Structure-function multilayer network integration and cognition in multiple sclerosis
Poster No:
735
Submission Type:
Abstract Submission
Authors:
Lucas Breedt1, Giuseppe Pontillo2, Fernando Santos3, Chris Vriend4, Ferran Prados2, Alle Meije Wink4, Alvino Bisecco5, Alessandro Cagol6, Massimiliano Calabrese7, Marco Castellaro8, Rosa Cortese9, Nicola de Stefano9, Christian Enzinger10, Massimo Filippi11, Michael Foster2, Antonio Gallo5, Gabriel Gonzalez-Escamilla12, Christina Granziera6, Sergiu Groppa12, Einar Hogestol13, Sara Llufriu14, Eloy Martinez-Heras14, Elisabeth Solana14, Silvia Messina15, Marcello Moccia2, Gro Nygaard13, Jacqueline Palace15, Daniela Pinter10, Maria Rocca11, Ahmed Toosy2, Olga Ciccarelli2, Eva Strijbis4, Frederik Barkhof4, Menno Schoonheim4, Linda Douw16
Institutions:
1Amsterdam UMC, Amsterdam, NA, 2University College London, London, United Kingdom, 3University of Amsterdam, Amsterdam, Netherlands, 4Amsterdam UMC, Amsterdam, Netherlands, 5University of Campania, Naples, Italy, 6University Hospital Basel, Basel, Switzerland, 7University of Verona, Verona, Italy, 8University of Padova, Padova, Italy, 9University of Siena, Siena, Italy, 10Medical University of Graz, Graz, Austria, 11IRCCS San Raffaele Scientific Institute, Milan, Italy, 12University Medicine Mainz, Mainz, Germany, 13Oslo University Hospital, Oslo, Norway, 14University of Barcelona, Barcelona, Spain, 15University of Oxford, Oxford, United Kingdom, 16Amsterdam University Medical Center, Amsterdam, Netherlands
First Author:
Co-Author(s):
Introduction:
People with multiple sclerosis (pwMS) often present with cognitive deficits that cannot fully be attributed to local structural brain alterations. Whole-brain network features extracted from various imaging modalities, such as diffusion MRI (dMRI) or resting-state functional MRI (rsfMRI), show stronger relations with cognition. However, most studies have focused on single-layer networks, resulting in mixed findings. Recent work has shown the importance of multilayer frontoparietal network (FPN) integration for cognition in the healthy brain as well as in other neurological and psychiatric populations (Breedt et al., 2023; van Lingen et al., 2023). These findings suggest that such a multilayer approach may be more sensitive than single-layer analyses to individual differences in cognition across different populations. Here, we explored the association between cognitive performance and structure-function multilayer integration of the frontoparietal network in MS.
Methods:
PwMS with relapsing-remitting phenotype (n=780) were recruited across 13 European centers of the MAGNIMS consortium (www.magnims.eu). They underwent cognitive testing through the Symbol-Digit Modalities Test (SDMT, the recommended screening test in MS) as well as 3D T1-weighted MRI, rsfMRI, and dMRI. The MRI analysis pipeline can be found in figure 1. In short, lesion-filled T1 images were used to parcellate the brain into 100 cortical Schaefer atlas regions. An additional 14 subcortical regions were segmented using FSL-FIRST. Preprocessing of rsfMRI data was done using fMRIPrep. Pearson correlation coefficients were computed between all pairs of timeseries extracted from all atlas regions, Fisher z-transformed, and absolutized to obtain a 114x114 functional connectivity matrix. Preprocessing of dMRI was performed using QSIPrep. A tissue response function was estimated using the Dhollander algorithm. Probabilistic anatomically-constrained tractography was used to generate 10 million streamlines and spherical-deconvolution informed filtering of tractograms (SIFT2) was performed to obtain weights for each streamline. A 114x114 structural connectivity matrix was obtained by summing the weights of all streamlines between all pairs of atlas regions. ComBat harmonization (Johnson et al., 2007) was used to correct connectivity matrices for center-specific effects while maintaining biological associations with sex, age, and education. For each participant, we constructed binary structural and functional single-layer networks as well as a structure-function multilayer. For both single-layers and the multilayer we then computed nodal eigenvector centrality (Fornito et al., 2016), averaged over FPN nodes; and, post-hoc, mean whole-network eccentricity. Associations with SDMT scores were assessed through regression analyses.
Results:
Of all pwMS, 200 (25.6%) showed SDMT impairment at Z<-1.5 compared to healthy controls. Higher multilayer FPN centrality related weakly to lower SDMT performance (β=-0.117 , p=0.005, figure 2), while sex and age correlated more strongly. Mean eccentricity of single-layer diffusion (β=-0.123, p<0.001) and multilayer networks (β=0.085 , p=0.018) related weakly to SDMT. However, none of the results could not be replicated when using an alternative Brainnetome atlas parcellation.
Conclusions:
We observed significant associations between FPN integration and eccentricity and cognition using a structure-function multilayer network approach in pwMS. Nevertheless, correlations were weak and atlas-specific, highlighting the shortcomings of a binary structure-function multilayer comprised of only dMRI and fMRI as a correlate of cognition in MS. Our study underscores the importance of further exploring the complex interplay between multimodal neuroimaging network dynamics and cognition, and future studies may consider additional (functional) modalities or alternative multilayer approaches to further elucidate these associations.
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 1
Modeling and Analysis Methods:
Diffusion MRI Modeling and Analysis
fMRI Connectivity and Network Modeling
Multivariate Approaches 2
Task-Independent and Resting-State Analysis
Keywords:
Cognition
Computational Neuroscience
FUNCTIONAL MRI
MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of brain network analysis. Academic Press.
Johnson, W. E., Li, C., & Rabinovic, A. (2007). Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics, 8(1), 118-127.
van Lingen, M. R., Breedt, L. C., Geurts, J. J., Hillebrand, A., Klein, M., Kouwenhoven, M. C., Kulik, S. D., Reijneveld, J. C., Stam, C. J., & De Witt Hamer, P. C. (2023). The longitudinal relation between executive functioning and multilayer network topology in glioma patients. Brain Imaging and Behavior, 17(4), 425-435.