Dataset Information

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

ABSTRACT: Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTMS-EEG artifact rejection. A key step of this algorithm is to decompose the spTMS-EEG data into statistically independent components (ICs), and then train a pattern classifier to automatically identify artifact components based on knowledge of the spatio-temporal profile of both neural and artefactual activities. The autocleaned and hand-cleaned data yield qualitatively similar group evoked potential waveforms. The algorithm achieves a 95% IC classification accuracy referenced to expert artifact rejection performance, and does so across a large number of spTMS-EEG data sets (n?=?90 stimulation sites), retains high accuracy across stimulation sites/subjects/populations/montages, and outperforms current automated algorithms. Moreover, the algorithm was superior to the artifact rejection performance of relatively novice individuals, who would be the likely users of spTMS-EEG as the technique becomes more broadly disseminated. In summary, our algorithm provides an automated, fast, objective, and accurate method for cleaning spTMS-EEG data, which can increase the utility of TMS-EEG in both clinical and basic neuroscience settings.

SUBMITTER: Wu W

PROVIDER: S-EPMC6866546 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

Wu Wei W Keller Corey J CJ Rogasch Nigel C NC Longwell Parker P Shpigel Emmanuel E Rolle Camarin E CE Etkin Amit A

Human brain mapping 20180113 4

Concurrent single-pulse TMS-EEG (spTMS-EEG) is an emerging noninvasive tool for probing causal brain dynamics in humans. However, in addition to the common artifacts in standard EEG data, spTMS-EEG data suffer from enormous stimulation-induced artifacts, posing significant challenges to the extraction of neural information. Typically, neural signals are analyzed after a manual time-intensive and often subjective process of artifact rejection. Here we describe a fully automated algorithm for spTM ...[more]

PMID: 29331054

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Project description:Objective.Simultaneous electroencephalography-functional magnetic resonance imaging (EEG-fMRI) recordings offer a high spatiotemporal resolution approach to study human brain and understand the underlying mechanisms mediating cognitive and behavioral processes. However, the high susceptibility of EEG to MRI-induced artifacts hinders a broad adaptation of this approach. More specifically, EEG data collected during fMRI acquisition are contaminated with MRI gradients and ballistocardiogram artifacts, in addition to artifacts of physiological origin. There have been several attempts for reducing these artifacts with manual and time-consuming pre-processing, which may result in biasing EEG data due to variations in selecting steps order, parameters, and classification of artifactual independent components. Thus, there is a strong urge to develop a fully automatic and comprehensive pipeline for reducing all major EEG artifacts. In this work, we introduced an open-access toolbox with a fully automatic pipeline for reducing artifacts from EEG data collected simultaneously with fMRI (refer to APPEAR).Approach.The pipeline integrates average template subtraction and independent component analysis to suppress both MRI-related and physiological artifacts. To validate our results, we tested APPEAR on EEG data recorded from healthy control subjects during resting-state (n= 48) and task-based (i.e. event-related-potentials (ERPs);n= 8) paradigms. The chosen gold standard is an expert manual review of the EEG database.Main results.We compared manually and automated corrected EEG data during resting-state using frequency analysis and continuous wavelet transformation and found no significant differences between the two corrections. A comparison between ERP data recorded during a so-called stop-signal task (e.g. amplitude measures and signal-to-noise ratio) also showed no differences between the manually and fully automatic fMRI-EEG-corrected data.Significance.APPEAR offers the first comprehensive open-source toolbox that can speed up advancement of EEG analysis and enhance replication by avoiding experimenters' preferences while allowing for processing large EEG-fMRI cohorts composed of hundreds of subjects with manageable researcher time and effort.

Project description:IntroductionTranscranial magnetic stimulation (TMS)-evoked potentials (TEPs) allow for probing cortical functions in health and pathology. However, there is uncertainty whether long-latency TMS-evoked potentials reflect functioning of the targeted cortical area. It has been suggested that components such as the TMS-evoked N100 are stereotypical and related to nonspecific sensory processes rather than transcranial effects of the changing magnetic field. In contrast, TEPs that vary according to the targeted brain region and are systematically lateralized toward the stimulated hemisphere can be considered to reflect activity in the stimulated brain region resulting from transcranial electromagnetic induction.MethodsTMS with concurrent 64-channel electroencephalography (EEG) was sequentially performed in homologous areas of both hemispheres. One sample of healthy adults received TMS to the dorsolateral prefrontal cortex; another sample received TMS to the temporo-occipital cortex. We analyzed late negative TEP deflections corresponding to the N100 component in motor cortex stimulation.ResultsTEP topography varied according to the stimulation target site. Long-latency negative TEP deflections were systematically lateralized (higher in ipsilateral compared to contralateral electrodes) in electrodes over the stimulated brain region. A calculation that removes evoked components that are not systematically lateralized relative to the stimulated hemisphere revealed negative maxima located around the respective target sites.ConclusionTEPs contain long-latency negative components that are lateralized toward the stimulated hemisphere and have their topographic maxima at the respective stimulation sites. They can be differentiated from co-occurring components that are invariable across different stimulation sites (probably reflecting coactivation of peripheral sensory afferences) according to their spatiotemporal patterns. Lateralized long-latency TEP components located at the stimulation site likely reflect activity evoked in the targeted cortex region by direct transcranial effects and are therefore suitable for assessing cortical functions.

Dataset Information

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

Publications

ARTIST: A fully automated artifact rejection algorithm for single-pulse TMS-EEG data.

Similar Datasets

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