1. Neural network imaging to characterize brain injury in cardiac procedures: the emerging utility of connectomics 
B. Indja, J.P. Fanning, J.J. Maller, J.F. Fraser, P.G. Bannon, M. Vallely, S.M. Grieve 
British Journal of Anaesthesia 
Volume 118, Issue 5, Pages (May 2017)
DOI: /bja/aex088
Copyright © 2017 The Author(s) Terms and Conditions

2. Fig 1
(A) Maps of anatomical connectivity can be generated with diffusion tensor imaging (left); maps of functional connectivity can be generated with functional magnetic resonance imaging (fMRI; right). (B) Network nodes, corresponding to different brain regions, are identified by division of the brain into sections with a range of strategies, such as a priori anatomical templates (left), random division (middle), and functionally defined regions of interest (right). (C) After definition of brain regions, inter-regional connectivity can be measured using whole-brain tractography derived from diffusion tensor imaging (left) or through analysis of statistical covariance in regional activation during fMRI acquisitions (right), which corresponds to simultaneous local increases in blood flow. In the example given, regions 2 and 3 are more strongly coupled to each other than to region 1. (D) After some measure of connectivity has been calculated for every pair of brain regions, connectome architecture can be represented by a connectivity matrix encoding the strength and type of connectivity between each regional pair. In MRI studies, these matrices are typically symmetrical (i.e. connections are undirected), weighted (i.e. variations in the strength of inter-regional connectivity are captured), and unthresholded (i.e. the values are continuous, with few zero entries; left). A threshold is usually applied to distinguish real from spurious connections (middle) and to binarize the resulting matrix to encode the presence or absence of a connection (right). Figure from Filippi and colleagues,42 originally adapted from Fornito and colleagues,43 by permission of Elsevier. This image/content is not covered by the terms of the Creative Commons licence of this publication. For permission to reuse, please contact the rights holder.
British Journal of Anaesthesia  , DOI: ( /bja/aex088)
Copyright © 2017 The Author(s) Terms and Conditions

3. Fig 2
Summary of the significant networks shown to characterize major depressive disorder using network-based statistics. Two distinct circuits are represented. Network 1 involves many regions that form the default mode network involved in switching between active cognitive states and the ‘resting mode’. Network 2 contains a central cluster of six nodes involving the frontal–subcortical regions important for regulation of emotion and higher cognitive functions, such as working memory and executive function: the right thalamus, right caudate, right medial orbitofrontal cortex, right rostral middle frontal, and both the left and right superior frontal cortex.45 lh, left hemisphere; rh, right hemisphere. Image from Korgaonkar and co-workers,45 by permission of Elsevier. This image/content is not covered by the terms of the Creative Commons licence of this publication. For permission to reuse, please contact the rights holder.
British Journal of Anaesthesia  , DOI: ( /bja/aex088)
Copyright © 2017 The Author(s) Terms and Conditions

4. Fig 3
(A) Comparison of brain topology between reference adolescents and adolescents who had undergone repair of dextro-transposition of the great arteries (d-TGA). Graph theoretical analyses indicated that global network topology was optimally described as consisting of four modules or subnetworks, which have been mapped out and compared as follows: anteromedial interhemispheric subnetwork (red), posterior interhemispheric subnetwork (yellow–orange), right intrahemispheric subnetwork (cyan), and left intrahemispheric subnetwork (green). (B) Illustration of the use of small-world architecture to detect abnormal neural connections in d-TGA compared with control subjects. Data are visualized using a circle diagram representing the short-range (arcs) and long-range connection (lines) between different regions of the brain, including frontal lobes (blue and purple), subcortical nuclei (red), temporal lobes (orange), parietal lobes (yellow), and occipital lobes (green). The d-TGA subjects (left) demonstrated a tendency toward more short-range connections (in the frontal regions compared with occipital regions), compared with the control group (right), who have more short-range connections posteriorly. In addition, there is a difference in the spatial localization of long-range connections; long-range connections appear more clustered in the d-TGA cohort compared with the more uniform distribution found in the reference cohort. Figure adapted from Panigrahy and co-workers,50 by permission of Elsevier under creative commons attribution.
British Journal of Anaesthesia  , DOI: ( /bja/aex088)
Copyright © 2017 The Author(s) Terms and Conditions