<?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%">Blenkmann, Alejandro Omar</style></author><author><style face="normal" font="default" size="100%">Leske, Sabine Liliana</style></author><author><style face="normal" font="default" size="100%">Llorens, Anaïs</style></author><author><style face="normal" font="default" size="100%">Lin, Jack J</style></author><author><style face="normal" font="default" size="100%">Chang, Edward F</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Schalk, Gerwin</style></author><author><style face="normal" font="default" size="100%">Ivanovic, Jugoslav</style></author><author><style face="normal" font="default" size="100%">Larsson, Pål Gunnar</style></author><author><style face="normal" font="default" size="100%">Knight, Robert Thomas</style></author><author><style face="normal" font="default" size="100%">Endestad, Tor</style></author><author><style face="normal" font="default" size="100%">Solbakk, Anne-Kristin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Anatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurosci Methods</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neurosci Methods</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrodes</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrodes, Implanted</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">404</style></volume><pages><style face="normal" font="default" size="100%">110056</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;BACKGROUND: &lt;/b&gt;Intracranial electrodes are typically localized from post-implantation CT artifacts. Automatic algorithms localizing low signal-to-noise ratio artifacts and high-density electrode arrays are missing. Additionally, implantation of grids/strips introduces brain deformations, resulting in registration errors when fusing post-implantation CT and pre-implantation MR images. Brain-shift compensation methods project electrode coordinates to cortex, but either fail to produce smooth solutions or do not account for brain deformations.&lt;/p&gt;&lt;p&gt;&lt;b&gt;NEW METHODS: &lt;/b&gt;We first introduce GridFit, a model-based fitting approach that simultaneously localizes all electrodes' CT artifacts in grids, strips, or depth arrays. Second, we present CEPA, a brain-shift compensation algorithm combining orthogonal-based projections, spring-mesh models, and spatial regularization constraints.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;We tested GridFit on ∼6000 simulated scenarios. The localization of CT artifacts showed robust performance under difficult scenarios, such as noise, overlaps, and high-density implants (&lt;1 mm errors). Validation with data from 20 challenging patients showed 99% accurate localization of the electrodes (3160/3192). We tested CEPA brain-shift compensation with data from 15 patients. Projections accounted for simple mechanical deformation principles with &lt; 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.&lt;/p&gt;&lt;p&gt;&lt;b&gt;COMPARISON WITH EXISTING METHODS: &lt;/b&gt;GridFit succeeded in difficult scenarios that challenged available methods and outperformed visual localization by preserving the inter-electrode distance. CEPA registration errors were smaller than those obtained for well-established alternatives. Additionally, modeling resting-state high-frequency activity in five patients further supported CEPA.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;GridFit and CEPA are versatile tools for registering intracranial electrode coordinates, providing highly accurate results even in the most challenging implantation scenarios. The methods are implemented in the iElectrodes open-source toolbox.&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%">Gordon, Evan M</style></author><author><style face="normal" font="default" size="100%">Chauvin, Roselyne J</style></author><author><style face="normal" font="default" size="100%">Van, Andrew N</style></author><author><style face="normal" font="default" size="100%">Rajesh, Aishwarya</style></author><author><style face="normal" font="default" size="100%">Nielsen, Ashley</style></author><author><style face="normal" font="default" size="100%">Newbold, Dillan J</style></author><author><style face="normal" font="default" size="100%">Lynch, Charles J</style></author><author><style face="normal" font="default" size="100%">Seider, Nicole A</style></author><author><style face="normal" font="default" size="100%">Krimmel, Samuel R</style></author><author><style face="normal" font="default" size="100%">Scheidter, Kristen M</style></author><author><style face="normal" font="default" size="100%">Monk, Julia</style></author><author><style face="normal" font="default" size="100%">Miller, Ryland L</style></author><author><style face="normal" font="default" size="100%">Metoki, Athanasia</style></author><author><style face="normal" font="default" size="100%">Montez, David F</style></author><author><style face="normal" font="default" size="100%">Zheng, Annie</style></author><author><style face="normal" font="default" size="100%">Elbau, Immanuel</style></author><author><style face="normal" font="default" size="100%">Madison, Thomas</style></author><author><style face="normal" font="default" size="100%">Nishino, Tomoyuki</style></author><author><style face="normal" font="default" size="100%">Myers, Michael J</style></author><author><style face="normal" font="default" size="100%">Kaplan, Sydney</style></author><author><style face="normal" font="default" size="100%">Badke D'Andrea, Carolina</style></author><author><style face="normal" font="default" size="100%">Demeter, Damion V</style></author><author><style face="normal" font="default" size="100%">Feigelis, Matthew</style></author><author><style face="normal" font="default" size="100%">Ramirez, Julian S B</style></author><author><style face="normal" font="default" size="100%">Xu, Ting</style></author><author><style face="normal" font="default" size="100%">Barch, Deanna M</style></author><author><style face="normal" font="default" size="100%">Smyser, Christopher D</style></author><author><style face="normal" font="default" size="100%">Rogers, Cynthia E</style></author><author><style face="normal" font="default" size="100%">Zimmermann, Jan</style></author><author><style face="normal" font="default" size="100%">Botteron, Kelly N</style></author><author><style face="normal" font="default" size="100%">Pruett, John R</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Shimony, Joshua S</style></author><author><style face="normal" font="default" size="100%">Kay, Benjamin P</style></author><author><style face="normal" font="default" size="100%">Marek, Scott</style></author><author><style face="normal" font="default" size="100%">Norris, Scott A</style></author><author><style face="normal" font="default" size="100%">Gratton, Caterina</style></author><author><style face="normal" font="default" size="100%">Sylvester, Chad M</style></author><author><style face="normal" font="default" size="100%">Power, Jonathan D</style></author><author><style face="normal" font="default" size="100%">Liston, Conor</style></author><author><style face="normal" font="default" size="100%">Greene, Deanna J</style></author><author><style face="normal" font="default" size="100%">Roland, Jarod L</style></author><author><style face="normal" font="default" size="100%">Petersen, Steven E</style></author><author><style face="normal" font="default" size="100%">Raichle, Marcus E</style></author><author><style face="normal" font="default" size="100%">Laumann, Timothy O</style></author><author><style face="normal" font="default" size="100%">Fair, Damien A</style></author><author><style face="normal" font="default" size="100%">Dosenbach, Nico U F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A somato-cognitive action network alternates with effector regions in motor cortex.</style></title><secondary-title><style face="normal" font="default" size="100%">Nature</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nature</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Child</style></keyword><keyword><style  face="normal" font="default" size="100%">Cognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Datasets as Topic</style></keyword><keyword><style  face="normal" font="default" size="100%">Foot</style></keyword><keyword><style  face="normal" font="default" size="100%">Hand</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant</style></keyword><keyword><style  face="normal" font="default" size="100%">Infant, Newborn</style></keyword><keyword><style  face="normal" font="default" size="100%">Macaca</style></keyword><keyword><style  face="normal" font="default" size="100%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Mouth</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2023</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">617</style></volume><pages><style face="normal" font="default" size="100%">351-359</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Motor cortex (M1) has been thought to form a continuous somatotopic homunculus extending down the precentral gyrus from foot to face representations, despite evidence for concentric functional zones and maps of complex actions. Here, using precision functional magnetic resonance imaging (fMRI) methods, we find that the classic homunculus is interrupted by regions with distinct connectivity, structure and function, alternating with effector-specific (foot, hand and mouth) areas. These inter-effector regions exhibit decreased cortical thickness and strong functional connectivity to each other, as well as to the cingulo-opercular network (CON), critical for action and physiological control, arousal, errors and pain. This interdigitation of action control-linked and motor effector regions was verified in the three largest fMRI datasets. Macaque and pediatric (newborn, infant and child) precision fMRI suggested cross-species homologues and developmental precursors of the inter-effector system. A battery of motor and action fMRI tasks documented concentric effector somatotopies, separated by the CON-linked inter-effector regions. The inter-effectors lacked movement specificity and co-activated during action planning (coordination of hands and feet) and axial body movement (such as of the abdomen or eyebrows). These results, together with previous studies demonstrating stimulation-evoked complex actions and connectivity to internal organs such as the adrenal medulla, suggest that M1 is punctuated by a system for whole-body action planning, the somato-cognitive action network (SCAN). In M1, two parallel systems intertwine, forming an integrate-isolate pattern: effector-specific regions (foot, hand and mouth) for isolating fine motor control and the SCAN for integrating goals, physiology and body movement.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7960</style></issue></record></records></xml>