Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

IMAGEN Consortium

doi:10.1007/s12021-022-09604-4

Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

IMAGEN Consortium

Research output: Contribution to journal › Article › peer-review

Abstract

With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

Original language	American English
Pages (from-to)	287-301
Number of pages	15
Journal	Neuroinformatics
Volume	21
Issue number	2
DOIs	https://doi.org/10.1007/s12021-022-09604-4
State	Published - Apr 2023
Externally published	Yes

ASJC Scopus subject areas

Software
General Neuroscience
Information Systems

Keywords

Adolescent health
COINSTAC
CVEDA
Decentralized analysis
IMAGEN
Neuromark

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10.1007/s12021-022-09604-4

Cite this

@article{ab5a923cb8ee4aec9fe0ea63402725d3,

title = "Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains",

abstract = "With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.",

keywords = "Adolescent health, COINSTAC, CVEDA, Decentralized analysis, IMAGEN, Neuromark",

author = "\{IMAGEN Consortium\} and Harshvardhan Gazula and Kelly Rootes-Murdy and Bharath Holla and Sunitha Basodi and Zuo Zhang and Eric Verner and Ross Kelly and Pratima Murthy and Amit Chakrabarti and Debasish Basu and \{Bhagyalakshmi Nanjayya\}, Subodh and \{Lenin Singh\}, Rajkumar and \{Lourembam Singh\}, Roshan and Kartik Kalyanram and Kamakshi Kartik and Kumaran Kalyanaraman and Krishnaveni Ghattu and Rebecca Kuriyan and Kurpad, \{Sunita Simon\} and Barker, \{Gareth J.\} and Bharath, \{Rose Dawn\} and Sylvane Desrivieres and Meera Purushottam and Orfanos, \{Dimitri Papadopoulos\} and Eesha Sharma and Matthew Hickman and Mireille Toledano and Nilakshi Vaidya and Tobias Banaschewski and Bokde, \{Arun L.W.\} and Herta Flor and Antoine Grigis and Hugh Garavan and Penny Gowland and Andreas Heinz and R{\"u}diger Br{\"u}hl and Martinot, \{Jean Luc\} and \{Paill{\'e}re Martinot\}, \{Marie Laure\} and Eric Artiges and Frauke Nees and Tom{\'a}s Paus and Luise Poustka and Fr{\"o}hner, \{Juliane H.\} and Lauren Robinson and Smolka, \{Michael N.\} and Henrik Walter and Jeanne Winterer and Robert Whelan and Turner, \{Jessica A.\} and Sarwate, \{Anand D.\}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.",

year = "2023",

month = apr,

doi = "10.1007/s12021-022-09604-4",

language = "American English",

volume = "21",

pages = "287--301",

journal = "Neuroinformatics",

issn = "1539-2791",

publisher = "Humana Press",

number = "2",

}

TY - JOUR

T1 - Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

AU - IMAGEN Consortium

AU - Gazula, Harshvardhan

AU - Rootes-Murdy, Kelly

AU - Holla, Bharath

AU - Basodi, Sunitha

AU - Zhang, Zuo

AU - Verner, Eric

AU - Kelly, Ross

AU - Murthy, Pratima

AU - Chakrabarti, Amit

AU - Basu, Debasish

AU - Bhagyalakshmi Nanjayya, Subodh

AU - Lenin Singh, Rajkumar

AU - Lourembam Singh, Roshan

AU - Kalyanram, Kartik

AU - Kartik, Kamakshi

AU - Kalyanaraman, Kumaran

AU - Ghattu, Krishnaveni

AU - Kuriyan, Rebecca

AU - Kurpad, Sunita Simon

AU - Barker, Gareth J.

AU - Bharath, Rose Dawn

AU - Desrivieres, Sylvane

AU - Purushottam, Meera

AU - Orfanos, Dimitri Papadopoulos

AU - Sharma, Eesha

AU - Hickman, Matthew

AU - Toledano, Mireille

AU - Vaidya, Nilakshi

AU - Banaschewski, Tobias

AU - Bokde, Arun L.W.

AU - Flor, Herta

AU - Grigis, Antoine

AU - Garavan, Hugh

AU - Gowland, Penny

AU - Heinz, Andreas

AU - Brühl, Rüdiger

AU - Martinot, Jean Luc

AU - Paillére Martinot, Marie Laure

AU - Artiges, Eric

AU - Nees, Frauke

AU - Paus, Tomás

AU - Poustka, Luise

AU - Fröhner, Juliane H.

AU - Robinson, Lauren

AU - Smolka, Michael N.

AU - Walter, Henrik

AU - Winterer, Jeanne

AU - Whelan, Robert

AU - Turner, Jessica A.

AU - Sarwate, Anand D.

PY - 2023/4

Y1 - 2023/4

N2 - With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

AB - With the growth of decentralized/federated analysis approaches in neuroimaging, the opportunities to study brain disorders using data from multiple sites has grown multi-fold. One such initiative is the Neuromark, a fully automated spatially constrained independent component analysis (ICA) that is used to link brain network abnormalities among different datasets, studies, and disorders while leveraging subject-specific networks. In this study, we implement the neuromark pipeline in COINSTAC, an open-source neuroimaging framework for collaborative/decentralized analysis. Decentralized exploratory analysis of nearly 2000 resting-state functional magnetic resonance imaging datasets collected at different sites across two cohorts and co-located in different countries was performed to study the resting brain functional network connectivity changes in adolescents who smoke and consume alcohol. Results showed hypoconnectivity across the majority of networks including sensory, default mode, and subcortical domains, more for alcohol than smoking, and decreased low frequency power. These findings suggest that global reduced synchronization is associated with both tobacco and alcohol use. This proof-of-concept work demonstrates the utility and incentives associated with large-scale decentralized collaborations spanning multiple sites.

KW - Adolescent health

KW - COINSTAC

KW - CVEDA

KW - Decentralized analysis

KW - IMAGEN

KW - Neuromark

UR - https://www.scopus.com/pages/publications/85142937870

UR - https://www.scopus.com/pages/publications/85142937870#tab=citedBy

U2 - 10.1007/s12021-022-09604-4

DO - 10.1007/s12021-022-09604-4

M3 - Article

SN - 1539-2791

VL - 21

SP - 287

EP - 301

JO - Neuroinformatics

JF - Neuroinformatics

IS - 2

ER -

Federated Analysis in COINSTAC Reveals Functional Network Connectivity and Spectral Links to Smoking and Alcohol Consumption in Nearly 2,000 Adolescent Brains

Abstract

ASJC Scopus subject areas

Keywords

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