Cortical Surface extraction with Freesurfer. 2. ... Connectome-based cortical surface registration ... information with cortical surface anatomy for a joint.
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Boris A. Gutman, Neda Jahanshad, Derek Hibar, Cassandra Leonardo, Kristian Eschenburg, Talia Nir, Julio Villalon-Reina, Paul M. Thompson
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Continuous Connectomics: An Exploratory Framework for Connectivity Analysis in Brain Imaging
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MRI tractography. Medical Image Analysis 15, 414-425 (2011) [2] Gutman, B.A., et al.: A Family of Fast Spherical Registration Algorithms for Cortical Shapes. Multimodal Brain Image Analysis (MBIA) (2013)
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[1]. Aganj, I., et al.: A Hough transform global probabilistic approach to multiple-subject diffusion
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We have presented a framework for fusing connectivity information with cortical surface anatomy for a joint connectivity analysis. There are four distinct contributions: (1) the definition of a continuous connectome space and a method for estimating continuous kernels from fiber models; (2) an algorithm for defining a mutual connectome shared by two brains; (3) a spatial correspondence search between two connectome kernels, directly registering the brainsโ structural connectivities; (4) an adaptation of graph theory measures to the continuous setting.
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โข Continuous Clustering Coefficient:
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โข Continuous Nodal degree (connectedness): ๐ ๐ = ๐ด ๐ฒ(๐, ๐)๐
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Corrected p-maps for AD-NC difference in (A) clustering coefficient , (B) connectedness, a.k.a. continuous nodal degree
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5. Continuous Connectomics
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4. Connectome-based cortical surface registration
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Both measures passed False Discovery Rate (FDR) correction. Connectedness: q = 1.0x10-3 for the right -3 hemisphere, and q = 1.9x10 for the left. Clustering -3 coefficient: q = 1.7x10 for the right hemisphere and q = 2.5x10-5 for the left.
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โข Registering connectomes reduces to multi-channel registration:
Individual improvement 0.098 +/-0.089 0.2 +/-0.089
4. Sensitivity of continuous connectomics to Alzheimerโs Disease
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โข By projecting networks of one personโs brain into appropriate eigenspaces of another we can match networks between individual brains. ๐ ๐ Mutual network set: ๐ฌ๐ด = ๐๐๐ , ๐๐ , ๐๐
Anatomy + connectivity 0.43 +/-0.12 0.12 +/-0.099
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Full kernel Mutual kernel
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โข Eigenfuncions of the connectome kernel (eigennetworks) fully describe the connectome per Mercerโs Theorem: ๐ฒ ๐, ๐ = ๐ ๐๐ ๐๐ ๐ ๐๐ ๐
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3. Eigen-network matching
Anatomy only 0.528 +/-0.101 0.32 +/-0.082
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โข Spherical fluid framework [2] โข Connectome mismatch reduced compared to purely anatomical registration:
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3. Connectome registration
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2. Matched eigen-networks (right)
โข Treat each fiber as an obseravtion in a connectome space โข Use kernel density estimation to compute a continuous connectivity profile for each brain:
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Pre-processing
โข Tractogaphy with Hough Transform [1] โข Cortical Surface extraction with Freesurfer
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Method
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โข Current connectivity analysis assumes a small discrete graph โ not flexible enough for connectome matching
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1. Visualizing individual fiber model contribution to the continuous connectome
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โข Matching connectivity profiles across brains may therefore improve brain surface registration
2. Continuous Connectome
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Results
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โข Structural connectivity may be a better indicator of functional role of brain regions than gross anatomy alone
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Data
ADNI 2 dataset: 64-direction DTI images & T1-weighed images for cortical surface extraction. 48 Alzheimerโs patients. 50 age-matched controls.
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Motivation: matching connectomes across different brains
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Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA.
