Evidence map›Paper›PMID 39040639›Full record

ArticleArXiv2024

Simulation-based Inference of Developmental EEG Maturation with the Spectral Graph Model.

Danilo Bernardo, Xihe Xie, Parul Verma, Jonathan Kim, Virginia Liu, Adam L Numis, Ye Wu, Hannah C Glass, Pew-Thian Yap, Srikantan S Nagarajan and 1 more

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Danilo BernardoDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Xihe XieDepartment of Neuroscience, Weill Cornell Medicine, New York, NY, USA.
Parul VermaDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Jonathan KimDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Virginia LiuDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Adam L NumisDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Ye WuDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
Hannah C GlassDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Pew-Thian YapDepartment of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
Srikantan S NagarajanDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Ashish RajDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.

Funding

Understanding disorder-specific neural pathophysiology in laryngeal dystonia and voice tremorP50DC019900 · NIDCD · MASSACHUSETTS EYE AND EAR INFIRMARY · PI Kristina Simonyan · 2021 to 2026
$11.9M
Understanding the dynamic interactions between tau pathology and microgliamediated inflammation in Alzheimer's DiseaseR01AG072753 · NIA · WEILL MEDICAL COLL OF CORNELL UNIV · PI BUTLER, TRACY A., GUPTA, AJAY · 2021 to 2025
$5.3M
Longitudinal Mapping of Human Brain Development in the First Years of LifeR01EB008374 · NIBIB · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2009 to 2024
$5.1M
The role of the cerebellum in speechR01DC017091 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI John Francis Houde, RICHARD IVRY · 2019 to 2026
$4.5M
Dynamic Brain Imaging of Speech in Primary Progressive AphasiaRF1NS100440 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI GORNO TEMPINI, MARIA LUISA, HOUDE, JOHN FRANCIS · 2023 to 2023
$2.4M
Computational Diffusion MRI for Studying Early Human Brain DevelopmentR01MH125479 · NIMH · UNIV OF NORTH CAROLINA CHAPEL HILL · PI YAP, PEW-THIAN · 2021 to 2025
$2.1M
NIA NIH HHS R01 AG072753NIBIB NIH HHS R01 EB008374NIDCD NIH HHS P50 DC019900NIDCD NIH HHS R01 DC017091NIMH NIH HHS R01 MH125479NINDS NIH HHS RF1 NS100440
6 · The paper itself

Abstract

The spectral content of macroscopic neural activity evolves throughout development, yet how this maturation relates to underlying brain network formation and dynamics remains unknown. Here, we assess the developmental maturation of electroencephalogram spectra via Bayesian model inversion of the spectral graph model, a parsimonious whole-brain model of spatiospectral neural activity derived from linearized neural field models coupled by the structural connectome. Simulation-based inference was used to estimate age-varying spectral graph model parameter posterior distributions from electroencephalogram spectra spanning the developmental period. This model-fitting approach accurately captures observed developmental electroencephalogram spectral maturation via a neurobiologically consistent progression of key neural parameters: long-range coupling, axonal conduction speed, and excitatory:inhibitory balance. These results suggest that the spectral maturation of macroscopic neural activity observed during typical development is supported by age-dependent functional adaptations in localized neural dynamics and their long-range coupling across the macroscopic structural network.

Indexed as

Bayesian inferenceBrain modelingEEGNeurodevelopmentSimulation-based inferenceSpectral graph model

Identifiers

PMID39040639
PMCPMC11261974

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.