Cerebral network disorders after stroke: evidence from imaging-based connectivity analyses of active and resting brain states in humans

Abstract

Stroke causes a sudden disruption of physiological brain function which leads to impairments of functional brain networks involved in voluntary movements. In some cases, the brain has the intrinsic capacity to reorganize itself, thereby compensating for the disruption of motor networks. In humans, such reorganization can be investigated in vivo using neuroimaging. Recent developments in connectivity analyses based on functional neuroimaging data have provided new insights into the network pathophysiology underlying neurological symptoms. Here we review recent neuroimaging studies using functional resting-state correlations, effective connectivity models or graph theoretical analyses to investigate changes in neural motor networks and recovery after stroke. The data demonstrate that network disturbances after stroke occur not only in the vicinity of the lesion but also between remote cortical areas in the affected and unaffected hemisphere. The reorganization of motor networks encompasses a restoration of interhemispheric functional coherence in the resting state, particularly between the primary motor cortices. Furthermore, reorganized neural networks feature strong excitatory interactions between fronto-parietal areas and primary motor cortex in the affected hemisphere, suggesting that greater top-down control over primary motor areas facilitates motor execution in the lesioned brain. In addition, there is evidence that motor recovery is accompanied by a more random network topology with reduced local information processing. In conclusion, Stroke induces changes in functional and effective connectivity within and across hemispheres which relate to motor impairments and recovery thereof. Connectivity analyses may hence provide new insights into the pathophysiology underlying neurological deficits and may be further used to develop novel, neurobiologically informed treatment strategies.

Figures

Figure 1. Activation likelihood estimation (ALE) meta-analysis of motor-related neural activity after stroke

The activation likelihood for affected upper limb movements as compared with rest is depicted in blue (based on 452 activation maxima from 54 experiments in a total sample of 472 stroke patients). Enhanced activation likelihood for movements of patients as compared with healthy subjects is depicted in yellow (based on 113 activation maxima from 20 experiments of 177 patients). Movements of the affected upper limb are associated with significant local convergence in primary sensorimotor cortices, lateral PMC, SMA, pre-SMA, parietal operculum and cerebellum of both hemispheres (P < 0.05, cluster-level familywise error (FWE) corrected for multiple comparisons). Compared with healthy controls, activation likelihood is specifically enhanced in contralesional sensorimotor cortex as well as in ventral PMC and SMA of both hemispheres. Hence, neural activity in these areas distinguishes patients most consistently from healthy subjects (adopted from Rehme et al. 2012, with permission).

Figure 2. Resting-state functional connectivity

A, seed-voxel analysis in a group of 16 right-handed healthy volunteers (P < 0.05 false discovery rate (FDR) corrected). The colour scales indicate the correlation between a seed voxel located in left or right primary motor cortex (M1) and any other voxel time-course in the brain. B, Pearson correlation coefficients between BOLD signal time-courses from seed voxels of homologous motor areas in both hemispheres in 16 healthy subjects. Coordinates are reported in Montreal Neurological Institute (MNI) reference space. M1, primary motor cortex; PMC, premotor cortex; SMA, supplementary motor area.

Figure 3. Effective connectivity as estimated using dynamic causal modelling (DCM)

A, top panel, example of a network model including three areas and a priori assumptions about their connectivity. Bottom panel, bilinear neuronal state equation which describes changes in the system over time based on: (1) endogenous coupling, which is computed independently from experimental conditions (A-matrix, grey); (2) context-dependent coupling (B-matrix, blue); and (3) direct experimental input to the system (C-matrix, red). B, context-independent (endogenous) and context-dependent connectivity between key areas of the cortical motor system for rhythmic fist closures of the left or right hand. Coupling parameters reflect the strength and direction of coupling between regions and are measured as rate of change (Hz). Positive coupling (green arrows) indicates promoting influences, whereas negative coupling (red arrows) indicates inhibitory influences on the activity in the target region (adopted from Grefkes et al. 2008a, with permission).

Figure 4. Examples of different graph network topologies based on a model with 16 nodes and 32 edges

The average shortest path length L refers to the minimum number of edges that have to be traversed to get from one node to another. Hence, shorter paths (i.e. a few number of edges indicated by a low shortest path length L) represent a greater ability for global information exchange. The clustering coefficient C represents an index for the number of edges between nearest neighbours of a given node relative to the maximum number of possible connections (Rubinov & Sporns, 2010). A, regular network, which is highly clustered or ‘cliquish’ and in which high number of edges has to be traversed to get from one node to another. Thus, regular networks feature a high efficiency of local information processing at each node, but less global information transfer. B, random network with low local clustering, but short paths between nodes. This topology provides a high global efficiency, but reduced local information processing. C, small-world network representing an intermediate network configuration with some highly clustered nodes and a medium number of paths to enable both local and global information processing.

Figure 5. Synopsis of changes in motor networks after stroke

The figure summarizes those areas which were included in network models of functional and effective connectivity studies: cerebellum (Cereb), primary motor cortex (M1), prefrontal cortex (PFC), lateral premotor cortex (PMC), supplementary motor area (SMA), superior parietal cortex (SPC) and thalamus (Thal). The numbers displayed on connections refer to the respective publication which reported connectivity disturbances. Correlations with motor impairment are marked in green. A, resting-state functional connectivity after stroke. The figure shows connections which either positively correlate with motor impairment or were related to disrupted anatomical connectivity. The most consistent finding pertains to reduced interhemispheric interactions, particularly between primary motor cortices, which correlate with more severe impairments. For reasons of clarity, performance-related disturbances in interhemispheric connections between cerebellum and SMA, as well as between ipsilesional thalamus and contralesional premotor areas reported in the study of Wang et al. (2010) are not depicted here. B, effective connectivity after stroke. The figure depicts changes in excitatory (left panel) and inhibitory (right panel) interactions relative to healthy subjects as revealed by DCM and SEM analyses of fMRI motor tasks and one resting-state fMRI study (Inman et al. 2012). For intervention studies (numbers 4 and 7), only findings from baseline assessments are presented. Strongest convergence across studies was observed for reduced positive influences between premotor areas and ipsilesional M1. Likewise, inhibitory interhemispheric influences between the primary motor cortices are often attenuated, which suggests that motor deficits after stroke are maintained by a disinhibition of contralesional M1 activity. At the subacute stage, there is evidence that contralesional M1 exerts a positive (i.e., supportive) influence onto ipsilesional M1 activity in patients with severe impairments. Together, there seems to be a time-dependent role of contralesional M1 in motor recovery after stroke.