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

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 multi-neuronal 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, which enables neurons to fire spikes with a certain delay and heterosynaptic plasticity, which allows the own regulation of synaptic weights. From the perspective of the information representation, the structure allows mapping a spatio-temporal stimulus into a multi-dimensional, 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 results show that out method exhibits similar performance compared with other classification methods, indicating the effectiveness of our approach. In addition, 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

Resting-State Beta-Band Recovery Network Related to Cognitive Improvement After Stroke

Stroke is the second leading cause of death worldwide and it causes important long-term cognitive and physical deficits that hamper patients' daily activity. Neuropsychological rehabilitation (NR) has increasingly become more important to recover from cognitive disability and to improve the functionality and quality of life of these patients. Since in most stroke cases, restoration of functional connectivity (FC) precedes or accompanies cognitive and behavioral recovery, understanding the electrophysiological signatures underlying stroke recovery mechanisms is a crucial scientific and clinical goal. For this purpose, a longitudinal study was carried out with a sample of 10 stroke patients, who underwent two neuropsychological assessments and two resting-state magnetoencephalographic (MEG) recordings, before and after undergoing a NR program. Moreover, to understand the degree of cognitive and neurophysiological impairment after stroke and the mechanisms of recovery after cognitive rehabilitation, stroke patients were compared to 10 healthy controls matched for age, sex, and educational level. After intra and inter group comparisons, we found the following results: (1) Within the stroke group who received cognitive rehabilitation, almost all cognitive domains improved relatively or totally; (2) They exhibit a pattern of widespread increased in FC within the beta band that was related to the recovery process (there were no significant differences between patients who underwent rehabilitation and controls); (3) These FC recovery changes were related with the enhanced of cognitive performance. Furthermore, we explored the capacity of the neuropsychological scores before rehabilitation, to predict the FC changes in the brain network. Significant correlations were found in global indexes from the WAIS-III: Performance IQ (PIQ) and Perceptual Organization index (POI) (i.e., Picture Completion, Matrix Reasoning, and Block Design)

Noninvasive prediction of shunt operation outcome in idiopathic normal pressure hydrocephalus

Idiopathic normal pressure hydrocephalus (iNPH) is a syndrome characterized by gait disturbance, cognitive deterioration and urinary incontinence in elderly individuals. These symptoms can be improved by shunt operation in some but not all patients. Therefore, discovering predictive factors for the surgical outcome is of great clinical importance. We used normalized power variance (NPV) of electroencephalography (EEG) waves, a sensitive measure of the instability of cortical electrical activity, and found significantly higher NPV in beta frequency band at the right fronto-temporo-occipital electrodes (Fp2, T4 and O2) in shunt responders compared to non-responders. By utilizing these differences, we were able to correctly identify responders and non-responders to shunt operation with a positive predictive value of 80% and a negative predictive value of 88%. Our findings indicate that NPV can be useful in noninvasively predicting the clinical outcome of shunt operation in patients with iNPH

MEG functional network disorganization associates with cerebrospinal fluid biomarkers in early Alzheimer’s disease

To determine whether functional connectivity patterns, as an index of synaptic dysfunction, associate with cerebrospinal fluid (CSF) biomarkers (i.e., phospho-tau and amyloid beta -A 42- levels) in patients with Mild Cognitive Impairment due to Alzheimer?s disease. We also assessed orrelations of aberrant functional connections with structural connectivity abnormalities and with cognitive deficit

Predictive factors of occupational noise-induced hearing loss in Spanish workers: A prospective study

The purpose of our study was to identify the main factors associated with objective noise-induced hearing loss (NIHL), as indicated by abnormal audiometric testing, in Spanish workers exposed to occupational noise in the construction industry. We carried out a prospective study in Tenerife, Spain, using 150 employees exposed to occupational noise and 150 age-matched controls who were not working in noisy environments. The variables analyzed included sociodemographic data, noise-related factors, types of hearing protection, self-report hearing loss, and auditory-related symptoms (e.g., tinnitus, vertigo). Workers with pathological audiograms had significantly longer noise-exposure duration (16.2 ± 11.4 years) relative to those with normal audiograms (10.2 ± 7.0 years; t = 3.99, P < 0.001). The vast majority of those who never used hearing protection measures had audiometric abnormalities (94.1%). Additionally, workers using at least one of the protection devices (earplugs or earmuffs) had significantly more audiometric abnormalities than those using both protection measures simultaneously (Chi square = 16.07; P < 0.001). The logistic regression analysis indicates that the use of hearing protection measures [odds ratio (OR) = 12.30, confidence interval (CI) = 4.36-13.81, P < 0.001], and noise-exposure duration (OR = 1.35, CI = 1.08-1.99, P = 0.040) are significant predictors of NIHL. This regression model correctly predicted 78.2% of individuals with pathological audiograms. The combined use of hearing protection measures, in particular earplugs and earmuffs, associates with a lower rate of audiometric abnormalities in subjects with high occupational noise exposure. The use of hearing protection measures at work and noise-exposure duration are best predictive factors of NIHL. Auditory-related symptoms and self-report hearing loss do not represent good indicators of objective NIHL. Routine monitoring of noise levels and hearing status are of great importance as part of effective hearing conservation programs

New Insights on Basic and Clinical Aspects of EEG and MEG Connectome

Recent advances in the neuroimaging field areas allow us to visualize the aggregate of neural connections at the macroscopic level within the brain, the so-called “connectome”. In order to promote the development of the neurophysiological investigation of connectome of brain oscillations, this eBook aims at bringing together contributions from researchers in basic and clinical neuroscience using EEG and MEG connectome analysis. The most important focal point will be to address the functional roles of connectome of brain oscillations in contributing to understandings of higher cognitive processes in normal subjects and pathophysiology of psychiatric diseases. This Research Topic presented novel methodologies and various applications of neurophysiological connectome analysis. As a result, these papers were cited more than 120 times in these four years in total and threw light and impact on new directions for investigating the connectome of human brain

Editorial: New Insights on Basic and Clinical Aspects of EEG and MEG Connectome

Recent advances in the neuroimaging field areas allow us to visualize the aggregate of neural connections at the macroscopic level within the brain, the so-called “connectome”. In order to promote the development of the neurophysiological investigation of connectome of brain oscillations, this eBook aims at bringing together contributions from researchers in basic and clinical neuroscience using EEG and MEG connectome analysis. The most important focal point will be to address the functional roles of connectome of brain oscillations in contributing to understandings of higher cognitive processes in normal subjects and pathophysiology of psychiatric diseases. This Research Topic presented novel methodologies and various applications of neurophysiological connectome analysis. As a result, these papers were cited more than 120 times in these four years in total and threw light and impact on new directions for investigating the connectome of human brain

Automated Source Estimation of Scalp EEG Epileptic Activity Using eLORETA Kurtosis Analysis

Objectives: eLORETA (exact low-resolution brain electromagnetic tomography) is a technique created by Pascual-Marqui et al. [Int J Psychophysiol. 1994 Oct; 18(1): 49–65] for the 3-dimensional representation of current source density in the brain by electroencephalography (EEG) data. Kurtosis analysis allows for the identification of spiky activity in the brain. In this study, we focused on the evaluation of the reliability of eLORETA kurtosis analysis. For this purpose, the results of eLORETA kurtosis source localization of paroxysmal activity in EEG were compared with those of eLORETA current source density (CSD) analysis of EEG data in 3 epilepsy patients with partial seizures. Methods: EEG was measured using a digital EEG system with 19 channels. We set the bandpass filter at traditional frequency band settings (1–4, 4–8, 8–15, 15–30, and 30–60 Hz) and 5–10 and 20–70 Hz and performed eLORETA kurtosis to compare the source localization of paroxysmal activity with that of visual interpretation of EEG data and CSD analysis of eLORETA in focal epilepsy patients. Results: The eLORETA kurtosis analysis of EEG data preprocessed by bandpass filtering from 20 to 70 Hz and traditional frequency band settings did not show any discrete paroxysmal source activity compatible with the results of CSD analysis of eLORETA. In all 3 cases, eLORETA kurtosis analysis filtered at 5–10 Hz showed paroxysmal activities in the theta band, which were all consistent with the visual inspection results and the CSD analysis results. Discussion: Our findings suggested that eLORETA kurtosis analysis of EEG data might be useful for the identification of spiky paroxysmal activity sources in epilepsy patients. Since EEG is widely used in the clinical practice of epilepsy, eLORETA kurtosis analysis is a promising method that can be applied to epileptic activity mapping