scholarly journals The Decision Decoding ToolBOX (DDTBOX) – A multivariate pattern analysis toolbox for event-related potentials

2017 ◽  
Author(s):  
Stefan Bode ◽  
Daniel Feuerriegel ◽  
Daniel Bennett ◽  
Phillip M. Alday
Keyword(s):  
Open Source ◽  
Pattern Analysis ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Statistical Testing ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Related Potentials ◽  
Multivariate Pattern

AbstractIn recent years, neuroimaging research in cognitive neuroscience has increasingly used multivariate pattern analysis (MVPA) to investigate higher cognitive functions. Here we present DDTBOX, an open-source MVPA toolbox for electroencephalography (EEG) data. DDTBOX runs under MATLAB and is well integrated with the EEGLAB/ERPLAB and Fieldtrip toolboxes (Delorme and Makeig, 2004; Lopez-Calderon and Luck, 2014; Oostenveld et al. 2011). It trains support vector machines (SVMs) on patterns of event-related potential (ERP) amplitude data, following or preceding an event of interest, for classification or regression of experimental variables. These amplitude patterns can be extracted across space/electrodes (spatial decoding), time (temporal decoding), or both (spatiotemporal decoding). DDTBOX can also extract SVM feature weights, generate empirical chance distributions based on shuffled-labels decoding for group-level statistical testing, provide estimates of the prevalence of decodable information in the population, and perform a variety of corrections for multiple comparisons. It also includes plotting functions for single subject and group results. DDTBOX complements conventional analyses of ERP components, as subtle multivariate patterns can be detected that would be overlooked in standard analyses. It further allows for a more explorative search for information when no ERP component is known to be specifically linked to a cognitive process of interest. In summary, DDTBOX is an easy-to-use and open-source toolbox that allows for characterising the time-course of information related to various perceptual and cognitive processes. It can be applied to data from a large number of experimental paradigms and could therefore be a valuable tool for the neuroimaging community.

Multivariate pattern analysis of event-related potentials predicts the subjective relevance of everyday objects

2017 ◽  
Vol 55 ◽  
pp. 46-58 ◽  
Author(s):  
William Francis Turner ◽  
Phillip Johnston ◽  
Kathleen de Boer ◽  
Carmen Morawetz ◽  
Stefan Bode
Keyword(s):  
Pattern Analysis ◽  
Event Related Potentials ◽  
Multivariate Pattern Analysis ◽  
Everyday Objects ◽  
Related Potentials ◽  
Multivariate Pattern

scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractCognitive gains following cognitive training interventions are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single-subject level. We employed whole-brain multivariate pattern analysis with support vector machine (SVM) modeling to identify gray matter (GM) patterns that predicted higher vs. lower functioning after 40 h of ABCT at the single-subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an out-of-sample cross-validation analysis to unseen SCZ patients from an independent validation sample who underwent 50 h of ABCT. The whole-brain GM volume-based pattern classification predicted higher vs. lower functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%). In particular, greater baseline GM volumes in regions within superior temporal gyrus, thalamus, anterior cingulate, and cerebellum predicted improved functioning at the single-subject level following ABCT in SCZ participants. The present findings provide a structural MRI fingerprint associated with preserved GM volumes at a single baseline timepoint, which predicted improved functioning following an ABCT intervention, and serve as a model for how to facilitate precision clinical therapies for SCZ based on imaging data, operating at the single-subject level.

scholarly journals Multivariate pattern analysis of brain structure predicts functional outcome after auditory-based cognitive training interventions

2020 ◽  
Author(s):  
Lana Kambeitz-Ilankovic ◽  
Sophia Vinogradov ◽  
Julian Wenzel ◽  
Melissa Fisher ◽  
Shalaila S. Haas ◽  
...  
Keyword(s):  
Cognitive Training ◽  
Cross Validation ◽  
Pattern Analysis ◽  
Anterior Cingulate ◽  
Support Vector ◽  
Single Subject ◽  
Multivariate Pattern Analysis ◽  
Whole Brain ◽  
Training Interventions ◽  
Multivariate Pattern

AbstractBackgroundCognitive gains following cognitive training interventions (CT) are associated with improved functioning in people with schizophrenia (SCZ). However, considerable inter-individual variability is observed. Here, we evaluate the sensitivity of brain structural features to predict functional response to auditory-based cognitive training (ABCT) at a single subject level.MethodsWe employed whole-brain multivariate pattern analysis (MVPA) with support vector machine (SVM) modeling to identify grey matter (GM) patterns that predicted ‘higher’ vs. ‘lower’ functioning after 40 hours of ABCT at the single subject level in SCZ patients. The generalization capacity of the SVM model was evaluated by applying the original model through an Out-Of-Sample Cross Validation analysis (OOCV) to unseen SCZ patients from an independent sample that underwent 50 hours of ABCT.ResultsThe whole-brain GM volume-based pattern classification predicted ‘higher’ vs. ‘lower’ functioning at follow-up with a balanced accuracy (BAC) of 69.4% (sensitivity 72.2%, specificity 66.7%) as determined by nested cross-validation. The neuroanatomical model was generalizable to an independent cohort with a BAC of 62.1% (sensitivity 90.9%, specificity 33.3%).ConclusionsIn particular, greater baseline GM volume in regions within superior temporal gyrus, thalamus, anterior cingulate and cerebellum -- predicted improved functioning at the single-subject level following ABCT in SCZ participants.

scholarly journals Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine

NeuroImage ◽  
2016 ◽  
Vol 132 ◽  
pp. 157-166 ◽  
Author(s):  
Kristin A. Linn ◽  
Bilwaj Gaonkar ◽  
Theodore D. Satterthwaite ◽  
Jimit Doshi ◽  
Christos Davatzikos ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Pattern Analysis ◽  
Structural Mri ◽  
Support Vector ◽  
Control Group ◽  
Multivariate Pattern Analysis ◽  
Feature Normalization ◽  
Multivariate Pattern

scholarly journals Functional connectivity differences in early infancy precede autism symptoms: a multivariate pattern analysis

2019 ◽  
Author(s):  
Abigail Dickinson ◽  
Manjari Daniel ◽  
Andrew Marin ◽  
Bilwaj Goanker ◽  
Mirella Dapretto ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Cognitive Abilities ◽  
Pattern Analysis ◽  
Brain Connectivity ◽  
Familial Risk ◽  
Autism Spectrum ◽  
Support Vector ◽  
Multivariate Pattern Analysis ◽  
Autism Symptoms ◽  
Multivariate Pattern

AbstractFunctional brain connectivity is altered in children and adults with autism spectrum disorder (ASD). Mapping pre-symptomatic functional disruptions in ASD could identify infants based on neural risk, providing a crucial opportunity to mediate outcomes before behavioral symptoms emerge.Here we quantify functional connectivity using scalable EEG measures of oscillatory phase coherence (6-12Hz). Infants at high and low familial risk for ASD (N=65) underwent an EEG recording at 3 months of age and were assessed for ASD symptoms at 18 months using the Autism Diagnostic Observation Schedule-Toddler Module. Multivariate pattern analysis was used to examine early functional patterns that are associated with later ASD symptoms.Support vector regression (SVR) algorithms accurately predicted observed ASD symptoms at 18 months from EEG data at 3 months (r=0.76, p=0.02). Specifically, lower frontal connectivity and higher right temporo-parietal connectivity predicted higher ASD symptoms. The SVR model did not predict non-verbal cognitive abilities at 18 months (r=0.15, p=0.36), suggesting specificity of these brain alterations to ASD.These data suggest that frontal and temporo-parietal dysconnectivity play important roles in the early pathophysiology of ASD. Early functional differences in ASD can be captured using EEG during infancy and may inform much-needed advancements in the early detection of ASD.

scholarly journals Electrophysiological Correlates of Refreshing: Event-related Potentials Associated with Directing Reflective Attention to Face, Scene, or Word Representations

2015 ◽  
Vol 27 (9) ◽  
pp. 1823-1839 ◽  
Author(s):  
Matthew R. Johnson ◽  
Gregory McCarthy ◽  
Kathleen A. Muller ◽  
Samuel N. Brudner ◽  
Marcia K. Johnson
Keyword(s):  
Temporal Resolution ◽  
Time Course ◽  
Event Related Potentials ◽  
Multivariate Pattern Analysis ◽  
Top Down ◽  
Two Phase ◽  
Electrophysiological Response ◽  
Related Potentials ◽  
Multivariate Pattern ◽  
Directing Attention

Refreshing is the component cognitive process of directing reflective attention to one of several active mental representations. Previous studies using fMRI suggested that refresh tasks involve a component process of initiating refreshing as well as the top–down modulation of representational regions central to refreshing. However, those studies were limited by fMRI's low temporal resolution. In this study, we used EEG to examine the time course of refreshing on the scale of milliseconds rather than seconds. ERP analyses showed that a typical refresh task does have a distinct electrophysiological response as compared to a control condition and includes at least two main temporal components: an earlier (∼400 msec) positive peak reminiscent of a P3 response and a later (∼800–1400 msec) sustained positivity over several sites reminiscent of the late directing attention positivity. Overall, the evoked potentials for refreshing representations from three different visual categories (faces, scenes, words) were similar, but multivariate pattern analysis showed that some category information was nonetheless present in the EEG signal. When related to previous fMRI studies, these results are consistent with a two-phase model, with the first phase dominated by frontal control signals involved in initiating refreshing and the second by the top–down modulation of posterior perceptual cortical areas that constitutes refreshing a representation. This study also lays the foundation for future studies of the neural correlates of reflective attention at a finer temporal resolution than is possible using fMRI.

scholarly journals A Novel Cognition-Guided Neurofeedback BCI Dataset on Nicotine Addiction

2021 ◽  
Vol 15 ◽  
Author(s):  
Junjie Bu ◽  
Chang Liu ◽  
Huixing Gou ◽  
Hefan Gan ◽  
Yan Cheng ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Cue Reactivity ◽  
Nicotine Addiction ◽  
Event Related Potential ◽  
Multivariate Pattern Analysis ◽  
Neurofeedback Training ◽  
The Mean ◽  
Two Phases ◽  
Multivariate Pattern ◽  
Electroencephalogram Eeg

Compared with the traditional neurofeedback paradigm, the cognition-guided neurofeedback brain–computer interface (BCI) is a novel paradigm with significant effect on nicotine addiction. However, the cognition-guided neurofeedback BCI dataset is extremely lacking at present. This paper provides a BCI dataset based on a novel cognition-guided neurofeedback on nicotine addiction. Twenty-eight participants are recruited and involved in two visits of neurofeedback training. This cognition-guided neurofeedback includes two phases: an offline classifier construction and a real-time neurofeedback training. The original electroencephalogram (EEG) raw data of two phases are provided and evaluated in this paper. The event-related potential (ERP) amplitude and channel waveform suggest that our BCI dataset is of good quality and consistency. During neurofeedback training, the participants’ smoking cue reactivity patterns have a significant reduction. The mean accuracy of the multivariate pattern analysis (MVPA) classifier can reach approximately 70%. This novel cognition-guided neurofeedback BCI dataset can be used to develop comparisons with other neurofeedback systems and provide a reference for the development of other BCI algorithms and neurofeedback paradigms on addiction.

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