<?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%">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%">others</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%">Journal of Neuroscience Methods</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></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></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%">Cross, Zachariah R</style></author><author><style face="normal" font="default" size="100%">Gray, Samantha M</style></author><author><style face="normal" font="default" size="100%">Dede, Adam J O</style></author><author><style face="normal" font="default" size="100%">Rivera, Yessenia M</style></author><author><style face="normal" font="default" size="100%">Yin, Qin</style></author><author><style face="normal" font="default" size="100%">Vahidi, Parisa</style></author><author><style face="normal" font="default" size="100%">Rau, Elias M B</style></author><author><style face="normal" font="default" size="100%">Cyr, Christopher</style></author><author><style face="normal" font="default" size="100%">Holubecki, Ania M</style></author><author><style face="normal" font="default" size="100%">Asano, Eishi</style></author><author><style face="normal" font="default" size="100%">Lin, Jack J</style></author><author><style face="normal" font="default" size="100%">McManus, Olivia Kim</style></author><author><style face="normal" font="default" size="100%">Sattar, Shifteh</style></author><author><style face="normal" font="default" size="100%">Saez, Ignacio</style></author><author><style face="normal" font="default" size="100%">Girgis, Fady</style></author><author><style face="normal" font="default" size="100%">King-Stephens, David</style></author><author><style face="normal" font="default" size="100%">Weber, Peter B</style></author><author><style face="normal" font="default" size="100%">Laxer, Kenneth D</style></author><author><style face="normal" font="default" size="100%">Schuele, Stephan U</style></author><author><style face="normal" font="default" size="100%">Rosenow, Joshua M</style></author><author><style face="normal" font="default" size="100%">Wu, Joyce Y</style></author><author><style face="normal" font="default" size="100%">Lam, Sandi K</style></author><author><style face="normal" font="default" size="100%">Raskin, Jeffrey S</style></author><author><style face="normal" font="default" size="100%">Chang, Edward F</style></author><author><style face="normal" font="default" size="100%">Shaikhouni, Ammar</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Roland, Jarod L</style></author><author><style face="normal" font="default" size="100%">Braga, Rodrigo M</style></author><author><style face="normal" font="default" size="100%">Knight, Robert T</style></author><author><style face="normal" font="default" size="100%">Ofen, Noa</style></author><author><style face="normal" font="default" size="100%">Johnson, Elizabeth L</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The development of aperiodic neural activity in the human brain.</style></title><secondary-title><style face="normal" font="default" size="100%">bioRxiv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">bioRxiv</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2024 Nov 09</style></date></pub-dates></dates><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The neurophysiological mechanisms supporting brain maturation are fundamental to attention and memory capacity across the lifespan. Human brain regions develop at different rates, with many regions developing into the third and fourth decades of life. Here, in this preregistered study (https://osf.io/gsru7), we analyzed intracranial EEG (iEEG) recordings from widespread brain regions in a large developmental cohort. Using task-based (i.e., attention to-be-remembered visual stimuli) and task-free (resting-state) data from 101 children and adults (5.93 - 54.00 years, 63 males;  electrodes = 5691), we mapped aperiodic (1/ƒ-like) activity, a proxy of excitation:inhibition (E:I) balance with steeper slopes indexing inhibition and flatter slopes indexing excitation. We reveal that aperiodic slopes flatten with age into young adulthood in both association and sensorimotor cortices, challenging models of early sensorimotor development based on brain structure. In prefrontal cortex (PFC), attentional state modulated age effects, revealing steeper task-based than task-free slopes in adults and the opposite in children, consistent with the development of cognitive control. Age-related differences in task-based slopes also explained age-related gains in memory performance, linking the development of PFC cognitive control to the development of memory. Last, with additional structural imaging measures, we reveal that age-related differences in gray matter volume are differentially associated with aperiodic slopes in association and sensorimotor cortices. Our findings establish developmental trajectories of aperiodic activity in localized brain regions and illuminate the development of PFC inhibitory control during adolescence in the development of attention and memory.&lt;/p&gt;</style></abstract></record></records></xml>