CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave.
TL;DR: CoSMoMVPA is a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens.
read more
Abstract: Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto- and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: cosmomvpa.org
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Distributed and hierarchical neural encoding of multidimensional biological motion attributes in the human brain.
Ruidi Wang,Xiqian Lu,Yi Jiang +2 more
TL;DR: In this paper , the authors used multiple regression representational similarity analysis to identify distributed brain areas, respectively, related to the processing of facing direction, gender, and emotional state conveyed by BM.
5
Neural Representations of Death in the Cortical Midline Structures Promote Temporal Discounting.
Kuniaki Yanagisawa,Emiko S. Kashima,Yayoi Shigemune,Ryusuke Nakai,Nobuhito Abe +4 more
- 01 Apr 2021
TL;DR: It is suggested that death-related neural representations are distributed across default mode regions, and neural populations in the cortical midline structures play a crucial role in the integration of one's own death into economic decision-making.
Auditory dyadic interactions through the “eye” of the social brain: How visual is the posterior STS interaction region?
Julia Landsiedel,Kami Koldewyn +1 more
TL;DR: The posterior STS interaction region is sensitive to auditory social interactions, suggesting its involvement in heteromodal representation of social interactions.
5
Similarity judgments and cortical visual responses reflect different properties of object and scene categories in naturalistic images
TL;DR: There is no simple mapping between responses in high-level visual cortex and behavior – each domain reflects different visual properties of the images and responses inHigh level visual cortex may correspond to intermediate stages of processing between basic visual features and the conceptual categories that dominate the behavioral response.
Structural and functional connectivity of the inferior temporal numeral area
TL;DR: In this article , the authors used diffusion-weighted imaging and functional magnetic resonance imaging to contrast structural and functional connectivity between left and right hemisphere ITNAs and a left hemisphere letter-prefering region.
5
References
•Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
•Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
28.9K
EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis.
Arnaud Delorme,Scott Makeig +1 more
TL;DR: EELAB as mentioned in this paper is a toolbox and graphic user interface for processing collections of single-trial and/or averaged EEG data of any number of channels, including EEG data, channel and event information importing, data visualization (scrolling, scalp map and dipole model plotting, plus multi-trial ERP-image plots), preprocessing (including artifact rejection, filtering, epoch selection, and averaging), Independent Component Analysis (ICA) and time/frequency decomposition including channel and component cross-coherence supported by bootstrap statistical methods based on data resampling.
22.4K
The Psychophysics Toolbox.
TL;DR: The Psychophysics Toolbox is a software package that supports visual psychophysics and its routines provide an interface between a high-level interpreted language and the video display hardware.