Global InformationBrain connectivity estimators information
 | This article may be too technical for most readers to understand. (April 2015) |
Brain connectivity estimators[1] represent patterns of links in the brain. Connectivity can be considered at different levels of the brain's organisation: from neurons, to neural assemblies and brain structures. Brain connectivity involves different concepts such as: neuroanatomical or structural connectivity (pattern of anatomical links), functional connectivity (usually understood as statistical dependencies) and effective connectivity (referring to causal interactions).[2]
Neuroanatomical connectivity is inherently difficult to define given the fact that at the microscopic scale of neurons, new synaptic connections or elimination of existing ones are formed dynamically and are largely dependent on the function executed, but may be considered as pathways extending over regions of the brain, which are in accordance with general anatomical knowledge. Diffusion Weighted Imaging (DWI) can be used to provide such information. The distinction between functional and effective connectivity is not always sharp; sometimes causal or directed connectivity is called functional connectivity. Functional connectivity, may be defined as the temporal correlation (in terms of statistically significant dependence between distant brain regions) among the activity of different neural assemblies, whereas effective connectivity may be defined as the direct or indirect influence that one neural system exerts over another.[3] Some brain connectivity estimators evaluate connectivity from brain activity time series such as Electroencephalography (EEG), Local field potential (LFP) or spike trains, with an effect on the directed connectivity. These estimators can be applied to fMRI data, if the required image sequences are available. Among estimators of connectivity, there are linear and non-linear, bivariate and multivariate measures. Certain estimators also indicate directionality. Different methods of connectivity estimation vary in their effectiveness.[4][5][6] This article provides an overview of these measures, with an emphasis on the most effective methods.
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Sakkalis1was invoked but never defined (see the help page). - ^ Colombo, Matteo; Weinberger, Naftali (June 1, 2018). "Discovering Brain Mechanisms Using Network Analysis and Causal Modeling". Minds and Machines. 28 (2): 265–286. doi:10.1007/s11023-017-9447-0. PMC 6438494. PMID 30996522 – via Springer Link.
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