Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia - PubMed (original) (raw)
Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia
Jong H Yoon et al. Biol Psychiatry. 2008.
Abstract
Background: Multivariate pattern analysis is an alternative method of analyzing functional magnetic resonance imaging (fMRI) data, which is capable of decoding distributed neural representations. We applied this method to test the hypothesis of the impairment in distributed representations in schizophrenia. We also compared the results of this method with traditional general linear model (GLM)-based univariate analysis.
Methods: Nineteen schizophrenia and 15 control subjects viewed two runs of stimuli-exemplars of faces, scenes, objects, and scrambled images. To verify engagement with stimuli, subjects completed a 1-back matching task. A multivoxel pattern classifier was trained to identify category-specific activity patterns on one run of fMRI data. Classification testing was conducted on the remaining run. Correlation of voxelwise activity across runs evaluated variance over time in activity patterns.
Results: Patients performed the task less accurately. This group difference was reflected in the pattern analysis results with diminished classification accuracy in patients compared with control subjects, 59% and 72%, respectively. In contrast, there was no group difference in GLM-based univariate measures. In both groups, classification accuracy was significantly correlated with behavioral measures. Both groups showed highly significant correlation between interrun correlations and classification accuracy.
Conclusions: Distributed representations of visual objects are impaired in schizophrenia. This impairment is correlated with diminished task performance, suggesting that decreased integrity of cortical activity patterns is reflected in impaired behavior. Comparisons with univariate results suggest greater sensitivity of pattern analysis in detecting group differences in neural activity and reduced likelihood of nonspecific factors driving these results.
Figures
Figure 1. Visual Processing Task
The subjects were shown a series of exemplars of four categories of visual objects—faces (F), scenes (S), everyday objects (O), and scrambled images of everyday objects (Sc). Within each category block, 20 exemplars were shown. In order to verify task engagement, the subjects were required to conduct a 1-back match. A fixation baseline (B) was displayed between series of category blocks.
Figure 2. Multi-voxel Pattern Analysis Classification Accuracy
A) The outcome, in terms of proportion of correct classification, for patients and controls across all stimulus types. * Significant difference between groups, 2-tailed t-test, p < .05.
Figure 3. Correlations Between Classification Accuracy and Behavioral Measures
A) Controls and B) patients behavioral accuracy in the 1-back task vs. classification accuracy. C) Controls and D) patients reaction times (RT) in the 1-back task vs. classification accuracy.
Figure 4. Inter-run Correlation in Univariate Activity
A) Inter-run correlation values (Pearson’s r) for patients and controls across all stimulus types. * Significant difference between groups, 2-tailed t-test, p < .05.
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