Geometric and connectomic constraints on mouse brain organisation
Poster No:
1708
Submission Type:
Abstract Submission
Authors:
Mehul Gajwani1, James Pang1, Jean-Charles Mariani2, Ludovico Coletta3, Alessandro Gozzi2, Alex Fornito1
Institutions:
1Monash University, Melbourne, Australia, 2Istituto Italiano di Tecnologia, Trento, Italy, 3Fondazione Bruno Kessler, Trento, Italy
First Author:
Co-Author(s):
Introduction:
Recent work has shown that human brain function can be parsimoniously modelled by geometric features derived only from cortical surfaces (Pang et al., 2023). These geometric features, called eigenmodes, primarily capture information pertaining to local and global curvature of the surface being examined. These geometric models have been shown to accurately and parsimoniously reconstruct features of human brain function and organisation. Although these models utilise no information from brain connectivity studies, they contain as much information as models that rely on tractography data – a measure that is difficult to acquire and interpret in humans given the lack of a ground truth connectome (Gajwani et al., 2023).
However, the ground truth connectome is now available in other species. In particular, the mouse connectome has recently been mapped at voxelwise resolution using invasive tract-tracing (Coletta et al., 2020; Knox et al., 2018; Oh et al., 2014). This high-resolution connectome now allows for the generation of mouse connectome eigenmode models more accurately than in human. Here, we aim to assess whether more accurate connectivity information – in the form of this whole-brain mouse tract tracing data – can improve the accuracy of connectome eigenmode models.
However, the ground truth connectome is now available in other species. In particular, the mouse connectome has recently been mapped at voxelwise resolution using invasive tract-tracing (Coletta et al., 2020; Knox et al., 2018; Oh et al., 2014). This high-resolution connectome now allows for the generation of mouse connectome eigenmode models more accurately than in human. Here, we aim to assess whether more accurate connectivity information – in the form of this whole-brain mouse tract tracing data – can improve the accuracy of connectome eigenmode models.
Methods:
We use mouse whole brain tract tracing studies (Coletta et al., 2020; Knox et al., 2018; Oh et al., 2014), resting state functional MRI recordings (Grandjean, 2020), gene expression maps (Fulcher et al., 2019), and anatomical masks (Coletta et al., 2020) that have been processed as previously described. We restrict our analysis to isocortex due to poor fMRI coverage in other structures.
Eigenmodes (Figure 1A) are computed as previously described (Pang et al., 2023) using the MASSIVE high-performing computing facility (Goscinski et al., 2014). The LaPy library (Reuter et al., 2006) uses the cubic finite element method on the isocortical volume to compute the geometric eigenfunctions of the Laplace-Beltrami operator, thus capturing features of the intrinsic geometry of the mouse isocortex. Connectome eigenmodes are obtained by solving the eigenvalue problem on an adjacency matrix that encodes short range adjacencies in the isocortical volume as well as long-range connections from tract-tracing.
Eigenmodes (Figure 1A) are computed as previously described (Pang et al., 2023) using the MASSIVE high-performing computing facility (Goscinski et al., 2014). The LaPy library (Reuter et al., 2006) uses the cubic finite element method on the isocortical volume to compute the geometric eigenfunctions of the Laplace-Beltrami operator, thus capturing features of the intrinsic geometry of the mouse isocortex. Connectome eigenmodes are obtained by solving the eigenvalue problem on an adjacency matrix that encodes short range adjacencies in the isocortical volume as well as long-range connections from tract-tracing.
Results:
First, we compare the accuracy of geometric vs connectome eigenmodes in reconstructing gene expression maps in mouse isocortex (Figure 1B-E). Across 4385 genes, we find that geometric eigenmodes (average Pearson's r = 0.70 using 100 modes; r = 0.81 using 200 modes) are able to more accurately reconstruct gene expression profiles than connectome eigenmodes (Figure 1B; r = 0.61 using 100 modes; r = 0.71 using 200 modes). Similarly, geometric eigenmodes are able to reconstruct the principal components of gene expression more accurately than connectome eigenmodes (Figure 1C and 1E).
Second, geometric eigenmodes also outperform connectome eigenmodes when reconstructing fMRI maps (geometric: r = 0.92; connectome: r = 0.87; Figure 1F-G). When concatenated, the reconstructions of FC matrices are also more accurate using geometric eigenmodes (geometric: r = 0.47; connectome: r = 0.41; Figure 1H).
Finally, we compare two generative models' reconstruction of empirical fMRI (Figure 1I). We use a wave model derived from generative eigenmodes with one free parameter, and compare this to an existing neural mass model derived from the mouse connectome (Melozzi et al., 2019). When generating (rather than reconstructing) putative patterns of isocortical function, geometric models (average r = 0.68) remain more accurate than connectome models (r = 0.49).
Second, geometric eigenmodes also outperform connectome eigenmodes when reconstructing fMRI maps (geometric: r = 0.92; connectome: r = 0.87; Figure 1F-G). When concatenated, the reconstructions of FC matrices are also more accurate using geometric eigenmodes (geometric: r = 0.47; connectome: r = 0.41; Figure 1H).
Finally, we compare two generative models' reconstruction of empirical fMRI (Figure 1I). We use a wave model derived from generative eigenmodes with one free parameter, and compare this to an existing neural mass model derived from the mouse connectome (Melozzi et al., 2019). When generating (rather than reconstructing) putative patterns of isocortical function, geometric models (average r = 0.68) remain more accurate than connectome models (r = 0.49).
·Figure 1. Geometric constraints on mouse brain function and organisation.
Conclusions:
Here, we are able to use more reliable measurements of brain connectivity to recalculate connectome eigenmodes. However, geometric eigenmodes continue to reconstruct spatial phenotypes more parsimoniously than connectome eigenmodes. Overall, geometric eigenmodes remain a promising avenue to explore brain structure-function relationships.
Genetics:
Genetic Modeling and Analysis Methods
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 1
Cortical Anatomy and Brain Mapping
Normal Development 2
Keywords:
ANIMAL STUDIES
Cross-Species Homologues
FUNCTIONAL MRI
Neurotransmitter
STRUCTURAL MRI
Tractography
1|2Indicates the priority used for review
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Grandjean, J. (2020). A common mouse fMRI resource through unified preprocessing (Version 1, p. 73 GB, 2265 files) [Dataset]. Radboud University. https://doi.org/10.34973/1HE1-5C70
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