Multi-site analysis of functional brain connectivity in bipolar disorder
Poster No:
481
Submission Type:
Abstract Submission
Authors:
Melody Kang1, Leila Nabulsi1, Sophia Thomopoulos1, Yanghee Im1, Genevieve McPhilemy2, Fiona Martyn3, Brian Hallahan3, Colm McDonald3, Dara Cannon3, Jair Soares4, Giovana Zunta-Soares4, Benson Mwangi4, Mon-Ju Wu4, Janice Fullerton5, Philip Mitchell5, Gloria Roberts5, Luisa Klahn6, Mikael Landén6, Enric Vilajosana7, Joaquim Radua7, Clara Moreau8, Ole Andreassen9, Paul Thompson1, Chris Ching1, ENIGMA BD Working Group1
Institutions:
1University of Southern California, Los Angeles, USA, 2University of Galway, Ireland, Galway, Ireland, 3University of Galway, Galway, Ireland, 4UTHealth Houston, Houston, USA, 5University of New South Wales, Sydney, Australia, 6University of Gothenburg, Gothenburg, Sweden, 7Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain, 8University of Montréal, Montréal, CA, 9University of Oslo, Oslo, Oslo
First Author:
Co-Author(s):
Enric Vilajosana
Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
Barcelona, Spain
Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)
Barcelona, Spain
Introduction:
Bipolar disorder (BD) is a severe mental illness with no validated biomarkers to help guide diagnosis or treatment. BD has been associated with altered functional connectivity measured with resting-state functional MRI (rsfMRI; Nabulsi et al., 2020, Philemy et al., 2020), but varied data processing methods make it challenging to compare findings across studies. Few studies have evaluated the impact of various rsfMRI processing pipeline settings on downstream results, especially when combining multisite data. Here, we test several rsfMRI processing pipelines, and two commonly used brain atlases, to understand their effects on case-control differences across 5 independently collected cohorts.
Methods:
rsfMRI data from five Enhancing Neuro Imaging Genetics through Meta-Analysis Bipolar Disorder Working Group (ENIGMA-BD) cohorts (231 BD, 283 controls (CN); ages 13-81, 57% female) were processed using ENIGMA-HALFPipe (Waller et al., 2022) (v1.2.2, based on fMRIPrep; Esteban et al., 2018) to derive rsfMRI connectomes using two common cortical atlases: the Yeo 7-network (Yeo et al., 2011), and Schaefer 17-network 400 parcellation atlas (Schaefer et al., 2018). Seven data processing pipeline settings were tested: 1) ICA-AROMA only, 2) ICA-AROMA + aCompCor, 3) ICA-AROMA + aCompCor + Global Signal Regression (GSR), 4) aCompCor only, 5) aCompCor + GSR, 6) Motion Parameters + White Matter (WM) + Cerebrospinal fluid (CSF), and 7) Motion Parameters + WM + CSF + GSR (Figure 1). Within- and between-network functional connectivity outputs from the 400-parcellation atlas were analyzed at the network level, as opposed to using each ROI. Linear mixed effect models were used to compare BD and CN groups, adjusting for age, sex, and scan site. All results were corrected for multiple comparisons using the false discovery rate (q<0.05).
Results:
Different rsfMRI processing settings resulted in variable detection of BD versus CN differences. When using the Schaefer 17-network atlas, the BD group showed patterns of hypoconnectivity across all 17 networks when using setting 1, and hyperconnectivity between dorsal attention network B ("DorsalAttention B") and frontoparietal control network C ("Control C") across settings 2, 3, 5 and 7 (Figure 2). Hypoconnectivity within networks default mode A ("DefaultA"), and dorsal attention A ("DorsalAttention A"), were replicated in settings 1, 3, 5, and 7. Setting 4 (aCompCor only) showed no significant connectivity differences between BD and CN. Results from the Yeo-7 atlas were less consistent across processing settings, though hyperconnectivity between frontoparietal and visual networks, as well as between visual and default mode networks were replicated in settings 2, 4, and 5. Settings 6 and 7 showed no significant connectivity differences between groups.
Conclusions:
Standardized and scalable data processing pipelines are needed to improve replication and generalizability in multisite rsfMRI studies of BD. ENIGMA-HALFPipe aids in this effort, allowing researchers to apply standardized processing and analysis steps across sites in large-scale consortium projects (Bruin et al., 2023). In this preliminary analysis, different processing steps and atlases had a significant impact on the detection of BD versus CN group differences both within and between brain networks. The application of different brain atlases and processing settings like GSR may be expected to alter downstream case versus control findings. Ongoing work is focused on pooling more samples to boost statistical power, as well as testing different atlases, edge filtering, and site adjustment methods to examine the generalizability of rsfMRI across ENIGMA-BD sites.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Neuroinformatics and Data Sharing:
Brain Atlases
Workflows
Keywords:
FUNCTIONAL MRI
Psychiatric
Psychiatric Disorders
Statistical Methods
Other - Bipolar Disorder
1|2Indicates the priority used for review
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Provide references using APA citation style.
Esteban, O., et al. (2019). ‘fMRIPrep: a robust preprocessing pipeline for functional MRI.’ Nat Methods 16, 111–116. https://doi-org/10.1038/s41592-018-0235-4
Nabulsi, L., et al. (2020), ‘Frontolimbic, Frontoparietal, and Default Mode Involvement in Functional Dysconnectivity in Psychotic Bipolar Disorder’, Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 5(2), 140–151. https://doi.org/10.1016/j.bpsc.2019.10.015
Philemy, G. M., et al. (2020), ‘Resting-state network patterns underlying cognitive function in bipolar disorder: A graph theoretical analysis’, Brain Connectivity, 10(7), 355–367. https://doi.org/10.1089/brain.2019.0709
Schaefer, A., et al. (2018), ‘Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI’, Cerebral Cortex (New York, N.Y.: 1991), 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179
Waller, L., et al. (2022), ‘ENIGMA HALFpipe: Interactive, reproducible, and efficient analysis for resting-state and task-based fMRI data’, Human Brain Mapping, 43(9), 2727–2742. https://doi.org/10.1002/hbm.25829
Yeo, T., et al (2011). ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity’, J Neurophysiol, 106(3), 1125-1165. https://doi:10.1152/jn.00338.2011