mapping (dSPM). MAIN RESULTS Our results show that the spectral and non-linear parameters yield similar results at sensor- and source-level, showing high correlation values between them for all the source inversion methods evaluated and both modalities of signal, EEG and MEG. Furthermore, the correlation values remain high when performing coarse-grained spatial analyses. SIGNIFICANCE To the best of our knowledge, this is the first study analysing how field spread and volume conduction effects impact on local activation parameters computed from resting-state neural activity. Our findings evidence that local activation parameters are robust against field spread and volume conduction effects and provide equivalent information at sensor- and source-level even when performing regional analyses.
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Rodriguez-Gonzalez V, Poza J, Nunez P, Gomez C, Garcia M, Shigihara Y, Hoshi H, Santamaria-Vazquez E, Hornero R. Towards Automatic Artifact Rejection in Resting-State MEG Recordings: Evaluating the Performance of the SOUND Algorithm. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:4807-4810. [PMID: 31946937 DOI: 10.1109/embc.2019.8856587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
In this study, a new automated noise rejection algorithm, the SOurce-estimate-Utilizing Noise-Discarding algorithm (SOUND), was evaluated on magnetoencephalographic (MEG) resting-state signals in order to select its optimal configuration parameters. Different values of the epoch length and the regularization parameter λ0 were assessed in three scenarios with ascending noise levels. Results show that it is possible to remarkably improve the Signal-to-Noise Ratio, without overly altering the signal of interest. An optimal λ0 value of 0.1 was obtained. However, the epoch length should be adapted to the specific problem. In conclusion, our results suggest that the SOUND algorithm is an appropriate and useful tool to be applied in a preprocessing pipeline for MEG restingstate signals.
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44
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Gross J. Magnetoencephalography in Cognitive Neuroscience: A Primer. Neuron 2020; 104:189-204. [PMID: 31647893 DOI: 10.1016/j.neuron.2019.07.001] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Revised: 06/25/2019] [Accepted: 06/28/2019] [Indexed: 12/31/2022]
Abstract
Magnetoencephalography (MEG) is an invaluable tool to study the dynamics and connectivity of large-scale brain activity and their interactions with the body and the environment in functional and dysfunctional body and brain states. This primer introduces the basic concepts of MEG, discusses its strengths and limitations in comparison to other brain imaging techniques, showcases interesting applications, and projects exciting current trends into the near future, in a way that might more fully exploit the unique capabilities of MEG.
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Affiliation(s)
- Joachim Gross
- Institute for Biomagnetism and Biosignalanalysis (IBB), University of Muenster, 48149 Muenster, Germany; Otto-Creutzfeldt-Center for Cognitive and Behavioral Neuroscience, University of Muenster, 48149 Muenster, Germany; Centre for Cognitive Neuroimaging (CCNi), University of Glasgow, Glasgow, UK.
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Yan Y, Yang H, Xie Y, Ding Y, Kong D, Yu H. Research Progress on Alzheimer's Disease and Resveratrol. Neurochem Res 2020; 45:989-1006. [PMID: 32162143 DOI: 10.1007/s11064-020-03007-0] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 02/27/2020] [Accepted: 03/03/2020] [Indexed: 12/13/2022]
Abstract
Alzheimer's disease (AD), a common irreversible neurodegenerative disease characterized by amyloid-β plaques, neurofibrillary tangles, and changes in tau phosphorylation, is accompanied by memory loss and symptoms of cognitive dysfunction. Increases in disease incidence due to the ageing of the population have placed a great burden on society. To date, the mechanism of AD and the identities of adequate drugs for AD prevention and treatment have eluded the medical community. It has been confirmed that phytochemicals have certain neuroprotective effects against AD. For example, some progress has been made in research on the use of resveratrol, a natural polyphenolic phytochemical, for the prevention and treatment of AD in recent years. Elucidation of the pathogenesis of AD will create a solid foundation for drug treatment. In addition, research on resveratrol, including its mechanism of action, the roles of signalling pathways and its therapeutic targets, will provide new ideas for AD treatment, which is of great significance. In this review, we discuss the possible relationships between AD and the following factors: synapses, α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA)-type glutamate receptors (AMPARs), silent information regulator 1 (SIRT1), and estrogens. We also discuss the findings of previous studies regarding these relationships in the context of AD treatment and further summarize research progress related to resveratrol treatment.
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Affiliation(s)
- Yan Yan
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Huihuang Yang
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Yuxun Xie
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Yuanlin Ding
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China
| | - Danli Kong
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China.
| | - Haibing Yu
- The Department of Epidemiology and Health Statistics, Public Health School of Guangdong Medical University, Dongguan, 523808, Guangdong, China.
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Sun J, Wang B, Niu Y, Tan Y, Fan C, Zhang N, Xue J, Wei J, Xiang J. Complexity Analysis of EEG, MEG, and fMRI in Mild Cognitive Impairment and Alzheimer's Disease: A Review. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E239. [PMID: 33286013 PMCID: PMC7516672 DOI: 10.3390/e22020239] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/15/2020] [Accepted: 02/17/2020] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is a degenerative brain disease with a high and irreversible incidence. In recent years, because brain signals have complex nonlinear dynamics, there has been growing interest in studying complex changes in the time series of brain signals in patients with AD. We reviewed studies of complexity analyses of single-channel time series from electroencephalogram (EEG), magnetoencephalogram (MEG), and functional magnetic resonance imaging (fMRI) in AD and determined future research directions. A systematic literature search for 2000-2019 was performed in the Web of Science and PubMed databases, resulting in 126 identified studies. Compared to healthy individuals, the signals from AD patients have less complexity and more predictable oscillations, which are found mainly in the left parietal, occipital, right frontal, and temporal regions. This complexity is considered a potential biomarker for accurately responding to the functional lesion in AD. The current review helps to reveal the patterns of dysfunction in the brains of patients with AD and to investigate whether signal complexity can be used as a biomarker to accurately respond to the functional lesion in AD. We proposed further studies in the signal complexities of AD patients, including investigating the reliability of complexity algorithms and the spatial patterns of signal complexity. In conclusion, the current review helps to better understand the complexity of abnormalities in the AD brain and provide useful information for AD diagnosis.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China; (J.S.); (B.W.); (Y.N.); (Y.T.); (C.F.); (N.Z.); (J.X.); (J.W.)
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Echegoyen I, López-Sanz D, Martínez JH, Maestú F, Buldú JM. Permutation Entropy and Statistical Complexity in Mild Cognitive Impairment and Alzheimer's Disease: An Analysis Based on Frequency Bands. ENTROPY 2020; 22:e22010116. [PMID: 33285891 PMCID: PMC7516422 DOI: 10.3390/e22010116] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 01/15/2020] [Accepted: 01/16/2020] [Indexed: 12/14/2022]
Abstract
We present one of the first applications of Permutation Entropy (PE) and Statistical Complexity (SC) (measured as the product of PE and Jensen-Shanon Divergence) on Magnetoencephalography (MEG) recordings of 46 subjects suffering from Mild Cognitive Impairment (MCI), 17 individuals diagnosed with Alzheimer's Disease (AD) and 48 healthy controls. We studied the differences in PE and SC in broadband signals and their decomposition into frequency bands ( δ , θ , α and β ), considering two modalities: (i) raw time series obtained from the magnetometers and (ii) a reconstruction into cortical sources or regions of interest (ROIs). We conducted our analyses at three levels: (i) at the group level we compared SC in each frequency band and modality between groups; (ii) at the individual level we compared how the [PE, SC] plane differs in each modality; and (iii) at the local level we explored differences in scalp and cortical space. We recovered classical results that considered only broadband signals and found a nontrivial pattern of alterations in each frequency band, showing that SC does not necessarily decrease in AD or MCI.
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Affiliation(s)
- Ignacio Echegoyen
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Correspondence:
| | - David López-Sanz
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
| | - Johann H. Martínez
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
- Biomedical Engineering Department, Universidad de los Andes, Bogotá 111711, Colombia
| | - Fernando Maestú
- Laboratory of Cognitive and Computational Neuroscience, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain; (D.L.-S.); (F.M.)
- Department of Experimental Psychology, Complutense University of Madrid, 28223 Madrid, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine, 28029 Zaragoza, Spain
| | - Javier M. Buldú
- Laboratory of Biological Networks, Centre for Biomedical Technology, Universidad Politécnica de Madrid (UPM), 28223 Madrid, Spain;
- Complex Systems Group, Rey Juan Carlos University, 28933 Madrid, Spain
- Grupo Interdisciplinar de Sistemas Complejos (GISC), 28911 Madrid, Spain;
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48
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Takeuchi Y, Berényi A. Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond. Neurosci Res 2020; 152:87-107. [PMID: 31954733 DOI: 10.1016/j.neures.2020.01.002] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Revised: 12/18/2019] [Accepted: 12/19/2019] [Indexed: 02/09/2023]
Abstract
Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.
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Affiliation(s)
- Yuichi Takeuchi
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 6720, Hungary; Department of Neuropharmacology, Graduate School of Pharmaceutical Sciences, Nagoya City University, Nagoya, 467-8603, Japan.
| | - Antal Berényi
- MTA-SZTE 'Momentum' Oscillatory Neuronal Networks Research Group, Department of Physiology, University of Szeged, Szeged, 6720, Hungary; HCEMM-SZTE Magnetotherapeutics Research Group, University of Szeged, Szeged, 6720, Hungary; Neuroscience Institute, New York University, New York, NY 10016, USA.
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49
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Furutani N, Nariya Y, Takahashi T, Noto S, Yang AC, Hirosawa T, Kameya M, Minabe Y, Kikuchi M. Decomposed Temporal Complexity Analysis of Neural Oscillations and Machine Learning Applied to Alzheimer's Disease Diagnosis. Front Psychiatry 2020; 11:531801. [PMID: 33101073 PMCID: PMC7495507 DOI: 10.3389/fpsyt.2020.531801] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 08/17/2020] [Indexed: 12/25/2022] Open
Abstract
Despite growing evidence of aberrant neuronal complexity in Alzheimer's disease (AD), it remains unclear how this variation arises. Neural oscillations reportedly comprise different functions depending on their own properties. Therefore, in this study, we investigated details of the complexity of neural oscillations by decomposing the oscillations into frequency, amplitude, and phase for AD patients. We applied resting-state magnetoencephalography (MEG) to 17 AD patients and 21 healthy control subjects. We first decomposed the source time series of the MEG signal into five intrinsic mode functions using ensemble empirical mode decomposition. We then analyzed the temporal complexities of these time series using multiscale entropy. Results demonstrated that AD patients had lower complexity on short time scales and higher complexity on long time scales in the alpha band in temporal regions of the brain. We evaluated the alpha band complexity further by decomposing it into amplitude and phase using Hilbert spectral analysis. Consequently, we found lower amplitude complexity and higher phase complexity in AD patients. Correlation analyses between spectral complexity and decomposed complexities revealed scale-dependency. Specifically, amplitude complexity was positively correlated with spectral complexity on short time scales, whereas phase complexity was positively correlated with spectral complexity on long time scales. Regarding the relevance of cognitive function to the complexity measures, the phase complexity on the long time scale was found to be correlated significantly with the Mini-Mental State Examination score. Additionally, we examined the diagnostic utility of the complexity characteristics using machine learning (ML) methods. We prepared a feature pool using multiple sparse autoencoders (SAEs), chose some discriminating features, and applied them to a support vector machine (SVM). Compared to the simple SVM and the SVM after feature selection (FS + SVM), the SVM with multiple SAEs (SAE + FS + SVM) had improved diagnostic accuracy. Through this study, we 1) advanced the understanding of neuronal complexity in AD patients using decomposed temporal complexity analysis and 2) demonstrated the effectiveness of combining ML methods with information about signal complexity for the diagnosis of AD.
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Affiliation(s)
- Naoki Furutani
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yuta Nariya
- Faculty of Medicine, The University of Tokyo, Tokyo, Japan
| | - Tetsuya Takahashi
- Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
| | - Sarah Noto
- Faculty of Nursing, National College of Nursing, Tokyo, Japan
| | - Albert C Yang
- Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, MA, United States.,Institute of Brain Science, National Yang-Ming University, Taipei, Taiwan
| | - Tetsu Hirosawa
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Masafumi Kameya
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Yoshio Minabe
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan
| | - Mitsuru Kikuchi
- Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.,Research Center for Child Mental Development, Kanazawa University, Kanazawa, Japan
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50
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Núñez P, Poza J, Gómez C, Rodríguez-González V, Hillebrand A, Tola-Arribas MA, Cano M, Hornero R. Characterizing the fluctuations of dynamic resting-state electrophysiological functional connectivity: reduced neuronal coupling variability in mild cognitive impairment and dementia due to Alzheimer’s disease. J Neural Eng 2019; 16:056030. [DOI: 10.1088/1741-2552/ab234b] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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