03432nas a2200361   4500008004100000022001400041245014400055210006900199260001300268300001100281490000800292520223300300653001002533653002002543653001502563653002602578653002702604653001102631653003102642100003102673700002702704700002002731700001702751700002102768700001902789700001902808700002302827700002602850700002702876700001802903700002602921856012302947       2024                        eng d  a1872-678X00aAnatomical registration of intracranial electrodes. Robust model-based localization and deformable smooth brain-shift compensation methods.0 aAnatomical registration of intracranial electrodes Robust modelb  c2024 Apr  a1100560 v4043 a<p><b>BACKGROUND: </b>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.</p><p><b>NEW METHODS: </b>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.</p><p><b>RESULTS: </b>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 (<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 < 0.4 mm errors. The inter-electrode distances smoothly changed across neighbor electrodes, while changes in inter-electrode distances linearly increased with projection distance.</p><p><b>COMPARISON WITH EXISTING METHODS: </b>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.</p><p><b>CONCLUSION: </b>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.</p>10aBrain10aCerebral Cortex10aElectrodes10aElectrodes, Implanted10aElectroencephalography10aHumans10aMagnetic Resonance Imaging1 aBlenkmann, Alejandro, Omar1 aLeske, Sabine, Liliana1 aLlorens, Anaïs1 aLin, Jack, J1 aChang, Edward, F1 aBrunner, Peter1 aSchalk, Gerwin1 aIvanovic, Jugoslav1 aLarsson, Pål, Gunnar1 aKnight, Robert, Thomas1 aEndestad, Tor1 aSolbakk, Anne-Kristin  uhttps://www.neurotechcenter.org/publications/2024/anatomical-registration-intracranial-electrodes-robust-model-based-002340nas 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-002504nas a2200385   4500008004100000022001400041245010500055210006900160260001600229300000900245490000700254520128700261653003001548653001701578653002701595653001101622653002501633653001801658653004601676653001901722100002201741700001901763700001701782700001301799700001901812700001601831700002201847700001801869700002301887700002101910700001901931700002401950700002301974856012101997       2024                        eng d  a2045-232200aA novel theta-controlled vibrotactile brain-computer interface to treat chronic pain: a pilot study.0 anovel thetacontrolled vibrotactile braincomputer interface to tr  c2024 Feb 10  a34330 v143 a<p>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'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 ± 0.25 MAD, p = 0.03, q = 0.05) and pain interference (1.79 ± 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.</p>10abrain-computer interfaces10aChronic Pain10aElectroencephalography10aHumans10aLongitudinal Studies10aNeurofeedback10aNon-Randomized Controlled Trials as Topic10aPilot Projects1 aDemarest, Phillip1 aRustamov, Nabi1 aSwift, James1 aXie, Tao1 aAdamek, Markus1 aCho, Hohyun1 aWilson, Elizabeth1 aHan, Zhuangyu1 aBelsten, Alexander1 aLuczak, Nicholas1 aBrunner, Peter1 aHaroutounian, Simon1 aLeuthardt, Eric, C  uhttps://www.neurotechcenter.org/publications/2024/novel-theta-controlled-vibrotactile-brain-computer-interface-treat