01915nas a2200349   4500008004100000022001400041245004100055210004000096260001600136300000900152490000700161520102100168653001001189653001001199653002001209653002001229653001101249653001101260653000901271653001801280653001601298653001601314100001301330700001901343700001601362700002201378700002501400700001901425700001901444700001701463856008501480       2024                        eng d  a2041-172300aGraded decisions in the human brain.0 aGraded decisions in the human brain  c2024 May 21  a43080 v153 a<p>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.</p>10aAdult10aBrain10aChoice Behavior10aDecision Making10aFemale10aHumans10aMale10aParietal Lobe10aUncertainty10aYoung Adult1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aAdamo, Matthew, A1 aRitaccio, Anthony, L1 aWillie, Jon, T1 aBrunner, Peter1 aKubanek, Jan  uhttps://www.neurotechcenter.org/publications/2024/graded-decisions-human-brain-002594nas a2200325   4500008004100000022001400041245010900055210006900164260001600233300000900249490000700258520156700265653001001832653002301842653002701865653002401892653001301916653001101929653001801940100002101958700001801979700001601997700001902013700002502032700002102057700002302078700001902101700002502120856012302145       2024                        eng d  a2041-172300aMultisensory flicker modulates widespread brain networks and reduces interictal epileptiform discharges.0 aMultisensory flicker modulates widespread brain networks and red  c2024 Apr 11  a31560 v153 a<p>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.</p>10aBrain10aCross-Over Studies10aElectroencephalography10aEpilepsies, Partial10aEpilepsy10aHumans10aTemporal Lobe1 aBlanpain, Lou, T1 aCole, Eric, R1 aChen, Emily1 aPark, James, K1 aWalelign, Michael, Y1 aGross, Robert, E1 aCabaniss, Brian, T1 aWillie, Jon, T1 aSinger, Annabelle, C  uhttps://www.neurotechcenter.org/publications/2024/multisensory-flicker-modulates-widespread-brain-networks-and-reduces02340nas a2200229   4500008004100000022001400041245012600055210006900181260001600250490000700266520152600273653001001799653002501809653002701834653001101861653004101872100001601913700001901929700001901948700001901967856012401986       2024                        eng d  a2050-084X00aNovel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.0 aNovel cyclic homogeneous oscillation detection method for high a  c2024 Sep 060 v123 a<p>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.</p>10aBrain10aElectrocorticography10aElectroencephalography10aHumans10aSignal Processing, Computer-Assisted1 aCho, Hohyun1 aAdamek, Markus1 aWillie, Jon, T1 aBrunner, Peter  uhttps://www.neurotechcenter.org/publications/2024/novel-cyclic-homogeneous-oscillation-detection-method-high-accuracy-0