Jennifer Foss-Feig - US grants

Sensory and motor deficits represent core features of autism spectrum disorder (ASD) and contribute to significant functional impairment. In the current application, we hypothesize a relationship between alterations in sensation and action in ASD, highlighting the importance of sensorimotor loops in attempting to understand mechanisms of impairment. In particular, we predict - to our knowledge, for the first time - that a breakdown in the link between action and perception leads to a different perceptual quality of self-generated motor acts in ASD. We propose a battery of translational experimental paradigms to test this novel hypothesis. All mobile organisms are equipped with a mechanism that serves to attenuate the sensory consequences of self-generated action, allowing enhanced processing of external information. Specifically, corollary discharge (CD) signals are sent to sensory brain areas and represent a copy of movement signals sent to lower motor regions. CD signals allow organisms to predict the sensory consequences of an imminent movement, such that sensory brain regions can attenuate their response to self-initiated action. In the auditory domain, CD allows dampening of the sensory response to self-generated sounds (e.g., speech). In the oculomotor domain, CD allows the visual system to prepare for change in retinal input following an eye movement. We propose that ASD is characterized by disturbances in CD signaling, such that affected individuals experience increased response to their own actions, potentially resulting in hypo-responsiveness to external sensory stimuli and internal preoccupation. Critically, these putative consequences of CD deficits are well- replicated ASD features, but CD itself has never been tested in ASD. Our approach is to capitalize on elegant behavioral paradigms derived from animal neurophysiology, in combination with eye tracking and electrophysiology (EEG), to evaluate the integrity of CD signals in children and adolescents with ASD, as compared to well-matched typically developing controls. We hypothesize that disturbances in CD in ASD will be evidenced in: (1) reduced attenuation of auditory EEG responses to self-generated sounds; and (2) altered visual perception and movement planning following a saccadic eye movement, consistent with a failure to use CD to compensate for this movement. We will explore whether CD deficits relate to clinical features, including not only sensory and motor symptoms, but also higher order deficits in social and empathic functioning, which could reflect downstream effects of basic sensorimotor alterations. To our knowledge, this study is the first investigation of CD in ASD. Thus, this innovative, translationally- grounded project addresses a key gap in ASD research and knowledge, using cognitive neuroscience techniques to probe a specific, well-characterized brain mechanism that may underlie core ASD features. Our findings have the potential to link core ASD features to activity of single neurons, providing unique insight into potential neural mechanisms driving symptoms in ASD and potentially offering novel targets for intervention.

PROJECT SUMMARY Marked heterogeneity among individuals with autism spectrum disorder (ASD) is a significant problem facing the autism community. Heterogeneity makes it difficult to identify core biological disruptions. It also hampers the development and validation of targeted and consistently effective therapeutics. In particular, the lack of precise, psychometrically robust, change sensitive outcome measures for core symptomatology represents a key barrier for clinical trials and for detecting mechanisms underlying manifest symptoms. This project proposes to develop and validate an innovative new observer report measure for ASD that attempts to capture heterogeneity in core symptoms along positive and negative dimensions, akin to those that have been successful in quantifying heterogeneity and facilitating key biological and therapeutic discoveries in schizophrenia. Using state-of-the-art procedures put forth by the NIH Patient-Reported Outcome Measurement Information System (PROMIS®) initiative, this project will develop, calibrate, and validate a new tool - the Positive and Negative Inventory for ASD (PNI) - for assessing core ASD symptoms with unprecedented precision of symptom description and discrimination, along conceptually novel dimensions. First, our preliminary item pool will be refined with key stakeholder input from caregivers and national experts. Next, caregivers of 1,000 3-11 year old children with ASD and 400 typically developing and non-ASD clinical controls will complete the PNI. A combination of classical item analysis and factor analysis will be used to identify the best-performing items, which will then be calibrated using Item Response Theory (IRT) and co-calibrated with legacy measures to demonstrate superiority of PNI item and scale function. Baseline inter-rater and 6-week test-retest reliability, 24-week change sensitivity, and sensitivity in the context of an upcoming clinical trial all will be assessed, both convergent and divergent validity will be tested, and a short form will be developed. Preliminary data demonstrate both the utility of parsing individual behaviors, currently subsumed within larger categories, into positive and negative dimensions and the success of PROMIS® methods and IRT for developing sensitive tools for ASD. We anticipate that this innovative, scientifically rigorous study will result in a calibrated and validated assessment tool for ASD with an empirically-supported factor structure and items that are both precise in their descriptive capturing of symptoms and sensitive to variability in the latent dimensions, within and across children. We also expect that the PNI will show excellent reliability and change sensitivity, offering a promising outcome measure for use in ASD clinical trials. Long term, we expect this research to have significant clinical benefits, including: 1) offering a new tool for assessing experimental therapeutics, 2) providing a new framework within which to test brain mechanisms and genetic contributions to specific feature manifestations, and 3) contributing to more nuanced application of targeted treatment in community clinical practice by offering a rapid, innovative, precise, and sensitive way to quantify heterogeneity in ASD.

Project Summary Social interaction deficits are at the crux of autism spectrum disorder (ASD) and contribute to significant functional impairment, including poorer relationship quality and low employment rates in individuals with ASD. Despite an enormous amount of research dollars invested and thousands of research papers published on the topic, we remain far from understanding the basic neural computations underlying social processes in ASD. In the current proposal, we posit that this information gap is due in part to the rarity with which computational model- based analyses are used in ASD neuroimaging research. Additionally, most studies use passive paradigms (e.g. face perception) rather than examining brain functioning while participants engage in ecologically-relevant, interactive social tasks more akin to the type of interactions with which people with ASD struggle in their daily lives. This proposal takes an innovative computational psychiatry approach to understanding aberrant neural computations of social interactions in ASD, using high-resolution (7T) functional magnetic resonance imaging (fMRI) and virtual reality-like tasks that test individuals? abilities to proactively and dynamically engage in simulated social interactions. In particular, we focus on the ability of individuals with ASD to: 1) discriminate and track levels of closeness and power when navigating social interactions in a choose-your-own-adventure style interactive paradigm, and 2) understand and adapt to social norms and exert control over social others in the context of a proactive social exchange paradigm. We use novel computational models to examine the neural computations and connectivity underlying proactive social behavior, focusing on brain regions (e.g., hippocampus) that have been understudied in the context of social deficits in ASD. Finally, we use machine learning approaches to explore ASD heterogeneity along dimensions of dynamic and proactive social interactions and apply these indices to make clinically-meaningful predictions. We hypothesize that: 1) hippocampal tracking of social space will be less robust in ASD as compared to neurotypical controls and will correlate with social symptoms; 2) ASD individuals will show slower norm adaptation rate, greater aversion to norm violation, and reduced social controllability, accompanied by reduced neural encoding of social values in anterior insula and ventral striatum; and 3) these parameters will help identify subtypes of ASD and predict ASD- relevant outcomes (e.g. social skills, adaptive social functioning, quality of life). We expect that findings from this project will break new ground and fill critical knowledge gaps regarding the neurobiology of ASD. In particular, we expect our findings will greatly enhance understanding of the neural and computational mechanisms underlying deficits in proactive social behavior in ASD and will allow us to identify distinct, neurobiologically- driven clusters. In so doing, the results of this project could offer new tools by which to subtype the ASD phenotype and provide novel insights into treatment targets.