Evidence map›Paper›PMID 35700943›Full record

ArticleNeuroImage2022

Empirical Bayesian localization of event-related time-frequency neural activity dynamics.

Chang Cai, Leighton Hinkley, Yijing Gao, Ali Hashemi, Stefan Haufe, Kensuke Sekihara, Srikantan S Nagarajan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Chang CaiNational Engineering Research Center for E-Learning, Central China Normal University, Wuhan, China; Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States. Electronic address: ccai@ccnu.edu.cn.
Leighton HinkleyDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States.
Yijing GaoDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States.
Ali HashemiBerlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Berlin, Germany; Machine Learning Group, Electrical Engineering and Computer Science Faculty, Technische Universität Berlin, Germany; Institut für Mathematik, Technische Universität Berlin, Germany.
Stefan HaufeBerlin Center for Advanced Neuroimaging, Charité Universitätsmedizin Berlin, Berlin, Germany; Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.
Kensuke SekiharaDepartment of Advanced Technology in Medicine, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo 113-8519, Japan; Signal Analysis Inc., Hachioji, Tokyo, Japan.
Srikantan S NagarajanDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143-0628, United States. Electronic address: sri@ucsf.edu.

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
Biomedical Imaging for Clinician ScientistsT32EB001631 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI THOMAS M LINK · 2005 to 2026
$5.6M
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 AphasiaR01NS100440 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MARIA LUISA GORNO TEMPINI, John Francis Houde · 2017 to 2026
$3.6M
Network Modelling of Multimodal Dynamics in Alzheimer’s Disease and DementiaRF1AG062196 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2019 to 2019
$3.0M
Imaging sensorimotor adaptation and compensation of speechR01DC013979 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2014 to 2018
$1.7M
Multimodal modeling framework for fusing structural and functional connectome dataR01EB022717 · NIBIB · WEILL MEDICAL COLL OF CORNELL UNIV · PI NAGARAJAN, SRIKANTAN S., RAJ, ASHISH · 2016 to 2018
$1.6M
Role of auditory feedback in speech motor control in Alzheimer's diseaseR01DC017696 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI HOUDE, JOHN FRANCIS, NAGARAJAN, SRIKANTAN S. · 2018 to 2020
$1.5M
Brain Plasticity and Clinical Consequences of Adult-Onset Asymmetric Hearing LossR56DC019282 · NIDCD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI CHEUNG, STEVEN WAN, NAGARAJAN, SRIKANTAN S. · 2021 to 2021
$685k
NIA NIH HHS RF1 AG062196NIBIB NIH HHS R01 EB022717NIBIB NIH HHS T32 EB001631NIDCD NIH HHS P50 DC019900NIDCD NIH HHS R01 DC013979NIDCD NIH HHS R01 DC017091NIDCD NIH HHS R01 DC017696NIDCD NIH HHS R56 DC019282NINDS NIH HHS R01 NS100440
6 · The paper itself

Abstract

Accurate reconstruction of the spatio-temporal dynamics of event-related cortical oscillations across human brain regions is an important problem in functional brain imaging and human cognitive neuroscience with magnetoencephalography (MEG) and electroencephalography (EEG). The problem is challenging not only in terms of localization of complex source configurations from sensor measurements with unknown noise and interference but also for reconstruction of transient event-related time-frequency dynamics of cortical oscillations. We recently proposed a robust empirical Bayesian algorithm for simultaneous reconstruction of complex brain source activity and noise covariance, in the context of evoked and resting-state data. In this paper, we expand upon this empirical Bayesian framework for optimal reconstruction of event-related time-frequency dynamics of regional cortical oscillations, referred to as time-frequency Champagne (TFC). This framework enables imaging of five-dimensional (space, time, and frequency) event-related brain activity from M/EEG data, and can be viewed as a time-frequency optimized adaptive Bayesian beamformer. We evaluate TFC in both simulations and several real datasets, with comparisons to benchmark standards - variants of time-frequency optimized adaptive beamformers (TFBF) as well as the sLORETA algorithm. In simulations, we demonstrate several advantages in estimating time-frequency cortical oscillatory dynamics compared to benchmarks. With real MEG data, we demonstrate across many datasets that the proposed approach is robust to highly correlated brain activity and low SNR data, and is able to accurately reconstruct cortical dynamics with data from just a few epochs.

Indexed as

Brain MappingMagnetoencephalographyAlgorithmsBayes TheoremBrainElectroencephalographyHumansBayesian inferenceBrain source power changesDynamics of neural activityElectromagnetic brain imagingFive-dimensional neuroimaging

Identifiers

PMID35700943
PMCPMC10411635

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.