Improving Between-Group Effect Size for Multi-Site Functional Connectivity Data via Site-Wise De-Meaning

• Published in: Frontiers in computational neuroscience Vol. 15; p. 762781
• Main Authors: Reardon, Alexandra M., Li, Kaiming, Hu, Xiaoping P.
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 02.12.2021; Frontiers Media S.A
• Subjects: Autism; autism spectrum disorder; Bipolar disorder; Brain mapping; Computational Neuroscience; Consortia; Data exchange; effect size; functional connectivity; Functional magnetic resonance imaging; Generalized linear models; Magnetic resonance imaging; Medical imaging; Mental disorders; Methods; multi-site; Neural networks; Neurodevelopmental disorders; Neuroimaging; Phenotypes; Regression analysis; resting-state functional MRI; Sample size; Scanners; Schizophrenia; Statistical analysis; Schizophrenia; multi-site; functional connectivity; autism spectrum disorder; effect size; resting-state functional MRI
• DOI: 10.3389/fncom.2021.762781
• ISSN: 1662-5188

Background: Multi-site functional MRI (fMRI) databases are becoming increasingly prevalent in the study of neurodevelopmental and psychiatric disorders. However, multi-site databases are known to introduce site effects that may confound neurobiological and measures such as functional connectivity (FC). Although studies have been conducted to mitigate site effects, these methods often result in reduced effect size in FC comparisons between controls and patients. Methods: We present a site-wise de-meaning (SWD) strategy in multi-site FC analysis and compare its performance with two common site-effect mitigation methods, i.e., generalized linear model (GLM) and Combining Batches (ComBat) Harmonization. For SWD, after FC was calculated and Fisher z-transformed, the site-wise FC mean was removed from each subject before group-level statistical analysis. The above methods were tested on two multi-site psychiatric consortiums [Autism Brain Imaging Data Exchange (ABIDE) and Bipolar and Schizophrenia Network on Intermediate Phenotypes (B-SNIP)]. Preservation of consistent FC alterations in patients were evaluated for each method through the effect sizes (Hedge’s g) of patients vs. controls. Results: For the B-SNIP dataset, SWD improved the effect size between schizophrenic and control subjects by 4.5–7.9%, while GLM and ComBat decreased the effect size by 22.5–42.6%. For the ABIDE dataset, SWD improved the effect size between autistic and control subjects by 2.9–5.3%, while GLM and ComBat decreased the effect size by up to 11.4%. Conclusion: Compared to the original data and commonly used methods, the SWD method demonstrated superior performance in preserving the effect size in FC features associated with disorders.