Neuroplastic Changes Following Brain Ischemia and their Contribution to Stroke Recovery: Novel Approaches in Neurorehabilitation

Claudia Alia, Cristina Spalletti, Stefano Lai, Alessandro Panarese, Giuseppe Lamola, Federica Bertolucci, Fabio Vallone, Angelo Di Garbo, Carmelo Chisari, Silvestro Micera, Matteo Caleo, Claudia Alia, Cristina Spalletti, Stefano Lai, Alessandro Panarese, Giuseppe Lamola, Federica Bertolucci, Fabio Vallone, Angelo Di Garbo, Carmelo Chisari, Silvestro Micera, Matteo Caleo

Abstract

Ischemic damage to the brain triggers substantial reorganization of spared areas and pathways, which is associated with limited, spontaneous restoration of function. A better understanding of this plastic remodeling is crucial to develop more effective strategies for stroke rehabilitation. In this review article, we discuss advances in the comprehension of post-stroke network reorganization in patients and animal models. We first focus on rodent studies that have shed light on the mechanisms underlying neuronal remodeling in the perilesional area and contralesional hemisphere after motor cortex infarcts. Analysis of electrophysiological data has demonstrated brain-wide alterations in functional connectivity in both hemispheres, well beyond the infarcted area. We then illustrate the potential use of non-invasive brain stimulation (NIBS) techniques to boost recovery. We finally discuss rehabilitative protocols based on robotic devices as a tool to promote endogenous plasticity and functional restoration.

Keywords: callosal connections; local field potentials; motor cortex; non-invasive brain stimulation; plasticity; rehabilitation; robotics; stroke.

Figures

Figure 1
Figure 1
Effect of DMCM on motor recovery after focal cortical ischemia in mice. (A) The number of foot faults in the gridwalk task is persistently decreased after transient DMCM treatment (two way RM ANOVA followed by Tukey test, *p < 0.05; n = 10). (B) In the single-pellet retrieval task, the fraction of incorrect graspings decreases after DMCM treatment (two way RM ANOVA, followed by Tukey test, **p < 0.01). Data are shown as percentage of the initial deficit, i.e., the difference in the fraction of foot faults/incorrect graspings between day 2 and baseline (before stroke). Modified from Alia et al. (2016).
Figure 2
Figure 2
Functional interhemispheric coupling in stroke mice. (A) Schematic drawing of stroke and electrode location for local field potential (LFP) recordings. Unilateral phototrombotic stroke was induced in caudal forelimb area (CFA) and bipolar recording (El1 and El2) and reference (Ref1 and Ref2) electrodes were inserted in both rostral forelimb areas (RFAs). A surgical screw (Ground) was placed in the occipital bone and used as ground reference. Orange dot represents Bregma position. Cross correlation (B) and Mutual Information (C) measures between the two hemispheres in control (black) and ischemic (red) animals are shown. Data are means ± standard errors. Modified from Vallone et al. (2016). *P < 0.05, **P < 0.01.
Figure 3
Figure 3
Example of a novel robotic system that integrates functional grasping, active reaching arm training and bimanual tasks. An example of a novel robotic system that integrates functional grasping, active reaching arm training and bimanual tasks, consisting of: (i) Virtual Reality: software applications composed of rehabilitative and evaluation tasks; (ii) TrackHold: robotic device to support the weight of the user’s limb during tasks execution; (iii) Robotic Hand Exos: active hand exoskeleton to assist grasping tasks; and (iv) Handgrip sensors to support the bilateral grasping training and evaluation (modified from Sgherri et al., 2017).
Figure 4
Figure 4
M-Platform for training and measuring motor performance of mice forelimb. Schematic of the robotic device. The main components are indicated: head fixation system, peristaltic pump with a gavage-feeding needle for liquid reward delivery, restrainer, linear actuator, camera to record forelimb position, plastic handle for the retraction task, linear slide and load cell for forces detection. Modified from Spalletti et al. (2014).

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