Topography of functional connectivity in human multichannel EEG during second language processing

We analyze the topography of nonlinear interdependence in the EEG of two group German-native speakers, divided according to their English proficiency level (high or low), when listening to one text in German and one in English. Global functional connectivity was assessed in the full band EEGs using the nonlinear correlation integration entropy, an index of multivariate interdependence derived from the normalized cross-mutual information between every two electrodes within each region of interest (ROI): three interhemispheric (frontal, centro-temporal and parieto-occipital) and two intrahemispheric ones (left and right hemisphere). The results show clear topographic differences between the interhemispheric ROIs, but no differences between the intrahemispheric ROIs Furthermore, there were also differences in language processing that depend on the proficiency level. We discuss these results and their implications along with recent findings about phase synchronization in the gamma band during second language processing

A neuro-inspired system for online learning and recognition of parallel spike trains, based on spike latency and heterosynaptic STDP

Humans perform remarkably well in many cognitive tasks including pattern recognition. However, the neuronal mechanisms underlying this process are not well understood. Nevertheless, artificial neural networks, inspired in brain circuits, have been designed and used to tackle spatio-temporal pattern recognition tasks. In this paper we present a multineuronal spike pattern detection structure able to autonomously implement online learning and recognition of parallel spike sequences (i.e., sequences of pulses belonging to different neurons/neural ensembles). The operating principle of this structure is based on two spiking/synaptic neurocomputational characteristics: spike latency, that enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, that allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multidimensional, temporal, feature space. In this space, the parameter coordinate and the time at which a neuron fires represent one specific feature. In this sense, each feature can be considered to span a single temporal axis. We applied our proposed scheme to experimental data obtained from a motor inhibitory cognitive task. The test exhibits good classification performance, indicating the adequateness of our approach. In addition to its effectiveness, its simplicity and low computational cost suggest a large scale implementation for real time recognition applications in several areas, such as brain computer interface, personal biometrics authentication or early detection of diseases.Comment: Submitted to Frontiers in Neuroscienc

Choice of Magnetometers and Gradiometers after Signal Space Separation

Background: Modern Elekta Neuromag MEG devices include 102 sensor triplets containing one magnetometer and two planar gradiometers. The first processing step is often a signal space separation (SSS), which provides a powerful noise reduction. A question commonly raised by researchers and reviewers relates to which data should be employed in analyses: (1) magnetometers only, (2) gradiometers only, (3) magnetometers and gradiometers together. The MEG community is currently divided with regard to the proper answer. Methods: First, we provide theoretical evidence that both gradiometers and magnetometers result from the backprojection of the same SSS components. Then, we compare resting state and task-related sensor and source estimations from magnetometers and gradiometers in real MEG recordings before and after SSS. Results: SSS introduced a strong increase in the similarity between source time series derived from magnetometers and gradiometers (r2 = 0.3–0.8 before SSS and r2 > 0.80 after SSS). After SSS, resting state power spectrum and functional connectivity, as well as visual evoked responses, derived from both magnetometers and gradiometers were highly similar (Intraclass Correlation Coefficient > 0.8, r 2 > 0.8). Conclusions: After SSS, magnetometer and gradiometer data are estimated from a single set of SSS components (usually ≤ 80). Equivalent results can be obtained with both sensor types in typical MEG experiments

Assessment of ADHD Through Electroencephalographic Measures of Functional Connectivity

The main objective of the chapter is to review the types of electroencephalographic measures of functional connectivity that have been used so far in the study/diagnosis of ADHD. The review will include the methods and results so far reported in the literature as well as those conducted by our research group

Functional connectivity of the hippocampus and its subfields in resting-state networks

First published: 30 March 2021Many neuroimaging studies have shown that the hippocampus participates in a resting-state network called the default mode network. However, how the hippocampus connects to the default mode network, whether the hippocampus connects to other resting-state networks and how the different hippocampal subfields take part in resting-state networks remains poorly understood. Here, we examined these issues using the high spatial-resolution 7T resting-state fMRI dataset from the Human Connectome Project. We used data-driven techniques that relied on spatially-restricted Independent Component Analysis, Dual Regression and linear mixed-effect group-analyses based on participant-specific brain morphology. The results revealed two main activity hotspots inside the hippocampus. The first hotspot was located in an anterior location and was correlated with the somatomotor network. This network was subserved by co-activity in the CA1, CA3, CA4 and Dentate Gyrus fields. In addition, there was an activity hotspot that extended from middle to posterior locations along the hippocampal long-axis and correlated with the default mode network. This network reflected activity in the Subiculum, CA4 and Dentate Gyrus fields. These results show how different sections of the hippocampus participate in two known resting-state networks and how these two resting-state networks depend on different configurations of hippocampal subfield co-activity.Agencia Canaria de Investigación, Innovación y Sociedad de la Información; Ministerio de Ciencia, Innovación y Universidades, Grant/Award Number: PSI2017-84933- P, PSI2017-91955- EXP and TEC2016-80063- C3- 2- R; NIH Blueprint for Neuroscience Research, Grant/Award Number: 1U54MH091657; McDonnell Center for Systems Neuroscience; European Social Fund (ESF

Non-linear dynamical analysis of resting tremor for demand-driven deep brain stimulation.

Parkinson's Disease (PD) is currently the second most common neurodegenerative disease. One of the most characteristic symptoms of PD is resting tremor. Local Field Potentials (LFPs) have been widely studied to investigate deviations from the typical patterns of healthy brain activity. However, the inherent dynamics of the Sub-Thalamic Nucleus (STN) LFPs and their spatiotemporal dynamics have not been well characterized. In this work, we study the non-linear dynamical behaviour of STN-LFPs of Parkinsonian patients using ε -recurrence networks. RNs are a non-linear analysis tool that encodes the geometric information of the underlying system, which can be characterised (for example, using graph theoretical measures) to extract information on the geometric properties of the attractor. Results show that the activity of the STN becomes more non-linear during the tremor episodes and that ε -recurrence network analysis is a suitable method to distinguish the transitions between movement conditions, anticipating the onset of the tremor, with the potential for application in a demand-driven deep brain stimulation system