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Background: Knowledge regarding neuropsychological training in Rett syndrome (RS) is scarce. The aim of this study was to assess the outcome and the duration of the effect of cognitive stimulation on topographic EEG data in RS. Method: Twenty female children diagnosed with RS were included in the analysis. Girls with RS conducted a cognitive task using an eye-tracker designed to evaluate access and choice skills. EEG data was acquired during the experimental procedure including two 10-minutes baseline stages before and after the task. Topographical changes of several EEG spectral markers including absolute and relative powers, brain symmetry index and entropy were assessed. Results: Topographic significance probability maps suggested statistical decreases on delta activity and increases on beta rhythm associated with the cognitive task. Entropy increased during and after the task, likely related to more complex brain activity. A significant positive interaction was obtained between brain symmetry index (BSI) and age showing that the improvement of interhemispheric symmetry was higher in younger girls (5-10 years). Conclusions: According to our findings, significant alterations of brain rhythms were observed during and after cognitive stimulation, suggesting that cognitive stimulation may have effects on brain activity beyond the stimulation period. Finally, our promising results also showed an increased brain symmetry that was especially relevant for the younger group. This could suggest an interaction of the eye-tracking cognitive task, however, further studies in this field are needed to assess the relation between brain asymmetries and age. We would like to acknowledge specific funding support from the Spanish Patient Associations Mi Princesa Rett and Rettando al Síndrome de Rett. This project has also received funding from Torrons Vicens and the Ministry of Science and Innovation (MICINN), Spain, under contract PID2020-117751RB-I00. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. A. Bachiller is a Serra Húnter Fellow. A.García-Cazorla is supported by FIS P118/00111 “Instituto de Salud Carlos III (ISCIII)” and “Fondo Europeo de desarrollo regional (FEDER)”. A. Tost has received the predoctoral scholarship FI-AGAUR from the Generalitat de Catalunya.

The data that support the findings of this study are openly available at the following URL/DOI: https://doi.org/10.1371/journal.pone.0182578. Objective. Improvements in electroencephalography enable the study of the localization of active brain regions during motor tasks. Movement-related cortical potentials (MRCPs), and event-related desynchronization (ERD) and synchronization are the main motor-related cortical phenomena/neural correlates observed when a movement is elicited. When assessing neurological diseases, averaging techniques are commonly applied to characterize motor related processes better. In this case, a large number of trials is required to obtain a motor potential that is representative enough of the subject’s condition. This study aimed to assess the effect of a limited number of trials on motor-related activity corresponding to different upper limb movements (elbow flexion/extension, pronation/supination and hand open/close). Approach. An open dataset consisting on 15 healthy subjects was used for the analysis. A Monte Carlo simulation approach was applied to analyse, in a robust way, different typical time- and frequency-domain features, topography, and low-resolution electromagnetic tomography. Main results. Grand average potentials, and topographic and tomographic maps showed few differences when using fewer trials, but shifts in the localization of motor-related activity were found for several individuals. MRCP and beta ERD features were more robust to a limited number of trials, yielding differences lower than 20% for cases with 50 trials or more. Strong correlations between features were obtained for subsets above 50 trials. However, the inter-subject variability increased as the number of trials decreased. The elbow flexion/extension movement showed a more robust performance for a limited number of trials, both in population and in individual-based analysis. Significance. Our findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features in terms of differences in the averaged motor features, correlation, and changes in topography and tomography. This study has been funded by the Ministry of Science and Innovation (MICINN), Spain, under contract PID2020-117751RB-I00. CIBER-BBN is an initiative of the Instituto de Salud Carlos III, Spain. Marta Borràs gratefully acknowledges the Universitat Politècnica de Catalunya and Banco Santander for the financial support of her predoctoral Grant FPI-UPC. A B is a Serra Hunter Fellow.

Spectral entropy (SE) is a measurement from information theory field that provides an estimation of EEG regularity and may be useful as a summary of its spectral properties. Previous studies using small samples reported a deficit of EEG entropy modulation in schizophrenia during cognitive activity. The present study is aimed at replicating this finding in a larger sample, to explore its cognitive and clinical correlates and to discard antipsychotic treatment as the main source of that deficit. We included 64 schizophrenia patients (21 first episodes, FE) and 65 healthy controls. We computed SE during performance of an odd-ball paradigm, at the windows prior (-300 to 0ms) and following (150 to 450ms) stimulus presentation. Modulation of SE was defined as the difference between post- and pre-stimulus windows. In comparison to controls, patients showed a deficit of SE modulation over frontal and central regions, also shown by FE patients. Baseline SE did not differ between patients and controls. Modulation deficit was directly associated with cognitive deficits and negative symptoms, and inversely with positive symptoms. SE modulation was not related to antipsychotic doses. Patients also showed a smaller change of median frequency (i.e., smaller slowing of oscillatory activity) of the EEG from pre- to post-stimulus windows. These results support that a deficit of fast modulation contributes to cognitive deficits and symptoms in schizophrenia patients.

Objective. High-frequency oscillations (HFOs) have emerged as a promising clinical biomarker for presurgical evaluation in childhood epilepsy. HFOs are commonly classified in stereo-encephalography as ripples (80–200 Hz) and fast ripples (200–500 Hz). Ripples are less specific and not so directly associated with epileptogenic activity because of their physiological and pathological origin. The aim of this paper is to distinguish HFOs in the ripple band and to improve the evaluation of the epileptogenic zone (EZ). Approach. This study constitutes a novel modeling approach evaluated in ten patients from Sant Joan de Deu Pediatric Hospital (Barcelona, Spain), with clearly-defined seizure onset zones (SOZ) during presurgical evaluation. A subject-by-subject basis analysis is proposed: a probabilistic Gaussian mixture model (GMM) based on the combination of specific ripple features is applied for estimating physiological and pathological ripple subpopulations. Main Results. Clear pathological and physiological ripples are identified. Features differ considerably among patients showing within-subject variability, suggesting that individual models are more appropriate than a traditional whole-population approach. The difference in rates inside and outside the SOZ for pathological ripples is significantly higher than when considering all the ripples. These significant differences also appear in signal segments without epileptiform activity. Pathological ripple rates show a sharp decline from SOZ to non-SOZ contacts and a gradual decrease with distance. Significance. This novel individual GMM approach improves ripple classification and helps to refine the delineation of the EZ, as well as being appropriate to investigate the interaction of epileptogenic and propagation networks.

Current sleep analyses have used electroencephalography (EEG) to establish sleep intensity through linear and nonlinear measures. Slow wave activity (SWA) and entropy are the most commonly used markers of sleep depth. The purpose of this study is to evaluate changes in brain EEG connectivity during sleep in healthy subjects and compare them with SWA and entropy. Four different connectivity metrics: coherence (MSC), synchronization likelihood (SL), cross mutual information function (CMIF), and phase locking value (PLV), were computed focusing on their correlation with sleep depth. These measures provide different information and perspectives about functional connectivity. All connectivity measures revealed to have functional changes between the different sleep stages. The averaged CMIF seemed to be a more robust connectivity metric to measure sleep depth (correlations of 0.78 and 0.84 with SWA and entropy, respectively), translating greater linear and nonlinear interdependences between brain regions especially during slow wave sleep. Potential changes of brain connectivity were also assessed throughout the night. Connectivity measures indicated a reduction of functional connectivity in N2 as sleep progresses. The validation of connectivity indexes is necessary because they can reveal the interaction between different brain regions in physiological and pathological conditions and help understand the different functions of deep sleep in humans.

Producción Científica, The aim of the present study was to explore the interchannel relationships of resting-state brain activity in patients with attentiondeficit/hyperactivity disorder (ADHD), one of the most common mental disorders that develop in children. Magnetoencephalographic (MEG) signals were recorded using a 148-channel whole-head magnetometer in 13 patients with ADHD (range: 8–12 years) and 14 control subjects (range: 8–13 years).Three complementary measures (coherence, phase-locking value, and Euclidean distance) were calculated in the conventionalMEG frequency bands: delta, theta, alpha, beta, and gamma. Our results showed that the interactions among MEG channels are higher for ADHD patients than for control subjects in all frequency bands. Statistically significant differences were observed for short-distance values within right-anterior and central regions, especially at delta, beta, and gamma-frequency bands (𝑝 < 0.05; Mann-Whitney 𝑈 test with false discovery rate correction). These frequency bands also showed statistically significant differences in long-distance interactions, mainly among anterior and central regions, as well as among anterior, central, and other areas. These differences might reflect alterations during brain development in children with ADHD. Our results support the role of frontal abnormalities in ADHD pathophysiology, which may reflect a delay in cortical maturation in the frontal cortex., Ministerio de Economía, Industria y Competitividad (TEC2014-53196-R), Junta de Castilla y León (programa de apoyo a proyectos de investigación - Ref. VA059U13

Objective. We propose a novel automated method called the S-Transform Gaussian Mixture detection algorithm (SGM) to detect high-frequency oscillations (HFO) combining the strengths of different families of previously published detectors. Approach. This algorithm does not depend on parameter tuning on a subject (or database) basis, uses time-frequency characteristics, and relies on non-supervised classification to determine if the events standing out from the baseline activity are HFO or not. SGM consists of three steps: the first stage computes the signal baseline using the entropy of the autocorrelation; the second uses the S-Transform to obtain several time-frequency features (area, entropy, and time and frequency widths); and in the third stage Gaussian mixture models cluster time-frequency features to decide if events correspond to HFO-like activity. To validate the SGM algorithm we tested its performance in simulated and real environments. Main results. We assessed the algorithm on a publicly available simulated stereoelectroencephalographic (SEEG) database with varying signal-to-noise ratios (SNR), obtaining very good results for medium and high SNR signals. We further tested the SGM algorithm on real signals from patients with focal epilepsy, in which HFO detection was performed visually by experts, yielding a high agreement between experts and SGM. Significance. The SGM algorithm displayed proper performance in simulated and real environments and therefore can be used for non-supervised detection of HFO. This non-supervised algorithm does not require previous labelling by experts or parameter adjustment depending on the subject or database considered. SGM is not a computationally intensive algorithm, making it suitable to detect and characterize HFO in long-term SEEG recordings.

Background The study of cerebral underpinnings of schizophrenia may benefit from the high temporal resolution of electromagnetic techniques, but its spatial resolution is low. However, source imaging approaches such as low-resolution brain electromagnetic tomography (LORETA) allow for an acceptable compromise between spatial and temporal resolutions. Methods We combined LORETA with 32 channels and 3-Tesla diffusion magnetic resonance (Dmr) to study cerebral dysfunction in 38 schizophrenia patients (17 first episodes, FE), compared to 53 healthy controls. The EEG was acquired with subjects performing an odd-ball task. Analyses included an adaptive window of interest to take into account the interindividual variability of P300 latency. We compared source activation patters to distractor (P3a) and target (P3b) tones within- and between-groups. Results Patients showed a reduced activation in anterior cingulate and lateral and medial prefrontal cortices, as well as inferior/orbital frontal regions. This was also found in the FE patients alone. The activation was directly related to IQ in the patients and controls and to working memory performance in controls. Symptoms were unrelated to source activation. Fractional anisotropy in the tracts connecting lateral prefrontal and anterior cingulate regions predicted source activation in these regions in the patients. Conclusions These results replicate the source activation deficit found in a previous study with smaller sample size and a lower number of sensors and suggest an association between structural connectivity deficits and functional alterations.

In schizophrenia, both increased baseline metabolic and electroencephalographic (EEG) activities as well as decreased task-related modulation of neural dynamics have been reported. Noise power (NP) can measure the background EEG activity during task performance, and Shannon entropy (SE) is useful for quantifying the global modulation of EEG activity with a high temporal resolution. In this study, we have assessed the possible relationship between increased NP in theta and gamma bands and decreased SE modulation in 24 patients with schizophrenia and 26 controls over the parietal and central regions during a P300 task. SE modulation was calculated as the change from baseline to the active epoch (i.e., 150-550 ms following the target stimulus onset). Patients with schizophrenia displayed statistically significant higher NP values and lower SE modulation than healthy controls. We found a significant association between gamma NP and SE in all of the participants. Specifically, a NP increase in the gamma band was followed by a decrease in SE change. These results support the notion that an excess of gamma activity, unlocked to the task being performed, is accompanied by a decreased modulation of EEG activity in schizophrenia.

This study was aimed at exploring phase-amplitude coupling (PAC) patterns of neural activity in dementia due to Alzheimer's disease (AD). For this task, five minutes of spontaneous electroencephalographic (EEG) activity from 22 patients with mild AD and 16 cognitively healthy controls were studied. To assess PAC patterns, phase-locking value was computed between the phase of low frequencies and the power of high frequencies within each sensor. Our results showed that high-frequency gamma power is phase-locked to the alpha peak in EEG signals. Furthermore, statistically significant differences (p

Producción Científica, Objective. The aim of this paper was to characterize brain non-stationarity during an auditory oddball task in schizophrenia (SCH). The level of non-stationarity was measured in the baseline and response windows of relevant tones in SCH patients and healthy controls. Approach. Event-related potentials were recorded from 28 SCH patients and 51 controls. Non-stationarity was estimated in the conventional electroencephalography frequency bands by means of Kullback-Leibler divergence (KLD). Relative power (RP) was also computed to assess a possible complementarity with KLD. Main results. Results showed a widespread statistically significant increase in the level of non-stationarity from baseline to response in all frequency bands for both groups. Statistically significant differences in non-stationarity were found between SCH patients and controls in beta-2 and especially in the alpha band. SCH patients showed more non-stationarity in the left parieto-occipital region during the baseline window in the beta-2 band. A leave-one-out cross validation classification study with feature selection based on binary stepwise logistic regression to discriminate between SCH patients and controls provided an accuracy of 89.87% and area under ROC of 0.9510. Significance. KLD can characterize transient neural reorganization during an attentional task in response to novelty and relevance. Our findings suggest anomalous reorganization of neural dynamics in SCH during an oddball task. The abnormal frequency-dependent modulation found in SCH patients during relevant tones is in agreement with the hypothesis of aberrant salience detection in SCH. The increase in non-stationarity in the alpha band during the active task supports the notion that this band is involved in top-down processing. The baseline differences in the beta-2 band suggest that hyperactivation of the default mode network during attention tasks may be related to SCH symptoms. Furthermore, the binary stepwise logistic regression procedure selected features from both KLD and RP, supporting the idea that these measures can be complementary., This research project was supported in part by the projects TEC2014-53196-R of ‘Ministerio de Economía y Competitividad’ and FEDER; the project VA037U16 from the “Consejería de Educación de la Junta de Castilla y León”, the “Fondo de Investigaciones Sanitarias (Instituto de Salud Carlos III)” under projects FIS PI11/02203 and PI15/00299; and the “Gerencia Regional de Salud de Castilla y León” under projects GRS 932/A/14 and GRS 1134/A/15. P. Núñez was in receipt of a ‘Promoción de empleo joven e implantación de la Garantía Juvenil en I+D+i’ grant from ‘Ministerio de Economía y Competitividad’ and the University of Valladolid, A. Bachiller and J. Gomez-Pilar were in receipt of a PIF-UVA grant from the University of Valladolid. A. Lubeiro has a predoctoral scholarship from the “Junta de Castilla y León” and European Social Fund.

The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p < 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.

Schizophrenia is a disabling, chronic and severe mental illness characterized by disintegration of the process of thinking, contact with reality and emotional responsiveness. Schizophrenia has been related to an aberrant assignment of salience to external objects and internal representations. In addition, schizophrenia has been identified as a dysconnection syndrome, which is associated with a reduced capacity to integrate information among different brain regions. Relevance attribution likely involves diverse cerebral regions and their interconnections. As a consequence, many efforts have been devoted to identifying abnormalities in the cortical connections and their relation to schizophrenia symptoms and cognitive performance. Neural oscillations are one of the largest contributing mechanism for enabling coordinated activity during normal brain functioning. Alterations in neural oscillations and cognitive processing in schizophrenia have long been assessed using electroencephalographic (EEG) recordings (i.e. time-varying voltages on the human scalp generated by the electrical activity on the cerebral cortex). Event-related potentials (ERP) depict EEG data as a response to a cognitive task. ERP analyses are used to gain further insights into the neural mechanisms underlying cognitive dysfunctions. In this Doctoral Thesis, a 3-stimulus auditory-oddball paradigm was used for examining cognitive processing as response to both relevant and irrelevant stimuli. A total of 69 ERP recordings were analyzed in the research papers included in the Thesis, which comprises 20 chronic schizophrenia patients, 11 first episode patients and 38 healthy controls. This Doctoral Thesis is focused on the study, design and application of biomedical signal processing methodologies in order to facilitate the understanding of cognitive processes altered by the schizophrenia. EEG data were examined using a two-level analysis: (I) local activation studies to quantify functional segregation of the brain network, by means of spectral analysis and by assessing neural source generators of P3a and P3b components; and (II) EEG interactions studies to explore functional integration across brain regions, including pair-wise couplings and exploring hierarchical organization of neural rhythms., Departamento de Teoría de la Señal y Comunicaciones e Ingeniería Telemática, Doctorado en Tecnologías de la Información y las Telecomunicaciones

The analysis of the interaction between novelty and relevance may be of interest to test the aberrant salience hypothesis of schizophrenia (SCH). In comparison with other neuroimaging techniques, such as functional magnetic resonance imaging, electroencephalography (EEG) provides high temporal resolution. Therefore, EEG is useful to analyze transient dynamics in neural activity, even in the range of milliseconds. In this study, EEG activity from 31 patients with SCH and 38 controls was analyzed using Shannon spectral entropy (SE) and median frequency (MF). The aim of the study was to quantify differences between distractor (i.e., novelty) and target (i.e., novelty and relevance) tones in an auditory oddball paradigm. Healthy controls displayed a larger SE decrease in response to target stimulus than in response to distractor tones. SE decrease was accompanied by a significant and widespread reduction of MF (i.e., a significant slowing of EEG activity). In comparison with controls, patients showed a significant reduction of changes in SE in response to both target and distractor tones. These differences were also observed in patients that only received a minimal treatment prior to EEG recording. Furthermore, significant changes in SE were inversely correlated to positive and total symptoms severity for SCH patients. Our findings support the notion that SCH is associated with a reduced response to both novelty and relevance during an auditory P300 task.

The aim of this study was to explore the coupling among neural oscillations in different frequency bands using two approaches: conventional phase-amplitude coupling (PAC) and a novel event-related PAC. Both measures were applied to the electroencephalographic activity from 20 healthy volunteers. The results showed that the phase of alpha band modulated gamma power. Event-related PAC measures the coupling between frequency rhythms without losing of temporal resolution. Therefore, it may provide further insights into the characterization of brain dynamics.

The aim of this study was to evaluate neural coupling patterns in schizophrenia (SCH) patients and healthy controls during an auditory oddball task. Two measures of functional connectivity were applied to 28 SCH patients and 51 healthy controls to characterize electroencephalographic (EEG) activity. Specifically, magnitude squared coherence (MSC) and the imaginary part of coherency (ICOH) were computed for five frequency bands: theta, alpha, beta-1, beta-2 and gamma. The results showed a statistically significant modulation increase in MSC and ICOH for controls with respect to SCH in the theta band, and a decrease in ICOH for the beta-2 band. Furthermore, controls showed more significant changes from the baseline and active task windows than SCH patients. Our findings suggest that SCH patients show coupling abnormalities during an auditory oddball task compared to healthy controls.

Producción Científica, The aim of this study was to assess brain complexity dynamics in schizophrenia (SCH) patients during an auditory oddball task. For that task, we applied a novel graph measure based on the balance of the node weighs distribution. Previous studies applied complexity parameters that were strongly dependent on network topology. This fact could bias the results besides being necessary correction techniques as surrogating process. In the present study, we applied a novel graph complexity measure from the information theory: Shannon Graph Complexity (SGC). Complexity patterns form electroencephalographic recordings of 20 healthy controls and 20 SCH patients during an auditory oddball task were analyzed. Results showed a significantly more pronounced decrease of SGC for controls than for SCH patients during the cognitive task. These findings suggest an important change in the brain configuration towards more balanced networks, mainly in the connections related to long-range interactions. Since these changes are significantly more pronounced in controls, it implies a deficit in the neural network reorganization in SCH patients. In addition, SGC showed a suitable discrimination ability using a leave-one-out cross-validation: 0.725 accuracy and 0.752 area under receiver operating characteristics curve. The novel complexity measure proposed in this study demonstrated to be independent of network topology and, therefore, it complements traditional graph measures to characterize brain networks., Ministerio de Economía y Competitividad (TEC2014-53196-R), Junta de Castilla y León (VA059U13)

Producción Científica, The aim of this study was to characterize brain dynamics during an auditory oddball task. For this purpose, a measure of the non-stationarity of a given time-frequency representation (TFR) was applied to electroencephalographic (EEG) signals. EEG activity was acquired from 20 schizophrenic (SCH) patients and 20 healthy controls while they underwent a three-stimulus auditory oddball task. The Degree of Stationarity (DS), a measure of the non-stationarity of the TFR, was computed using the continuous wavelet transform. DS was calculated for both the baseline [-300 0] ms and active task [150 550] ms windows of a P300 auditory oddball task. Results showed a statistically significant increase (p, Ministerio de Economía y Competitividad (TEC2014-53196-R), Junta de Castilla y León (VA059U13)

Producción Científica, Hydrocephalus comprises a number of conditions characterised by clinical symptoms, dilated ventricles and anomalous cerebrospinal fluid (CSF) dynamics. Infusion tests (ITs) are usually performed to study CSF circulation and in the preoperatory evaluation of patients with hydrocephalus. The study of intracranial pressure (ICP) signals recorded during ITs could be useful to gain insight into the underlying pathophysiology of this condition and to further support treatment decisions. In this study, two wavelet parameters, wavelet turbulence (WT) and wavelet entropy (WE), were analysed in order to characterise the variability, irregularity and similarity in spectral content of ICP signals in hydrocephalus., Ministerio de Economía y Competitividad (TEC2014-53196-R), Junta de Castilla y León (project VA059U13)

The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC). This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs. The proposed complexity measure was designed to capture the interplay between two properties of a system: the ‘information’ (calculated by means of Shannon entropy) and the ‘order’ of the system (estimated by means of a disequilibrium measure). SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution. In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients. SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity. Our results showed that SGC is invariant with respect to GD and independent of node degree distribution. Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text]. Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls. Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process. In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.

The aim of this study was to analyze the changes that mild cognitive impairment (MCI) and Alzheimer's disease (AD) elicit in brain dynamics. For this task, the spontaneous magnetoencephalographic (MEG) activity from 36 AD patients, 18 MCI subjects and 24 healthy controls was analyzed. A disequilibrium measure, Jensen's divergence, was used to estimate the irregularity of neural dynamics. Results revealed that AD patients displayed significant changes (p

The aim of this research was to analyze the changes in magnetoencephalographic (MEG) activity across the life span. For this task, 220 healthy subjects with ages ranging from 7 to 84 years were enrolled in the study. A statistical complexity measure based on the Shannon entropy and the Euclidean distance were used to assess changes in MEG oscillations during brain development. An increasing quadratic relationship between entropy and age was found, whereas the opposite behavior was observed between statistical complexity and age. Entropy and statistical complexity significantly increased and decreased, respectively, as a function of age from childhood to adolescence (p < 0.0071; Mann-Whitney U-test, Bonferroni corrected). Our results suggest that brain development is accompanied by several changes in the irregularity and statistical complexity patterns. These findings provide new insights into the delimitation of ‘normal behavior’ of neural dynamics during maturation and ageing.

The aim of this research was to study the organization of the brain functional network during an auditory oddball task in schizophrenia (SZ). Electroencephalographic (EEG) activity was recorded from 31 schizophrenic patients and 38 healthy controls. In a first step, coherence was used to estimate the similarity between the spectral content of each pair of electrodes. In a second step, a graph was generated from the similarity matrix and two network parameters were computed: the clustering coefficient and the path length. Our results indicate that SZ patients obtained lower clustering coefficient and longer path length variations between the baseline and the P300 response than controls. These findings suggest an abnormal organization of the brain functional network in SZ.

Spectral entropy (SE), also known as Shannon entropy, is a useful parameter for quantifying the global regularity of the electroencephalographic (EEG) signal. Hence, it is of interest in the assessment of the electrophysiological correlates of cognitive processing in schizophrenia. However, to date, SE has been barely used in studies comparing resting EEG recordings between patients and controls. In this work, we compared SE between resting baseline [−250 0] ms and active task [150 550] ms windows of a P300 task in 31 patients with schizophrenia and 38 controls. Moreover, we also calculated the median frequency (MF) and relative power in each frequency band for these windows to assess the correlates of the possible SE differences. Controls showed a significant (p < 0.0029) SE decrease (i.e., meaning higher signal regularity) from baseline to the active task window at parietal and central electrode sites. This SE decrease from baseline to active conditions was significantly lower in patients. In controls, this SE decrease was accompanied by a statistically significant decrease in MF (i.e., a significant slowing of the EEG activity), not observed in patients. In this latter group, the difference in SE between resting baseline and active task windows was inversely correlated to positive and total symptoms scores, as measured with the positive and negative symptoms scale. Our data support the relevance of SE in the study of cerebral processing in schizophrenia.

The aim of this research was to study the changes that Alzheimer's disease (AD) elicits in the organization of brain networks. For this task, the electroencephalographic (EEG) activity from 32 AD patients and 25 healthy controls was analyzed. In a first step, a disequilibrium measure, the Euclidean distance (ED), was used to estimate the similarity between the spectral content of each pair of electrodes. In a second step, the similarity matrices were used to generate the corresponding graphs, from which two parameters were computed to characterize the network structure: the mean clustering coefficient and the mean path length. Results revealed significant changes (p

Alzheimer's Disease (AD) is considered the most prevalent form of dementia. A definite AD diagnosis is established after examination of brain tissue. However, an accurate identification should be attempted to effectively apply therapeutic strategies. The aim of the present study was to perform regional analysis of spontaneous magnetoencephalographic (MEG) activity to describe brain dynamics in AD. Several spectral and non-linear parameters were calculated to obtain a comprehensive description of the spatial abnormalities in brain dynamics. Our findings showed a significant global slowing of MEG activity in AD, as well as a significant loss of irregularity and complexity in several brain regions. Spectral and non-linear parameters reached classification accuracies of around 80%. The results suggest the potential usefulness of spectral and non-linear parameters to characterize the cognitive and functional abnormalities of dementia. These parameters can yield information useful in clinical AD diagnosis and provide further insights on underlying brain dynamics.

Objective. The aim of this research is to explore the coupling patterns of brain dynamics during an auditory oddball task in schizophrenia (SCH). Approach. Event-related electroencephalographic (ERP) activity was recorded from 20 SCH patients and 20 healthy controls. The coupling changes between auditory response and pre-stimulus baseline were calculated in conventional EEG frequency bands (theta, alpha, beta-1, beta-2 and gamma), using three coupling measures: coherence, phase-locking value and Euclidean distance. Main results. Our results showed a statistically significant increase from baseline to response in theta coupling and a statistically significant decrease in beta-2 coupling in controls. No statistically significant changes were observed in SCH patients. Significance. Our findings support the aberrant salience hypothesis, since SCH patients failed to change their coupling dynamics between stimulus response and baseline when performing an auditory cognitive task. This result may reflect an impaired communication among neural areas, which may be related to abnormal cognitive functions.

Producción Científica, The present study investigates the neural substrates underlying cognitive processing in schizophrenia (Sz) patients. To this end, an auditory 3-stimulus oddball paradigm was used to identify P3a and P3b components, elicited by rare-distractor and rare-target tones, respectively. Event-related potentials (ERP) were recorded from 31 Sz patients and 38 healthy controls. The P3a and P3b brain-source generators were identified by timeaveraging of low-resolution brain electromagnetic tomography (LORETA) current density images. In contrast with the commonly used fixed window of interest (WOI), we proposed to apply an adaptive WOI, which takes into account subjects' P300 latency variability. Our results showed different P3a and P3b source activation patterns in both groups. P3b sources included frontal, parietal and limbic lobes,whereas P3a response generators were localized over bilateral frontal and superior temporal regions. These areas have been related to the discrimination of auditory stimulus and to the inhibition (P3a) or the initiation (P3b) of motor response in a cognitive task. In addition, differences in source localization between Sz and control groups were observed. Sz patients showed lower P3b source activity in bilateral frontal structures and the cingulate. P3a generators were less widespread for Sz patients than for controls in right superior, medial and middle frontal gyrus. Our findings suggest that target and distractor processing involves distinct attentional subsystems, both being altered in Sz. Hence, the study of neuroelectric brain information can provide further insights to understand cognitive processes and underlying mechanisms in Sz., Ministerio de Economía y Competitividad (projects TEC2014-53196-R, DPI2011-22680 and DPI2014-59049-R), Junta de Castilla y León (VA059U13), Instituto de Salud Carlos III’, ISCIII (FIS PI1102303), Gerencia Regional de Salud de Castilla y León (GRS 613/A/11; GRS 932/A/14)

In this chapter, we discuss how intracranial or scalp electroencephalographic and magnetoencephalographic recordings could be used for epileptic seizure prediction and onset zone localization. The signal processing methods and the challenges of the state-of-the-art are discussed.