Abstract
The recent advances in neurological imaging and sensing technologies have led to rapid increase in the volume, rate of data generation, and variety of neuroscience data. This “neuroscience Big data” represents a significant opportunity for the biomedical research community to design experiments using data with greater timescale, large number of attributes, and statistically significant data size. The results from these new data-driven research techniques can advance our understanding of complex neurological disorders, help model long-term effects of brain injuries, and provide new insights into dynamics of brain networks. However, many existing neuroinformatics data processing and analysis tools were not built to manage large volume of data, which makes it difficult for researchers to effectively leverage this available data to advance their research. We introduce a new toolkit called NeuroPigPen that was developed using Apache Hadoop and Pig data flow language to address the challenges posed by large-scale electrophysiological signal data. NeuroPigPen is a modular toolkit that can process large volumes of electrophysiological signal data, such as Electroencephalogram (EEG), Electrocardiogram (ECG), and blood oxygen levels (SpO2), using a new distributed storage model called Cloudwave Signal Format (CSF) that supports easy partitioning and storage of signal data on commodity hardware. NeuroPigPen was developed with three design principles: (a) Scalability—the ability to efficiently process increasing volumes of data; (b) Adaptability—the toolkit can be deployed across different computing configurations; and (c) Ease of programming—the toolkit can be easily used to compose multi-step data processing pipelines using high-level programming constructs. The NeuroPigPen toolkit was evaluated using 750 GB of electrophysiological signal data over a variety of Hadoop cluster configurations ranging from 3 to 30 Data nodes. The evaluation results demonstrate that the toolkit is highly scalable and adaptable, which makes it suitable for use in neuroscience applications as a scalable data processing toolkit. As part of the ongoing extension of NeuroPigPen, we are developing new modules to support statistical functions to analyze signal data for brain connectivity research. In addition, the toolkit is being extended to allow integration with scientific workflow systems. NeuroPigPen is released under BSD license at: https://sites.google.com/a/case.edu/neuropigpen/.
Highlights
Rapid technological and methodological advances in sensing as well as recording neurological data in patients with epileptic seizures, stroke, and psychiatric disorders have dramatically improved the availability of high-resolution multimodal neurological data for both biomedical research as well as patient care (Bargmann et al, 2014)
In this article we describe the architecture of NeuroPigPen, the functionalities of the toolkit, and evaluate its performance using de-identified electrophysiological signal data collected at the University Hospitals Case Medical Center (UH-CMC) epilepsy monitoring unit (EMU)
By leveraging the features of the MapReduce parallel programming approach and the high-level data flow programming design of Apache Pig, NeuroPigPen can be integrated in neuroinformatics software without compromising on computing performance
Summary
Rapid technological and methodological advances in sensing as well as recording neurological data in patients with epileptic seizures, stroke, and psychiatric disorders have dramatically improved the availability of high-resolution multimodal neurological data for both biomedical research as well as patient care (Bargmann et al, 2014). These multi-modal datasets are playing a key role in neuroscience research efforts, for example they are advancing research in the brain connectivity networks using multiple data modalities representing both structural and functional networks (Swann et al, 2012; Wendling et al, 2016). Signal data from SEEG is used as gold standard during presurgical evaluation of epilepsy patients to identify brain tissues responsible for epileptic seizures, which can be removed during surgery and to identify important brain regions such as the speech center that need to be protected during surgery (Lüders et al, 2012; Schuele et al, 2012)
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