MÉTHODES
FiNN: A toolbox for neurophysiological
network analysis
Maximilian Scherer1,2
, Tianlu Wang1, Robert Guggenberger1,
Luka Milosevic1,2, and Alireza Gharabaghi1
1Institute for Neuromodulation and Neurotechnology, University Hospital and University of Tübingen, Tübingen, Allemagne
2Krembil Brain Institute, University Health Network, and Institute of Biomedical Engineering, University of Toronto,
Toronto, Canada
Mots clés: Connectivity, Cross-frequency coupling, Neural oscillations, Phase-amplitude coupling
un accès ouvert
journal
ABSTRAIT
Recently, neuroscience has seen a shift from localist approaches to network-wide
investigations of brain function. Neurophysiological signals across different spatial and
temporal scales provide insight into neural communication. Cependant, additional
methodological considerations arise when investigating network-wide brain dynamics
rather than local effects. Spécifiquement, larger amounts of data, investigated across a higher
dimensional space, are necessary. Ici, we present FiNN (Find Neurophysiological
Networks), a novel toolbox for the analysis of neurophysiological data with a focus on
functional and effective connectivity. FiNN provides a wide range of data processing methods
and statistical and visualization tools to facilitate inspection of connectivity estimates and the
resulting metrics of brain dynamics. The Python toolbox and its documentation are freely
available as Supporting Information. We evaluated FiNN against a number of established
frameworks on both a conceptual and an implementation level. We found FiNN to require
much less processing time and memory than other toolboxes. En outre, FiNN adheres to a
design philosophy of easy access and modifiability, while providing efficient data processing
implementations. Since the investigation of network-level neural dynamics is experiencing
increasing interest, we place FiNN at the disposal of the neuroscientific community as open-
source software.
INTRODUCTION
Analyzing dependence between neurophysiological signals, and the definition of large-scale
réseaux, has become an important field of research that greatly enhances our comprehension
of communication between distinct neural structures (Bressler & Menon, 2010; Siegel et al.,
2012). Neural connectivity in particular is commonly quantified by estimating the degree to
which neural oscillations within the same frequency band or across different frequency bands
relate to each other (Fries, 2005). These two types of communication modes are known as
same-frequency coupling (sfc) and cross-frequency coupling (cfc), respectivement (Friston,
2011; Hyafil et al., 2015).
Neural communication on a network level can be quantified on the basis of neurophysio-
logical data from a wide variety of data sources including electroencephalography (EEG), mag-
netoencephalography (MEG), and local field potentials (LFPs) (Engel et al., 2013; Ganzetti &
Citation: Scherer, M., Wang, T.,
Guggenberger, R., Milosevic, L., &
Gharabaghi, UN. (2022). FiNN: A toolbox
for neurophysiological network
analyse. Neurosciences en réseau, 6(4),
1205–1218. https://doi.org/10.1162/netn
_a_00265
EST CE QUE JE:
https://doi.org/10.1162/netn_a_00265
Informations complémentaires:
https://github.com/neurophysiological
-analysis/ FiNN;
https://neurophysiological-analysis
.github.io/ FiNN
Reçu: 9 Mars 2022
Accepté: 23 Juin 2022
Intérêts concurrents: Les auteurs ont
a déclaré qu'aucun intérêt concurrent
exister.
Auteur correspondant:
Alireza Gharabaghi
alireza.gharabaghi@uni-tuebingen.de
Éditeur de manipulation:
Cornelis Jan Stam
droits d'auteur: © 2022 Maximilian Scherer,
Tianlu Wang, Robert Guggenberger,
Luka Milosevic, and Alireza Gharabaghi.
Publié sous Creative Commons
Attribution 4.0 International
(CC PAR 4.0) Licence
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FiNN: A toolbox for neurophysiological network analysis
sfc:
Coupling between signals with the
same frequency.
cfc:
Coupling between signals with
different frequencies.
EEG:
Head-attached sensors used to
record information about cortical
activation patterns.
MEG:
Electromagnetism based sensors used
to record information about cortical
activation patterns.
LFP:
Low-frequency (<300 Hz)
electrophysiological activity
recorded from within the brain.
FiNN:
Find Neurophysiological Networks is
the framework presented in this
paper. It offers a wide range of
functions for analysis of
electrophysiological data.
GitHub:
An online repository for
programming code.
Mantini, 2013). An estimation of connectivity across regions and>
