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PreprintCC BY-NC-NDbioRxiv

Multi-parametric spectral modeling disentangles synaptic kinetics and neural excitability from non-invasive electrophysiological recordings

  • V. Philippe1
  • L. Tombroff
  • F. Akbarian
  • S. De Witte
  • N. Trotta
  • J. Van Schependom
  • S. Engelborghs
  • J.-C. Bier
  • M. Strauss
  • X. De Tiege
  • V. Wens
  1. 1Université libre de Bruxelles (ULB)
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Abstract

Spectral modeling of electrophysiological recordings enables non-invasive characterization of neural excitability. However, current approaches rely on a single spectral index, limiting insight into the multiple biophysical processes underlying excitation-inhibition balance. Here, we introduce a biologically informed, multi-parametric spectral analysis that disentangles neural excitability and synaptic kinetics from recordings. As a proof of concept, we demonstrate that this approach captures signatures of hyperexcitability and glutamate reuptake dysfunction characteristic of Alzheimer's disease.

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Cite

BibTeXRIS
BibTeX
@misc{philippe2026multiparametric,
  title = {{Multi-parametric spectral modeling disentangles synaptic kinetics and neural excitability from non-invasive electrophysiological recordings}},
  author = {V. Philippe and L. Tombroff and F. Akbarian and S. De Witte and N. Trotta and J. Van Schependom and S. Engelborghs and J.-C. Bier and M. Strauss and X. De Tiege and V. Wens},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.15.759313},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.15.759313v1},
  note = {CC BY-NC-ND},
}
RIS
TY  - UNPB
TI  - Multi-parametric spectral modeling disentangles synaptic kinetics and neural excitability from non-invasive electrophysiological recordings
AU  - V. Philippe
AU  - L. Tombroff
AU  - F. Akbarian
AU  - S. De Witte
AU  - N. Trotta
AU  - J. Van Schependom
AU  - S. Engelborghs
AU  - J.-C. Bier
AU  - M. Strauss
AU  - X. De Tiege
AU  - V. Wens
PY  - 2026
DA  - 2026/10/15
PB  - bioRxiv
DO  - 10.64898/2026.10.15.759313
UR  - https://www.biorxiv.org/content/10.64898/2026.10.15.759313v1
AB  - Spectral modeling of electrophysiological recordings enables non-invasive characterization of neural excitability. However, current approaches rely on a single spectral index, limiting insight into the multiple biophysical processes underlying excitation-inhibition balance. Here, we introduce a biologically informed, multi-parametric spectral analysis that disentangles neural excitability and synaptic kinetics from recordings. As a proof of concept, we demonstrate that this approach captures signatures of hyperexcitability and glutamate reuptake dysfunction characteristic of Alzheimer's disease.
ER  -