It has become increasingly recognized that the major functional deficits following brain damage are largely due to ‘negative’ features such as weakness and loss of dexterity rather than spasticity. A variety of studies suggest that spasticity is a distinct problem and separate from the loss of dexterity, but that it may be implicated in the formation of muscle contracture and even in the recovery of strength. In order to address these issues, we examined the relationship between spasticity, contracture, strength and dexterity in the affected upper limb following stroke. Spasticity was measured both as increased tonic stretch reflexes and increased resistance to passive stretch (hypertonia). Twenty-four patients were recruited non-selectively from three rehabilitation units within 13 months of their stroke. Few patients exhibited increased tonic reflexes but half were found to have muscle contracture, the earliest at 2 months following stroke. Hypertonia was associated with contracture but not with reflex hyperexcitability. Increased tonic stretch reflexes were observed only in a subgroup of those with contracture and where present could usually be elicited only at the end of muscle range. This finding suggests that instead of spasticity causing contracture, contracture may actually potentiate spasticity in some patients. However, the majority of patients with contracture did not have increased tonic stretch reflexes. In addition, we found no relationship between spasticity and either weakness or loss of dexterity. Therefore, while hypertonia remains an important problem following cerebral lesions, it would appear that the amount of attention directed to reflex hyperexcitability associated with spasticity is out of proportion with its effects. Consequently, hypertonia needs to be clearly distinguished from reflex hyperexcitability in patients with spasticity.

Adaptive model theory (AMT) is a computational theory that addresses the difficult control problem posed by the musculoskeletal system in interaction with the environment. It proposes that the nervous system creates motor maps and task-dependent synergies to solve the problems of redundancy and limited central resources. These lead to the adaptive formation of task-dependent feedback/feedforward controllers able to generate stable, noninteractive control and render nonlinear interactions unobservable in sensory-motor relationships. AMT offers a unified account of how the nervous system might achieve these solutions by forming internal models. This is presented as the design of a simulator consisting of neural adaptive filters based on cerebellar circuitry. It incorporates a new network module that adaptively models (in real time) nonlinear relationships between inputs with changing and uncertain spectral and amplitude probability density functions as is the case for sensory and motor signals.