@article{60c99ce8b5624e0796ad81dd0876d9ec,
title = "Decentralized Multisite VBM Analysis During Adolescence Shows Structural Changes Linked to Age, Body Mass Index, and Smoking: a COINSTAC Analysis",
abstract = "There has been an upward trend in developing frameworks that enable neuroimaging researchers to address challenging questions by leveraging data across multiple sites all over the world. One such open-source framework is the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) that works on Windows, macOS, and Linux operating systems and leverages containerized analysis pipelines to analyze neuroimaging data stored locally across multiple physical locations without the need for pooling the data at any point during the analysis. In this paper, the COINSTAC team partnered with a data collection consortium to implement the first-ever decentralized voxelwise analysis of brain imaging data performed outside the COINSTAC development group. Decentralized voxel-based morphometry analysis of over 2000 structural magnetic resonance imaging data sets collected at 14 different sites across two cohorts and co-located in different countries was performed to study the structural changes in brain gray matter which linked to age, body mass index (BMI), and smoking. Results produced by the decentralized analysis were consistent with and extended previous findings in the literature. In particular, a widespread cortical gray matter reduction (resembling a 'default mode network' pattern) and hippocampal increase with age, bilateral increases in the hypothalamus and basal ganglia with BMI, and cingulate and thalamic decreases with smoking. This work provides a critical real-world test of the COINSTAC framework in a {"}Large-N{"} study. It showcases the potential benefits of performing multivoxel and multivariate analyses of large-scale neuroimaging data located at multiple sites.",
author = "Harshvardhan Gazula and Bharath Holla and Zuo Zhang and Jiayuan Xu and Eric Verner and Ross Kelly and Sanjeev Jain and Bharath, {Rose Dawn} and Barker, {Gareth J} and Debasish Basu and Amit Chakrabarti and Kartik Kalyanram and Kalyanaraman Kumaran and Lenin Singh and Rebecca Kuriyan and Pratima Murthy and Vivek Benega and Plis, {Sergey M} and Sarwate, {Anand D} and Turner, {Jessica A} and Gunter Schumann and Calhoun, {Vince D}",
note = "Funding Information: This work was funded by the National Institutes of Health (grants: R01EB005846, 1R01DA040487) and the National Science Foundation (grants: 1539067, 1631819 and CCF-1909468). The study was supported in part by grant LSHM-CT-2007-037286 from the European Union?funded FP6 Integrated Project IMAGEN; the European Research Council Advanced Grant STRATIFY 695313 from the Horizon 2020; grant PR-ST-0416-10004 from the European Research Area Network on Illicit Drugs; grant MR/N027558/1 from BRIDGET (Brain Imaging Cognition Dementia and Next Generation Genomics); the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01), the European Union?s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). cVEDA is jointly funded by the Indian Council for Medical Research (ICMR/MRC/3/M/2015-NCD-I) and the Newton Grant from the Medical Research Council(MR/N000390/1), United Kingdom. One of the co-authors, BH, was supported by Department of Biotechnology, Government of India grant (BT/PR17316/MED/31/326/2015) for ?Accelerating program for discovery in brain disorders using stem cells?. Last but not least, we would like to acknowledge Matt Hickmann (University of Bristol), Mireille B. Toledano (Imperial College London) and Sylvane Desrivieres (King?s College London) for their important contributions to cVEDA. Funding Information: This work was funded by the National Institutes of Health (grants: R01EB005846, 1R01DA040487) and the National Science Foundation (grants: 1539067, 1631819 and CCF-1909468). The study was supported in part by grant LSHM-CT-2007-037286 from the European Union–funded FP6 Integrated Project IMAGEN; the European Research Council Advanced Grant STRATIFY 695313 from the Horizon 2020; grant PR-ST-0416-10004 from the European Research Area Network on Illicit Drugs; grant MR/N027558/1 from BRIDGET (Brain Imaging Cognition Dementia and Next Generation Genomics); the National Institutes of Health (NIH) funded ENIGMA (grants 5U54EB020403-05 and 1R56AG058854-01), the European Union{\textquoteright}s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2). cVEDA is jointly funded by the Indian Council for Medical Research (ICMR/MRC/3/M/2015-NCD-I) and the Newton Grant from the Medical Research Council(MR/N000390/1), United Kingdom. One of the co-authors, BH, was supported by Department of Biotechnology, Government of India grant (BT/PR17316/MED/31/326/2015) for “Accelerating program for discovery in brain disorders using stem cells”. Last but not least, we would like to acknowledge Matt Hickmann (University of Bristol), Mireille B. Toledano (Imperial College London) and Sylvane Desrivieres (King{\textquoteright}s College London) for their important contributions to cVEDA. Publisher Copyright: {\textcopyright} 2021, Springer Science+Business Media, LLC, part of Springer Nature.",
year = "2021",
month = oct,
doi = "10.1007/s12021-020-09502-7",
language = "English",
volume = "19",
pages = "553--566",
journal = "Neuroinformatics",
issn = "1539-2791",
publisher = "Humana Press",
number = "4",
}