03428nas a2200361 4500008004100000022001400041245014400055210006900199260001300268300001100281490000800292520223300300653001002533653002002543653001502563653002602578653002702604653001102631653003102642100003102673700002702704700002002731700001702751700002102768700001902789700001902808700002302827700002602850700002702876700001802903700002602921856011902947 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

BACKGROUND: 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.

NEW METHODS: 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.

RESULTS: 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.

COMPARISON WITH EXISTING METHODS: 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.

CONCLUSION: 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.

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://neurotechcenter.org/publications/2024/anatomical-registration-intracranial-electrodes-robust-model-based-001911nas a2200349 4500008004100000022001400041245004100055210004000096260001600136300000900152490000700161520102100168653001001189653001001199653002001209653002001229653001101249653001101260653000901271653001801280653001601298653001601314100001301330700001901343700001601362700002201378700002501400700001901425700001901444700001701463856008101480 2024 eng d a2041-172300aGraded decisions in the human brain.0 aGraded decisions in the human brain c2024 May 21 a43080 v153 a

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.

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://neurotechcenter.org/publications/2024/graded-decisions-human-brain-002336nas a2200229 4500008004100000022001400041245012600055210006900181260001600250490000700266520152600273653001001799653002501809653002701834653001101861653004101872100001601913700001901929700001901948700001901967856012001986 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

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.

10aBrain10aElectrocorticography10aElectroencephalography10aHumans10aSignal Processing, Computer-Assisted1 aCho, Hohyun1 aAdamek, Markus1 aWillie, Jon, T1 aBrunner, Peter uhttps://neurotechcenter.org/publications/2024/novel-cyclic-homogeneous-oscillation-detection-method-high-accuracy-002549nas a2200325 4500008004100000022001400041245011400055210006900169260001200238300001000250490000800260520151900268653002401787653001001811653001801821653002001839653002501859653001101884653001301895100002401908700002001932700002401952700002301976700002201999700002102021700002502042700001902067700001902086856011802105 2023 eng d a1872-895200aPassive functional mapping of receptive language cortex during general anesthesia using electrocorticography.0 aPassive functional mapping of receptive language cortex during g c03/2023 a31-440 v1473 a

OBJECTIVE: To investigate the feasibility of passive functional mapping in the receptive language cortex during general anesthesia using electrocorticographic (ECoG) signals.

METHODS: We used subdurally placed ECoG grids to record cortical responses to speech stimuli during awake and anesthesia conditions. We identified the cortical areas with significant responses to the stimuli using the spectro-temporal consistency of the brain signal in the broadband gamma (BBG) frequency band (70-170 Hz).

RESULTS: We found that ECoG BBG responses during general anesthesia effectively identify cortical regions associated with receptive language function. Our analyses demonstrated that the ability to identify receptive language cortex varies across different states and depths of anesthesia. We confirmed these results by comparing them to receptive language areas identified during the awake condition. Quantification of these results demonstrated an average sensitivity and specificity of passive language mapping during general anesthesia to be 49±7.7% and 100%, respectively.

CONCLUSION: Our results demonstrate that mapping receptive language cortex in patients during general anesthesia is feasible.

SIGNIFICANCE: Our proposed protocol could greatly expand the population of patients that can benefit from passive language mapping techniques, and could eliminate the risks associated with electrocortical stimulation during an awake craniotomy.

10aAnesthesia, General10aBrain10aBrain Mapping10aCerebral Cortex10aElectrocorticography10aHumans10aLanguage1 aNourmohammadi, Amin1 aSwift, James, R1 ade Pesters, Adriana1 aGuay, Christian, S1 aAdamo, Matthew, A1 aDalfino, John, C1 aRitaccio, Anthony, L1 aSchalk, Gerwin1 aBrunner, Peter uhttps://neurotechcenter.org/publications/2023/passive-functional-mapping-receptive-language-cortex-during-general