CRHD Connectomes Related to Anxiety & Depression

Study Overview

The Connectomes Related to Anxiety and Depression in Adolescents Project is a collaborative effort among researchers at the Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), McLean Hospital, and Boston University. We will focus on understanding psychiatric disorders in adolescence, in particular those associated with two leading causes of death in adolescents and young adults (suicide and substance-abuse related accidents). Our research is guided by the “Acute Threat/Fear” and the “Reward/Prediction Error” construct.

Project Timespan: Sept. 16, 2015 - June 20, 2019


Investigators

Susan Whitfield-Gabrieli

Susan Whitfield-Gabrieli, Ph.D. - MIT Principal Investigator

Contact: Email

John Gabrieli

John Gabrieli, Ph.D. - MIT Principal Investigator

Contact: Email

Satra Ghosh

Satra Ghosh, Ph.D. - MIT

Contact: Email

Anastasia Yendiki

Anastasia Yendiki, Ph.D. - MGH

Contact: Email

Diego Pizzagalli

Diego Pizzagalli, Ph.D. - McLean

Contact: Email

Randy Auerbach

Randy Auerbach, Ph.D. - McLean

Contact: Email

Stefan Hofmann

Stefan Hofmann, Ph.D. - BU

Contact: Email

Aude Henin

Aude Henin, Ph.D. - MGH

Nicholas Hubbard

Nicholas Hubbard, Ph.D. - MIT

Contact: Email

Study Protocol Overview

Data being collected

All imaging will be conducted at Massachusetts General Hospital on one of three scanners:  A 3T Siemens Prisma, 3T Siemens ConnectomA, and 7T Siemens.    A limited version of the HCP Lifespan scanning protocol will be implemented with the intent of keeping the total MR scanning time to under 2 hours.

  • Standard HCP demographics.
  • Imaging: The imaging modalities are structural, diffusion, and functional (both resting state and task) with the following tasks:  emotion processing, incentive processing, social cognition, working memory/category-specific representations.
  • Clinical: Child Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version, Revised Child Anxiety and Depression Scale, Beck Depression Inventory-II, Snaith-Hamilton Pleasure Scale, State-Trait Anxiety Inventory, Behavioral Inhibition System and Behavioral Activation System Questionnaire, Beck Scale for Suicidal Ideation, Risky Behavior Questionnaire for Adolescents, Stress and Adversity Inventory.
  • Behavioral: NIH toolbox, HCP behavioral measures, fear learning and extinction, biased attention to threat.


Cohort Description

The study includes of 225 adolescents ages 14-15.  Of these, 45 are healthy controls, and 180 are participants with and without anxiety and/or depression.  


Data Release Plans

  • The first data release includes 75 participants.
  • The second data release includes 150 participants.
  • The third data release includes 225 participants.


Keywords:

Adolescent; Anxiety; Mental Depression; Mood Disorders; White Matter


For More Information: 

Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA)

Publications

  • Predictive analytics in mental health: applications, guidelines, challenges and perspectives.

    T Hahn, A A Nierenberg, S Whitfield-Gabrieli
    Molecular psychiatry, Nov 16, 2016 PMID: 27843153
    Show Summary

    The emerging field of 'predictive analytics in mental health' has recently generated tremendous interest with the bold promise to revolutionize clinical practice in psychiatry paralleling similar developments in personalized and precision medicine. Here, we provide an overview of the key questions and challenges in the field, aiming to (1) propose general guidelines for predictive analytics projects in psychiatry, (2) provide a conceptual introduction to core aspects of predictive modeling technology, and (3) foster a broad and informed discussion involving all stakeholders including researchers, clinicians, patients, funding bodies and policymakers.

  • Altered Intrinsic Functional Brain Architecture in Children at Familial Risk of Major Depression.

    Xiaoqian J Chai, Dina Hirshfeld-Becker, Joseph Biederman, Mai Uchida, Oliver Doehrmann, Julia A Leonard, John Salvatore, Tara Kenworthy, Ariel Brown, Elana Kagan, Carlo de Los Angeles, John D E Gabrieli, Susan Whitfield-Gabrieli
    Biological psychiatry, Feb 02, 2016 PMID: 26826874
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    Neuroimaging studies of patients with major depression have revealed abnormal intrinsic functional connectivity measured during the resting state in multiple distributed networks. However, it is unclear whether these findings reflect the state of major depression or reflect trait neurobiological underpinnings of risk for major depression.

  • Brain connectomics predict response to treatment in social anxiety disorder.

    S Whitfield-Gabrieli, S S Ghosh, A Nieto-Castanon, Z Saygin, O Doehrmann, X J Chai, G O Reynolds, S G Hofmann, M H Pollack, J D E Gabrieli
    Molecular psychiatry, Aug 12, 2015 PMID: 26260493
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    We asked whether brain connectomics can predict response to treatment for a neuropsychiatric disorder better than conventional clinical measures. Pre-treatment resting-state brain functional connectivity and diffusion-weighted structural connectivity were measured in 38 patients with social anxiety disorder (SAD) to predict subsequent treatment response to cognitive behavioral therapy (CBT). We used a priori bilateral anatomical amygdala seed-driven resting connectivity and probabilistic tractography of the right inferior longitudinal fasciculus together with a data-driven multivoxel pattern analysis of whole-brain resting-state connectivity before treatment to predict improvement in social anxiety after CBT. Each connectomic measure improved the prediction of individuals' treatment outcomes significantly better than a clinical measure of initial severity, and combining the multimodal connectomics yielded a fivefold improvement in predicting treatment response. Generalization of the findings was supported by leave-one-out cross-validation. After dividing patients into better or worse responders, logistic regression of connectomic predictors and initial severity combined with leave-one-out cross-validation yielded a categorical prediction of clinical improvement with 81% accuracy, 84% sensitivity and 78% specificity. Connectomics of the human brain, measured by widely available imaging methods, may provide brain-based biomarkers (neuromarkers) supporting precision medicine that better guide patients with neuropsychiatric diseases to optimal available treatments, and thus translate basic neuroimaging into medical practice.

  • Functional and structural brain correlates of risk for major depression in children with familial depression.

    Xiaoqian J Chai, Dina Hirshfeld-Becker, Joseph Biederman, Mai Uchida, Oliver Doehrmann, Julia A Leonard, John Salvatore, Tara Kenworthy, Ariel Brown, Elana Kagan, Carlo de Los Angeles, Susan Whitfield-Gabrieli, John D E Gabrieli
    NeuroImage. Clinical, Jun 25, 2015 PMID: 26106565
    Show Summary

    Despite growing evidence for atypical amygdala function and structure in major depression, it remains uncertain as to whether these brain differences reflect the clinical state of depression or neurobiological traits that predispose individuals to major depression. We examined function and structure of the amygdala and associated areas in a group of unaffected children of depressed parents (at-risk group) and a group of children of parents without a history of major depression (control group). Compared to the control group, the at-risk group showed increased activation to fearful relative to neutral facial expressions in the amygdala and multiple cortical regions, and decreased activation to happy relative to neutral facial expressions in the anterior cingulate cortex and supramarginal gyrus. At-risk children also exhibited reduced amygdala volume. The extensive hyperactivation to negative facial expressions and hypoactivation to positive facial expressions in at-risk children are consistent with behavioral evidence that risk for major depression involves a bias to attend to negative information. These functional and structural brain differences between at-risk children and controls suggest that there are trait neurobiological underpinnings of risk for major depression.

  • Prediction as a humanitarian and pragmatic contribution from human cognitive neuroscience.

    John D E Gabrieli, Satrajit S Ghosh, Susan Whitfield-Gabrieli
    Neuron, Jan 09, 2015 PMID: 25569345
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    Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the human brain and its variation across individuals (neurodiversity) in both health and disease. Such progress has not yet, however, propelled changes in educational or medical practices that improve people's lives. We review neuroimaging findings in which initial brain measures (neuromarkers) are correlated with or predict future education, learning, and performance in children and adults; criminality; health-related behaviors; and responses to pharmacological or behavioral treatments. Neuromarkers often provide better predictions (neuroprognosis), alone or in combination with other measures, than traditional behavioral measures. With further advances in study designs and analyses, neuromarkers may offer opportunities to personalize educational and clinical practices that lead to better outcomes for people.

  • Selective development of anticorrelated networks in the intrinsic functional organization of the human brain.

    Xiaoqian J Chai, Noa Ofen, John D E Gabrieli, Susan Whitfield-Gabrieli
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    We examined the normal development of intrinsic functional connectivity of the default network (brain regions typically deactivated for attention-demanding tasks) as measured by resting-state fMRI in children, adolescents, and young adults ages 8-24 years. We investigated both positive and negative correlations and employed analysis methods that allowed for valid interpretation of negative correlations and that also minimized the influence of motion artifacts that are often confounds in developmental neuroimaging. As age increased, there were robust developmental increases in negative correlations, including those between medial pFC (MPFC) and dorsolateral pFC (DLPFC) and between lateral parietal cortices and brain regions associated with the dorsal attention network. Between multiple regions, these correlations reversed from being positive in children to negative in adults. Age-related changes in positive correlations within the default network were below statistical threshold after controlling for motion. Given evidence in adults that greater negative correlation between MPFC and DLPFC is associated with superior cognitive performance, the development of an intrinsic anticorrelation between MPFC and DLPFC may be a marker of the large growth of working memory and executive functions that occurs from childhood to young adulthood.

  • Predicting treatment response in social anxiety disorder from functional magnetic resonance imaging.

    Oliver Doehrmann, Satrajit S Ghosh, Frida E Polli, Gretchen O Reynolds, Franziska Horn, Anisha Keshavan, Christina Triantafyllou, Zeynep M Saygin, Susan Whitfield-Gabrieli, Stefan G Hofmann, Mark Pollack, John D Gabrieli
    JAMA psychiatry, Sep 05, 2012 PMID: 22945462
    Show Summary

    Current behavioral measures poorly predict treatment outcome in social anxiety disorder (SAD). To our knowledge, this is the first study to examine neuroimaging-based treatment prediction in SAD.

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