The clinical presentation and neurobiology of mild traumatic brain injury (mTBI) - also referred to as concussion - are complex and multifaceted, and interrelationships between neurobiological measures derived from neuroimaging are poorly understood. This study applied machine learning (ML) to multimodal whole-brain functional connectomes from magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI), and structural connectomes from diffusion tensor imaging (DTI) in a test of discriminative accuracy in cases of mTBI. Resting state MEG (amplitude envelope correlations), fMRI (BOLD correlations), and DTI (fractional anisotropy, FA; streamline count, SC) connectome data was acquired in 26 controls without mTBI (all male; 27.6 -/+ 4.7 years) and 24 participants with mTBI (all male; 29.7 -/+ 6.7 years) in the acute-subacute phase of injury. ML with data fusion was used to optimally identify modalities and brain features for discriminating individuals with mTBI from those without. Univariate group differences were only found for MEG functional connectivity, while no differences were found for fMRI or DTI. Functional connectivity (fMRI and MEG) showed robust unimodal classification accuracy for mTBI, followed by structural connectivity (DTI), where FA showed marginally better classification performance than SC, but SC outperformed FA in data interpretation and fusion. Perfect, unsupervised separation of participants with and without mTBI was achieved through participant fusion maps featuring all three data modalities. Finally, the MEG-only full feature fusion map showed group differences, and this effect was eliminated upon integrating DTI and fMRI datasets. The markers identified here align well with prior multimodal findings in concussion and highlight modality-specific considerations for their use in understanding network abnormalities of mTBI.
Autism is a neurodevelopmental condition with varied trajectories through the lifespan, leading to individualized patterns of strengths and challenges. Longitudinal autism cohort studies show the importance of developmental and adaptive skills starting in the early years, followed by emerging co-occurring conditions, and opportunities for autonomy and community participation when approaching adulthood. Studies of interventions to support developmental outcomes in autistic children have shown benefits; however, adverse events from therapies and outcomes prioritized by autistic people must be incorporated. Programs for autistic children and youth are making some progress by including members of diverse communities, developing and adapting interventions to meet their needs. Most importantly, autistic people have highlighted the many contributors to a 'good life', prominent among which are acceptance and meaningful inclusion. This review summarizes the latest evidence about developmental trajectories and outcomes among autistic children and youth, and how this translates into clinical practice and policy.
INTRODUCTION:Implementation science frameworks - including process models, determinant frameworks, classic theories, implementation theories, and evaluation frameworks - are increasingly used to guide the translation of evidence-based interventions into practice. In paediatric rehabilitation, where interventions are complex and often require multidisciplinary collaboration, these frameworks can support systematic and context-sensitive implementation. However, the extent to which these frameworks have been used has not been comprehensively reviewed. OBJECTIVE:Determine the extent, nature, and specific contexts of the existing literature on the use of implementation science models, theories, and/or frameworks (MTFs) in paediatric rehabilitation. METHODS:This scoping review will follow the Joanna Briggs Institute (JBI) methodological guidance for scoping reviews. A comprehensive search strategy will be developed with a health sciences librarian and applied across multiple electronic databases: MEDLINE (Ovid), Embase, CINAHL, PsycINFO, ACM Digital Library, Web of Science, the Cochrane Central Register of Controlled Trials, PEDro, and RehabData. We will search English language articles published since 2006. Studies will be included if they report on the application of implementation science MTFs in the context of paediatric rehabilitation. Screening of titles and abstracts and full texts will be performed independently and in duplicate using Covidence. Discrepancies will be resolved through discussion or a third reviewer. Data will be extracted using a standardized form. Quantitative data will be summarized using numerical counts. Qualitative data will be analyzed using content analyses. RESULTS:This review will report on the use of implementation science MTFs in paediatric rehabilitation, identifying trends on the specific types applied, highlight gaps and/or underutilization across domains or developmental stages, and potentially uncover emerging frameworks. Finally, the results may inform the development of future implementation strategies and capacity-building initiatives within the field.
Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD) have considerable overlap in clinical presentation, supporting the need for a framework of symptom domains which crosses traditional diagnostic boundaries. We aimed to identify latent groups based on similarities in factor scores across ASD and ADHD neurodevelopmental domains and to compare these groups on adaptive functioning and neuroimaging measures. Participants included children and youth with a clinical diagnosis of ASD (n = 727) or ADHD (n = 770). Parents completed measures of autism and ADHD symptoms. We identified latent profiles of four symptom factors (inattention, hyperactivity/impulsivity, social communication, and restricted, repetitive behaviours and interests). The profiles were then compared on adaptive functioning and global structural and functional neuroimaging measures. A four-profile model was the best fitting model. All four profiles had varying levels of co-occurring ASD and ADHD symptoms, regardless of whether the class consisted of predominately ASD or ADHD participants. There was no profile with all ASD or ADHD participants. Adaptive Behavior Assessment System scores were significantly different among latent profiles after adjusting for age, sex and diagnosis (Χ2(3) = 360.747, p < 0.001). Total subcortical volume was significantly different among latent profiles after adjusting for age, sex, and brain volume (Χ2(4) = 10.868, p = 0.028). We identified four latent profiles with varying levels of co-occurring ASD and ADHD symptoms, spanning diagnostic labels, and between-profile variation on adaptive functioning and subcortical volume. These results provide support for phenotypic overlap between ASD and ADHD and reinforce the need to focus on dimensionality in ASD and ADHD.
The increasing prevalence of autism has led to considerable system challenges as specialist-driven approaches have struggled to keep pace. Recent research has challenged status quo models by demonstrating that community-based pediatric clinicians can accurately diagnose autism in young children. This research provides an opportunity to further expand capacity of community systems to conduct developmentally relevant, context-specific assessments of autistic people over their lifespan. In this Commentary, we propose a personalized lifespan approach to autism assessment as a precision care and system organization framework that considers the ongoing and evolving needs of autistic people and aligns the necessary expertise and resources to provide focused assessment when it is most informative. This framework consists of three key principles: (1) assessment is not a one-time, one-size-fits-all event; instead, assessment continues across the lifespan; (2) assessments should generate information that is relevant to the individual's current needs and life stage; and (3) wherever possible, community-based expertise should be engaged to promote the right assessment at the right time in the right place. Adopting this framework can reduce system access challenges, while also addressing the ongoing assessment needs of an autistic person across their life course.
