Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke

Abstract

Background and Purpose: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. Methods: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. Results: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the metric to be the sum of the entries in the disconnectome and, more importantly, the to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPR<LLPR), for , this relationship was reversed (MLPR>LLPR) or showed no difference for . Conclusions: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

Coherent neural oscillations inform early stroke motor recovery

“…These findings parallel earlier task-oriented fMRI studies depicting brain activation in areas other than primary motor cortex involving cingulate, temporal, and striate cortices, for example (Ward, Brown, Thompson, & Frackowiak, 2003b). Further supporting these findings and ours is an increased appreciation that input from nonmotor brain regions and networks influence motor system function in healthy participants and plasticity after stroke (Egger et al, 2021;Lin et al, 2021). Our finding of EEG leads overlying nonmotor regions in bilateral hemispheres may reflect one of many ongoing injury-or recovery-related processes, such as compensatory neuroplasticity mechanisms (Park et al, 2011;Ward, Brown, Thompson, & Frackowiak, 2003a), diaschisis (Fornito, Zalesky, & Breakspear, 2015), and contributions from widespread network modulation.…”

Adaptive brain changes to virtual lesions interfering with visual processing

“…TMS signals are differently integrated and propagated when targeting processing in early visual areas or in a higher-level visual area, such as hMT+/V5. These findings motivate the use of TMS-fMRI coupling as a complementary tool to assess 'disconnectomics' and their network and behavioural effects after brain lesions, such as in stroke (Egger et al, 2021). This technique could be leveraged to precisely map how a local perturbation propagates to largescale behavioural deficits by comparing the neural impact of…”

“…For instance, focal or incomplete brain lesions can sometimes fail to produce any behavioural effect (Sperber, 2020). In contrast, small lesions located in highly interconnected hubs can have devastating effects (Egger et al, 2021). Today, a comprehensive understanding of the causal relationship between a focal lesion and brain network organization is still missing, leading to the inability to accurately predict resulting symptoms and recovery processes.…”

Differential impact of brain network efficiency on post-stroke motor and attentional deficits

“…For each patient, a structural connectome was built with 13,202 pairs of areas obtained through the parcellation. 10,27 As in our previous work, 10 we did not limit the analyses to considering the numbers of streamlines between areas, a method which suffers from serious pitfalls. 28 Instead, we computed whole brain connectomes as follows: for each pair of regions of interest, we compute the sum of the COMMIT weights of the streamlines that run between the two regions.…”