Showing papers in "Journal of Neuroscience Methods in 2018"

TL;DR: A suite of software tools, called EpiTools, able to precisely and automatically localize the position of each SEEG contact and display the results of signal analysis in each patient's anatomy, can help speeding up the interpretation of SEEG data and improving its precision.

TL;DR: In this paper, enzyme-linked immunosorbent assays (ELISAs) were used for the measurement of salivary Oxytocin (OT) and Vasopressin (AVP) in domestic dogs.

TL;DR: Results support the feasibility and both the ecological and construct validity of the Virtual Shop, a fully immersive task developed to assess memory in a more ecological way than traditional neuropsychological assessments.

TL;DR: Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification, and resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data.

TL;DR: This review deepens and extends contents recently published by adding new concepts and examples concerning the applications of TPA in the study of behaviour both in human and non-human subjects.

TL;DR: A robust method has been proposed for the international challenge on MCI prediction based on MRI data and yielded the second best performance during the competition with an accuracy rate of 56.25% when evaluated on the real subjects of the test set.

TL;DR: Performing dynamic filtering of EEG-signals using WOLA increases the recognition rate of ERD/ERS patterns of motor imagery brain activity and reduces the overall classification error rate for the three datasets of the BCI-competition.

TL;DR: The Wide Field-of-View Nematode Tracking Platform (WF-NTP) is described, which enables massively parallel data acquisition and automated multi-parameter behavioural profiling of thousands of worms simultaneously and shows that the sensitivity and reproducibility of behavioural assays is very greatly increased.

TL;DR: The peripheral sensations and discomfort caused by TMS pulses significantly and systematically influence RTs during single-pulse, online TMS experiments.

TL;DR: In this article, the identification of individual rhesus macaques within a group is addressed. But the problem is not solved as pair-and group-housing of macaques becomes standard and current published methods require either implantation or wearing a tracking device.

TL;DR: The usefulness of 50-kHz USVs as an experimental tool to investigate how different neurotransmitter systems regulate the manifestations of positive emotional states is discussed, and also use of this tool in preclinical modeling of psychiatric diseases is discussed.

TL;DR: A single task -Attentional Networks Test for Interactions and Vigilance - executive and arousal components (ANTI-Vea) to simultaneously assess both components of vigilance while measuring the classic attentional functions.

TL;DR: A hierarchical process is proposed by turning the 4-way classification into five binary classification problems and a new feature selection technology based on relative importance is proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features.

TL;DR: MR-guided CED infusion into thalamus provides a simplified approach to transduce large cortical areas by thalamo-cortico-thalamic projections in primate brain and achieves large expressing areas in comparison to convectional techniques.

TL;DR: The neural spheroid is a new model to study the formation of capillary-like networks in a three-dimensional environment that incorporates both neuronal and glial cell types, and does not require exogenous vasculogenic growth factors.

TL;DR: The advantages of floatation iodixanol density gradient isolation of small EVs are demonstrated, and evidence of EV enrichment by electron microscopy, immunoblot analysis, and quantitative mass spectrometry is provided.

TL;DR: Increased attention on model quality will lead to lower false-positive rates in cognitive neuroscience and increased application of the MACS toolbox will increase the reproducibility of GLM analyses and is likely to increase the replicability of fMRI studies.

TL;DR: A novel method to rigorously enrich the characterization of scale-free brain activity using a robust wavelet-based assessment of self-similarity and multifractality is offered, showing that the wavelet leader based multifractal approach extends power spectrum estimation methods in the way of characterizing finely scale- free brain dynamics.

TL;DR: Activity-independent t-GRASP is a novel targeted GRASP method for assessing synaptic connectivity between Drosophila neurons and its flexibility of use with all three Drosophile binary transcription systems significantly expands the potential use of GRasP in Drosphila.

TL;DR: It is argued that ICA already performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and will identify activity that is local in human S-EEG.

TL;DR: Simulations investigated the effects of signal averaging on SNR, measurement error and group-level variance against a known ground truth and found that SNR increased with the square root of the number of averages.

TL;DR: The architecture and general workflow of this toolbox, as well as a simulated data set demonstrating some of its functions, are presented, indicating that SEREEGA is a general-purpose toolbox to simulate ground-truth EEG data.

TL;DR: Electric pain-evoked potentials are equally modulated by expectation, within the same participants, and a difference is shown between the pain types in anticipation, which may reflect different processes.

TL;DR: The ALICE pipeline provides a fast and accurate detection, discrimination and localization of ECoG electrodes spaced down to 4 mm apart and integrates the multiple-step method into a user-friendly GUI in Matlab®, thus providing an easy, automated and standard tool for E coG electrode localization.

TL;DR: A novel algorithm is developed, which extracts respiration signals based on pixel time series, removing the need for nose-tracking and image segmentation, and finds the algorithm reliably detects inhalation onsets with high temporal precision.

TL;DR: The CS algorithm is shown to have high sensitivity and specificity for HFOs, be robust to common forms of artifact in EEG, and have performance adequate for use in a clinical setting.

TL;DR: The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study, and the boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods.

TL;DR: An automated, stepwise method for fitting a neuron model to data with fine spatial resolution and reliably reproduces the membrane-potential dynamics across the dendrites as predicted by the full-morphology model is developed and applied.

TL;DR: A new automatic muscle-removal approach based on the traditional Blind Source Separation-Canonical Correlation Analysis method and the spectral slope that can reduce high-frequency muscle contamination of EEG, especially at peripheral channels, while preserving steady-state brain responses in cognitive tasks is introduced.

TL;DR: An existing performance-check methodology in the BioVoxxel toolbox is applied, validated and extended to z-projected images of ex vivo glia cells and yields improved binary representation of glial cells compared with the conventional user-chosen single threshold approach for confocal z-stacks acquired from ex vivo slice cultures.