Recording neural activity during neurosurgical interventions is an invaluable tool for both improving patient outcomes and advancing our understanding of neural mechanisms and organization. However, increasing clinical electrodes' signal-to-noise and spatial specificity requires overcoming substantial physical barriers due to the compromised metal electrochemical interface properties. The electrochemical properties of poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) based interfaces surpass those of current clinical electrocorticography electrodes. Here, robust fabrication process of PEDOT:PSS microelectrode arrays is demonstrated for safe and high fidelity intraoperative monitoring of human brain. PEDOT:PSS microelectrodes measure significant differential neural modulation under various clinically relevant conditions. This study reports the first evoked (stimulus-locked) cognitive activity with changes in amplitude across pial surface distances as small as 400 µm, potentially enabling basic neurophysiology studies at the scale of neural micro-circuitry.

Stroke patients are monitored hourly by physicians and nurses in an attempt to better understand their physical state. To quantify the patients’ level of mobility, hourly movement (i.e. motor) assessment scores are performed, which can be taxing and time-consuming for nurses and physicians. In this paper, we attempt to find a correlation between patient motor scores and continuous accelerometer data recorded in subjects who are unilaterally impaired due to stroke. The accelerometers were placed on both upper and lower extremities of four severely unilaterally impaired patients and their movements were recorded continuously for 7 to 14 days. Features that incorporate movement smoothness, strength, and characteristic movement patterns were extracted from the accelerometers using time-frequency analysis. Support vector classifiers were trained with the extracted features to test the ability of the long term accelerometer recordings in predicting dependent and antigravity sides, and significantly above baseline performance was obtained in most instances ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$P < 0.05$ \end{document} ). Finally, a leave-one-subject-out approach was carried out to assess the generalizability of the proposed methodology, and above baseline performance was obtained in two out of the three tested subjects. The methodology presented in this paper provides a simple, yet effective approach to perform long term motor assessment in neurocritical care patients.

Electrocorticography (ECoG) is becoming more prevalent due to improvements in fabrication and recording technology as well as its ease of implantation compared to intracortical electrophysiology, larger cortical coverage, and potential advantages for use in long term chronic implantation. Given the flexibility in the design of ECoG grids, which is only increasing, it remains an open question what geometry of the electrodes is optimal for an application. Conductive polymer, PEDOT:PSS, coated microelectrodes have an advantage that they can be made very small without losing low impedance. This makes them suitable for evaluating the required granularity of ECoG recording in humans and experimental animals. We used two-dimensional (2D) micro-ECoG grids to record intra-operatively in humans and during acute implantations in mouse with separation distance between neighboring electrodes (i.e., pitch) of 0.4 mm and 0.2/0.25 mm respectively. To assess the spatial properties of the signals, we used the average correlation between electrodes as a function of the pitch. In agreement with prior studies, we find a strong frequency dependence in the spatial scale of correlation. By applying independent component analysis (ICA), we find that the spatial pattern of correlation is largely due to contributions from multiple spatially extended, time-locked sources present at any given time. Our analysis indicates the presence of spatially structured activity down to the sub-millimeter spatial scale in ECoG despite the effects of volume conduction, justifying the use of dense micro-ECoG grids.

Electrocorticography (ECoG), electrophysiological recording from the pial surface of the brain, is a critical measurement technique for clinical neurophysiology, basic neurophysiology studies, and demonstrates great promise for the development of neural prosthetic devices for assistive applications and the treatment of neurological disorders. Recent advances in device engineering are poised to enable orders of magnitude increase in the resolution of ECoG without comprised measurement quality. This enhancement in cortical sensing enables the observation of neural dynamics from the cortical surface at the micrometer scale. While these technical capabilities may be enabling, the extent to which finer spatial scale recording enhances functionally relevant neural state inference is unclear. We examine this question by employing a high-density and low impedance 400 μm pitch microECoG (μECoG) grid to record neural activity from the human cortical surface during cognitive tasks. By applying machine learning techniques to classify task conditions from the envelope of high-frequency band (70-170Hz) neural activity collected from two study participants, we demonstrate that higher density grids can lead to more accurate binary task condition classification. When controlling for grid area and selecting task informative sub-regions of the complete grid, we observed a consistent increase in mean classification accuracy with higher grid density; in particular, 400 μm pitch grids outperforming spatially sub-sampled lower density grids up to 23%. We also introduce a modeling framework to provide intuition for how spatial properties of measurements affect the performance gap between high and low density grids. To our knowledge, this work is the first quantitative demonstration of human sub-millimeter pitch cortical surface recording yielding higher-fidelity state estimation relative to devices at the millimeter-scale, motivating the development and testing of μECoG for basic and clinical neurophysiology as well as towards the realization of high-performance neural prostheses.

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized μECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, μECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to £±200 μm, i.e., a single cortical column. Biophysical simulations reproduce experimental findings, and indicate that neurons in cortical layers V and VI contribute ~85% of evoked high-gamma signal recorded at the surface. Cell number and synchronicity were the primary biophysical properties determining laminar contributions to evoked μECoG signals, while distance was only a minimal factor. Thus, evoked μECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.