An individualised, geometrically informed approach to brain parcellation
Poster No:
1636
Submission Type:
Abstract Submission
Authors:
Priscila Thalenberg Levi1, Jace Cruddas2, Mehul Gajwani2, James Pang3, Alex Fornito3
Institutions:
1Monash University, Carnegie, VIC, 2Monash University, Melbourne, Victoria, 3Monash University, Clayton, Victoria
First Author:
Co-Author(s):
Introduction:
Most neuroimaging studies model the brain as a network of interconnected discrete areas. An important challenge in this approach involves defining such areas so that they optimally correspond to distinct, functionally homogeneous units of the brain (Eickhoff et al., 2018; Fornito et al., 2016). Choosing an appropriate parcellation is crucial as it can greatly influence subsequent analyses such as in the construction of the connectome (Gajwani et al., 2023). Most existing parcellation methods employ descriptive approaches to delineate regional boundaries based on brain phenotypes (e.g., functional coupling) measured in healthy young adults (Eickhoff et al., 2018). These approaches fail to capture the underlying generative mechanisms that shape regional organisation and inter-individual differences. Our team has recently developed a parcellation approach that leverages intrinsic geometric properties of the brain to mimic the formative effect of early cortical patterns, created by morphogen inteactions, which are fundamental for region formation in brain development (Pang et al.). This approach, however, does not account for individual differences in regional organisation that result from activity-dependent processes occurring during later developmental stages (O'Leary et al., 2007).
Methods:
Here, we developed an individualised parcellation approach that starts with a geometric parcellation and adapts individual regional borders based on functional coupling (FC) profiles. Briefly, we created a group-average geometric parcellation on an fsaverage5 standard surface. This template was then used to initialize an individualisation algorithm named Group Prior Individualised Parcellation (GPIP; Chong et al, 2017). GPIP uses a Bayesian formulation that combines two priors to update individual maps: (1) a group prior based on a group sparsity constraint model of FC that ensures regional matching between subjects; and (2) an individualised prior based on a Markov Random Field in the form of a Potts model to define individual-specific regional boundaries. We created individualised parcellations for 954 healthy individuals using one run of resting-state fMRI data collected and preprocessed by the Human Connectome Project (Glasser et al., 2013). We created parcellations with 200 and 300 parcels. To validate the parcellation, we calculated within-parcel activity homogeneity normalised for parcel size, out of sample. We then compared homogeneity scores between the group geometric parcellation, the popular Schaefer atlas (Schaefer et al, 2018), and our individualised parcellation.
Results:
We show that our method maintains regional comparability between participants while accounting for individual differences in organisation across all scales (figure 1). Our individualised parcellation creates parcels with greater FC homogeneity (figure 2) than the group geometric parcellation (200 parcels: mean percentage difference (μ) = 5.2%, p < 0.0001; 300 parcels: μ = 5.7%, p < 0.0001) and the Schafer parcellation (200 parcels: mean percentage difference (μ) = 3.1%, p < 0.0001; 300 parcels: μ = 3.7%, p < 0.0001).
Conclusions:
Here, we developed a new multi-scale individualised parcellation approach that creates homogenous parcels, which is grounded in neurodevelopmental mechanisms.
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Methods Development
Segmentation and Parcellation 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Neuroinformatics and Data Sharing:
Brain Atlases
Keywords:
Atlasing
Cortex
Development
Morphometrics
Segmentation
Other - parcellation
1|2Indicates the priority used for review
Abstract Information
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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.
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Provide references using APA citation style.
Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018). Imaging-based parcellations of the human brain. Nature Reviews Neuroscience, 19(11), 672–686. https://doi.org/10.1038/s41583-018-0071-7
Fornito, A., Zalesky, A., & Bullmore, E. T. (2016). Fundamentals of brain network analysis. 476.
Gajwani, M., Oldham, S., Pang, J. C., Arnatkevičiūtė, A., Tiego, J., Bellgrove, M. A., & Fornito, A. (2023). Can hubs of the human connectome be identified consistently with diffusion MRI? Network Neuroscience, 7(4), 1326–1350. https://doi.org/10.1162/netn_a_00324
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., Van Essen, D. C., & Jenkinson, M. (2013). The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage, 80, 105–124. https://doi.org/10.1016/j.neuroimage.2013.04.127
O’Leary, D. D. M., Chou, S.-J., & Sahara, S. (2007). Area Patterning of the Mammalian Cortex. Neuron, 56(2), 252–269. https://doi.org/10.1016/j.neuron.2007.10.010
Pang J. C., Aquino K. M., Robinson P. A., Levi P. T., Markicevic M.,
Shen X., Sabaroedin K., Funck T., Kong R., Yeo B. T.,
Palomero-Gallagher N., Constable R. T., Lake E., Breakspear M., Fornito A. Geometric influences on the regional organization of the mammalian brain, submitted.
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex, 28(9), 3095–3114. https://doi.org/10.1093/cercor/bhx179