A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing
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) activ...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-07-01
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| Series: | Alzheimer’s Research & Therapy |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13195-022-01041-4 |
| _version_ | 1811222917074124800 |
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| author | Anne M. van Nifterick Alida A. Gouw Ronald E. van Kesteren Philip Scheltens Cornelis J. Stam Willem de Haan |
| author_facet | Anne M. van Nifterick Alida A. Gouw Ronald E. van Kesteren Philip Scheltens Cornelis J. Stam Willem de Haan |
| author_sort | Anne M. van Nifterick |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:24:19Z |
| format | Article |
| id | doaj.art-5669b09c0ddf4ea79393ad97c469127f |
| institution | Directory Open Access Journal |
| issn | 1758-9193 |
| language | English |
| last_indexed | 2024-04-12T08:24:19Z |
| publishDate | 2022-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj.art-5669b09c0ddf4ea79393ad97c469127f2022-12-22T03:40:27ZengBMCAlzheimer’s Research & Therapy1758-91932022-07-0114112010.1186/s13195-022-01041-4A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowingAnne M. van Nifterick0Alida A. Gouw1Ronald E. van Kesteren2Philip Scheltens3Cornelis J. Stam4Willem de Haan5Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAmsterdam Neuroscience, NeurodegenerationAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcClinical Neurophysiology and MEG Center, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAlzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmcAbstract 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.https://doi.org/10.1186/s13195-022-01041-4MEGAlzheimer’s diseaseOscillatory slowingHyperexcitabilityNeural mass modelsNeuronal network |
| spellingShingle | Anne M. van Nifterick Alida A. Gouw Ronald E. van Kesteren Philip Scheltens Cornelis J. Stam Willem de Haan A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing Alzheimer’s Research & Therapy MEG Alzheimer’s disease Oscillatory slowing Hyperexcitability Neural mass models Neuronal network |
| title | A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing |
| title_full | A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing |
| title_fullStr | A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing |
| title_full_unstemmed | A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing |
| title_short | A multiscale brain network model links Alzheimer’s disease-mediated neuronal hyperactivity to large-scale oscillatory slowing |
| title_sort | multiscale brain network model links alzheimer s disease mediated neuronal hyperactivity to large scale oscillatory slowing |
| topic | MEG Alzheimer’s disease Oscillatory slowing Hyperexcitability Neural mass models Neuronal network |
| url | https://doi.org/10.1186/s13195-022-01041-4 |
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