Reliable and specific biomarkers that can distinguish autism spectrum disorders (ASDs) from commonly co-occurring attention-deficit/hyperactivity disorder (ADHD) are lacking, causing misses and delays in diagnosis, and reducing access to interventions and quality of life.

To examine whether an innovative, brief (1-min), videogame method called Computerised Assessment of Motor Imitation (CAMI), can identify ASD-specific imitation differences compared with neurotypical children and children with ADHD.

This cross-sectional study used CAMI alongside standardised parent-report (Social Responsiveness Scale, Second Edition) and observational measures of autism (Autism Diagnostic Observation Schedule-Second Edition; ADOS-2), ADHD (Conners) and motor ability (Physical and Neurological Examination for Soft Signs). The sample comprised 183 children aged 7–13 years, with ADHD (without ASD), with ASD (with and without ADHD) and who were neurotypical.

Regardless of co-occurring ADHD, children with ASD showed poorer CAMI performance than neurotypical children (P < 0.0001; adjusted R2 = 0.28), whereas children with ADHD and neurotypical children showed similar CAMI performance. Receiver operating curve and support vector machine analyses showed that CAMI distinguishes ASD from both neurotypical children (80% true positive rate) and children with ADHD (70% true positive rate), with a high success rate significantly above chance. Among children with ASD, poor CAMI performance was associated with increased autism traits, particularly ADOS-2 measures of social affect and restricted and repetitive behaviours (adjusted R2 = 0.23), but not with ADHD traits or motor ability.

Four levels of analyses confirm that poor imitation measured by the low-cost and scalable CAMI method specifically distinguishes ASD not only from neurotypical development, but also from commonly co-occurring ADHD.

Prominent clinical perspectives posit that the interface of autism and (borderline) personality disorder manifests as either a misdiagnosis of the former as the latter or a comorbidity of both. In this editorial, we integrate these disparate viewpoints by arguing that personality difficulties are inherent to the autistic spectrum.

The aim of the present study was to determine whether a combination of baseline features and early post-baseline depressive symptom changes have clinical value in predicting out-patient non-response in depressed out-patients after 8 weeks of medication treatment.

We analysed data from the Combining Medications to Enhance Depression Outcomes study for 447 participants with complete 16-item Quick Inventory of Depressive Symptomatology – Self-Report (QIDS-SR16) ratings at baseline and at treatment weeks 2, 4 and 8. We used a multi-time point, recursive subsetting approach that included baseline features and changes in QIDS-SR16 scores from baseline to weeks 2 and 4, to identify non-responders (<50% reduction in QIDS-SR16) at week 8 with a pre-specified accuracy level.

Pretreatment clinical features alone were not clinically useful predictors of non-response after 8 weeks of treatment. Baseline to week 2 symptom change identified 48 non-responders (of which 36 were true non-responders). This approach gave a clinically meaningful negative predictive value of 0.75. Symptom change from baseline to week 4 identified 79 non-responders (of which 60 were true non-responders), achieving the same accuracy. Symptom change at both weeks 2 and 4 identified 87 participants (almost 20% of the sample) as non-responders with the same accuracy. More participants with chronic than non-chronic index episodes could be accurately identified by week 4.

Specific baseline clinical features combined with symptom changes by weeks 2–4 can provide clinically actionable results, enhancing the efficiency of care by personalizing the treatment of depression.