Epilepsy in Parry-Romberg syndrome and linear scleroderma en coup de sabre: Case series and systematic review including 140 patients.

Parry-Romberg syndrome (PRS) and linear sclerosis en coup de sabre (LScs) are rare, related, autoimmune conditions of focal atrophy and sclerosis of head and face which are associated with the development of focal epilepsy. The scarcity of PRS and LScs cases has made an evidence-based approach to optimal treatment of seizures difficult. Here we present a large systematic review of the literature evaluating 137 cases of PRS or LScs, as well as three new cases with epilepsy that span the spectrum of severity, treatments, and outcomes in these syndromes. Analysis showed that intracranial abnormalities and epileptic foci localized ipsilateral to the external (skin, eye, mouth) manifestations by imaging or EEG in 92% and 80% of cases, respectively. Epilepsy developed before external abnormalities in 19% of cases and after external disease onset in 66% of cases, with decreasing risk the further from the start of external symptoms. We found that over half of individuals affected may achieve seizure freedom with anti-seizure medications (ASMs) alone or in combination with immunomodulatory therapy (IMT), while a smaller number of individuals benefitted from epilepsy surgery. Although analysis of case reports has the risk of bias or omission, this is currently the best source of clinical information on epilepsy in PRS/LScs-spectrum disease. The paucity of higher quality information requires improved case identification and tracking. Toward this effort, all data have been deposited in a Synapse.org database for case collection with the potential for international collaboration.

Inferring sleep stage from local field potentials recorded in the subthalamic nucleus of Parkinson's patients.

Parkinson's disease (PD) is highly comorbid with sleep dysfunction. In contrast to motor symptoms, few therapeutic interventions exist to address sleep symptoms in PD. Subthalamic nucleus (STN) deep brain stimulation (DBS) treats advanced PD motor symptoms and may improve sleep architecture. As a proof of concept toward demonstrating that STN-DBS could be used to identify sleep stages commensurate with clinician-scored polysomnography (PSG), we developed a novel artificial neural network (ANN) that could trigger targeted stimulation in response to inferred sleep state from STN local field potentials (LFPs) recorded from implanted DBS electrodes. STN LFP recordings were collected from nine PD patients via a percutaneous cable attached to the DBS lead, during a full night's sleep (6-8 hr) with concurrent polysomnography (PSG). We trained a feedforward neural network to prospectively identify sleep stage with PSG-level accuracy from 30-s epochs of LFP recordings. Our model's sleep-stage predictions match clinician-identified sleep stage with a mean accuracy of 91% on held-out epochs. Furthermore, leave-one-group-out analysis also demonstrates 91% mean classification accuracy for novel subjects. These results, which classify sleep stage across a typical heterogenous sample of PD patients, may indicate spectral biomarkers for automatically scoring sleep stage in PD patients with implanted DBS devices. Further development of this model may also focus on adapting stimulation during specific sleep stages to treat targeted sleep deficits.

Using deep learning to probe the neural code for images in primary visual cortex.

Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in systems neuroscience is devoted to understanding how it encodes information about visual stimuli. Within V1, many neurons respond selectively to edges of a given preferred orientation: These are known as either simple or complex cells. Other neurons respond to localized center-surround image features. Still others respond selectively to certain image stimuli, but the specific features that excite them are unknown. Moreover, even for the simple and complex cells-the best-understood V1 neurons-it is challenging to predict how they will respond to natural image stimuli. Thus, there are important gaps in our understanding of how V1 encodes images. To fill this gap, we trained deep convolutional neural networks to predict the firing rates of V1 neurons in response to natural image stimuli, and we find that the predicted firing rates are highly correlated (\(\def\upalpha{\unicode[Times]{x3B1}}\)\(\def\upbeta{\unicode[Times]{x3B2}}\)\(\def\upgamma{\unicode[Times]{x3B3}}\)\(\def\updelta{\unicode[Times]{x3B4}}\)\(\def\upvarepsilon{\unicode[Times]{x3B5}}\)\(\def\upzeta{\unicode[Times]{x3B6}}\)\(\def\upeta{\unicode[Times]{x3B7}}\)\(\def\uptheta{\unicode[Times]{x3B8}}\)\(\def\upiota{\unicode[Times]{x3B9}}\)\(\def\upkappa{\unicode[Times]{x3BA}}\)\(\def\uplambda{\unicode[Times]{x3BB}}\)\(\def\upmu{\unicode[Times]{x3BC}}\)\(\def\upnu{\unicode[Times]{x3BD}}\)\(\def\upxi{\unicode[Times]{x3BE}}\)\(\def\upomicron{\unicode[Times]{x3BF}}\)\(\def\uppi{\unicode[Times]{x3C0}}\)\(\def\uprho{\unicode[Times]{x3C1}}\)\(\def\upsigma{\unicode[Times]{x3C3}}\)\(\def\uptau{\unicode[Times]{x3C4}}\)\(\def\upupsilon{\unicode[Times]{x3C5}}\)\(\def\upphi{\unicode[Times]{x3C6}}\)\(\def\upchi{\unicode[Times]{x3C7}}\)\(\def\uppsy{\unicode[Times]{x3C8}}\)\(\def\upomega{\unicode[Times]{x3C9}}\)\(\def\bialpha{\boldsymbol{\alpha}}\)\(\def\bibeta{\boldsymbol{\beta}}\)\(\def\bigamma{\boldsymbol{\gamma}}\)\(\def\bidelta{\boldsymbol{\delta}}\)\(\def\bivarepsilon{\boldsymbol{\varepsilon}}\)\(\def\bizeta{\boldsymbol{\zeta}}\)\(\def\bieta{\boldsymbol{\eta}}\)\(\def\bitheta{\boldsymbol{\theta}}\)\(\def\biiota{\boldsymbol{\iota}}\)\(\def\bikappa{\boldsymbol{\kappa}}\)\(\def\bilambda{\boldsymbol{\lambda}}\)\(\def\bimu{\boldsymbol{\mu}}\)\(\def\binu{\boldsymbol{\nu}}\)\(\def\bixi{\boldsymbol{\xi}}\)\(\def\biomicron{\boldsymbol{\micron}}\)\(\def\bipi{\boldsymbol{\pi}}\)\(\def\birho{\boldsymbol{\rho}}\)\(\def\bisigma{\boldsymbol{\sigma}}\)\(\def\bitau{\boldsymbol{\tau}}\)\(\def\biupsilon{\boldsymbol{\upsilon}}\)\(\def\biphi{\boldsymbol{\phi}}\)\(\def\bichi{\boldsymbol{\chi}}\)\(\def\bipsy{\boldsymbol{\psy}}\)\(\def\biomega{\boldsymbol{\omega}}\)\(\def\bupalpha{\unicode[Times]{x1D6C2}}\)\(\def\bupbeta{\unicode[Times]{x1D6C3}}\)\(\def\bupgamma{\unicode[Times]{x1D6C4}}\)\(\def\bupdelta{\unicode[Times]{x1D6C5}}\)\(\def\bupepsilon{\unicode[Times]{x1D6C6}}\)\(\def\bupvarepsilon{\unicode[Times]{x1D6DC}}\)\(\def\bupzeta{\unicode[Times]{x1D6C7}}\)\(\def\bupeta{\unicode[Times]{x1D6C8}}\)\(\def\buptheta{\unicode[Times]{x1D6C9}}\)\(\def\bupiota{\unicode[Times]{x1D6CA}}\)\(\def\bupkappa{\unicode[Times]{x1D6CB}}\)\(\def\buplambda{\unicode[Times]{x1D6CC}}\)\(\def\bupmu{\unicode[Times]{x1D6CD}}\)\(\def\bupnu{\unicode[Times]{x1D6CE}}\)\(\def\bupxi{\unicode[Times]{x1D6CF}}\)\(\def\bupomicron{\unicode[Times]{x1D6D0}}\)\(\def\buppi{\unicode[Times]{x1D6D1}}\)\(\def\buprho{\unicode[Times]{x1D6D2}}\)\(\def\bupsigma{\unicode[Times]{x1D6D4}}\)\(\def\buptau{\unicode[Times]{x1D6D5}}\)\(\def\bupupsilon{\unicode[Times]{x1D6D6}}\)\(\def\bupphi{\unicode[Times]{x1D6D7}}\)\(\def\bupchi{\unicode[Times]{x1D6D8}}\)\(\def\buppsy{\unicode[Times]{x1D6D9}}\)\(\def\bupomega{\unicode[Times]{x1D6DA}}\)\(\def\bupvartheta{\unicode[Times]{x1D6DD}}\)\(\def\bGamma{\bf{\Gamma}}\)\(\def\bDelta{\bf{\Delta}}\)\(\def\bTheta{\bf{\Theta}}\)\(\def\bLambda{\bf{\Lambda}}\)\(\def\bXi{\bf{\Xi}}\)\(\def\bPi{\bf{\Pi}}\)\(\def\bSigma{\bf{\Sigma}}\)\(\def\bUpsilon{\bf{\Upsilon}}\)\(\def\bPhi{\bf{\Phi}}\)\(\def\bPsi{\bf{\Psi}}\)\(\def\bOmega{\bf{\Omega}}\)\(\def\iGamma{\unicode[Times]{x1D6E4}}\)\(\def\iDelta{\unicode[Times]{x1D6E5}}\)\(\def\iTheta{\unicode[Times]{x1D6E9}}\)\(\def\iLambda{\unicode[Times]{x1D6EC}}\)\(\def\iXi{\unicode[Times]{x1D6EF}}\)\(\def\iPi{\unicode[Times]{x1D6F1}}\)\(\def\iSigma{\unicode[Times]{x1D6F4}}\)\(\def\iUpsilon{\unicode[Times]{x1D6F6}}\)\(\def\iPhi{\unicode[Times]{x1D6F7}}\)\(\def\iPsi{\unicode[Times]{x1D6F9}}\)\(\def\iOmega{\unicode[Times]{x1D6FA}}\)\(\def\biGamma{\unicode[Times]{x1D71E}}\)\(\def\biDelta{\unicode[Times]{x1D71F}}\)\(\def\biTheta{\unicode[Times]{x1D723}}\)\(\def\biLambda{\unicode[Times]{x1D726}}\)\(\def\biXi{\unicode[Times]{x1D729}}\)\(\def\biPi{\unicode[Times]{x1D72B}}\)\(\def\biSigma{\unicode[Times]{x1D72E}}\)\(\def\biUpsilon{\unicode[Times]{x1D730}}\)\(\def\biPhi{\unicode[Times]{x1D731}}\)\(\def\biPsi{\unicode[Times]{x1D733}}\)\(\def\biOmega{\unicode[Times]{x1D734}}\)\({\overline {{\bf{CC}}} _{{\bf{norm}}}}\) = 0.556 ± 0.01) with the neurons' actual firing rates over a population of 355 neurons. This performance value is quoted for all neurons, with no selection filter. Performance is better for more active neurons: When evaluated only on neurons with mean firing rates above 5 Hz, our predictors achieve correlations of \({\overline {{\bf{CC}}} _{{\bf{norm}}}}\) = 0.69 ± 0.01 with the neurons' true firing rates. We find that the firing rates of both orientation-selective and non-orientation-selective neurons can be predicted with high accuracy. Additionally, we use a variety of models to benchmark performance and find that our convolutional neural-network model makes more accurate predictions.

Year in Review 2023: Noteworthy Literature in Cardiac Anesthesiology.

Notable clinical research published in 2023 related to cardiac anesthesia included studies focused on resuscitation and pharmacology, regional anesthesia, technological advances, and novel gene therapies. We reviewed 241 articles to identify 25 noteworthy studies that represent the most significant research related to cardiac anesthesia from the past year. Overall, improvements in clinical practice have enabled decreased morbidity and mortality with a renewed focus on mechanical circulatory support and transplantation.

Year in Review 2022: Noteworthy Literature in Cardiac Anesthesiology.

Last year researchers made substantial progress in work relevant to the practice of cardiac anesthesiology. We reviewed 389 articles published in 2022 focused on topics related to clinical practice to identify 16 that will impact the current and future practice of cardiac anesthesiology. We identified 4 broad themes including risk prediction, postoperative outcomes, clinical practice, and technological advances. These articles are representative of the best work in our field in 2022.

Year in Review 2021: Noteworthy Literature in Cardiothoracic Anesthesia.

In 2021, progress in clinical science related to Cardiac Anesthesiology continued, but at a slower rate due to the ongoing pandemic and disruptions to clinical research. Most progress was incremental and addressed persistent questions related to our field. To identify articles for this review, we completed a structured review using our previously reported methods (1). Specifically, we used the search terms: "cardiac anesthesiology and outcomes" (n = 177), "cardiothoracic anesthesiology" (n = 34), "cardiac anesthesia," and "clinical outcomes" (n = 42) filtered on clinical trials and the year 2021 in PubMed. We also reviewed clinical trials from the most prominent clinical journals to identify additional studies for a narrative review. We then selected the most noteworthy publications for inclusion in this review and identified key themes.

Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of concept study.

Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson's disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016-0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.

Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking.

The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largely unchanged for decades. Utilizing deep learning-based computer vision and related computational methods, we developed an effective and simple intraoperative approach to objectively correlate neural signals with movements, automating and standardizing the otherwise manual and subjective process of identifying ideal DBS electrode placements. Kinematics are extracted from video recordings of intraoperative motor testing using a trained deep neural network and compared to multi-unit activity recorded from the subthalamic nucleus. Neuro-motor correlations were quantified using dynamic time warping with the strength of a given comparison measured by comparing against a null distribution composed of related neuro-motor correlations. This objective measure was then compared to clinical determinations as recorded in surgical case notes. In seven DBS cases for treatment of Parkinson's disease, 100 distinct motor testing epochs were extracted for which clear clinical determinations were made. Neuro-motor correlations derived by our automated system compared favorably with expert clinical decision making in post-hoc comparisons, although follow-up studies are necessary to determine if improved correlation detection leads to improved outcomes. By improving the classification of neuro-motor relationships, the automated system we have developed will enable clinicians to maximize the therapeutic impact of DBS while also providing avenues for improving continued care of treated patients.

Year in Review 2020: Noteworthy Literature in Cardiothoracic Anesthesiology.

The year 2020 was marred by the emergence of a deadly pandemic that disrupted every aspect of life. Despite the disruption, notable research accomplishments in the practice of cardiothoracic anesthesiology occurred in 2020 with an emphasis on optimizing care, improving outcomes, and expanding what is possible for patients undergoing cardiac surgery. This year's edition of Noteworthy Literature Review will focus on specific themes in cardiac anesthesiology that include preoperative anemia, predictors of acute kidney injury following cardiac surgery, pain management modalities, anticoagulation strategies after transcatheter aortic valve replacement, mechanical circulatory support, and future directions in research.

Whole-Genome Sequences of Two Pseudomonas fluorescens Strains Isolated from Roots of Tomato and Cucumber Plants.

Pseudomonas fluorescens strain EC1 was isolated from Cucumis sativus (cucumber) roots, and P. fluorescens SC1 was isolated from Solanum lycopersicum (tomato) roots. The P. fluorescens SC1 genome has a total sequence length of 6,157,842 bp, and the P. fluorescens EC1 genome has a total sequence length of 6,125,428 bp.

Multiplexed inkjet functionalization of silicon photonic biosensors.

The transformative potential of silicon photonics for chip-scale biosensing is limited primarily by the inability to selectively functionalize and exploit the extraordinary density of integrated optical devices on this platform. Silicon biosensors, such as the microring resonator, can be routinely fabricated to occupy a footprint of less than 50 × 50 µm; however, chemically addressing individual devices has proven to be a significant challenge due to their small size and alignment requirements. Herein, we describe a non-contact piezoelectric (inkjet) method for the rapid and efficient printing of bioactive proteins, glycoproteins and neoglycoconjugates onto a high-density silicon microring resonator biosensor array. This approach demonstrates the scalable fabrication of multiplexed silicon photonic biosensors for lab-on-a-chip applications, and is further applicable to the functionalization of any semiconductor-based biosensor chip.