NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Meunier, David; Pascarella, Annalisa; Altukhov, Dmitrii; Jas, Mainak; Combrisson, Etienne; Lajnef, Tarek; Bertrand-Dubois, Daphné; Hadid, Vanessa; Alamian, Golnoush; Alves, Jordan; Barlaam, Fanny; Saive, Anne-Lise; Dehgan, Arthur; Jerbi, Karim

doi:10.1016/j.neuroimage.2020.117020

Cited by 30 publications

(18 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guidelines might recommend specific steps and strategies of best practice to improve neurobiological relevance and reduce erroneous localization/connections (He et al, 2019). Openly available toolkits can further help to streamline this process (Meunier et al, 2020; Schirner et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Wirsich

Jorge

Iannotti

et al. 2020

Preprint

View full text Add to dashboard Cite

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite their differences in probing brain activity, both electrophysiology and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage).Here, we investigated the reliability of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reliably correlates across different datasets and that the crossmodal correlation between EEG and fMRI connectivity of r≈0.3 can be reliably extracted in low and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and withing intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship.This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects being organized into reliable ICNs across different timescales. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. Alterations of this coupling could be explored as a potential clinical marker of pathological brain function.

show abstract

Section: Discussionmentioning

confidence: 99%

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Wirsich

Jorge

Iannotti

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Python and Matlab example code shows how to import the various files. This assures compatibility with commonly used EEG and MRI software, including MNE-Python 53 , NeuroPycon 54 , Fieldtrip 55 and SPM 56 . The code also shows how to calculate one time-series of activity from all source points contained in a region using singular-value decomposition.…”

Section: Usage Notesmentioning

confidence: 92%

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Pascucci

Tourbier

Rué-Queralt

et al. 2021

Preprint

View full text Add to dashboard Cite

We describe the multimodal neuroimaging dataset VEPCON (OpenNeuro Dataset ds003505). It includes raw data and derivatives of high-density EEG, structural MRI, diffusion weighted images (DWI) and single-trial behavior (accuracy, reaction time). Visual evoked potentials (VEPs) were recorded while participants (n=20) discriminated briefly presented faces from scrambled faces, or coherently moving stimuli from incoherent ones. EEG and MRI were recorded separately from the same participants. The dataset contains pre-processed EEG of single trials in each condition, behavioral measures, structural MRIs, individual brain parcellations at 5 spatial resolutions (83 to 1015 regions), and the corresponding structural connectomes computed from fiber count, fiber density, average fractional anisotropy and mean diffusivity maps. For source imaging, VEPCON provides EEG inverse solutions based on individual anatomy, with Python and Matlab scripts to derive activity time-series in each brain region, for each parcellation level. The BIDS-compatible dataset can contribute to multimodal methods development, studying structure-function relations, and to unimodal optimization of source imaging and graph analyses, among many other possibilities.

show abstract

“…An in-house open-source python pipeline NeuroPycon ( Meunier et al, 2020 ) was used for the preprocessing and source-reconstruction analysis. The continuous raw data was filtered with a zero-phase bandpass using a finite impulse response filtering (FIR 1, order = 3) between 0.1 Hz and 150 Hz, and a Hamming window.…”

Section: Methodsmentioning

confidence: 99%

“…The lead field matrix was computed using the single layer model boundary element method implemented in MNE-python ( Gramfort et al, 2013 ). Finally, the inverse solution was computed using the inverse pipeline provided by NeuroPycon ( Meunier et al, 2020 ) where we chose the weighted Minimum Norm Estimate ( Dale and Sereno, 1993 , Hämäläinen and Ilmoniemi, 1994 , Hincapié, 2016 ), implemented in the MNE-python package ( Gramfort et al, 2013 , Hyvarinen, 1999 ). The dipoles of the cortical source-space were constrained to have an orientation normal to the surface, while the dipoles of the subcortical volumes were left with free orientation.…”

Section: Methodsmentioning

confidence: 99%

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia

Alamian

Pascarella

Lajnef

et al. 2020

NeuroImage: Clinical

Self Cite

View full text Add to dashboard Cite

Highlights Temporal autocorrelation is attenuated in the neural dynamics of schizophrenia. Alterations are primarily observed in the alpha and beta frequency bands. Machine-learning significantly classified patients and controls with 82% accuracy. Brain regions showing disrupted temporal connectivity include (para)limbic areas. Long-range temporal correlation informs on temporal dysconnectivity in schizophrenia.

show abstract

NeuroPycon: An open-source python toolbox for fast multi-modal and reproducible brain connectivity pipelines

Cited by 30 publications

References 39 publications

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T

Source imaging of high-density visual evoked potentials with multi-scale brain parcellations and connectomes

Patient, interrupted: MEG oscillation dynamics reveal temporal dysconnectivity in schizophrenia

Contact Info

Product

Resources

About