Integrated open-source software for multiscale electrophysiology

被引:15
作者
Nasiotis, Konstantinos [1 ]
Cousineau, Martin [1 ]
Tadel, Francois [2 ]
Peyrache, Adrien [1 ]
Leahy, Richard M. [3 ]
Pack, Christopher C. [1 ]
Baillet, Sylvain [1 ]
机构
[1] McGill Univ, Montreal Neurol Inst, Montreal, PQ, Canada
[2] Grenoble Inst Neurosci, Grenoble, France
[3] Univ Southern Calif, Inst Signal & Image Proc, Los Angeles, CA 90089 USA
基金
美国国家卫生研究院; 加拿大自然科学与工程研究理事会;
关键词
OSCILLATORY NEURONAL SYNCHRONIZATION; VISUAL-CORTEX; FUNCTIONAL CONNECTIVITY; EEG; STIMULATION; INFORMATION; VARIABILITY; MODULATION; FORMAT; MEMORY;
D O I
10.1038/s41597-019-0242-z
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The methods for electrophysiology in neuroscience have evolved tremendously over the recent years with a growing emphasis on dense-array signal recordings. Such increased complexity and augmented wealth in the volume of data recorded, have not been accompanied by efforts to streamline and facilitate access to processing methods, which too are susceptible to grow in sophistication. Moreover, unsuccessful attempts to reproduce peer-reviewed publications indicate a problem of transparency in science. This growing problem could be tackled by unrestricted access to methods that promote research transparency and data sharing, ensuring the reproducibility of published results. Here, we provide a free, extensive, open-source software that provides data-analysis, data-management and multi-modality integration solutions for invasive neurophysiology. Users can perform their entire analysis through a user-friendly environment without the need of programming skills, in a tractable (logged) way. This work contributes to open-science, analysis standardization, transparency and reproducibility in invasive neurophysiology.
引用
收藏
页数:13
相关论文
共 68 条
[1]   The effect of correlated variability on the accuracy of a population code [J].
Abbott, LF ;
Dayan, P .
NEURAL COMPUTATION, 1999, 11 (01) :91-101
[2]   Machine learning for neuroirnaging with scikit-learn [J].
Abraham, Alexandre ;
Pedregosa, Fabian ;
Eickenberg, Michael ;
Gervais, Philippe ;
Mueller, Andreas ;
Kossaifi, Jean ;
Gramfort, Alexandre ;
Thirion, Bertrand ;
Varoquaux, Gael .
FRONTIERS IN NEUROINFORMATICS, 2014, 8
[3]  
[Anonymous], 2016, NIPS P
[4]   Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior [J].
Arce-McShane, F., I ;
Sessle, B. J. ;
Ross, C. F. ;
Hatsopoulos, N. G. .
JOURNAL OF NEUROPHYSIOLOGY, 2018, 120 (01) :226-238
[5]   Neural correlations, population coding and computation [J].
Averbeck, BB ;
Latham, PE ;
Pouget, A .
NATURE REVIEWS NEUROSCIENCE, 2006, 7 (05) :358-366
[6]   Electromagnetic brain mapping [J].
Baillet, S ;
Mosher, JC ;
Leahy, RM .
IEEE SIGNAL PROCESSING MAGAZINE, 2001, 18 (06) :14-30
[7]   Academic Software Applications for Electromagnetic Brain Mapping Using MEG and EEG [J].
Baillet, Sylvain ;
Friston, Karl ;
Oostenveld, Robert .
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2011, 2011
[8]  
Baker M, 2016, NATURE, V533, P452, DOI 10.1038/533452a
[9]   AN INFORMATION MAXIMIZATION APPROACH TO BLIND SEPARATION AND BLIND DECONVOLUTION [J].
BELL, AJ ;
SEJNOWSKI, TJ .
NEURAL COMPUTATION, 1995, 7 (06) :1129-1159
[10]  
Buzaki G., 2006, Rhythms of the Brain, DOI DOI 10.1093/ACPROF:OSO/9780195301069.001.0001