The validity of diagnostic labels of autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and obsessive compulsive disorder (OCD) is an open question given the mounting evidence that these categories may not correspond to conditions with distinct etiologies, biologies, or phenotypes. The objective of this study was to determine the agreement between existing diagnostic labels and groups discovered based on a data-driven, diagnosis-agnostic approach integrating cortical neuroanatomy and core-domain phenotype features. A machine learning pipeline, called bagged-multiview clustering, was designed to discover homogeneous subgroups by integrating cortical thickness data and measures of core-domain phenotypic features of ASD, ADHD, and OCD. This study was conducted using data from the Province of Ontario Neurodevelopmental Disorders (POND) Network, a multi-center study in Ontario, Canada. Participants (n = 226) included children between the ages of 6 and 18 with a diagnosis of ASD (n = 112, median [IQR] age = 11.7[4.8], 21% female), ADHD (n = 58, median [IQR] age = 10.2[3.3], 14% female), or OCD (n = 34, median [IQR] age = 12.1[4.2], 38% female), as well as typically developing controls (n = 22, median [IQR] age = 11.0[3.8], 55% female). The diagnosis-agnostic groups were significantly different than each other in phenotypic characteristics (SCQ: χ2(9) = 111.21, p < 0.0001; SWAN: χ2(9) = 142.44, p < 0.0001) as well as cortical thickness in 75 regions of the brain. The analyses revealed disagreement between existing diagnostic labels and the diagnosis-agnostic homogeneous groups (normalized mutual information < 0.20). Our results did not support the validity of existing diagnostic labels of ASD, ADHD, and OCD as distinct entities with respect to phenotype and cortical morphology.

A binary switch based on the detection of periodic vocal cord vibrations is proposed for individuals with multiple and severe disabilities. The system offers three major advantages over existing speech-based access technologies, namely, insensitivity to environment noise, increased robustness against user-generated artifacts such as coughs, and reduced exertion during prolonged usage periods. The proposed system makes use of a dual-axis accelerometer placed noninvasively in proximity of the vocal cords by means of a neckband. Periodic vocal cord vibrations are detected using the normalized cross-correlation function computed from anterior-posterior and superior-inferior accelerometry signals. Experiments with a participant with hypotonic cerebral palsy show the proposed system outperforming a popular commercial sound-based system in terms of sensitivity, task time, and user-perceived exertion.

This study proposed a single-switch text entry system by hierarchical scanning of character strokes for an 11-year-old girl with severe physical disabilities and low vision. She could only perceive magnified straight line segments and chords presented against high-contrast, colored backgrounds. In a descriptive case study, the participant used the proposed system in the community for 8 months. Assessment included theoretical evaluation of text entry performance and empirical evaluation of the participant's proficiency. The proposed system had a lower error-free text entry rate but comparable proneness to user error as a real-world implementation of row-column virtual scanning keyboard with character frequency layout. The participant's proficiency, in terms of mean number of single-switch activations and time to type one character, showed statistically significant improvements as the case study progressed. The proposed system feasibly addressed the participant's typing needs, in a context where traditional row-column scanning and codeword-based text entry systems were not successful.