Electroencephalogram aperiodic power spectral slope can be reliably measured and predicts ADHD risk in early development.

The aperiodic exponent of the electroencephalogram (EEG) power spectrum has received growing attention as a physiological marker of neurodevelopmental psychopathology, including attention-deficit/hyperactivity disorder (ADHD). However, its use as a marker of ADHD risk across development, and particularly in very young children, is limited by unknown reliability, difficulty in aligning canonical band-based measures across development periods, and unclear effects of treatment in later development. Here, we investigate the internal consistency of the aperiodic EEG power spectrum slope and its association with ADHD risk in both infants (n = 69, 1-month-old) and adolescents (n = 262, ages 11-17 years). Results confirm good to excellent internal consistency in infancy and adolescence. In infancy, a larger aperiodic exponent was associated with greater family history of ADHD. In contrast, in adolescence, ADHD diagnosis was associated with a smaller aperiodic exponent, but only in children with ADHD who had not received stimulant medication treatment. Results suggest that disruptions in cortical development associated with ADHD risk may be detectable shortly after birth via this approach. Together, findings imply a dynamic developmental shift in which the developmentally normative flattening of the EEG power spectrum is exaggerated in ADHD, potentially reflecting imbalances in cortical excitation and inhibition that could contribute to long-lasting differences in brain connectivity.

Momentary Dynamics Implicate Emotional Features in the ADHD Phenotype.

Emotional dysregulation is increasingly recognized as important to the attention-deficit/hyperactivity disorder (ADHD) phenotype alongside inattention and hyperactivity-impulsivity. Studies of ADHD have relied primarily on trait-based conceptualizations that emphasize stability of symptoms across moderate developmental timescales (i.e., months to years). Trait-based conceptualizations provide a critical view but fail to account for short-term dynamic variations in the expression of ADHD symptoms and emotion. This leaves a gap in our understanding of the short-term variation in ADHD symptom expression and the dynamic relationships among ADHD symptoms and emotion. Here, we assessed caregiver report of ADHD symptoms and positive and negative emotion using ecological momentary approaches over 2 weeks in a sample of 36 children with and without ADHD between the ages of 7-12 years old. Between-person (RKF) and within-person (RC) reliability were estimated. Multilevel models tested specific covariation hypotheses between ADHD symptoms and emotion. Analyses confirmed that ADHD and emotion ratings were reliable as individual differences (i.e., between-person; RKF range 0.93-1.0) and moment-to-moment change (i.e., within-person; Rc range 0.66-0.88) measures. Multilevel models found little evidence for lagged effects between domains, but consistently identified concurrent expression of ADHD symptoms and emotions; inattention covaried most strongly with negative emotion and hyperactivity-impulsivity covaried most strongly with positive emotion. Results demonstrate the importance of complementing trait-level conceptualizations with assessment of momentary dynamics. Momentary assessment suggests important covariation of ADHD symptoms and emotion as part of the ADHD phenotype.

Domain-Specific Outcomes for Stroke Clinical Trials: What the Modified Rankin Isn't Ranking.

Global outcome measures that are widely used in stroke clinical trials, such as the modified Rankin Scale (mRS), lack sufficient detail to detect changes within specific domains (e.g., sensory, motor, visual, linguistic, or cognitive function). Yet such data are vital for understanding stroke recovery and its mechanisms. Poststroke deficits in specific domains differ in their rate and degree of recovery and in their effects on overall independence and quality of life. For example, even in a patient with complete recovery of strength, persistent deficits in the nonmotor domains such as language and cognition may make a return to independent living impossible. In such cases, global measures based solely on the patient's degree of independence would overlook a complete recovery in the motor domain. Capturing these important aspects of recovery demands a domain-specific approach. If stroke outcomes trials are to incorporate finer-grained recovery metrics-which can require substantial time, effort, and expertise to implement-efficiency must be a priority. In this article, we discuss how commonly collected clinical data from the NIH Stroke Scale can guide the judicious selection of relevant recovery domains for more detailed testing. Our overarching goal is to make the implementation of domain-specific testing more feasible for large-scale clinical trials on stroke recovery.