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Ladd A, Kim KG, Balewski J, Bouchard K, Ben-Shalom R. Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models. Front Neuroinform 2022; 16:882552. [PMID: 35784184 PMCID: PMC9248031 DOI: 10.3389/fninf.2022.882552] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 05/18/2022] [Indexed: 11/28/2022] Open
Abstract
Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.
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Affiliation(s)
- Alexander Ladd
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
- *Correspondence: Alexander Ladd
| | - Kyung Geun Kim
- Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, CA, United States
| | - Jan Balewski
- NERSC, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Kristofer Bouchard
- Helen Wills Neuroscience Institute & Redwood Center for Theoretical Neuroscience, University of California, Berkeley, Berkeley, CA, United States
- Scientific Data Division and Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Roy Ben-Shalom
- Neurology Department, MIND Institute, University of California, Davis, Sacramento, CA, United States
- Roy Ben-Shalom
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Carr SJA, Gershon A, Shafiabadi N, Lhatoo SD, Tatsuoka C, Sahoo SS. An Integrative Approach to Study Structural and Functional Network Connectivity in Epilepsy Using Imaging and Signal Data. Front Integr Neurosci 2021; 14:491403. [PMID: 33510622 PMCID: PMC7835283 DOI: 10.3389/fnint.2020.491403] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/30/2020] [Indexed: 12/22/2022] Open
Abstract
A key area of research in epilepsy neurological disorder is the characterization of epileptic networks as they form and evolve during seizure events. In this paper, we describe the development and application of an integrative workflow to analyze functional and structural connectivity measures during seizure events using stereotactic electroencephalogram (SEEG) and diffusion weighted imaging data (DWI). We computed structural connectivity measures using electrode locations involved in recording SEEG signal data as reference points to filter fiber tracts. We used a new workflow-based tool to compute functional connectivity measures based on non-linear correlation coefficient, which allows the derivation of directed graph structures to represent coupling between signal data. We applied a hierarchical clustering based network analysis method over the functional connectivity data to characterize the organization of brain network into modules using data from 27 events across 8 seizures in a patient with refractory left insula epilepsy. The visualization of hierarchical clustering values as dendrograms shows the formation of connected clusters first within each insulae followed by merging of clusters across the two insula; however, there are clear differences between the network structures and clusters formed across the 8 seizures of the patient. The analysis of structural connectivity measures showed strong connections between contacts of certain electrodes within the same brain hemisphere with higher prevalence in the perisylvian/opercular areas. The combination of imaging and signal modalities for connectivity analysis provides information about a patient-specific dynamical functional network and examines the underlying structural connections that potentially influences the properties of the epileptic network. We also performed statistical analysis of the absolute changes in correlation values across all 8 seizures during a baseline normative time period and different seizure events, which showed decreased correlation values during seizure onset; however, the changes during ictal phases were varied.
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Affiliation(s)
- Sarah J. A. Carr
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Neuroimaging Department, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Arthur Gershon
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Nassim Shafiabadi
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Samden D. Lhatoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
| | - Curtis Tatsuoka
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Satya S. Sahoo
- Department of Neurology, School of Medicine Case Western Reserve University, Cleveland, OH, United States
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
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Lhatoo SD, Bernasconi N, Blumcke I, Braun K, Buchhalter J, Denaxas S, Galanopoulou A, Josephson C, Kobow K, Lowenstein D, Ryvlin P, Schulze-Bonhage A, Sahoo SS, Thom M, Thurman D, Worrell G, Zhang GQ, Wiebe S. Big data in epilepsy: Clinical and research considerations. Report from the Epilepsy Big Data Task Force of the International League Against Epilepsy. Epilepsia 2020; 61:1869-1883. [PMID: 32767763 DOI: 10.1111/epi.16633] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 12/25/2022]
Abstract
Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.
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Affiliation(s)
- Samden D Lhatoo
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Ingmar Blumcke
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Kees Braun
- Department of Child Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Jeffrey Buchhalter
- Department of Neurology, St Joseph's Hospital and Medical Center, Phoenix, Arizona
| | - Spiros Denaxas
- Institute of Health Informatics, University College London, London, UK
| | - Aristea Galanopoulou
- Saul Korey Department of Neurology, Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, New York
| | - Colin Josephson
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
| | - Katja Kobow
- Friedrich-Alexander University Erlangen-Nürnberg, University Hospital Erlangen, Erlangen, Germany
| | - Daniel Lowenstein
- Department of Neurology, University of California, San Francisco, San Francisco, California
| | - Philippe Ryvlin
- Department of Neurosciences, University of Lausanne, Lausanne, Switzerland
| | | | - Satya S Sahoo
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Maria Thom
- Institute of Neurology, University College London, London, UK
| | | | - Greg Worrell
- Department of Neurology, Mayo Clinic, Rochester, Minnesota
| | - Guo-Qiang Zhang
- University of Texas Health Sciences Center at Houston, Houston, Texas
| | - Samuel Wiebe
- Department of Clinical Neurosciences, University of Calgary, Calgary, Canada
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Foreman B. Neurocritical Care: Bench to Bedside (Eds. Claude Hemphill, Michael James) Integrating and Using Big Data in Neurocritical Care. Neurotherapeutics 2020; 17:593-605. [PMID: 32152955 PMCID: PMC7283405 DOI: 10.1007/s13311-020-00846-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The critical care environment drives huge volumes of data, and clinicians are tasked with quickly processing this data and responding to it urgently. The neurocritical care environment increasingly involves EEG, multimodal intracranial monitoring, and complex imaging which preclude comprehensive human synthesis, and requires new concepts to integrate data into clinical care. By definition, Big Data is data that cannot be handled using traditional infrastructures and is characterized by the volume, variety, velocity, and variability of the data being produced. Big Data in the neurocritical care unit requires rethinking of data storage infrastructures and the development of tools and analytics to drive advancements in the field. Preprocessing, feature extraction, statistical inference, and analytic tools are required in order to achieve the primary goals of Big Data for clinical use: description, prediction, and prescription. Barriers to its use at bedside include a lack of infrastructure development within the healthcare industry, lack of standardization of data inputs, and ultimately existential and scientific concerns about the outputs that result from the use of tools such as artificial intelligence. However, as implied by the fundamental theorem of biomedical informatics, physicians remain central to the development and utility of Big Data to improve patient care.
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Affiliation(s)
- Brandon Foreman
- Department of Neurology & Rehabilitation Medicine, University of Cincinnati Medical Center, 231 Albert Sabin Way, Cincinnati, OH, 45267-0517, USA.
- Collaborative for Research on Acute Neurological Injuries (CRANI), University of Cincinnati, Cincinnati, OH, USA.
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Big data analytics in health sector: Theoretical framework, techniques and prospects. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2020. [DOI: 10.1016/j.ijinfomgt.2019.05.003] [Citation(s) in RCA: 68] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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PlantES: A Plant Electrophysiological Multi-Source Data Online Analysis and Sharing Platform. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8112269] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
At present, plant electrophysiological data volumes and complexity are increasing rapidly. It causes the demand for efficient management of big data, data sharing among research groups, and fast analysis. In this paper, we proposed PlantES (Plant Electrophysiological Data Sharing), a distributed computing-based prototype system that can be used to store, manage, visualize, analyze, and share plant electrophysiological data. We deliberately designed a storage schema to manage the multi-source plant electrophysiological data by integrating distributed storage systems HDFS and HBase to access all kinds of files efficiently. To improve the online analysis efficiency, parallel computing algorithms on Spark were proposed and implemented, e.g., plant electrical signals extraction method, the adaptive derivative threshold algorithm, and template matching algorithm. The experimental results indicated that Spark efficiently improves the online analysis. Meanwhile, the online visualization and sharing of multiple types of data in the web browser were implemented. Our prototype platform provides a solution for web-based sharing and analysis of plant electrophysiological multi-source data and improves the comprehension of plant electrical signals from a systemic perspective.
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