Conference Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 195 - Section on Statistics in Imaging Student Paper Award Winners
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistics in Imaging
Abstract #322394
Title: Harmonizing Functional Connectivity Reduces Scanner Effects in Community Detection
Author(s): Andrew An Chen* and Russell Shinohara and Haochang Shou
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Keywords: Harmonization; Functional connectivity; Site effects; Community detection; Brain networks; Network analyses

Community detection on graphs constructed from functional magnetic resonance imaging (fMRI) data has led to important insights into brain functional organization. Large studies of brain community structure often include images acquired on multiple scanners across different studies. Differences in scanner can introduce variability into the downstream results, and these differences are often referred to as scanner effects. Such effects have been previously shown to significantly impact common network metrics. In this study, we identify scanner effects in data-driven community detection results and related network metrics. We assess a commonly employed harmonization method and propose new methodology for harmonizing functional connectivity that leverage existing knowledge about network structure as well as patterns of covariance in the data. Finally, we demonstrate that our new methods reduce scanner effects in community structure and network metrics. Our results highlight scanner effects in studies of brain functional organization and provide additional tools to address these unwanted effects. These findings and methods can be incorporated into future functional connectivity studies.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2022 program