Assessing the spatiotemporal evolution of neuronal activation with single-trial event-related potentials and functional MRI. Proc Natl Acad Sci U S A

Department of Biological and Medical Psychology, University of Bergen, 5009 Bergen, Norway. tom.eichele@
Proceedings of the National Academy of Sciences (Impact Factor: 9.67). 01/2006; 102(49):17798-803. DOI: 10.1073/pnas.0505508102
Source: PubMed


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.

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Available from: Tom Eichele, Oct 01, 2015
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    • "Modern neuroscience benefits greatly from a multitude of imaging techniques that, individually, have helped to further our understanding of cognitive processing [1], [2] and improved clinical diagnostics [3], [4]. The combination of several imaging modalities originated in the context of epilepsy imaging [5]–[7] but has since then become an important asset in cognitive neuroscience. "
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    ABSTRACT: Multimodal data is ubiquitous in engineering, communication, robotics, vision or more generally speaking in industry and the sciences. All disciplines have developed their respective sets of analytic tools to fuse the information that is available in all measured modalities. In this paper we provide a review of classical as well as recent machine learning methods (specifically factor models) for fusing information from functional neuroimaging techniques such as LFP, EEG, MEG, fNIRS and fMRI. Early and late fusion scenarios are distinguished and appropriate factor models for the respective scenarios are presented along with example applications from selected multimodal neuroimaging studies. Further emphasis is given to the interpretability of the resulting model parameters, in particular by highlighting how factor models relate to physical models needed for source localization. The methods we discuss allow to extract information from neural data, which ultimately contributes to (a) better neuroscientific understanding, (b) enhance diagnostic performance and (c) discover neural signals of interest that correlate maximally with a given cognitive paradigm. While we clearly study the multimodal functional neuroimaging challenge, the discussed machine learning techniques have a wide applicability beyond, i.e. in general data fusion and may thus be informative to the general interested reader.
    Proceedings of the IEEE 08/2015; 109(9):1507 - 1530. DOI:10.1109/JPROC.2015.2425807 · 4.93 Impact Factor
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    • " - specified sum ( Hugdahl et al . , 2004 ) . Beliefs about others , Theory of Mind test ( panel #8 ) , infer the reason of an observed action ( Specht et al . , in preparation ) . Context updating , oddball detection task ( panel #9 ) , press a button whenever heard a tone with a deviating pitch in a stream of standard tones with the same pitch ( Eichele et al . , 2005 ) . All tasks were standard cognitive or neuropsychology tasks or tests that had been adapted to the MR scanner environment , with a slight modification of the Stroop task that also had a working memory component . All tasks had about the same duration and involved either a motor or a verbal response , with approximately the same freque"
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    ABSTRACT: In this paper we suggest the existence of a generalized task-related cortical network that is up-regulated whenever the task to be performed requires the allocation of generalized non-specific cognitive resources, independent of the specifics of the task to be performed. We have labeled this general purpose network, the extrinsic mode network (EMN) as complementary to the default mode network (DMN), such that the EMN is down-regulated during periods of task-absence, when the DMN is up-regulated, and vice versa. We conceptualize the EMN as a cortical network for extrinsic neuronal activity, similar to the DMN as being a cortical network for intrinsic neuronal activity. The EMN has essentially a fronto-temporo-parietal spatial distribution, including the inferior and middle frontal gyri, inferior parietal lobule, supplementary motor area, inferior temporal gyrus. We hypothesize that this network is always active regardless of the cognitive task being performed. We further suggest that failure of network up- and down-regulation dynamics may provide neuronal underpinnings for cognitive impairments seen in many mental disorders, such as, e.g., schizophrenia. We start by describing a common observation in functional imaging, the close overlap in fronto-parietal activations in healthy individuals to tasks that denote very different cognitive processes. We now suggest that this is because the brain utilizes the EMN network as a generalized response to tasks that exceeds a cognitive demand threshold and/or requires the processing of novel information. We further discuss how the EMN is related to the DMN, and how a network for extrinsic activity is related to a network for intrinsic activity. Finally, we discuss whether the EMN and DMN networks interact in a common single brain system, rather than being two separate and independent brain systems.
    Frontiers in Human Neuroscience 08/2015; 9. DOI:10.3389/fnhum.2015.00430 · 2.99 Impact Factor
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    • "The data were then low-pass filtered below 40 Hz and re-referenced to the algebraic average of the left and right mastoid electrodes. Artifacts caused by eye movements and muscular activity were removed using independent component analysis (a similar approach can be found in Debener et al. 2005; Eichele et al. 2005; Scheibe et al. 2010; San Martín et al. 2013). To analyze cue-locked ERP responses, the continuous EEG data were divided into 900-ms epochs, spanning from 400 ms before to 500 ms after the onset of the cue-pair stimulus. "
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    ABSTRACT: Adaptive choice behavior depends critically on identifying and learn-ing from outcome-predicting cues. We hypothesized that attention may be preferentially directed toward certain outcome-predicting cues. We studied this possibility by analyzing event-related potential (ERP) responses in humans during a probabilistic decision-making task. Participants viewed pairs of outcome-predicting visual cues and then chose to wager either a small (i.e., loss-minimizing) or large (i.e., gain-maximizing) amount of money. The cues were bilaterally presented, which allowed us to extract the relative neural responses to each cue by using a contralateral-versus-ipsilateral ERP contrast. We found an early lateralized ERP response, whose features matched the attention-shift-related N2pc component and whose amplitude scaled with the learned reward-predicting value of the cues as predicted by an attention-for-reward model. Consistently, we found a double dissociation involving the N2pc. Across participants, gain-maximization positively correlated with the N2pc amplitude to the most reliable gain-predicting cue, suggesting an attentional bias toward such cues. Conversely, loss-minimization was negatively cor-related with the N2pc amplitude to the most reliable loss-predicting cue, suggesting an attentional avoidance toward such stimuli. These results indicate that learned stimulus–reward associations can influ-ence rapid attention allocation, and that differences in this process are associated with individual differences in economic decision-making performance.
    Cerebral Cortex 08/2014; DOI:10.1093/cercor/bhu160 · 8.67 Impact Factor
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