Multi-voxel Pattern Analysis Methods Research Articles

Tools of the Trade Multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists.

The family of neuroimaging analytical techniques known as multivoxel pattern analysis (MVPA) has dramatically increased in popularity over the past decade, particularly in social and affective neuroscience research using functional magnetic resonance imaging (fMRI). MVPA examines patterns of neural responses, rather than analyzing single voxel- or region-based values, as is customary in conventional univariate analyses. Here, we provide a practical introduction to MVPA and its most popular variants (namely, representational similarity analysis (RSA) and decoding analyses, such as classification using machine learning) for social and affective neuroscientists of all levels, particularly those new to such methods. We discuss how MVPA differs from traditional mass-univariate analyses, the benefits MVPA offers to social neuroscientists, experimental design and analysis considerations, step-by-step instructions for how to implement specific analyses in one’s own dataset and issues that are currently facing research using MVPA methods.

Statistical inference and multiple testing correction in classification-based multi-voxel pattern analysis (MVPA): Random permutations and cluster size control

An ever-increasing number of functional magnetic resonance imaging (fMRI) studies are now using information-based multi-voxel pattern analysis (MVPA) techniques to decode mental states. In doing so, they achieve a significantly greater sensitivity compared to when they use univariate frameworks. However, the new brain-decoding methods have also posed new challenges for analysis and statistical inference on the group level. We discuss why the usual procedure of performing t-tests on accuracy maps across subjects in order to produce a group statistic is inappropriate. We propose a solution to this problem for local MVPA approaches, which achieves higher sensitivity than other procedures. Our method uses random permutation tests on the single-subject level, and then combines the results on the group level with a bootstrap method. To preserve the spatial dependency induced by local MVPA methods, we generate a random permutation set and keep it fixed across all locations. This enables us to later apply a cluster size control for the multiple testing problem. More specifically, we explicitly compute the distribution of cluster sizes and use this to determine the p-values for each cluster. Using a volumetric searchlight decoding procedure, we demonstrate the validity and sensitivity of our approach using both simulated and real fMRI data sets. In comparison to the standard t-test procedure implemented in SPM8, our results showed a higher sensitivity. We discuss the theoretical applicability and the practical advantages of our approach, and outline its generalization to other local MVPA methods, such as surface decoding techniques.

Loss of reliable temporal structure in event-related averaging of naturalistic stimuli

To separate neural signals from noise, brain responses measured in neuroimaging are routinely averaged across space and time. However, such procedures may obscure some properties of neural activity. Recently, multi-voxel pattern analysis methods have demonstrated that patterns of activity across voxels contain valuable information that is concealed by spatial averaging. Here we show that temporal patterns of neural activity contain information that can discriminate different stimuli, even within brain regions that show no net activation to that stimulus class. Furthermore, we find that in many brain regions, responses to natural stimuli are highly context dependent. In such cases, prototypical event-related responses do not even exist for individual stimuli, so that averaging responses to the same stimulus within different contexts may worsen the effective signal-to-noise. As a result, analysis of the temporal structures of single events can reveal aspects of neural dynamics which cannot be detected using standard event-related averaging methods.

Viewed actions are mapped in retinotopic coordinates in the human visual pathways

Viewed object-oriented actions elicit widespread fMRI activation in the dorsal and ventral visual pathways. This activation is typically stronger in the hemisphere contralateral to the visual field in which action is seen. However, since in previous studies participants kept fixation at the same screen position throughout the scan, it was impossible to infer if the viewed actions are represented in retina-based coordinates or in a more elaborated coordinate system. Here, participants changed their gaze between experimental conditions, such that some conditions shared the same retinotopic coordinates (but differed in their screen position), while other pairs of conditions shared the opposite trait. The degree of similarity between the patterns of activation elicited by the various conditions was assessed using multivoxel pattern analysis methods. Regions of interest, showing robust overall activation, included the intraparietal sulcus (IPS) and the occipitotemporal cortex. In these areas, the correlation between activation patterns for conditions sharing the same retinotopic coordinates was significantly higher than that of those having different retinotopic coordinates. In contrast, the correlations between activation patterns for conditions with the same spatiotopic coordinates were not significantly greater than for non-spatiotopic conditions. These results suggest that viewed object-oriented actions are likely to be maintained in retinotopic-framed coordinates.

Commonality of neural representations of words and pictures

In this work we explore whether the patterns of brain activity associated with thinking about concrete objects are dependent on stimulus presentation format, whether an object is referred to by a written or pictorial form. Multi-voxel pattern analysis methods were applied to brain imaging (fMRI) data to identify the item category associated with brief viewings of each of 10 words (naming 5 tools and 5 dwellings) and, separately, with brief viewings of each of 10 pictures (line drawings) of the objects named by the words. These methods were able to identify the category of the picture the participant was viewing, based on neural activation patterns observed during word-viewing, and identify the category of the word the participant was viewing, based on neural activation patterns observed during picture-viewing, using data from only that participant or only from other participants. These results provide an empirical demonstration of object category identification across stimulus formats and across participants. In addition, we were able to identify the category of the word that the participant was viewing based on the patterns of neural activation generated during word-viewing by that participant or by all other participants. Similarly, we were able to identify with even higher accuracy the category of the picture the participant was viewing, based on the patterns of neural activation demonstrated during picture-viewing by that participant or by all other participants. The brain locations that were important for category identification were similar across participants and were distributed throughout the cortex where various object properties might be neurally represented. These findings indicate consistent triggering of semantic representations using different stimulus formats and suggest the presence of stable, distributed, and identifiable neural states that are common to pictorial and verbal input referring to object categories.

Disease state prediction from resting state functional connectivity

The application of multivoxel pattern analysis methods has attracted increasing attention, particularly for brain state prediction and real-time functional MRI applications. Support vector classification is the most popular of these techniques, owing to reports that it has better prediction accuracy and is less sensitive to noise. Support vector classification was applied to learn functional connectivity patterns that distinguish patients with depression from healthy volunteers. In addition, two feature selection algorithms were implemented (one filter method, one wrapper method) that incorporate reliability information into the feature selection process. These reliability feature selections methods were compared to two previously proposed feature selection methods. A support vector classifier was trained that reliably distinguishes healthy volunteers from clinically depressed patients. The reliability feature selection methods outperformed previously utilized methods. The proposed framework for applying support vector classification to functional connectivity data is applicable to other disease states beyond major depression.

Multivariate Patterns in Object-Selective Cortex Dissociate Perceptual and Physical Shape Similarity

Prior research has identified the lateral occipital complex (LOC) as a critical cortical region for the representation of object shape in humans. However, little is known about the nature of the representations contained in the LOC and their relationship to the perceptual experience of shape. We used human functional MRI to measure the physical, behavioral, and neural similarity between pairs of novel shapes to ask whether the representations of shape contained in subregions of the LOC more closely reflect the physical stimuli themselves, or the perceptual experience of those stimuli. Perceptual similarity measures for each pair of shapes were obtained from a psychophysical same-different task; physical similarity measures were based on stimulus parameters; and neural similarity measures were obtained from multivoxel pattern analysis methods applied to anterior LOC (pFs) and posterior LOC (LO). We found that the pattern of pairwise shape similarities in LO most closely matched physical shape similarities, whereas shape similarities in pFs most closely matched perceptual shape similarities. Further, shape representations were similar across participants in LO but highly variable across participants in pFs. Together, these findings indicate that activation patterns in subregions of object-selective cortex encode objects according to a hierarchy, with stimulus-based representations in posterior regions and subjective and observer-specific representations in anterior regions.

The Representation of Behavioral Choice for Motion in Human Visual Cortex

Single-unit recording studies have demonstrated a close link between neural activity in the middle temporal (MT) area and motion perception. In contrast, researchers using functional magnetic resonance imaging and multivoxel pattern analysis methods have recently documented direction-specific responses within many regions of the visual system (e.g., visual cortical areas V1-V4v) not normally associated with motion processing. Our goal was to determine how these direction-selective response patterns directly relate to the conscious perception of motion. We dissociated neuronal responses associated with the perceptual experience of motion from the physical presence of motion in the display by asking observers to report the perceived direction of an ambiguous stimulus. Activation patterns in the human MT complex closely matched the reported perceptual state of the observer, whereas patterns in other visual areas did not. These results suggest that, even when selective responses to a given feature are distributed relatively broadly across the visual system, the conscious experience of that feature may be primarily based on activity within specialized cortical areas.