BACKGROUND: Functional connectivity analysis of resting-state functional magnetic resonance imaging data (fcrs-fMRI) has been shown to be a robust non-invasive method for localization of functional networks (without using specific tasks) and to be promising for presurgical planning. However, in order to transfer the approach to everyday clinical practice, fcrs-fMRI needs to be further validated and made easily accessible to neurosurgeons. This paper addresses the latter by presenting a software tool designed for neurosurgeons for analyzing and visualizing fcrs-fMRI data. METHODS: A prototypical interactive visualization tool was developed to enable neurosurgeons to explore functional connectivity data and evaluate its usability. The implementation builds upon LIPSIA, an established software package for the assessment of functional neuroimaging data, and integrates the selection of a region-of-interest with the computation and visualization of functionally connected areas. The tool was used to explore data from a healthy participant and eight brain lesion patients. The usability of the software was evaluated with four neurosurgeons previously unacquainted with the methodology, who were asked to identify prominent, large-scale cortical networks. FINDINGS: With this novel tool, previously published findings, such as tumor displacement of the sensorimotor cortex and other disturbances of functional networks, were reproduced. The neurosurgeons were able to consistently obtain results similar to the results of an expert, with the exception of the language network. Immediate feedback helped to pinpoint functional networks quickly and intuitively, with even inexperienced users requiring less than 3 min per network. CONCLUSIONS: Although fcrs-fMRI is a nascent method still undergoing evaluation with respect to established standards, the interactive software is nonetheless a promising tool for non-invasive exploration of individual functional connectivity networks in neurosurgical practice, both for well-known networks and for those less typically addressed.
BACKGROUND: Functional connectivity analysis of resting-state functional magnetic resonance imaging data (fcrs-fMRI) has been shown to be a robust non-invasive method for localization of functional networks (without using specific tasks) and to be promising for presurgical planning. However, in order to transfer the approach to everyday clinical practice, fcrs-fMRI needs to be further validated and made easily accessible to neurosurgeons. This paper addresses the latter by presenting a software tool designed for neurosurgeons for analyzing and visualizing fcrs-fMRI data. METHODS: A prototypical interactive visualization tool was developed to enable neurosurgeons to explore functional connectivity data and evaluate its usability. The implementation builds upon LIPSIA, an established software package for the assessment of functional neuroimaging data, and integrates the selection of a region-of-interest with the computation and visualization of functionally connected areas. The tool was used to explore data from a healthy participant and eight brain lesionpatients. The usability of the software was evaluated with four neurosurgeons previously unacquainted with the methodology, who were asked to identify prominent, large-scale cortical networks. FINDINGS: With this novel tool, previously published findings, such as tumor displacement of the sensorimotor cortex and other disturbances of functional networks, were reproduced. The neurosurgeons were able to consistently obtain results similar to the results of an expert, with the exception of the language network. Immediate feedback helped to pinpoint functional networks quickly and intuitively, with even inexperienced users requiring less than 3 min per network. CONCLUSIONS: Although fcrs-fMRI is a nascent method still undergoing evaluation with respect to established standards, the interactive software is nonetheless a promising tool for non-invasive exploration of individual functional connectivity networks in neurosurgical practice, both for well-known networks and for those less typically addressed.
Authors: Michael P Catalino; Shun Yao; Deborah L Green; Edward R Laws; Alexandra J Golby; Yanmei Tie Journal: Neurosurg Focus Date: 2020-02-01 Impact factor: 4.047
Authors: Maxime Chamberland; Benoit Scherrer; Sanjay P Prabhu; Joseph Madsen; David Fortin; Kevin Whittingstall; Maxime Descoteaux; Simon K Warfield Journal: Hum Brain Mapp Date: 2016-09-20 Impact factor: 5.038
Authors: F Xavier Castellanos; Adriana Di Martino; R Cameron Craddock; Ashesh D Mehta; Michael P Milham Journal: Neuroimage Date: 2013-04-28 Impact factor: 6.556
Authors: Donna Dierker; Jarod L Roland; Mudassar Kamran; Jerrel Rutlin; Carl D Hacker; Daniel S Marcus; Mikhail Milchenko; Michelle M Miller-Thomas; Tammie L Benzinger; Abraham Z Snyder; Eric C Leuthardt; Joshua S Shimony Journal: Neuroimaging Clin N Am Date: 2017-11 Impact factor: 2.264