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tutorial:shared:connectivity_simulation [2013/10/03 16:54]
jan-mathijs
tutorial:shared:connectivity_simulation [2018/10/21 15:12]
42.49.180.224 [Background]
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 ===== Introduction ===== ===== Introduction =====
  
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 The brain is organized in functional units, which at the smallest level consists of neurons, and at higher levels consists of larger neuronal populations. Functional localization studies consider the brain to be organized in specialized neuronal modules corresponding to specific areas in the brain. These functionally specialized brain areas (e.g. visual cortex area V1, V2, V4, MT, ...) have to pass information back and forth along anatomical connections. The identification of these functional connections and determining their functional relevance comprises connectivity analysis and can be done using the **[[reference:​ft_connectivityanalysis]]** and associated functions. The brain is organized in functional units, which at the smallest level consists of neurons, and at higher levels consists of larger neuronal populations. Functional localization studies consider the brain to be organized in specialized neuronal modules corresponding to specific areas in the brain. These functionally specialized brain areas (e.g. visual cortex area V1, V2, V4, MT, ...) have to pass information back and forth along anatomical connections. The identification of these functional connections and determining their functional relevance comprises connectivity analysis and can be done using the **[[reference:​ft_connectivityanalysis]]** and associated functions.
  
-The nomenclature for connectivity analysis can be adopted from [[http://​en.wikipedia.org/​wiki/​Graph_theory|graph theory]], in which brain areas correspond to nodes or vertices and the connections between the nodes is given by edges. One of the fundamental challenges in the analysis of brain networks from MEG and EEG data lies not only in identifying the "edges", i.e. the functional connections,​ but also the "nodes". The remainder of this tutorial will only explain the methods to characterize the edges, but will not go into detail into identifying the nodes. The [[tutorial:​beamformer]] and [[tutorial:​coherence|corticomuscular coherence]] tutorial provide more pointers to localizing the nodes.+The nomenclature for connectivity analysis can be adopted from [[http://​en.wikipedia.org/​wiki/​Graph_theory|graph theory]], in which brain areas correspond to nodes or vertices and the connections between the nodes is given by edges. One of the fundamental challenges in the analysis of brain networks from MEG and EEG data lies not only in identifying the "edges", i.e. the functional connections,​ but also the "nodes". The remainder of this tutorial will only explain the methods to characterize the edges, but will not go into detail into identifying the nodes. The [[tutorial:​beamformer]] and [[tutorial:​coherence|corticomuscular coherence]] tutorial provide more pointers to localizing the nodes.
  
 Many measures of connectivity exist, and they can be broadly divided into measures of functional connectivity (denoting statistical dependencies between measured signals, without information about causality/​directionality),​ and measures of effective connectivity,​ which describe directional interactions. ​ Many measures of connectivity exist, and they can be broadly divided into measures of functional connectivity (denoting statistical dependencies between measured signals, without information about causality/​directionality),​ and measures of effective connectivity,​ which describe directional interactions. ​
  
 After the identification of the network nodes and the characterization of the edges between the nodes, it is possible to analyze and describe certain network features in more detail. This network analysis is also not covered in this tutorial, although FieldTrip provides some functionality in this direction (see **[[reference:​ft_networkanalysis]]** to get started). After the identification of the network nodes and the characterization of the edges between the nodes, it is possible to analyze and describe certain network features in more detail. This network analysis is also not covered in this tutorial, although FieldTrip provides some functionality in this direction (see **[[reference:​ft_networkanalysis]]** to get started).
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 ===== Procedure ===== ===== Procedure =====