Caltech Conte Center, a multimodal data resource for exploring social cognition and decision-making

Creators: Kliemann, Dorit; Adolphs, Ralph; Armstrong, Tim; Galdi, Paola; Kahn, David A.; Rusch, Tessa; Enkavi, A. Zeynep; Liang, Deuhua; Lograsso, Steven; Zhu, Wenying; Yu, Rona; Nair, Remya; Paul, Lynn K.; Tyszka, J. Michael

Abstract

This data release of 117 healthy community-dwelling adults provides multimodal high-quality neuroimaging and behavioral data for the investigation of brain-behavior relationships. We provide structural MRI, resting-state functional MRI, movie functional MRI, together with questionnaire-based and task-based psychological variables; many of the participants have multiple datasets from retesting over the course of several years. Our dataset is distinguished by utilizing open-source data formats and processing tools (BIDS, FreeSurfer, fMRIPrep, MRIQC), providing data that is thoroughly quality checked, preprocessed to various extents and available in multiple anatomical spaces. A customizable denoising pipeline is provided as open-source code that includes tools for the generation of functional connectivity matrices and initialization of individual difference analyses. Behavioral data include a comprehensive set of psychological assessments on gold-standard instruments encompassing cognitive function, mood and personality, together with exploratory factor analyses. The dataset provides an in-depth, multimodal resource for investigating associations between individual differences, brain structure and function, with a focus on the domains of social cognition and decision-making.

Additional Information

© The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 11 November 2021; Accepted 18 January 2022; Published 31 March 2022. Conceptualization (DK, RA, RN, LKP, JMT), Data Curation (DK, PG, DAK, TR, AZE, DL, SL, WZ, RN, JMT), Formal analysis (DK, TA, PG, RLY, LKP, JMT), Funding Acquisition (RA, LKP, JMT), Data Collection (DK, TA, RN, JMT), Protocol/Task Implementation (TA, JMT, LKP), Recruitment & Enrollment (TA, LKP), Project administration & Supervision (DK, RA, RN, LKP, JMT), Writing of the manuscript (DK, RA, TA, PG, DAK, TR, AZE, DL, SL, WZ, RLY, RN, LKP, JMT). Given the complex set of contributions from many authors, we provide a figure in the supplementary material (Figure S1) to summarize their contributions. Contributions reflect the organization of the Caltech Conte Center, with R.A. as director, R.N. as staff responsible for NIH-compliant data-sharing, L.K.P. as PI of a Psychological Assessment Core, and J.M.T. as PI of a Neuroimaging Core. Other notable contributions were essentially all in-person subject testing by T.A., and generation of the denoising code we are sharing here (rsDenoise) by P.G.. These authors jointly supervised this work: Remya Nair, Lynn K. Paul, J. Michael Tyszka. Contributions: Given the complex set of contributions from many authors, we provide the figure below (Figure S1) to summarize their contributions. Contributions reflect the organization of the Caltech Conte Center, with R.A. as director, R.N. as staff responsible for NIH-compliant data-sharing, L.K.P. as PI of a Psychological Assessment Core, and J.M.T. as PI of a Neuroimaging Core. Other notable contributions were essentially all in-person subject testing by T.A. and generation of the denoising code we are sharing here (rsDenoise) by P.G. The authors declare no competing interests. Code availability: We used containerized versions of fMRIPrep 20.2.1 and MRIQC for data preprocessing and quality control. Example calling scripts for fMRIPrep, jupyter lab notebooks for figure recreation and R code for the example factor analysis are provided at https://github.com/adolphslab/ConteDataRelease. The code to reproduce resting-state and movie analyses are provided at https://github.com/adolphslab/rsDenoise. As outlined in detail in the source, this codebase can easily be adapted to run many different configurations of denoising decisions on the data.

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