Cognitive and affective theory of mind share the same local patterns of activity in posterior temporal but not medial prefrontal cortex

Understanding emotions in others engages specific brain regions in temporal and medial prefrontal cortices. These activations are often attributed to more general cognitive 'mentalizing' functions, associated with theory of mind and also necessary to represent people's non-emotional mental states, such as beliefs or intentions. Here, we directly investigated whether understanding emotional feelings recruit similar or specific brain systems, relative to other non-emotional mental states. We used functional magnetic resonance imaging with multivoxel pattern analysis in 46 volunteers to compare activation patterns in theory-of-mind tasks for emotions, relative to beliefs or somatic states accompanied with pain. We found a striking dissociation between the temporoparietal cortex, that exhibited a remarkable voxel-by-voxel pattern overlap between emotions and beliefs (but not pain), and the dorsomedial prefrontal cortex, that exhibited distinct (and yet nearby) patterns of activity during the judgment of beliefs and emotions in others. Pain judgment was instead associated with activity in the supramarginal gyrus, middle cingulate cortex and middle insular cortex. Our data reveal for the first time a functional dissociation within brain networks sub-serving theory of mind for different mental contents, with a common recruitment for cognitive and affective states in temporal regions, and distinct recruitment in prefrontal areas.

The neural decoding toolbox

Population decoding is a powerful way to analyze neural data, however, currently only a small percentage of systems neuroscience researchers use this method. In order to increase the use of population decoding, we have created the Neural Decoding Toolbox (NDT) which is a Matlab package that makes it easy to apply population decoding analyses to neural activity. The design of the toolbox revolves around four abstract object classes which enables users to interchange particular modules in order to try different analyses while keeping the rest of the processing stream intact. The toolbox is capable of analyzing data from many different types of recording modalities, and we give examples of how it can be used to decode basic visual information from neural spiking activity and how it can be used to examine how invariant the activity of a neural population is to stimulus transformations. Overall this toolbox will make it much easier for neuroscientists to apply population decoding analyses to their data, which should help increase the pace of discovery in neuroscience.

The pathways for intelligible speech: multivariate and univariate perspectives

An anterior pathway, concerned with extracting meaning from sound, has been identified in nonhuman primates. An analogous pathway has been suggested in humans, but controversy exists concerning the degree of lateralization and the precise location where responses to intelligible speech emerge. We have demonstrated that the left anterior superior temporal sulcus (STS) responds preferentially to intelligible speech (Scott SK, Blank CC, Rosen S, Wise RJS. 2000. Identification of a pathway for intelligible speech in the left temporal lobe. Brain. 123:2400–2406.). A functional magnetic resonance imaging study in Cerebral Cortex used equivalent stimuli and univariate and multivariate analyses to argue for the greater importance of bilateral posterior when compared with the left anterior STS in responding to intelligible speech (Okada K, Rong F, Venezia J, Matchin W, Hsieh IH, Saberi K, Serences JT,Hickok G. 2010. Hierarchical organization of human auditory cortex: evidence from acoustic invariance in the response to intelligible speech. 20: 2486–2495.). Here, we also replicate our original study, demonstrating that the left anterior STS exhibits the strongest univariate response and, in decoding using the bilateral temporal cortex, contains the most informative voxels showing an increased response to intelligible speech. In contrast, in classifications using local “searchlights” and a whole brain analysis, we find greater classification accuracy in posterior rather than anterior temporal regions. Thus, we show that the precise nature of the multivariate analysis used will emphasize different response profiles associated with complex sound to speech processing.

Dynamic representations of race: processing goals shape race decoding in the fusiform gyri

People perceive and evaluate others on the basis of social categories, such as race, gender and age. Initial processing of targets in terms of visually salient social categories is often characterized as inevitable. In the current study, we investigated the influence of processing goals on the representation of race in the visual processing stream. Participants were assigned to one of two mixed-race teams and categorized faces according to their group membership or skin color. To assess neural representations of race, we employed multivariate pattern analysis to examined neural activity related to the presentation of Black and White faces. As predicted, patterns of neural activity within the early visual cortex and fusiform gyri (FG) could decode the race of face stimuli above chance and were moderated by processing goals. Race decoding in early visual cortex was above chance in both categorization tasks and below chance in a prefrontal control region. More importantly, race decoding was greater in the FG during the group membership vs skin color categorization task. The results suggest that, ironically, explicit racial categorization can diminish the representation of race in the FG. These findings suggest that representations of race are dynamic, reflecting current processing goals.

Neural similarity between encoding and retrieval is related to memory via hippocampal interactions

A fundamental principle in memory research is that memory is a function of the similarity between encoding and retrieval operations. Consistent with this principle, many neurobiological models of declarative memory assume that memory traces are stored in cortical regions, and the hippocampus facilitates the reactivation of these traces during retrieval. The present investigation tested the novel prediction that encoding-retrieval similarity can be observed and related to memory at the level of individual items. Multivariate representational similarity analysis was applied to functional magnetic resonance imaging data collected during encoding and retrieval of emotional and neutral scenes. Memory success tracked fluctuations in encoding-retrieval similarity across frontal and posterior cortices. Importantly, memory effects in posterior regions reflected increased similarity between item-specific representations during successful recognition. Mediation analyses revealed that the hippocampus mediated the link between cortical similarity and memory success, providing crucial evidence for hippocampal-cortical interactions during retrieval. Finally, because emotional arousal is known to modulate both perceptual and memory processes, similarity effects were compared for emotional and neutral scenes. Emotional arousal was associated with enhanced similarity between encoding and retrieval patterns. These findings speak to the promise of pattern similarity measures for evaluating memory representations and hippocampal-cortical interactions.

Is race erased? Decoding race from patterns of neural activity when skin color is not diagnostic of group boundaries

Several theories suggest that people do not represent race when it does not signify group boundaries. However, race is often associated with visually salient differences in skin tone and facial features. In this study, we investigated whether race could be decoded from distributed patterns of neural activity in the fusiform gyri and early visual cortex when visual features that often covary with race were orthogonal to group membership. To this end, we used multivariate pattern analysis to examine an fMRI dataset that was collected while participants assigned to mixed-race groups categorized own-race and other-race faces as belonging to their newly assigned group. Whereas conventional univariate analyses provided no evidence of race-based responses in the fusiform gyri or early visual cortex, multivariate pattern analysis suggested that race was represented within these regions. Moreover, race was represented in the fusiform gyri to a greater extent than early visual cortex, suggesting that the fusiform gyri results do not merely reflect low-level perceptual information (e.g. color, contrast) from early visual cortex. These findings indicate that patterns of activation within specific regions of the visual cortex may represent race even when overall activation in these regions is not driven by racial information.

Predicting vocal emotion expressions from the human brain

Speech is an important carrier of emotional information. However, little is known about how different vocal emotion expressions are recognized in a receiver's brain. We used multivariate pattern analysis of functional magnetic resonance imaging data to investigate to which degree distinct vocal emotion expressions are represented in the receiver's local brain activity patterns. Specific vocal emotion expressions are encoded in a right fronto-operculo-temporal network involving temporal regions known to subserve suprasegmental acoustic processes and a fronto-opercular region known to support emotional evaluation, and, moreover, in left temporo-cerebellar regions covering sequential processes. The right inferior frontal region, in particular, was found to differentiate distinct emotional expressions. The present analysis reveals vocal emotion to be encoded in a shared cortical network reflected by distinct brain activity patterns. These results shed new light on theoretical and empirical controversies about the perception of distinct vocal emotion expressions at the level of large-scale human brain signals.

Rapid transfer of abstract rules to novel contexts in human lateral prefrontal cortex

Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances.

Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism

BACKGROUND

Autism spectrum disorders (ASD) are neurodevelopmental disorders with a prevalence of nearly 1:100. Structural imaging studies point to disruptions in multiple brain areas, yet the precise neuroanatomical nature of these disruptions remains unclear. Characterization of brain structural differences in children with ASD is critical for development of biomarkers that may eventually be used to improve diagnosis and monitor response to treatment.

METHODS

We use voxel-based morphometry along with a novel multivariate pattern analysis approach and searchlight algorithm to classify structural magnetic resonance imaging data acquired from 24 children and adolescents with autism and 24 age-, gender-, and IQ-matched neurotypical participants.

RESULTS

Despite modest voxel-based morphometry differences, multivariate pattern analysis revealed that the groups could be distinguished with accuracies of approximately 90% based on gray matter in the posterior cingulate cortex, medial prefrontal cortex, and bilateral medial temporal lobes-regions within the default mode network. Abnormalities in the posterior cingulate cortex were associated with impaired Autism Diagnostic Interview communication scores. Gray matter in additional prefrontal, lateral temporal, and subcortical structures also discriminated between groups with accuracies between 81% and 90%. White matter in the inferior fronto-occipital and superior longitudinal fasciculi, and the genu and splenium of the corpus callosum, achieved up to 85% classification accuracy.

CONCLUSIONS

Multiple brain regions, including those belonging to the default mode network, exhibit aberrant structural organization in children with autism. Brain-based biomarkers derived from structural magnetic resonance imaging data may contribute to identification of the neuroanatomical basis of symptom heterogeneity and to the development of targeted early interventions.

Multivariate pattern analysis of functional magnetic resonance imaging data reveals deficits in distributed representations in schizophrenia

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.