F Wave Analyzer, a system for repeater F-waves detection: Application in patients with amyotrophic lateral sclerosis.

OBJECTIVES: We assessed the clinical usefulness of repeater F-waves (Freps) analysis in amyotrophic lateral sclerosis (ALS), using an automated computerized system (F Wave Analyzer). METHODS: Forty consecutive F-waves were recorded from the ulnar and peroneal nerve in 52 patients with ALS and 52 healthy control subjects. Data were imported into the F Wave Analyzer which identifies Freps and groups them. Parameters of Freps and non repeater F-waves (Fnonreps) were compared. RESULTS: Total number of repeating neurons, Freps persistence (100xFreps/40stimuli) and Index Total Freps (100xFreps/total number of F-waves) were significantly higher in the ALS compared to the control group (P ≤ 0.005). There were no consistent differences of F-wave latency or amplitude measurements between Freps and Fnonreps for both studied groups, with the exception of prolonged Freps minimum latency in ALS. CONCLUSION: In ALS, the high numbers of Freps, reduced overall F-wave persistence and increased F-wave amplitude measurements in a relatively unaffected nerve-muscle complex reflects excitability alterations of the corresponding motor neuron pool. Overall, automatic analysis facilitates accurate and fast detection of Freps and could be useful in other clinical settings. SIGNIFICANCE: Analysis of repeater F-waves is expected to provide new insight regarding ALS pathophysiology and utilized for monitoring in clinical drug trials.

Integrating Brain and Biomechanical Models-A New Paradigm for Understanding Neuro-muscular Control.

To date, realistic models of how the central nervous system governs behavior have been restricted in scope to the brain, brainstem or spinal column, as if these existed as disembodied organs. Further, the model is often exercised in relation to an in vivo physiological experiment with input comprising an impulse, a periodic signal or constant activation, and output as a pattern of neural activity in one or more neural populations. Any link to behavior is inferred only indirectly via these activity patterns. We argue that to discover the principles of operation of neural systems, it is necessary to express their behavior in terms of physical movements of a realistic motor system, and to supply inputs that mimic sensory experience. To do this with confidence, we must connect our brain models to neuro-muscular models and provide relevant visual and proprioceptive feedback signals, thereby closing the loop of the simulation. This paper describes an effort to develop just such an integrated brain and biomechanical system using a number of pre-existing models. It describes a model of the saccadic oculomotor system incorporating a neuromuscular model of the eye and its six extraocular muscles. The position of the eye determines how illumination of a retinotopic input population projects information about the location of a saccade target into the system. A pre-existing saccadic burst generator model was incorporated into the system, which generated motoneuron activity patterns suitable for driving the biomechanical eye. The model was demonstrated to make accurate saccades to a target luminance under a set of environmental constraints. Challenges encountered in the development of this model showed the importance of this integrated modeling approach. Thus, we exposed shortcomings in individual model components which were only apparent when these were supplied with the more plausible inputs available in a closed loop design. Consequently we were able to suggest missing functionality which the system would require to reproduce more realistic behavior. The construction of such closed-loop animal models constitutes a new paradigm of computational neurobehavior and promises a more thoroughgoing approach to our understanding of the brain's function as a controller for movement and behavior.