Perinatal asphyxia: current status and approaches towards neuroprotective strategies, with focus on sentinel proteins

Delivery is a stressful and risky event menacing the newborn. The mother-dependent respiration has to be replaced by autonomous pulmonary breathing immediately after delivery. If delayed, it may lead to deficient oxygen supply compromising survival and development of the central nervous system. Lack of oxygen availability gives rise to depletion of NAD+ tissue stores, decrease of ATP formation, weakening of the electron transport pump and anaerobic metabolism and acidosis, leading necessarily to death if oxygenation is not promptly re-established. Re-oxygenation triggers a cascade of compensatory biochemical events to restore function, which may be accompanied by improper homeostasis and oxidative stress. Consequences may be incomplete recovery, or excess reactions that worsen the biological outcome by disturbed metabolism and/or imbalance produced by over-expression of alternative metabolic pathways. Perinatal asphyxia has been associated with severe neurological and psychiatric sequelae with delayed clinical onset. No specific treatments have yet been established. In the clinical setting, after resuscitation of an infant with birth asphyxia, the emphasis is on supportive therapy. Several interventions have been proposed to attenuate secondary neuronal injuries elicited by asphyxia, including hypothermia. Although promising, the clinical efficacy of hypothermia has not been fully demonstrated. It is evident that new approaches are warranted. The purpose of this review is to discuss the concept of sentinel proteins as targets for neuroprotection. Several sentinel proteins have been described to protect the integrity of the genome (e.g. PARP-1; XRCC1; DNA ligase IIIα; DNA polymerase β, ERCC2, DNA-dependent protein kinases). They act by eliciting metabolic cascades leading to (i) activation of cell survival and neurotrophic pathways; (ii) early and delayed programmed cell death, and (iii) promotion of cell proliferation, differentiation, neuritogenesis and synaptogenesis. It is proposed that sentinel proteins can be used as markers for characterising long-term effects of perinatal asphyxia, and as targets for novel therapeutic development and innovative strategies for neonatal care

Tensor decompositions for signal processing applications: from two-way to multiway component analysis

The widespread use of multisensor technology and the emergence of big data sets have highlighted the limitations of standard flat-view matrix models and the necessity to move toward more versatile data analysis tools. We show that higher-order tensors (i.e., multiway arrays) enable such a fundamental paradigm shift toward models that are essentially polynomial, the uniqueness of which, unlike the matrix methods, is guaranteed under very mild and natural conditions. Benefiting from the power of multilinear algebra as their mathematical backbone, data analysis techniques using tensor decompositions are shown to have great flexibility in the choice of constraints which match data properties and extract more general latent components in the data than matrix-based methods

Assessing the Potential of Data Augmentation in EEG Functional Connectivity for Early Detection of Alzheimer’s Disease

Acknowledgements: This work was carried out as part of the doctoral program in Experimental Science and Technology at the University of Vic - Central University of Catalonia. The authors would like to thank Pau Solé-Vilaró for the English proofreading of this manuscript.Funder: Universitat de VicAbstractElectroencephalographic (EEG) signals are acquired non-invasively from electrodes placed on the scalp. Experts in the field can use EEG signals to distinguish between patients with Alzheimer’s disease (AD) and normal control (NC) subjects using classification models. However, the training of deep learning or machine learning models requires a large number of trials. Datasets related to Alzheimer’s disease are typically small in size due to the lack of AD patient samples. The lack of data samples required for the training process limits the use of deep learning techniques for further development in clinical settings. We propose to increase the number of trials in the training set by means of a decomposition–recombination system consisting of three steps. Firstly, the original signals from the training set are decomposed into multiple intrinsic mode functions via multivariate empirical mode decomposition. Next, these intrinsic mode functions are randomly recombined across trials. Finally, the recombined intrinsic mode functions are added together as artificial trials, which are used for training the models. We evaluated the decomposition–recombination system on a small dataset using each subject’s functional connectivity matrices as inputs. Three different neural networks, including ResNet, BrainNet CNN, and EEGNet, were used. Overall, the system helped improve ResNet training in both the mild AD dataset, with an increase of 5.24%, and in the mild cognitive impairment dataset, with an increase of 4.50%. The evaluation of the proposed data augmentation system shows that the performance of neural networks can be improved by enhancing the training set with data augmentation. This work shows the need for data augmentation on the training of neural networks in the case of small-size AD datasets.</jats:p

A Brain-Controlled Vehicle System Based on Steady State Visual Evoked Potentials

Acknowledgements: We thank the participants for their willingness and predisposition to participate in the experiments. We also thank the reviewers for their comments and suggestions that greatly improved the manuscript. Finally, we thank Pau Solé-Vilaró for reviewing the grammar, style and syntax of this manuscript.Funder: Universitat de VicAbstractIn this paper, we propose a human-vehicle cooperative driving system. The objectives of this research are twofold: (1) providing a feasible brain-controlled vehicle (BCV) mode; (2) providing a human-vehicle cooperative control mode. For the first aim, through a brain-computer interface (BCI), we can analyse the EEG signal and get the driving intentions of the driver. For the second aim, the human-vehicle cooperative control is manifested in the BCV combined with the obstacle detection assistance. Considering the potential dangers of driving a real motor vehicle in the outdoor, an obstacle detection module is essential in the human-vehicle cooperative driving system. Obstacle detection and emergency braking can ensure the safety of the driver and the vehicle during driving. EEG system based on steady-state visual evoked potential (SSVEP) is used in the BCI. Simulation and real vehicle driving experiment platform are designed to verify the feasibility of the proposed human-vehicle cooperative driving system. Five subjects participated in the simulation experiment and real the vehicle driving experiment. The outdoor experimental results show that the average accuracy of intention recognition is 90.68 ± 2.96% on the real vehicle platform. In this paper, we verified the feasibility of the SSVEP-based BCV mode and realised the human-vehicle cooperative driving system. </jats:p

Learning fast dictionaries for sparse representations using low-rank tensor decompositions

International audienceA new dictionary learning model is introduced where the dictionary matrix is constrained as a sum of R Kronecker products of K terms. It offers a more compact representation and requires fewer training data than the general dictionary learning model, while generalizing Tucker dictionary learning. The proposed Higher Order Sum of Kroneckers model can be computed by merging dictionary learning approaches with the tensor Canonic Polyadic Decomposition. Experiments on image denoising illustrate the advantages of the proposed approach

Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach

One of the current issues in brain-computer interface (BCI) is how to deal with noisy electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al. Chemom Intell Lab Syst. 106:41-56, 2011), 3DPB-TC (Caiafa et al. 2013), BCPF (Zhao et al. IEEE Trans Pattern Anal Mach Intell. 37(9):1751-1763, 2015), and HaLRT (Liu et al. IEEE Trans Pattern Anal Mach Intell. 35(1):208-220, 2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by 7–9% (LDA–SVD) for random missing entries and 15–8% (LDA–SVD) for random missing channels. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.Fil: Solé Casals, J.. Universitat de Vic; EspañaFil: Caiafa, César Federico. Indiana University; Estados Unidos. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Zhao, Q.. Guangdong University Of Technology; China. Riken; JapónFil: Cichocki, A.. Skolkovo Institute Of Science And Technology; Rusia. Hangzhou Dianzi University; Chin