A NWB-based dataset and processing pipeline of human single-neuron activity during a declarative memory task

A challenge for data sharing in systems neuroscience is the multitude of different data formats used. Neurodata Without Borders: Neurophysiology 2.0 (NWB:N) has emerged as a standardized data format for the storage of cellular-level data together with meta-data, stimulus information, and behavior. A key next step to facilitate NWB:N adoption is to provide easy to use processing pipelines to import/export data from/to NWB:N. Here, we present a NWB-formatted dataset of 1863 single neurons recorded from the medial temporal lobes of 59 human subjects undergoing intracranial monitoring while they performed a recognition memory task. We provide code to analyze and export/import stimuli, behavior, and electrophysiological recordings to/from NWB in both MATLAB and Python. The data files are NWB:N compliant, which affords interoperability between programming languages and operating systems. This combined data and code release is a case study for how to utilize NWB:N for human single-neuron recordings and enables easy re-use of this hard-to-obtain data for both teaching and research on the mechanisms of human memory

TSDF: A simple yet comprehensive, unified data storage and exchange format standard for digital biosensor data in health applications

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a consensus on a common storage, exchange and archival data format standard, has yet to be reached. To address this gap, we propose Time Series Data Format (TSDF): a unified, standardized format for storing all types of physiological sensor data, across diverse disease areas. We pose a series of format design criteria and review in detail current storage and exchange formats. When judged against these criteria, we find these current formats lacking, and propose a very simple, intuitive standard for both numerical sensor data and metadata, based on raw binary data and JSON-format text files, for sensor measurements/timestamps and metadata, respectively. By focusing on the common characteristics of diverse biosensor data, we define a set of necessary and sufficient metadata fields for storing, processing, exchanging, archiving and reliably interpreting, multi-channel biological time series data. Our aim is for this standardized format to increase the interpretability and exchangeability of data, thereby contributing to scientific reproducibility in studies where digital biosensor data forms a key evidence base

Preparing Laboratory and Real-World EEG Data for Large-Scale Analysis: A Containerized Approach.

Large-scale analysis of EEG and other physiological measures promises new insights into brain processes and more accurate and robust brain-computer interface models. However, the absence of standardized vocabularies for annotating events in a machine understandable manner, the welter of collection-specific data organizations, the difficulty in moving data across processing platforms, and the unavailability of agreed-upon standards for preprocessing have prevented large-scale analyses of EEG. Here we describe a "containerized" approach and freely available tools we have developed to facilitate the process of annotating, packaging, and preprocessing EEG data collections to enable data sharing, archiving, large-scale machine learning/data mining and (meta-)analysis. The EEG Study Schema (ESS) comprises three data "Levels," each with its own XML-document schema and file/folder convention, plus a standardized (PREP) pipeline to move raw (Data Level 1) data to a basic preprocessed state (Data Level 2) suitable for application of a large class of EEG analysis methods. Researchers can ship a study as a single unit and operate on its data using a standardized interface. ESS does not require a central database and provides all the metadata data necessary to execute a wide variety of EEG processing pipelines. The primary focus of ESS is automated in-depth analysis and meta-analysis EEG studies. However, ESS can also encapsulate meta-information for the other modalities such as eye tracking, that are increasingly used in both laboratory and real-world neuroimaging. ESS schema and tools are freely available at www.eegstudy.org and a central catalog of over 850 GB of existing data in ESS format is available at studycatalog.org. These tools and resources are part of a larger effort to enable data sharing at sufficient scale for researchers to engage in truly large-scale EEG analysis and data mining (BigEEG.org)

odML-Tables as a Metadata Standard in Microneurography

Metadata is essential for handling medical data according to FAIR principles. Standards are well-established for many types of electrophysiological methods but are still lacking for microneurographic recordings of peripheral sensory nerve fibers in humans. Developing a new concept to enhance laboratory workflows is a complex process. We propose a standard for structuring and storing microneurography metadata based on odML and odML-tables. Further, we present an extension to the odML-tables GUI that enables user-friendly search functionality of the database. With our open-source repository, we encourage other microneurography labs to incorporate odML-based metadata into their experimental routines

Using delay differential equations in models of cardiac electrophysiology

In cardiac physiology, electrical alternans is a phenomenon characterized by long-short alternations in the action potential duration of cardiac myocytes that give rise to complex spatiotemporal dynamics in tissue. Experiments and clinical measurements indicate that alternans can be a precursor of life-threatening arrhythmias, such as cardiac _brillation. Despite the importance of alternans in the study of cardiac disease, many mathematical models developed to describe cardiac electrophysiology at the cellular level are not able to produce this phenomenon. As a potential remedy to this de_ciency, we introduce short time-delays in some formulations of existing cardiac cell models that are based on Ordinary Di_erential Equations (ODEs). Many processes within cardiac cells involve delays in sensing and responding to changes. In addition, delay di_erential equations (DDEs) are known to give rise to complex dynamical properties in mathematical models. In biological modeling, DDEs have been applied to epidemiology, population dynamics, immunology, and neural networks. Therefore, DDEs can potentially represent mechanisms that result in complex dynamics both at the cellular level and at the tissue level. In this thesis, we propose DDE-based formulations for ion channel models based on the Hodgkin-Huxley formalism that can induce alternans in single-cell simulations in many models found in the literature. We also show that these modi_cations can destabilize spiral waves and produce spiral breakups in two-dimensional simulations, which is a typical model of cardiac _brillation. However, the new DDE-based formulations introduce new computational challenges due to the need for storing and retrieving past values of variables. Therefore, we present novel numerical methods to overcome these challenges and enable e_cient DDE-based studies at the tissue level in standard computational environments. We _nd that the proposed methods decrease memory usage by up to 95% in cardiac tissue simulations compared to straightforward history management algorithms available in widely used DDE solvers.Em fisiologia cardíaca, alternans elétrica _e um fenômeno caracterizado pela alternância entre potenciais de ação longos e curtos que dá origem a complexos comportamentos espaço-temporais em tecido. Experimentos e medições clínicas indicam que alternans pode ser um precursor de perigosas arritmias, como fibrilação ventricular ou morte súbita. Apesar da importância do alternans no estudo de doenças cardíacas, muitos modelos matemáticos para a eletrofisiologia de células cardíacas não são capazes de reproduzir este fenômeno. Como um potencial remédio para esta deficiência, introduzimos curtos atrasos de tempo em algumas formulações de modelos preexistentes para células cardíacas que são baseados em Equações Diferenciais Ordinárias (EDOs). Vários processos em células cardíacas envolvem atrasos de sensibilidade e de resposta a mudanças em variáveis fisiológicas. Além disso, equações diferenciais com atraso (DDEs) são conhecidas por dar origem a complexas propriedades dinâmicas em modelos matemáticos. Em modelagem biológica, DDEs têm sido aplicadas em epidemiologia, dinâmica populacional, imunologia e redes neurais. Portanto, DDEs podem representar mecanismos que resultam em dinâmicas complexas tanto no nível celular, quanto no nível do tecido. Nesta tese, propomos formulações baseadas em DDEs para modelos de canais iônicos descritos pelo formalismo de Hodgkin-Huxley. Tais formulações são capazes de induzir alternans em simulações celulares envolvendo vários modelos encontrados na literatura. Nós também mostramos que essas modificações podem desestabilizar e quebrar ondas espirais em simulações bidimensionais de propagação elétrica, o que é típico de fibrilação cardíaca. Entretanto, as formulações propostas introduzem novos desafios computacionais devido à necessidade de armazenar e recuperar valores passados de variáveis. Deste modo, nós apresentamos novos métodos numéricos para superar tais desafios e permitir a eficiente simulação de modelos baseados em DDEs no nível do tecido cardíaco. Os métodos propostos foram capazes de diminuir o uso de memória em até 95% em comparação aos algoritmos largamente utilizados na solução numérica de DDEs. Assim, os novos modelos baseados em DDEs e os eficientes métodos numéricos propostos nesta tese contribuem para o estudo de arritmias cardíacas fatais através de modelagem computacional