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Periodic/Aperiodic parameterization of transient oscillations (PAPTO)–Implications for healthy ageing

2022· article· en· W4210709080 on OpenAlexafffund
Brendan Brady, Tim Bardouille

Bibliographic record

VenueNeuroImage · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeural dynamics and brain function
Canadian institutionsDalhousie University
FundersBiotechnology and Biological Sciences Research CouncilDalhousie UniversityMedical Research CouncilNova Scotia Department of Health and WellnessNatural Sciences and Engineering Research Council of CanadaUniversity of Cambridge
KeywordsAperiodic graphMagnetoencephalographyTransient (computer programming)Somatosensory systemNeurophysiologyNeuroscienceRhythmResting state fMRIPsychologyElectroencephalographyComputer scienceSpeech recognitionPhysicsMathematicsAcoustics

Abstract

fetched live from OpenAlex

Two techniques for analyzing human extracranial neurophysiological signals, namely the periodic/aperiodic parameterization of neural power spectra and the transient events framework of oscillatory activity, have recently emerged in the scientific literature. In this work, we integrate these two analysis perspectives to analyze extracranial neurophysiological signals as a series of transient rhythmic events disambiguated from the background aperiodic activity. We call this novel technique the periodic/aperiodic parametrization of transient oscillations (PAPTO). We demonstrate PAPTO by investigating resting-state sensorimotor magnetoencephalography recordings from the Cambridge Center for Ageing and Neuroscience cross-sectional study on healthy ageing (n = 600, ages 18-88). We show that PAPTO is more sensitive to neocortical transient beta rhythms compared to more conventional transient event detection algorithms and captures more variance in the resting-state occurrence rate of beta events across participants. The improved sensitivity of PAPTO reveals that the beta occurrence rate almost doubles over the adult lifespan which we discuss in terms of thalamocortical beta generation in the somatosensory cortex and the age-related decline of sensory perception.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.294
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations64
Published2022
Admission routes2
Has abstractyes

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