Analysis-rcs-data: Open-Source Toolbox for the Ingestion, Time-Alignment, and Visualization of Sense and Stimulation Data From the Medtronic Summit RC+S System

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these devices necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, a functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

Download Full-text

Analysis-rcs-data: Open-source toolbox for the ingestion, time-alignment, and visualization of sense and stimulation data from the Medtronic Summit RC+S system

10.1101/2021.06.07.447439 ◽

2021 ◽

Author(s):

Kristin K Sellers ◽

Ro'ee Gilron ◽

Juan Anso ◽

Kenneth H Louie ◽

Prasad R Shirvalkar ◽

...

Keyword(s):

Open Source ◽

Biomarker Discovery ◽

Neural Data ◽

Neural Recordings ◽

Time Frequency ◽

Psychiatric Illnesses ◽

Time Alignment ◽

Stimulation Parameters ◽

Multiple Data ◽

Clinical Advances

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these device necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

Download Full-text

GhostiPy: an efficient signal processing and spectral analysis toolbox for large data

10.1101/2021.04.30.442217 ◽

2021 ◽

Author(s):

Joshua P Chu ◽

Caleb Kemere

Keyword(s):

Signal Processing ◽

Large Scale ◽

Large Data ◽

Neural Data ◽

Neural Recordings ◽

Time Frequency ◽

Technological Advances ◽

Blocked Algorithms ◽

User Friendly ◽

Time And Space Complexity

Recent technological advances have enabled neural recordings consisting of hundreds to thousands of channels. As the pace of these developments continues to grow rapidly, it is imperative to have fast, flexible tools supporting the analysis of neural data gathered by such large scale modalities. Here we introduce ghostipy (general hub of spectral techniques in Python), a Python open source software toolbox implementing various signal processing and spectral analyses including optimal digital filters and time-frequency transforms. ghostipy prioritizes performance and efficiency by using parallelized, blocked algorithms. As a result, it is able to outperform commercial software in both time and space complexity for high channel count data and can handle out-of-core computation in a user-friendly manner. Overall, our software suite reduces frequently encountered bottlenecks in the experimental pipeline, and we believe this toolset will enhance both the portability and scalability of neural data analysis.

Download Full-text

Predicting Change Prone Classes in Open Source Software

International Journal of Information Retrieval Research ◽

10.4018/ijirr.2018100101 ◽

2018 ◽

Vol 8 (4) ◽

pp. 1-23 ◽

Cited By ~ 2

Author(s):

Deepa Godara ◽

Amit Choudhary ◽

Rakesh Kumar Singh

Keyword(s):

Prediction Model ◽

Open Source ◽

Open Source Software ◽

Prediction Models ◽

New Technology ◽

Modern Technology ◽

Time Frequency ◽

Rigorous Testing ◽

Technology Changes ◽

Sensitivity Specificity

In today's world, the heart of modern technology is software. In order to compete with pace of new technology, changes in software are inevitable. This article aims at the association between changes and object-oriented metrics using different versions of open source software. Change prediction models can detect the probability of change in a class earlier in the software life cycle which would result in better effort allocation, more rigorous testing and easier maintenance of any software. Earlier, researchers have used various techniques such as statistical methods for the prediction of change-prone classes. In this article, some new metrics such as execution time, frequency, run time information, popularity and class dependency are proposed which can help in prediction of change prone classes. For evaluating the performance of the prediction model, the authors used Sensitivity, Specificity, and ROC Curve. Higher values of AUC indicate the prediction model gives significant accurate results. The proposed metrics contribute to the accurate prediction of change-prone classes.

Download Full-text

The Significance of Neural Inter-Frequency Correlations

10.21203/rs.3.rs-329644/v1 ◽

2021 ◽

Author(s):

Oscar Wiljam Savolainen

Keyword(s):

Multiple Testing ◽

Statistical Significance ◽

Significance Test ◽

Stationary Time Series ◽

Frequency Bands ◽

Neural Recordings ◽

Time Frequency ◽

Common Information ◽

Neural Processes ◽

Long Term Trends

Abstract It is of great interest in neuroscience to determine what frequency bands in the brain contain common information. However, to date, a comprehensive statistical approach to this question has been lacking. As such, this work presents a novel statistical significance test for correlated power across frequency bands in non-stationary time series. The test accounts for biases that often go untreated in time-frequency analysis, i.e. intra-frequency autocorrelation, inter-frequency non-dyadicity, and multiple testing under dependency. It is used to test all of the inter-frequency correlations between 0.2 and 8500 Hz in continuous intracortical extracellular neural recordings, using a very large, publicly available dataset. The results show that neural processes have signatures across a very broad range of frequency bands. In particular, LFP frequency bands as low as 20 Hz were found to almost always be significantly correlated to kHz frequency ranges. This test also has applications in a broad range of fields, e.g. biological signal processing, economics, finance, climatology, etc. It is useful whenever one wants to robustly determine whether short-term components in a signal are robustly related to long-term trends, or what frequencies contain common information.

Download Full-text

Using computational theory to constrain statistical models of neural data

10.1101/104737 ◽

2017 ◽

Cited By ~ 1

Author(s):

Scott W. Linderman ◽

Samuel J. Gershman

Keyword(s):

Large Scale ◽

Recent Literature ◽

Theory Of Computation ◽

Temporal Difference Learning ◽

Computational Theory ◽

Neural Data ◽

Neural Recordings ◽

First Order ◽

Clear Cut ◽

Worked Example

AbstractComputational neuroscience is, to first order, dominated by two approaches: the “bottom-up” approach, which searches for statistical patterns in large-scale neural recordings, and the “top-down” approach, which begins with a theory of computation and considers plausible neural implementations. While this division is not clear-cut, we argue that these approaches should be much more intimately linked. From a Bayesian perspective, computational theories provide constrained prior distributions on neural data—albeit highly sophisticated ones. By connecting theory to observation via a probabilistic model, we provide the link necessary to test, evaluate, and revise our theories in a data-driven and statistically rigorous fashion. This review highlights examples of this theory-driven pipeline for neural data analysis in recent literature and illustrates it with a worked example based on the temporal difference learning model of dopamine.

Download Full-text

Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2021-1007 ◽

2021 ◽

Vol 7 (1) ◽

pp. 30-34

Author(s):

Ramy A. Zeineldin ◽

Pauline Weimann ◽

Mohamed E. Karar ◽

Franziska Mathis-Ullrich ◽

Oliver Burgert

Keyword(s):

Deep Learning ◽

Open Source ◽

Brain Tumour ◽

Brain Research ◽

3D Visualization ◽

High Grade Glioma ◽

3D Slicer ◽

Tumour Segmentation ◽

Multiple Data ◽

Brain Tumour Segmentation

Abstract Purpose Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer- DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.

Download Full-text

Evaluation and resolution of many challenges of neural spike-sorting: a new sorter

10.1101/2021.01.19.427297 ◽

2021 ◽

Author(s):

Nathan J. Hall ◽

David J. Herzfeld ◽

Stephen G. Lisberger

Keyword(s):

Failure Modes ◽

Ground Truth ◽

Spike Sorting ◽

Neural Data ◽

Neural Recordings ◽

Recording Session ◽

Neural Spike

AbstractWe evaluate existing spike sorters and present a new one that resolves many sorting challenges. The new sorter, called “full binary pursuit” or FBP, comprises multiple steps. First, it thresholds and clusters to identify the waveforms of all unique neurons in the recording. Second, it uses greedy binary pursuit to optimally recognize the spike events in the original voltages. Third, it resolves spike events that are described more accurately as the superposition of spikes from two other neurons. Fourth, it resolves situations where the recorded neurons drift in amplitude or across electrode contacts during a long recording session. Comparison with other sorters on real and simulated ground-truth datasets reveals many of the failure modes of spike sorters. We suggest a set of post-sorting analyses that can improve the veracity of neural recordings by minimizing the intrusion of those failure modes into analysis and interpretation of neural data.

Download Full-text

Twister3: a simple and fast microwire twister

10.1101/727644 ◽

2019 ◽

Cited By ~ 2

Author(s):

Jonathan P. Newman ◽

Jakob Voigts ◽

Maxim Borius ◽

Mattias Karlsson ◽

Mark T. Harnett ◽

...

Keyword(s):

Open Source ◽

Source Code ◽

Neural Probes ◽

Neural Recordings

AbstractWe present Twister3, a microwire twisting machine. This device greatly increases the speed and repeatability of constructing twisted microwire neural probes (e.g. stereotrodes and tetrodes) compared to existing options. It is cheap, well documented, and all associated designs and source code are open-source. Twister3 is of interest to any lab performing twisted microwire neural recordings, for example, using tetrode drives.

Download Full-text

Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

10.1101/2021.03.05.434053 ◽

2021 ◽

Author(s):

Furkan M. Torun ◽

Sebastian Virreira Winter ◽

Sophia Doll ◽

Felix M. Riese ◽

Artem Vorobyev ◽

...

Keyword(s):

Machine Learning ◽

Open Source ◽

Biomarker Discovery ◽

Expert Knowledge ◽

State Of The Art ◽

Open Science ◽

Easy Access ◽

Health State ◽

List Type ◽

Web Based

AbstractBiomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.Graphical AbstractHighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats

Download Full-text

The digital biomarker discovery pipeline: An open-source software platform for the development of digital biomarkers using mHealth and wearables data

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.511 ◽

2020 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Brinnae Bent ◽

Ke Wang ◽

Emilia Grzesiak ◽

Chentian Jiang ◽

Yuankai Qi ◽

...

Keyword(s):

Health Outcomes ◽

Open Source ◽

Open Source Software ◽

Biomarker Discovery ◽

Digital Health ◽

Personal Data ◽

Software Platform ◽

Biomarker Research ◽

Digital Biomarkers ◽

Discovery Pipeline

Abstract Introduction: Digital health is rapidly expanding due to surging healthcare costs, deteriorating health outcomes, and the growing prevalence and accessibility of mobile health (mHealth) and wearable technology. Data from Biometric Monitoring Technologies (BioMeTs), including mHealth and wearables, can be transformed into digital biomarkers that act as indicators of health outcomes and can be used to diagnose and monitor a number of chronic diseases and conditions. There are many challenges faced by digital biomarker development, including a lack of regulatory oversight, limited funding opportunities, general mistrust of sharing personal data, and a shortage of open-source data and code. Further, the process of transforming data into digital biomarkers is computationally expensive, and standards and validation methods in digital biomarker research are lacking. Methods: In order to provide a collaborative, standardized space for digital biomarker research and validation, we present the first comprehensive, open-source software platform for end-to-end digital biomarker development: The Digital Biomarker Discovery Pipeline (DBDP). Results: Here, we detail the general DBDP framework as well as three robust modules within the DBDP that have been developed for specific digital biomarker discovery use cases. Conclusions: The clear need for such a platform will accelerate the DBDP’s adoption as the industry standard for digital biomarker development and will support its role as the epicenter of digital biomarker collaboration and exploration.

Download Full-text