<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luckett, Patrick H</style></author><author><style face="normal" font="default" size="100%">Park, Ki Yun</style></author><author><style face="normal" font="default" size="100%">Lee, John J</style></author><author><style face="normal" font="default" size="100%">Lenze, Eric J</style></author><author><style face="normal" font="default" size="100%">Wetherell, Julie Loebach</style></author><author><style face="normal" font="default" size="100%">Eyler, Lisa T</style></author><author><style face="normal" font="default" size="100%">Snyder, Abraham Z</style></author><author><style face="normal" font="default" size="100%">Ances, Beau M</style></author><author><style face="normal" font="default" size="100%">Shimony, Joshua S</style></author><author><style face="normal" font="default" size="100%">Leuthardt, Eric C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Data-efficient resting-state functional magnetic resonance imaging brain mapping with deep learning.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurosurg</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neurosurg</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2023</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">1-12</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;Resting-state functional MRI (RS-fMRI) enables the mapping of function within the brain and is emerging as an efficient tool for the presurgical evaluation of eloquent cortex. Models capable of reliable and precise mapping of resting-state networks (RSNs) with a reduced scanning time would lead to improved patient comfort while reducing the cost per scan. The aims of the present study were to develop a deep 3D convolutional neural network (3DCNN) capable of voxel-wise mapping of language (LAN) and motor (MOT) RSNs with minimal quantities of RS-fMRI data.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Imaging data were gathered from multiple ongoing studies at Washington University School of Medicine and other thoroughly characterized, publicly available data sets. All study participants (n = 2252 healthy adults) were cognitively screened and completed structural neuroimaging and RS-fMRI. Random permutations of RS-fMRI regions of interest were used to train a 3DCNN. After training, model inferences were compared using varying amounts of RS-fMRI data from the control data set as well as 5 patients with glioblastoma multiforme.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;The trained model achieved 96% out-of-sample validation accuracy on data encompassing a large age range collected on multiple scanner types and varying sequence parameters. Testing on out-of-sample control data showed 97.9% similarity between results generated using either 50 or 200 RS-fMRI time points, corresponding to approximately 2.5 and 10 minutes, respectively (96.9% LAN, 96.3% MOT true-positive rate). In evaluating data from patients with brain tumors, the 3DCNN was able to accurately map LAN and MOT networks despite structural and functional alterations.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Functional maps produced by the 3DCNN can inform surgical planning in patients with brain tumors in a time-efficient manner. The authors present a highly efficient method for presurgical functional mapping and thus improved functional preservation in patients with brain tumors.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luckett, Patrick H</style></author><author><style face="normal" font="default" size="100%">Lee, John J</style></author><author><style face="normal" font="default" size="100%">Park, Ki Yun</style></author><author><style face="normal" font="default" size="100%">Raut, Ryan V</style></author><author><style face="normal" font="default" size="100%">Meeker, Karin L</style></author><author><style face="normal" font="default" size="100%">Gordon, Evan M</style></author><author><style face="normal" font="default" size="100%">Snyder, Abraham Z</style></author><author><style face="normal" font="default" size="100%">Ances, Beau M</style></author><author><style face="normal" font="default" size="100%">Leuthardt, Eric C</style></author><author><style face="normal" font="default" size="100%">Shimony, Joshua S</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Resting state network mapping in individuals using deep learning.</style></title><secondary-title><style face="normal" font="default" size="100%">Front Neurol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Neurol</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">13</style></volume><pages><style face="normal" font="default" size="100%">1055437</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;INTRODUCTION: &lt;/b&gt;Resting state functional MRI (RS-fMRI) is currently used in numerous clinical and research settings. The localization of resting state networks (RSNs) has been utilized in applications ranging from group analysis of neurodegenerative diseases to individual network mapping for pre-surgical planning of tumor resections. Reproducibility of these results has been shown to require a substantial amount of high-quality data, which is not often available in clinical or research settings.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this work, we report voxelwise mapping of a standard set of RSNs using a novel deep 3D convolutional neural network (3DCNN). The 3DCNN was trained on publicly available functional MRI data acquired in  = 2010 healthy participants. After training, maps that represent the probability of a voxel belonging to a particular RSN were generated for each participant, and then used to calculate mean and standard deviation (STD) probability maps, which are made publicly available. Further, we compared our results to previously published resting state and task-based functional mappings.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Our results indicate this method can be applied in individual subjects and is highly resistant to both noisy data and fewer RS-fMRI time points than are typically acquired. Further, our results show core regions within each network that exhibit high average probability and low STD.&lt;/p&gt;&lt;p&gt;&lt;b&gt;DISCUSSION: &lt;/b&gt;The 3DCNN algorithm can generate individual RSN localization maps, which are necessary for clinical applications. The similarity between 3DCNN mapping results and task-based fMRI responses supports the association of specific functional tasks with RSNs.&lt;/p&gt;</style></abstract></record></records></xml>