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Many different types of microelectrodes have been developed for use as a direct Brain-Machine Interface (BMI) to chronically recording single neuron action potentials from ensembles of neurons. Unfortunately, the recordings from these microelectrode devices are not consistent and often last for only a few weeks. For most microelectrode types, the loss of these recordings is not due to failure of the electrodes but most likely due to damage to surrounding tissue that results in the formation of nonconductive glial-scar. Since the extracellular matrix consists of nanostructured microtubules, we have postulated that neurons may prefer a more complex surface structure than the smooth surface typical of thin-film microelectrodes. We, therefore, investigated the suitability of a nano-porous silicon surface layer to increase the biocompatibility of our thin film ceramic-insulated multisite electrodes. In-vitro testing demonstrated, for the first time, decreased adhesion of astrocytes and increased extension of neurites from pheochromocytoma cells on porous silicon surfaces compared to smooth silicon sufaces. Moreover, nano-porous surfaces were more biocompatible than macroporous surfaces. Collectively, these results support our hypothesis that nano-porous silicon may be an ideal material to improve biocompatibility of chronically implanted microelectrodes. We next developed a method to apply nano-porous surfaces to ceramic insulated, thin-film, microelectrodes and tested them in vivo. Chronic testing demonstrated that the nano-porous surface modification did not alter the electrical properties of the recording sites and did not interfere with proper functioning of the microelectrodes in vivo.

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Objectives: To measure the diagnostic accuracy, timeliness, and ease of use of Ceribell rapid response electroencephalography. We assessed physicians’ diagnostic assessments and treatment plans before and after rapid response electroencephalography assessment. Primary outcomes were changes in physicians’ diagnostic and therapeutic decision making and their confidence in these decisions based on the use of the rapid response electroencephalography system. Secondary outcomes were time to electroencephalography, setup time, ease of use, and quality of electroencephalography data. Design: Prospective multicenter nonrandomized observational study. Setting: ICUs in five academic hospitals in the United States. Subjects: Patients with encephalopathy suspected of having nonconvulsive seizures and physicians evaluating these patients. Interventions: Physician bedside assessment of sonified electroencephalography (30 s from each hemisphere) and visual electroencephalography (60 s) using rapid response electroencephalography. Measurements and Main Results: Physicians (29 fellows or residents, eight attending neurologists) evaluated 181 ICU patients; complete clinical and electroencephalography data were available in 164 patients (average 58.6 ± 18.7 yr old, 45% females). Relying on rapid response electroencephalography information at the bedside improved the sensitivity (95% CI) of physicians’ seizure diagnosis from 77.8% (40.0%, 97.2%) to 100% (66.4%, 100%) and the specificity (95% CI) of their diagnosis from 63.9% (55.8%, 71.4%) to 89% (83.0%, 93.5%). Physicians’ confidence in their own diagnosis and treatment plan were also improved. Time to electroencephalography (median [interquartile range]) was 5 minutes (4–10 min) with rapid response electroencephalography while the conventional electroencephalography was delayed by several hours (median [interquartile range] delay = 239 minutes [134–471 min] [p < 0.0001 using Wilcoxon signed rank test]). The device was rated as easy to use (mean ± sd: 4.7 ± 0.6 [1 = difficult, 5 = easy]) and was without serious adverse effects. Conclusions: Rapid response electroencephalography enabled timely and more accurate assessment of patients in the critical care setting. The use of rapid response electroencephalography may be clinically beneficial in the assessment of patients with high suspicion for nonconvulsive seizures and status epilepticus.

Objective Race/ethnic differences in carotid arterial function and structure exist among those with cerebrovascular disease, but whether differences persist among healthy populations is unknown. Our objective was to investigate differences in carotid artery diameter and stiffness between race/ethnic groups, and examine whether these race/ethnic differences were age-dependent. Methods Carotid diameters were assessed by B-mode ultrasound among 1536 participants from the Northern Manhattan Study (NOMAS), and carotid stiffness metrics were calculated. We used multivariable linear regression models to determine the relationship between race/ethnicity and both carotid arterial stiffness and carotid diastolic diameter. Results Mean participant age was 70 ± 9 years (Hispanics=68 ± 8, blacks=72 ± 9, and whites=74 ± 9, p<0.0001). Mean DDIAM was 6.2 ± 1.0mm (Hispanics=6.2 ± 0.9mm, blacks=6.3 ± 1.0mm, and whites=6.3 ± 1.0mm, p<0.005) and mean STIFF was 8.7 ± 6.3 (Hispanics=8.5 ± 5.7, blacks=9.2 ± 6.2 and whites=8.9 ± 6.9, p<0.02). In a model that adjusted for sociodemographics and vascular risk factors including hypertension, diabetes, dislipidemia, renal function, physical acticity and a history of known coronary artery diseases; age was positively associated with greater DDIAM in Hispanics (p<0.0001) but not among blacks or whites. Older age was associated with greater stiffness among Hispanics (p<0.0001) and blacks (p<0.003), but not among whites. Conclusions We found race/ethnic differences in the association between age and arterial stiffness and diameter, including age-dependent arterial dilation observed in Hispanics that was not observed among blacks or whites.

Objective: Since arterial stiffness is a functional measure of arterial compliance and may be an important marker of cerebrovascular disease, we examined the association of carotid artery stiffness with white matter hyperintensity volume (WMHV) in a cross-sectional study of 1,166 strokefree participants.Methods: Carotid beta stiffness index (STIFF) was assessed by M-mode ultrasound of the common carotid artery and calculated as the ratio of natural log of the difference between systolic and diastolic blood pressure over STRAIN, a ratio of the difference between carotid systolic and diastolic diameter (DD) divided by DD. WMHV was measured by fluid-attenuated inversion recovery MRI. The associations of STIFF, DD, and STRAIN with WMHV were examined using linear regression after adjusting for sociodemographic, lifestyle, and vascular risk factors. Results:In a fully adjusted model, larger carotid DD was significantly associated with greater log-WMHV (b 5 0.09, p 5 0.001). STIFF and STRAIN were not significantly associated with WMHV. In adjusted analyses stratified by race-ethnicity, STRAIN (b 5 21.78, p 5 0.002) and DD (b 5 0.11, p 5 0.001) were both associated with greater log-WMHV among Hispanic participants, but not among black or white participants.Conclusions: Large carotid artery diameters are associated with greater burden of white matter hyperintensity (WMH) in this multiethnic population. The association between increased diameters, decreased STRAIN, and greater WMH burden is more pronounced among Hispanics. These associations suggest a potential important pathophysiologic role of extracranial large artery remodeling in the burden of WMH. Neurology ® 2017;88:2036-2042 GLOSSARY BMI 5 body mass index; CCA 5 common carotid artery; CI 5 confidence interval; cIMT 5 carotid intima media thickness; DBP 5 diastolic blood pressure; DD 5 diastolic diameter; FLAIR 5 fluid-attenuated inversion recovery; HDL 5 high-density lipoprotein; ICA 5 internal carotid artery; ICV 5 intracranial volume; IMT 5 intima media thickness; LDL 5 low-density lipoprotein; NOMAS 5 Northern Manhattan Study; OR 5 odds ratio; PWV 5 pulse wave velocity; SBI 5 silent brain infarction; SBP 5 systolic blood pressure; WMH 5 white matter hyperintensity; WMHV 5 white matter hyperintensity volume.

Larger cIMT was associated with greater burden of cerebral WM lesions independently of demographics and traditional vascular risk factors, particularly among elderly and Hispanic participants, who are at high risk for stroke and cognitive decline.

Labeling training datasets has become a key barrier to building medical machine learning models. One strategy is to generate training labels programmatically, for example by applying natural language processing pipelines to text reports associated with imaging studies. We propose cross-modal data programming, which generalizes this intuitive strategy in a theoretically-grounded way that enables simpler, clinician-driven input, reduces required labeling time, and improves with additional unlabeled data. In this approach, clinicians generate training labels for models defined over a target modality (e.g. images or time series) by writing rules over an auxiliary modality (e.g. text reports). The resulting technical challenge consists of estimating the accuracies and correlations of these rules; we extend a recent unsupervised generative modeling technique to handle this cross-modal setting in a provably consistent way. Across four applications in radiography, computed tomography, and electroencephalography, and using only several hours of clinician time, our approach matches or exceeds the efficacy of physician-months of hand-labeling with statistical significance, demonstrating a fundamentally faster and more flexible way of building machine learning models in medicine.Modern machine learning approaches have achieved impressive empirical successes on diverse clinical tasks that include predicting cancer prognosis from digital pathology, 1, 2 classifying skin lesions from dermatoscopy, 3 characterizing retinopathy from fundus photographs, 4 detecting intracranial hemorrhage through computed tomography, 5, 6 and performing automated interpretation of chest radiographs. 7,8 Remarkably, these applications typically build on standardized reference neural network architectures 9 supported in professionally-maintained open source frameworks, 10, 11 suggesting that model design is no longer a major barrier to entry in medical machine learning. However, each of these application successes was predicated on a not-so-hidden cost: massive hand-labeled training datasets, often produced through years of institutional investment and expert clinician labeling time, at a cost of hundreds of thousands of dollars per task or more. 4,12 In addition to being extremely costly, these training sets are inflexible: given a new classification schema, imaging system, patient population, or other change in the data distribution or modeling task, the training set generally needs to be relabeled from scratch. These factors suggest 1