Abstract
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.
Highlights
Naturalistic stimuli are becoming increasingly more common in cognitive neuroscience (Nishimoto et al, 2011; Wehbe et al, 2014a; Huth et al, 2016; Sonkusare et al, 2019; Hamilton and Huth, 2020; Nastase et al, 2020)
We demonstrate the use of this framework for denoising Magnetoencephalography (MEG) data from a naturalistic reading comprehension task
While Transfer Learning (TL) approaches attempt to improve the performance of a classifier or regression model in a target domain by utilizing examples in a source domain, our goal is more general than classification/regression: We seek to obtain a new version of the data that amplifies stimulusrelated effects by minimizing stimulus-unrelated noise
Summary
Naturalistic stimuli are becoming increasingly more common in cognitive neuroscience (Nishimoto et al, 2011; Wehbe et al, 2014a; Huth et al, 2016; Sonkusare et al, 2019; Hamilton and Huth, 2020; Nastase et al, 2020). These stimuli are often presented only once to each participant in order to maximize the diversity of the data recorded in a fixed session, thereby sampling the stimulus space broadly (Nishimoto and Gallant, 2011; Nishimoto et al, 2011) and limiting the effect of habituation and repetition suppression. The paradigm shift toward naturalistic stimuli requires new ways of analyzing the resulting brain recordings.
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