Similar representation of names and faces in the network for person perception.

Person-knowledge encompasses the diverse types of knowledge we have about other people. This knowledge spans the social, physical, episodic, semantic & nominal information we possess about others and is served by a distributed cortical network including core (perceptual) and extended (non-perceptual) subsystems. Our understanding of this cortical system is tightly linked to the perception of faces and the extent to which cortical knowledge-access processes are independent of perception is unclear. In this study, participants were presented with the written names of famous people and performed ten different semantic access tasks drawn from five cognitive domains (biographic, episodic, nominal, social and physical). We used representational similarity analysis, adapted to investigate network-level representations (NetRSA) to characterise the inter-regional functional coordination within the non-perceptual extended subsystem across access to varied forms of person-knowledge. Results indicate a hierarchical cognitive taxonomy consistent with that seen during face-processing and forming the same three macro-domains: socio-perceptual judgements, episodic-semantic memory and nominal knowledge. The coordination across regions was largely preserved within elements of the extended system associated with internalised cognition but differed in prefrontal regions. Results suggest the elements of the extended system work together in a consistent way to access knowledge when viewing faces and names but that coordination patterns also change as a function of input-processing demands.

Angular Gyrus Responses Show Joint Statistical Dependence with Brain Regions Selective for Different Categories.

Category selectivity is a fundamental principle of organization of perceptual brain regions. Human occipitotemporal cortex is subdivided into areas that respond preferentially to faces, bodies, artifacts, and scenes. However, observers need to combine information about objects from different categories to form a coherent understanding of the world. How is this multicategory information encoded in the brain? Studying the multivariate interactions between brain regions of male and female human subjects with fMRI and artificial neural networks, we found that the angular gyrus shows joint statistical dependence with multiple category-selective regions. Adjacent regions show effects for the combination of scenes and each other category, suggesting that scenes provide a context to combine information about the world. Additional analyses revealed a cortical map of areas that encode information across different subsets of categories, indicating that multicategory information is not encoded in a single centralized location, but in multiple distinct brain regions.SIGNIFICANCE STATEMENT Many cognitive tasks require combining information about entities from different categories. However, visual information about different categorical objects is processed by separate, specialized brain regions. How is the joint representation from multiple category-selective regions implemented in the brain? Using fMRI movie data and state-of-the-art multivariate statistical dependence based on artificial neural networks, we identified the angular gyrus encoding responses across face-, body-, artifact-, and scene-selective regions. Further, we showed a cortical map of areas that encode information across different subsets of categories. These findings suggest that multicategory information is not encoded in a single centralized location, but at multiple cortical sites which might contribute to distinct cognitive functions, offering insights to understand integration in a variety of domains.

Contrastive machine learning reveals the structure of neuroanatomical variation within autism.

Autism spectrum disorder (ASD) is highly heterogeneous. Identifying systematic individual differences in neuroanatomy could inform diagnosis and personalized interventions. The challenge is that these differences are entangled with variation because of other causes: individual differences unrelated to ASD and measurement artifacts. We used contrastive deep learning to disentangle ASD-specific neuroanatomical variation from variation shared with typical control participants. ASD-specific variation correlated with individual differences in symptoms. The structure of this ASD-specific variation also addresses a long-standing debate about the nature of ASD: At least in terms of neuroanatomy, individuals do not cluster into distinct subtypes; instead, they are organized along continuous dimensions that affect distinct sets of regions.

Controlling for Spurious Nonlinear Dependence in Connectivity Analyses.

Recent analysis methods can capture nonlinear interactions between brain regions. However, noise sources might induce spurious nonlinear relationships between the responses in different regions. Previous research has demonstrated that traditional denoising techniques effectively remove noise-induced linear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. To address this question, we analyzed fMRI responses while participants watched the film Forrest Gump. We tested whether nonlinear Multivariate Pattern Dependence Networks (MVPN) outperform linear MVPN in non-denoised data, and whether this difference is reduced after CompCor denoising. Whereas nonlinear MVPN outperformed linear MVPN in the non-denoised data, denoising removed these nonlinear interactions. We replicated our results using different neural network architectures as the bases of MVPN, different activation functions (ReLU and sigmoid), different dimensionality reduction techniques for CompCor (PCA and ICA), and multiple datasets, demonstrating that CompCor's ability to remove nonlinear interactions is robust across these analysis choices and across different groups of participants. Finally, we asked whether information contributing to the removal of nonlinear interactions is localized to specific anatomical regions of no interest or to specific principal components. We denoised the data 8 separate times by regressing out 5 principal components extracted from combined white matter (WM) and cerebrospinal fluid (CSF), each of the 5 components separately, 5 components extracted from WM only, and 5 components extracted solely from CSF. In all cases, denoising was sufficient to remove the observed nonlinear interactions.

General and feature-based semantic representations in the semantic network.

How semantic representations are manifest over the brain remains a topic of active debate. A semantic representation may be determined by specific semantic features (e.g. sensorimotor information), or may abstract away from specific features and represent generalized semantic characteristics (general semantic representation). Here we tested whether nodes of the semantic system code for a general semantic representation and/or possess representational spaces linked to particular semantic features. In an fMRI study, eighteen participants performed a typicality judgment task with written words drawn from sixteen different categories. Multivariate pattern analysis (MVPA) and representational similarity analysis (RSA) were adopted to investigate the sensitivity of the brain regions to semantic content and the type of semantic representation coded (general or feature-based). We replicated previous findings of sensitivity to general semantic similarity in posterior middle/inferior temporal gyrus (pMTG/ITG) and precuneus (PC) and additionally observed general semantic representations in ventromedial prefrontal cortex (PFC). Finally, two brain regions of the semantic network were sensitive to semantic features: the left pMTG/ITG was sensitive to haptic perception and the left ventral temporal cortex (VTC) to size. This finding supports the involvement of both general semantic representation and feature-based representations in the brain's semantic system.

Regional Specialization and Coordination Within the Network for Perceiving and Knowing About Others.

Seeing familiar faces prompts the recall of diverse kinds of person-related knowledge. How this information is encoded within the well-characterized face-/person-selective network remains an outstanding question. In this functional magnetic resonance imaging study, participants rated famous faces in 10 tasks covering 5 domains of person knowledge (social, episodic, semantic, physical, and nominal). Comparing different cognitive domains enabled us to 1) test the relative roles of brain regions in specific cognitive processes and 2) apply a multivariate network-level representational similarity analysis (NetRSA) to gain insight into underlying system-level organization. Comparing across cognitive domains revealed the importance of multiple domains in most regions, the importance of social over nominal knowledge in the anterior temporal lobe, and the functional subdivision of the temporoparietal junction into perceptual superior temporal sulcus and knowledge-related angular gyrus. NetRSA revealed a strong divide between regions implicated in "default-mode" cognition and the fronto-lateral elements that coordinated more with "core" perceptual components (fusiform/occipital face areas and posterior superior temporal sulcus). NetRSA also revealed a taxonomy of cognitive processes, with semantic retrieval being more similar to episodic than nominal knowledge. Collectively, these results illustrate the importance of coordinated activity of the person knowledge network in the instantiation of the diverse cognitive capacities of this system.

Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity.

Watching other people move elicits engagement of a collection of sensorimotor brain regions collectively termed the Action Observation Network (AON). An extensive literature documents more robust AON responses when observing or executing familiar compared to unfamiliar actions, as well as a positive correlation between amplitude of AON response and an observer's familiarity with an observed or executed movement. On the other hand, emerging evidence shows patterns of AON activity counter to these findings, whereby in some circumstances, unfamiliar actions lead to greater AON engagement than familiar actions. In an attempt to reconcile these conflicting findings, some have proposed that the relationship between AON response amplitude and action familiarity is nonlinear in nature. In the present study, we used an elaborate guitar training intervention to probe the relationship between movement familiarity and AON engagement during action execution and action observation tasks. Participants underwent fMRI scanning while executing one set of guitar sequences with a scanner-compatible bass guitar and observing a second set of sequences. Participants then acquired further physical practice or observational experience with half of these stimuli outside the scanner across 3 days. Participants then returned for an identical scanning session, wherein they executed and observed equal numbers of familiar (trained) and unfamiliar (untrained) guitar sequences. Via region of interest analyses, we extracted activity within AON regions engaged during both scanning sessions, and then fit linear, quadratic and cubic regression models to these data. The data best support the cubic regression models, suggesting that the response profile within key sensorimotor brain regions associated with the AON respond to action familiarity in a nonlinear manner. Moreover, by probing the subjective nature of the prediction error signal, we show results consistent with a predictive coding account of AON engagement during action observation and execution that also takes into account effects of changes in neural efficiency.