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Grants Awarded

Congratulations to these VTCAR Investigators!

 

Development and Testing of a Novel Neurotechnology to Promote Emotion Recognition in Autism

Investigators: Susan White (PI, Psychology, sww@vt.edu) and Co-PIs John Richey (Psychology), Martha Ann Bell (Psychology), Denis Gracanin (CHCI), Stephen LaConte (VTCRI), and Inyoung Kim (Statistics)   

Description: The goal of this project is to develop an assistive technology to promote facial emotion recognition in ASD. The investigators propose that facial emotion recognition can be rehabilitated using a brain-computer interface device.  The investigators plan to create a facial emotion recognition, which would be a virtual reality-based iPad application to assist users with emotion recognition by manipulating the avatar’s emotion intensity until it is recognized by the user at the neural level. The interface is user-friendly and game-like, to promote ease of use and eventual dissemination. The purpose of this randomized controlled trial is to assess feasibility including acceptability of the intervention, recruitment and randomization procedures, intervention implementation, blinded assessment procedures, and participant retention within the context of a randomized controlled trial.

 

Data Mining for Autism Endophenotypes in a Large-Scale Resting State fMRI Repository

Investigator: John Richey (richey@vt.edu)

Description: The purpose of this project is to use computational neuroscience to empirically identify endophenotypes of ASD in a large scale resting-state fMRI (rs-fMRI) repository (the Autism Brain Imaging Exchange [ABIDE], N~1,112 [ASD N=539]). Specifically, we propose to use resting-state connectivity maps in conjunction with group iterative multiple model estimation (Gates & Molenaar, 2012) and community structure detection (Newman, 2006) to generate empirically-derived subgroups of ASD who share similar brain network properties. The rationale for this approach is as follows. It has been widely acknowledged that ASD is a vastly heterogeneous disorder (e.g. Volkmar et al., 2004). It is also increasingly accepted that ASD is a “network-disorder”, involving complex degradation of brain networks. However, prior work in network-analysis of ASD has generally ignored this heterogeneity, and proceeded in traditional between-groups (ASD vs. Control) comparison. Our objective here is to determine if heterogeneity within ASD can actually be useful information, which facilitates the identification of subgroups (communities) whose brain network properties are similar [AIM 1], and whose symptoms cluster together [AIM 2]. Our target network will be the Default-Mode Network (DMN), a brain system that is 1) anchored in the posteromedial cortex, and 2) involved in multiple forms of social cognition that are known to be disrupted in ASD. Based on recent, well-conducted studies of DMN in autism, (e.g., Lynch et al., 2013; Rudie et al., 2012; Washington et al., 2013), we hypothesize that DMN has a heterogeneous connectivity profile in ASD, and that connectivity within the DMN can be used to parse subtypes of autism. We further predict that these individual patterns of connectivity are strongly related to individual differences in the phenotypic presentation of ASD based on the topography of connections. Our approach represents a substantially different way of using heterogeneity, and we provide extensive simulations to demonstrate the computational feasibility of endophenotype generation, and also the method by which endophenotypes will be linked to behavioral data available through ABIDE. We feel that our novel approach, in combination with the largest ASD resting-state fMRI repository ever created will stimulate vertical progress by overcoming problems associated with small sample size, univariate approaches and missing or inconsistent phenotypic data.

 

STEPS: Stepped Transition in Education Program for Students with ASD

Investigator: Susan White (sww@vt.edu)

Description: Young adults who have Autism Spectrum Disorder (ASD) without co-occurring intellectual impairment face a fairly unique set of challenges as they transition out of secondary school. These students are often quite capable of succeeding in higher education and many of them have interest in pursuing advanced degrees, but the nature of their disability and associated deficits (e.g., poor time management and poor self-regulation) may impede success. Individualized, appropriately timed, and developmentally sensitive transition and support services may promote realization of optimal outcomes for these young people. The goal of this project is to develop a comprehensive program to promote successful transition of students with ASD from high school to post-secondary education. We propose to refine and then evaluate a novel transition support and intervention program for adolescents and young adults with ASD: STEPS [Stepped Transition in Education Program for Students with ASD]. By targeting improved self-regulation (SR) and self-determination (SD) in young people with ASD, we assert that this program may have positive outcomes with respective to college adjustment and functional behavior.

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