Optimization of an anatomically and electrically detailed rodent subthalamic nucleus neuron model.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for Parkinson's disease, but its mechanisms of action remain unclear. Detailed multicompartment computational models of STN neurons are often used to study how DBS electric fields modulate the neurons. However, currently available STN neuron models have some limitations in their biophysical realism. In turn, the goal of this study was to update a detailed rodent STN neuron model originally developed by Gillies and Willshaw in 2006. Our design requirements consisted of explicitly representing an axon connected to the neuron and updating the ion channel distributions based on the experimental literature to match established electrophysiological features of rodent STN neurons. We found that adding an axon to the STN neuron model substantially altered its firing characteristics. We then used a genetic algorithm to optimize biophysical parameters of the model. The updated model exhibited spontaneous firing, action potential shape, hyperpolarization response, and frequency-current curve that aligned well with experimental recordings from STN neurons. Subsequently, we evaluated the general compatibility of the updated biophysics by applying them to 26 different STN neuron morphologies derived from three-dimensional anatomical reconstructions. The different morphologies affected the firing behavior of the model, but the updated biophysics were robustly capable of maintaining the desired electrophysiological features. The new STN neuron model developed in this work offers a valuable tool for studying STN neuron firing properties and may find application in simulating STN local field potentials and analyzing the effects of STN DBS.NEW & NOTEWORTHY This study presents an anatomically and biophysically realistic rodent STN neuron model. The work showcases the use of a genetic algorithm to optimize the model parameters. We noted a substantial influence of the axon on the electrophysiological characteristics of STN neurons. The updated model offers a valuable tool to investigate the firing of STN neurons and their modulation by intrinsic and/or extrinsic factors.

Development and evaluation of a computerized algorithm for the interpretation of pulmonary function tests.

Pulmonary function tests (PFTs) are usually interpreted by clinicians using rule-based strategies and pattern recognition. The interpretation, however, has variabilities due to patient and interpreter errors. Most PFTs have recognizable patterns that can be categorized into specific physiological defects. In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians' interpretation. We downloaded PFT reports in the electronic medical record system that were in PDF format. We digitized the flow volume loop (FVL) and extracted numeric values from the reports. The algorithm used FEV1/FVC<0.7 for obstruction, TLC<80%pred for restriction and <80% or >120%pred for abnormal DLCO. The algorithm also used a small airway disease index (SADI) to quantify late expiratory flattening of the FVL to assess small airway dysfunction. We devised keywords for the python Natural Language Processing (NLP) package (spaCy) to identify obstruction, restriction, abnormal DLCO and small airway dysfunction in the reports. The algorithm was compared to clinicians' interpretation in 6,889 PFTs done between March 1st, 2018, and September 30th, 2020. The agreement rates (Cohen's kappa) for obstruction, restriction and abnormal DLCO were 94.4% (0.868), 99.0% (0.979) and 87.9% (0.750) respectively. In 4,711 PFTs with FEV1/FVC≥0.7, the algorithm identified 190 tests with SADI < lower limit of normal (LLN), suggesting small airway dysfunction. Of these, the clinicians (67.9%) also flagged 129 tests. When SADI was ≥ LLN, no clinician's reports indicated small airway dysfunction. Our results showed the computerized algorithm agreed with clinicians' interpretation in approximately 90% of the tests and provided a sensitive objective measure for assessing small airway dysfunction. The algorithm can improve efficiency and consistency and decrease human errors in PFT interpretation. The computerized algorithm works directly on PFT reports in PDF format and can be adapted to incorporate a different interpretation strategy and platform.

Development and Evaluation of a Small Airway Disease Index Derived From Modeling the Late-Expiratory Flattening of the Flow-Volume Loop.

Excessive decrease in the flow of the late expiratory portion of a flow volume loop (FVL) or "flattening", reflects small airway dysfunction. The assessment of the flattening is currently determined by visual inspection by the pulmonary function test (PFT) interpreters and is highly variable. In this study, we developed an objective measure to quantify the flattening. We downloaded 172 PFT reports in PDF format from the electronic medical records and digitized and extracted the expiratory portion of the FVL. We located point A (the point of the peak expiratory flow), point B (the point corresponding to 75% of the expiratory vital capacity), and point C (the end of the expiratory portion of the FVL intersecting with the x-axis). We did a linear fitting to the A-B segment and the B-C segment. We calculated: 1) the AB-BC angle (∠ABC), 2) BC-x-axis angle (∠BCX), and 3) the log ratio of the BC slope over the vertical distance between point A and x-axis [log (BC/A-x)]. We asked an expert pulmonologist to assess the FVLs and separated the 172 PFTs into the flattening and the non-flattening groups. We defined the cutoff value as the mean minus one standard deviation using data from the non-flattening group. ∠ABC had the best concordance rate of 80.2% with a cutoff value of 149.7°. We then asked eight pulmonologists to evaluate the flattening with and without ∠ABC in another 168 PFTs. The Fleiss' kappa was 0.320 (lower and upper confidence intervals [CIs]: 0.293 and 0.348 respectively) without ∠ABC and increased to 0.522 (lower and upper CIs: 0.494 and 0.550) with ∠ABC. There were 147 CT scans performed within 6 months of the 172 PFTs. Twenty-six of 55 PFTs (47.3%) with ∠ABC <149.7° had CT scans showing small airway disease patterns while 44 of 92 PFTs (47.8%) with ∠ABC ≥149.7° had no CT evidence of small airway disease. We concluded that ∠ABC improved the inter-rater agreement on the presence of the late expiratory flattening in FVL. It could be a useful addition to the assessment of small airway disease in the PFT interpretation algorithm and reporting.

Application of Machine Learning in Pulmonary Function Assessment Where Are We Now and Where Are We Going?

Analysis of pulmonary function tests (PFTs) is an area where machine learning (ML) may benefit clinicians, researchers, and the patients. PFT measures spirometry, lung volumes, and carbon monoxide diffusion capacity of the lung (DLCO). The results are usually interpreted by the clinicians using discrete numeric data according to published guidelines. PFT interpretations by clinicians, however, are known to have inter-rater variability and the inaccuracy can impact patient care. This variability may be caused by unfamiliarity of the guidelines, lack of training, inadequate understanding of lung physiology, or simply mental lapses. A rules-based automated interpretation system can recapitulate expert's pattern recognition capability and decrease errors. ML can also be used to analyze continuous data or the graphics, including the flow-volume loop, the DLCO and the nitrogen washout curves. These analyses can discover novel physiological biomarkers. In the era of wearables and telehealth, particularly with the COVID-19 pandemic restricting PFTs to be done in the clinical laboratories, ML can also be used to combine mobile spirometry results with an individual's clinical profile to deliver precision medicine. There are, however, hurdles in the development and commercialization of the ML-assisted PFT interpretation programs, including the need for high quality representative data, the existence of different formats for data acquisition and sharing in PFT software by different vendors, and the need for collaboration amongst clinicians, biomedical engineers, and information technologists. Hurdles notwithstanding, the new developments would represent significant advances that could be the future of PFT, the oldest test still in use in clinical medicine.