Revisiting the Endoscopic Third Ventriculostomy Success Score using machine learning: can we do better?

Objective: The Endoscopic Third Ventriculostomy Success Score (ETVSS) is a useful decision-making heuristic when considering the probability of surgical success, defined traditionally as no repeat cerebrospinal fluid diversion surgery needed within 6 months. Nonetheless, the performance of the logistic regression (LR) model in the original 2009 study was modest, with an area under the receiver operating characteristic curve (AUROC) of 0.68.

Improved 3D Markerless Mouse Pose Estimation Using Temporal Semi-Supervision.

Three-dimensional markerless pose estimation from multi-view video is emerging as an exciting method for quantifying the behavior of freely moving animals. Nevertheless, scientifically precise 3D animal pose estimation remains challenging, primarily due to a lack of large training and benchmark datasets and the immaturity of algorithms tailored to the demands of animal experiments and body plans. Existing techniques employ fully supervised convolutional neural networks (CNNs) trained to predict body keypoints in individual video frames, but this demands a large collection of labeled training samples to achieve desirable 3D tracking performance.

Gigapixel imaging with a novel multi-camera array microscope.

The dynamics of living organisms are organized across many spatial scales. However, current cost-effective imaging systems can measure only a subset of these scales at once. We have created a scalable multi-camera array microscope (MCAM) that enables comprehensive high-resolution recording from multiple spatial scales simultaneously, ranging from structures that approach the cellular scale to large-group behavioral dynamics.

Leaving flatland: Advances in 3D behavioral measurement.

Animals move in three dimensions (3D). Thus, 3D measurement is necessary to report the true kinematics of animal movement. Existing 3D measurement techniques draw on specialized hardware, such as motion capture or depth cameras, as well as deep multi-view and monocular computer vision.

Machine Learning to Predict Successful Opioid Dose Reduction or Stabilization After Spinal Cord Stimulation.

Background: Spinal cord stimulation (SCS) effectively reduces opioid usage in some patients, but preoperatively, there is no objective measure to predict who will most benefit.

Objective: To predict successful reduction or stabilization of opioid usage after SCS using machine learning models we developed and to assess if deep learning provides a significant benefit over logistic regression (LR).

Methods: We used the IBM MarketScan national databases to identify patients undergoing SCS from 2010 to 2015.

Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.

Background: Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints.

Objective: To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models.

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

Objective: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of enhancing TBI triage in LMICs, we aim to develop the first deep learning model to predict outcomes after TBI and compare its performance with that of less complex algorithms.

Machine Learning for Predicting In-Hospital Mortality After Traumatic Brain Injury in Both High-Income and Low- and Middle-Income Countries.

Background: Machine learning (ML) holds promise as a tool to guide clinical decision making by predicting in-hospital mortality for patients with traumatic brain injury (TBI). Previous models such as the international mission for prognosis and clinical trials in TBI (IMPACT) and the corticosteroid randomization after significant head injury (CRASH) prognosis calculators can potentially be improved with expanded clinical features and newer ML approaches.

Objective: To develop ML models to predict in-hospital mortality for both the high-income country (HIC) and the low- and middle-income country (LMIC) settings.

Geometric deep learning enables 3D kinematic profiling across species and environments.

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors.

Surgical intervention and patient factors associated with poor outcomes in patients with traumatic brain injury at a tertiary care hospital in Uganda.

Objective: The purpose of this study was to investigate whether neurosurgical intervention for traumatic brain injury (TBI) is associated with reduced risks of death and clinical deterioration in a low-income country with a relatively high neurosurgical capacity. The authors further aimed to assess whether the association between surgical intervention and acute poor outcomes differs according to TBI severity and various patient factors.

Methods: Using TBI registry data collected from a national referral hospital in Uganda between July 2016 and April 2020, the authors performed Cox regression analyses of poor outcomes in admitted patients who did and did not undergo surgery for TBI, with surgery as a time-varying treatment variable.

Continuous Whole-Body 3D Kinematic Recordings across the Rodent Behavioral Repertoire.

In mammalian animal models, high-resolution kinematic tracking is restricted to brief sessions in constrained environments, limiting our ability to probe naturalistic behaviors and their neural underpinnings. To address this, we developed CAPTURE (Continuous Appendicular and Postural Tracking Using Retroreflector Embedding), a behavioral monitoring system that combines motion capture and deep learning to continuously track the 3D kinematics of a rat's head, trunk, and limbs for week-long timescales in freely behaving animals. CAPTURE realizes 10- to 100-fold gains in precision and robustness compared with existing convolutional network approaches to behavioral tracking.

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in optimizing patient outcomes and healthcare resource utilization. Machine learning may allow for effective predictions of patient outcomes both with and without surgery.

Sociocultural determinants and patterns of healthcare utilization for epilepsy care in Uganda.

Objective: Epilepsy is a global public health concern, with the majority of cases occurring in lower- and middle-income countries where the treatment gap remains formidable. In this study, we simultaneously explore how beliefs about epilepsy causation, perceived barriers to care, seizure disorder characteristics, and demographics influence the initial choice of healthcare for epilepsy and its impact on attaining biomedical care (BMC).

Methods: This study utilized the baseline sample (n = 626) from a prospective cohort study of people with epilepsy (PWE) attending three public hospitals in Uganda (Mulago National Referral Hospital, Butabika National Referral Mental Hospital, and Mbarara Regional Referral Hospital) for epilepsy care.

An Attitude Survey and Assessment of the Feasibility, Acceptability, and Usability of a Traumatic Brain Injury Decision Support Tool in Uganda.

Background: Traumatic brain injury (TBI) prognostic models are potential solutions to severe human and technical shortages. Although numerous TBI prognostic models have been developed, none are widely used in clinical practice, largely because of a lack of feasibility research to inform implementation. We previously developed a prognostic model and Web-based application for in-hospital TBI care in low-resource settings.

Correcting for physical distortions in visual stimuli improves reproducibility in zebrafish neuroscience.

Optical refraction causes light to bend at interfaces between optical media. This phenomenon can significantly distort visual stimuli presented to aquatic animals in water, yet refraction has often been ignored in the design and interpretation of visual neuroscience experiments. Here we provide a computational tool that transforms between projected and received stimuli in order to detect and control these distortions.

From Whole-Brain Data to Functional Circuit Models: The Zebrafish Optomotor Response.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion.

Brain-wide mapping of neural activity controlling zebrafish exploratory locomotion.

In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right.

Neural Circuits Underlying Visually Evoked Escapes in Larval Zebrafish.

Escape behaviors deliver organisms away from imminent catastrophe. Here, we characterize behavioral responses of freely swimming larval zebrafish to looming visual stimuli simulating predators. We report that the visual system alone can recruit lateralized, rapid escape motor programs, similar to those elicited by mechanosensory modalities.

Spinal projection neurons control turning behaviors in zebrafish.

Discrete populations of brainstem spinal projection neurons (SPNs) have been shown to exhibit behavior-specific responses during locomotion [1-9], suggesting that separate descending pathways, each dedicated to a specific behavior, control locomotion. In an alternative model, a large variety of motor outputs could be generated from different combinations of a small number of basic motor pathways. We examined this possibility by studying the precise role of ventromedially located hindbrain SPNs (vSPNs) in generating turning behaviors.

Identification of nonvisual photomotor response cells in the vertebrate hindbrain.

Nonvisual photosensation enables animals to sense light without sight. However, the cellular and molecular mechanisms of nonvisual photobehaviors are poorly understood, especially in vertebrate animals. Here, we describe the photomotor response (PMR), a robust and reproducible series of motor behaviors in zebrafish that is elicited by visual wavelengths of light but does not require the eyes, pineal gland, or other canonical deep-brain photoreceptive organs.

Optogenetic photochemical control of designer K+ channels in mammalian neurons.

Currently available optogenetic tools, including microbial light-activated ion channels and transporters, are transforming systems neuroscience by enabling precise remote control of neuronal firing, but they tell us little about the role of indigenous ion channels in controlling neuronal function. Here, we employ a chemical-genetic strategy to engineer light sensitivity into several mammalian K(+) channels that have different gating and modulation properties. These channels provide the means for photoregulating diverse electrophysiological functions.

Photochemical control of endogenous ion channels and cellular excitability.

Light-activated ion channels provide a precise and noninvasive optical means for controlling action potential firing, but the genes encoding these channels must first be delivered and expressed in target cells. Here we describe a method for bestowing light sensitivity onto endogenous ion channels that does not rely on exogenous gene expression. The method uses a synthetic photoisomerizable small molecule, or photoswitchable affinity label (PAL), that specifically targets K+ channels.