Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling

A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repe...

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Autores principales:	Srinivas Ravishankar, Mariya Toneva, Leila Wehbe
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	MEG single-trial denoising predictive modeling shared response N400m Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/f154222573404fb6933ed163550aec8f
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id	oai:doaj.org-article:f154222573404fb6933ed163550aec8f
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spelling	oai:doaj.org-article:f154222573404fb6933ed163550aec8f2021-11-11T09:38:42ZSingle-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling1662-518810.3389/fncom.2021.737324https://doaj.org/article/f154222573404fb6933ed163550aec8f2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fncom.2021.737324/fullhttps://doaj.org/toc/1662-5188A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data by leveraging correlations in neural responses across subjects as they experience the same stimulus. We demonstrate its use in a naturalistic reading comprehension task with 8 subjects, with MEG data collected while they read the same story a single time. We find that our procedure results in data with reduced noise and allows for better discovery of neural phenomena. As proof-of-concept, we show that the N400m's correlation with word surprisal, an established finding in literature, is far more clearly observed in the denoised data than the original data. The denoised data also shows higher decoding and encoding accuracy than the original data, indicating that the neural signals associated with reading are either preserved or enhanced after the denoising procedure.Srinivas RavishankarMariya TonevaMariya TonevaLeila WehbeLeila WehbeFrontiers Media S.A.articleMEGsingle-trialdenoisingpredictive modelingshared responseN400mNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Computational Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	MEG single-trial denoising predictive modeling shared response N400m Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	MEG single-trial denoising predictive modeling shared response N400m Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Srinivas Ravishankar Mariya Toneva Mariya Toneva Leila Wehbe Leila Wehbe Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
description	A pervasive challenge in brain imaging is the presence of noise that hinders investigation of underlying neural processes, with Magnetoencephalography (MEG) in particular having very low Signal-to-Noise Ratio (SNR). The established strategy to increase MEG's SNR involves averaging multiple repetitions of data corresponding to the same stimulus. However, repetition of stimulus can be undesirable, because underlying neural activity has been shown to change across trials, and repeating stimuli limits the breadth of the stimulus space experienced by subjects. In particular, the rising popularity of naturalistic studies with a single viewing of a movie or story necessitates the discovery of new approaches to increase SNR. We introduce a simple framework to reduce noise in single-trial MEG data by leveraging correlations in neural responses across subjects as they experience the same stimulus. We demonstrate its use in a naturalistic reading comprehension task with 8 subjects, with MEG data collected while they read the same story a single time. We find that our procedure results in data with reduced noise and allows for better discovery of neural phenomena. As proof-of-concept, we show that the N400m's correlation with word surprisal, an established finding in literature, is far more clearly observed in the denoised data than the original data. The denoised data also shows higher decoding and encoding accuracy than the original data, indicating that the neural signals associated with reading are either preserved or enhanced after the denoising procedure.
format	article
author	Srinivas Ravishankar Mariya Toneva Mariya Toneva Leila Wehbe Leila Wehbe
author_facet	Srinivas Ravishankar Mariya Toneva Mariya Toneva Leila Wehbe Leila Wehbe
author_sort	Srinivas Ravishankar
title	Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
title_short	Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
title_full	Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
title_fullStr	Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
title_full_unstemmed	Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling
title_sort	single-trial meg data can be denoised through cross-subject predictive modeling
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/f154222573404fb6933ed163550aec8f
work_keys_str_mv	AT srinivasravishankar singletrialmegdatacanbedenoisedthroughcrosssubjectpredictivemodeling AT mariyatoneva singletrialmegdatacanbedenoisedthroughcrosssubjectpredictivemodeling AT mariyatoneva singletrialmegdatacanbedenoisedthroughcrosssubjectpredictivemodeling AT leilawehbe singletrialmegdatacanbedenoisedthroughcrosssubjectpredictivemodeling AT leilawehbe singletrialmegdatacanbedenoisedthroughcrosssubjectpredictivemodeling
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Single-Trial MEG Data Can Be Denoised Through Cross-Subject Predictive Modeling

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