The link between synaptic plasticity and reorganization of brain activity in health and disease remains a scientific challenge. We examined this question in Parkinson’s disease (PD) where functional up-regulation of postsynaptic D2 receptors has been documented while its significance at the neural activity level has never been identified. We investigated cortico-subcortical plasticity in PD using the oculomotor system as a model to study reorganization of dopaminergic networks. This model is ideal because this system reorganizes due to frontal-to-parietal shifts in blood oxygen level–dependent (BOLD) activity. We tested the prediction that functional activation plasticity is associated with postsynaptic dopaminergic modifications by combining positron emission tomography/functional magnetic resonance imaging to investigate striatal postsynaptic reorganization of dopamine D2 receptors (using 11C-raclopride) and neural activation in PD. We used covariance (connectivity) statistics at molecular and functional levels to probe striato-cortical reorganization in PD in on/off medication states to show that functional and molecular forms of reorganization are related. D2 binding across regions defined by prosaccades showed increased molecular connectivity between both caudate/putamen and hyperactive parietal eye fields in PD in contrast with frontal eye fields in controls, in line with the shift model. Concerning antisaccades, parietal-striatal connectivity dominated in again in PD, unlike frontal regions. Concerning molecular–BOLD covariance, a striking sign reversal was observed: PD patients showed negative frontal-putamen functional–molecular associations, consistent with the reorganization shift, in contrast with the positive correlations observed in controls. Follow-up analysis in off-medication PD patients confirmed the negative BOLD–molecular correlation. These results provide a link among BOLD responses, striato-cortical synaptic reorganization, and neural plasticity in PD.
Abstract:This paper investigates the possibility to classify isolated human activities from biosignal sensors integrated into a knee orthosis. An intelligent orthosis that is capable to recognize its wearers activity would be able to adapt itself to the users situation for enhanced comfort. We use a setup with three modalities: accelerometry, electromyography and goniometry to measure leg motion and muscle activity of the wearer. We segment signals in motion primitives and apply Hidden Markov Models to classify these isolated motion primitives. We discriminate between seven activities like for example walking stairs and ascend or descend a hill. In a small user study we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%.
Time series unsupervised clustering is accurate in various domains, and there is an increased interest in time series clustering algorithms for human behavior recognition. The authors have developed an algorithm for biosignals clustering, which captures the general morphology of a signal’s cycles in one mean wave. In this chapter, they further validate and consolidate it and make a quantitative comparison with a state-of-the-art algorithm that uses distances between data’s cepstral coefficients to cluster the same biosignals. They are able to successfully replicate the cepstral coefficients algorithm, and the comparison showed that the mean wave approach is more accurate for the type of signals analyzed, having a 19% higher accuracy value. They authors also test the mean wave algorithm with biosignals with three different activities in it, and achieve an accuracy of 96.9%. Finally, they perform a noise immunity test with a synthetic signal and notice that the algorithm remains stable for signal-to-noise ratios higher than 2, only decreasing its accuracy with noise of amplitude equal to the signal. The necessary validation tests performed in this study confirmed the high accuracy level of the developed clustering algorithm for biosignals that express human behavior.
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