Quantifying tics: Best practices and design considerations for video-based tic coding in research.

Tic disorders (TD), including Tourette Syndrome, are characterized by involuntary, repetitive movements and/or vocalizations that can lead to persistent disability and impairment across the lifespan. Existing research demonstrates that video-based behavioral coding (VBBC) methods can be used to reliably quantify tics, enabling a more objective approach to tic measurement above and beyond standardly used TD questionnaires. VBBC is becoming more popular given the ease and ubiquity of obtaining patient videos. However, rigor and reproducibility of this work has been limited by undescribed and unstandardized approaches to using VBBC methods in TD research. The current paper describes "best practices" for VBBC in TD research, which have been tested and refined in our research over the past 15+ years, including considerations for data acquisition, coding implementation, interrater reliability demonstration, and methods reporting. We also address ethical considerations for researchers using this method.

Automated Quantification of Eye Tics Using Computer Vision and Deep Learning Techniques.

BACKGROUND: Tourette syndrome (TS) tics are typically quantified using "paper and pencil" rating scales that are susceptible to factors that adversely impact validity. Video-based methods to more objectively quantify tics have been developed but are challenged by reliance on human raters and procedures that are resource intensive. Computer vision approaches that automate detection of atypical movements may be useful to apply to tic quantification. OBJECTIVE: The current proof-of-concept study applied a computer vision approach to train a supervised deep learning algorithm to detect eye tics in video, the most common tic type in patients with TS. METHODS: Videos (N = 54) of 11 adolescent patients with TS were rigorously coded by trained human raters to identify 1.5-second clips depicting "eye tic events" (N = 1775) and "non-tic events" (N = 3680). Clips were encoded into three-dimensional facial landmarks. Supervised deep learning was applied to processed data using random split and disjoint split regimens to simulate model validity under different conditions. RESULTS: Area under receiver operating characteristic curve was 0.89 for the random split regimen, indicating high accuracy in the algorithm's ability to properly classify eye tic vs. non-eye tic movements. Area under receiver operating characteristic curve was 0.74 for the disjoint split regimen, suggesting that algorithm generalizability is more limited when trained on a small patient sample. CONCLUSIONS: The algorithm was successful in detecting eye tics in unseen validation sets. Automated tic detection from video is a promising approach for tic quantification that may have future utility in TS screening, diagnostics, and treatment outcome measurement. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Auditory Domain Sensitivity and Neuroplasticity-Based Targeted Cognitive Training in Autism Spectrum Disorder.

Sensory processing, along with the integration of external inputs into stable representations of the environment, is integral to social cognitive functioning; challenges in these processes have been reported in Autism Spectrum Disorder (ASD) since the earliest descriptions of autism. Recently, neuroplasticity-based targeted cognitive training (TCT) has shown promise as an approach to improve functional impairments in clinical patients. However, few computerized and adaptive brain-based programs have been trialed in ASD. For individuals with sensory processing sensitivities (SPS), the inclusion of some auditory components in TCT protocols may be aversive. Thus, with the goal of developing a web-based, remotely accessible intervention that incorporates SPS concerns in the auditory domain, we assessed auditory SPS in autistic adolescents and young adults (N = 25) who started a novel, computerized auditory-based TCT program designed to improve working memory and information processing speed and accuracy. We found within-subject gains across the training program and between pre/post-intervention assessments. We also identified auditory, clinical, and cognitive characteristics that are associated with TCT outcomes and program engagement. These initial findings may be used to inform therapeutic decisions about which individuals would more likely engage in and benefit from an auditory-based, computerized TCT program.

Utility of Downstream Biomarkers to Assess and Optimize Intranasal Delivery of Oxytocin.

Oxytocin (OT), a mammalian neurohormone associated with social cognition and behavior, can be administered in its synthetic form intranasally (IN) and impact brain chemistry and behavior. IN-OT shows potential as a noninvasive intervention for disorders characterized by social challenges, e.g., autism spectrum disorder (ASD) and anorexia nervosa (AN). To evaluate IN-OT's efficacy, we must quantify OT uptake, availability, and clearance; thus, we assessed OT levels in urine (uOT) before and after participants (26 ASD, 7 AN, and 7 healthy controls) received 40 IU IN-OT or placebo across two sessions using double-blind, placebo-controlled crossover designs. We also measured uOT and plasma (pOT) levels in a subset of participants to compare the two sampling methods. We found significantly higher uOT and pOT following intranasal delivery of active compound versus placebo, but analyses yielded larger effect sizes and more clearly differentiated pre-post-OT levels for uOT than pOT. Further, we applied a two-step cluster (TSC), blinded backward-chaining approach to determine whether active/placebo groups could be identified by uOT and pOT change alone; uOT levels may serve as an accessible and accurate systemic biomarker for OT dose-response. Future studies will explore whether uOT levels correlate directly with behavioral targets to improve dosing for therapeutic goals.

Novel linguistic evaluation of prefrontal synthesis (LEPS) test measures prefrontal synthesis acquisition in neurotypical children and predicts high-functioning versus low-functioning class assignment in individuals with autism.

In order to grasp the difference between "the cat on the mat" and "the mat on the cat," understanding the words and the grammar is not enough. Rather it is essential to visualize the cat and the mat together to appreciate their relations. This type of imagination, which involves juxtaposition of mental objects is conducted by the prefrontal cortex and is therefore called Prefrontal Synthesis (PFS). PFS acquisition has a strong experience-dependent critical period putting children with language delay in danger of never acquiring PFS and, consequently, not mastering complex language comprehension. In typical children, the timeline of PFS acquisition correlates with vocabulary expansion. Conversely, atypically developing children may learn many words but never acquire PFS. In these individuals, intelligence tests based on vocabulary assessment may miss the profound deficit in PFS. Accordingly, we developed a test specific for PFS - Linguistic Evaluation of Prefrontal Synthesis or LEPS - and administered it to 50 neurotypical children, age 4.1 ± 1.3 years and to 23 individuals with impairments, age 16.4 ± 3.0 years. All neurotypical children older than 4 years received the LEPS score 7/10 or greater indicating good PFS ability. Among individuals with impairments only 39% received the LEPS score 7/10 or greater. LEPS was 90% correct in predicting high-functioning vs. low-functioning class assignment in individuals with impairments.

Predicting Core Characteristics of ASD Through Facial Emotion Recognition and Eye Tracking in Youth.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder (NDD) with a high rate of comorbidity. The implementation of eye-tracking methodologies has informed behavioral and neurophysiological patterns of visual processing across ASD and comorbid NDDs. In this study, we propose a machine learning method to predict measures of two core ASD characteristics: impaired social interactions and communication, and restricted, repetitive, and stereotyped behaviors and interests. Our method extracts behavioral features from task performance and eye-tracking data collected during a facial emotion recognition paradigm. We achieved high regression accuracy using a Random Forest regressor trained to predict scores on the SRS-2 and RBS-R assessments; this approach may serve as a classifier for ASD diagnosis.

The Use of Immersive Environments for the Early Detection and Treatment of Neuropsychiatric Disorders.

Neuropsychiatric disorders are highly prevalent conditions with significant individual, societal, and economic impacts. A major challenge in the diagnosis and treatment of these conditions is the lack of sensitive, reliable, objective, quantitative tools to inform diagnosis, and measure symptom severity. Currently available assays rely on self-reports and clinician observations, leading to subjective analysis. As a step toward creating quantitative assays of neuropsychiatric symptoms, we propose an immersive environment to track behaviors relevant to neuropsychiatric symptomatology and to systematically study the effect of environmental contexts on certain behaviors. Moreover, the overarching theme leads to connected tele-psychiatry which can provide effective assessment.