Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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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

@article{Arslan:2017:10.1016/j.neuroimage.2017.04.014,
author = {Arslan, S and Ktena, SI and Makropoulos, A and Robinson, EC and Rueckert, D and Parisot, S},
doi = {10.1016/j.neuroimage.2017.04.014},
journal = {Neuroimage},
pages = {5--30},
title = {Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex},
url = {http://dx.doi.org/10.1016/j.neuroimage.2017.04.014},
volume = {170},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The macro-connectome elucidates the pathways through which brain regions are structurally connected or functionally coupled to perform a specific cognitive task. It embodies the notion of representing and understanding all connections within the brain as a network, while the subdivision of the brain into interacting functional units is inherent in its architecture. As a result, the definition of network nodes is one of the most critical steps in connectivity network analysis. Although brain atlases obtained from cytoarchitecture or anatomy have long been used for this task, connectivity-driven methods have arisen only recently, aiming to delineate more homogeneous and functionally coherent regions. This study provides a systematic comparison between anatomical, connectivity-driven and random parcellation methods proposed in the thriving field of brain parcellation. Using resting-state functional MRI data from the Human Connectome Project and a plethora of quantitative evaluation techniques investigated in the literature, we evaluate 10 subject-level and 24 groupwise parcellation methods at different resolutions. We assess the accuracy of parcellations from four different aspects: (1) reproducibility across different acquisitions and groups, (2) fidelity to the underlying connectivity data, (3) agreement with fMRI task activation, myelin maps, and cytoarchitectural areas, and (4) network analysis. This extensive evaluation of different parcellations generated at the subject and group level highlights the strengths and shortcomings of the various methods and aims to provide a guideline for the choice of parcellation technique and resolution according to the task at hand. The results obtained in this study suggest that there is no optimal method able to address all the challenges faced in this endeavour simultaneously.
AU  - Arslan,S
AU  - Ktena,SI
AU  - Makropoulos,A
AU  - Robinson,EC
AU  - Rueckert,D
AU  - Parisot,S
DO  - 10.1016/j.neuroimage.2017.04.014
EP  - 30
PY  - 2017///
SN  - 1095-9572
SP  - 5
TI  - Human brain mapping: a systematic comparison of parcellation methods for the human cerebral cortex
T2  - Neuroimage
UR  - http://dx.doi.org/10.1016/j.neuroimage.2017.04.014
UR  - http://www.ncbi.nlm.nih.gov/pubmed/28412442
UR  - http://hdl.handle.net/10044/1/48328
VL  - 170
ER  -
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