Methods for analysis of brain connectivity: An IFCN-sponsored review.

TLDR: This paper examines existing methods to study the "Human Brain Connectome" with a specific focus on the neurophysiological ones, including the most commonly used structural and functional MRI, EEG, MEG and non-invasive brain stimulation techniques and measures of local and global brain connectivity.

About: This article is published in Clinical Neurophysiology.The article was published on 2019-10-01 and is currently open access. It has received 91 citations till now. The article focuses on the topics: Brain stimulation & Connectome.

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Fig. 3. Methods to study long-range connections in the human brain. (A) Transections of nerves or CNS fiber bundles invariably cause anterograde (Wallerian) degeneration, i.e. destruction and elimination of the portions of axons and terminal ramifications distal to the lesion; depending on lesion location and size, degeneration can also follow a retrograde path and involve neuronal cell bodies. (B) Diffusion-weighted magnetic resonance imaging is used to identify in vivo the spatial orientation of fiber bundles in the brain, making it possible to reconstruct central pathways. An important limitation of the technique is that in areas where fibers intersect, the signal averages out and accurate directions cannot be established.

Fig. 9. Network science is developing along a network of measures and models, introducing new concepts, such as cost-efficiency, hierarchical modularity, vulnerability to random or targeted attack, and the notion of rich clubs (from Vecchio et al., 2018).

Fig. 8. Factorization method for the LiFE model, memory efficiency & model accuracy. (A by a 3D indicator array (/) and dictionary of diffusion predictions (D; colors indicate pr reduces LiFE model size from over 30 GB of RAM per brain (old LiFE, low saturation symbo is indistinguishable from the original LiFE. Insets. Errors in reconstructing fascicles‘weig

Fig. 4. Frontal section of the human brain (m

Fig. 7. Ensemble tractography (ET). Connectomes generated with multiple tracking algorithms are merged. LiFE is used to ‘‘learn” from the data. The ET connectomes contain all fascicles best contributing to predicting diffusion.

Fig. 5. (A) Connectome nodes. Cortical terminations of the arcuate fasciculus. Yellow higher, red lower termination density. (B) Connectome edges. Two major WM tracts, cortico-spinal tract (CST) & arcuate fasciculus.