Julia O Linke1, Rany Abend2, Katharina Kircanski2, Michal Clayton2, Caitlin Stavish2, Brenda E Benson2, Melissa A Brotman2, Olivier Renaud3, Stephen M Smith4, Thomas E Nichols5, Ellen Leibenluft2, Anderson M Winkler2, Daniel S Pine2. 1. Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland. Electronic address: julia.linke@nih.gov. 2. Emotion and Development Branch, National Institute of Mental Health, National Institutes of Health, Bethesda, Maryland. 3. Methodology and Data Analysis, Department of Psychology, University of Geneva, Switzerland. 4. Wellcome Trust Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom. 5. Big Data Institute, University of Oxford, Oxford, United Kingdom.
Abstract
BACKGROUND: Imaging research has not yet delivered reliable psychiatric biomarkers. One challenge, particularly among youth, is high comorbidity. This challenge might be met through canonical correlation analysis designed to model mutual dependencies between symptom dimensions and neural measures. We mapped the multivariate associations that intrinsic functional connectivity manifests with pediatric symptoms of anxiety, irritability, and attention-deficit/hyperactivity disorder (ADHD) as common, impactful, co-occurring problems. We evaluate the replicability of such latent dimensions in an independent sample. METHODS: We obtained ratings of anxiety, irritability, and ADHD, and 10 minutes of resting-state functional magnetic resonance imaging data, from two independent cohorts. Both cohorts (discovery: n = 182; replication: n = 326) included treatment-seeking youth with anxiety disorders, with disruptive mood dysregulation disorder, with ADHD, or without psychopathology. Functional connectivity was modeled as partial correlations among 216 brain areas. Using canonical correlation analysis and independent component analysis jointly we sought maximally correlated, maximally interpretable latent dimensions of brain connectivity and clinical symptoms. RESULTS: We identified seven canonical variates in the discovery and five in the replication cohort. Of these canonical variates, three exhibited similarities across datasets: two variates consistently captured shared aspects of irritability, ADHD, and anxiety, while the third was specific to anxiety. Across cohorts, canonical variates did not relate to specific resting-state networks but comprised edges interconnecting established networks within and across both hemispheres. CONCLUSIONS: Findings revealed two replicable types of clinical variates, one related to multiple symptom dimensions and a second relatively specific to anxiety. Both types involved a multitude of broadly distributed, weak brain connections as opposed to strong connections encompassing known resting-state networks. Published by Elsevier Inc.
BACKGROUND: Imaging research has not yet delivered reliable psychiatric biomarkers. One challenge, particularly among youth, is high comorbidity. This challenge might be met through canonical correlation analysis designed to model mutual dependencies between symptom dimensions and neural measures. We mapped the multivariate associations that intrinsic functional connectivity manifests with pediatric symptoms of anxiety, irritability, and attention-deficit/hyperactivity disorder (ADHD) as common, impactful, co-occurring problems. We evaluate the replicability of such latent dimensions in an independent sample. METHODS: We obtained ratings of anxiety, irritability, and ADHD, and 10 minutes of resting-state functional magnetic resonance imaging data, from two independent cohorts. Both cohorts (discovery: n = 182; replication: n = 326) included treatment-seeking youth with anxiety disorders, with disruptive mood dysregulation disorder, with ADHD, or without psychopathology. Functional connectivity was modeled as partial correlations among 216 brain areas. Using canonical correlation analysis and independent component analysis jointly we sought maximally correlated, maximally interpretable latent dimensions of brain connectivity and clinical symptoms. RESULTS: We identified seven canonical variates in the discovery and five in the replication cohort. Of these canonical variates, three exhibited similarities across datasets: two variates consistently captured shared aspects of irritability, ADHD, and anxiety, while the third was specific to anxiety. Across cohorts, canonical variates did not relate to specific resting-state networks but comprised edges interconnecting established networks within and across both hemispheres. CONCLUSIONS: Findings revealed two replicable types of clinical variates, one related to multiple symptom dimensions and a second relatively specific to anxiety. Both types involved a multitude of broadly distributed, weak brain connections as opposed to strong connections encompassing known resting-state networks. Published by Elsevier Inc.
Authors: Jonathan D Power; Anish Mitra; Timothy O Laumann; Abraham Z Snyder; Bradley L Schlaggar; Steven E Petersen Journal: Neuroimage Date: 2013-08-29 Impact factor: 6.556
Authors: Dag Alnæs; Tobias Kaufmann; Andre F Marquand; Stephen M Smith; Lars T Westlye Journal: Proc Natl Acad Sci U S A Date: 2020-05-14 Impact factor: 11.205
Authors: Andrew T Drysdale; Logan Grosenick; Jonathan Downar; Katharine Dunlop; Farrokh Mansouri; Yue Meng; Robert N Fetcho; Benjamin Zebley; Desmond J Oathes; Amit Etkin; Alan F Schatzberg; Keith Sudheimer; Jennifer Keller; Helen S Mayberg; Faith M Gunning; George S Alexopoulos; Michael D Fox; Alvaro Pascual-Leone; Henning U Voss; B J Casey; Marc J Dubin; Conor Liston Journal: Nat Med Date: 2016-12-05 Impact factor: 53.440
Authors: Alexander Schaefer; Ru Kong; Evan M Gordon; Timothy O Laumann; Xi-Nian Zuo; Avram J Holmes; Simon B Eickhoff; B T Thomas Yeo Journal: Cereb Cortex Date: 2018-09-01 Impact factor: 5.357
Authors: Karla L Miller; Fidel Alfaro-Almagro; Neal K Bangerter; David L Thomas; Essa Yacoub; Junqian Xu; Andreas J Bartsch; Saad Jbabdi; Stamatios N Sotiropoulos; Jesper L R Andersson; Ludovica Griffanti; Gwenaëlle Douaud; Thomas W Okell; Peter Weale; Iulius Dragonu; Steve Garratt; Sarah Hudson; Rory Collins; Mark Jenkinson; Paul M Matthews; Stephen M Smith Journal: Nat Neurosci Date: 2016-09-19 Impact factor: 24.884
Authors: Lindsay M Alexander; Jasmine Escalera; Lei Ai; Charissa Andreotti; Karina Febre; Alexander Mangone; Natan Vega-Potler; Nicolas Langer; Alexis Alexander; Meagan Kovacs; Shannon Litke; Bridget O'Hagan; Jennifer Andersen; Batya Bronstein; Anastasia Bui; Marijayne Bushey; Henry Butler; Victoria Castagna; Nicolas Camacho; Elisha Chan; Danielle Citera; Jon Clucas; Samantha Cohen; Sarah Dufek; Megan Eaves; Brian Fradera; Judith Gardner; Natalie Grant-Villegas; Gabriella Green; Camille Gregory; Emily Hart; Shana Harris; Megan Horton; Danielle Kahn; Katherine Kabotyanski; Bernard Karmel; Simon P Kelly; Kayla Kleinman; Bonhwang Koo; Eliza Kramer; Elizabeth Lennon; Catherine Lord; Ginny Mantello; Amy Margolis; Kathleen R Merikangas; Judith Milham; Giuseppe Minniti; Rebecca Neuhaus; Alexandra Levine; Yael Osman; Lucas C Parra; Ken R Pugh; Amy Racanello; Anita Restrepo; Tian Saltzman; Batya Septimus; Russell Tobe; Rachel Waltz; Anna Williams; Anna Yeo; Francisco X Castellanos; Arno Klein; Tomas Paus; Bennett L Leventhal; R Cameron Craddock; Harold S Koplewicz; Michael P Milham Journal: Sci Data Date: 2017-12-19 Impact factor: 6.444
Authors: Oscar Esteban; Daniel Birman; Marie Schaer; Oluwasanmi O Koyejo; Russell A Poldrack; Krzysztof J Gorgolewski Journal: PLoS One Date: 2017-09-25 Impact factor: 3.240
Authors: Russell A Poldrack; Krzysztof J Gorgolewski; Oscar Esteban; Christopher J Markiewicz; Ross W Blair; Craig A Moodie; A Ilkay Isik; Asier Erramuzpe; James D Kent; Mathias Goncalves; Elizabeth DuPre; Madeleine Snyder; Hiroyuki Oya; Satrajit S Ghosh; Jessey Wright; Joke Durnez Journal: Nat Methods Date: 2018-12-10 Impact factor: 28.547