Transdiagnostic Connectome Project: A open dataset for brain-based models of behavior in psychiatry
Poster No:
2221
Submission Type:
Abstract Submission
Authors:
Sidhant Chopra1, Carrisa Cocuzza1, Connor Lawhead1, Jocelyn Ricard2, Loic Labache1, Lauren Patrick1, Poornima Kumar3, Arielle Rubenstein1, Julia Moses1, Lia Chen1, Crystal Blankenbaker3, Bryce Gillis4, Laura Germine3, Ilan Harpaz-Rotem5, B. T. Thomas Yeo6, Justin Baker3, Avram Holmes7
Institutions:
1Yale University, New Haven, CT, 2Stanford University, Stanford, CA, 3Havard University, Boston, MA, 4Havard University, Boston, CT, 5Yale University School of Medicine, New Haven, CT, 6National University of Singapore, Singapore, Singapore, 7Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ
First Author:
Co-Author(s):
Introduction:
A primary aim of computational psychiatry is to establish models that link individual differences in brain functioning to clinically relevant symptoms and behaviors. Progress in this field has been hindered by an overemphasis on discrete diagnostic categories and limited behavioral measures. Converging evidence from epidemiology, genetics, and neuroscience suggests that the boundaries between nominally distinct disorders are not phenotypically discontinuous, either between diagnoses or in comparison to healthy populations. Furthermore, behavior measures in clinical samples often consist of a narrow selection of self-report scales, constraining the ability to establish precise and reliable brain-behavior correlations. To advance the field of psychiatry, datasets that are richly phenotyped with a diverse range of both self-report and clinician-assessed behavioral measures, along with neuroimaging data, are essential.
Methods:
We introduce a large, open repository containing behavioral and neuroimaging data from 244 individuals, including 150 meeting diagnostic criteria for a broad range affective and/or psychotic illnesses (Fig1A), including major depressive disorder, bipolar disorder, post-traumatic stress disorder, generalized anxiety disorder, substance use disorder and schizophrenia, as well as a comparison group of 95 individuals without a mental health diagnosis. Participants were aged 18 to 70 and recruited from two sites (Fig1B-D): Yale University, New Haven, Connecticut and McLean Hospital, Boston, Massachusetts. Each participant underwent a neuroimaging session that included high-resolution anatomical scans (T1w and T2w), resting-state functional MRI (4 runs), and task-based fMRI (3 runs). Additionally, participants completed over 50 self-report, computerized, and clinician-assessed tests across multiple in-person and online sessions.
·Overview of sample for the Transdiagnostic Connectome Project (TCP) data release
Results:
Behavioral data, raw and processed MRI data will soon be made openly available via the National Institute of Mental Health Data Archive (NDA). MRI data were processed using Human Connectome Project pipelines (v4.3.0), with each functional neuroimaging run denoised using ICA-FIX. This processing significantly reduced the association between functional connectivity and quality control metrics, such as head motion (Fig2A). Processed data also displayed known characteristics, including inter-hemispheric connectivity and canonical functional network structure (Fig2B). Group-level analysis revealed similar correlation structures across 101 behavioral scales and subscales for both diagnosed and non-diagnosed individuals (Fig2C). Principal component analysis of the behavioral data revealed that the first component, accounting for 21% of the variance (Fig2D), represented a general functioning and wellbeing factor. The second, third, and fourth components were associated with internalizing, externalizing, and cognitive scales, respectively.
·Overview of functional MRI and behavioural data for the Transdiagnostic Connectome Project (TCP) data release
Conclusions:
We provide a comprehensive, high-quality, and analysis-ready transdiagnostic dataset comprising individuals with a range of psychiatric diagnoses and a comparison group without diagnoses. This dataset can facilitate research for purposes such as identifying disease-relevant biotypes, predicting individual symptom profiles, establish brain-behavior associations and recommending personalized therapeutic interventions.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Keywords:
Open Data
Psychiatric
Psychiatric Disorders
1|2Indicates the priority used for review