Carlos Gómez

Nonlinear analysis of electroencephalogram and magnetoencephalogram recordings in patients with Alzheimer's disease

The aim of the present study is to show the usefulness of nonlinear methods to analyse the electroencephalogram (EEG) and magnetoencephalogram (MEG) in patients with Alzheimers disease (AD). The following nonlinear methods have been applied to study the EEG and MEG background activity in AD patients and control subjects: approximate entropy, sample entropy, multiscale entropy, auto-mutual information and Lempel–Ziv complexity. We discuss why these nonlinear methods are appropriate to analyse the EEG and MEG. Furthermore, the performance of all these methods has been compared when applied to the same databases of EEG and MEG recordings. Our results show that EEG and MEG background activities in AD patients are less complex and more regular than in healthy control subjects. In line with previous studies, our work suggests that nonlinear analysis techniques could be useful in AD diagnosis.

Complexity analysis of spontaneous brain activity in attention-deficit/hyperactivity disorder: diagnostic implications.

BACKGROUND Attention-deficit/hyperactivity disorder (ADHD) is defined as the most common neurobehavioral disorder of childhood, but an objective diagnostic test is not available yet to date. Neurophychological, neuroimaging, and neurophysiological research offer ample evidence of brain and behavioral dysfunctions in ADHD, but these findings have not been useful as a diagnostic test. METHODS Whole-head magnetoencephalographic recordings were obtained from 14 diagnosed ADHD patients and 14 healthy children during resting conditions. Lempel-Ziv complexity (LZC) values were obtained for each channel and child and averaged in five sensor groups: anterior, central, left lateral, right lateral, and posterior. RESULTS Lempel-Ziv complexity scores were significantly higher in control subjects, with the maximum value in anterior region. Combining age and anterior complexity values allowed the correct classification of ADHD patients and control subjects with a 93% sensitivity and 79% specificity. Control subjects showed an age-related monotonic increase of LZC scores in all sensor groups, while children with ADHD exhibited a nonsignificant tendency toward decreased LZC scores. The age-related divergence resulted in a 100% specificity in children older than 9 years. CONCLUSIONS Results support the role of a frontal hypoactivity in the diagnosis of ADHD. Moreover, the age-related divergence of complexity scores between ADHD patients and control subjects might reflect distinctive developmental trajectories. This interpretation of our results is in agreement with recent investigations reporting a delay of cortical maturation in the prefrontal cortex.

Complexity analysis of spontaneous brain activity: effects of depression and antidepressant treatment:

Magnetoencephalography (MEG) allows the real-time recording of neural activity and oscillatory activity in distributed neural networks. We applied a non-linear complexity analysis to resting-state neural activity as measured using whole-head MEG. Recordings were obtained from 20 unmedicated patients with major depressive disorder and 19 matched healthy controls. Subsequently, after 6 months of pharmacological treatment with the antidepressant mirtazapine 30 mg/day, patients received a second MEG scan. A measure of the complexity of neural signals, the Lempel–Ziv Complexity (LZC), was derived from the MEG time series. We found that depressed patients showed higher pre-treatment complexity values compared with controls, and that complexity values decreased after 6 months of effective pharmacological treatment, although this effect was statistically significant only in younger patients. The main treatment effect was to recover the tendency observed in controls of a positive correlation between age and complexity values. Importantly, the reduction of complexity with treatment correlated with the degree of clinical symptom remission. We suggest that LZC, a formal measure of neural activity complexity, is sensitive to the dynamic physiological changes observed in depression and may potentially offer an objective marker of depression and its remission after treatment.

Disturbed Beta Band Functional Connectivity in Patients With Mild Cognitive Impairment: An MEG Study

Mild cognitive impairment (MCI) refers to the clinical state of subjects who suffer from some degree of cognitive deterioration but do not meet clinical criteria for dementia. The aim of this study was to analyze the magnetoencephalogram (MEG) background activity in MCI subjects using two connectivity measures: coherence and synchronization likelihood (SL). Our results showed that coherence and SL mean values were lower in the MCI group than in control group at all frequency bands (delta, theta, alpha-1, alpha-2, beta, and gamma). Significant differences were found in the beta frequency band with both measures (p Lt 0.05, Mann-Whitney U-test). Coherence analysis also revealed significant differences between controls and MCIs in the gamma band. Additionally, coherence and SL mean values at each frequency band were analyzed by means of receiver operating characteristic curves. The highest accuracy (69.8%) was achieved in the beta band with both connectivity measures. Our results suggest that spontaneous MEG rhythms show disconnection problems in MCI, especially in the beta band. In conclusion, both coherence and SL may be useful measures for discriminating MCI patients from control subjects.

Reduced predictable information in brain signals in autism spectrum disorder

Autism spectrum disorder (ASD) is a common developmental disorder characterized by communication difficulties and impaired social interaction. Recent results suggest altered brain dynamics as a potential cause of symptoms in ASD. Here, we aim to describe potential information-processing consequences of these alterations by measuring active information storage (AIS)—a key quantity in the theory of distributed computation in biological networks. AIS is defined as the mutual information between the past state of a process and its next measurement. It measures the amount of stored information that is used for computation of the next time step of a process. AIS is high for rich but predictable dynamics. We recorded magnetoencephalography (MEG) signals in 10 ASD patients and 14 matched control subjects in a visual task. After a beamformer source analysis, 12 task-relevant sources were obtained. For these sources, stationary baseline activity was analyzed using AIS. Our results showed a decrease of AIS values in the hippocampus of ASD patients in comparison with controls, meaning that brain signals in ASD were either less predictable, reduced in their dynamic richness or both. Our study suggests the usefulness of AIS to detect an abnormal type of dynamics in ASD. The observed changes in AIS are compatible with Bayesian theories of reduced use or precision of priors in ASD.

Analysis of spontaneous MEG activity in mild cognitive impairment and Alzheimer's disease using spectral entropies and statistical complexity measures

Alzheimers disease (AD) is the most common cause of dementia. Over the last few years, a considerable effort has been devoted to exploring new biomarkers. Nevertheless, a better understanding of brain dynamics is still required to optimize therapeutic strategies. In this regard, the characterization of mild cognitive impairment (MCI) is crucial, due to the high conversion rate from MCI to AD. However, only a few studies have focused on the analysis of magnetoencephalographic (MEG) rhythms to characterize AD and MCI. In this study, we assess the ability of several parameters derived from information theory to describe spontaneous MEG activity from 36 AD patients, 18 MCI subjects and 26 controls. Three entropies (Shannon, Tsallis and Rényi entropies), one disequilibrium measure (based on Euclidean distance ED) and three statistical complexities (based on Lopez Ruiz-Mancini-Calbet complexity LMC) were used to estimate the irregularity and statistical complexity of MEG activity. Statistically significant differences between AD patients and controls were obtained with all parameters (p < 0.01). In addition, statistically significant differences between MCI subjects and controls were achieved by ED and LMC (p < 0.05). In order to assess the diagnostic ability of the parameters, a linear discriminant analysis with a leave-one-out cross-validation procedure was applied. The accuracies reached 83.9% and 65.9% to discriminate AD and MCI subjects from controls, respectively. Our findings suggest that MCI subjects exhibit an intermediate pattern of abnormalities between normal aging and AD. Furthermore, the proposed parameters provide a new description of brain dynamics in AD and MCI.

A comparative study of event-related coupling patterns during an auditory oddball task in schizophrenia

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.

Neural Network Reorganization Analysis During an Auditory Oddball Task in Schizophrenia Using Wavelet Entropy

The aim of the present study was to characterize the neural network reorganization during a cognitive task in schizophrenia (SCH) by means of wavelet entropy (WE). Previous studies suggest that the cognitive impairment in patients with SCH could be related to the disrupted integrative functions of neural circuits. Nevertheless, further characterization of this effect is needed, especially in the time-frequency domain. This characterization is sensitive to fast neuronal dynamics and their synchronization that may be an important component of distributed neuronal interactions; especially in light of the disconnection hypothesis for SCH and its electrophysiological correlates. In this work, the irregularity dynamics elicited by an auditory oddball paradigm were analyzed through synchronized-averaging (SA) and single-trial (ST) analyses. They provide complementary information on the spatial patterns involved in the neural network reorganization. Our results from 20 healthy controls and 20 SCH patients showed a WE decrease from baseline to response both in controls and SCH subjects. These changes were significantly more pronounced for healthy controls after ST analysis, mainly in central and frontopolar areas. On the other hand, SA analysis showed more widespread spatial differences than ST results. These findings suggest that the activation response is weakly phase-locked to stimulus onset in SCH and related to the default mode and salience networks. Furthermore, the less pronounced changes in WE from baseline to response for SCH patients suggest an impaired ability to reorganize neural dynamics during an oddball task.

Analysis of neural dynamics in mild cognitive impairment and Alzheimer's disease using wavelet turbulence

OBJECTIVE Current diagnostic guidelines encourage further research for the development of novel Alzheimers disease (AD) biomarkers, especially in its prodromal form (i.e. mild cognitive impairment, MCI). Magnetoencephalography (MEG) can provide essential information about AD brain dynamics; however, only a few studies have addressed the characterization of MEG in incipient AD. APPROACH We analyzed MEG rhythms from 36 AD patients, 18 MCI subjects and 27 controls, introducing a new wavelet-based parameter to quantify their dynamical properties: the wavelet turbulence. MAIN RESULTS Our results suggest that AD progression elicits statistically significant regional-dependent patterns of abnormalities in the neural activity (p < 0.05), including a progressive loss of irregularity, variability, symmetry and Gaussianity. Furthermore, the highest accuracies to discriminate AD and MCI subjects from controls were 79.4% and 68.9%, whereas, in the three-class setting, the accuracy reached 67.9%. SIGNIFICANCE Our findings provide an original description of several dynamical properties of neural activity in early AD and offer preliminary evidence that the proposed methodology is a promising tool for assessing brain changes at different stages of dementia.

Effects of a multi-sensory environment on brain-injured patients: Assessment of spectral patterns

Snoezelen(®) multi-sensory (SMS) environment has been commonly applied as a therapeutic strategy to alleviate the symptoms associated to a wide variety of pathologies. Despite most studies have reported a wide range of positive revealed short-term changes associated to SMS intervention, little has been done to systematically quantify its effects. The present study examined electroencephalographic (EEG) changes in 18 individuals with brain-injury and 18 healthy controls during SMS stimulation. The experimental design included a multi-sensory stimulation session carried out in a Snoezelen(®) room, preceded and followed by a 5 min quiet rest condition. Spontaneous EEG activity was analyzed by computing the relative power in conventional EEG frequency bands. The results suggest that SMS stimulation induces a significant increase (p < 0.05, Wilcoxon sign-ranked test) of relative power for low frequency bands (i.e., theta and alpha bands) and a significant decrease (p < 0.05, Wilcoxon sign-ranked test) for fast rhythms (i.e., beta1, beta2 and gamma bands). In addition, statistically significant differences (p < 0.05, Mann-Whitney U-test) between both groups were found in relative power of theta band. Our findings suggest that the slowing of EEG oscillatory activity may reflect the state of relaxation induced by the SMS stimulation. Furthermore, this study presents a new strategy to assess the short-term effects of SMS stimulation therapy in comparison to previous studies using subjective observations and qualitative data.