Assessment of brain age in posttraumatic stress disorder: Findings from the ENIGMA PTSD and brain age working groups.

BACKGROUND: Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. METHOD: Adult subjects (N = 2229; 56.2% male) aged 18-69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age - chronological age) controlling for chronological age, sex, and scan site. RESULTS: BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. DISCUSSION: Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan.

Defining Episodes of Care in Children's Mental Health Using Administrative Data.

Criteria to define an episode of care in children's mental health services are needed. Various criteria were applied to 5 years of visit data from children 4-11 years (N = 5,206) at their first visit to 1 of 3 children's mental health agencies. A minimum of 3 visits with 180 days between episodes optimized agreement with other dates (e.g., telephone intake assessment) marking the start and end of an episode, and clinician-rated number of episodes. Grouping visits into episodes provides a clearer representation of how services are distributed over extended periods of time, facilitating research and enhancing accuracy in service planning.

Help-seeking for children with mental health problems: parents' efforts and experiences.

Parents who contacted 1 of 15 children's mental health agencies in Ontario, Canada reported on where and why they were seeking mental health services for their 4- to 17-year-old children. Parents contacted an average of four agencies (± 1.7; range = 1-14) in the previous year. Approximately one-half of parents were looking for either multiple types of treatment, or help for different problems, across agencies. The complex pattern of help-seeking evidenced in our study likely increases the burden on the mental health care system and on families, and may reduce the likelihood that families will connect with the most appropriate treatment.

Introduction to the Special Section on Cognitive Science and Psychological Assessment.

This introduction to the Special Section on Cognitive Science and Psychological Assessment begins with a brief overview of cognitive science and its interface with the clinical assessment of cognitive functioning. Concepts and themes permeating the articles in this section are then taken up. Included are cognitive-model architectures and model parameters, differential emphasis on efficiency versus content of cognition, and implementation of stochastic and dynamical aspects of cognitive processes. Selected distinguishing attributes of the respective contributions are highlighted.

Application of stochastic modeling to the assessment of group and individual differences in cognitive functioning.

This article begins with a guiding schema of relations among cognitive science, clinical science, and assessment technology. Emphasis is placed on stochastic modeling of cognitive processes. Basic models are adjusted so as to parsimoniously accommodate performance deviations occurring with psychopathology. Modified portions of models indicate functions affected by disorder, whereas portions remaining intact indicate spared functions. Findings from clinical cognitive science are applied to the individual case using Bayesian procedures. Methods are instantiated with respect to cognitive psychopathology of paranoid schizophrenia. The authors address observations and issues arising from this application, including integration of these methods with current assessment practices.

Trajectory of cognitive science and psychological assessment: current status and expectations.

Salient commonalities and distinctions of the set of contributions to this special section are synthesized and discussed. The examination provides a springboard for observations on future developments in cognitive-science applications. Issues considered include imminence and nature of clinical implementation, prediction of selected client transactions outside the assessment context, integration with complementing modes of clinical assessment, and reciprocal information sharing by clinical and nonclinical cognitive scientists.