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Connectome analysis for pre-operative brain mapping in neurosurgery
Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences o...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Taylor & Francis
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5152559/ https://www.ncbi.nlm.nih.gov/pubmed/27447756 http://dx.doi.org/10.1080/02688697.2016.1208809 |
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author | Hart, Michael G. Price, Stephen J. Suckling, John |
author_facet | Hart, Michael G. Price, Stephen J. Suckling, John |
author_sort | Hart, Michael G. |
collection | PubMed |
description | Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. |
format | Online Article Text |
id | pubmed-5152559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Taylor & Francis |
record_format | MEDLINE/PubMed |
spelling | pubmed-51525592016-12-21 Connectome analysis for pre-operative brain mapping in neurosurgery Hart, Michael G. Price, Stephen J. Suckling, John Br J Neurosurg Original Article Object: Brain mapping has entered a new era focusing on complex network connectivity. Central to this is the search for the connectome or the brains ‘wiring diagram’. Graph theory analysis of the connectome allows understanding of the importance of regions to network function, and the consequences of their impairment or excision. Our goal was to apply connectome analysis in patients with brain tumours to characterise overall network topology and individual patterns of connectivity alterations. Methods: Resting-state functional MRI data were acquired using multi-echo, echo planar imaging pre-operatively from five participants each with a right temporal–parietal–occipital glioblastoma. Complex networks analysis was initiated by parcellating the brain into anatomically regions amongst which connections were identified by retaining the most significant correlations between the respective wavelet decomposed time-series. Results: Key characteristics of complex networks described in healthy controls were preserved in these patients, including ubiquitous small world organization. An exponentially truncated power law fit to the degree distribution predicted findings of general network robustness to injury but with a core of hubs exhibiting disproportionate vulnerability. Tumours produced a consistent reduction in local and long-range connectivity with distinct patterns of connection loss depending on lesion location. Conclusions: Connectome analysis is a feasible and novel approach to brain mapping in individual patients with brain tumours. Applications to pre-surgical planning include identifying regions critical to network function that should be preserved and visualising connections at risk from tumour resection. In the future one could use such data to model functional plasticity and recovery of cognitive deficits. Taylor & Francis 2016-09-02 2016-07-22 /pmc/articles/PMC5152559/ /pubmed/27447756 http://dx.doi.org/10.1080/02688697.2016.1208809 Text en © 2016 The Author(s) Published by The Neurosurgical Foundation http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Hart, Michael G. Price, Stephen J. Suckling, John Connectome analysis for pre-operative brain mapping in neurosurgery |
title | Connectome analysis for pre-operative brain mapping in neurosurgery
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title_full | Connectome analysis for pre-operative brain mapping in neurosurgery
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title_fullStr | Connectome analysis for pre-operative brain mapping in neurosurgery
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title_full_unstemmed | Connectome analysis for pre-operative brain mapping in neurosurgery
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title_short | Connectome analysis for pre-operative brain mapping in neurosurgery
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title_sort | connectome analysis for pre-operative brain mapping in neurosurgery |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5152559/ https://www.ncbi.nlm.nih.gov/pubmed/27447756 http://dx.doi.org/10.1080/02688697.2016.1208809 |
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