Stroke Connectome and Its Implications for Cognitive and Behavioral Sequela of Stroke

Abstract

Systems-based approaches to neuroscience, using network analysis and the human connectome, have been adopted by many researchers by virtue of recent progress in neuroimaging and computational technologies. Various neurological disorders have been evaluated from a network perspective, including stroke, Alzheimer's disease, Parkinson's disease, and traumatic brain injury. Until now, dynamic processes after stroke and during recovery were investigated through multimodal neuroimaging techniques. Many studies have shown disruptions in structural and functional connectivity, including in large-scale neural networks, in patients with stroke sequela such as motor weakness, aphasia, hemianopia, neglect, and general cognitive dysfunction. A connectome-based approach might shed light on the underlying mechanisms of stroke sequela and the recovery process, and could identify candidates for individualized rehabilitation programs. In this review, we briefly outline the basic concepts of structural and functional connectivity, and the connectome. Then, we explore current evidence regarding how stroke lesions cause changes in connectivity and network architecture parameters. Finally, the clinical implications of perspectives on the connectome are discussed in relation to the cognitive and behavioral sequela of stroke.

Keywords: Connectivity; Connectome; Diffusion tensor imaging; Network; Resting-state functional MRI; Stroke.

Figure 1.

Alteration of functional and structural connectivity after stroke in the posterior cerebral artery territory (n=30). (A) Spatial pattern of infarct frequency across subjects. (B) Group level spatial patterns of visual network estimated by independent component analysis using resting-state fMRI. The one sample t test was adopted to estimate the group level spatial patterns for each of time points (i.e., 1 week and 3 months from stroke onset). (C) Spatial pattern of overlapped probabilistic tracts connected with the visual cortex (regions-of-interest are calcarine, lingual and cuneus). The spatial patterns of functional and structural connectivity were superimposed over standard MNI T1 image. White colored outline indicates the infarct location which is greater than 20% of infarct frequency across subjects.

Figure 2.

Representative diagram for the network characteristics. (A) This diagram consists of 12 nodes and their interconnections. (B) Node ‘a’ is interconnected with node ‘b’ through edge ‘ab’. (C) Degree of node ‘c’ is 3, which denotes the edges ‘cd’, ‘bc’, and ‘cg’. Likewise, the degree of node ‘b’ is 4, and that of node ‘a’ is 6. (D) Shortest path length between node ‘g’ and ‘j’ is 4, which is visualized with arrows.

Figure 3.

Exemplar networks of regular network (left), small-world network (center) and random network (right). The exemplar small-world network was constructed using the functional connectivity in patients with visual field defect at 1 week after posterior cerebral artery territory infarction (n=30). Colors of connectivity matrix and circular lattice represent statistical significances from one sample t test (n=30) from red (P=10^-5) to yellow colors (P=10^-10).

Figure 4.

Network visualization by (A) connectivity matrix, (B) circular representation, and (C) graph theoretical methods. The vision-related regions-of-interest were selected as network nodes based on Automated Anatomical Labeling (AAL) atlas. Statistical significance from one sample t test (n=30) was used to represent the network connections between nodes from red (P=10^-5) to yellow colors (P=10^-10). Fisher’s z-score was used in statistical tests. IL, ipsilesional hemisphere; CL, contralesional hemisphere; Sup, superior; Mid, middle; Inf, inferior; Occ, occipital cortex.