A randomized controlled trial of everolimus for neurocognitive symptoms in PTEN hamartoma tumor syndrome.

PTEN hamartoma tumor syndrome (PHTS) is a complex neurodevelopmental disorder characterized by mechanistic target of rapamycin (mTOR) overactivity. Limited data suggest that mTOR inhibitors may be therapeutic. No placebo-controlled studies have examined mTOR inhibition on cognition and behavior in humans with PHTS with/without autism. We conducted a 6-month phase II, randomized, double-blinded, placebo-controlled trial to examine the safety profile and efficacy of everolimus (4.5 mg/m2) in individuals (5-45 years) with PHTS. We measured several cognitive and behavioral outcomes, and electroencephalography (EEG) biomarkers. The primary endpoint was a neurocognitive composite derived from Stanford Binet-5 (SB-5) nonverbal working memory score, SB-5 verbal working memory, Conners' Continuous Performance Test hit reaction time and Purdue Pegboard Test score. Forty-six participants underwent 1:1 randomization: n = 24 (everolimus) and n = 22 (placebo). Gastrointestinal adverse events were more common in the everolimus group (P < 0.001). Changes in the primary endpoint between groups from baseline to Month 6 were not apparent (Cohen's d = -0.10, P = 0.518). However, several measures were associated with modest effect sizes (≥0.2) in the direction of improvement, including measures of nonverbal IQ, verbal learning, autism symptoms, motor skills, adaptive behavior and global improvement. There was a significant difference in EEG central alpha power (P = 0.049) and central beta power (P = 0.039) 6 months after everolimus treatment. Everolimus is well tolerated in PHTS; adverse events were similar to previous reports. The primary efficacy endpoint did not reveal improvement. Several secondary efficacy endpoints moved in the direction of improvement. EEG measurements indicate target engagement following 6 months of daily oral everolimus. Trial Registration Information: ClinicalTrials.gov NCT02991807 Classification of Evidence: I.

Prediction of autism spectrum disorder diagnosis using nonlinear measures of language-related EEG at 6 and 12 months.

BACKGROUND: Early identification of autism spectrum disorder (ASD) provides an opportunity for early intervention and improved developmental outcomes. The use of electroencephalography (EEG) in infancy has shown promise in predicting later ASD diagnoses and in identifying neural mechanisms underlying the disorder. Given the high co-morbidity with language impairment, we and others have speculated that infants who are later diagnosed with ASD have altered language learning, including phoneme discrimination. Phoneme learning occurs rapidly in infancy, so altered neural substrates during the first year of life may serve as early, accurate indicators of later autism diagnosis. METHODS: Using EEG data collected at two different ages during a passive phoneme task in infants with high familial risk for ASD, we compared the predictive accuracy of a combination of feature selection and machine learning models at 6 months (during native phoneme learning) and 12 months (after native phoneme learning), and we identified a single model with strong predictive accuracy (100%) for both ages. Samples at both ages were matched in size and diagnoses (n = 14 with later ASD; n = 40 without ASD). Features included a combination of power and nonlinear measures across the 10­20 montage electrodes and 6 frequency bands. Predictive features at each age were compared both by feature characteristics and EEG scalp location. Additional prediction analyses were performed on all EEGs collected at 12 months; this larger sample included 67 HR infants (27 HR-ASD, 40 HR-noASD). RESULTS: Using a combination of Pearson correlation feature selection and support vector machine classifier, 100% predictive diagnostic accuracy was observed at both 6 and 12 months. Predictive features differed between the models trained on 6- versus 12-month data. At 6 months, predictive features were biased to measures from central electrodes, power measures, and frequencies in the alpha range. At 12 months, predictive features were more distributed between power and nonlinear measures, and biased toward frequencies in the beta range. However, diagnosis prediction accuracy substantially decreased in the larger, more behaviorally heterogeneous 12-month sample. CONCLUSIONS: These results demonstrate that speech processing EEG measures can facilitate earlier identification of ASD but emphasize the need for age-specific predictive models with large sample sizes to develop clinically relevant classification algorithms.