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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"We decided to focus on the subsequent P3 that is thought to reflect a neural representation of a sensory process where the incoming stimulus is compared to the mental representation of the previous stimuli and the stimulus environment is updated. This is closely linked to concepts of orienting/surprise and predictive coding (Eichele et al., 2005). A later aspect of P3, the late positive complex (LPC) is thought to more closely represent working memory and response selection (Donchin, 1981; Coles, 1998, 2010; Polich, 2007). "
[Show abstract][Hide abstract]ABSTRACT: Background:
Tourette syndrome (TS) is a childhood-onset neurodevelopmental disorder and its impact on cognitive development needs further study. Evidence from neuropsychological, neuroimaging and electrophysiological studies suggests that the decline in tic severity and the ability to suppress tics relate to the development of self-regulatory functions in late childhood and adolescence. Hence, tasks measuring performance monitoring might provide insight into the regulation of tics in children with TS.
Twenty-five children with TS, including 14 with comorbid Attention-deficit/ hyperactivity disorder (ADHD), 39 children with ADHD and 35 typically developing children aged 8-12 years were tested with a modified Eriksen-Flanker task during a 34-channel electroencephalography (EEG) recording. Task performance, as well as stimulus-locked and response-locked event-related potentials (ERP) were analyzed and compared across groups.
Participants did not differ in their behavioral performance. Children with TS showed higher amplitudes of an early P3 component of the stimulus-locked ERPs in ensemble averages and in separate trial outcomes, suggesting heightened orienting and/or attention during stimulus evaluation. In response-locked averages, children with TS had a slightly higher positive complex before the motor response, likely also reflecting a late P3. Groups did not differ in post-response components, particularly in the error-related negativity (ERN) and error-related positivity (Pe).
These findings suggest that children with TS may employ additional attentional resources as a compensatory mechanism to maintain equal behavioral performance.
Full-text · Article · Apr 2016 · Frontiers in Neuroscience
"Given the orthogonal nature of the benefits and limitations of EEG and fMRI, it is a natural question to ask whether these measures can be combined in an effort to capitalize on the temporal and spatial resolutions provided by each modality. There currently exist several methods for " fusing " multimodal functional neuroimaging data together, such as partial least squares correlation (Lin et al., 2003; Martinez-Montes et al., 2004 ), independent component analysis (Beckmann and Smith, 2005; Liu & Calhoun, 2007; Calhoun et al., 2006; Eichele et al., 2009; Franco et al., 2008; Teipel et al., 2010; Xu et al., 2009; Calhoun et al., 2011;), structural equation modeling (Astolfi et al., 2004; Hamandi et al., 2008), multiple regression (De Martino et al., 2010; Eichele et al., 2005), and canonical correlation analysis (Correa et al., 2010a; Correa et al., 2010b; Correa et al., 2008). However, the focus of these methods is either source localization (for reviews, see Sui et al. (2012); Dähne et al. (2015)), or relating behavioral measures (e.g., response times) to brain data (for a review, see Krishnan et al. (2015) . "
[Show abstract][Hide abstract]ABSTRACT: The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.
"Modern neuroscience benefits greatly from a multitude of imaging techniques that, individually, have helped to further our understanding of cognitive processing ,  and improved clinical diagnostics ,  . The combination of several imaging modalities originated in the context of epilepsy imaging – but has since then become an important asset in cognitive neuroscience. "
[Show abstract][Hide abstract]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.
Full-text · Article · Aug 2015 · Proceedings of the IEEE