Mania symptoms in youth predict poor long-term mental health outcomes, yet their developmental trajectories and associated risk factors remain unclear. Leveraging data from the Adolescent Brain Cognitive Development Study (N = 10,474; 9-10 years at baseline; 48% female, 65% white), we used latent growth mixture models to identify trajectories of mania symptoms across two years in early adolescence. We used multinomial logistic regressions to examine associations between trajectories and risk factors across mental and physical health, cognition, and family/environmental domains. We identified three trajectories: Low (58%), Moderate (32%), and High/Variable Mania Symptoms (10%). All mental health symptoms (depression, attention deficit hyperactivity disorder, conduct and oppositional defiant disorders and anxiety), two physical health factors (sleep disturbances, irritable bowel syndrome symptoms), one cognitive factor (verbal learning impairment), and three family/environmental factors (trauma, parent- and youth-reported family conflict) significantly differentiated between all three trajectories, reflecting incremental increases in risk factor severity with increasing mania symptoms. Other physical, cognitive, family and environmental factors were also associated with more severe mania symptom trajectories. More severe mania symptom trajectories in early adolescence are associated with multiple mental, physical, cognitive, family and environmental risk factors, underscoring the need for comprehensive risk prediction approaches in youth.
Jasmin Dönicke, Rebecca Cooper, Adriane M. Soehner et al.· Development and Psychopathol...· 0 citations
Portable low-field MRI systems are a promising complement to conventional high-field systems, enabling broader access to MRI. However, correspondence in cortical thickness estimates between low- and high-field MRI in young people remains limited despite its importance for neurodevelopment and psychopathology. To evaluate how multiple low-field image processing approaches improve cortical thickness correspondence with high-field MRI in a large sample of young individuals, we collected ultra-low-field (64mT) and high-field (3T) MRI data from a community sample of young people. We applied deep learning–based image processing approaches (SynthSR v1.0, SynthSR v2.0, recon-all-clinical, and recon-any) to low-field data acquired across multiple sequences (T1- and T2-weighted) and orientations (axial, coronal, sagittal, and multi-orientation), with and without resampling and/or co-registration. We assessed global, lobar, and regional cortical thickness correspondence with 3T MRI measures using Pearson and intraclass correlations. We compared pipelines using Steiger’s Z-tests and Fisher’s Z-tests. A total of 150 individuals (mean age, 18.63±5.07; 80 female) were included. We observed the highest global correspondence with recon-all-clinical applied to coronal T1-weighted images (r=0.40, pFDR=2.6e-05). At the lobar and regional levels, multi-orientation T2-weighted images processed with recon-all-clinical showed the highest correspondence across the greatest number of regions (4/12 lobes; 13/68 regions). The highest correspondence and largest improvements were in frontal, cingulate, and temporal regions, including the right pars triangularis (r=0.52, pFDR=4.78e-11; Z=4.78, pFDR=4.25e-06), right caudal anterior cingulate (r=0.47, pFDR=3.83e-09; Z=5.46, pFDR=1.32e-07), and left parahippocampal (r=0.58, pFDR=2.98e-14; Z=5.17, pFDR=6.01e-07). We observed significantly improved cortical thickness correspondence in low-field MRI in young people. The recon-all-clinical pipeline yielded moderate correspondence, particularly in frontal, cingulate, and temporal regions. Our results highlight the potential of low-field MRI as an affordable and scalable approach for assessing cortical thickness in young people.
Sun-Ho Choi, Julia Shaw, Rebecca Cooper et al.· bioRxiv· 0 citations
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