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.

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.

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. Conceptual and semantic knowledge are fundamental aspects of human cognition and the investigation of the neural substrates underlying these processes is an ongoing topic of research in the cognitive neurosciences. Although current evidence has demonstrated that semantic knowledge is represented in a distributed manner over the brain 1,2 , the manner in which semantic representation is manifest remains a topic of active debate. The association damage to the anterior temporal lobe primary progressive aphasia, herpes encephalitis and lesions led to an emphasis of this brain regions as a critical locus for semantic processing 3,4. However, functional neuroimaging has suggested a broader range of regions are involved in semantic processing 5. A meta-analysis of 120 studies 6 identified a "general semantic network"-a left-lateralized network consisting of seven brain regions that were activated in a variety of semantic tasks: angular gyrus, lateral and ventral temporal cortex, ventromedial prefrontal cortex, inferior frontal gyrus, dorsal medial prefrontal cortex and the posterior cingulate gyrus. However, not all brain regions activated in semantic tasks necessarily represent semantic content, for instance regions may control access to semantic information rather than contain that information themselves 2,7,8. Starting from this assumption, Fairhall and Caramazza (2013) 9 identified a set of regions representing semantic content by means of Multivariate Pattern Analysis (MVPA) and Representational Similarity Analysis (RSA) 10. The authors showed that a left-lateralized netwo...

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 noiseinduced linear relationships between brain areas, but it is unknown whether these techniques can remove spurious nonlinear relationships. Among existing denoising methods, CompCor has been hypothesized to remove noise in BOLD responses that is nonlinearly related to its source. In this paper, we investigated whether CompCor additionally removes spurious nonlinear interactions between different brain regions. To test this, we analyzed fMRI responses while participants watched the film Forrest Gump using both linear and nonlinear Multivariate Pattern Dependence Networks (MVPN).We found nonlinear interactions between the nondenoised responses in face-selective regions and nondenoised responses in the anterior frontal and temporal lobes. CompCor denoising removed these nonlinear interactions. We then 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. Finally, we replicated our results using different types of neural networks as the bases of MVPN, demonstrating that CompCor's ability to remove nonlinear interactions is independent of network architecture.Impact Statement: With the growing popularity of nonlinear connectivity analyses, this research provides key insight into the ability of common denoising approaches to reduce specious nonlinear interactions between brain regions. This work can help guide the selection of data analysis procedures for connectivity studies across multiple domains of cognition.