NeuroRA: A Python Toolbox of Representational Analysis From Multi-Modal Neural Data

In studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities and even bridge data from different species. However, previous toolboxes have been made to fit specific datasets. Here, we develop NeuroRA, a novel and easy-to-use toolbox for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g., EEG, MEG, fNIRS, fMRI, and other sources of neruroelectrophysiological data), behavioral data, and computer-simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. Using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity among different task conditions and conduct a representational analysis among different RDMs to achieve a cross-modal comparison. Besides, users can calculate neural pattern similarity (NPS), spatiotemporal pattern similarity (STPS), and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage, and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

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NeuroRA: A Python Toolbox of Representational Analysis from Multi-modal Neural Data

10.1101/2020.03.25.008086 ◽

2020 ◽

Author(s):

Zitong Lu ◽

Yixuan Ku

Keyword(s):

Univariate Analysis ◽

Simulated Data ◽

Spatiotemporal Pattern ◽

Multivariate Pattern Analysis ◽

Dissimilarity Matrix ◽

Neural Data ◽

Software Packages ◽

Pattern Similarity ◽

Multivariate Pattern ◽

The Brain

AbstractIn studies of cognitive neuroscience, multivariate pattern analysis (MVPA) is widely used as it offers richer information than traditional univariate analysis. Representational similarity analysis (RSA), as one method of MVPA, has become an effective decoding method based on neural data by calculating the similarity between different representations in the brain under different conditions. Moreover, RSA is suitable for researchers to compare data from different modalities, and even bridge data from different species. However, previous toolboxes have been made to fit for specific datasets. Here, we develop a novel and easy-to-use toolbox based on Python named NeuroRA for representational analysis. Our toolbox aims at conducting cross-modal data analysis from multi-modal neural data (e.g. EEG, MEG, fNIRS, ECoG, sEEG, neuroelectrophysiology, fMRI), behavioral data, and computer simulated data. Compared with previous software packages, our toolbox is more comprehensive and powerful. By using NeuroRA, users can not only calculate the representational dissimilarity matrix (RDM), which reflects the representational similarity between different conditions, but also conduct a representational analysis among different RDMs to achieve a cross-modal comparison. In addition, users can calculate neural pattern similarity, spatiotemporal pattern similarity (STPS) and inter-subject correlation (ISC) with this toolbox. NeuroRA also provides users with functions performing statistical analysis, storage and visualization of results. We introduce the structure, modules, features, and algorithms of NeuroRA in this paper, as well as examples applying the toolbox in published datasets.

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Stable Task Representations under Attentional Load Revealed with Multivariate Pattern Analysis of Human Brain Activity

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_00819 ◽

2015 ◽

Vol 27 (9) ◽

pp. 1789-1800 ◽

Cited By ~ 5

Author(s):

Jason L. Chan ◽

Aaron Kucyi ◽

Joseph F. X. DeSouza

Keyword(s):

Neural Activity ◽

Pattern Analysis ◽

Univariate Analysis ◽

Visual Presentation ◽

Error Rates ◽

Oculomotor Control ◽

Multivariate Pattern Analysis ◽

Attentional Load ◽

Multivariate Pattern ◽

Task Representations

Performing multiple tasks concurrently places a load on limited attentional resources and results in disrupted task performance. Although human neuroimaging studies have investigated the neural correlates of attentional load, how attentional load affects task processing is poorly understood. Here, task-related neural activity was investigated using fMRI with conventional univariate analysis and multivariate pattern analysis (MVPA) while participants performed blocks of prosaccades and antisaccades, either with or without a rapid serial visual presentation (RSVP) task. Performing prosaccades and antisaccades with RSVP increased error rates and RTs, decreased mean activation in frontoparietal brain areas associated with oculomotor control, and eliminated differences in activation between prosaccades and antisaccades. However, task identity could be decoded from spatial patterns of activation both in the absence and presence of an attentional load. Furthermore, in the FEFs and intraparietal sulcus, these spatial representations were found to be similar using cross-trial-type MVPA, which suggests stability under attentional load. These results demonstrate that attentional load may disrupt the strength of task-related neural activity, rather than the identity of task representations.

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Multi-voxel pattern analysis reveals a neural phenotype for trust bias in adolescents

10.31234/osf.io/pcynu ◽

2019 ◽

Author(s):

Sarah M. Tashjian ◽

Joao F Guassi Moreira ◽

Adriana Galván

Keyword(s):

Decision Making ◽

Individual Differences ◽

Social Functioning ◽

Pattern Analysis ◽

Neural Mechanisms ◽

Female Adolescents ◽

Multivariate Pattern Analysis ◽

Brain Response ◽

Pattern Similarity ◽

Multivariate Pattern

The extent to which individuals are inclined to judge unfamiliar others as trustworthy can have important implications for social functioning. Using multivariate pattern analysis (MVPA), a neural phenotype of trust bias was identified in 48 human adolescents (ages 14-18 years, 26 female). Adolescents who exhibited more similar brain response to faces at the extremes of a trustworthy gradient were more likely to rate neutral faces as trustworthy. This relation between neural pattern representation and trust bias was evinced in the amygdala. Amygdala-insula connectivity dissimilarity to faces at the extremes of the trustworthy gradient was associated with greater trust bias to neutral faces, serving as a distinct circuit-level contributor to decision making over and above of amygdala pattern similarity. These findings aid understanding of neural mechanisms contributing to individual differences in social evaluations of ambiguity.

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Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data

10.1101/2020.05.28.122036 ◽

2020 ◽

Author(s):

Andrew E. Silva ◽

Benjamin Thompson ◽

Zili Liu

Keyword(s):

Visual Cortex ◽

Visual Processing ◽

Pattern Analysis ◽

Flicker Noise ◽

Univariate Analysis ◽

Motion Processing ◽

List Type ◽

Multivariate Pattern Analysis ◽

Motion Energy ◽

Multivariate Pattern

AbstractThis study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy which is processed by low-level motion detectors. Models of motion processing propose a mechanism called motion opponency that reduces the processing of flicker noise. Motion opponency involves the pooling of local motion signals to calculate an overall motion direction. A neural correlate of motion opponency has been observed in human area MT+/V5 using fMRI, whereby stimuli with perfectly balanced motion energy constructed from dots moving in counter-phase elicit a weaker BOLD response than non-balanced (in-phase) motion stimuli. Building on this previous work, we used multivariate pattern analysis to examine whether the patterns of brain activation elicited by motion opponent stimuli resemble the activation elicited by flicker noise across the human visual cortex. Robust multivariate signatures of opponency were observed in V5 and in V3A. Our results support the notion that V5 is centrally involved in motion opponency and in the reduction of flicker noise during visual processing. Furthermore, these results demonstrate the utility of powerful multivariate analysis methods in revealing the role of additional visual areas, such as V3A, in opponency and in motion processing more generally.HighlightsOpponency is demonstrated in multivariate and univariate analysis of V5 data.Multivariate fMRI also implicates V3A in motion opponency.Multivariate analyses are useful for examining opponency throughout visual cortex.

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Neural Pattern Similarity Unveils the Integration of Social Information and Aversive Learning

Cerebral Cortex ◽

10.1093/cercor/bhaa122 ◽

2020 ◽

Vol 30 (10) ◽

pp. 5410-5419

Author(s):

Irem Undeger ◽

Renée M Visser ◽

Andreas Olsson

Keyword(s):

Inferior Frontal Gyrus ◽

Learning Task ◽

Anterior Cingulate ◽

Neural Activation ◽

Multivariate Pattern Analysis ◽

Aversive Learning ◽

Activation Patterns ◽

Pattern Similarity ◽

Making Choices ◽

Multivariate Pattern

Abstract Attributing intentions to others’ actions is important for learning to avoid their potentially harmful consequences. Here, we used functional magnetic resonance imaging multivariate pattern analysis to investigate how the brain integrates information about others’ intentions with the aversive outcome of their actions. In an interactive aversive learning task, participants (n = 33) were scanned while watching two alleged coparticipants (confederates)—one making choices intentionally and the other unintentionally—leading to aversive (a mild shock) or safe (no shock) outcomes to the participant. We assessed the trial-by-trial changes in participants’ neural activation patterns related to observing the coparticipants and experiencing the outcome of their choices. Participants reported a higher number of shocks, more discomfort, and more anger to shocks given by the intentional player. Intentionality enhanced responses to aversive actions in the insula, anterior cingulate cortex, inferior frontal gyrus, dorsal medial prefrontal cortex, and the anterior superior temporal sulcus. Our findings indicate that neural pattern similarities index the integration of social and threat information across the cortex.

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Multivoxel Pattern Analysis Reveals a Neural Phenotype for Trust Bias in Adolescents

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01448 ◽

2019 ◽

Vol 31 (11) ◽

pp. 1726-1741 ◽

Cited By ~ 1

Author(s):

Sarah M. Tashjian ◽

João F. Guassi Moreira ◽

Adriana Galván

Keyword(s):

Decision Making ◽

Individual Differences ◽

Pattern Analysis ◽

Neural Mechanisms ◽

Female Adolescents ◽

Multivariate Pattern Analysis ◽

Brain Response ◽

Multivoxel Pattern Analysis ◽

Pattern Similarity ◽

Multivariate Pattern

The extent to which individuals are inclined to judge unfamiliar others as trustworthy can have important implications for social functioning. Using multivariate pattern analysis, a neural phenotype of trust bias was identified in 48 human adolescents (ages 14–18 years, 26 female). Adolescents who exhibited more similar brain response to faces at the extremes of a trustworthy gradient were more likely to rate neutral faces as trustworthy. This relation between neural pattern representation and trust bias was evinced in the amygdala. Amygdala–insula connectivity dissimilarity to faces at the extremes of the trustworthy gradient was associated with greater trust bias to neutral faces, serving as a distinct circuit-level contributor to decision-making over and above of amygdala pattern similarity. These findings aid understanding of neural mechanisms contributing to individual differences in social evaluations of ambiguity.

Download Full-text