Elsevier

NeuroImage

Volume 57, Issue 1, 1 July 2011, Pages 113-123
NeuroImage

Multi-voxel pattern analysis of fMRI data predicts clinical symptom severity

https://doi.org/10.1016/j.neuroimage.2011.04.016Get rights and content

Abstract

Multi-voxel pattern analysis (MVPA) has been applied successfully to a variety of fMRI research questions in healthy participants. The full potential of applying MVPA to functional data from patient groups has yet to be fully explored. Our goal in this study was to investigate whether MVPA might yield a sensitive predictor of patient symptoms. We also sought to demonstrate that this benefit can be realized from existing datasets, even when they were not designed with MVPA in mind. We analyzed data from an fMRI study of the neural basis for face processing in individuals with an Autism Spectrum Disorder (ASD), who often show fusiform gyrus hypoactivation when presented with unfamiliar faces, compared to controls. We found reliable correlations between MVPA classification performance and standardized measures of symptom severity that exceeded those observed using a univariate measure; a relation that was robust across variations in ROI definition. A searchlight analysis across the ventral temporal lobes identified regions with relationships between classification performance and symptom severity that were not detected using mean activation. These analyses illustrate that MVPA has the potential to act as a sensitive functional biomarker of patient severity.

Research highlights

► MVPA can be used to sensitively predict patient symptoms from fMRI data. ► More clinically severe ASD patients have less classifiable fusiform face patterns. ► Relating MVPA to symptoms can identify regions not found using mean activation.

Introduction

In 2001, Haxby and colleagues demonstrated that information that was not evident from univariate analyses could be decoded from patterns of fMRI activation across voxels (Haxby et al., 2001). Since the publication of this seminal study, a class of techniques referred to as multi-voxel pattern analysis (MVPA) has been applied to a variety of questions (e.g., Cox and Savoy, 2003, O'Toole et al., 2005, Spiridon and Kanwisher, 2002; for reviews see Haynes and Rees, 2006, Norman et al., 2006, O'Toole et al., 2007). These techniques analyze recorded activity patterns using tools such as machine learning classifiers, to measure the information present within populations of voxels.

Despite the success of applying MVPA in a wide range of contexts, few studies have extended the method to investigations of atypical neural activity. Several functional studies have used multivariate approaches to classify individuals into different groups (in contrast to classifying trials into conditions) for depression (Fu et al., 2008) and drug addiction (Zhang et al., 2005). Similarly, functional models have predicted future responses to Cognitive Behavioral Therapy (full vs. partial) in depressed patients (Costafreda et al., 2009) and estimated years-to-onset of Huntington's disease symptoms (Rizk-Jackson et al., 2010). Only a small number of clinical studies have conducted within-subject between-condition MVPA: abnormal activity patterns have been reported during object representation and working memory processes in schizophrenia (Kim et al., 2010, Yoon et al., 2008), and unusual patterns have been detected in the medial prefrontal cortex of participants on the autism spectrum during mental state reflections (Gilbert et al., 2009). None of these patient studies used MVPA measures to predict individual differences in clinical symptom severity.

The primary goal of this paper is to report the potential for MVPA to give high levels of sensitivity in relating fMRI data to patient symptoms. By incorporating the unique contributions of individual voxels, subtleties within activation patterns are reflected in MVPA outcome measures, such as classification performance. Such subtleties are often ignored in univariate analyses, where the levels of voxel activation, or mean activation of a region, are evaluated. In a region that is functionally relevant to a disorder with atypical cognitive or behavioral symptoms, this multivariate characterization could act as a sensitive measure of variation among affected individuals. To investigate this possibility here, we use MVPA to examine a dataset from a study of fusiform gyrus activation in individuals with an Autism Spectrum Disorder (ASD; Schultz et al., 2008).

Investigating the face processing differences in people with autism has been a very active area of research, not least because of the importance of face processing to successful social functioning. Among other deficits, ASD patients show large impairments in recognizing facial identity across changes in viewing conditions (Wolf et al., 2008), despite typical performance at processing complex objects (Boucher and Lewis, 1992, Wolf et al., 2008). A large number of functional neuroimaging investigations have studied the neural substrates of these behavioral abnormalities, particularly in the fusiform gyrus, a highly face-selective brain region, which has come to be known as the fusiform face area (FFA; Kanwisher et al., 1997, Kanwisher and Yovel, 2006, Winston et al., 2004), although the specificity of fusiform computations is much debated (Gauthier et al., 1999, Kanwisher and Yovel, 2006, Schultz et al., 2003). The FFA is strongly activated when typically-developing individuals view faces, but is frequently hypoactive when individuals with an ASD view unfamiliar faces (Critchley et al., 2000, Deeley et al., 2007, Grelotti et al., 2005, Hall et al., 2003, Hubl et al., 2003, Koshino et al., 2008, Pierce et al., 2001, Piggot et al., 2004, Schultz et al., 2000, Schultz et al., 2008, Wang et al., 2004). The processes responsible for this relative hypoactivation are an area of ongoing debate.1 Although it is a related and important question, the present study is neutral on the proximate causes of hypoactivation, focusing instead on the potential for MVPA to give a sensitive functional biomarker.

A secondary aim of the paper is to provide an example of the way MVPA can be used to realize this benefit in studies designed without MVPA in mind. This suggestion may resonate with patient-group investigators looking to make the most of existing datasets, which are often expensive, time-consuming and logistically difficult to obtain. In this study, we illustrate this point with an extreme case; analyzing a dataset from an fMRI study that was not planned with MVPA in mind and that was, in many ways, sub-optimal for this purpose. Designing a study for subsequent MVPA typically involves a number of considerations: The established sensitivity of MVPA to subtle visual differences (e.g., Kamitani and Tong, 2005) makes controlling visual properties, such as luminance and the visual angle of presented images, particularly important. Additionally, in order to draw conclusions about a specific category, an appropriate number of classes are required. Any two-way classification is affected by the activity patterns of both classes, so multiple comparisons are required for drawing conclusions about one condition of interest. For example, above-chance classification between class A and class B could be due to encoded class A information (where the classifier succeeds based on ‘A vs. not-A’) or class B information (‘B vs. not-B’). The successful separation of A vs. B, C and D, but not among B, C and D, however, gives some confidence that A, or at least certain features of A, are central to an area's encoded information (as applied in O'Toole et al., 2005, Spiridon and Kanwisher, 2002).

The above design considerations are recommended where possible; however, it is still possible to benefit from the MVPA approach when a dataset has been designed for univariate analyses. The dataset analyzed here was collected to examine how fusiform activation varies during face tasks that differ in their attentional and perceptual loads (Schultz et al., 2008), in individuals with autism. Designed specifically for univariate analyses, the study was organized in a way contrary to the optimal design considerations reviewed above: the stimuli in each condition were not individually matched for luminance, and two categories of stimuli, faces and houses, were presented to participants. Additionally, the house condition required participants to make a ‘same’ vs. ‘different’ judgment about two side-by-side houses, while the face condition was intentionally varied along several dimensions by run, including the perceptual judgments required, number of face stimuli, and presence or absence of emotional expression. A constant house condition was included in the study to act as a common baseline for between-run comparisons of the different face tasks. Finally, each face condition was allocated a relatively short amount of fMRI time (five 20-second blocks each) giving a small number of trials for each of the face conditions.

In this study, we applied MVPA to four of the six runs in this fMRI dataset by classifying the activity patterns for viewing faces and houses within the participants. By grouping together the different face trials into one class, we were able to increase the number of trials to a suitable level for performing MVPA (see Pereira et al., 2009 for a discussion of the factors relevant to selecting classifier exemplars), while allowing us to investigate underlying commonalities in face activity patterns between the different face tasks. Our results showed that classification performance was more strongly related to symptom severity than a univariate measure of mean activation. The greater sensitivity of MVPA was consistent across a variety of approaches to defining the regions of interest, including an anatomical definition, face-responsive voxels defined in the control group, and even in an area defined based on the mean activation difference itself. Furthermore, using a roaming searchlight analysis across the ventral temporal (VT) lobe, we found a symptom severity relationship with MVPA in regions of cortex that were not highlighted using a univariate measure. This is the first study, to our knowledge, that reports a link between functional MVPA results and standardized measures of symptom severity: an important target for many patient-based investigations. We also hope this study will be encouraging to patient-group researchers who are looking to maximize the utility of existing fMRI datasets, and to those searching for functional techniques that are sensitive to patient symptoms.

Section snippets

Participants

Twelve males on the autism spectrum (ages 9.3–24.2, mean (M) = 13.9 years) and twelve typically-developing male controls (ages 9.4–23.3, M = 13.6 years) were selected for these analyses from a total sample of more than twenty in each group on the basis of having the lowest scanner movement, while matching the groups by age. All participants were recruited and studied at the Yale Child Study Center. All participants or their legal guardians gave informed written consent and were compensated for their

MVPA results

Before relating MVPA results to symptom severity, we tested for significant classification performance in the regions of interest. Three approaches were taken to define these regions, as described in the Methods, in part because of the difficulty of using a traditional face localizer in a group characterized by face hypoactivation. In the first approach, three overlapping spheres were placed at coordinates from previous FFA studies, giving a 29-voxel cluster in the right fusiform gyrus. Both

Acknowledgments

This work was supported by NIMH grant R01MH073084 (R.T. Schultz), with further support from NIH grant R01MH070850 (S.L. Thompson-Schill). We thank members of the Thompson-Schill lab and CHOP Center for Autism Research for helpful discussions. We thank Lauren Hallion for valuable comments on an earlier version of the manuscript, and the anonymous reviewers for their insightful suggestions.

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