Showing papers in "Journal of Neuroscience Methods in 2020"

TL;DR: DA increasingly used and considerably improved DL decoding accuracy on EEG and holds transformative promise for EEG processing, possibly like DL revolutionized computer vision, etc.

TL;DR: The applications of NODDI in clinical research are reviewed and future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice are discussed.

TL;DR: This review dwells on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues.

TL;DR: The explored frameworks reflected the high potential of deep learning architectures in learning subtle predictive features and utility in critical applications such as predicting and understanding disease progression.

TL;DR: Osprey is the first MRS software to combine uniform pre-processing, linear-combination modelling, tissue correction and quantification into a coherent ecosystem, Compared to existing compiled, often closed-source modelling software, Osprey's open-source code philosophy allows researchers to integrate state-of-the-art data processing and modelling routines, and potentially converge towards standardization of analysis.

TL;DR: The findings suggest that the frontal lobe contains the most discriminative power towards the classification of ADHD, and the proposed end-to-end deep learning architecture achieves better performance as compared to the other state-of-the-art methods.

TL;DR: Fundamental and practical principles guiding PNI design are reviewed, followed by an updated and critical account of existing PNI designs and strategies, and a brief survey of in vitro and in vivo PNI characterization methods.

TL;DR: This review presents enough evidence that provides motivation for consideration in the future research using EEG source localization methods, and investigates the effect of the head model on EEG source imaging results.

TL;DR: The proposed system consists of a convolutional neural network with modified softmax loss function and regularization, which has higher accuracy by almost 2% and less processing time of 40∼50 milliseconds compared to other current solutions.

TL;DR: In this paper, a modified Kalman filter (MKF) was used to provide intuitive, independent and proportional control over six-DOF prostheses such as the DEKA “LUKE” arm.

TL;DR: SfEBs can be used in combination with HTS to compensate for experimental variability common in 3D cultures, while significantly decreasing processing speed, making this an efficient starting point for phenotypic drug screening.

TL;DR: This study concludes that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN.

TL;DR: Methodological differences in past silent period work are discussed, highlighting how these choices affect silent period outcome measures and challenges and possible solutions for measuring silent periods in the unique case of the lower limbs are outlined.

TL;DR: This comprehensive review provides evidence that, while the authors are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system.

TL;DR: It is suggested that hybrid EEG-fNIRS systems are a promising tool that may enhance the AD diagnosis and assessment process and the right prefrontal and left parietal regions are associated with the progression of AD.

TL;DR: Results show the validity of the proposed Deep Transfer-Learning based technique as a state of the art technique for MI classification in BCI as well as significant improvement over other studies.

TL;DR: The SVM plus AFBD method represents a useful contribution to olfactory-induced emotion recognition and is considerably higher than those of other combination methods, such as the combinations of AFBD or EEG rhythm-based features with naive Bayesian, k-nearest neighbor classification, voting-extreme learning machine, and backpropagation neural network methods.

TL;DR: The results suggest that the proposed ASD identification method is efficient to improve the performance of ASD identification, and is promising for ASD clinical diagnosis.

TL;DR: It is demonstrated that children with ASD showed weaker internal logic, but stronger memory and persistence to random shocks than TD children, and the deployed deep learning framework with an integration manner has the potential for screening children with risk of ASD.

TL;DR: Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis and were the most suitable methods to find theNumber of clusters in both simulation and real data.

TL;DR: Benefiting from the hierarchical structure with attention model, the proposed network is capable of exploiting low-level and high-level features extracted from the multi-modal data and improving the accuracy of AD diagnosis.

TL;DR: A fully automated method to localize and segment AIS lesions in variable locations for 192 multimodal 3D-magnetic resonance images (MRI) including 106 stroke and 86 healthy cases is developed and shows good agreement with the lesions identified by human experts.

TL;DR: The early stage of MCI is introduced to assess more thoroughly the earliest signs of disease manifestation and progression to help clinicians and researchers determine essential measures that can help in the early detection of AD.

TL;DR: The efficiency and reliability of vector delivery has been considerably improved by the development of new methods and instruments and this development should facilitate the translation of chemo- and optogenetic studies performed in smaller animals to larger animals such as rhesus monkeys.

TL;DR: The Kaggle competition as discussed by the authors leveraged crowdsourcing to build a pool of machine learning pipelines for neurological disorder diagnosis with application to ASD diagnosis using cortical morphological networks derived from T1-weighted MRI Results.

TL;DR: The results demonstrate the feasibility of combining TE with HOG for emotion recognition with improved classification accuracy by taking advantage of both network and gradient features.

TL;DR: The findings of this study demonstrate that the proposed prediction strategy is suitable for the prediction of seizure onset, and surpasses the accuracy of pure graph theory and pure non-linear methods with a significantly increased prediction time.

TL;DR: The proposed study provides an objective assessment of tremor and bradykinesia in Parkinson's disease and may help movement disorder clinicians to detect, diagnose and monitor treatment efficacy in Parkinson’s disease.

TL;DR: In using ECM-collagen gels, the importance of optimizing seeding parameters and thorough 3D culture characterization to understand the neurophysiological responses of these 3D systems is highlighted.

TL;DR: A novel approach, based on Fourier theory, known as Fourier decomposition method (FDM), for automatic identification of alcoholism using electroencephalogram (EEG) signals and can be employed in real-time alcoholism detection.