<?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%">Staveland, Brooke R</style></author><author><style face="normal" font="default" size="100%">Oberschulte, Julia</style></author><author><style face="normal" font="default" size="100%">Kim-McManus, Olivia</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%">Dastjerdi, Mohammad</style></author><author><style face="normal" font="default" size="100%">Lin, Jack J</style></author><author><style face="normal" font="default" size="100%">Hsu, Ming</style></author><author><style face="normal" font="default" size="100%">Knight, Robert T</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Circuit dynamics of approach-avoidance conflict in humans.</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%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Jan 01</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;Debilitating anxiety is pervasive in the modern world. Choices to approach or avoid are common in everyday life and excessive avoidance is a cardinal feature of all anxiety disorders. Here, we used intracranial EEG to define a distributed prefrontal-limbic circuit dynamics supporting approach and avoidance. Presurgical epilepsy patients (n=20) performed an approach-avoidance conflict decision-making task inspired by the arcade game Pac-Man, where participants trade-off real-time harvesting rewards with potential losses from attack. As patients approached increasing rewards and threats, we found evidence of a limbic circuit mediated by increased theta power in the hippocampus, amygdala, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), which then drops rapidly during avoidance. Theta band connectivity between these regions increases during approach and falls during avoidance, with OFC serving as a connector in this circuit with high theta coherence across limbic regions, but also with regions outside of the limbic system, including the lateral prefrontal cortex. Importantly, the degree of OFC-driven connectivity predicts how long participants approach, with enhanced network synchronicity extending approach times. Finally, under ghost attack, the system dynamically switches to a sustained increase in high-frequency activity (70-150Hz) in the middle frontal gyrus (MFG), marking the retreat from the ghost. The results provide evidence for a distributed prefrontal-limbic circuit, mediated by theta oscillations, underlying approach-avoidance conflict.&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%">Cao, Runnan</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Brandmeir, Nicholas J</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Wang, Shuo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A human single-neuron dataset for object recognition.</style></title><secondary-title><style face="normal" font="default" size="100%">Sci Data</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Sci Data</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amygdala</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Hippocampus</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Recognition, Psychology</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2025</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2025 Jan 15</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">79</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Object recognition is fundamental to how we interact with and interpret the world around us. The human amygdala and hippocampus play a key role in object recognition, contributing to both the encoding and retrieval of visual information. Here, we recorded single-neuron activity from the human amygdala and hippocampus when neurosurgical epilepsy patients performed a one-back task using naturalistic object stimuli. We employed two sets of naturalistic object images from leading datasets extensively used in primate neural recordings and computer vision models: we recorded 1204 neurons using the ImageNet stimuli, which included broader object categories (10 different images per category for 50 categories), and we recorded 512 neurons using the Microsoft COCO stimuli, which featured a higher number of images per category (50 different images per category for 10 categories). Together, our extensive dataset, offering the highest spatial and temporal resolution currently available in humans, will not only facilitate a comprehensive analysis of the neural correlates of object recognition but also provide valuable opportunities for training and validating computational models.&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%">Tan, Gansheng</style></author><author><style face="normal" font="default" size="100%">Adams, Josh</style></author><author><style face="normal" font="default" size="100%">Donovan, Kara</style></author><author><style face="normal" font="default" size="100%">Demarest, Phillip</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%">Gorlewicz, Jenna L</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%">Does vibrotactile stimulation of the auricular vagus nerve enhance working memory? A behavioral and physiological investigation.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain Stimul</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain Stimul</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Galvanic Skin Response</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%">Memory, Short-Term</style></keyword><keyword><style  face="normal" font="default" size="100%">Pupil</style></keyword><keyword><style  face="normal" font="default" size="100%">Vagus Nerve</style></keyword><keyword><style  face="normal" font="default" size="100%">Vagus Nerve Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Vibration</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 Mar-Apr</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">17</style></volume><pages><style face="normal" font="default" size="100%">460-468</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;Working memory is essential to a wide range of cognitive functions and activities. Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising method to improve working memory performance. However, the feasibility and scalability of electrical stimulation are constrained by several limitations, such as auricular discomfort and inconsistent electrical contact.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;We aimed to develop a novel and practical method, vibrotactile taVNS, to improve working memory. Further, we investigated its effects on arousal, measured by skin conductance and pupil diameter.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHOD: &lt;/b&gt;This study included 20 healthy participants. Behavioral response, skin conductance, and eye tracking data were concurrently recorded while the participants performed N-back tasks under three conditions: vibrotactile taVNS delivered to the cymba concha, earlobe (sham control), and no stimulation (baseline control).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;In 4-back tasks, which demand maximal working memory capacity, active vibrotactile taVNS significantly improved the performance metric d compared to the baseline but not to the sham. Moreover, we found that the reduction rate of d with increasing task difficulty was significantly smaller during vibrotactile taVNS sessions than in both baseline and sham conditions. Arousal, measured as skin conductance and pupil diameter, declined over the course of the tasks. Vibrotactile taVNS rescued this arousal decline, leading to arousal levels corresponding to optimal working memory levels. Moreover, pupil diameter and skin conductance level were higher during high-cognitive-load tasks when vibrotactile taVNS was delivered to the concha compared to baseline and sham.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our findings suggest that vibrotactile taVNS modulates the arousal pathway and could be a potential intervention for enhancing working memory.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</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%">Tan, Gansheng</style></author><author><style face="normal" font="default" size="100%">Adams, Josh</style></author><author><style face="normal" font="default" size="100%">Donovan, Kara</style></author><author><style face="normal" font="default" size="100%">Demarest, Phillip</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%">Gorlewicz, Jenna L</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%">Does Vibrotactile Stimulation of the Auricular Vagus Nerve Enhance Working Memory? A Behavioral and Physiological Investigation.</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 Mar 27</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;&lt;b&gt;BACKGROUND: &lt;/b&gt;Working memory is essential to a wide range of cognitive functions and activities. Transcutaneous auricular VNS (taVNS) is a promising method to improve working memory performance. However, the feasibility and scalability of electrical stimulation are constrained by several limitations, such as auricular discomfort and inconsistent electrical contact.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;We aimed to develop a novel and practical method, vibrotactile taVNS, to improve working memory. Further, we investigated its effects on arousal, measured by skin conductance and pupil diameter.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHOD: &lt;/b&gt;This study included 20 healthy participants. Behavioral response, skin conductance, and eye tracking data were concurrently recorded while the participants performed N-back tasks under three conditions: vibrotactile taVNS delivered to the cymba concha, earlobe (sham control), and no stimulation (baseline control).&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;In 4-back tasks, which demand maximal working memory capacity, active vibrotactile taVNS significantly improved the performance metric  ' compared to the baseline but not to the sham. Moreover, we found that the reduction rate of  ' with increasing task difficulty was significantly smaller during vibrotactile taVNS sessions than in both baseline and sham conditions. Arousal, measured as skin conductance and pupil diameter, declined over the course of the tasks. Vibrotactile taVNS rescued this arousal decline, leading to arousal levels corresponding to optimal working memory levels. Moreover, pupil diameter and skin conductance level were higher during high-cognitive-load tasks when vibrotactile taVNS was delivered to the concha compared to baseline and sham.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSION: &lt;/b&gt;Our findings suggest that vibrotactile taVNS modulates the arousal pathway and could be a potential intervention for enhancing working memory.&lt;/p&gt;&lt;p&gt;&lt;b&gt;HIGHLIGHTS: &lt;/b&gt;Vibrotactile stimulation of the auricular vagus nerve increases general arousal.Vibrotactile stimulation of the auricular vagus nerve mitigates arousal decreases as subjects continuously perform working memory tasks.6 Hz Vibrotactile auricular vagus nerve stimulation is a potential intervention for enhancing working memory performance.&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%">Tan, Gansheng</style></author><author><style face="normal" font="default" size="100%">Adams, Josh</style></author><author><style face="normal" font="default" size="100%">Donovan, Kara</style></author><author><style face="normal" font="default" size="100%">Demarest, Phillip</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%">Gorlewicz, Jenna L</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%">Does vibrotactile stimulation of the auricular vagus nerve enhance working memory? A behavioral and physiological investigation</style></title><secondary-title><style face="normal" font="default" size="100%">Brain Stimulation</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%">17</style></volume><pages><style face="normal" font="default" size="100%">460–468</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%">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%">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%">Nature communications</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%">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></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%">Pedersen, Nigel P</style></author><author><style face="normal" font="default" size="100%">Raghu, Ashley</style></author><author><style face="normal" font="default" size="100%">Shivamurthy, Veeresh Kumar N</style></author><author><style face="normal" font="default" size="100%">Chern, Joshua J</style></author><author><style face="normal" font="default" size="100%">Gross, Robert E</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Dingledine, Raymond J</style></author><author><style face="normal" font="default" size="100%">Kheder, Ammar</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The involvement of the piriform cortex in non-lesional temporal lobe epilepsy: an uncommon component of the epileptogenic network.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain Commun</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain Commun</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</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">fcae179</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The piriform cortex is recognized as highly epileptogenic in rodents, yet its electrophysiological role in human epilepsy remains understudied. Recent surgical outcomes have suggested potential benefits in resecting the piriform cortex for cases of medial temporal lobe epilepsy. However, little is known about its electrophysiological activity in human epilepsy. This case-series study aimed to explore the electrophysiological role of the piriform cortex within the epileptogenic network among patients with suspected temporal lobe epilepsy. Participants were recruited from Emory University Hospital or Children's Healthcare of Atlanta, with non-lesional frontotemporal or temporal lobe hypotheses, undergoing stereoelectroencephalographic studies. Specifically, focus was placed on patients with one or more electrode contacts in the piriform cortex. Primary objectives included determining piriform cortex involvement within the electrophysiologically defined epileptogenic network and assessing the effects of electrical stimulation. Twenty-two patients were included in the study. Notably, only one patient exhibited piriform cortex involvement at seizure onset, associated with an olfactory aura. Two patients showed early piriform cortex involvement, while others displayed late or no involvement. Electrical stimulation of the piriform cortex induced after-discharges in three patients and replicated a habitual seizure in one. These findings present a contrast to surgical outcome studies, suggesting that the piriform cortex may not typically play a significant role in the epileptogenic network among patients with non-lesional temporal lobe epilepsy.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">4</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%">Cao, Runnan</style></author><author><style face="normal" font="default" size="100%">Wang, Jinge</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Li, Xin</style></author><author><style face="normal" font="default" size="100%">Rutishauser, Ueli</style></author><author><style face="normal" font="default" size="100%">Brandmeir, Nicholas J</style></author><author><style face="normal" font="default" size="100%">Wang, Shuo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus.</style></title><secondary-title><style face="normal" font="default" size="100%">Cell Rep</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Cell Rep</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amygdala</style></keyword><keyword><style  face="normal" font="default" size="100%">Facial Recognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Hippocampus</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Recognition, Psychology</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 Jan 23</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">43</style></volume><pages><style face="normal" font="default" size="100%">113520</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Recognizing familiar faces and learning new faces play an important role in social cognition. However, the underlying neural computational mechanisms remain unclear. Here, we record from single neurons in the human amygdala and hippocampus and find a greater neuronal representational distance between pairs of familiar faces than unfamiliar faces, suggesting that neural representations for familiar faces are more distinct. Representational distance increases with exposures to the same identity, suggesting that neural face representations are sharpened with learning and familiarization. Furthermore, representational distance is positively correlated with visual dissimilarity between faces, and exposure to visually similar faces increases representational distance, thus sharpening neural representations. Finally, we construct a computational model that demonstrates an increase in the representational distance of artificial units with training. Together, our results suggest that the neuronal population geometry, quantified by the representational distance, encodes face familiarity, similarity, and learning, forming the basis of face recognition and memory.&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%">Cao, Runnan</style></author><author><style face="normal" font="default" size="100%">Wang, Jinge</style></author><author><style face="normal" font="default" size="100%">Brunner, Peter</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Li, Xin</style></author><author><style face="normal" font="default" size="100%">Rutishauser, Ueli</style></author><author><style face="normal" font="default" size="100%">Brandmeir, Nicholas J</style></author><author><style face="normal" font="default" size="100%">Wang, Shuo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural mechanisms of face familiarity and learning in the human amygdala and hippocampus</style></title><secondary-title><style face="normal" font="default" size="100%">Cell reports</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%">43</style></volume><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%">Regev, Tamar I</style></author><author><style face="normal" font="default" size="100%">Casto, Colton</style></author><author><style face="normal" font="default" size="100%">Hosseini, Eghbal A</style></author><author><style face="normal" font="default" size="100%">Adamek, Markus</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%">Fedorenko, Evelina</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neural populations in the language network differ in the size of their temporal receptive windows</style></title><secondary-title><style face="normal" font="default" size="100%">Nature Human Behaviour</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%">8</style></volume><pages><style face="normal" font="default" size="100%">1924–1942</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%">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%">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 Mar 23</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;Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). 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><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></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">RP91605</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%">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><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%">Metzger, Brian A</style></author><author><style face="normal" font="default" size="100%">Kalva, Prathik</style></author><author><style face="normal" font="default" size="100%">Mocchi, Madaline M</style></author><author><style face="normal" font="default" size="100%">Cui, Brian</style></author><author><style face="normal" font="default" size="100%">Adkinson, Joshua A</style></author><author><style face="normal" font="default" size="100%">Wang, Zhengjia</style></author><author><style face="normal" font="default" size="100%">Mathura, Raissa</style></author><author><style face="normal" font="default" size="100%">Kanja, Kourtney</style></author><author><style face="normal" font="default" size="100%">Gavvala, Jay</style></author><author><style face="normal" font="default" size="100%">Krishnan, Vaishnav</style></author><author><style face="normal" font="default" size="100%">Lin, Lu</style></author><author><style face="normal" font="default" size="100%">Maheshwari, Atul</style></author><author><style face="normal" font="default" size="100%">Shofty, Ben</style></author><author><style face="normal" font="default" size="100%">Magnotti, John F</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Sheth, Sameer A</style></author><author><style face="normal" font="default" size="100%">Bijanki, Kelly R</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Intracranial stimulation and EEG feature analysis reveal affective salience network specialization.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain</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%">06/2023</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;Emotion is represented in limbic and prefrontal brain areas herein termed the Affective Salience Network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed - specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach (&quot;specparam&quot;) was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula (aINS) and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. AIC model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC was predictive of the extent of affective bias in the ratings of facial expressions - a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli.&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%">Blanpain, Lou T</style></author><author><style face="normal" font="default" size="100%">Chen, Emily</style></author><author><style face="normal" font="default" size="100%">Park, James</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 in Humans.</style></title><secondary-title><style face="normal" font="default" size="100%">medRxiv</style></secondary-title><alt-title><style face="normal" font="default" size="100%">medRxiv</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%">03/2023</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;Modulating brain oscillations has strong therapeutic potential. However, commonly used non-invasive interventions such as transcranial magnetic or direct current stimulation have limited effects on deeper cortical structures like the medial temporal lobe. Repetitive audio- visual stimulation, or sensory flicker, modulates such structures in mice but little is known about its effects in humans. Using high spatiotemporal resolution, we mapped and quantified the neurophysiological effects of sensory flicker in human subjects undergoing presurgical intracranial seizure monitoring. We found that flicker modulates both local field potential and single neurons in higher cognitive regions, including the medial temporal lobe and prefrontal cortex, and that local field potential modulation is likely mediated via resonance of involved circuits. We then assessed how flicker affects pathological neural activity, specifically interictal epileptiform discharges, a biomarker of epilepsy also implicated in Alzheimerâ€™s and other diseases. In our patient population with focal seizure onsets, sensory flicker decreased the rate interictal epileptiform discharges. Our findings support the use of sensory flicker to modulate deeper cortical structures and mitigate pathological activity in humans.&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%">Block, Cady K</style></author><author><style face="normal" font="default" size="100%">Patel, Margi</style></author><author><style face="normal" font="default" size="100%">Risk, Benjamin B</style></author><author><style face="normal" font="default" size="100%">Staikova, Ekaterina</style></author><author><style face="normal" font="default" size="100%">Loring, David</style></author><author><style face="normal" font="default" size="100%">Esper, Christine D</style></author><author><style face="normal" font="default" size="100%">Scorr, Laura</style></author><author><style face="normal" font="default" size="100%">Higginbotham, Lenora</style></author><author><style face="normal" font="default" size="100%">Aia, Pratibha</style></author><author><style face="normal" font="default" size="100%">DeLong, Mahlon R</style></author><author><style face="normal" font="default" size="100%">Wichmann, Thomas</style></author><author><style face="normal" font="default" size="100%">Factor, Stewart A</style></author><author><style face="normal" font="default" size="100%">Au Yong, Nicholas</style></author><author><style face="normal" font="default" size="100%">Willie, Jon T</style></author><author><style face="normal" font="default" size="100%">Boulis, Nicholas M</style></author><author><style face="normal" font="default" size="100%">Gross, Robert E</style></author><author><style face="normal" font="default" size="100%">Buetefisch, Cathrin</style></author><author><style face="normal" font="default" size="100%">Miocinovic, Svjetlana</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Patients with Cognitive Impairment in Parkinson's Disease Benefit from Deep Brain Stimulation: A Case-Control Study.</style></title><secondary-title><style face="normal" font="default" size="100%">Mov Disord Clin Pract</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Mov Disord Clin Pract</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%">03/2023</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">10</style></volume><pages><style face="normal" font="default" size="100%">382-391</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;Deep brain stimulation (DBS) for Parkinson's disease (PD) is generally contraindicated in persons with dementia but it is frequently performed in people with mild cognitive impairment or normal cognition, and current clinical guidelines are primarily based on these cohorts.&lt;/p&gt;&lt;p&gt;&lt;b&gt;OBJECTIVES: &lt;/b&gt;To determine if moderately cognitive impaired individuals including those with mild dementia could meaningfully benefit from DBS in terms of motor and non-motor outcomes.&lt;/p&gt;&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;In this retrospective case-control study, we identified a cohort of 40 patients with PD who exhibited moderate (two or more standard deviations below normative scores) cognitive impairment (CI) during presurgical workup and compared their 1-year clinical outcomes to a cohort of 40 matched patients with normal cognition (NC). The surgery targeted subthalamus, pallidus or motor thalamus, in a unilateral, bilateral or staged approach.&lt;/p&gt;&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;At preoperative baseline, the CI cohort had higher Unified Parkinson's Disease Rating Scale (UPDRS) subscores, but similar levodopa responsiveness compared to the NC cohort. The NC and CI cohorts demonstrated comparable degrees of postoperative improvement in the OFF-medication motor scores, motor fluctuations, and medication reduction. There was no difference in adverse event rates between the two cohorts. Outcomes in the CI cohort did not depend on the target, surgical staging, or impaired cognitive domain.&lt;/p&gt;&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;Moderately cognitively impaired patients with PD can experience meaningful motor benefit and medication reduction with DBS.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</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%">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>