scholarly journals Temporal Multivariate Pattern Analysis (tMVPA): a single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Temporal Information ◽  
Single Subject ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
Over Time

AbstractBackgroundfMRI provides spatial resolution that is unmatched by any non-invasive neuroimaging technique. Its temporal dynamics however are typically neglected due to the sluggishness of the hemodynamic based fMRI signal.New MethodsWe present temporal multivariate pattern analysis (tMVPA), a method for investigating the temporal evolution of neural representations in fMRI data, computed using pairs of single-trial BOLD time-courses, leveraging both spatial and temporal components of the fMRI signal. We implemented an expanding sliding window approach that allows identifying the time-window of an effect.ResultsWe demonstrate that tMVPA can successfully detect condition-specific multivariate modulations over time, in the absence of univariate differences. Using Monte Carlo simulations and synthetic data, we quantified family-wise error rate (FWER) and statistical power. Both at the group and at the single subject level, FWER was either at or significantly below 5%. For the group level, we reached the desired power with 18 subjects and 12 trials; for the single subject scenario, 14 trials were required to achieve comparable power.Comparison with existing methodstMVPA adds a temporal multivariate dimension to the tools available for fMRI analysis, enabling investigations of the evolution of neural representations over time. Moreover, tMVPA permits performing single subject inferential statistics by considering single-trial distribution.ConclusionThe growing interest in fMRI temporal dynamics, motivated by recent evidence suggesting that the BOLD signal carries temporal information at a finer scale than previously thought, advocates the need for analytical tools, such as the tMVPA approach proposed here, tailored to investigating BOLD temporal information.

scholarly journals Temporal multivariate pattern analysis (tMVPA): A single trial approach exploring the temporal dynamics of the BOLD signal

2018 ◽  
Vol 308 ◽  
pp. 74-87 ◽  
Author(s):  
Luca Vizioli ◽  
Alexander Bratch ◽  
Junpeng Lao ◽  
Kamil Ugurbil ◽  
Lars Muckli ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Bold Signal ◽  
Multivariate Pattern Analysis ◽  
Single Trial ◽  
Multivariate Pattern

scholarly journals Decoding the Brain: Neural Representation and the Limits of Multivariate Pattern Analysis in Cognitive Neuroscience

2017 ◽  
Author(s):  
J. Brendan Ritchie ◽  
David Michael Kaplan ◽  
Colin Klein
Keyword(s):  
Cognitive Neuroscience ◽  
Strong Evidence ◽  
Neural Activity ◽  
Pattern Analysis ◽  
Neural Representation ◽  
Neural Decoding ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Multivariate Pattern ◽  
The Brain

AbstractSince its introduction, multivariate pattern analysis (MVPA), or “neural decoding”, has transformed the field of cognitive neuroscience. Underlying its influence is a crucial inference, which we call the Decoder’s Dictum: if information can be decoded from patterns of neural activity, then this provides strong evidence about what information those patterns represent. Although the Dictum is a widely held and well-motivated principle in decoding research, it has received scant philosophical attention. We critically evaluate the Dictum, arguing that it is false: decodability is a poor guide for revealing the content of neural representations. However, we also suggest how the Dictum can be improved on, in order to better justify inferences about neural representation using MVPA.

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 Attention Biases Competition for Visual Representation via Dissociable Influences from Frontal and Parietal Cortex

2021 ◽  
pp. 1-17
Author(s):  
Andrew D. Sheldon ◽  
Elyana Saad ◽  
Muhammet I. Sahan ◽  
Emma E. Meyering ◽  
Michael J. Starrett ◽  
...  
Keyword(s):  
Parietal Cortex ◽  
Attentional Control ◽  
Pattern Analysis ◽  
Relevant Information ◽  
Time Varying ◽  
Multivariate Pattern Analysis ◽  
Neural Representations ◽  
Change State ◽  
Multivariate Pattern ◽  
Central Fixation

What mechanisms underlie the prioritization of neural representations of visually perceived information to guide behavior? We assessed the dynamics whereby attention biases competition for representation of visual stimuli by enhancing representations of relevant information and suppressing the irrelevant. Multivariate pattern analysis (MVPA) classifiers were trained to discriminate patterns of fMRI activity associated with each of three stimuli, within several predefined ROIs. Participants performed a change-detection task wherein two of three presented items flashed at 1 Hz, one to each side of central fixation. Both flashing stimuli would unpredictably change state, but participants covertly counted the number of changes only for the cued item. In the ventral occipito-temporal ROI, MVPA evidence (a proxy for representational fidelity) was dynamically enhanced for attended stimuli and suppressed for unattended stimuli, consistent with a mechanism of biased competition between stimulus representations. Frontal and parietal ROIs displayed a qualitatively distinct, more “source-like” profile, wherein MVPA evidence for only the attended stimulus could be observed above baseline levels. To assess how attentional modulation of ventral occipito-temporal representations might relate to signals originating in the frontal and/or parietal ROIs, we analyzed informational connectivity (IC), which indexes time-varying covariation between regional levels of MVPA evidence. Parietal-posterior IC was elevated during the task, but did not differ for cued versus uncued items. Frontal-posterior IC, in contrast, was sensitive to an item's priority status. Thus, although regions of frontal and parietal cortex act as sources of top–down attentional control, their precise functions likely differ.

scholarly journals Multivariate pattern analysis of MEG and EEG: a comparison of representational structure in time and space

2016 ◽  
Author(s):  
Radoslaw Martin Cichy ◽  
Dimitrios Pantazis
Keyword(s):  
Visual Cortex ◽  
Visual Processing ◽  
Pattern Analysis ◽  
Temporal Dynamics ◽  
Multivariate Pattern Analysis ◽  
Eeg Data ◽  
Early Visual Cortex ◽  
Spatio Temporal ◽  
High Level ◽  
Multivariate Pattern

1AbstractMultivariate pattern analysis of magnetoencephalography (MEG) and electroencephalography (EEG) data can reveal the rapid neural dynamics underlying cognition. However, MEG and EEG have systematic differences in sampling neural activity. This poses the question to which degree such measurement differences consistently bias the results of multivariate analysis applied to MEG and EEG activation patterns. To investigate, we conducted a concurrent MEG/EEG study while participants viewed images of everyday objects. We applied multivariate classification analyses to MEG and EEG data, and compared the resulting time courses to each other, and to fMRI data for an independent evaluation in space. We found that both MEG and EEG revealed the millisecond spatio-temporal dynamics of visual processing with largely equivalent results. Beyond yielding convergent results, we found that MEG and EEG also captured partly unique aspects of visual representations. Those unique components emerged earlier in time for MEG than for EEG. Identifying the sources of those unique components with fMRI, we found the locus for both MEG and EEG in high-level visual cortex, and in addition for MEG in early visual cortex. Together, our results show that multivariate analyses of MEG and EEG data offer a convergent and complimentary view on neural processing, and motivate the wider adoption of these methods in both MEG and EEG research.

scholarly journals SUPERNORMALS’ OVER-TIME BRAIN MAP LINKS TO ALZHEIMER’S PATHOLOGY: A MULTIVARIATE PATTERN ANALYSIS

2017 ◽  
Vol 1 (suppl_1) ◽  
pp. 434-434
Author(s):  
X. Wang ◽  
T. Baran ◽  
P. Ren ◽  
R.D. Raizada ◽  
F.V. Lin
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Alzheimer’S Pathology ◽  
Multivariate Pattern ◽  
Over Time

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.

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