Insights into the dependence of post-stroke motor recovery on the initial corticospinal tract connectivity from a computational model

Abstract

There is a consensus that motor recovery post-stroke primarily depends on the degree of the initial connectivity of the ipsilesional corticospinal tract (CST). Indeed, if the residual CST connectivity is sufficient to convey motor commands, the neuromotor system continues to use the CST predominantly, and motor function recovers up to 80%. In contrast, if the residual CST connectivity is insufficient, hand/arm dexterity barely recovers, even as the phases of stroke progress. Instead, the functional upregulation of the reticulospinal tract (RST) often occurs. In this study, we construct a computational model that reproduces the dependence of post-stroke motor recovery on the initial CST connectivity. The model emulates biologically plausible evolutions of primary motor descending tracts, based on activity-dependent or use-dependent plasticity and the preferential use of more strongly connected neural circuits. The model replicates several elements of the empirical evidence presented by the Fugl-Meyer Assessment (FMA) subscores, which evaluate the capabilities for out-of-synergy and in-synergy movements. These capabilities presumably change differently depending on the degree of the initial CST connectivity post-stroke, providing insights into the interactive dynamics of the primary descending motor tracts. We discuss findings derived from the proposed model in relation to the well-known proportional recovery rule. This modeling study aims to present a way to differentiate individuals who can achieve 70 to 80% recovery in the chronic phase from those who cannot by examining the interactive evolution of out-of-synergy and in-synergy movement capabilities during the subacute phase, as assessed by the FMA.

Keywords: Corticospinal tract; Fugl-Meyer assessment; Reticulospinal tract; Stroke; Synergy.

Fig. 1

Functional capabilities of the CST (left) and RST (right) of 6 simulated subjects (color corresponding) across trials (Minor CST Damage case). The learning gains for w and x vary arbitrarily

Fig. 2

An example of the functional capabilities and connectivities (values of the weights) of the CST and RST in an individual with strong initial CST connectivity across trials (Minor CST Damage case). We assume that the summation of spinal connection to each cell determines tract connectivity. If a cell is dead, its corresponding connection does not work

Fig. 3

Examples of the functional capabilities of the CST and RST and connectivities (values of the weights) of the CST and RST in an individual with different numbers of dead CST cells across trials (Substantial CST Damage case). The learning gains for w and x vary arbitrarily. We assume that the summation of spinal connection to each cell determines tract connectivity. If a cell is dead, its corresponding connection does not work

Fig. 4

Empirical results: (a) Fitters and non-fitters: fitters (red asterisk) reach 70% recovery or show mild impairment (FMA score ≥ 43) and non-fitters (black dot) do not reach 70% recovery. (b) Time evolutions of the total scores of (in-synergy) and (out-of-synergy) test items of 6 representative participants in each group (color corresponding) after stroke. (c) Time evolutions of the subtotal scores of (in-synergy), (out-of-synergy), and (in-synergy and out-of-synergy) test items averaged across participants in each group after stroke. Cloud: 1 standard deviation

Fig. 5

Simulation results: Time evolutions of the subtotal scores of (in-synergy), (out-of-synergy), and (in-synergy and out-of-synergy) test items averaged across simulated subjects (n = 100) in each group (fitters and non-fitters) after stroke. Cloud: 1 standard deviation

Fig. 6

Time evolutions of the simulated functional capabilities of the CST and RST, with (a) the initial values of the firing rate and weight dependent on the number of alive CST cells and the learning gains varying across subjects, (b) the initial values independent (randomly chosen between 0.1 and 0.9) and the learning gains varying, (c) the initial values dependent and the learning gains fixed, and (d) the initial values independent and the learning gains fixed