Tau protein spreads through functionally connected neurons in Alzheimer’s disease: a combined MEG/PET study
Abstract Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatom...
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Published in | Brain (London, England : 1878) Vol. 146; no. 10; pp. 4040 - 4054 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
US
Oxford University Press
03.10.2023
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Subjects | |
Online Access | Get full text |
ISSN | 0006-8950 1460-2156 1460-2156 |
DOI | 10.1093/brain/awad189 |
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Abstract | Abstract
Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum.
In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8–13 Hz) and beta (13–30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds.
In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage.
These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential.
Schoonhoven et al. combine MEG data with 18F-flortaucipir PET to construct epidemic spreading models that mimic the propagation of tau tangles throughout the brain. The results show that the functional architecture of the brain plays a key role in determining the pattern by which tau pathology spreads in Alzheimer’s disease. |
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AbstractList | Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum.
In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8–13 Hz) and beta (13–30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds.
In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage.
These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with [18F]flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-minutes [18F]flortaucipir PET from 57 subjects positive for amyloid-beta (Aβ)-pathology (preclinical AD (n=16), mild cognitive impairment (MCI) due to AD (n=16) and AD dementia (n=25)). Cognitively healthy subjects without Aβ-pathology were included as controls (n=25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks (in alpha (8-13Hz) and beta (13-30Hz) bands), a structural, or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in 3 stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with [18F]flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET (AEC-c alpha C=0.584; AEC-c beta C=0.569), followed by the structural network (C=0.451) and simple diffusion (C=0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C=0.384; C=0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8-13 Hz) and beta (13-30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. Abstract Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8–13 Hz) and beta (13–30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. Schoonhoven et al. combine MEG data with 18F-flortaucipir PET to construct epidemic spreading models that mimic the propagation of tau tangles throughout the brain. The results show that the functional architecture of the brain plays a key role in determining the pattern by which tau pathology spreads in Alzheimer’s disease. Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18 F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18 F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD ( n = 16), mild cognitive impairment (MCI) due to AD ( n = 16) and AD dementia ( n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls ( n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8–13 Hz) and beta (13–30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18 F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. Schoonhoven et al. combine MEG data with 18 F-flortaucipir PET to construct epidemic spreading models that mimic the propagation of tau tangles throughout the brain. The results show that the functional architecture of the brain plays a key role in determining the pattern by which tau pathology spreads in Alzheimer’s disease. Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8-13 Hz) and beta (13-30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential.Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be involved in this process: spreading between brain regions that interact strongly (functional connectivity); through the pattern of anatomical connections (structural connectivity); or simple diffusion. Using magnetoencephalography (MEG), we investigated which spreading pathways influence tau protein spreading by modelling the tau propagation process using an epidemic spreading model. We compared the modelled tau depositions with 18F-flortaucipir PET binding potentials at several stages of the AD continuum. In this cross-sectional study, we analysed source-reconstructed MEG data and dynamic 100-min 18F-flortaucipir PET from 57 subjects positive for amyloid-β pathology [preclinical AD (n = 16), mild cognitive impairment (MCI) due to AD (n = 16) and AD dementia (n = 25)]. Cognitively healthy subjects without amyloid-β pathology were included as controls (n = 25). Tau propagation was modelled as an epidemic process (susceptible-infected model) on MEG-based functional networks [in alpha (8-13 Hz) and beta (13-30 Hz) bands], a structural or diffusion network, starting from the middle and inferior temporal lobe. The group-level network of the control group was used as input for the model to predict tau deposition in three stages of the AD continuum. To assess performance, model output was compared to the group-specific tau deposition patterns as measured with 18F-flortaucipir PET. We repeated the analysis by using networks of the preceding disease stage and/or using regions with most observed tau deposition during the preceding stage as seeds. In the preclinical AD stage, the functional networks predicted most of the modelled tau-PET binding potential, with best correlations between model and tau-PET [corrected amplitude envelope correlation (AEC-c) alpha C = 0.584; AEC-c beta C = 0.569], followed by the structural network (C = 0.451) and simple diffusion (C = 0.451). Prediction accuracy declined for the MCI and AD dementia stages, although the correlation between modelled tau and tau-PET binding remained highest for the functional networks (C = 0.384; C = 0.376). Replacing the control-network with the network from the preceding disease stage and/or alternative seeds improved prediction accuracy in MCI but not in the dementia stage. These results suggest that in addition to structural connections, functional connections play an important role in tau spread, and highlight that neuronal dynamics play a key role in promoting this pathological process. Aberrant neuronal communication patterns should be taken into account when identifying targets for future therapy. Our results also suggest that this process is more important in earlier disease stages (preclinical AD/MCI); possibly, in later stages, other processes may be influential. |
Author | Visser, Denise Hillebrand, Arjan Ossenkoppele, Rik van Nifterick, Anne M Schoonhoven, Deborah N van Berckel, Bart N M Coomans, Emma M Golla, Sandeep S V van der Flier, Wiesje M Gouw, Alida A Stam, Cornelis J Tuncel, Hayel Scheltens, Philip Millán, Ana P |
Author_xml | – sequence: 1 givenname: Deborah N orcidid: 0000-0001-6712-0347 surname: Schoonhoven fullname: Schoonhoven, Deborah N email: d.schoonhoven@amsterdamumc.nl – sequence: 2 givenname: Emma M orcidid: 0000-0002-4289-9101 surname: Coomans fullname: Coomans, Emma M – sequence: 3 givenname: Ana P surname: Millán fullname: Millán, Ana P – sequence: 4 givenname: Anne M surname: van Nifterick fullname: van Nifterick, Anne M – sequence: 5 givenname: Denise surname: Visser fullname: Visser, Denise – sequence: 6 givenname: Rik surname: Ossenkoppele fullname: Ossenkoppele, Rik – sequence: 7 givenname: Hayel surname: Tuncel fullname: Tuncel, Hayel – sequence: 8 givenname: Wiesje M surname: van der Flier fullname: van der Flier, Wiesje M – sequence: 9 givenname: Sandeep S V surname: Golla fullname: Golla, Sandeep S V – sequence: 10 givenname: Philip surname: Scheltens fullname: Scheltens, Philip – sequence: 11 givenname: Arjan surname: Hillebrand fullname: Hillebrand, Arjan – sequence: 12 givenname: Bart N M surname: van Berckel fullname: van Berckel, Bart N M – sequence: 13 givenname: Cornelis J surname: Stam fullname: Stam, Cornelis J – sequence: 14 givenname: Alida A orcidid: 0000-0003-1481-3881 surname: Gouw fullname: Gouw, Alida A |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37279597$$D View this record in MEDLINE/PubMed https://lup.lub.lu.se/record/6f08382d-89cb-451c-b278-7ecdecd203cd$$DView record from Swedish Publication Index oai:portal.research.lu.se:publications/6f08382d-89cb-451c-b278-7ecdecd203cd$$DView record from Swedish Publication Index |
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Copyright | The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. 2023 The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. |
Copyright_xml | – notice: The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. 2023 – notice: The Author(s) 2023. Published by Oxford University Press on behalf of the Guarantors of Brain. |
CorporateAuthor | Lunds universitets profilområden LU Profile Area: Proactive Ageing MultiPark: Multidisciplinary research focused on Parkinson's disease Lunds universitet Profile areas and other strong research environments Department of Clinical Sciences, Malmö Lund University Lund University Profile areas Strategiska forskningsområden (SFO) Faculty of Medicine Strategic research areas (SRA) Clinical Memory Research Klinisk minnesforskning Medicinska fakulteten LU profilområde: Proaktivt åldrande Profilområden och andra starka forskningsmiljöer Institutionen för kliniska vetenskaper, Malmö |
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Keywords | functional connectivity spreading model Alzheimer’s disease tau protein magnetoencephalography |
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Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could... Recent studies on Alzheimer’s disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be... Recent studies on Alzheimer's disease (AD) suggest that tau proteins spread through the brain following neuronal connections. Several mechanisms could be... |
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SubjectTerms | Alzheimer's disease Basic Medicine functional connectivity magnetoencephalography Medical and Health Sciences Medicin och hälsovetenskap Medicinska och farmaceutiska grundvetenskaper Neurosciences Neurovetenskaper Original spreading model tau protein |
Title | Tau protein spreads through functionally connected neurons in Alzheimer’s disease: a combined MEG/PET study |
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