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An open resource for transdiagnostic research in pediatric mental health and learning disorders.

ABSTRACT: Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5-21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).

SUBMITTER: Alexander LM

PROVIDER: S-EPMC5735921 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Alexander Lindsay M LM Escalera Jasmine J Ai Lei L Andreotti Charissa C Febre Karina K Mangone Alexander A Vega-Potler Natan N Langer Nicolas N Alexander Alexis A Kovacs Meagan M Litke Shannon S O'Hagan Bridget B Andersen Jennifer J Bronstein Batya B Bui Anastasia A Bushey Marijayne M Butler Henry H Castagna Victoria V Camacho Nicolas N Chan Elisha E Citera Danielle D Clucas Jon J Cohen Samantha S Dufek Sarah S Eaves Megan M Fradera Brian B Gardner Judith J Grant-Villegas Natalie N Green Gabriella G Gregory Camille C Hart Emily E Harris Shana S Horton Megan M Kahn Danielle D Kabotyanski Katherine K Karmel Bernard B Kelly Simon P SP Kelly Simon P SP Kleinman Kayla K Koo Bonhwang B Kramer Eliza E Lennon Elizabeth E Lord Catherine C Mantello Ginny G Margolis Amy A Merikangas Kathleen R KR Milham Judith J Minniti Giuseppe G Neuhaus Rebecca R Levine Alexandra A Osman Yael Y Parra Lucas C LC Pugh Ken R KR Racanello Amy A Restrepo Anita A Saltzman Tian T Septimus Batya B Tobe Russell R Waltz Rachel R Williams Anna A Yeo Anna A Castellanos Francisco X FX Klein Arno A Paus Tomas T Leventhal Bennett L BL Craddock R Cameron RC Koplewicz Harold S HS Milham Michael P MP

Scientific data 20171219

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The ...[more]

PMID: 29257126

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Project description:Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

Project description:ObjectivesTransdiagnostic risk factors-disrupted processes common to psychopathology-link adverse childhood experiences to severe mental disorders (i.e. major depressive disorder, bipolar disorder, and schizophrenia spectrum disorders); however, transdiagnostic protective factors are understudied. The present study investigated the association between a positive mental health framework of protective intra- and interpersonal resources and severe mental disorders in individuals with adverse childhood experiences. We hypothesized that (1) individuals with adverse childhood experiences will experience more severe mental disorders and poorer intra- and interpersonal resources than those without adverse childhood experiences; (2) intrapersonal (e.g. general coping) and interpersonal resources (e.g. emotional support) will interact to predict severe mental disorders.MethodsA total of 1929 adults participated in this population-based study. Participants were assessed for adverse childhood experiences, severe mental disorders, and intra- and interpersonal resources (general coping, general affect, emotional support, interpersonal skills, spirituality, and personal growth and autonomy) via structured interviews and self-reports.ResultsAs hypothesized, individuals with adverse childhood experiences (62.6%) experienced more severe mental disorders and poorer intra- and interpersonal resources than those without adverse childhood experiences. Among those with adverse childhood experiences, emotional support interacted with general coping and general affect to predict severe mental disorders; general coping and general affect were negatively associated with severe mental disorders at high (+1 SD) and low (-1 SD) emotional support, respectively.ConclusionsThe present study identified interactions between specific intrapersonal (i.e. general coping and general affect) and interpersonal resources (i.e. emotional support); knowing among whom and when to intervene are essential for optimal treatment of adverse childhood experiences and severe mental disorders.

Dataset Information

An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Publications

An open resource for transdiagnostic research in pediatric mental health and learning disorders.

Similar Datasets

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