Autism Spectrum Disorder (ASD) has traditionally been evaluated and diagnosed via behavioral assessments. However, increasing research suggests that neuroimaging as early as infancy can reliably identify structural and functional differences between autistic and non-autistic brains. The current review provides a systematic overview of imaging approaches used to identify differences between infants at familial risk and without risk and predictive biomarkers. Two primary themes emerged after reviewing the literature: (1) neuroimaging methods can be used to describe structural and functional differences between infants at risk and infants not at risk for ASD (descriptive), and (2) neuroimaging approaches can be used to predict ASD diagnosis among high-risk infants and developmental outcomes beyond infancy (predicting later diagnosis). Combined, the articles highlighted that several neuroimaging studies have identified a variety of neuroanatomical and neurological differences between infants at high and low risk for ASD, and among those who later receive an ASD diagnosis. Incorporating neuroimaging into ASD evaluations alongside traditional behavioral assessments can provide individuals with earlier diagnosis and earlier access to supportive resources.
With the onset of the COVID-19 pandemic, researchers have been faced with challenges in maintaining interdisciplinary research collaborations. The purpose of this article is to apply and expand a previously introduced model to sustaining new interdisciplinary research collaborations: Forging Alliances in Interdisciplinary Rehabilitation Research (FAIRR). FAIRR is a logic model that can be used as a guide to create interdisciplinary rehabilitation research teams. In this article, the authors propose expanding FAIRR by including strategies for sustaining interdisciplinary rehabilitation research collaborations: modifying inputs (resources needed to assemble a team and to conduct research activities), shifting activities (steps taken to move the interdisciplinary collaboration forward), and examining what impacts the fit between inputs and activities. Two examples are used to highlight the application of the FAIRR model to interdisciplinary collaborations during COVID-19.
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