Local field potentials (LFPs) represent the relatively slow varying components of the neural signal, and their analysis is instrumental in understanding normal brain function. To be properly analyzed, this signal needs to be separated in its fundamental frequency bands. Recent studies have shown that empirical mode decomposition (EMD) can be exploited to pre-process LFP recordings in order to achieve a proper separation. However, depending on the analyzed signal, EMD is known to generate components that may cover a too wide frequency range to be considered as narrow banded. As an alternative, we present here an improved version of the singular spectrum analysis (SSA) algorithm, validated by numerical simulations, and applied to LFP recordings in V1 of a macaque monkey exposed to simple visual stimuli. The components generated by the improved SSA algorithm are shown to be more meaningful than those generated by EMD, paving the way for its use in LFP analysis.

Aims To present a comparison of electrocardiogram-based non-invasive measures of atrial fibrillation (AF) substrate complexity computed on invasive animal recordings to discriminate between short-term and long-term AF. The final objective is the selection of an optimal sub-set of measures for AF complexity assessment. Methods and results High-density epicardial direct contact mapping recordings (234 leads) were acquired from the right and the left atria of 17 goats in which AF was induced for 3 weeks (short-term AF group, N = 10) and 6 months (long-term AF group, N = 7). Several non-invasive measures of AF organization proposed in the literature in the last decade were investigated to assess their power in discriminating between the short-term and long-term group. The best performing measures were identified, which when combined attained a correct classification rate of 100%. Their ability to predict standard invasive AF complexity measures was also tested, showing an average R 2 of 0.73 ± 0.04. Conclusion An optimal set of measures of the AF substrate complexity was identified out of the set of non-invasive measures analysed in this study. These measures may contribute to improve patient-tailored diagnosis and therapy of sustained AF.

Introduction: Electrical contact mapping provides a detailed view of conduction patterns in the atria during atrial fibrillation (AF). Identification of repetitive wave front propagation mechanisms potentially initiating or sustaining AF might provide more insights into temporal and spatial distribution of candidate AF mechanism and identify targets for catheter ablation. We developed a novel tool based on recurrence plots to automatically identify and characterize repetitive conduction patterns in high-density contact mapping of AF. Materials and Methods: Recurrence plots were constructed by first transforming atrial electrograms recorded by a multi-electrode array to activation-phase signals and then quantifying the degree of similarity between snapshots of the activation-phase in the electrode array. An AF cycle length dependent distance threshold was applied to discriminate between repetitive and non-repetitive snapshots. Intervals containing repetitive conduction patterns were detected in a recurrence plot as regions with a high recurrence rate. Intervals that contained similar repetitive patterns were then grouped into clusters. To demonstrate the ability to detect and quantify the incidence, duration and size of repetitive patterns, the tool was applied to left and right atrial recordings in a goat model of different duration of persistent AF [3 weeks AF (3 wkAF, n = 8) and 22 weeks AF (22 wkAF, n = 8)], using a 249-electrode mapping array (2.4 mm inter-electrode distance). Results: Recurrence plots identified frequent recurrences of activation patterns in all recordings and indicated a strong correlation between recurrence plot threshold and AF cycle length. Prolonged AF duration was associated with shorter repetitive pattern duration [mean maximum duration 3 wkAF: 74 cycles, 95% confidence interval (54–94) vs. 22 wkAF: 41 cycles (21–62), p = 0.03], and smaller recurrent regions within repetitive patterns [3 wkAF 1.7 cm² (1.0–2.3) vs. 22 wkAF 0.5 cm² (0.0–1.2), p = 0.02]. Both breakthrough patterns and re-entry were identified as repetitive conduction patterns. Conclusion: Recurrence plots provide a novel way to delineate high-density contact mapping of AF. Dominant repetitive conduction patterns were identified in a goat model of sustained AF. Application of the developed methodology using the new generation of multi-electrode catheters could identify additional targets for catheter ablation of AF.

The electrocardiogram (ECG) is the standard method in clinical practice to non-invasively analyze the electrical activity of the heart, from electrodes placed on the body’s surface. The ECG can provide a cardiologist with relevant information to assess the condition of the heart and the possible presence of cardiac pathology. Nonetheless, the global view of the heart’s electrical activity given by the ECG cannot provide fully detailed and localized information about abnormal electrical propagation patterns and corresponding substrates on the surface of the heart. Electrocardiographic imaging, also known as the inverse problem in electrocardiography, tries to overcome these limitations by non-invasively reconstructing the heart surface potentials, starting from the corresponding body surface potentials, and the geometry of the torso and the heart. This problem is ill-posed, and regularization techniques are needed to achieve a stable and accurate solution. The standard approach is to use zero-order Tikhonov regularization and the L-curve approach to choose the optimal value for the regularization parameter. However, different methods have been proposed for computing the optimal value of the regularization parameter. Moreover, regardless of the estimation method used, this may still lead to over-regularization or under-regularization. In order to gain a better understanding of the effects of the choice of regularization parameter value, in this study, we first focused on the regularization parameter itself, and investigated its influence on the accuracy of the reconstruction of heart surface potentials, by assessing the reconstruction accuracy with high-precision simultaneous heart and torso recordings from four dogs. For this, we analyzed a sufficiently large range of parameter values. Secondly, we evaluated the performance of five different methods for the estimation of the regularization parameter, also in view of the results of the first analysis. Thirdly, we investigated the effect of using a fixed value of the regularization parameter across all reconstructed beats. Accuracy was measured in terms of the quality of reconstruction of the heart surface potentials and estimation of the activation and recovery times, when compared with ground truth recordings from the experimental dog data. Results show that values of the regularization parameter in the range (0.01–0.03) provide the best accuracy, and that the three best-performing estimation methods (L-Curve, Zero-Crossing, and CRESO) give values in this range. Moreover, a fixed value of the regularization parameter could achieve very similar performance to the beat-specific parameter values calculated by the different estimation methods. These findings are relevant as they suggest that regularization parameter estimation methods may provide the accurate reconstruction of heart surface potentials only for specific ranges of regularization parameter values, and that using a fixed value of the regularization parameter may represent a valid alternative, especially when computational efficiency or consistency across time is required.

Rett syndrome (RTT) is a X-linked neurodevelopmental disorder which represents the leading cause of severe incurable intellectual disability in females worldwide. The vast majority of RTT cases are caused by mutations in the X-linked MECP2 gene, and preclinical studies on RTT largely benefit from the use of mouse models of Mecp2, which present a broad spectrum of symptoms phenocopying those manifested by RTT patients. Neurons represent the core targets of the pathology; however, neuroanatomical abnormalities that regionally characterize the Mecp2 deficient mammalian brain remain ill-defined. Neuroimaging techniques, such as MRI and MRS, represent a key approach for assessing in vivo anatomic and metabolic changes in brain. Being non-invasive, these analyses also permit to investigate how the disease progresses over time through longitudinal studies. To foster the biological comprehension of RTT and identify useful biomarkers, we have performed a thorough in vivo longitudinal study of MRI and MRS in Mecp2 deficient mouse brains. Analyses were performed on both genders of two different mouse models of RTT, using an automatic atlas-based segmentation tool that permitted to obtain a detailed and unbiased description of the whole RTT mouse brain. We found that the most robust alteration of the RTT brain consists in an overall reduction of the brain volume. Accordingly, Mecp2 deficiency generally delays brain growth, eventually leading, in heterozygous older animals, to stagnation and/or contraction. Most but not all brain regions participate in the observed deficiency in brain size; similarly, the volumetric defect progresses diversely in different brain areas also depending on the specific Mecp2 genetic lesion and gender. Interestingly, in some regions volumetric defects anticipate overt symptoms, possibly revealing where the pathology originates and providing a useful biomarker for assessing drug efficacy in pre-clinical studies.