Antonio R. Hidalgo-Muñoz

Disentangling the attention network test: behavioral, event related potentials, and neural source analyses.

Background: The study of the attentional system remains a challenge for current neuroscience. The “Attention Network Test” (ANT) was designed to study simultaneously three different attentional networks (alerting, orienting, and executive) based in subtraction of different experimental conditions. However, some studies recommend caution with these calculations due to the interactions between the attentional networks. In particular, it is highly relevant that several interpretations about attentional impairment have arisen from these calculations in diverse pathologies. Event related potentials (ERPs) and neural source analysis can be applied to disentangle the relationships between these attentional networks not specifically shown by behavioral measures. Results: This study shows that there is a basic level of alerting (tonic alerting) in the no cue (NC) condition, represented by a slow negative trend in the ERP trace prior to the onset of the target stimuli. A progressive increase in the CNV amplitude related to the amount of information provided by the cue conditions is also shown. Neural source analysis reveals specific modulations of the CNV related to a task-related expectancy presented in the NC condition; a late modulation triggered by the central cue (CC) condition and probably representing a generic motor preparation; and an early and late modulation for spatial cue (SC) condition suggesting specific motor and sensory preactivation. Finally, the first component in the information processing of the target stimuli modulated by the interaction between orienting network and the executive system can be represented by N1. Conclusions: The ANT is useful as a paradigm to study specific attentional mechanisms and their interactions. However, calculation of network effects is based in subtractions with non-comparable experimental conditions, as evidenced by the present data, which can induce misinterpretations in the study of the attentional capacity in human subjects.

Neural Correlates of Alerting and Orienting Impairment in Multiple Sclerosis Patients

Background A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. Results After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (p<0.05). ANOVAs showed for the patient group a significant latency delay for P1 and N1 components (p<0.05) and a decrease of P3 amplitude for congruent and incongruent stimuli (p<0.01). With regard to correlation analysis, PASAT-3s and SDMT showed significant correlations with behavioral measures of the Attention Network Test (p<0.01) and an ERP parameter (CNV amplitude). Conclusions Behavioral data are highly correlated with the neuropsychological scores and show that the alerting and orienting mechanisms in the patient group were impaired. Reduced amplitude for the Contingent Negative Variation in the patient group suggests that this component could be a physiological marker related to the alerting and orienting impairment in relapsing-remitting multiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention impairment, which could help in the assessment and treatment of relapsing-remitting multiple sclerosis patients.

Retest Reliability of Individual P3 Topography Assessed by High Density Electroencephalography

Background Some controversy remains about the potential applicability of cognitive potentials for evaluating the cerebral activity associated with cognitive capacity. A fundamental requirement is that these neurophysiological parameters show a high level of stability over time. Previous studies have shown that the reliability of diverse parameters of the P3 component (latency and amplitude) ranges between moderate and high. However, few studies have paid attention to the retest reliability of the P3 topography in groups or individuals. Considering that changes in P3 topography have been related to different pathologies and healthy aging, the main objective of this article was to evaluate in a longitudinal study (two sessions) the reliability of P3 topography in a group and at the individual level. Results The correlation between sessions for P3 topography in the grand average of groups was high (r = 0.977, p<0.001). The within-subject correlation values ranged from 0.626 to 0.981 (mean: 0.888). In the between-subjects topography comparisons, the correlation was always lower for comparisons between different subjects than for within-subjects correlations in the first session but not in the second session. Conclusions The present study shows that P3 topography is highly reliable for group analysis (comprising the same subjects) in different sessions. The results also confirmed that retest reliability for individual P3 maps is suitable for follow-up studies for a particular subject. Moreover, P3 topography appears to be a specific marker considering that the between-subjects correlations were lower than the within-subject correlations. However, P3 topography appears more similar between subjects in the second session, demonstrating that is modulated by experience. Possible clinical applications of all these results are discussed.

EEG study on affective valence elicited by novel and familiar pictures using ERD/ERS and SVM-RFE

Abstract EEG signals have been widely explored in emotional processing analyses, both in time and frequency domains. However, in such studies, habituation phenomenon is barely considered in the discrimination of different emotional responses. In this work, spectral features of the event-related potentials (ERPs) are studied by means of event-related desynchronization/synchronization computation. In order to determine the most relevant ERP features for distinguishing how positive and negative affective valences are processed within the brain, support vector machine-recursive feature elimination is employed. The proposed approach was applied for investigating in which way the familiarity of stimuli affects the affective valence processing as well as which frequency bands and scalp regions are more involved in this process. In a group composed of young adult women, results prove that parietooccipital region and theta band are especially involved in the processing of novelty in emotional stimuli. Furthermore, the proposed method has shown to perform successfully using a moderated number of trials.

Non-invasive prediction of catheter ablation outcome in persistent atrial fibrillation by fibrillatory wave amplitude computation in multiple electrocardiogram leads

BACKGROUND Catheter ablation (CA) of persistent atrial fibrillation (AF) is challenging, and reported results are capable of improvement. A better patient selection for the procedure could enhance its success rate while avoiding the risks associated with ablation, especially for patients with low odds of favorable outcome. CA outcome can be predicted non-invasively by atrial fibrillatory wave (f-wave) amplitude, but previous works focused mostly on manual measures in single electrocardiogram (ECG) leads only. AIM To assess the long-term prediction ability of f-wave amplitude when computed in multiple ECG leads. METHODS Sixty-two patients with persistent AF (52 men; mean age 61.5±10.4years) referred for CA were enrolled. A standard 1-minute 12-lead ECG was acquired before the ablation procedure for each patient. F-wave amplitudes in different ECG leads were computed by a non-invasive signal processing algorithm, and combined into a mutivariate prediction model based on logistic regression. RESULTS During an average follow-up of 13.9±8.3months, 47 patients had no AF recurrence after ablation. A lead selection approach relying on the Wald index pointed to I, V1, V2 and V5 as the most relevant ECG leads to predict jointly CA outcome using f-wave amplitudes, reaching an area under the curve of 0.854, and improving on single-lead amplitude-based predictors. CONCLUSION Analysing the f-wave amplitude in several ECG leads simultaneously can significantly improve CA long-term outcome prediction in persistent AF compared with predictors based on single-lead measures.

Atrial Signal Extraction in Atrial Fibrillation Electrocardiograms Using a Tensor Decomposition Approach

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice and remains a major challenge in cardiology. The noninvasive analysis of AF usually requires the estimation of the atrial activity (AA) signal in surface electrocardiogram (ECG) recordings. The present contribution puts forward a tensor decomposition approach for noninvasive AA extraction in AF ECG recordings. As opposed to the matrix approach, tensor decompositions are generally unique under mild conditions and have the potential to perform source separation in scenarios with a limited number of electrodes. An experimental study on a synthethic signal model and a real AF ECG recording evaluates the performance of the so-called block term tensor decomposition approach as compared to matrix techniques such as principal component analysis and independent component analysis.

A tensor decomposition approach to noninvasive atrial activity extraction in atrial fibrillation ECG

Atrial fibrillation (AF), the most common arrhythmia in adults, is still considered as the last great frontier of cardiac electrophysiology, since its mechanisms are not completely understood. Analysis of the atrial activity (AA) signal contained in electrocardiograms during AF episodes is a noninvasive and inexpensive solution for obtaining useful information about AF. This work presents tensor decompositions as an alternative to classic blind source separation methods based on matrix decompositions due to their appealing uniqueness properties and considers in particular the block term decomposition (BTD). The practical usefulness of BTD is evaluated by comparing its AA estimation quality, measured by spectral concentration, to those oftwo benchmark methods, revealing that BTD presents a better performance. The results presented in this work motivate further investigation oftensor decompositions for AF analysis.

Empirical mode decomposition for noninvasive atrial fibrillation dominant frequency estimation

The dominant frequency (DF) of the atrial activity signal is arguably one of the most relevant features characterizing atrial fibrillation (AF), the most common cardiac arrhythmia. Its accurate estimation from noninvasive acquisition modalities such as the electrocardiogram (ECG) can avoid risks of potential complications to patients in a cost-effective manner. However, the approximation of the underlying intracardiac atrial activity by noninvasive techniques such as average beat subtraction or blind source separation has not always been satisfactory. In the present work, a new approach based on the ensemble empirical mode decomposition (EEMD) is proposed for AF DF estimation. Our results suggest that EEMD provides more accurate estimates of intracardiac AF DF than alternative noninvasive methods. In addition, the empirical nature of EEMD overcomes important drawbacks of other techniques, simplifying its implementation in automatic tools for diagnosis aid.

F-wave amplitude stability on multiple electrocardiogram leads in atrial fibrillation

Fibrillatory wave (f-wave) amplitude correlates with left atrium (LA) size in certain electrocardiogram (ECG) leads and it is regarded as a predictor of ablation therapy outcome for atrial fibrillation (AF). This study aims at assessing the temporal stability of f-wave amplitude measures throughout the recording and determining the minimum signal length necessary to characterize them accurately in ECG leads. In a set of standard ECGs acquired in 34 persistent AF patients, we determined the minimum temporal window length W such that the related amplitude value accurately correlated with that from the whole atrial activity (AA) signal in leads I, II, V1-V6 (threshold Pearsons correlation coefficient R = 0.9). Subsequently, we tested intrarecording correlation between amplitude values obtained in two distinct W -second AA signal excerpts. This procedure was performed both on the original AA signal and on its principal component analysis (PCA) rank-1 approximation. The first experimental step yielded W = 5 seconds. Amplitude intrarecording correlation was generally accurate in all leads for W = 5 seconds (Rmin = 0.799, V1; Rmax = 0.999, V3). Interestingly, PCA revealed that amplitude measures are more stable in proximity to LA (R(V1) = 0.975; R(V2) = 0.993; R(V3) = 0.989). Our findings confirm the temporal stability off-wave amplitude measures and their robustness to signal duration. Moreover, a preprocessing stage based on PCA improves the stability of this parameter in leads closer to LA.

Individual EEG differences in affective valence processing in women with low and high neuroticism

OBJECTIVE In this study, individual differences in brain electrophysiology during positive and negative affective valence processing in women with different neuroticism scores are quantified. METHODS Twenty-six women scoring high and low on neuroticism participated on this experiment. A support vector machine (SVM)-based classifier was applied on the EEG single trials elicited by high arousal pictures with negative and positive valence scores. Based on the accuracy values obtained from subject identification tasks, the most distinguishing EEG channels among participants were detected, pointing which scalp regions show more distinct patterns. RESULTS Significant differences were obtained, in the EEG heterogeneity between positive and negative valence stimuli, yielding higher accuracy in subject identification using negative pictures. Regarding the topographical analysis, significantly higher accuracy values were reached in occipital areas and in the right hemisphere (p < 0.001). CONCLUSIONS Mainly, individual differences in EEG can be located in parietooccipital regions. These differences are likely to be due to the different reactivity and coping strategies to unpleasant stimuli in individuals with high neuroticism. In addition, the right hemisphere shows a greater individual specificity. SIGNIFICANCE An SVM-based classifier asserts the individual specificity and its topographical differences in electrophysiological activity for women with high neuroticism compared to low neuroticism.