Standards for data acquisition and software-based analysis of in vivo electroencephalography recordings from animals. A TASK1-WG5 report of the AES/ILAE Translational Task Force of the ILAE
Jazyk angličtina Země Spojené státy americké Médium print
Typ dokumentu časopisecké články, přehledy, Research Support, N.I.H., Extramural, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S.
Grantová podpora
U24 NS063930
NINDS NIH HHS - United States
R01 NS099348
NINDS NIH HHS - United States
K01 ES025436
NIEHS NIH HHS - United States
K08 NS069783
NINDS NIH HHS - United States
R01 NS094399
NINDS NIH HHS - United States
R01 NS091170
NINDS NIH HHS - United States
R01 NS029709
NINDS NIH HHS - United States
PubMed
29105070
PubMed Central
PMC5683416
DOI
10.1111/epi.13909
Knihovny.cz E-zdroje
- Klíčová slova
- Data sharing, Data storage, Electrocorticography, Electroencephalography, Signal processing,
- MeSH
- elektroencefalografie * přístrojové vybavení metody normy MeSH
- epilepsie patofyziologie MeSH
- modely nemocí na zvířatech MeSH
- mozek patofyziologie MeSH
- poradní výbory * MeSH
- software * normy MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- přehledy MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Electroencephalography (EEG)-the direct recording of the electrical activity of populations of neurons-is a tremendously important tool for diagnosing, treating, and researching epilepsy. Although standard procedures for recording and analyzing human EEG exist and are broadly accepted, there are no such standards for research in animal models of seizures and epilepsy-recording montages, acquisition systems, and processing algorithms may differ substantially among investigators and laboratories. The lack of standard procedures for acquiring and analyzing EEG from animal models of epilepsy hinders the interpretation of experimental results and reduces the ability of the scientific community to efficiently translate new experimental findings into clinical practice. Accordingly, the intention of this report is twofold: (1) to review current techniques for the collection and software-based analysis of neural field recordings in animal models of epilepsy, and (2) to offer pertinent standards and reporting guidelines for this research. Specifically, we review current techniques for signal acquisition, signal conditioning, signal processing, data storage, and data sharing, and include applicable recommendations to standardize collection and reporting. We close with a discussion of challenges and future opportunities, and include a supplemental report of currently available acquisition systems and analysis tools. This work represents a collaboration on behalf of the American Epilepsy Society/International League Against Epilepsy (AES/ILAE) Translational Task Force (TASK1-Workgroup 5), and is part of a larger effort to harmonize video-EEG interpretation and analysis methods across studies using in vivo and in vitro seizure and epilepsy models.
Department of Neurology Baylor College of Medicine Houston Texas U S A
Department of Neurosurgery National Nagasaki Medical Center Omura Nagasaki Japan
Departments of Neurology and Biomedical Engineering University of Michigan Ann Arbor Michigan U S A
Institute of Physiology Czech Academy of Sciences Prague Czech Republic
Zobrazit více v PubMed
Niedermeyer E. Historical aspects. In: Niedermeyer E, Lopes da Silva F, editors. Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2005. pp. 1–16.
Westbrook G. Seizures and epilepsy. In: Kandel E, Schwartz T, Jessell T, et al., editors. Principles of neural science. McGraw Hill Professional; 2013. pp. 1116–1139.
American Clinical Neurophysiology S. Guideline 6: A proposal for standard montages to be used in clinical eeg. J Clin Neurophysiol. 2006;23:111–117. PubMed
Hirsch LJ, LaRoche SM, Gaspard N, et al. American clinical neurophysiology society's standardized critical care eeg terminology: 2012 version. J Clin Neurophysiol. 2013;30:1–27. PubMed
Noachtar S, Binnie C, Ebersole J, et al. A glossary of terms most commonly used by clinical electroencephalographers and proposal for the report form for the eeg findings. The international federation of clinical neurophysiology. Electroencephalogr Clin Neurophysiol Suppl. 1999;52:21–41. PubMed
Institution IE. Iec 60601-1: Medical electrical equipment - part 1: General requirements for basic safety and essential performance. Editor (Ed)^(Eds) Book Iec 60601-1: Medical electrical equipment - part 1: General requirements for basic safety and essential performance. 2005
Sherman-Gold R. The axon guide for electrophysiology and biophysics: Laboratory techniques. Foster City, CA, USA: 1993.
Isley MR, Krauss GL, Levin KH, et al. Electromyography/electroencephalography. SpaceLabs Medical. 1993
Nagel JH. Biopotential amplifiers. In: Bronzino JD, Peterson DR, editors. The biomedical engineering handbook, vol. 4: Medical devices and engineering. CRC Press, LLC; Boca Raton, FL, USA: 2014.
Olson WH. Electrical safety. In: Webster JG, editor. Medical instrumentation: Application and design. John Wiley & Sons; New York, NY, USA: 2010. pp. 638–675.
Mathew G. Medical devices isolation: How safe is safe enough. [Accessed July 31, 2016];2002 Available at: https://www.wipro.com/documents/whitepaper/Whitepaper-MedicalDevicesIsolation-%C3%B4Howsafeissafeenough%C3%B6.pdf.
Prutchi D, Norris M. Design and development of medical electronic instrumentation: A practical perspective of the deisng, construction, and test of medical devices. John Wiley & Sons; New York, NY, USA: 2005.
Webster JG. Amplifiers and signal processing. In: Webster JG, editor. Medical instrumentation: Application and design. John Wiley & Sons; New York, NY, USA: 2010. pp. 91–125.
Reilly EL. Eeg recording and operation of the apparatus. In: Niedermeyer E, Lopes da Silva F, editors. Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2005. pp. 139–159.
Bertram EH. Monitoring for seizures in rodents. In: Pitkanen A, Schwartzkroin PA, Moshe SL, editors. Models of seizures and epilepsy. Elsevier Academic Press; Burlington, MA, USA: 2006. pp. 569–582.
Rensing NR, Guo D, Wong M. Video-eeg monitoring methods for characterizing rodent models of tuberous sclerosis and epilepsy. In: Weichhart T, editor. Mtor: Methods and protocols. Humana Press; New York, NY, USA: 2012. pp. 373–391. PubMed
Galanopoulou AS, Kokaia M, Loeb JA, et al. Epilepsy therapy development: Technical and methodologic issues in studies with animal models. Epilepsia. 2013;54(Suppl 4):13–23. PubMed PMC
Smith SW. The scientist and engineer's guide to digital signal processing. Califronia Technical Publishers; 1997.
Krauss GL, Webber WRS. Digital eeg. In: Niedermeyer E, Lopes da Silva F, editors. Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2005. pp. 797–813.
Lesser RP, Webber WRS. Principles of computerized epilepsy monitoring. In: Niedermeyer E, Lopes da Silva F, editors. Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2005. pp. 791–796.
Shannon CE. Communication in the presence of noise. Proceedings of the Institue of Radio Engineers. 1949;37:10–21.
Nyquist H. Certain topics in telegraph transmission theory. Transactions of the American Institute of Electrical Engineers. 1928;47:617–644.
Mainardi LT, Bianchi AM, Cerutti S. Digital biomedical signal acquisition and processing. In: Bronzino JD, Peterson DR, editors. The biomedical engineering handbook, vol. 3: Biomedical signals, imaging, and informatics. CRC Press LLC; 2014.
van Drongelen W. Signal processing for neuroscientists: An introduction to the analysis of physiological signals. Academic Press; Burlington, MA, USA: 2006.
Widmann A, Schroger E, Maess B. Digital filter design for electrophysiological data--a practical approach. J Neurosci Methods. 2015;250:34–46. PubMed
Ifeachor E, Jervis B. Digital signal processing: A practical approach. 2002
Gliske SV, Irwin ZT, Chestek C, et al. Effect of sampling rate and filter settings on high frequency oscillation detections. Clin Neurophysiol. 2016;127:3042–3050. PubMed PMC
Practical introduction to digital filter design. [Accessed July 31, 2016]; Available at: http://www.mathworks.com/help/signal/examples/practical-introduction-to-digital-filter-design.html?requestedDomain=www.mathworks.com.
Wesson KD, Ochshorn RM, Land BR. Low-cost, high-fidelity, adaptive cancellation of periodic 60 hz noise. J Neurosci Methods. 2009;185:50–55. PubMed
Northrop RB. Signals and systems analysis in biomedical engineering. CRC Press, Taylor & Francis Group; Boca Raton, FL: 2010.
Qian S, Chen D. Joint time-frequency analysis : Methods and applications. PTR Prentice Hall; Upper Saddle River, NJ: 1996.
Walker JS. A primer on wavelets and their scientific applications. Chapman and Hall/CRC; 2008.
Castellanos NP, Makarov VA. Recovering eeg brain signals: Artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods. 2006;158:300–312. PubMed
Wilson SB, Emerson R. Spike detection: A review and comparison of algorithms. Clin Neurophysiol. 2002;113:1873–1881. PubMed
Latka M, Was Z, Kozik A, et al. Wavelet analysis of epileptic spikes. Phys Rev E Stat Nonlin Soft Matter Phys. 2003;67:052902. PubMed
del Campo CM, Velazquez JL, Freire MA. Eeg recording in rodents, with a focus on epilepsy. Curr Protoc Neurosci. 2009;(Unit 6):24. Chapter 6. PubMed
Nolan H, Whelan R, Reilly RB. Faster: Fully automated statistical thresholding for eeg artifact rejection. J Neurosci Methods. 2010;192:152–162. PubMed
Kelleher D, Temko A, Orregan S, et al. Parallel artefact rejection for epileptiform activity detection in routine eeg. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:7953–7956. PubMed
LeVan P, Urrestarazu E, Gotman J. A system for automatic artifact removal in ictal scalp eeg based on independent component analysis and bayesian classification. Clin Neurophysiol. 2006;117:912–927. PubMed
Chaumon M, Bishop DV, Busch NA. A practical guide to the selection of independent components of the electroencephalogram for artifact correction. J Neurosci Methods. 2015;250:47–63. PubMed
Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in eeg data using higher-order statistics and independent component analysis. Neuroimage. 2007;34:1443–1449. PubMed PMC
Hamaneh MB, Chitravas N, Kaiboriboon K, et al. Automated removal of ekg artifact from eeg data using independent component analysis and continuous wavelet transformation. IEEE Trans Biomed Eng. 2014;61:1634–1641. PubMed
Lopes da Silva F. Eeg analysis: Theory and practice. In: Niedermeyer E, Lopes da Silva F, editors. Electroencephalography: Basic principles, clinical applications, and related fields. Lippincott Williams and Wilkins; Philadelphia, PA, USA: 2005. pp. 1199–1231.
Brinkmann BH, Bower MR, Stengel KA, et al. Multiscale electrophysiology format: An open-source electrophysiology format using data compression, encryption, and cyclic redundancy check. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:7083–7086. PubMed PMC
Brinkmann BH, Bower MR, Stengel KA, et al. Large-scale electrophysiology: Acquisition, compression, encryption, and storage of big data. J Neurosci Methods. 2009;180:185–192. PubMed PMC
Stead M, Halford JJ. A proposal for a standard format for neurophysiology data recording and exchange. J Clin Neurophysiol. 2016 PubMed PMC
Teeters JL, Godfrey K, Young R, et al. Neurodata without borders: Creating a common data format for neurophysiology. Neuron. 2015;88:629–634. PubMed
Kemp B, Varri A, Rosa AC, et al. A simple format for exchange of digitized polygraphic recordings. Electroencephalogr Clin Neurophysiol. 1992;82:391–393. PubMed
Kemp B, Olivan J. European data format 'plus' (edf+), an edf alike standard format for the exchange of physiological data. Clin Neurophysiol. 2003;114:1755–1761. PubMed
Wagenaar JB, Worrell GA, Ives Z, et al. Collaborating and sharing data in epilepsy research. J Clin Neurophysiol. 2015;32:235–239. PubMed PMC
Liang SF, Shaw FZ, Young CP, et al. A closed-loop brain computer interface for real-time seizure detection and control. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:4950–4953. PubMed
Nelson TS, Suhr CL, Freestone DR, et al. Closed-loop seizure control with very high frequency electrical stimulation at seizure onset in the gaers model of absence epilepsy. Int J Neural Syst. 2011;21:163–173. PubMed
Zayachkivsky A, Lehmkuhle MJ, Dudek FE. Long-term continuous eeg monitoring in small rodent models of human disease using the epoch wireless transmitter system. J Vis Exp. 2015:e52554. PubMed PMC
Kuzum D, Takano H, Shim E, et al. Transparent and flexible low noise graphene electrodes for simultaneous electrophysiology and neuroimaging. Nat Commun. 2014;5:5259. PubMed PMC
Vanhatalo S, Palva JM, Andersson S, et al. Slow endogenous activity transients and developmental expression of k+-cl- cotransporter 2 in the immature human cortex. Eur J Neurosci. 2005;22:2799–2804. PubMed
Myers KA, Bello-Espinosa LE, Wei XC, et al. Infraslow eeg changes in infantile spasms. J Clin Neurophysiol. 2014;31:600–605. PubMed
Thordstein M, Lofgren N, Flisberg A, et al. Infraslow eeg activity in burst periods from post asphyctic full term neonates. Clin Neurophysiol. 2005;116:1501–1506. PubMed
Vanhatalo S, Holmes MD, Tallgren P, et al. Very slow eeg responses lateralize temporal lobe seizures: An evaluation of non-invasive dc-eeg. Neurology. 2003;60:1098–1104. PubMed
Bragin A, Engel J, Jr, Staba RJ. High-frequency oscillations in epileptic brain. Curr Opin Neurol. 2010;23:151–156. PubMed PMC
Open, industry-standard file format for neurophysiological data. [Accessed July 31, 2016];2000 Available at: http://neuroshare.sourceforge.net/API-Documentation/sfn00meeting_agenda.pdf.