Large-scale brain networks account for sustained and transient activity during target detection.
- Authors
- Mantini, Dante; Corbetta, Maurizio; Perrucci, Mauro Gianni; Romani, Gian Luca; Del Gratta, Cosimo
- Year
- 2009
- Journal
- NeuroImage
- PMID
- 18793734
- DOI
- 10.1016/j.neuroimage.2008.08.019
- PMCID
- PMC5745806
Target detection paradigms have been widely applied in the study of human cognitive functions, particularly those associated with arousal, attention, stimulus processing and memory. In EEG recordings, the detection of task-relevant stimuli elicits the P300 component, a transient response with latency around 300 ms. The P300 response has been shown to be affected by the amount of mental effort and learning, as well as habituation. Furthermore, trial-by-trial variability of the P300 component has been associated with inter-stimulus interval, target-to-target interval or target probability; however, understanding the mechanisms underlying this variability is still an open question. In order to investigate whether it could be related to the distinct cortical networks in which coherent intrinsic activity is organized, and to understand the contribution of those networks to target detection processes, we carried out a simultaneous EEG-fMRI study, collecting data from 13 healthy subjects during a visual oddball task. We identified five large-scale networks, that largely overlap with the dorsal attention, the ventral attention, the core, the visual and the sensory-motor networks. Since the P300 component has been consistently associated with target detection, we concentrated on the first two brain networks, the time-course of which showed a modulation with the P300 response as detected in simultaneous EEG recordings. A trial-by-trial EEG-fMRI correlation approach revealed that they are involved in target detection with different functional roles: the ventral attention network, dedicated to revealing salient stimuli, was transiently activated by the occurrence of targets; the dorsal attention network, usually engaged during voluntary orienting, reflected sustained activity, possibly related to search for targets.
ICA-based procedure for EEG artifact correction. (1) Pre-processed EEG signals are obtained by slice-based AAS method for imaging artifact attenuation, downsampling to 1 kHz and filtering in the band 0.5β40 Hz; (2) EEG artifact templates for subsequent artifact detection are prepared using the simultaneously recorded EOG and EKG, as well as simulated signals for BCG, slice MRI and volume MRI artifacts; (3) ICA full decomposition is performed on the pre-processed EEG data, and the resulting ICs are classified into brain signals and artifacts on the basis of the correlations with the EEG artifact templates; (4) Artifact-corrected EEG signals are obtained by subtracting the detected artifacts using the weights provided by the ICA decomposition.
Single-subject ERP analysis of artifact-corrected EEG signals. (Top panel) Scalp topography of ERPs associated with rare and frequent events; (Bottom panel) Scalp maps showing the temporal evolution of the P300 response.
Single-subject single-trial analysis of artifact-corrected EEG signals. (a) Raster plot of all trials at electrode Cz, aligned to stimulus onset, with electric potential expressed in color scale; (b) separate raster plots of trials at electrode Pz, corresponding to rare and frequent events; (c) reconstructed fluctuations of the P300 response across trials, with red bars placed in correspondence of rare events; (d) P300 reference time-course obtained from the convolution of the P300 response fluctuations with a canonical HRF.
Spatio-temporal analysis of the five networks consistently found across subjects. The first two networks, marked with a star, are significantly correlated with the P300 response fluctuations. For each network, sagittal, coronal and axial maps are shown, along with the average time-courses in response to rare events. The maps contain regions with positive (warm colors, yellow-orange) or negative (cool colors, azure-blue) z-scores. The average time-courses are normalized because they refer to the average activity of the brain patterns. The thick red line in each graph represents the best-fit to all data points.
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