Motor problems in autism: Co-occurrence or feature?

Motor features of autism have long been acknowledged by clinicians, researchers, and community stakeholders. Current DSM-5 and ICD-11 guidelines allow clinicians to assign a co-occurring diagnosis of developmental [motor] coordination disorder (DCD) for autistic individuals with significant motor problems. DCD is characterized by poor motor proficiency with an onset of symptoms in early development. Studies have shown considerable overlap in the behavioral motor features observed in autism and DCD. However, others indicate that motor problems in autism and DCD may stem from different underlying sensorimotor mechanisms. Regardless of whether autism has a unique motor phenotype or an overlap with DCD, changes need to be made in the clinical pipeline to address motor problems in autism at the stages of recognition, assessment, diagnosis, and intervention. Consensus is needed to address unmet needs in research on the etiology of motor problems in autism and their overlap with DCD, to optimize clinical practice guidelines. The development of screening and assessment tools for motor problems that are valid and reliable for use with autistic individuals is essential, and an evidence-based clinical pipeline for motor problems in autism is urgently needed. WHAT THIS PAPER ADDS: Motor problems in autism are highly prevalent, yet underdiagnosed and poorly managed. An evidence-based clinical pipeline for motor problems in autism is urgently needed.

Functional MRI Preprocessing in Lesioned Brains: Manual Versus Automated Region of Interest Analysis.

Functional magnetic resonance imaging (fMRI) has significant potential in the study and treatment of neurological disorders and stroke. Region of interest (ROI) analysis in such studies allows for testing of strong a priori clinical hypotheses with improved statistical power. A commonly used automated approach to ROI analysis is to spatially normalize each participant's structural brain image to a template brain image and define ROIs using an atlas. However, in studies of individuals with structural brain lesions, such as stroke, the gold standard approach may be to manually hand-draw ROIs on each participant's non-normalized structural brain image. Automated approaches to ROI analysis are faster and more standardized, yet are susceptible to preprocessing error (e.g., normalization error) that can be greater in lesioned brains. The manual approach to ROI analysis has high demand for time and expertise, but may provide a more accurate estimate of brain response. In this study, commonly used automated and manual approaches to ROI analysis were directly compared by reanalyzing data from a previously published hypothesis-driven cognitive fMRI study, involving individuals with stroke. The ROI evaluated is the pars opercularis of the inferior frontal gyrus. Significant differences were identified in task-related effect size and percent-activated voxels in this ROI between the automated and manual approaches to ROI analysis. Task interactions, however, were consistent across ROI analysis approaches. These findings support the use of automated approaches to ROI analysis in studies of lesioned brains, provided they employ a task interaction design.