Literature DB >> 29902585

Elucidating relations between fMRI, ECoG, and EEG through a common natural stimulus.

Stefan Haufe1, Paul DeGuzman2, Simon Henin3, Michael Arcaro4, Christopher J Honey5, Uri Hasson6, Lucas C Parra7.   

Abstract

Human brain mapping relies heavily on fMRI, ECoG and EEG, which capture different physiological signals. Relationships between these signals have been established in the context of specific tasks or during resting state, often using spatially confined concurrent recordings in animals. But it is not certain whether these correlations generalize to other contexts relevant for human cognitive neuroscience. Here, we address the case of complex naturalistic stimuli and ask two basic questions. First, how reliable are the responses evoked by a naturalistic audio-visual stimulus in each of these imaging methods, and second, how similar are stimulus-related responses across methods? To this end, we investigated a wide range of brain regions and frequency bands. We presented the same movie clip twice to three different cohorts of subjects (NEEG = 45, NfMRI = 11, NECoG = 5) and assessed stimulus-driven correlations across viewings and between imaging methods, thereby ruling out task-irrelevant confounds. All three imaging methods had similar repeat-reliability across viewings when fMRI and EEG data were averaged across subjects, highlighting the potential to achieve large signal-to-noise ratio by leveraging large sample sizes. The fMRI signal correlated positively with high-frequency ECoG power across multiple task-related cortical structures but positively with low-frequency EEG and ECoG power. In contrast to previous studies, these correlations were as strong for low-frequency as for high frequency ECoG. We also observed links between fMRI and infra-slow EEG voltage fluctuations. These results extend previous findings to the case of natural stimulus processing.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  ECoG; EEG; Inter-method correlation; Repeat-reliability; fMRI

Mesh:

Year:  2018        PMID: 29902585      PMCID: PMC6063527          DOI: 10.1016/j.neuroimage.2018.06.016

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  61 in total

1.  Neurophysiological investigation of the basis of the fMRI signal.

Authors:  N K Logothetis; J Pauls; M Augath; T Trinath; A Oeltermann
Journal:  Nature       Date:  2001-07-12       Impact factor: 49.962

2.  Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy.

Authors:  Matthias Moosmann; Petra Ritter; Ina Krastel; Andrea Brink; Sebastian Thees; Felix Blankenburg; Birol Taskin; Hellmuth Obrig; Arno Villringer
Journal:  Neuroimage       Date:  2003-09       Impact factor: 6.556

3.  Large-scale EEG/MEG source localization with spatial flexibility.

Authors:  Stefan Haufe; Ryota Tomioka; Thorsten Dickhaus; Claudia Sannelli; Benjamin Blankertz; Guido Nolte; Klaus-Robert Müller
Journal:  Neuroimage       Date:  2010-09-09       Impact factor: 6.556

4.  Visual stimulation elicits locked and induced gamma oscillations in monkey intracortical- and EEG-potentials, but not in human EEG.

Authors:  E Juergens; A Guettler; R Eckhorn
Journal:  Exp Brain Res       Date:  1999-11       Impact factor: 1.972

5.  Coupling between neuronal firing rate, gamma LFP, and BOLD fMRI is related to interneuronal correlations.

Authors:  Yuval Nir; Lior Fisch; Roy Mukamel; Hagar Gelbard-Sagiv; Amos Arieli; Itzhak Fried; Rafael Malach
Journal:  Curr Biol       Date:  2007-08-07       Impact factor: 10.834

6.  Infra-slow EEG fluctuations are correlated with resting-state network dynamics in fMRI.

Authors:  Tuija Hiltunen; Jussi Kantola; Ahmed Abou Elseoud; Pasi Lepola; Kalervo Suominen; Tuomo Starck; Juha Nikkinen; Jukka Remes; Osmo Tervonen; Satu Palva; Vesa Kiviniemi; J Matias Palva
Journal:  J Neurosci       Date:  2014-01-08       Impact factor: 6.167

7.  Finding brain oscillations with power dependencies in neuroimaging data.

Authors:  Sven Dähne; Vadim V Nikulin; David Ramírez; Peter J Schreier; Klaus-Robert Müller; Stefan Haufe
Journal:  Neuroimage       Date:  2014-04-08       Impact factor: 6.556

8.  Attention Strongly Modulates Reliability of Neural Responses to Naturalistic Narrative Stimuli.

Authors:  Jason J Ki; Simon P Kelly; Lucas C Parra
Journal:  J Neurosci       Date:  2016-03-09       Impact factor: 6.167

9.  Unbiased average age-appropriate atlases for pediatric studies.

Authors:  Vladimir Fonov; Alan C Evans; Kelly Botteron; C Robert Almli; Robert C McKinstry; D Louis Collins
Journal:  Neuroimage       Date:  2010-07-23       Impact factor: 6.556

10.  Intersubject consistency of cortical MEG signals during movie viewing.

Authors:  K Lankinen; J Saari; R Hari; M Koskinen
Journal:  Neuroimage       Date:  2014-02-12       Impact factor: 6.556

View more
  11 in total

1.  Brain-optimized extraction of complex sound features that drive continuous auditory perception.

Authors:  Julia Berezutskaya; Zachary V Freudenburg; Umut Güçlü; Marcel A J van Gerven; Nick F Ramsey
Journal:  PLoS Comput Biol       Date:  2020-07-02       Impact factor: 4.475

2.  Measuring shared responses across subjects using intersubject correlation.

Authors:  Samuel A Nastase; Valeria Gazzola; Uri Hasson; Christian Keysers
Journal:  Soc Cogn Affect Neurosci       Date:  2019-08-07       Impact factor: 3.436

3.  Naturalistic Stimuli: A Paradigm for Multi-Scale Functional Characterization of the Human Brain.

Authors:  Yizhen Zhang; Jung-Hoon Kim; David Brang; Zhongming Liu
Journal:  Curr Opin Biomed Eng       Date:  2021-06-02

4.  Enhancing Brain Connectivity With Infra-Low Frequency Neurofeedback During Aging: A Pilot Study.

Authors:  Olga R Dobrushina; Larisa A Dobrynina; Galina A Arina; Elena I Kremneva; Evgenia S Novikova; Mariia V Gubanova; Ekaterina V Pechenkova; Anastasia D Suslina; Vlada V Aristova; Viktoriya V Trubitsyna; Marina V Krotenkova
Journal:  Front Hum Neurosci       Date:  2022-05-30       Impact factor: 3.473

5.  Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network.

Authors:  Marco Marino; Giorgio Arcara; Camillo Porcaro; Dante Mantini
Journal:  Front Neurosci       Date:  2019-10-04       Impact factor: 4.677

6.  Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement.

Authors:  Avgusta Y Shestyuk; Karthik Kasinathan; Viswajith Karapoondinott; Robert T Knight; Ram Gurumoorthy
Journal:  PLoS One       Date:  2019-03-28       Impact factor: 3.240

7.  Functional connectivity of EEG is subject-specific, associated with phenotype, and different from fMRI.

Authors:  Maximilian Nentwich; Lei Ai; Jens Madsen; Qawi K Telesford; Stefan Haufe; Michael P Milham; Lucas C Parra
Journal:  Neuroimage       Date:  2020-05-31       Impact factor: 6.556

8.  Multi-modal Mapping of the Face Selective Ventral Temporal Cortex-A Group Study With Clinical Implications for ECS, ECoG, and fMRI.

Authors:  Takahiro Sanada; Christoph Kapeller; Michael Jordan; Johannes Grünwald; Takumi Mitsuhashi; Hiroshi Ogawa; Ryogo Anei; Christoph Guger
Journal:  Front Hum Neurosci       Date:  2021-03-15       Impact factor: 3.169

9.  Probabilistic comparison of gray and white matter coverage between depth and surface intracranial electrodes in epilepsy.

Authors:  Daria Nesterovich Anderson; Chantel M Charlebois; Elliot H Smith; Amir M Arain; Tyler S Davis; John D Rolston
Journal:  Sci Rep       Date:  2021-12-17       Impact factor: 4.379

10.  The Variability of Neural Responses to Naturalistic Videos Change with Age and Sex.

Authors:  Agustin Petroni; Samantha S Cohen; Lei Ai; Nicolas Langer; Simon Henin; Tamara Vanderwal; Michael P Milham; Lucas C Parra
Journal:  eNeuro       Date:  2018-01-27
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.