Spiveyfournier0454

Merkulyeva, Kalinina, Musienko, Rocchi, Mus, Sotnikova and Gainetdinov.Ensemble classifiers have been proven to result in better classification accuracy than that of a single strong learner in many machine learning studies. Although many studies on electroencephalography-brain-computer interface (BCI) used ensemble classifiers to enhance the BCI performance, ensemble classifiers have hardly been employed for near-infrared spectroscopy (NIRS)-BCIs. In addition, since there has not been any systematic and comparative study, the efficacy of ensemble classifiers for NIRS-BCIs remains unknown. In this study, four NIRS-BCI datasets were employed to evaluate the efficacy of linear discriminant analysis ensemble classifiers based on the bootstrap aggregating. From the analysis results, significant (or marginally significant) increases in the bitrate as well as the classification accuracy were found for all four NIRS-BCI datasets employed in this study. Moreover, significant bitrate improvements were found in two of the four datasets. Copyright © 2020 Shin and Im.This study presents a computational model of closed-loop control of deep brain stimulation (DBS) for Parkinson's disease (PD) to investigate clinically viable control schemes for suppressing pathological beta-band activity. Closed-loop DBS for PD has shown promising results in preliminary clinical studies and offers the potential to achieve better control of patient symptoms and side effects with lower power consumption than conventional open-loop DBS. However, extensive testing of algorithms in patients is difficult. The model presented provides a means to explore a range of control algorithms in silico and optimize control parameters before preclinical testing. The model incorporates (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The performance of on-off and dual-threshol of that utilized by open-loop DBS. The network model presented captures sufficient physiological detail to act as a surrogate for preclinical testing of closed-loop DBS algorithms using a clinically accessible biomarker, providing a first step for deriving and testing novel, clinically suitable closed-loop DBS controllers. Copyright © 2020 Fleming, Dunn and Lowery.Background Anorexia nervosa (AN) is a debilitating illness whose neural basis remains unclear. Studies using tract-based spatial statistics (TBSS) with diffusion tensor imaging (DTI) have demonstrated differences in white matter (WM) microarchitecture in AN, but the findings are inconclusive and controversial. Objectives To identify the most consistent WM abnormalities among previous TBSS studies of differences in WM microarchitecture in AN. Methods By systematically searching online databases, a total of 11 datasets were identified, including 245 patients with AN and 246 healthy controls (HC). We used Seed-based d Mapping to analyze fractional anisotropy (FA) differences between AN patients and HC, and performed meta-regression analysis to explore the effects of clinical characteristics on WM abnormalities in AN. Results The pooled results of all AN patients showed robustly lower FA in the corpus callosum (CC) and the cingulum compared to HC. These two regions preserved significance in the sensitivity analysis as well as in all subgroup analyses. Fiber tracking showed that the WM tracts primarily involved were the body of the CC and the cingulum bundle. Meta-regression analysis revealed that the body mass index and mean age were not linearly correlated with the lower FA. Conclusions The most consistent WM microstructural differences in AN were in the interhemispheric connections and limbic association fibers. These common "targets" advance our understanding of the complex neural mechanisms underlying the puzzling symptoms of AN, and may help in developing early treatment approaches. Copyright © 2020 Zhang, Wang, Su, Li, Yang, Su, Tan, Zhao, Sun, Kemp, Gong and Yue.We hypothesized that assessment of brain connectivity may shed light on the underpinnings of ocular hypertension (OHT), characterized by raised intraocular pressure (IOP) and no typical glaucomatous findings. OHT carries a risk for future glaucoma development, thus representing a model of presymptomatic condition. In previous studies on glaucoma, we showed altered brain connectivity since the early stage and in case of normal IOP. In this pilot study, we used a multimodal MRI approach by modeling voxelwise measures of gray matter volume, anatomical connectivity along white matter(WM) tracts, and large-scale functional connectivity in OHT subjects (n = 18, age 58.3 ± 9.8 years) and demographically matched normal controls (n = 29). While OHT brain had no structural alterations, it showed significantly decreased functional connectivity in key cognitive networks [default mode network, frontoparietal working memory network (WMN), ventral attention network (VAN), and salience network (SN)] and altered long-range functional connectivity, which was decreased between default mode and SNs and increased between primary and secondary visual networks (VN). Overall, such findings seem to delineate a complex neuroplasticity in the OHT brain, where decreased functional connectivity in non-visual networks may reflect a type of temporarily downregulated functional reserve while increased functional connectivity between VN may be viewed as a very early attempt of adaptive functional reorganization of the visual system. Copyright © 2020 Giorgio, Zhang, Costantino, De Stefano and Frezzotti.The olfactory neuroepithelium is located in the upper vault of the nasal cavity, lying on the olfactory cleft and projecting into the dorsal portion of the superior and middle turbinates beyond the mid-portion of the nasal septum. It is composed of a variety of cell types including olfactory sensory neurons, supporting glial-like cells, microvillar cells, and basal stem cells. The cells of the neuroepithelium are often intermingled with respiratory and metaplastic epithelial cells. Olfactory neurons undergo a constant self-renewal in the timespan of 2-3 months; they are directly exposed to the external environment, and thus they are vulnerable to physical and chemical injuries. The latter might induce metabolic perturbations and ultimately be the cause of cell death. However, the lifespan of olfactory neurons is biologically programmed, and for this reason, these cells have an accelerated metabolic cycle leading to an irreversible apoptosis. These characteristics make these cells suitable for research relatede protein misfolding occurring in the olfactory neuroepithelium. Copyright © 2020 Brozzetti, Sacchetto, Cecchini, Avesani, Perra, Bongianni, Portioli, Scupoli, Ghetti, Monaco, Buffelli and Zanusso.Alzheimer's disease (AD) is a neurodegenerative disease with a complex and not fully understood pathogenesis. Besides brain-intrinsic hallmarks such as abnormal deposition of harmful proteins, i.e., amyloid beta in plaques and hyperphosphorylated Tau in neurofibrillary tangles, blood-derived elements, in particular, platelets have been discussed to be involved in AD pathogenesis. The underlying mechanisms, however, are rather unexplored. Here, we investigate a potential role of platelets in an AD transgenic animal model with severe amyloid plaque formation, the APP-PS1 transgenic mice, and analyzed the presence, spatial location and activation status of platelets within the brain. In APP-PS1 mice, a higher number of platelets were located within the brain parenchyma, i.e., outside the cerebral blood vessels compared to WT controls. Such platelets were activated according to the expression of the platelet activation marker CD62P and to morphological hallmarks such as membrane protrusions. In the brain, platelets were in close contact exclusively with astrocytes suggesting an interaction between these two cell types. In the bloodstream, although the percentage of activated platelets did not differ between transgenic and age-matched control animals, APP-PS1 blood-derived platelets showed remarkable ultrastructural peculiarities in platelet-specific organelles such as the open canalicular system (OCS). This work urges for further investigations on platelets and their yet unknown functional roles in the brain, which might go beyond AD pathogenesis and be relevant for various age-related neurodegenerative diseases. Copyright © 2020 Kniewallner, de Sousa, Unger, Mrowetz and Aigner.Robust cross-subject emotion recognition based on multichannel EEG has always been hard work. In this work, we hypothesize that there exist default brain variables across subjects in emotional processes. Hence, the states of the latent variables that relate to emotional processing must contribute to building robust recognition models. Specifically, we propose to utilize an unsupervised deep generative model (e.g., variational autoencoder) to determine the latent factors from the multichannel EEG. Through a sequence modeling method, we examine the emotion recognition performance based on the learnt latent factors. The performance of the proposed methodology is verified on two public datasets (DEAP and SEED) and compared with traditional matrix factorization-based (ICA) and autoencoder-based approaches. Experimental results demonstrate that autoencoder-like neural networks are suitable for unsupervised EEG modeling, and our proposed emotion recognition framework achieves an inspiring performance. As far as we know, it is the first work that introduces variational autoencoder into multichannel EEG decoding for emotion recognition. We think the approach proposed in this work is not only feasible in emotion recognition but also promising in diagnosing depression, Alzheimer's disease, mild cognitive impairment, etc., whose specific latent processes may be altered or aberrant compared with the normal healthy control. Copyright © 2020 Li, Zhao, Song, Zhang, Pan, Wu, Huo, Niu and Wang.Oxidative stress has long been implicated in the pathophysiology and progression of Huntington's disease (HD). Uric acid (UA) is a naturally occurring antioxidant that is present in the brain and periphery. Growing evidence has implicated UA as a molecular biomarker for several neurodegenerative diseases, most notably Parkinson's disease (PD). In this study, we investigated UA levels in clinical samples from HD patients and normal controls (NCs) and assessed potential relationships between UA levels and disease and clinical data. UA levels were measured in plasma (n = 107) and saliva (n = 178) samples from premanifest (pre-HD) and manifest HD patients and control subjects. Gender effects of UA levels were observed in both biofluids, with male patients showing higher UA levels compared to female patients. Comparisons of UA levels across diagnostic groups, separated by gender, revealed that both plasma and salivary UA levels were significantly lower in female pre-HD and manifest HD patients compared to NCs. Salse symptoms and burden. Our findings further highlight the impact of sexual dimorphism in HD pathophysiology. Copyright © 2020 Corey-Bloom, Haque, Aboufadel, Snell, Fischer, Granger, Granger and Thomas.