Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

Hengyi Cao, Sarah C McEwen ... Tyrone D Cannon

Authors

Hengyi Cao¹, Sarah C McEwen², Jennifer K Forsyth³, Dylan G Gee¹, Carrie E Bearden², Jean Addington⁴, Bradley Goodyear⁵, Kristin S Cadenhead⁶, Heline Mirzakhanian⁶, Barbara A Cornblatt⁷, Ricardo E Carrión⁷, Daniel H Mathalon⁸, Thomas H McGlashan⁹, Diana O Perkins¹⁰, Aysenil Belger¹⁰, Larry J Seidman¹¹, Heidi Thermenos¹¹, Ming T Tsuang⁶, Theo G M van Erp¹², Elaine F Walker¹³, Stephan Hamann¹³, Alan Anticevic⁹, Scott W Woods⁹, Tyrone D Cannon^{1

9}

Affiliations

¹ Department of Psychology, Yale University, New Haven, CT, USA.
² Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
³ Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA.
⁴ Department of Psychiatry, University of Calgary, Calgary, Canada.
⁵ Departments of Radiology, Clinical Neuroscience and Psychiatry, University of Calgary, Calgary, Canada.
⁶ Department of Psychiatry, University of California San Diego, San Diego, CA, USA.
⁷ Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.
⁸ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
⁹ Department of Psychiatry, Yale University, New Haven, CT, USA.
¹⁰ Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
¹¹ Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹² Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
¹³ Department of Psychology, Emory University, Atlanta, GA, USA.

PMID: 29522112
PMCID: PMC6676966 (available on 2020-03-01)
DOI: 10.1093/cercor/bhy032

Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

Hengyi Cao et al. Cereb Cortex. 2019

, 29 (3), 1263-1279

Authors

Affiliations

¹ Department of Psychology, Yale University, New Haven, CT, USA.
² Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles, Los Angeles, CA, USA.
³ Department of Psychology, University of California Los Angeles, Los Angeles, CA, USA.
⁴ Department of Psychiatry, University of Calgary, Calgary, Canada.
⁵ Departments of Radiology, Clinical Neuroscience and Psychiatry, University of Calgary, Calgary, Canada.
⁶ Department of Psychiatry, University of California San Diego, San Diego, CA, USA.
⁷ Department of Psychiatry Research, Zucker Hillside Hospital, Glen Oaks, NY, USA.
⁸ Department of Psychiatry, University of California San Francisco, San Francisco, CA, USA.
⁹ Department of Psychiatry, Yale University, New Haven, CT, USA.
¹⁰ Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
¹¹ Department of Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
¹² Department of Psychiatry and Human Behavior, University of California Irvine, Irvine, CA, USA.
¹³ Department of Psychology, Emory University, Atlanta, GA, USA.

PMID: 29522112
PMCID: PMC6676966 (available on 2020-03-01)
DOI: 10.1093/cercor/bhy032

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Abstract

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data.

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Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

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Toward Leveraging Human Connectomic Data in Large Consortia: Generalizability of fMRI-Based Brain Graphs Across Sites, Sessions, and Paradigms

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