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Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

ABSTRACT: The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modulations of simultaneously acquired single trial ERPs. The method was used to study a variant of a two-stimulus auditory target detection (odd-ball) paradigm that manipulated predictability through alternations of stimulus sequences with random or regular target-to-target intervals. In addition to electrophysiologic and hemodynamic evoked responses to auditory targets per se, single-trial modulations were expressed during the latencies of the P2 (170-ms), N2 (200-ms), and P3 (320-ms) components and predicted spatially separated fMRI activation patterns. These spatiotemporal matches, i.e., the prediction of hemodynamic activation by time-variant information from single trial ERPs, permit inferences about regional responses using fMRI with the temporal resolution provided by electrophysiology.

SUBMITTER: Eichele T

PROVIDER: S-EPMC1295592 | biostudies-literature | 2005 Dec

REPOSITORIES: biostudies-literature

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Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

Eichele Tom T Specht Karsten K Moosmann Matthias M Jongsma Marijtje L A ML Quiroga Rodrigo Quian RQ Nordby Helge H Hugdahl Kenneth K

Proceedings of the National Academy of Sciences of the United States of America 20051128 49

The brain acts as an integrated information processing system, which methods in cognitive neuroscience have so far depicted in a fragmented fashion. Here, we propose a simple and robust way to integrate functional MRI (fMRI) with single trial event-related potentials (ERP) to provide a more complete spatiotemporal characterization of evoked responses in the human brain. The idea behind the approach is to find brain regions whose fMRI responses can be predicted by paradigm-induced amplitude modul ...[more]

PMID: 16314575

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Project description:Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone 'cues' and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential 'prediction signal' that reflects this fundamental learning process.

Project description:Event-related potentials (ERPs) summarize electrophysiological brain response to specific stimuli. They can be considered as correlated functions of time with both spatial correlation across electrodes and nested correlations within subjects. Commonly used analytical methods for ERPs often focus on pre-determined extracted components and/or ignore the correlation among electrodes or subjects, which can miss important insights, and tend to be sensitive to outlying subjects, time points or electrodes. Motivated by ERP data in a smoking cessation study, we introduce a Bayesian spatial functional regression framework that models the entire ERPs as spatially correlated functional responses and the stimulus types as covariates. This novel framework relies on mixed models to characterize the effects of stimuli while simultaneously accounting for the multilevel correlation structure. The spatial correlation among the ERP profiles is captured through basis-space Matérn assumptions that allow either separable or nonseparable spatial correlations over time. We induce both adaptive regularization over time and spatial smoothness across electrodes via a correlated normal-exponential-gamma (CNEG) prior on the fixed effect coefficient functions. Our proposed framework includes both Gaussian models as well as robust models using heavier-tailed distributions to make the regression automatically robust to outliers. We introduce predictive methods to select among Gaussian vs. robust models and models with separable vs. non-separable spatiotemporal correlation structures. Our proposed analysis produces global tests for stimuli effects across entire time (or time-frequency) and electrode domains, plus multiplicity-adjusted pointwise inference based on experiment-wise error rate or false discovery rate to flag spatiotemporal (or spatio-temporal-frequency) regions that characterize stimuli differences, and can also produce inference for any prespecified waveform components. Our analysis of the smoking cessation ERP data set reveals numerous effects across different types of visual stimuli.

Dataset Information

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

Publications

Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI.

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