scholarly journals Time-resolved multivariate pattern analysis of infant EEG data

2021 ◽  
Author(s):  
Kira Ashton ◽  
Benjamin Zinszer ◽  
Radoslaw Cichy ◽  
Charles Nelson ◽  
Richard Aslin ◽  
...  
Keyword(s):  
Time Course ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Time Resolved ◽  
Promising Tool ◽  
Eeg Data ◽  
Neuroimaging Data ◽  
Linear Svm ◽  
Multivariate Pattern ◽  
The Impact

Time-resolved multivariate pattern analysis (MVPA), a popular technique for analyzing magneto- and electro-encephalography (M/EEG) neuroimaging data, quantifies the extent and time-course by which neural representations support the discrimination of relevant stimuli dimensions. As EEG is widely used for infant neuroimaging, time-resolved MVPA of infant EEG data is a particularly promising tool for infant cognitive neuroscience. MVPA methods have recently been applied to common infant imaging methods such as EEG and fNIRS. In this tutorial, we provide and describe code to implement time-resolved, within-subject MVPA with infant EEG data. A pipeline for time-resolved MVPA based on linear SVM classification is described and implemented with accompanying code in both Matlab and Python. Results from a test dataset indicated that in both infants and adults this method reliably produced above chance classification accuracy. Extensions of the core pipeline are presented including both geometric- and accuracy-based representational similarity analysis, implemented in Python. Common choices of implementation are presented and discussed. As the amount of artifact-free EEG data contributed by each participant is lower in studies of infants than in studies of children and adults, we also explore and discuss the impact of varying participant-level inclusion thresholds on resulting MVPA findings in these datasets.

Testing key predictions of the associative account of mirror neurons in humans using multivariate pattern analysis

2014 ◽  
Vol 37 (2) ◽  
pp. 213-215 ◽  
Author(s):  
Nikolaas N. Oosterhof ◽  
Alison J. Wiggett ◽  
Emily S. Cross
Keyword(s):  
Mirror Neurons ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neuroimaging Data ◽  
Associative Account ◽  
Multivariate Pattern

AbstractCook et al. overstate the evidence supporting their associative account of mirror neurons in humans: most studies do not address a key property, action-specificity that generalizes across the visual and motor domains. Multivariate pattern analysis (MVPA) of neuroimaging data can address this concern, and we illustrate how MVPA can be used to test key predictions of their account.

scholarly journals Decoding Predictions and Violations of Object Position and Category in Time-resolved EEG

2020 ◽  
Author(s):  
Christopher J. Whyte ◽  
Amanda K. Robinson ◽  
Tijl Grootswagers ◽  
Hinze Hogendoorn ◽  
Thomas A. Carlson
Keyword(s):  
Sensory Input ◽  
Pattern Analysis ◽  
Prior Experience ◽  
Predictive Coding ◽  
Multivariate Pattern Analysis ◽  
Time Resolved ◽  
Current Input ◽  
Multivariate Pattern ◽  
Input Prediction ◽  
Decoding Accuracy

AbstractClassic models of predictive coding propose that sensory systems use information retained from prior experience to predict current sensory input. Any mismatch between predicted and current input (prediction error) is then fed forward up the hierarchy leading to a revision of the prediction. We tested this hypothesis in the domain of object vision using a combination of multivariate pattern analysis and time-resolved electroencephalography. We presented participants with sequences of images that stepped around fixation in a predictable order. On the majority of presentations, the images conformed to a consistent pattern of position order and object category order, however, on a subset of presentations the last image in the sequence violated the established pattern by either violating the predicted category or position of the object. Contrary to classic predictive coding when decoding position and category we found no differences in decoding accuracy between predictable and violation conditions. However, consistent with recent extensions of predictive coding, exploratory analyses showed that a greater proportion of predictions was made to the forthcoming position in the sequence than to either the previous position or the position behind the previous position suggesting that the visual system actively anticipates future input as opposed to just inferring current input.

scholarly journals Representational confusion: the plausible consequence of demeaning your data

2017 ◽  
Author(s):  
Fernando M. Ramírez
Keyword(s):  
Neural Coding ◽  
Pattern Analysis ◽  
Brain Area ◽  
Multivariate Pattern Analysis ◽  
Class Membership ◽  
Multivariate Pattern ◽  
Potential Interactions ◽  
The Impact ◽  
The Brain ◽  
Extract Information

AbstractThe use of multivariate pattern analysis (MVPA) methods has enjoyed this past decade a rapid increase in popularity among neuroscientists. More recently, similarity-based multivariate methods aiming not only to extract information regarding the class membership of stimuli from their associated brain patterns, say, decode a face from a potato, but to understand the form of the underlying representational structure associated with stimulus dimensions of interest, say, 2D grating or 3D face orientation, have flourished under the name of Representational Similarity Analysis (RSA). However, data-preprocessing steps implemented prior to RSA can significantly change the covariance (and correlation) structure of the data, hence possibly leading to representational confusion—i.e., a researcher inferring that brain area A encodes information according to representational scheme X, and not Y, when the opposite is true. Here, I demonstrate with simulations that time-series demeaning (including z-scoring) can plausibly lead to representational confusion. Further, I expose potential interactions between the effects of data demeaning and how the brain happens to encode information. Finally, I emphasize the importance in the context of similarity analyses of at least occasionally explicitly considering the direction of pattern vectors in multivariate space, rather than focusing exclusively on the relative location of their endpoints. Overall, I expect this article will promote awareness of the impact of data demeaning on inferences regarding representational structure and neural coding.

Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis

NeuroImage ◽  
2017 ◽  
Vol 159 ◽  
pp. 449-458 ◽  
Author(s):  
Sarah Alizadeh ◽  
Hamidreza Jamalabadi ◽  
Monika Schönauer ◽  
Christian Leibold ◽  
Steffen Gais
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neuroimaging Data ◽  
Multivariate Pattern

scholarly journals Deep-Learning-Based Multivariate Pattern Analysis (dMVPA): A Tutorial and a Toolbox

2021 ◽  
Vol 15 ◽  
Author(s):  
Karl M. Kuntzelman ◽  
Jacob M. Williams ◽  
Phui Cheng Lim ◽  
Ashok Samal ◽  
Prahalada K. Rao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Pattern Analysis ◽  
Time Frame ◽  
Multivariate Pattern Analysis ◽  
Similar Time ◽  
Learning Techniques ◽  
Neuroimaging Data ◽  
Pros And Cons ◽  
Multivariate Pattern

In recent years, multivariate pattern analysis (MVPA) has been hugely beneficial for cognitive neuroscience by making new experiment designs possible and by increasing the inferential power of functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and other neuroimaging methodologies. In a similar time frame, “deep learning” (a term for the use of artificial neural networks with convolutional, recurrent, or similarly sophisticated architectures) has produced a parallel revolution in the field of machine learning and has been employed across a wide variety of applications. Traditional MVPA also uses a form of machine learning, but most commonly with much simpler techniques based on linear calculations; a number of studies have applied deep learning techniques to neuroimaging data, but we believe that those have barely scratched the surface of the potential deep learning holds for the field. In this paper, we provide a brief introduction to deep learning for those new to the technique, explore the logistical pros and cons of using deep learning to analyze neuroimaging data – which we term “deep MVPA,” or dMVPA – and introduce a new software toolbox (the “Deep Learning In Neuroimaging: Exploration, Analysis, Tools, and Education” package, DeLINEATE for short) intended to facilitate dMVPA for neuroscientists (and indeed, scientists more broadly) everywhere.

scholarly journals Decoding Digits and Dice with Magnetoencephalography: Evidence for a Shared Representation of Magnitude

2018 ◽  
Vol 30 (7) ◽  
pp. 999-1010 ◽  
Author(s):  
Lina Teichmann ◽  
Tijl Grootswagers ◽  
Thomas Carlson ◽  
Anina N. Rich
Keyword(s):  
Time Course ◽  
Numerical Cognition ◽  
Pattern Analysis ◽  
Brain Activity ◽  
High Temporal Resolution ◽  
Multivariate Pattern Analysis ◽  
Ongoing Debate ◽  
Activation Patterns ◽  
Magnitude Processing ◽  
Multivariate Pattern

Numerical format describes the way magnitude is conveyed, for example, as a digit (“3”) or Roman numeral (“III”). In the field of numerical cognition, there is an ongoing debate of whether magnitude representation is independent of numerical format. Here, we examine the time course of magnitude processing when using different symbolic formats. We presented participants with a series of digits and dice patterns corresponding to the magnitudes of 1 to 6 while performing a 1-back task on magnitude. Magnetoencephalography offers an opportunity to record brain activity with high temporal resolution. Multivariate pattern analysis applied to magnetoencephalographic data allows us to draw conclusions about brain activation patterns underlying information processing over time. The results show that we can cross-decode magnitude when training the classifier on magnitude presented in one symbolic format and testing the classifier on the other symbolic format. This suggests a similar representation of these numerical symbols. In addition, results from a time generalization analysis show that digits were accessed slightly earlier than dice, demonstrating temporal asynchronies in their shared representation of magnitude. Together, our methods allow a distinction between format-specific signals and format-independent representations of magnitude showing evidence that there is a shared representation of magnitude accessed via different symbols.

scholarly journals Inter-subject pattern analysis: a straightforward and powerful scheme for group-level MVPA

2019 ◽  
Author(s):  
Qi Wang ◽  
Bastien Cagna ◽  
Thierry Chaminade ◽  
Sylvain Takerkart
Keyword(s):  
Cross Validation ◽  
Pattern Analysis ◽  
Functional Neuroimaging ◽  
Individual Variability ◽  
Group Level ◽  
Multivariate Pattern Analysis ◽  
Neuroimaging Data ◽  
Partial Overlap ◽  
Multivariate Pattern ◽  
Level Analysis

AbstractMultivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature. The standard one is hierarchical, combining the outcomes of within-subject decoding results in a second-level analysis. The alternative one, inter-subject pattern analysis, directly works at the group-level by using, e.g, a leave-one-subject-out cross-validation. This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual variability are parametrically controlled, as well as two real fMRI datasets comprising respectively 15 and 39 subjects. We show that these two strategies uncover distinct significant regions with partial overlap, and that inter-subject pattern analysis is able to detect smaller effects and to facilitate the interpretation. The core source code and data are openly available, allowing to fully reproduce most of these results.

scholarly journals Removal of Scanner Effects in Covariance Improves Multivariate Pattern Analysis in Neuroimaging Data

2019 ◽  
Author(s):  
Andrew A. Chen ◽  
Joanne C. Beer ◽  
Nicholas J. Tustison ◽  
Philip A. Cook ◽  
Russell T. Shinohara ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Neuroimaging Data ◽  
Mean And Variance ◽  
The Mean ◽  
Improved Performance ◽  
Sensitivity Specificity ◽  
Multivariate Pattern ◽  
The Brain ◽  
Multivariate Image

AbstractTo acquire larger samples for answering complex questions in neuroscience, researchers have increasingly turned to multi-site neuroimaging studies. However, these studies are hindered by differences in images acquired across multiple scanners. These effects have been shown to bias comparison between scanners, mask biologically meaningful associations, and even introduce spurious associations. To address this, the field has focused on harmonizing data by removing scanner-related effects in the mean and variance of measurements. Contemporaneously with the increase in popularity of multi-center imaging, the use of multivariate pattern analysis (MVPA) has also become commonplace. These approaches have been shown to provide improved sensitivity, specificity, and power due to their modeling the joint relationship across measurements in the brain. In this work, we demonstrate that methods for removing scanner effects in mean and variance may not be sufficient for MVPA. This stems from the fact that such methods fail to address how correlations between measurements can vary across scanners. Data from the Alzheimer’s Disease Neuroimaging Initiative is used to show that considerable differences in covariance exist across scanners and that popular harmonization techniques do not address this issue. We also propose a novel methodology that harmonizes covariance of multivariate image measurements across scanners and demonstrate its improved performance in data harmonization.

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