Worsening of verbal fluency after treatment with deep brain stimulation in Parkinson's disease patients is one of the most often reported cognitive adverse effect. The underlying mechanisms of this decline are not well understood. The present focused review assesses the evidence for the reliability of the often-reported decline of verbal fluency, as well as the evidence for the suggested mechanisms including disease progression, reduced medication levels, electrode positions, and stimulation effect vs. surgical effects. Finally, we highlight the need for more systematic investigations of the large degree of heterogeneity in the prevalence of verbal fluency worsening after DBS, as well as provide suggestions for future research.
Extracellular neuronal microelectrode recordings can include action potentials from multiple neurons. To separate spikes from different neurons, they can be sorted according to their shape, a procedure referred to as spike-sorting. Several algorithms have been reported to solve this task. However, when clustering outcomes are unsatisfactory, most of them are difficult to adjust to achieve the desired results. We present an online spike-sorting framework that uses feature normalization and weighting to maximize the distinctiveness between different spike shapes. Furthermore, multiple criteria are applied to either facilitate or prevent cluster fusion, thereby enabling experimenters to fine-tune the sorting process. We compare our method to established unsupervised offline (Wave_Clus (WC)) and online (OSort (OS)) algorithms by examining their performance in sorting various test datasets using two different scoring systems (AMI and the Adamos metric). Furthermore, we evaluate sorting capabilities on intra-operative recordings using established quality metrics. Compared to WC and OS, our algorithm achieved comparable or higher scores on average and produced more convincing sorting results for intra-operative datasets. Thus, the presented framework is suitable for both online and offline analysis and could substantially improve the quality of microelectrode-based data evaluation for research and clinical application.
Prediction of infection trends, estimating the efficacy of contact tracing, testing or impact of influx of infected are of vital importance for administration during an ongoing epidemic. Most effective methods currently are empirical in nature and their relation to parameters of interest to administrators are not evident. We thus propose a modified SEIRD model that is capable of modeling effect of interventions and inward migrations on the progress of an epidemic. The tunable parameters of this model bear relevance to monitoring of an epidemic. This model was used to show that some of the commonly seen features of cumulative infections in real data can be explained by piecewise constant changes in interventions and population influx. We also show that the data of cumulative infections from twelve Indian states between mid March and mid April 2020 can be generated from the model by applying interventions according to a set of heuristic rules. Prediction for the next ten days based on this model, reproduced real data very well. In addition, our model also reproduced the time series of recoveries and deaths. Our work constitutes an important first step towards an effective dashboard for the monitoring of epidemic by the administration, especially in an Indian context.
In this study, we explored the role of feedforward mechanisms in triggering cardiorespiratory adjustments before the onset of exercise. To isolate the feedforward aspects, we examined the effect of exercise anticipation on cardiorespiratory coherence. Twenty‐nine healthy males (age = 18.8 [0.96] years) were subjected to bicycle (BE) and handgrip exercise (H) at two different intensities, viz., low and high. Bicycle exercise was performed in a unilateral (left‐ and right‐sided) or bilateral mode, whereas handgrip was performed only in a unilateral mode. Single‐lead ECG and respiratory rhythm, measured in the 5 min of anticipation phase before the onset of exercise, were used for analysis. Coherence was computed between ECG‐derived instantaneous heart rate and respiratory signal. Average coherence in the high‐frequency band (0.15–0.4 Hz) was used to estimate respiratory sinus arrhythmia (RSA). We found that coherence decreased with the anticipation of exercise relative to baseline (baseline = 0.54 [0.16], BE = 0.41 [0.12], H = 0.39 [0.12], p < 0.001). The decrease was greater for high intensity exercise (low = 0.42 [0.11], high = 0.37 [0.1], p < 0.001). The fall of coherence with intensity was stronger for bicycle exercise (BE: low = 0.44 [0.12], high = 0.37 [0.12], H: low = 0.4 [0.12], high = 0.37 [0.12], p = 0.00433). The expectation of bilateral exercise resulted in lower coherence compared to unilateral exercise (right‐sided = 0.45 [0.16], left‐sided = 0.4 [0.16], bilateral = 0.36 [0.15], unilateral vs. bilateral: p < 0.001), and the left‐sided exercise had lower coherence compared to that of the right (left‐sided vs. right‐sided: p = 0.00925). Handgrip exercise showed similar trend (right‐sided = 0.4 [0.15], left‐sided = 0.37 [0.14], p = 0.0056). In conclusion, feedforward RSA adjustments in anticipation of exercise covaried with subsequent exercise‐related features like intensity, muscle mass (unilateral vs. bilateral), and the exercise side (left vs. right). The left versus the right difference in coherence indicates autonomic asymmetry. Feedforward changes in RSA are like those seen during actual exercise and might facilitate the rapid phase transition between rest and exercise.
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