Surface registration and cortical parcellation

Logan Williams

King’s College London

Disclosures

None

Overview

1. Modelling the cerebral cortex as a surface
2. Surface-based registration
3. Cortical parcellation

1. Why model the cerebral cortex as a surface?

Mountcastle, V. B. (1997). The columnar organization of the neocortex. Brain: a journal of neurology, 120(4), 701-722; Rakic, P. (1988). Specification of cerebral cortical areas. Science, 241

Eyre, M. et al. (2021). The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity. Brain, 144(7), 2199-2213.

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(44, 115, 134)

Subject 1

Subject 2

Subject 1

(not registered)

Subject 1

(registered)

Template

(MNI152)

Subject 1

(not registered)

Subject 1

(registered)

Subject 1

(not registered)

Subject 1

(registered)

Template

(MNI152)

Coalson, T. S. et al. (2018). The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proceedings of the National Academy of Sciences, 115(27), E6356-E6365.

Coalson, T. S. et al. (2018). The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proceedings of the National Academy of Sciences, 115(27), E6356-E6365.

2. What is surface registration?

Winkler, A. M. et al. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 53(3), 1135-1146.

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Chicago

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Steps to image registration

1. Specify a similarity or error measure describing the “goodness” of the match,
2. Specify a transformation model (e.g., rigid body, affine, elastic, fluid, B-spline),
3. Specify an interpolation strategy (e.g., nearest neighbour, trilinear, sinc),
4. Find the transformation parameters to maximise the similarity measure.

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Robinson, E. C. et al. (2014). MSM: a new flexible framework for multimodal surface matching. Neuroimage, 100, 414-426.

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Chicago

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Fischl, B. et al. (1999). High‐resolution intersubject averaging and a coordinate system for the cortical surface. Human brain mapping, 8(4), 272-284.

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Chicago

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Sprung-Much, T. et al. (2018). Morphological patterns and spatial probability maps of two defining sulci of the posterior ventrolateral frontal cortex of the human brain: the sulcus diagonalis and the anterior ascending ramus of the lateral fissure. Brain Structure and Function, 223, 4125-4152.

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Fischl, B. et al. (2008). Cortical folding patterns and predicting cytoarchitecture. Cerebral cortex, 18(8), 1973-1980.

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Chicago

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Sabuncu, M. R. et al. (2010). Function-based intersubject alignment of human cortical anatomy. Cerebral cortex, 20(1), 130-140.

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Chicago

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Robinson, E. C. et al. (2014). MSM: a new flexible framework for multimodal surface matching. Neuroimage, 100, 414-426.

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Chicago

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Coalson, T. S. et al. (2018). The impact of traditional neuroimaging methods on the spatial localization of cortical areas. Proceedings of the National Academy of Sciences, 115(27), E6356-E6365.

3. Why parcellate the cerebral cortex?

Palomero-Gallagher, N., & Zilles, K. (2019). Cortical layers: Cyto-, myelo-, receptor-and synaptic architecture in human cortical areas. Neuroimage, 197, 716-741.

Palomero-Gallagher, N., & Zilles, K. (2019). Cortical layers: Cyto-, myelo-, receptor-and synaptic architecture in human cortical areas. Neuroimage, 197, 716-741.

Desikan, R. S. et al. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968-980.

Yeo, B. T. et al. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of neurophysiology.

Multimodal parcellation

1. Registration should be driven by features that better reflect cortical organisation

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• Parcellation borders should be in agreement across different modalities

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• Cortex profile e.g. thickness, myelin, on either side of the boundary should be significantly different with reasonable effect size

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• Should reflect what we hope to see at a microscopic level

Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.

Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.

Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.

Glasser, M. F. et al. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178.

Limitations/disclaimer

Areas are only one level of cortical organization

There are many other tools we can use to study the cerebral cortex:

• Nodes/networks
• Gradients

Thanks

OHBM Education Committee

Supervisors: Dr. Emma Robinson & Professor David Edwards

MeTrICS lab @ KCL

Collaborators: Oxford WIN, WashU

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