Top-Down Attention Guidance Shapes Action Encoding in the pSTS.

The posterior superior temporal sulcus (pSTS) is a brain region characterized by perceptual representations of human body actions that promote the understanding of observed behavior. Increasingly, action observation is recognized as being strongly shaped by the expectations of the observer (Kilner 2011; Koster-Hale and Saxe 2013; Patel et al. 2019). Therefore, to characterize top-down influences on action observation, we evaluated the statistical structure of multivariate activation patterns from the action observation network (AON) while observers attended to the different dimensions of action vignettes (the action kinematics, goal, or identity of avatars jumping or crouching). Decoding accuracy varied as a function of attention instruction in the right pSTS and left inferior frontal cortex (IFC), with the right pSTS classifying actions most accurately when observers attended to the action kinematics and the left IFC classifying most accurately when observed attended to the actor's goal. Functional connectivity also increased between the right pSTS and right IFC when observers attended to the actions portrayed in the vignettes. Our findings are evidence that the attentive state of the viewer modulates sensory representations in the pSTS, consistent with proposals that the pSTS occupies an interstitial zone mediating top-down context and bottom-up perceptual cues during action observation.

Examining vocal attractiveness through articulatory working space.

Robust gender differences exist in the acoustic correlates of clearly articulated speech, with females, on average, producing speech that is acoustically and phonetically more distinct than that of males. This study investigates the relationship between several acoustic correlates of clear speech and subjective ratings of vocal attractiveness. Talkers were recorded producing vowels in /bVd/ context and sentences containing the four corner vowels. Multiple measures of working vowel space were computed from continuously sampled formant trajectories and were combined with measures of speech timing known to co-vary with clear articulation. Partial least squares regression (PLS-R) modeling was used to predict ratings of vocal attractiveness for male and female talkers based on the acoustic measures. PLS components that loaded on size and shape measures of working vowel space-including the quadrilateral vowel space area, convex hull area, and bivariate spread of formants-along with measures of speech timing were highly successful at predicting attractiveness in female talkers producing /bVd/ words. These findings are consistent with a number of hypotheses regarding human attractiveness judgments, including the role of sexual dimorphism in mate selection, the significance of traits signalling underlying health, and perceptual fluency accounts of preferences.

Network Connectivity of the Right STS in Three Social Perception Localizers.

The posterior STS (pSTS) is an important brain region for perceptual analysis of social cognitive cues. This study seeks to characterize the pattern of network connectivity emerging from the pSTS in three core social perception localizers: biological motion perception, gaze recognition, and the interpretation of moving geometric shapes as animate. We identified brain regions associated with all three of these localizers and computed the functional connectivity pattern between them and the pSTS using a partial correlations metric that characterizes network connectivity. We find a core pattern of cortical connectivity that supports the hypothesis that the pSTS serves as a hub of the social brain network. The right pSTS was the most highly connected of the brain regions measured, with many long-range connections to pFC. Unlike other highly connected regions, connectivity to the pSTS was distinctly lateralized. We conclude that the functional importance of right pSTS is revealed when considering its role in the large-scale network of brain regions involved in various aspects of social cognition.

Feature-based attentional tuning during biological motion detection measured with SSVEP.

Performance in detection tasks can be improved by directing attention to task-relevant features. In this study, we evaluate the direction tuning of selective attention to motion features when observers detect point-light biological motion in noise. Feature-based attention strategy is assessed by capitalizing on the sensitivity of unattended steady-state visual-evoked potential (SSVEP) to the spreading of feature-based attention to unattended regions of space. Participants monitored for the presence of a point-light walker embedded in uniform dynamic noise in the center of the screen. We analyzed the phase-locked electroencephalogram response to a flickering random-dot kinematogram (RDK) in an unattended peripheral annulus for the 1 s prior to the onset of the target. We found the highest SSVEP power to originate from electrodes over posterior parietal cortex (PPC), with power modulated by the direction of motion in the unattended annulus. The SSVEP was strongest on trials in which the unattended motion was opposite the facing direction of the walker, consistent with the backstroke of the feet and with the global direction of perceived background motion from a translating walker. Coherence between electrodes over PPC and other brain regions successfully predicted individual participant's d-prime, with the highest regression coefficients at electrodes over ventrolateral prefrontal cortex (VLPFC). The findings are evidence that functional connectivity between frontal and parietal cortex promote perceptual feature-based attention, and subsequent perceptual sensitivity, when segregating point-light figures from masking surround.

A quantitative comparison of atlas parcellations on the human superior temporal sulcus.

The superior temporal sulcus (STS) has a functional topography that has been difficult to characterize through traditional approaches. Automated atlas parcellations may be one solution while also being beneficial for both dimensional reduction and standardizing regions of interest, but they yield very different boundary definitions along the STS. Here we evaluate how well machine learning classifiers can correctly identify six social cognitive tasks from STS activation patterns dimensionally reduced using four popular atlases (Glasser et al., 2016; Gordon et al., 2016; Power et al., 2011 as projected onto the surface by Arslan et al., 2018; Schaefer et al., 2018). Functional data was summarized within each STS parcel in one of four ways, then subjected to leave-one-subject-out cross-validation SVM classification. We found that the classifiers could readily label conditions when data was parcellated using any of the four atlases, evidence that dimensional reduction to parcels did not compromise functional fingerprints. Mean activation for the social conditions was the most effective metric for classification in the right STS, whereas all the metrics classified equally well in the left STS. Interestingly, even atlases constructed from random parcellation schemes (null atlases) classified the conditions with high accuracy. We therefore conclude that the complex activation maps on the STS are readily differentiated at a coarse granular level, despite a strict topography having not yet been identified. Further work is required to identify what features have greatest potential to improve the utility of atlases in replacing functional localizers.

When shapes are more than shapes: perceptual, developmental, and neurophysiological basis for attributions of animacy and theory of mind.

Among a variety of entities in their environment, what do humans consider alive or animate and how does this attribution of animacy promote development of more abstract levels of mentalizing? By decontextualizing the environment of bodily features, we review how physical movements give rise to perceived animacy in Heider-Simmel style animations. We discuss the developmental course of how perceived animacy shapes our interpretation of the social world, and specifically discuss when and how children transition from perceiving actions as goal-directed to attributing behaviors to unobservable mental states. This transition from a teleological stance, asserting a goal-oriented interpretation to an agent's actions, to a mentalistic stance allows older children to reason about more complex actions guided by hidden beliefs. The acquisition of these more complex cognitive behaviors happens developmentally at the same time neural systems for social cognition are coming online in young children. We review perceptual, developmental, and neural evidence to identify the joint cognitive and neural changes associated with when children begin to mentalize and how this ability is instantiated in the brain.

The PyMVPA BIDS-App: a robust multivariate pattern analysis pipeline for fMRI data.

With the advent of multivariate pattern analysis (MVPA) as an important analytic approach to fMRI, new insights into the functional organization of the brain have emerged. Several software packages have been developed to perform MVPA analysis, but deploying them comes with the cost of adjusting data to individual idiosyncrasies associated with each package. Here we describe PyMVPA BIDS-App, a fast and robust pipeline based on the data organization of the BIDS standard that performs multivariate analyses using powerful functionality of PyMVPA. The app runs flexibly with blocked and event-related fMRI experimental designs, is capable of performing classification as well as representational similarity analysis, and works both within regions of interest or on the whole brain through searchlights. In addition, the app accepts as input both volumetric and surface-based data. Inspections into the intermediate stages of the analyses are available and the readability of final results are facilitated through visualizations. The PyMVPA BIDS-App is designed to be accessible to novice users, while also offering more control to experts through command-line arguments in a highly reproducible environment.

Configuration of the action observation network depends on the goals of the observer.

Observing the actions of others engages a core action observation network (AON) that includes the bilateral inferior frontal cortex (IFC), posterior superior temporal sulcus (pSTS) and inferior parietal lobule (IPL) (Caspers et al., 2010). Each region in the AON has functional properties that are heterogeneous and include representing the perceptual properties of action, predicting action outcomes and making inferences as to the goals of the actor. Critically, recent evidence shows that neural representations within the pSTS are sharpened when attending to the kinematics of the actor, such that the top-down guided attention reshapes underlying neural representations. In this study we evaluate how attention alters network connectivity within the AON as a system. Cues directed participant's attention to the goal, kinematics, or identity depicted in short action animations while brain responses were measured by fMRI. We identified those parcels within the AON with functional connectivity modulated by task. Results show that connectivity between the right pSTS and right IFC, and bilateral extended STS (STS+) were modulated during action observation such that connections were strengthened when the participant was attending to the action than goal. This finding is contrasted by the univariate results, which no univariate modulations in these brain regions except for right IFC. Using the functional networks defined by Yeo et al. (2011), we identified the parcels that are modulated by the attention to consist mainly of the fronto-parietal control network and default mode networks. These results are consistent with models of top-down feedback from executive system in the IFC to pSTS and implicates a right lateralized dual pathway model for action observation when focused on whole-body kinematics.

Optimizing multivariate pattern classification in rapid event-related designs.

BACKGROUND: Multivariate pattern analysis (MVPA or pattern decoding) has attracted considerable attention as a sensitive analytic tool for investigations using functional magnetic resonance imaging (fMRI) data. With the introduction of MVPA, however, has come a proliferation of methodological choices confronting the researcher, with few studies to date offering guidance from the vantage point of controlled datasets detached from specific experimental hypotheses. NEW METHOD: We investigated the impact of four data processing steps on support vector machine (SVM) classification performance aimed at maximizing information capture in the presence of common noise sources. The four techniques included: trial averaging (classifying on separate trial estimates versus condition-based averages), within-run mean centering (centering the data or not), method of cost selection (using a fixed or tuned cost value), and motion-related denoising approach (comparing no denoising versus a variety of nuisance regressions capturing motion-related reference signals). The impact of these approaches was evaluated on real fMRI data from two control ROIs, as well as on simulated pattern data constructed with carefully controlled voxel- and trial-level noise components. RESULTS: We find significant improvements in classification performance across both real and simulated datasets with run-wise trial averaging and mean centering. When averaging trials within conditions of each run, we note a simultaneous increase in the between-subject variability of SVM classification accuracies which we attribute to the reduced size of the test set used to assess the classifier's prediction error. Therefore, we propose a hybrid technique whereby randomly sampled subsets of trials are averaged per run and demonstrate that it helps mitigate the tradeoff between improving signal-to-noise ratio by averaging and losing exemplars in the test set. COMPARISON WITH EXISTING METHODS: Though a handful of empirical studies have employed run-based trial averaging, mean centering, or their combination, such studies have done so without theoretical justification or rigorous testing using control ROIs. CONCLUSIONS: Therefore, we intend this study to serve as a practical guide for researchers wishing to optimize pattern decoding without risk of introducing spurious results.

Perks of blindness: Enhanced verbal memory span in blind over sighted adults.

Blind individuals commonly use verbal encoding (i.e. text-to-speech) and memory-based strategies (i.e. serial recall) for situations in which sighted individuals use vision (i.e. finding items). These strategies may serve to train cognitive systems responsible for maintaining and manipulating verbal information. To test this hypothesis, we investigate whether early visual deprivation is linked to improved verbal short-term and working memory abilities, and thus might illustrate experience-dependent plasticity in memory systems. We also test whether the sensory modality for encoding information influences performance. Our data show that blind adults recalled more items on a verbal short-term memory span task than sighted participants. Furthermore, blind individuals performed equally well on auditory forward and backward conditions despite the fact that recalling items in reverse order is more difficult for the general population. However, the benefits of recalling items in reverse order did not extend to the tactile domain, specifically, a braille version of the short-term memory digit span task in blind individuals. Furthermore, we observed no differences between blind and sighted individuals on a more demanding auditory n-back task evaluating more complex working memory processes. We conclude that the memory benefits associated with blindness might be restricted to auditory-verbal short-term memory and likely reflect strategy use and practice.

Distinct cerebellar regions for body motion discrimination.

Visual processing of human movements is critical for adaptive social behavior. Cerebellar activations have been observed during biological motion discrimination in prior neuroimaging studies, and cerebellar lesions may be detrimental for this task. However, whether the cerebellum plays a causal role in biological motion discrimination has never been tested. Here, we addressed this issue in three different experiments by interfering with the posterior cerebellar lobe using transcranial magnetic stimulation (TMS) during a biological discrimination task. In Experiments 1 and 2, we found that TMS delivered at onset of the visual stimuli over the vermis (vermal lobule VI), but not over the left cerebellar hemisphere (left lobule VI/Crus I), interfered with participants' ability to distinguish biological from scrambled motion compared to stimulation of a control site (vertex). Interestingly, when stimulation was delivered at a later time point (300 ms after stimulus onset), participants performed worse when TMS was delivered over the left cerebellar hemisphere compared to the vermis and the vertex (Experiment 3). Our data show that the posterior cerebellum is causally involved in biological motion discrimination and suggest that different sectors of the posterior cerebellar lobe may contribute to the task at different time points.

Evoked potentials in large-scale cortical networks elicited by TMS of the visual cortex.

Single pulses of transcranial magnetic stimulation (TMS) result in distal and long-lasting oscillations, a finding directly challenging the virtual lesion hypothesis. Previous research supporting this finding has primarily come from stimulation of the motor cortex. We have used single-pulse TMS with simultaneous EEG to target seven brain regions, six of which belong to the visual system [left and right primary visual area V1, motion-sensitive human middle temporal cortex, and a ventral temporal region], as determined with functional MRI-guided neuronavigation, and a vertex "control" site to measure the network effects of the TMS pulse. We found the TMS-evoked potential (TMS-EP) over visual cortex consists mostly of site-dependent theta- and alphaband oscillations. These site-dependent oscillations extended beyond the stimulation site to functionally connected cortical regions and correspond to time windows where the EEG responses maximally diverge (40, 200, and 385 ms). Correlations revealed two site-independent oscillations ∼350 ms after the TMS pulse: a theta-band oscillation carried by the frontal cortex, and an alpha-band oscillation over parietal and frontal cortical regions. A manipulation of stimulation intensity at one stimulation site (right hemisphere V1-V3) revealed sensitivity to the stimulation intensity at different regions of cortex, evidence of intensity tuning in regions distal to the site of stimulation. Together these results suggest that a TMS pulse applied to the visual cortex has a complex effect on brain function, engaging multiple brain networks functionally connected to the visual system with both invariant and site-specific spatiotemporal dynamics. With this characterization of TMS, we propose an alternative to the virtual lesion hypothesis. Rather than a technique that simulates lesions, we propose TMS generates natural brain signals and engages functional networks.

Feature-based attention promotes biological motion recognition.

Motion perception is important for visually segregating and identifying objects from their surroundings, but in some cases extracting motion cues can be taxing to the human attention system. We measured the strength of feature salience required for individuals to correctly judge three types of moving events: biological motion, coherent motion, and multiple object tracking. The motion animations were embedded within a larger Gabor grid and constructed such that motion was conveyed by a salient single-feature dimension (second order) or by alternating across equisalient feature dimensions (third order). In the single-feature displays, we found biological motion to require less difference in the Gabor features (relative to background) to yield equivalent task performance as the coherent motion or multiple object tracking. This main effect of feature magnitude may reflect the inherent salience of biological motion as a visual stimulus. In the alternating-feature displays, both the biological motion and coherent motion discriminations needed additional salience, as compared to the single-feature displays, to achieve threshold discrimination levels. Accuracy in the multiple object tracking task did not vary as a function of salience. Together, these findings demonstrate the effectiveness with which attention-based motion mechanisms operate in complex dynamic sequences and argue for a critical role of feature-based attention in promoting biological motion perception.

Perceptual and computational analysis of critical features for biological motion.

Among the most common events in our daily lives is seeing people in action. Scientists have accumulated evidence suggesting humans may have developed specialized mechanisms for recognizing these visual events. In the current experiments, we apply the "bubbles" technique to construct space-time classification movies that reveal the key features human observers use to discriminate biological motion stimuli (point-light and stick figure walkers). We find that observers rely on similar features for both types of stimuli, namely, form information in the upper body and dynamic information in the relative motion of the limbs. To measure the contributions of motion and form analyses in this task, we computed classification movies from the responses of a biologically plausible model that can discriminate biological motion patterns (M. A. Giese & T. Poggio, 2003). The model classification movies reveal similar key features to observers, with the model's motion and form pathways each capturing unique aspects of human performance. In a second experiment, we computed classification movies derived from trials of varying exposure times (67-267 ms) and demonstrate the transition to form-based strategies as motion information becomes less available. Overall, these results highlight the relative contributions of motion and form computations to biological motion perception.

Understanding diaschisis models of attention dysfunction with rTMS.

Visual attentive tracking requires a balance of excitation and inhibition across large-scale frontoparietal cortical networks. Using methods borrowed from network science, we characterize the induced changes in network dynamics following low frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) as an inhibitory noninvasive brain stimulation protocol delivered over the intraparietal sulcus. When participants engaged in visual tracking, we observed a highly stable network configuration of six distinct communities, each with characteristic properties in node dynamics. Stimulation to parietal cortex had no significant impact on the dynamics of the parietal community, which already exhibited increased flexibility and promiscuity relative to the other communities. The impact of rTMS, however, was apparent distal from the stimulation site in lateral prefrontal cortex. rTMS temporarily induced stronger allegiance within and between nodal motifs (increased recruitment and integration) in dorsolateral and ventrolateral prefrontal cortex, which returned to baseline levels within 15 min. These findings illustrate the distributed nature by which inhibitory rTMS perturbs network communities and is preliminary evidence for downstream cortical interactions when using noninvasive brain stimulation for behavioral augmentations.

Motion opponency and transparency in the human middle temporal area.

Motion transparency is the perception of multiple, moving surfaces within the same retinal location (for example, a ripple on the surface of a drifting stream), and is an interesting challenge to motion models because multiple velocities must be represented within the same region of space. When these motion vectors are in opposite directions, brief in duration and spatially constrained within a very local region, the result is little or no perceived motion (motion opponency). Both motion transparency and motion opponency inhibit the firing rate of single middle temporal area (MT) neurons as compared with the preferred direction alone, but neither generally influences the firing rate of primary visual cortex neurons. Surprisingly, neuroimaging studies of human middle temporal area (hMT+) have found less activation due only to motion opponency and an increase in neural responses for motion transparency. Here we parametrically manipulate the local balance between competing motion vectors and find an interaction between motion opponency and transparency in the population blood oxygen level-dependent (BOLD) response. We find reduced BOLD amplitude for motion opponency throughout visual cortex, but weakened responses due to perceptual transparency that is most apparent only within the hMT+. We interpret our results as evidence for two distinct mechanisms mediating opponency and transparency.

Neural adaptation for novel objects during dynamic articulation.

Human observers readily identify objects with moving parts, and recognize their underlying structure even when the component parts undergo complex movement. This suggests the existence of neural representations that are invariant to motion and state of articulation, which together allow our visual system to maintain 'object constancy'. Ventral temporal cortex has previously been implicated in object perception and in coding object identity, but it is unclear where this is achieved when objects undergo motion-driven shape changes. In the present study, we use fMRI adaptation to probe the neural response properties when subjects view dynamic novel objects. Our results reveal neural selectivity for novel objects in the LOC region of the occipito-temporal lobe, even when those objects are viewed as moving and articulating. We also identify a bilateral area of posterior fusiform outside of the LOC with neural populations invariant to changes in the articulatory state of an object, a critical feature of object constancy. These results demonstrate the functional importance of ventral temporal cortex in the perception of moving objects, and the existence of neural populations coding for object constancy across movement and articulation.

Functional connectivity in ASD: Atypical pathways in brain networks supporting action observation and joint attention.

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication, including attending to and interpreting social cues, initiating and responding to joint attention, and engaging in abstract social cognitive reasoning. Current studies emphasize a underconnectivity in ASD, particularly for brain systems that support abstract social reasoning and introspective thought. Here, we evaluate intrinsic connectivity in children with ASD, targeting brain systems that support the developmental precursors to social reasoning, namely perception of social cues and joint attention. Using resting state fMRI made available through the Autism Brain Imaging Data Exchange (ABIDE), we compute functional connectivity within and between nodes in the action observation, attention and social cognitive networks in children and adolescents with ASD. We also compare connectivity strength to observational assessments that explicitly evaluate severity of ASD on two distinct subdomains using the ADOS-Revised schedule: social affective (SA) and restricted, repetitive behaviors (RRB). Compared to age-matched controls, children with ASD have decreased functional connectivity in a number of connections in the action observation network, particularly in the lateral occipital cortex (LOTC) and fusiform gyrus (FG). Distinct patterns of connections were also correlated with symptom severity on the two subdomains of the ADOS. ADOS-SA severity most strongly correlated with connectivity to the left TPJ, while ADOS-RRB severity correlated with connectivity to the dMPFC. We conclude that atypical connectivity in the action observation system may underlie some of the more complex deficits in social cognitive systems in ASD.

Prolonged Neuromodulation of Cortical Networks Following Low-Frequency rTMS and Its Potential for Clinical Interventions.

Non-invasive brain stimulation safely induces persistent large-scale neural modulation in functionally connected brain circuits. Interruption models of repetitive transcranial magnetic stimulation (rTMS) capitalize on the acute impact of brain stimulation, which decays over minutes. However, rTMS also induces longer-lasting impact on cortical functions, evident by the use of multi-session rTMS in clinical population for therapeutic purposes. Defining the persistent cortical dynamics induced by rTMS is complicated by the complex balance of excitation and inhibition among functionally connected networks. Nonetheless, it is these neuronal dynamic responses that are essential for the development of new neuromodulatory protocols for translational applications. We will review evidence of prolonged changes of cortical response, tens of minutes following one session of low frequency rTMS over the cortex. We will focus on the different methods which resulted in prolonged behavioral and brain changes, such as the combination of brain stimulation techniques, and individually tailored stimulation protocols. We will also highlight studies which apply these methods in multi-session stimulation practices to extend stimulation impact into weeks and months. Our data and others' indicate that delayed cortical dynamics may persist much longer than previously thought and have potential as an extended temporal window during which cortical plasticity may be enhanced.

Brain Areas Active during Visual Perception of Biological Motion.

Theories of vision posit that form and motion are represented by neural mechanisms segregated into functionally and anatomically distinct pathways. Using point-light animations of biological motion, we examine the extent to which form and motion pathways are mutually involved in perceiving figures depicted by the spatio-temporal integration of local motion components. Previous work discloses that viewing biological motion selectively activates a region on the posterior superior temporal sulcus (STSp). Here we report that the occipital and fusiform face areas (OFA and FFA) also contain neural signals capable of differentiating biological from nonbiological motion. EBA and LOC, although involved in perception of human form, do not contain neural signals selective for biological motion. Our results suggest that a network of distributed neural areas in the form and motion pathways underlie the perception of biological motion.