This proposal is submitted in response to NIH Funding Opportunity Connectome Related to Human Disease (U01) and in response to NIMH's priority/disease area of interest mood and anxiety disorders. This is a collaborative effort among researchers at the Massachusetts General Hospital (MGH), Massachusetts Institute of Technology (MIT), McLean Hospital, Boston University, and the Human Connectome Project (HCP) at Washington University in St. Louis. We believe that the combination of (1) state-of-the art MRI technology and methods at the MGH Martinos Center for Biomedical Imaging, (2) an active collaboration with the HCP to validate neuroimaging harmonization, (3) a Boston-wide consortium of experienced and expert clinical researchers, and (4) a transdiagnostic focus across the anxiety and depression spectrum can deliver a high- quality dataset that meets the specification of the Funding Opportunity. We propose to focus on an area of great clinical need and public health implication: better understanding of psychiatric disorders in adolescence. We target anxiety and depression as diseases that affect many adolescents across multiple traditional psychiatric diagnoses, that are strongly associated with two leading causes of death in adolescents and young adults (suicide and substance-abuse related accidents), and that are understood to frequently have developmental roots leading to lifelong psychiatric disorders. Our research approach is guided by two principles (1) careful adherence to HCP protocols so as to develop large-scale, integrated, and growing data sets available to the scientific community, and (2) a research approach aligned with two constructs from the NIMH Research Domain Criteria Project (RDoC), specifically: (a) the Acute Threat/Fear construct, which is associated with atypical structure and function in specific neural networks, especially amygdala, orbitofrontal cortex (OFC), and ventral medial prefrontal cortex (vmPFC); and (b) the Reward Prediction Error construct, which is associated with OFC, ventral striatum, and the midbrain ventral tegmental area. Across four years we aim to (1) operationalize MRI data collection and behavioral characterization that is harmonized and validated with the Human Connectome Project (HCP); (2) recruit and characterize clinically and behaviorally, 225 adolescents ages 14-15 with and without anxiety and/or depression (180 patients, 45 controls); and (3) perform and analyze HCP imaging with participants. We hypothesize that greater activation in the amygdala-OFC circuit will correlate with more severe scores on measures of fear, and that lesser activation of the striatal-OFC circuit will correlate with more severe scores on measures of reward-error expectancy. We will also (a) examine whether neuroimaging analyses are enhanced with artifact-detection tools and physiological aliasing correction that are publicly available and could be integrated with the HCP, and (b) create an age-specific human tract atlas and tools for automated reconstruction of white-matter tracts involved in the above circuits, which will also be made publicly available.

Public Health Relevance

Adolescent disorders of depression and anxiety are strongly related to leading causes of death (suicide, accidents related to substance abuse) in adolescents and young adults, and are also related to at least half of lifelong depression and anxiety in adulthood. This study aims to use state-of-the-art neuroimaging and behavioral characterization from the Human Connectome Project with adolescents with and without depression and/or anxiety to better understand the brain bases of these major psychiatric disorders. Understanding the brain basis of depression and anxiety at a relatively early stage of development ought to promote treatments that allow adolescents to be treated effectively and avoid lifelong consequences of these disorders.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project--Cooperative Agreements (U01)
Project #
5U01MH108168-04
Application #
9507963
Study Section
Special Emphasis Panel (ZRG1)
Program Officer
Kim, Douglas Sun-IL
Project Start
2015-09-16
Project End
2019-06-30
Budget Start
2018-07-01
Budget End
2019-06-30
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Miscellaneous
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
Gomez, Angelina F; Barthel, Abigail L; Hofmann, Stefan G (2018) Comparing the efficacy of benzodiazepines and serotonergic anti-depressants for adults with generalized anxiety disorder: a meta-analytic review. Expert Opin Pharmacother 19:883-894
Lutz, Wolfgang; Schwartz, Brian; Hofmann, Stefan G et al. (2018) Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: A methodological proof-of-concept study. Sci Rep 8:7819
Siless, Viviana; Chang, Ken; Fischl, Bruce et al. (2018) AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 166:32-45
David, Daniel; Cristea, Ioana; Hofmann, Stefan G (2018) Why Cognitive Behavioral Therapy Is the Current Gold Standard of Psychotherapy. Front Psychiatry 9:4
Hofmann, Stefan G; Hay, Aleena C (2018) Rethinking avoidance: Toward a balanced approach to avoidance in treating anxiety disorders. J Anxiety Disord 55:14-21
Huntenburg, Julia M; Bazin, Pierre-Louis; Goulas, Alexandros et al. (2017) A Systematic Relationship Between Functional Connectivity and Intracortical Myelin in the Human Cerebral Cortex. Cereb Cortex 27:981-997
Margulies, Daniel S; Ghosh, Satrajit S; Goulas, Alexandros et al. (2016) Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A 113:12574-12579