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Inhibitory control develops in early childhood, and atypical development may be a measurable marker of risk for the later development of psychosis. Additionally, inhibitory control may be a target for intervention.
Behavioral performance on a developmentally appropriate Go/No-Go task including a frustration manipulation completed by children ages 3–5 years (early childhood; n = 107) was examined in relation to psychotic-like experiences (PLEs; ‘tween’; ages 9–12), internalizing symptoms, and externalizing symptoms self-reported at long-term follow-up (pre-adolescence; ages 8–11). ERP N200 amplitude for a subset of these children (n = 34) with electrophysiological data during the task was examined as an index of inhibitory control.
Children with lower accuracy on No-Go trials compared to Go trials in early childhood (F(1,101) = 3.976, p = 0.049), evidenced higher PLEs at the transition to adolescence 4–9 years later, reflecting a specific deficit in inhibitory control. No association was observed with internalizing or externalizing symptoms. Decreased accuracy during the frustration manipulation predicted higher internalizing, F(2,202) = 5.618, p = 0.004, and externalizing symptoms, F(2,202) = 4.663, p = 0.010. Smaller N200 amplitudes were observed on No-Go trials for those with higher PLEs, F(1,101) = 6.075, p = 0.020; no relationship was observed for internalizing or externalizing symptoms.
Long-term follow-up demonstrates for the first time a specific deficit in inhibitory control behaviorally and electrophysiology, for individuals who later report more PLEs. Decreases in task performance under frustration induction indicated risk for internalizing and externalizing symptoms. These findings suggest that pathophysiological mechanisms for psychosis are relevant and discriminable in early childhood, and further, suggest an identifiable and potentially modifiable target for early intervention.
The National Institute of Mental Health's Research Domain Criteria (RDoC) framework has prompted a paradigm shift from categorical psychiatric disorders to considering multiple levels of vulnerability for probabilistic risk of disorder. However, the lack of neurodevelopmentally based tools for clinical decision making has limited the real-world impact of the RDoC. Integration with developmental psychopathology principles and statistical methods actualize the clinical implementation of RDoC to inform neurodevelopmental risk. In this conceptual paper, we introduce the probabilistic mental health risk calculator as an innovation for such translation and lay out a research agenda for generating an RDoC- and developmentally informed paradigm that could be applied to predict a range of developmental psychopathologies from early childhood to young adulthood. We discuss methods that weigh the incremental utility for prediction based on intensity and burden of assessment, the addition of developmental change patterns, considerations for assessing outcomes, and integrative data approaches. Throughout, we illustrate the risk calculator approach with different neurodevelopmental pathways and phenotypes. Finally, we discuss real-world implementation of these methods for improving early identification and prevention of developmental psychopathology. We propose that mental health risk calculators can build a needed bridge between the RDoC multiple units of analysis and developmental science.
Dyslexia, or difficulty in learning to read that is not caused by a sensory deficit or lack of effort or education, affects readers of all languages (Caravolas,2005). Based on this definition, dyslexia cannot be diagnosed until children have demonstrated trouble with reading acquisition. However, it would be ideal to identify which children will go on develop reading problems before they struggle or fail to learn to read. Children who are identified early and who receive early intervention are likely to have better reading outcomes (Bowyer-Crane et al., 2008; Torgesen, 2004; Schatschneider & Torgesen, 2004; Vellutino, Scanlon, & Tanzman, 1998) and may suffer fewer of the negative consequences associated with poor reading. Further, an understanding of which children are at greatest risk for reading difficulties would allow educators and clinicians to allocate limited intervention resources to students who need them most. Extensive behavioral research has sought to answer this question and yet models predicting reading are rarely employed in practice.
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