方法
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, 蒂宾根, 德国
2Krembil Brain Institute, University Health Network, and Institute of Biomedical Engineering, 多伦多大学,
多伦多, 加拿大
关键词: Connectivity, Cross-frequency coupling, Neural oscillations, Phase-amplitude coupling
开放访问
杂志
抽象的
最近, 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. 然而, additional
methodological considerations arise when investigating network-wide brain dynamics
rather than local effects. 具体来说, larger amounts of data, investigated across a higher
dimensional space, are necessary. 这里, we present FiNN (Find Neurophysiological
网络), 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. 此外, 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-
源软件.
介绍
Analyzing dependence between neurophysiological signals, and the definition of large-scale
网络, has become an important field of research that greatly enhances our comprehension
of communication between distinct neural structures (Bressler & Menon, 2010; 西格尔等人。,
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 (薯条, 2005). These two types of communication modes are known as
same-frequency coupling (sfc) and cross-frequency coupling (cfc), 分别 (弗里斯顿,
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 (乙二醇), and local field potentials (LFPs) (Engel et al., 2013; Ganzetti &
引文: Scherer, M。, 王, T。,
Guggenberger, R。, Milosevic, L。, &
Gharabaghi, A. (2022). FiNN: A toolbox
for neurophysiological network
分析. 网络神经科学, 6(4),
1205–1218. https://doi.org/10.1162/netn
_a_00265
DOI:
https://doi.org/10.1162/netn_a_00265
支持信息:
https://github.com/neurophysiological
-analysis/ FiNN;
https://neurophysiological-analysis
.github.io/ FiNN
已收到: 9 行进 2022
公认: 23 六月 2022
利益争夺: 作者有
声明不存在竞争利益
存在.
通讯作者:
Alireza Gharabaghi
alireza.gharabaghi@uni-tuebingen.de
处理编辑器:
Cornelis Jan Stam
版权: © 2022 Maximilian Scherer,
Tianlu Wang, Robert Guggenberger,
Luka Milosevic, and Alireza Gharabaghi.
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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.
乙二醇:
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>
