Psychiatric and neurological disorders have historically provided key insights into the structure-function relationships that subserve human social cognition and behavior, informing the concept of ...the ‘social brain’. In this review, we take stock of the current status of this concept, retaining a focus on disorders that impact social behavior. We discuss how the social brain, social cognition, and social behavior are interdependent, and emphasize the important role of development and compensation. We suggest that the social brain, and its dysfunction and recovery, must be understood not in terms of specific structures, but rather in terms of their interaction in large-scale networks.
The social difficulties that are a hallmark of autism spectrum disorder (ASD) are thought to arise, at least in part, from atypical attention toward stimuli and their features. To investigate this ...hypothesis comprehensively, we characterized 700 complex natural scene images with a novel three-layered saliency model that incorporated pixel-level (e.g., contrast), object-level (e.g., shape), and semantic-level attributes (e.g., faces) on 5,551 annotated objects. Compared with matched controls, people with ASD had a stronger image center bias regardless of object distribution, reduced saliency for faces and for locations indicated by social gaze, and yet a general increase in pixel-level saliency at the expense of semantic-level saliency. These results were further corroborated by direct analysis of fixation characteristics and investigation of feature interactions. Our results for the first time quantify atypical visual attention in ASD across multiple levels and categories of objects.
•A novel three-layered saliency model with 5,551 annotated natural scene semantic objects•People with ASD who have a stronger image center bias regardless of object distribution•Generally increased pixel-level saliency but decreased semantic-level saliency in ASD•Reduced saliency for faces and locations indicated by social gaze in ASD
Wang et al. use a comprehensive saliency model and eye tracking to quantify the relative contributions of each image attribute to visual saliency. People with ASD demonstrate atypical visual attention across multiple levels and categories of objects.
The ability to maintain an appropriate physical distance (i.e., interpersonal distance) from others is a critical aspect of social interaction and contributes importantly to real-life social ...functioning. In Study 1, using parent-report data that had been acquired on a large number of individuals (ages 4-18 years) for the Autism Genetic Resource Exchange and the Simons Simplex Collection, we found that those with Autism Spectrum Disorder (ASD; n = 766) more often violated the space of others compared to their unaffected siblings (n = 766). This abnormality held equally across ASD diagnostic categories, and correlated with clinical measures of communication and social functioning. In Study 2, laboratory experiments in a sample of high-functioning adults with ASD demonstrated an altered relationship between interpersonal distance and personal space, and documented a complete absence of personal space in 3 individuals with ASD. Furthermore, anecdotal self-report from several participants confirmed that violations of social distancing conventions continue to occur in real-world interactions through adulthood. We suggest that atypical social distancing behavior offers a practical and sensitive measure of social dysfunction in ASD, and one whose psychological and neurological substrates should be further investigated.
In higher functioning individuals with autism, a striking disparity exists between impaired social and emotional abilities and relatively preserved sustained attention and goal-directed cognitive ...abilities. As these two functional domains appear to map onto two distinct large-scale brain networks, the Task-Negative Network and the Task-Positive Network, respectively, we examined their intrinsically defined functional organization in individuals with autism. Using resting functional connectivity MRI (fcMRI), we found that, in autism, there was altered functional organization of the network involved in social and emotional processing, but no group difference in the functional organization of the network involved in sustained attention and goal-directed cognition. We suggest that these findings might serve to relate the seemingly disparate strengths and weaknesses of the autistic behavioral, perceptual, and cognitive phenotype into a tractable neurofunctional framework. These results also highlight the usefulness of resting fcMRI for studying the brain in neuropsychiatric and neurodevelopmental disorders.
A rapidly growing number of studies on autism spectrum disorder (ASD) have used resting‐state fMRI to identify alterations of functional connectivity, with the hope of identifying clinical biomarkers ...or underlying neural mechanisms. However, results have been largely inconsistent across studies, and there remains a pressing need to determine the primary factors influencing replicability. Here, we used resting‐state fMRI data from the Autism Brain Imaging Data Exchange to investigate two potential factors: denoising strategy and data site (which differ in terms of sample, data acquisition, etc.). We examined the similarity of both group‐averaged functional connectomes and group‐level differences (ASD vs. control) across 33 denoising pipelines and four independently‐acquired datasets. The group‐averaged connectomes were highly consistent across pipelines (r = 0.92 ± 0.06) and sites (r = 0.88 ± 0.02). However, the group differences, while still consistent within site across pipelines (r = 0.76 ± 0.12), were highly inconsistent across sites regardless of choice of denoising strategies (r = 0.07 ± 0.04), suggesting lack of replication may be strongly influenced by site and/or cohort differences. Across‐site similarity remained low even when considering the data at a large‐scale network level or when considering only the most significant edges. We further show through an extensive literature survey that the parameters chosen in the current study (i.e., sample size, age range, preprocessing methods) are quite representative of the published literature. These results highlight the importance of examining replicability in future studies of ASD, and, more generally, call for extra caution when interpreting alterations in functional connectivity across groups of individuals.
Individuals with autism spectrum disorder (ASD), including those who otherwise require less support, face severe difficulties in everyday social interactions. Research in this area has primarily ...focused on identifying the cognitive and neurological differences that contribute to these social impairments, but social interaction by definition involves more than one person and social difficulties may arise not just from people with ASD themselves, but also from the perceptions, judgments, and social decisions made by those around them. Here, across three studies, we find that first impressions of individuals with ASD made from thin slices of real-world social behavior by typically-developing observers are not only far less favorable across a range of trait judgments compared to controls, but also are associated with reduced intentions to pursue social interaction. These patterns are remarkably robust, occur within seconds, do not change with increased exposure, and persist across both child and adult age groups. However, these biases disappear when impressions are based on conversational content lacking audio-visual cues, suggesting that style, not substance, drives negative impressions of ASD. Collectively, these findings advocate for a broader perspective of social difficulties in ASD that considers both the individual's impairments and the biases of potential social partners.
Residual noise in the BOLD signal remains problematic for fMRI – particularly for techniques such as functional connectivity, where findings can be spuriously influenced by noise sources that can ...covary with individual differences. Many such potential noise sources – for instance, motion and respiration – can have a temporally lagged effect on the BOLD signal. Thus, here we present a tool for assessing residual lagged structure in the BOLD signal that is associated with nuisance signals, using a construction similar to a peri-event time histogram. Using this method, we find that framewise displacements – both large and very small – were followed by structured, prolonged, and global changes in the BOLD signal that depend on the magnitude of the preceding displacement and extend for tens of seconds. This residual lagged BOLD structure was consistent across datasets, and independently predicted considerable variance in the global cortical signal (as much as 30–40% in some subjects). Mean functional connectivity estimates varied similarly as a function of displacements occurring many seconds in the past, even after strict censoring. Similar patterns of residual lagged BOLD structure were apparent following respiratory fluctuations (which covaried with framewise displacements), implicating respiration as one likely mechanism underlying the displacement-linked structure observed. Global signal regression largely attenuates this artifactual structure. These findings suggest the need for caution in interpreting results of individual difference studies where noise sources might covary with the individual differences of interest, and highlight the need for further development of preprocessing techniques for mitigating such structure in a more nuanced and targeted manner.
•Introduces an approach for revealing residual lagged structure in the BOLD signal.•Reveals robust, predictable artifact; linked with variation in mean FC.•Artifact follows large & small displacements and is linked with respiration.•Global signal regression eliminates artifact, helping to avoid spurious conclusions.•A MATLAB script for general data exploration & quality assessment is provided.
Resting-state functional connectivity is used throughout neuroscience to study brain organization and to generate biomarkers of development, disease, and cognition. The processes that give rise to ...correlated activity are, however, poorly understood. Here we decompose resting-state functional connectivity using a temporal unwrapping procedure to assess the contributions of moment-to-moment activity cofluctuations to the overall connectivity pattern. This approach temporally resolves functional connectivity at a timescale of single frames, which enables us to make direct comparisons of cofluctuations of network organization with fluctuations in the blood oxygen level-dependent (BOLD) time series. We show that surprisingly, only a small fraction of frames exhibiting the strongest cofluctuation amplitude are required to explain a significant fraction of variance in the overall pattern of connection weights as well as the network’s modular structure. These frames coincide with frames of high BOLD activity amplitude, corresponding to activity patterns that are remarkably consistent across individuals and identify fluctuations in default mode and control network activity as the primary driver of resting-state functional connectivity. Finally, we demonstrate that cofluctuation amplitude synchronizes across subjects during movie watching and that high-amplitude frames carry detailed information about individual subjects (whereas low-amplitude frames carry little). Our approach reveals fine-scale temporal structure of resting-state functional connectivity and discloses that frame-wise contributions vary across time. These observations illuminate the relation of brain activity to functional connectivity and open a number of directions for future research.
Brain networks are flexible and reconfigure over time to support ongoing cognitive processes. However, tracking statistically meaningful reconfigurations across time has proven difficult. This has to ...do largely with issues related to sampling variability, making instantaneous estimation of network organization difficult, along with increased reliance on task-free (cognitively unconstrained) experimental paradigms, limiting the ability to interpret the origin of changes in network structure over time. Here, we address these challenges using time-varying network analysis in conjunction with a naturalistic viewing paradigm. Specifically, we developed a measure of inter-subject network similarity and used this measure as a coincidence filter to identify synchronous fluctuations in network organization across individuals. Applied to movie-watching data, we found that periods of high inter-subject similarity coincided with reductions in network modularity and increased connectivity between cognitive systems. In contrast, low inter-subject similarity was associated with increased system segregation and more rest-like architectures. We then used a data-driven approach to uncover clusters of functional connections that follow similar trajectories over time and are more strongly correlated during movie-watching than at rest. Finally, we show that synchronous fluctuations in network architecture over time can be linked to a subset of features in the movie. Our findings link dynamic fluctuations in network integration and segregation to patterns of inter-subject similarity, and suggest that moment-to-moment fluctuations in functional connectivity reflect shared cognitive processing across individuals.
Despite enthusiasm about the potential for using fMRI‐based functional connectomes in the development of biomarkers for autism spectrum disorder (ASD), the literature is full of negative ...findings—failures to distinguish ASD functional connectomes from those of typically developing controls (TD)—and positive findings that are inconsistent across studies. Here, we report on a new study designed to either better differentiate ASD from TD functional connectomes—or, alternatively, to refine our understanding of the factors underlying the current state of affairs. We scanned individuals with ASD and controls both at rest and while watching videos with social content. Using multiband fMRI across repeat sessions, we improved both data quantity and scanning duration by collecting up to 2 hr of data per individual. This is about 50 times the typical number of temporal samples per individual in ASD fcMRI studies. We obtained functional connectomes that were discriminable, allowing for near‐perfect individual identification regardless of diagnosis, and equally reliable in both groups. However, contrary to what one might expect, we did not consistently or robustly observe in the ASD group either reductions in similarity to TD functional connectivity (FC) patterns or shared atypical FC patterns. Accordingly, FC‐based predictions of diagnosis group achieved accuracy levels around chance. However, using the same approaches to predict scan type (rest vs. video) achieved near‐perfect accuracy. Our findings suggest that neither the limitations of resting state as a “task,” data resolution, data quantity, or scan duration can be considered solely responsible for failures to differentiate ASD from TD functional connectomes.
PDF
