An Inductively-Powered Wireless Neural Recording System with a Charge Sampling Analog Front-End.

An inductively-powered wireless integrated neural recording system (WINeR-7) is presented for wireless and battery less neural recording from freely-behaving animal subjects inside a wirelessly-powered standard homecage. The WINeR-7 system employs a novel wide-swing dual slope charge sampling (DSCS) analog front-end (AFE) architecture, which performs amplification, filtering, sampling, and analog-to-time conversion (ATC) with minimal interference and small amount of power. The output of the DSCS-AFE produces a pseudo-digital pulse width modulated (PWM) signal. A circular shift register (CSR) time division multiplexes (TDM) the PWM pulses to create a TDM-PWM signal, which is fed into an on-chip 915 MHz transmitter (Tx). The AFE and Tx are supplied at 1.8 V and 4.2 V, respectively, by a power management block, which includes a high efficiency active rectifier and automatic resonance tuning (ART), operating at 13.56 MHz. The 8-ch system-on-a-chip (SoC) was fabricated in a 0.35-µm CMOS process, occupying 5.0 × 2.5 mm2 and consumed 51.4 mW. For each channel, the sampling rate is 21.48 kHz and the power consumption is 19.3 µW. In vivo experiments were conducted on freely behaving rats in an energized homecage by continuously delivering 51.4 mW to the WINeR-7 system in a closed-loop fashion and recording local field potentials (LFP).

Less is more: novel less-invasive surgical techniques for mesial temporal lobe epilepsy that minimize cognitive impairment.

PURPOSE OF REVIEW: New minimally invasive techniques are becoming available to treat focal-onset epilepsy. The open surgical treatment of mesial temporal lobe epilepsy (MTLE), although associated with high rates of seizure freedom, is confounded by adverse impacts on neurocognitive function. This review covers new techniques being explored for surgical treatment of MTLE that in early studies have been achieving high seizure-free rates with preservation of memory and other functions referable to the mesial and lateral temporal regions. RECENT FINDINGS: Multiple subpial transections of the hippocampus, and stereotactic approaches including radiofrequency ablation and laser interstitial thermal therapy have achieved rates of seizure freedom comparable to open resection but with fewer neurocognitive adverse effects. Electrical neuromodulation approaches, including responsive neurostimulation, direct hippocampal stimulation, and thalamic deep brain stimulation preserve cognitive function and achieve significant seizure suppression, but have not yet achieved high seizure-free rates. SUMMARY: With the recent success in minimally invasive approaches with respect to seizure freedom and preservation of neurocognitive functions, it is predicted that fewer patients will be receiving 'classic' open resections for MTLE such as temporal lobectomy. These new approaches also promise to decrease discomfort, time away from work, and healthcare utilization.

A personalized earbud for non-invasive long-term EEG monitoring.

Objective. The primary objective of this study was to evaluate the reliability, comfort, and performance of a custom-fit, non-invasive long-term electrophysiologic headphone, known as Aware Hearable, for the ambulatory recording of brain activities. These recordings play a crucial role in diagnosing neurological disorders such as epilepsy and in studying neural dynamics during daily activities.Approach.The study uses commercial manufacturing processes common to the hearing aid industry, such as 3D scanning, computer-aided design modeling, and 3D printing. These processes enable the creation of the Aware Hearable with a personalized, custom-fit, thereby ensuring complete and consistent contact with the inner surfaces of the ear for high-quality data recordings. Additionally, the study employs a machine learning data analysis approach to validate the recordings produced by Aware Hearable, by comparing them to the gold standard intracranial electroencephalography recordings in epilepsy patients.Main results.The results indicate the potential of Aware Hearable to expedite the diagnosis of epilepsy by enabling extended periods of ambulatory recording.Significance.This offers significant reductions in burden to patients and their families. Furthermore, the device's utility may extend to a broader spectrum, making it suitable for other applications involving neurophysiological recordings in real-world settings.

Reinforcement learning for closed-loop regulation of cardiovascular system with vagus nerve stimulation: a computational study.

Objective. Vagus nerve stimulation (VNS) is being investigated as a potential therapy for cardiovascular diseases including heart failure, cardiac arrhythmia, and hypertension. The lack of a systematic approach for controlling and tuning the VNS parameters poses a significant challenge. Closed-loop VNS strategies combined with artificial intelligence (AI) approaches offer a framework for systematically learning and adapting the optimal stimulation parameters. In this study, we presented an interactive AI framework using reinforcement learning (RL) for automated data-driven design of closed-loop VNS control systems in a computational study.Approach.Multiple simulation environments with a standard application programming interface were developed to facilitate the design and evaluation of the automated data-driven closed-loop VNS control systems. These environments simulate the hemodynamic response to multi-location VNS using biophysics-based computational models of healthy and hypertensive rat cardiovascular systems in resting and exercise states. We designed and implemented the RL-based closed-loop VNS control frameworks in the context of controlling the heart rate and the mean arterial pressure for a set point tracking task. Our experimental design included two approaches; a general policy using deep RL algorithms and a sample-efficient adaptive policy using probabilistic inference for learning and control.Main results.Our simulation results demonstrated the capabilities of the closed-loop RL-based approaches to learn optimal VNS control policies and to adapt to variations in the target set points and the underlying dynamics of the cardiovascular system. Our findings highlighted the trade-off between sample-efficiency and generalizability, providing insights for proper algorithm selection. Finally, we demonstrated that transfer learning improves the sample efficiency of deep RL algorithms allowing the development of more efficient and personalized closed-loop VNS systems.Significance.We demonstrated the capability of RL-based closed-loop VNS systems. Our approach provided a systematic adaptable framework for learning control strategies without requiring prior knowledge about the underlying dynamics.

Quantifying the effects of vagus nerve stimulation on gastric myoelectric activity in ferrets using an interpretable machine learning approach.

Vagus nerve stimulation (VNS) is a potential treatment option for gastrointestinal (GI) diseases. The present study aimed to understand the physiological effects of VNS on gastrointestinal (GI) function, which is crucial for developing more effective adaptive closed-loop VNS therapies for GI diseases. Electrogastrography (EGG), which measures gastric electrical activities (GEAs) as a proxy to quantify GI functions, was employed in our investigation. We introduced a recording schema that allowed us to simultaneously induce electrical VNS and record EGG. While this setup created a unique model for studying the effects of VNS on the GI function and provided an excellent testbed for designing advanced neuromodulation therapies, the resulting data was noisy, heterogeneous, and required specialized analysis tools. The current study aimed at formulating a systematic and interpretable approach to quantify the physiological effects of electrical VNS on GEAs in ferrets by using signal processing and machine learning techniques. Our analysis pipeline included pre-processing steps, feature extraction from both time and frequency domains, a voting algorithm for selecting features, and model training and validation. Our results indicated that the electrophysiological changes induced by VNS were optimally characterized by a distinct set of features for each classification scenario. Additionally, our findings demonstrated that the process of feature selection enhanced classification performance and facilitated representation learning.

Automated Tuning of Closed-loop Neuromodulation Control Systems using Bayesian Optimization.

Tuning the parameters of controllers to attain the best performance is a challenging task in designing effective closed-loop neuromodulation systems. In this paper, we present a distributed architecture for automated tuning and adaptation of closed-loop neuromodulation control systems. We use this approach for the automated parameter tuning of a Proportional-Integral (PI) neuromodulation controller using Bayesian optimization. We use a biophysically-grounded mean-field model of neural populations under electrical stimulation as a simulation environment for testing and prototyping the proposed framework and characterizing its performance. Our results demonstrate the feasibility of using Bayesian optimization for performance-based automated tuning of a PI controller in closed-loop set-point neuromodulation control tasks.

Data Driven Control of Vagus Nerve Stimulation for the Cardiovascular System: An in Silico Computational Study.

Vagus nerve stimulation is an emerging therapy that seeks to offset pathological conditions by electrically stimulating the vagus nerve through cuff electrodes, where an electrical pulse is defined by several parameters such as pulse amplitude, pulse width, and pulse frequency. Currently, vagus nerve stimulation is under investigation for the treatment of heart failure, cardiac arrhythmia and hypertension. Through several clinical trials that sought to assess vagus nerve stimulation for the treatment of heart failure, stimulation parameters were determined heuristically and the results were inconclusive, which has led to the suggestion of using a closed-loop approach to optimize the stimulation parameters. A recent investigation has demonstrated highly specific control of cardiovascular physiology by selectively activating different fibers in the vagus nerve. When multiple locations and multiple stimulation parameters are considered for optimization, the design of closed-loop control becomes considerably more challenging. To address this challenge, we investigated a data-driven control scheme for both modeling and controlling the rat cardiovascular system. Using an existing in silico physiological model of a rat heart to generate synthetic input-output data, we trained a long short-term memory network (LSTM) to map the effect of stimulation on the heart rate and blood pressure. The trained LSTM was utilized in a model predictive control framework to optimize the vagus nerve stimulation parameters for set point tracking of the heart rate and the blood pressure in closed-loop simulations. Additionally, we altered the underlying in silico physiological model to consider intra-patient variability, and diseased dynamics from increased sympathetic tone in designing closed-loop VNS strategies. Throughout the different simulation scenarios, we leveraged the design of the controller to demonstrate alternative clinical objectives. Our results show that the controller can optimize stimulation parameters to achieve set-point tracking with nominal offset while remaining computationally efficient. Furthermore, we show a controller formulation that compensates for mismatch due to intra-patient variabilty, and diseased dynamics. This study demonstrates the first application and a proof-of-concept for using a purely data-driven approach for the optimization of vagus nerve stimulation parameters in closed-loop control of the cardiovascular system.

Automated deep brain stimulation programming with safety constraints for tremor suppression in patients with Parkinson's disease and essential tremor.

Objective.Deep brain stimulation (DBS) programming for movement disorders requires systematic fine tuning of stimulation parameters to ameliorate tremor and other symptoms while avoiding side effects. DBS programming can be a time-consuming process and requires clinical expertise to assess response to DBS to optimize therapy for each patient. In this study, we describe and evaluate an automated, closed-loop, and patient-specific framework for DBS programming that measures tremor using a smartwatch and automatically changes DBS parameters based on the recommendations from a closed-loop optimization algorithm thus eliminating the need for an expert clinician.Approach.Bayesian optimization which is a sample-efficient global optimization method was used as the core of this DBS programming framework to adaptively learn each patient's response to DBS and suggest the next best settings to be evaluated. Input from a clinician was used initially to define a maximum safe amplitude, but we also implemented 'safe Bayesian optimization' to automatically discover tolerable exploration boundaries.Main results.We tested the system in 15 patients (nine with Parkinson's disease and six with essential tremor). Tremor suppression at best automated settings was statistically comparable to previously established clinical settings. The optimization algorithm converged after testing15.1±0.7settings when maximum safe exploration boundaries were predefined, and17.7±4.9when the algorithm itself determined safe exploration boundaries.Significance.We demonstrate that fully automated DBS programming framework for treatment of tremor is efficient and safe while providing outcomes comparable to that achieved by expert clinicians.

CONTROL-CORE: A Framework for Simulation and Design of Closed-Loop Peripheral Neuromodulation Control Systems.

Closed-loop Vagus Nerve Stimulation (VNS) based on physiological feedback signals is a promising approach to regulate organ functions and develop therapeutic devices. Designing closed-loop neurostimulation systems requires simulation environments and computing infrastructures that support i) modeling the physiological responses of organs under neuromodulation, also known as physiological models, and ii) the interaction between the physiological models and the neuromodulation control algorithms. However, existing simulation platforms do not support closed-loop VNS control systems modeling without extensive rewriting of computer code and manual deployment and configuration of programs. The CONTROL-CORE project aims to develop a flexible software platform for designing and implementing closed-loop VNS systems. This paper proposes the software architecture and the elements of the CONTROL-CORE platform that allow the interaction between a controller and a physiological model in feedback. CONTROL-CORE facilitates modular simulation and deployment of closed-loop peripheral neuromodulation control systems, spanning multiple organizations securely and concurrently. CONTROL-CORE allows simulations to run on different operating systems, be developed in various programming languages (such as Matlab, Python, C++, and Verilog), and be run locally, in containers, and in a distributed fashion. The CONTROL-CORE platform allows users to create tools and testbenches to facilitate sophisticated simulation experiments. We tested the CONTROL-CORE platform in the context of closed-loop control of cardiac physiological models, including pulsatile and nonpulsatile rat models. These were tested using various controllers such as Model Predictive Control and Long-Short-Term Memory based controllers. Our wide range of use cases and evaluations show the performance, flexibility, and usability of the CONTROL-CORE platform.

Yin-Yang: Programming Abstractions for Cross-Domain Multi-Acceleration.

FPGA accelerators offer performance and efficiency gains by narrowing the scope of acceleration to one algorithmic domain. However, real-life applications are often not limited to a single domain, which naturally makes Cross-Domain Multi-Acceleration a crucial next step. The challenge is, existing FPGA accelerators are built upon their specific vertically-specialized stacks, which prevents utilizing multiple accelerators from different domains. To that end, we propose a pair of dual abstractions, called Yin-Yang, which work in tandem and enable programmers to develop cross-domain applications using multiple accelerators on a FPGA. The Yin abstraction enables cross-domain algorithmic specification, while the Yang abstraction captures the accelerator capabilities. We also develop a dataflow virtual machine, dubbed XLVM, that transparently maps domain functions (Yin) to best-fit accelerator capabilities (Yang). With six real-world cross-domain applications, our evaluations show that Yin-Yang unlocks 29.4× speedup, while the best single-domain acceleration achieves 12.0×.

Software-Defined Workflows for Distributed Interoperable Closed-Loop Neuromodulation Control Systems.

Closed-loop neuromodulation control systems facilitate regulating abnormal physiological processes by recording neurophysiological activities and modifying those activities through feedback loops. Designing such systems requires interoperable service composition, consisting of cycles. Workflow frameworks enable standard modular architectures, offering reproducible automated pipelines. However, those frameworks limit their support to executions represented by directed acyclic graphs (DAGs). DAGs need a pre-defined start and end execution step with no cycles, thus preventing the researchers from using the standard workflow languages as-is for closed-loop workflows and pipelines. In this paper, we present NEXUS, a workflow orchestration framework for distributed analytics systems. NEXUS proposes a Software-Defined Workflows approach, inspired by Software-Defined Networking (SDN), which separates the data flows across the service instances from the control flows. NEXUS enables creating interoperable workflows with closed loops by defining the workflows in a logically centralized approach, from microservices representing each execution step. The centralized NEXUS orchestrator facilitates dynamically composing and managing scientific workflows from the services and existing workflows, with minimal restrictions. NEXUS represents complex workflows as directed hypergraphs (DHGs) rather than DAGs. We illustrate a seamless execution of neuromodulation control systems by supporting loops in a workflow as the use case of NEXUS. Our evaluations highlight the feasibility, flexibility, performance, and scalability of NEXUS in modeling and executing closed-loop workflows.

Mapping Vagus Nerve Stimulation Parameters to Cardiac Physiology using Long Short-term Memory Network.

Vagus nerve stimulation (VNS) is an emerging therapeutic strategy for pathological conditions in a variety of diseases; however, several challenges arise for applying this stimulation paradigm in automated closed-loop control. In this work, we propose a data driven approach for predicting the impact of VNS on physiological variables. We apply this approach on a synthetic dataset created with a physiological model of a rat heart. Through training several neural network models, we found that a long short term memory (LSTM) architecture gave the best performance on a test set. Further, we found the neural network model was capable of mapping a set of VNS parameters to the correct response in the heart rate and the mean arterial blood pressure. In closed-loop control of biological systems, a model of the physiological system is often required and we demonstrate using a data driven approach to meet this requirement in the cardiac system.

Intraoperative neural signals predict rapid antidepressant effects of deep brain stimulation.

Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) is a promising intervention for treatment-resistant depression (TRD). Despite the failure of a clinical trial, multiple case series have described encouraging results, especially with the introduction of improved surgical protocols. Recent evidence further suggests that tractography targeting and intraoperative exposure to stimulation enhances early antidepressant effects that further evolve with ongoing chronic DBS. Accelerating treatment gains is critical to the care of this at-risk population, and identification of intraoperative electrophysiological biomarkers of early antidepressant effects will help guide future treatment protocols. Eight patients underwent intraoperative electrophysiological recording when bilateral DBS leads were implanted in the SCC using a connectomic approach at the site previously shown to optimize 6-month treatment outcomes. A machine learning classification method was used to discriminate between intracranial local field potentials (LFPs) recorded at baseline (stimulation-naïve) and after the first exposure to SCC DBS during surgical procedures. Spectral inputs (theta, 4-8 Hz; alpha, 9-12 Hz; beta, 13-30 Hz) to the model were then evaluated for importance to classifier success and tested as predictors of the antidepressant response. A decline in depression scores by 45.6% was observed after 1 week and this early antidepressant response correlated with a decrease in SCC LFP beta power, which most contributed to classifier success. Intraoperative exposure to therapeutic stimulation may result in an acute decrease in symptoms of depression following SCC DBS surgery. The correlation of symptom improvement with an intraoperative reduction in SCC beta power suggests this electrophysiological finding as a biomarker for treatment optimization.

Identifying the neurophysiological effects of memory-enhancing amygdala stimulation using interpretable machine learning.

BACKGROUND: Direct electrical stimulation of the amygdala can enhance declarative memory for specific events. An unanswered question is what underlying neurophysiological changes are induced by amygdala stimulation. OBJECTIVE: To leverage interpretable machine learning to identify the neurophysiological processes underlying amygdala-mediated memory, and to develop more efficient neuromodulation technologies. METHOD: Patients with treatment-resistant epilepsy and depth electrodes placed in the hippocampus and amygdala performed a recognition memory task for neutral images of objects. During the encoding phase, 160 images were shown to patients. Half of the images were followed by brief low-amplitude amygdala stimulation. For local field potentials (LFPs) recorded from key medial temporal lobe structures, feature vectors were calculated by taking the average spectral power in canonical frequency bands, before and after stimulation, to train a logistic regression classification model with elastic net regularization to differentiate brain states. RESULTS: Classifying the neural states at the time of encoding based on images subsequently remembered versus not-remembered showed that theta and slow-gamma power in the hippocampus were the most important features predicting subsequent memory performance. Classifying the post-image neural states at the time of encoding based on stimulated versus unstimulated trials showed that amygdala stimulation led to increased gamma power in the hippocampus. CONCLUSION: Amygdala stimulation induced pro-memory states in the hippocampus to enhance subsequent memory performance. Interpretable machine learning provides an effective tool for investigating the neurophysiological effects of brain stimulation.

Photocatalytic degradation of ketoconazole by Z-scheme Ag3PO4/graphene oxide: response surface modeling and optimization.

Ketoconazole is an imidazole fungicide which is commonly used as pharmaceutical and healthcare products. Residual amount of this compound can cause adverse ecological health problems. The present study investigated ketoconazole photocatalytic degradation using Ag3PO4/graphene oxide (GO). Ag3PO4/GO and Ag3PO4 as visible light-driven photocatalysts was synthesized using the in situ growth method. Degradation of ketoconazole at the concentration of 1-20 mg/L in aqueous solutions was optimized in the presence of Ag3PO4/GO nanocomposite with the dosage of 0.5-2 g/L, contact time of 15-20 min, and pH of 5-9 using response surface methodology. A second-order model was selected as the best fitted model with R2 value and lack of fit as 0.935 and 0.06, respectively. Under the optimized conditions, the Ag3PO4/GO catalyst achieved a photocatalytic efficiency of 96.53% after 93.34 min. The photocatalytic activity, reaction kinetics, and stability were also investigated. The results indicated that the Ag3PO4/GO nanocomposite exhibited higher photocatalytic activity for ketoconazole degradation, which was 2.4 times that of pure Ag3PO4. Finally, a direct Z-scheme mechanism was found to be responsible for enhanced photocatalytic activity in the Ag3PO4/GO nanocomposite. The high photocatalytic activity, acceptable reusability, and good aqueous stability make the Ag3PO4/GO nanocomposite a promising nanophotocatalyst for photocatalytic degradation of azoles contaminants.

Amygdala Stimulation Leads to Functional Network Connectivity State Transitions in the Hippocampus.

Several studies have shown that direct brain stimulation can enhance memory in humans and animal models. Investigating the neurophysiological changes induced by brain stimulation is an important step towards understanding the neural processes underlying memory function. Furthermore, it paves the way for developing more efficient neuromodulation approaches for memory enhancement. In this study, we utilized a combination of unsupervised and supervised machine learning approaches to investigate how amygdala stimulation modulated hippocampal network activities during the encoding phase. Using a sliding window in time, we estimated the hippocampal dynamic functional network connectivity (dFNC) after stimulation and during sham trials, based on the covariance of local field potential recordings in 4 subregions of the hippocampus. We extracted different network states by combining the dFNC samples from 5 subjects and applying k-means clustering. Next, we used the between-state transition numbers as the latent features to classify between amygdala stimulation and sham trials across all subjects. By training a logistic regression model, we could differentiate stimulated from sham trials with 67% accuracy across all subjects. Using elastic net regularization as a feature selection method, we identified specific patterns of hippocampal network state transition in response to amygdala stimulation. These results offer a new approach to better understanding of the causal relationship between hippocampal network dynamics and memory-enhancing amygdala stimulation.

Towards automated patient-specific optimization of deep brain stimulation for movement disorders.

In this paper we present a simulation framework for automated optimization of deep brain stimulation (DBS) parameters based on the hand kinematics signal as the feedback signal, in patients with essential tremor. We used Gaussian Process regression (GPR) models to develop patient-specific models for predicting the effect of DBS on the hand kinematics using the clinical data that was recorded during DBS programming. In this framework, we characterized the performance of a Bayesian Optimization method to identify the optimal DBS parameters that maximized the clinical efficacy. Our results demonstrate the feasibility of using black-box optimization methods for automated identification of optimal DBS parameters in clinical settings.

A Software-Defined Radio Receiver for Wireless Recording From Freely Behaving Animals.

To eliminate tethering effects on the small animals' behavior during electrophysiology experiments, such as neural interfacing, a robust and wideband wireless data link is needed for communicating with the implanted sensing elements without blind spots. We present a software-defined radio (SDR) based scalable data acquisition system, which can be programmed to provide coverage over standard-sized or customized experimental arenas. The incoming RF signal with the highest power among SDRs is selected in real-time to prevent data loss in the presence of spatial and angular misalignments between the transmitter (Tx) and receiver (Rx) antennas. A 32-channel wireless neural recording system-on-a-chip (SoC), known as WINeRS-8, is embedded in a headstage and transmits digitalized raw neural signals, which are sampled at 25 kHz/ch, at 9 Mbps via on-off keying (OOK) of a 434 MHz RF carrier. Measurement results show that the dual-SDR Rx system reduces the packet loss down to 0.12%, on average, by eliminating the blind spots caused by the moving Tx directionality. The system operation is verified in vivo on a freely behaving rat and compared with a commercial hardwired system.

Comparative health risk assessment of in-vehicle exposure to formaldehyde and acetaldehyde for taxi drivers and passengers: Effects of zone, fuel, refueling, vehicle's age and model.

This study aimed to assess the carcinogenic and non-carcinogenic risks of in-vehicle exposure in Tehran, Iran to formaldehyde and acetaldehyde for different models of taxis, and to explore the effects of city zone, taxi vehicle type, the taxi's age (<1, 1-5, 5-10), fuel type (gasoline, CNG, and LPG), and refueling activities on the estimated health risks based on previously measured concentrations. The overall and age-specific carcinogenic and non-carcinogenic risks of these compounds for taxi drivers and passengers were estimated separately using Monte Carlo simulations. Three scenarios of exposure frequency were defined for taxis commuting in different zones of city: Restricted Traffic Zone (RTZ) and Odd-Even Zone (OEZ) as two plans to reduce air pollution, and no-restriction zone (NRZ). The carcinogenic risks for drivers and passengers, the average risks of formaldehyde and acetaldehyde for most cases were above the 1 × 10-4. The health risks were greater in Restricted Traffic Zone (RTZ) and Odd-Even Zone (OEZ) in comparison to no-restriction zone (NRZ). The carcinogenic risk from formaldehyde exposures were higher than those for acetaldehyde in all cases. Taxis fueled with LPG showed lower cancer risks for both acetaldehyde and formaldehyde. Refueling increased the carcinogenic risk from both compounds. For non-carcinogenic risks from acetaldehyde, the average hazard ratios for both drivers and passengers were >1, indicating a non-negligible risk. Cancer and non-cancer risks for the taxi drivers were greater than the passengers given the higher time of occupancy. The present study showed that transportation in taxis can impose significant long-term health risks to both passengers and drivers. Development and investment in cleaner choices for public transportations are required.

A Cloud-based Framework for Implementing Portable Machine Learning Pipelines for Neural Data Analysis.

Cloud-based computing has created new avenues for innovative research. In recent years, numerous cloud-based, data analysis projects within the biomedical domain have been implemented. As this field is likely to grow, there is a need for a unified platform for the developing and testing of advanced analytic and modeling tools that enables those tools to be easily reused for biomedical data analysis by a broad set of users with diverse technical skills. A cloud-based platform of this nature could greatly assist future research endeavors. In this paper, we take the first step towards building such a platform. We define an approach by which containerized analytic pipelines can be distributed for use on cloud-based or on-premise computing platforms. We demonstrate our approach by implementing a portable biomarker identification pipeline using a logistic regression model with elastic net regularization (LR-ENR) and running it on Google Cloud. We used this pipeline for the diagnosis of Parkinson's disease based on a combination of clinical, demographic, and MRI-based features and for the identification of the most predictive biomarkers.