A multimodal pipeline for paediatric neuroimaging data to generate model inputs for TheVirtualBrain
Poster No:
779
Submission Type:
Abstract Submission
Authors:
Leanne Rokos1, Noah Frazier-Logue2, Justin Wang2, Margot Taylor3,1, Signe Bray4, Anthony McIntosh2,1, Kelly Shen2
Institutions:
1University of Toronto, Toronto, Canada, 2Simon Fraser University, Burnaby, Canada, 3Hospital for Sick Children, Toronto, Canada, 4University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
TheVirtualBrain (TVB, thevirtualbrain.org) is a neuroinformatics platform that is able to create individualized brain network models based on structural and functional neuroimaging data. Utilizing TVB to model brain dynamics in children can help characterize typical, healthy developmental changes. As subject-specific models can help link empirical findings across various modalities and scales, they can provide insight into potential causal mechanisms underlying longitudinal, developmental trajectories. The TVB-UKBiobank pipeline, is an automated and open-source multimodal magnetic resonance imaging (MRI) processing pipeline, that was developed to generate the model inputs required by TVB (Frazier-Logue et al., 2022). However, notable challenges in paediatric neuroimage processing, including small brain size and extreme head motion, are not addressed by the existing pipeline.
Methods:
An overview of the pipeline workflow is presented in Figure 1. In order to test the pipeline's functionality for processing data from children across early development, four multimodal neuroimaging paediatric datasets were used. The datasets included data collected by SLB (controls) (Dimond et al., 2020; Graff et al., 2022), MJT (children born very preterm [<32 weeks gestational age] and controls (Vandewouw et al., 2020), the Province of Ontario Neurodevelopmental Network (children diagnosed with various neurodevelopmental disorders [i.e., autism spectrum disorder, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder] and controls (e.g., Choi et al., 2020)), and the Calgary Preschool MRI dataset (controls) (Reynolds et al., 2020). Specifically, T1-weighted, functional MRI (resting or passive viewing state), and diffusion-weighted MRI data from children (N=848) between the ages of 3 and 24 were used for testing.
We added new capabilities to improve the pipeline's performance on paediatric MRI data. These included user-specified, age- or study-specific templates for brain extraction and registration. The option to use optiBET (Lutkenhoff et al., 2014), an optimized brain extraction tool that results in high-quality, robust brain extraction for brains with severe pathology, was also added (Lutkenhoff et al., 2014). The pipeline creates detailed quality control (QC) reports that were used to evaluate the pipeline's robustness and its new features.
We added new capabilities to improve the pipeline's performance on paediatric MRI data. These included user-specified, age- or study-specific templates for brain extraction and registration. The option to use optiBET (Lutkenhoff et al., 2014), an optimized brain extraction tool that results in high-quality, robust brain extraction for brains with severe pathology, was also added (Lutkenhoff et al., 2014). The pipeline creates detailed quality control (QC) reports that were used to evaluate the pipeline's robustness and its new features.
Results:
An example of the QC reports that can be used to evaluate the quality of the pipeline outputs is presented in Figure 2. The pipeline creates robust model inputs for TVB including average regional time series, whole-brain functional connectivity matrices and structural connectivity weights and tract lengths matrices. In addition to previously tested healthy aging and clinical adult populations, this work extends the pipeline's scope to support paediatric populations and thus large datasets across the entire lifespan.
Conclusions:
The pipeline offers greater accessibility to model brain network dynamics in early development. This work will support future investigations around brain maturation and individual differences to help predict varying outcomes for children.
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Motion Correction and Preprocessing 1
Novel Imaging Acquisition Methods:
Multi-Modal Imaging 2
Poster Session:
Poster Session 1
Poster Session 2
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Modeling
MRI
Open-Source Software
PEDIATRIC
STRUCTURAL MRI
Workflows
1|2Indicates the priority used for review
Abstract Information
My abstract is being submitted as a Software Demonstration.
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 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.
Was 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 author date format
Dimond, D. (2020), ‘Early childhood development of white matter fiber density and morphology’, Neuroimage, vol. 210, pp.116552
Frazier-Logue, N. (2022), 'A Robust Modular Automated Neuroimaging Pipeline for Model Inputs to TheVirtualBrain', Frontiers in Neuroinformatics, vol. 16, pp.883223
Graff, K. (2022), ‘Functional connectomes become more longitudinally self-stable, but not more distinct from others, across early childhood’, Neuroimage, vol. 258, pp.11967
Lutkenhoff, E.S. (2014), ‘Optimized Brain Extraction for Pathological Brain (optiBET)’, PLOS ONE, vol. 9, no. 12, pp.e115551
Reynolds, J.E. (2020), ‘Calgary Preschool magnetic resonance imaging (MRI) dataset’, Data in Brief, vol. 29, pp.105224
Vandewouw, M.M. (2020). 'Mapping the neuroanatomical impact of very preterm birth across childhood', Human Brain Mapping, vol. 41, no. 4, pp.892-905
Young, J.M. (2017). 'Longitudinal study of white matter development and outcomes in children born very preterm', Cerebral Cortex, vol. 27, no. 8, pp.4094-4105