Time-sensitive reorganization of the somatosensory cortex poststroke depends on interaction between Hebbian and homeoplasticity: a simulation study

Abstract

Bains AS, Schweighofer N. Time-sensitive reorganization of the somatosensory cortex poststroke depends on interaction between Hebbian and homeoplasticity: a simulation study. J Neurophysiol 112: 3240-3250, 2014. First published October 1, 2014 doi:10.1152/jn.00433.2013.-Together with Hebbian plasticity, homeoplasticity presumably plays a significant, yet unclear, role in recovery postlesion. Here, we undertake a simulation study addressing the role of homeoplasticity and rehabilitation timing poststroke. We first hypothesize that homeoplasticity is essential for recovery and second that rehabilitation training delivered too early, before homeoplasticity has compensated for activity disturbances postlesion, is less effective for recovery than training delivered after a delay. We developed a neural network model of the sensory cortex driven by muscle spindle inputs arising from a six-muscle arm. All synapses underwent Hebbian plasticity, while homeoplasticity adjusted cell excitability to maintain a desired firing distribution. After initial training, the network was lesioned, leading to areas of hyperand hypoactivity due to the loss of lateral synaptic connections. The network was then retrained through rehabilitative arm movements. We found that network recovery was unsuccessful in the absence of homeoplasticity, as measured by reestablishment of lesion-affected inputs. We also found that a delay preceding rehabilitation led to faster network recovery during the rehabilitation training than no delay. Our simulation results thus suggest that homeoplastic restoration of prelesion activity patterns is essential to functional network recovery via Hebbian plasticity.

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

“…After a lesion, because of the loss of interneuronal connections, the activities of neurons neighboring the lesions, or neurons previously connected to neurons within the lesion, are affected. Homeostatic plasticity may be crucial for network recovery, as measured by re-establishment of lesion-affected inputs [ 89 ]. In addition, sensorimotor activity might modulate this homeostatic plasticity, which is of importance for computational neurorehabilitation models, as it is one example of how sensorimotor activity appears to modulate spontaneous recovery [ 6 , 31 , 89 , 90 ].…”

“…Genetic changes in the perilesional area allow for a window of increased plasticity that makes it easier for the perilesional neurons to modify existing connections and form new ones in response to sensorimotor activity [ 81 ]. Increased LTP may also potentially lead to maladaptive plasticity and poor cortical reorganization if existing inputs are further strengthened at the expense of the reemergence of weak afferent synapses [ 89 ]. In summary, underlying mechanisms assumed to contribute to the non-linear time course of recovery of movement in the first 3 months after stroke presumably reflect the interaction between a period of heightened plasticity mechanisms, occurring in a limited time window, and sensorimotor activity [ 81 , 93 – 95 ].…”

“…One of the first models to study the effects of network changes on movement kinematics evaluated the effect of lesion size on post-stroke reach variability using cortical cells that were tuned to preferred reach directions, but did not simulate plastic processes after lesion [ 101 ]. A recent model studied the interaction between homeostatic plasticity and Hebbian-plasticity after stroke in the somatosensory cortex, and suggests that after a lesion, a delay preceding rehabilitation would allow a return of homeostatically-determined desired firing in cells neighboring the lesions, and thus may allow a faster network recovery in the rehabilitation training compared to no delay [ 89 ]. It will be increasingly important to compare models that incorporate spontaneous biological recovery mechanisms to ones that do not, to determine how modeling these phenomena improves explanatory power.…”

“…Several initial models of network dynamics after stroke incorporate unsupervised learning (see review [ 25 ]). Unsupervised learning, together with homeostatic plasticity, likely plays a role in map and neural reorganization post-stroke, and presumably in decreasing movement variability and thereby improving functional performance [ 89 , 112 ].…”

Optimal timing of speech and language therapy for aphasia after stroke: more evidence needed

“…Impairment directed restorative treatment is directed at specific linguistic processes such as phonology, semantics or syntax. This supposedly triggers the premorbid, yet weakened, language network and prevents the formation of new networks at the cost of the original one [48]. However, one may question whether it will ever be possible to restore such a complex system as the language system after stroke and whether the language system will ever function normally again.…”