In this study, we investigated the influence of motor imagery on the excitability of spinal nerve function by conducting an F-wave study in ten healthy participants (right-handed; mean age 27.2±7.8) and performing the Box and Block Test (BBT). Motor imagery was performed with the index finger and the thumb of the right hand at the earliest speed of the BBT. The change of F/M amplitudes ratios and F-wave persistence were examined at rest, during motor imagery, 2 min after motor imagery, and 4 min after motor imagery. Our findings indicated that the ratio of F/M amplitudes and F-wave persistence significantly increased during BBT motor imagery when compared to those of the resting state. Such results suggest that excitability of spinal nerve function increases during motor imagery in the BBT.
Resting state functional magnetic resonance imaging (RSfMRI) is an emerging method for measurement of brain function. Independent component analysis (ICA) allows for network-level analysis in patients with neurological disorders using RSfMRI. Although several research has been carried out on functional brain networks in amyotrophic lateral sclerosis (ALS), no studies examined network-network interaction. In terms of clinical practice, some patients with ALS cannot be diagnosed due to lack of upper motor neuron signs during course, and surrogate marker for upper motor neuron signs are required. ICA-based network analysis is expected to complement diagnostic assessment. The aim of this study was to assess the usefullness of ICA-based network analysis for diagnosis, and to reveal the network-network interaction in ALS. Twelve patients with ALS and 12 disease controls underwent MRI scan. Resting brain networks including sensorimotor network (SMN), salience network (SN) and right frontoparietal network (RFPN) were identified using ICA. No significant difference in network expression was found between groups. However, there was significant correlation among three networks in ALS, which were SMN, RFPN and SN. These findings suggested that ICA-based network analysis did not appear useful for diagnosis, but that abnormal network-network interaction was present in ALS.
Recent progress in Brain Machine Interface (BMI) research using signals from subdural electrocorticographical (ECoG) arrays implanted over primary motor cortex has demonstrated the potential for successful decoding of participant’s arm or hand movement. Concurrent advances in wireless implantable electrode technology are further shortening the gap between existing systems and real functioning rehabilitative neural prosthetics for use by patients with motor system disorders. However the long-term stability and effectiveness of BMI technologies depends crucially on the stability and effectiveness of the feature extraction and decoding algorithms used to translate the neural signals into behavioral commands. Given the limited density and coverage of ECoG channel arrays, it is critical to reliably extract as much of the available information as possible. While some success has been achieved using channel-based features, we have recently shown that improvement may be gained by first applying the Independent Component Analysis (ICA) method to the ECoG channel data to separate and isolate relevant cortical signals. In this talk, we will present some preliminary results of application of ICA to ECoG data for the derivation of features for BMI applications and comparison to standard approaches. We discuss the interpretation of the ICA features and suggest directions for future improvements.