Evidence map›Paper›PMID 30439389›Full record

ArticleJournal of neuroscience methods2019

Assessing recurrent interactions in cortical networks: Modeling EEG response to transcranial magnetic stimulation.

Jui-Yang Chang, Matteo Fecchio, Andrea Pigorini, Marcello Massimini, Giulio Tononi, Barry D Van Veen

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In one paragraph

Article in Journal of neuroscience methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jui-Yang ChangDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, WI, USA. Electronic address: jyryanchang@gmail.com.
Matteo FecchioDepartment of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy. Electronic address: fecchio.matteo@gmail.com.
Andrea PigoriniDepartment of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy. Electronic address: andreapigorini@gmail.com.
Marcello MassiminiDepartment of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy; IRCCS Fondazione Don Gnocchi, Milan, Italy. Electronic address: marcello.massimini@gmail.com.
Giulio TononiDepartment of Psychiatry, University of Wisconsin-Madison, WI, USA. Electronic address: gtononi@wisc.edu.
Barry D Van VeenDepartment of Electrical and Computer Engineering, University of Wisconsin-Madison, WI, USA. Electronic address: bvanveen@wisc.edu.

Funding

Cortico-thalamic mechanisms of anesthetic unconsciousnessR01GM116916 · NIGMS · UNIVERSITY OF WISCONSIN-MADISON · PI BANKS, MATTHEW I, TONONI, GIULIO · 2016 to 2019
$1.8M
Dynamic Cortical Network Estimation from TMS/EEGR21EB015542 · NIBIB · UNIVERSITY OF WISCONSIN-MADISON · PI VAN VEEN, BARRY D · 2013 to 2014
$352k
NIBIB NIH HHS R21 EB015542NIGMS NIH HHS R01 GM116916
6 · The paper itself

Abstract

backgroundThe basic mechanisms underlying the electroencephalograpy (EEG) response to transcranial magnetic stimulation (TMS) of the human cortex are not well understood. NEW

methodA state-space modeling methodology is developed to gain insight into the network nature of the TMS/EEG response. Cortical activity is modeled using a multivariariate autoregressive model with exogenous stimulation parameters representing the effect of TMS. An observation equation models EEG measurement of cortical activity. An expectation-maximization algorithm is developed to estimate the model parameters.

resultsThe methodology is used to assess two different hypotheses for the mechanisms underlying TMS/EEG in wakefulness and sleep. The integrated model hypothesizes that recurrent interactions between cortical regions are the source of TMS/EEG, while the segregated model hypothesizes that the TMS/EEG results from excitation of independent cortical oscillators. The results show that the relatively simple EEG response to TMS recorded during non-rapid-eye-movement sleep is described equally well by either the integrated or segregated model. However, the integrated model fits the more complex TMS/EEG of wakefulness much better than the segregated model. COMPARISON WITH EXISTING METHOD(S): Existing methods are limited to small numbers of cortical regions of interest or do not represent the effect of TMS. Our results are consistent with previous studies contrasting the complexity of TMS/EEG in wakefulness and sleep.

conclusionThe new method strongly suggests that effective feedback connections between cortical regions are required to produce the TMS/EEG in wakefulness.

Indexed as

Models, NeurologicalCerebral CortexData Interpretation, StatisticalElectroencephalographyHumansMultivariate AnalysisNeural PathwaysRegression AnalysisSignal Processing, Computer-AssistedTranscranial Magnetic StimulationElectroencephalographyExpectation–maximization algorithmMultivariate autoregressive model with exogenous stimulationState-space modelTranscranial magnetic stimulation

Identifiers

PMID30439389
PMCPMC6901095

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Registered trials

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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.