@article {4574, title = {Graded decisions in the human brain.}, journal = {Nat Commun}, volume = {15}, year = {2024}, month = {2024 May 21}, pages = {4308}, abstract = {

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{\textquoteright}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.

}, keywords = {Adult, Brain, Choice Behavior, Decision Making, Female, Humans, Male, Parietal Lobe, Uncertainty, Young Adult}, issn = {2041-1723}, doi = {10.1038/s41467-024-48342-w}, author = {Xie, Tao and Adamek, Markus and Cho, Hohyun and Adamo, Matthew A and Ritaccio, Anthony L and Willie, Jon T and Brunner, Peter and Kubanek, Jan} } @article {4555, title = {Graded decisions in the human brain}, journal = {Nature communications}, volume = {15}, year = {2024}, pages = {4308}, author = {Xie, Tao and Adamek, Markus and Cho, Hohyun and Adamo, Matthew A and Ritaccio, Anthony L and Willie, Jon T and Brunner, Peter and Kubanek, Jan} } @article {4560, title = {Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics}, journal = {Elife}, volume = {12}, year = {2024}, pages = {RP91605}, author = {Cho, Hohyun and Adamek, Markus and Willie, Jon T and Brunner, Peter} } @article {4568, title = {Novel cyclic homogeneous oscillation detection method for high accuracy and specific characterization of neural dynamics.}, journal = {Elife}, volume = {12}, year = {2024}, month = {2024 Sep 06}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, keywords = {Brain, Electrocorticography, Electroencephalography, Humans, Signal Processing, Computer-Assisted}, issn = {2050-084X}, doi = {10.7554/eLife.91605}, author = {Cho, Hohyun and Adamek, Markus and Willie, Jon T and Brunner, Peter} } @article {4581, title = {Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics.}, journal = {bioRxiv}, year = {2024}, month = {2024 Mar 23}, abstract = {

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{\textquoteright}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{\textquoteright}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.

}, issn = {2692-8205}, doi = {10.1101/2023.10.04.560843}, author = {Cho, Hohyun and Adamek, Markus and Willie, Jon T and Brunner, Peter} } @article {4583, title = {A novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.}, journal = {Sci Rep}, volume = {14}, year = {2024}, month = {2024 Feb 10}, pages = {3433}, abstract = {

Limitations in chronic pain therapies necessitate novel interventions that are effective, accessible, and safe. Brain-computer interfaces (BCIs) provide a promising modality for targeting neuropathology underlying chronic pain by converting recorded neural activity into perceivable outputs. Recent evidence suggests that increased frontal theta power (4-7~Hz) reflects pain relief from chronic and acute pain. Further studies have suggested that vibrotactile stimulation decreases pain intensity in experimental and clinical models. This longitudinal, non-randomized, open-label pilot study{\textquoteright}s objective was to reinforce frontal theta activity in six patients with chronic upper extremity pain using a novel vibrotactile neurofeedback BCI system. Patients increased their BCI performance, reflecting thought-driven control of neurofeedback, and showed a significant decrease in pain severity (1.29 {\textpm} 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 {\textpm} 1.10 MAD p = 0.03, q = 0.05) scores without any adverse events. Pain relief significantly correlated with frontal theta modulation. These findings highlight the potential of BCI-mediated cortico-sensory coupling of frontal theta with vibrotactile stimulation for alleviating chronic pain.

}, keywords = {brain-computer interfaces, Chronic Pain, Electroencephalography, Humans, Longitudinal Studies, Neurofeedback, Non-Randomized Controlled Trials as Topic, Pilot Projects}, issn = {2045-2322}, doi = {10.1038/s41598-024-53261-3}, author = {Demarest, Phillip and Rustamov, Nabi and Swift, James and Xie, Tao and Adamek, Markus and Cho, Hohyun and Wilson, Elizabeth and Han, Zhuangyu and Belsten, Alexander and Luczak, Nicholas and Brunner, Peter and Haroutounian, Simon and Leuthardt, Eric C} } @article {4557, title = {A novel theta-controlled vibrotactile brain{\textendash}computer interface to treat chronic pain: a pilot study}, journal = {Scientific reports}, volume = {14}, year = {2024}, pages = {3433}, author = {Demarest, Phillip and Rustamov, Nabi and Swift, James and Xie, Tao and Adamek, Markus and Cho, Hohyun and Wilson, Elizabeth and Han, Zhuangyu and Belsten, Alexander and Luczak, Nicholas and others} }