Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Arslan:2016:10.1007/978-3-319-46720-7_14,
author = {Arslan, S and Parisot, S and Rueckert, D},
doi = {10.1007/978-3-319-46720-7_14},
pages = {115--122},
title = {Boundary mapping through manifold learning for connectivity-based cortical parcellation},
url = {http://dx.doi.org/10.1007/978-3-319-46720-7_14},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - © Springer International Publishing AG 2016. The study of the human connectome is becoming more popular due to its potential to reveal the brain function and structure. A critical step in connectome analysis is to parcellate the cortex into coherent regions that can be used to build graphical models of connectivity. Computing an optimal parcellation is of great importance,as this stage can affect the performance of the subsequent analysis. To this end,we propose a new parcellation method driven by structural connectivity estimated from diffusion MRI. We learn a manifold from the local connectivity properties of an individual subject and identify parcellation boundaries as points in this low-dimensional embedding where the connectivity patterns change. We compute spatially contiguous and non-overlapping parcels from these boundaries after projecting them back to the native cortical surface. Our experiments with a set of 100 subjects show that the proposed method can produce parcels with distinct patterns of connectivity and a higher degree of homogeneity at varying resolutions compared to the state-of-the-art methods,hence can potentially provide a more reliable set of network nodes for connectome analysis.
AU - Arslan,S
AU - Parisot,S
AU - Rueckert,D
DO - 10.1007/978-3-319-46720-7_14
EP - 122
PY - 2016///
SN - 0302-9743
SP - 115
TI - Boundary mapping through manifold learning for connectivity-based cortical parcellation
UR - http://dx.doi.org/10.1007/978-3-319-46720-7_14
ER -