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Heritability estimates on resting state fMRI data using ENIGMA analysis pipeline.

Bhim M Adhikari1, Neda Jahanshad, Dinesh Shukla

  • 1Maryland Psychiatry Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA, badhikari@mprc.umaryland.edu.

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Summary

Big data initiatives like ENIGMA require harmonized resting state fMRI (rsfMRI) analysis. A new pipeline ensures consistent data processing, revealing heritability of brain networks up to 40%.

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Area of Science:

  • Neuroimaging Genetics
  • Computational Neuroscience
  • Big Data Analytics

Background:

  • Big data initiatives (e.g., ENIGMA) combine global neuroimaging data for robust findings.
  • Harmonizing resting state fMRI (rsfMRI) data is crucial but challenging due to varied acquisition protocols and scanner platforms.
  • Site-to-site variance in rsfMRI data quality (resolution, tSNR) necessitates effective harmonization methods.

Purpose of the Study:

  • To develop and validate a multi-site rsfMRI analysis pipeline for harmonized data processing.
  • To extract consistent, quantitative connectivity measurements for coordinated statistical testing.
  • To verify and replicate resting state network heritability estimates using harmonized rsfMRI data.

Main Methods:

  • Developed a multi-site rsfMRI analysis pipeline for global research groups.
  • Employed the ENIGMA rsfMRI preprocessing pipeline with model-free Marchenko-Pastur PCA denoising.
  • Analyzed two independent cohorts (GOBS, HCP) using seed-based connectivity and dual-regression.

Main Results:

  • Demonstrated consistent heritability of rsfMRI signal across twenty major functional networks.
  • Observed heritability estimates ranging from 20-40% in both conventional (GOBS) and connectomics-oriented (HCP) fMRI protocols.
  • The developed pipeline successfully processed rsfMRI data from different acquisition types.

Conclusions:

  • The novel rsfMRI pipeline enables harmonized processing and consistent quantitative measurements across diverse datasets.
  • Resting state functional connectivity exhibits significant heritability, replicable across different cohorts and acquisition methods.
  • This work facilitates large-scale, reproducible neuroimaging genetics studies by addressing data harmonization challenges.