An open-source toolbox for Multi-patient Intracranial EEG Analysis (MIA) *

被引:5
|
作者
Dubarry, A. -Sophie [1 ,2 ,6 ]
Liegeois-Chauvel, Catherine [3 ,4 ]
Trebuchon, Agnes [3 ,4 ,5 ]
Benar, Christian [4 ]
Alario, F. -Xavier [2 ,3 ]
机构
[1] Aix Marseille Univ, CNRS, LPL, Aix en Provence, France
[2] Aix Marseille Univ, CNRS, LPC, Aix en Provence, France
[3] Univ Pittsburgh, Cort Syst Lab, Pittsburgh, PA USA
[4] Aix Marseille Univ, Hop Timone, Inst Neurosci Syst, Serv Epileptol & Rythmol Cerebrale, Marseille, France
[5] Hopitalla Timone, APHM, Serv Epileptologieet Rythmol Cerebrale, Marseille, France
[6] Aix Marseille Univ, CNRS, LPL, Lab Parole & Langage 5 Ave Pasteur, F-13100 Aix en Provence, France
基金
欧洲研究理事会;
关键词
Software package; pipeline; intracranial recordings; group-level analysis; HGA; time-frequency decomposition; non-parametric statistics; Brainstorm; GAMMA ACTIVITY; DYNAMICS; LANGUAGE; SPEECH;
D O I
10.1016/j.neuroimage.2022.119251
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Intracranial EEG (iEEG) performed during the pre-surgical evaluation of refractory epilepsy provides a great opportunity to investigate the neurophysiology of human cognitive functions with exceptional spatial and temporal precisions. A difficulty of the iEEG approach for cognitive neuroscience, however, is the potential variability across patients in the anatomical location of implantations and in the functional responses therein recorded. In this context, we designed, implemented, and tested a user-friendly and efficient open-source toolbox for Multi Patient Intracranial data Analysis (MIA), which can be used as standalone program or as a Brainstorm plugin. MIA helps analyzing event related iEEG signals while following good scientific practice recommendations, such as building reproducible analysis pipelines and applying robust statistics. The signals can be analyzed in the temporal and time-frequency domains, and the similarity of time courses across patients or contacts can be assessed within anatomical regions. MIA allows visualizing all these results in a variety of formats at every step of the analysis. Here, we present the toolbox architecture and illustrate the different steps and features of the analysis pipeline using a group dataset collected during a language task.
引用
收藏
页数:14
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