Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age.

Abstract

OBJECTIVE: In this prospective, longitudinal study of young children, we examined whether a history of preschool generalized anxiety, separation anxiety, and/or social phobia is associated with amygdala-prefrontal dysregulation at school-age. As an exploratory analysis, we investigated whether distinct anxiety disorders differ in the patterns of this amygdala-prefrontal dysregulation. METHODS: Participants were children taking part in a 5-year study of early childhood brain development and anxiety disorders. Preschool symptoms of generalized anxiety, separation anxiety, and social phobia were assessed with the Preschool Age Psychiatric Assessment (PAPA) in the first wave of the study when the children were between 2 and 5 years old. The PAPA was repeated at age 6. We conducted functional MRIs when the children were 5.5 to 9.5 year old to assess neural responses to viewing of angry and fearful faces. RESULTS: A history of preschool social phobia predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces. Preschool generalized anxiety predicted less functional connectivity between the amygdala and dorsal prefrontal cortices in response to fearful faces. Finally, a history of preschool separation anxiety predicted less school-age functional connectivity between the amygdala and the ventral prefrontal cortices to angry faces and greater school-age functional connectivity between the amygdala and dorsal prefrontal cortices to angry faces. CONCLUSIONS: Our results suggest that there are enduring neurobiological effects associated with a history of preschool anxiety, which occur over-and-above the effect of subsequent emotional symptoms. Our results also provide preliminary evidence for the neurobiological differentiation of specific preschool anxiety disorders.

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Published Version (Please cite this version)

10.1371/journal.pone.0116854

Publication Info

Carpenter, Kimberly LH, Adrian Angold, Nan-Kuei Chen, William E Copeland, Pooja Gaur, Kevin Pelphrey, Allen W Song, Helen L Egger, et al. (2015). Preschool anxiety disorders predict different patterns of amygdala-prefrontal connectivity at school-age. PLoS One, 10(1). p. e0116854. 10.1371/journal.pone.0116854 Retrieved from https://hdl.handle.net/10161/9486.

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Carpenter

Kimberly Carpenter

Assistant Professor in Psychiatry and Behavioral Sciences

Dr. Kimberly Carpenter is a clinical neuroscientist specializing in understanding complex brain-behavior relationships in young children with autism and associated disorders. Her program of research includes four interrelated research themes: (1) Understanding the impact of comorbid disorders on clinical and behavioral outcomes of young autistic children; (2) Identification of early risk factors for the development of psychiatric and neurodevelopmental disorders; (3) Identification of brain-based biomarkers for group stratification and treatment response tracking in young children; and (4) Improving methods for screening, early identification, and treatment monitoring in autism and associated disorders. She currently leads an innovative research program exploring the shared and unique impacts that co-occurring anxiety and ADHD have on brain and behavioral biomarkers in young autistic children. She was the first to demonstrate that sensory over-responsivity, a symptom that has been described as part of a number of disorders including autism, anxiety, and ADHD, is a specific and unidirectional risk factor for the development of anxiety disorders in young children. She was also the first to demonstrate that, when accounting for comorbidity among individual anxiety disorders, specific anxiety disorders are associated with phenotypically meaningful differences in brain connectivity using MRI. Dr. Carpenter has also collaborated with experts in early childhood mental health, computer science, and engineering to develop novel technologies that utilize multi-modal methods via computer vision and machine learning to develop, refine, and test novel screening tools for early identification and treatment monitoring in young children with autism and related disorders.


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