Imperial College London

Professor Paul M. Matthews

Faculty of MedicineDepartment of Brain Sciences

Edmond and Lily Safra Chair. Head of Department
 
 
 
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Contact

 

+44 (0)20 7594 2855p.matthews

 
 
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Assistant

 

Ms Siobhan Dillon +44 (0)20 7594 2855

 
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Location

 

E502Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Nie:2016:10.1109/TBME.2016.2571221,
author = {Nie, L and Matthews, PM and Guo, Y},
doi = {10.1109/TBME.2016.2571221},
journal = {IEEE Transactions on Biomedical Engineering},
pages = {2505--2517},
title = {Inferring individual-level variations in the functional parcellation of the cerebral cortex},
url = {http://dx.doi.org/10.1109/TBME.2016.2571221},
volume = {63},
year = {2016}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Objective: Functional parcellation of the cerebral cortex is variable across different subjects or between cognitive states. Ignoring individual - or state - dependent variations in the functional parcellation may lead to inaccurate representations of individual functional connectivity, limiting the precision of interpretations of differences in individual connectivity profiles. However, it is difficult to infer the individual-level variations due to the relatively low robustness of methods for parcellation of individual subjects. Methods: We propose a method called “joint K-means” to robustly parcellate the cerebral cortex using fMRI data for contrasts between two states or subjects that intended to characterize variance in individual functional parcellations. The key idea of the proposed method is to jointly infer parcellations in contrasted datasets by iterative descent, while constraining the similarity of the two pathways in searches for local minima to reduce spurious variations. Results: Parcellations of resting-state fMRI datasets from the Human Connectome Project show that the similarity of parcellations for an individual subject studied on two sessions is greater than that between different subjects. Differences in parcellations between subjects are non-uniformly distributed across the cerebral cortex, with clusters of higher variance in the prefrontal, lateral temporal and occipito-parietal cortices. This pattern is reproducible across sessions, between groups and using different numbers of parcels. Conclusion: The individual-level variations inferred by the proposed method are plausible and consistent with the previously reported functional connectivity variability. Significance: The proposed method is a promising tool for investigating relationships between the cerebral functional organization and behavioral differences.
AU - Nie,L
AU - Matthews,PM
AU - Guo,Y
DO - 10.1109/TBME.2016.2571221
EP - 2517
PY - 2016///
SN - 0018-9294
SP - 2505
TI - Inferring individual-level variations in the functional parcellation of the cerebral cortex
T2 - IEEE Transactions on Biomedical Engineering
UR - http://dx.doi.org/10.1109/TBME.2016.2571221
UR - http://hdl.handle.net/10044/1/34152
VL - 63
ER -