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

2020 ◽  
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

AbstractIn 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.

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.

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 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.

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.

scholarly journals Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data

2017 ◽  
Vol 29 (4) ◽  
pp. 677-697 ◽  
Author(s):  
Tijl Grootswagers ◽  
Susan G. Wardle ◽  
Thomas A. Carlson
Keyword(s):  
Time Series ◽  
Cognitive Neuroscience ◽  
Pattern Analysis ◽  
Multivariate Pattern Analysis ◽  
Activation Patterns ◽  
Cognitive States ◽  
Classifier Selection ◽  
Computer Interfaces ◽  
Neuroimaging Data ◽  
Multivariate Pattern

Multivariate pattern analysis (MVPA) or brain decoding methods have become standard practice in analyzing fMRI data. Although decoding methods have been extensively applied in brain–computer interfaces, these methods have only recently been applied to time series neuroimaging data such as MEG and EEG to address experimental questions in cognitive neuroscience. In a tutorial style review, we describe a broad set of options to inform future time series decoding studies from a cognitive neuroscience perspective. Using example MEG data, we illustrate the effects that different options in the decoding analysis pipeline can have on experimental results where the aim is to “decode” different perceptual stimuli or cognitive states over time from dynamic brain activation patterns. We show that decisions made at both preprocessing (e.g., dimensionality reduction, subsampling, trial averaging) and decoding (e.g., classifier selection, cross-validation design) stages of the analysis can significantly affect the results. In addition to standard decoding, we describe extensions to MVPA for time-varying neuroimaging data including representational similarity analysis, temporal generalization, and the interpretation of classifier weight maps. Finally, we outline important caveats in the design and interpretation of time series decoding experiments.

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