706 research outputs found
An introduction to time-resolved decoding analysis for M/EEG
The human brain is constantly processing and integrating information in order
to make decisions and interact with the world, for tasks from recognizing a
familiar face to playing a game of tennis. These complex cognitive processes
require communication between large populations of neurons. The non-invasive
neuroimaging methods of electroencephalography (EEG) and magnetoencephalography
(MEG) provide population measures of neural activity with millisecond precision
that allow us to study the temporal dynamics of cognitive processes. However,
multi-sensor M/EEG data is inherently high dimensional, making it difficult to
parse important signal from noise. Multivariate pattern analysis (MVPA) or
"decoding" methods offer vast potential for understanding high-dimensional
M/EEG neural data. MVPA can be used to distinguish between different conditions
and map the time courses of various neural processes, from basic sensory
processing to high-level cognitive processes. In this chapter, we discuss the
practical aspects of performing decoding analyses on M/EEG data as well as the
limitations of the method, and then we discuss some applications for
understanding representational dynamics in the human brain
Decoding information in the human hippocampus: a user's guide
Multi-voxel pattern analysis (MVPA), or 'decoding', of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus
What's in a pattern? Examining the Type of Signal Multivariate Analysis Uncovers At the Group Level
Multivoxel pattern analysis (MVPA) has gained enormous popularity in the
neuroimaging community over the past few years. At the group level, most MVPA
studies adopt an "information based" approach in which the sign of the effect
of individual subjects is discarded and a non-directional summary statistic is
carried over to the second level. This is in contrast to a directional
"activation based" approach typical in univariate group level analysis, in
which both signal magnitude and sign are taken into account. The transition
from examining effects in one voxel at a time vs. several voxels (univariate
vs. multivariate) has thus tacitly entailed a transition from directional to
non-directional signal definition at the group level. While a directional
group-level MVPA approach implies that individuals have similar multivariate
spatial patterns of activity, in a non-directional approach each individual may
have a distinct spatial pattern. Using an experimental dataset, we show that
directional and non-directional group-level MVPA approaches uncover distinct
brain regions with only partial overlap. We propose a method to quantify the
degree of spatial similarity in activation patterns over subjects. Applied to
an auditory task, we find higher values in auditory regions compared to control
regions.Comment: Revised versio
Neural activity in the reward-related brain regions predicts implicit self-esteem: A novel validity test of psychological measures using neuroimaging
Self-esteem, arguably the most important attitudes an individual possesses, has been a premier research topic in psychology for more than a century. Following a surge of interest in implicit attitude measures in the 90s, researchers have tried to assess self-esteem implicitly in order to circumvent the influence of biases inherent in explicit measures. However, the validity of implicit self-esteem measures remains elusive. Critical tests are often inconclusive, as the validity of such measures is examined in the backdrop of imperfect behavioral measures. To overcome this serious limitation, we tested the neural validity of the most widely used implicit self-esteem measure, the implicit association test (IAT). Given (1) the conceptualization of self-esteem as attitude toward the self, and (2) neuroscience findings that the reward-related brain regions represent an individual’s attitude or preference for an object when viewing its image, individual differences in implicit self-esteem should be associated with neural signals in the reward-related regions during passive-viewing of self-face (the most obvious representation of the self). Using multi-voxel pattern analyses (MVPA) on functional magnetic resonance imaging (fMRI) data, we demonstrated that the neural signals in the reward-related regions were robustly associated with implicit (but not explicit) self-esteem, thus providing unique evidence for the neural validity of the self-esteem IAT. In addition, both implicit and explicit self-esteem were related, although differently, to neural signals in regions involved in self-processing. Our finding highlights the utility of neuroscience methods in addressing fundamental psychological questions and providing unique insights into important psychological constructs
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data
Multivariate pattern analysis (MVPA) or brain decoding methods have become
standard practice in analysing fMRI data. Although decoding methods have been
extensively applied in Brain Computing Interfaces (BCI), 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 generalisation, and
the interpretation of classifier weight maps. Finally, we outline important
caveats in the design and interpretation of time-series decoding experiments.Comment: 64 pages, 15 figure
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave
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: http://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA
- …