<?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%">Xie, Tao</style></author><author><style face="normal" font="default" size="100%">Adamek, Markus</style></author><author><style face="normal" font="default" size="100%">Cho, Hohyun</style></author><author><style face="normal" font="default" size="100%">Adamo, Matthew A</style></author><author><style face="normal" font="default" size="100%">Ritaccio, Anthony L</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%">Kubanek, Jan</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Graded decisions in the human brain.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Choice Behavior</style></keyword><keyword><style  face="normal" font="default" size="100%">Decision Making</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Parietal Lobe</style></keyword><keyword><style  face="normal" font="default" size="100%">Uncertainty</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</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 May 21</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">4308</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Decision-makers objectively commit to a definitive choice, yet at the subjective level, human decisions appear to be associated with a degree of uncertainty. Whether decisions are definitive (i.e., concluding in all-or-none choices), or whether the underlying representations are graded, remains unclear. To answer this question, we recorded intracranial neural signals directly from the brain while human subjects made perceptual decisions. The recordings revealed that broadband gamma activity reflecting each individual's decision-making process, ramped up gradually while being graded by the accumulated decision evidence. Crucially, this grading effect persisted throughout the decision process without ever reaching a definite bound at the time of choice. This effect was most prominent in the parietal cortex, a brain region traditionally implicated in decision-making. These results provide neural evidence for a graded decision process in humans and an analog framework for flexible choice behavior.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></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%">Blanpain, Lou T</style></author><author><style face="normal" font="default" size="100%">Cole, Eric R</style></author><author><style face="normal" font="default" size="100%">Chen, Emily</style></author><author><style face="normal" font="default" size="100%">Park, James K</style></author><author><style face="normal" font="default" size="100%">Walelign, Michael Y</style></author><author><style face="normal" font="default" size="100%">Gross, Robert E</style></author><author><style face="normal" font="default" size="100%">Cabaniss, Brian T</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Singer, Annabelle C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Multisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges.</style></title><secondary-title><style face="normal" font="default" size="100%">Nat Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Nat Commun</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Cross-Over Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsies, Partial</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Temporal Lobe</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 11</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">15</style></volume><pages><style face="normal" font="default" size="100%">3156</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Modulating brain oscillations has strong therapeutic potential. Interventions that both non-invasively modulate deep brain structures and are practical for chronic daily home use are desirable for a variety of therapeutic applications. Repetitive audio-visual stimulation, or sensory flicker, is an accessible approach that modulates hippocampus in mice, but its effects in humans are poorly defined. We therefore quantified the neurophysiological effects of flicker with high spatiotemporal resolution in patients with focal epilepsy who underwent intracranial seizure monitoring. In this interventional trial (NCT04188834) with a cross-over design, subjects underwent different frequencies of flicker stimulation in the same recording session with the effect of sensory flicker exposure on local field potential (LFP) power and interictal epileptiform discharges (IEDs) as primary and secondary outcomes, respectively. Flicker focally modulated local field potentials in expected canonical sensory cortices but also in the medial temporal lobe and prefrontal cortex, likely via resonance of stimulated long-range circuits. Moreover, flicker decreased interictal epileptiform discharges, a pathological biomarker of epilepsy and degenerative diseases, most strongly in regions where potentials were flicker-modulated, especially the visual cortex and medial temporal lobe. This trial met the scientific goal and is now closed. Our findings reveal how multi-sensory stimulation may modulate cortical structures to mitigate pathological activity in humans.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></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%">Cho, Hohyun</style></author><author><style face="normal" font="default" size="100%">Adamek, Markus</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></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.</style></title><secondary-title><style face="normal" font="default" size="100%">Elife</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Elife</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrocorticography</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%">Signal Processing, Computer-Assisted</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 Sep 06</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Determining the presence and frequency of neural oscillations is essential to understanding dynamic brain function. Traditional methods that detect peaks over 1/ noise within the power spectrum fail to distinguish between the fundamental frequency and harmonics of often highly non-sinusoidal neural oscillations. To overcome this limitation, we define fundamental criteria that characterize neural oscillations and introduce the cyclic homogeneous oscillation (CHO) detection method. We implemented these criteria based on an autocorrelation approach to determine an oscillation's fundamental frequency. We evaluated CHO by verifying its performance on simulated non-sinusoidal oscillatory bursts and validated its ability to determine the fundamental frequency of neural oscillations in electrocorticographic (ECoG), electroencephalographic (EEG), and stereoelectroencephalographic (SEEG) signals recorded from 27 human subjects. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.&lt;/p&gt;</style></abstract></record></records></xml>