Atrial fibrillation (AF) is both influenced by and contributes to atrial structural remodelling, including atrial enlargement and fibrosis. In this work, we aimed to understand how differences in atrial anatomy affect AF inducibility and dynamics, both without and with fibrosis, through in silico models. Atrial wall anatomies from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) were used to generate personalised models. Detailed intra- and inter-atrial structures (pectinate muscles, Bachmann’s bundle, fossa ovalis, coronary sinus, and fibre orientations) were mapped onto patient-specific atrial anatomies through a novel strategy using universal atrial coordinates and a highly detailed reference model. Patient-specific endomysial fibrosis was incorporated based on LGE-MRI. We quantified AF and macro-reentrant atrial tachycardia (MRAT) initiation rates in models with and without fibrosis, comparing reentry dynamics using renewal theory. We generated personalised models of 10 patients, all but one with low-fibrotic content (Utah stages ≤ 2). Anatomical variability did not significantly affect AF initiation rates in both non-fibrotic (54.0 ± 13.7

Data-driven signal decomposition methods decompose a signal into its underlying components in a flexible and adaptive way, taking into account the signal characteristics. Here we focus on univariate signals, whose decomposition is ill-posed. Classical univariate approaches – like variational mode decomposition – constrain the solution space (e.g. through narrowband priors), and often require the number of components to be known in advance. These assumption, however, limit the algorithm's usage in certain real-life applications. Instead, we exploit the flexibility of neural networks to replace fixed (narrowband) priors with data-driven priors. Our model, called Iterative Deep-Learning-Based Signal Decomposition (IDSD), iteratively extracts an adaptive number of various types of components from a signal, with no restrictions on the bandwidth of a component. We show superior performance of IDSD both in a controlled setup with synthetic data, and on two real datasets concerning tidal waves and physiological measurements.

For patients experiencing myocardial infarction (MI), localizing the affected cardiac region using electrocardiography (ECG) can reduce the time to reperfusion therapy, reducing morbidity and mortality. Extracting relevant information from ECG signals is not trivial, and computational methods have been developed aiming to assist physicians in making faster and better decisions in emergency situations. However, their clinical adoption remains limited due to the high false alarm rates consequence of the low generalizability of these methods. This research compares the performance of three machine learning techniques — Lasso, Support Vector Machine, and Gradient Boosting Machine — with varying degrees of complexity in localizing MI. Vectorcardiography-derived features were used as input to the models due to their ability to capture spatial and temporal information regarding the heart’s electrical activity. An autoencoder was employed to smooth the feature space, facilitating more efficient model training and improving generalization. To further address generalizability challenges, an inter-patient validation approach was employed. Models were trained on the PTB-XL dataset and externally validated on the PTB Diagnostic dataset. Results demonstrate that Lasso, a simpler model, achieved the highest AUC of 0.74 on the external dataset, outperforming more complex models such as SVM (0.72) and GBM (0.68). Also, the combination of Lasso with the autoencoder provided superior generalization compared to other state-of-the-art methods reported in the MI localization literature. This highlights the proposed method’s suitability for clinical settings, where model generalizability and reliability are critical. Furthermore, our method offers the advantage of explainability, allowing the extraction of clinical and physiological insights from the data and bridging the gap between computational methods and clinicians.

AIMS:Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation, but recurrences are frequent. Ablating AF sources beyond PVI may prevent re-initiations. This proof-of-principle in silico study compares a novel source-based ablation approach to conventional strategies in preventing AF re-initiation. METHODS AND RESULTS:We compared two conventional ablation strategies [PVI and PVI + posterior wall isolation (box ablation)] with our source-based approach. After PVI, a high-density mapping catheter was guided sequentially upstream of local repetitive conduction patterns until a source was identified. Located targets were ablated, connecting them to non-conducting boundaries. Strategies were compared based on their AF re-initiation rates after incremental pacing and ablated and electrically isolated areas. Analyses were performed in seven different scenarios with atria of different sizes, without (n = 3) and with fibrosis (n = 4), to assess different AF progression stages. Compared to no ablation, PVI reduced initiation rates in non-fibrotic atria (23 ± 8% control vs. 15 ± 0% PVI) but was less effective with fibrosis (60 ± 4% vs. 53 ± 10%). Box ablation was not superior to PVI while isolating more of the left atrium (isolated area in PVI: 31.5 ± 0.7% vs. box: 43.6 ± 0.5%). Conversely, source ablation completely prevented AF initiation in all scenarios, achieving comparable left atrial isolation with box ablation (isolated area without fibrosis: 36.3 ± 1.4%; with fibrosis: 43.2 ± 2.6%) and including right atrial lesions. Although macro-re-entrant tachycardias occurred frequently after source ablation, they were terminated with minimal lesions. CONCLUSION:Ablating AF sources using our high-density mapping approach was more efficient in preventing AF re-initiation in silico than anatomy-based strategies.

Objective: Repetitive atrial activation patterns (RAAPs) during complex atrial tachycardia could be associated with localized mechanisms that can be targeted. Clinically available electroanatomical mapping systems are limited by either the spatial coverage or electrode density of the mapping catheters, preventing the adequate visualization of transiently occurring RAAPs. This work proposes a technique to overcome this shortcoming by stitching spatially overlapping conduction patterns together to a larger image- called a composite map. Methods: Simulated stable mechanisms and meandering reentries are sequentially mapped (4 x 4 grid, 3 mm spacing) and then reconstructed back to the original sizes with the proposed recurrence plot-based algorithm. Results: The reconstruction of single linear waves presents minimal errors (local activation time (LAT) difference: 3.2 [1.6-4.9] ms, conduction direction difference: 5.2 [2.3-8.0] degrees). Errors significantly increase (p<0.05) for more complex patterns, being the highest with unstable reentries (LAT difference: 10.3 [3.5-16.2] ms, conduction direction difference: 18.2 [6.7-29.7] deg). In a second part of the analysis, 111 meandering reentries are reconstructed. Mapping 30 locations overlappingly around each reentry core was found to be the optimal mapping strategy. For this optimal setting, LAT, conduction direction, and core localization errors are low (6.1 [4.2-8.6] ms, 11.2 [8.6-15.5] deg and 4.1 [2.9-4.9] mm, respectively) and are weakly correlated with the degree of the meander (rho = 0.41, rho = 0.40 and rho = 0.20, respectively). Conclusion: Our findings underline the feasibility of generating composite maps by stitching spatially overlapping recordings. Significance: Composite maps can be instrumental in personalized ablation strategies.

Pulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation, but recurrence rates remain high. Ablating AF sources beyond PVI may prevent re-initiations. This proof-of-principle in silico study compares a novel source ablation approach guided by preferential conduction directions against conventional strategies in preventing AF re-initiation. We compared two conventional ablation strategies—PVI alone and PVI with posterior wall isolation (box ablation)—to our novel source-based approach (Abstract Figure 1). In this approach, after PVI, a virtual high-density mapping catheter (4x4 electrode grid, 3 mm spacing) was guided sequentially against the preferential conduction direction until a source was identified through local conduction analysis or by encircling the source region. This process was repeated starting from different positions to capture multiple regions harboring sources. The identified sources were then clustered and marked as ablation targets, which were subsequently ablated and connected to non-conducting boundaries. Ablation strategies were assessed by evaluating AF re-initiation rates on a realistic in-silico model of the atria. Incremental pacing (20 sites) was used to assess AF initiation rates, and the ablated and electrically isolated areas were quantified. We performed these analyses in anatomies without and with endomysial fibrosis to assess the approaches at different stages of AF progression. Compared to no ablation, PVI reduced initiation rates in non-fibrotic and fibrotic atria (30% vs. 15% [p=0.48] and 60% vs. 55% [p=0.66], respectively, Abstract Figure 2 [top]). Box ablation did not improve PVI re-initiation rates (15%) and isolated larger portions of the left atrium (total isolated area with PVI: 34%, box: 45%). In contrast, source ablation guided by preferential conduction direction completely prevented AF re-initiation, regardless of the degree of structural remodeling, while isolating smaller left atrial areas (without fibrosis: 38%; with fibrosis: 41%, Abstract Figure 2 [bottom]) and included right atrial lesions. Macro-reentrant tachycardias frequently occurred after source ablation but could all be terminated with minimal additional lesions. Source ablation guided by preferential conduction directions prevented AF re-initiation in silico while isolating smaller left atrial areas as compared to PVI alone and combined with box ablation.Figure 1:Methods overview Figure 2:Results overview

Complexity and signal recurrence metrics obtained from body surface potential mapping (BSPM) allow quantifying atrial fibrillation (AF) substrate complexity. This study aims to correlate electrocardiographic imaging (ECGI) detected reentrant patterns with BSPM-calculated signal complexity and recurrence metrics. BSPM signals were recorded from 28 AF patients (17 male, 11 women, 62.69 ± 8.09 y.o.), followed by ECGI calculation. Signal complexity and recurrence metrics were computed on BSPM and ECGI signals. Rotors per second and rotor duration were computed on ECGI signals for each atrium and the whole atrial surface. Correlation between BSPM metrics and ECGI reentrant patterns for the entire atrial surface and for left atrium (LA) and right atrium (RA) were analyzed. Atrial complexity and recurrence metrics strongly correlated when computed on BSPM and ECGI. Higher sample entropy and relative harmonic energy (RHE) correlated with rotors of short duration. The highest dominant frequency of the ECGI signals did not correlate with the reentrant activity of the ECGI. Higher short- and long-term recurrence of BSPM signals correlated with longer duration rotors, particularly for long-term recurrence (rLA=0.74 vs. rRA=0.42). Only ECGI-based reentrant parameters showed higher LA complexity compared to RA (p < 0.05). BSPM metrics strongly correlate with metrics measured on ECGI signals. BSPM metrics indicate a more elevated atrial electro-structural remodeling aligned with more short-duration rotors from ECGI computations. Although BSPM delivers qualitative AF reentry data, ECGI remains essential for identifying regional substrate complexity.

Ablation is an effective rhythm control strategy for patients with atrial fibrillation (AF); however, many patients experience recurrences of AF following the procedure. Accurately identifying patients who are most likely to benefit from ablation could be a crucial step towards personalized AF treatment. Traditionally, predicting ablation outcomes relies on predefined electrocardiogram (ECG) and clinical features. This approach, however, has shown only limited performance so far. We hypothesized that modern deep learning techniques can analyze subtle differences in normal sinus rhythm ECG (SR-ECG) to predict AF ablation outcomes more effectively than traditional techniques with predefined ECG features. We also hypothesized that incorporating supplementary clinical data (demographics, medical history) could enhance these prediction models. A 10-second 12-lead SR-ECG was collected from 232 AF patients scheduled for ablation. Predefined features—such as amplitude, duration, and complexity of the P-waves, along with QRS, T-wave, QTc, and PR interval durations—were extracted from beat-averaged ECG. A gradient boosting model was trained using these features to predict AF recurrence after a 12-month follow-up. The same ECG signals were also used to train an AI-based approach utilizing a convolutional neural network (AI-ECG). Both models were evaluated using 10-fold cross-validation across multiple metrics. Clinical data were integrated into the AI model to enhance performance. Finally, using saliency scores, we analyzed on which ECG features the AI-ECG model focused for its decisions. The AI-ECG approach outperformed the predefined ECG model, achieving an AUROC of 0.71±0.06, sensitivity of 0.72±0.21, specificity of 0.76±0.26, accuracy of 0.75±0.15, and F1-score of 0.52±0.08, compared to an AUROC of 0.61±0.06, sensitivity of 0.74±0.30, specificity of 0.63±0.28, accuracy of 0.66±0.13, and F1-score of 0.52±0.08 for the predefined approach (Table 1). Incorporating clinical data further improved both models, with a greater enhancement observed for the AI-ECG model. The best performance was achieved when combining all three approaches—AI-ECG, predefined ECG, and clinical data—yielding an AUROC of 0.78±0.05, sensitivity of 0.84±0.13, specificity of 0.74±0.15, accuracy of 0.76±0.08, and F1-score of 0.64±0.04. Beat-averaged saliency analysis showed that the AI model significantly focused on the P-waves in ECGs during correct predictions (p<0.05). Our findings demonstrate that the AI-ECG model outperforms traditional ECG feature-based approaches in predicting AF recurrence after ablation, particularly when combined with clinical data. The model's reliance on P-wave highlights its ability to capture valuable predictive information from P-waves that may be missed by conventional features. These results suggest that AI-augmented ECG analysis could play a crucial role in personalized AF treatment planning. Results

Electrocardiographic imaging (ECGI) aims to noninvasively estimate heart surface potentials starting from body surface potentials. This is classically based on geometric information on the torso and the heart from imaging, which complicates clinical application. In this study, we aim to develop a deep learning framework to estimate heart surface potentials solely from body surface potentials, enabling wider clinical use. The framework introduces two main components: the transformation of 3D torso and heart geometries into standard 2D representations, and the development of a customized deep learning network model. The 2D torso and heart representations maintain a consistent layout across different subjects, making the proposed framework applicable to different torso-heart geometries. With spatial information incorporated in the 2D representations, the torso-heart physiological relationship can be learnt by the network. The deep learning model is based on a Pix2Pix network, adapted to work with 2.5D data in our task, i.e., 2D body surface potential maps (BSPMs) and 2D heart surface potential maps (HSPMs) with time sequential information. We propose a new loss function tailored to this specific task, which uses a cosine similarity and different weights for different inputs. BSPMs and HSPMs from 11 healthy subjects (8 females and 3 males) and 29 idiopathic ventricular fibrillation (IVF) patients (11 females and 18 males) were used in this study. Performance was assessed on a test set by measuring the similarity and error between the output of the proposed model and the solution provided by mainstream ECGI, by comparing HSPMs, the concatenated electrograms (EGMs), and the estimated activation time (AT) and recovery time (RT). The mean of the mean absolute error (MAE) for the HSPMs was 0.012 ± 0.011, and the mean of the corresponding structural similarity index measure (SSIM) was 0.984 ± 0.026. The mean of the MAE for the EGMs was 0.004 ± 0.004, and the mean of the corresponding Pearson correlation coefficient (PCC) was 0.643 ± 0.352. Results suggest that the model is able to precisely capture the structural and temporal characteristics of the HSPMs. The mean of the absolute time differences between estimated and reference activation times was 6.048 ± 5.188 ms, and the mean of the absolute differences for recovery times was 18.768 ± 17.299 ms. Overall, results show similar performance between the proposed model and standard ECGI, exhibiting low error and consistent clinical patterns, without the need for CT/MRI. The model shows to be effective across diverse torso-heart geometries, and it successfully integrates temporal information in the input. This in turn suggests the possible use of this model in cost effective clinical scenarios like patient screening or post-operative follow-up.

Current therapies for atrial fibrillation (AF) rely on "one-size-fits-all" approaches that do not consider inter-patient differences. The mechanisms driving variability in AF inducibility and the role of the individual anatomy on AF inducibility are poorly understood. This study uses a recently developed patient-specific computer model of AF with realistic 3D bundle architecture to study how anatomical variability alone may affect AF inducibility and complexity. Patient-specific atrial wall anatomies were derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) to create personalized models for 10 patients undergoing catheter ablation for AF. Detailed intra- and inter-atrial structures, such as pectinate muscles, Bachmann's bundle, fossa ovalis, coronary sinus, and fiber orientations, were incorporated through a novel approach by using a highly detailed reference model as a template. All models were assigned identical electrophysiological properties, with optional endomysial fibrosis mapped according to LGE-MRI distributions. Incremental pacing was applied from 20 positions in each model, without and with fibrosis, to quantify the inducibility of AF and of supra-ventricular tachycardias (SVTs, including AF and macro-reentrant tachycardias). AF complexity was assessed by measuring functional reentry formation and termination rates, analyzed using renewal theory to estimate each patient's average number of simultaneous reentries. All 10 personalized models were successfully generated (Figure 1, left), with low fibrotic content in all but one patient (Utah stages <2). AF initiation rates showed no significant variation across models, regardless of fibrosis (Chi-squared test, 50.0% [IQR: 36.25%-53.75%] without fibrosis, p=0.45; 45.0% [41.25%-55.0%] with fibrosis, p=0.36). However, SVT initiation rates varied significantly only in models with fibrosis (57.5% [47.5%-60.0%], p=0.04, Figure 1, right). Reentry dynamics differed significantly across patients, revealing an impact of the anatomy on AF complexity irrespective of fibrosis. Reentry formation rates varied significantly among patients both without (ANOVA, p<0.01) and with fibrosis (p<0.03). Reentry termination rates varied only in the presence of fibrosis (p<0.001). These differences combined led to substantial variability in the expected number of simultaneous reentries among patients (ANOVA, p<0.001 without fibrosis; p<0.05 with fibrosis, Figure 2). Anatomical differences alone led to variability in AF reentry dynamics, contributing to inter-patient differences in SVT susceptibility and AF complexity irrespective of the presence of fibrosis. This variability highlights the importance of considering anatomical features in AF management and could support the development of more personalized therapeutic approaches.Fig. 1:model examples and initiation Fig. 2:AF complexity per patient model

The visualization and comparison of electrophysiological information in the atrium among different patients could be facilitated by a standardized 2D atrial mapping. However, due to the complexity of the atrial anatomy, unfolding the 3D geometry into a 2D atrial mapping is challenging. In this study, we aim to develop a standardized approach to achieve a 2D atrial mapping that connects the left and right atria, while maintaining fixed positions and sizes of atrial segments across individuals. Atrial segmentation is a prerequisite for the process. Segmentation includes 19 different segments with 12 segments from the left atrium, 5 segments from the right atrium, and two segments for the atrial septum. To ensure consistent and physiologically meaningful segment connections, an automated procedure is applied to open up the atrial surfaces and project the 3D information into 2D. The corresponding 2D atrial mapping can then be utilized to visualize different electrophysiological information of a patient, such as activation time patterns or phase maps. This can in turn provide useful information for guiding catheter ablation. The proposed standardized 2D maps can also be used to compare more easily structural information like fibrosis distribution with rotor presence and location. We show several examples of visualization of different electrophysiological properties for both healthy subjects and patients affected by atrial fibrillation. These examples show that the proposed maps provide an easy way to visualize and interpret intra-subject information and perform inter-subject comparison, which may provide a reference framework for the analysis of the atrial fibrillation substrate before treatment, and during a catheter ablation procedure.

Background: In persistent atrial fibrillation (AF), localized extra-pulmonary vein sources may contribute to arrhythmia recurrences after pulmonary vein isolation. This in-silico study proposes a high-density sequential mapping strategy to localize such sources. Method: Catheter repositioning was guided by repetitive conduction patterns, moving against the prevailing conduction direction (upstream) toward the sources. Sources were found either by locally identifying conduction patterns or by encircling the region harboring them. We simulated source tracking in an in-silico atrial model, comparing random vs. upstream-guided catheter repositioning (with and without encircling). To assess performance in increasing AF complexities, we simulated AF in 3 groups: atria with reentry-anchoring scars, without fibrosis, and with severe endomysial fibrosis. Results: Compared to random mapping, the upstream-guided approach successfully located sources more often (anchored reentries: 70.6% vs. 10.6%; no fibrosis: 87.9% vs. 22.1%; with fibrosis: 95.0% vs. 60.9% of tracking procedures, all p<0.001), using fewer steps (median [IQR]: 11 [7;23] vs. 26 [13;35]; 10 [6;19] vs. 19 [10;27]; 11 [7;19] vs. 16 [8;30], respectively, all p<0.05). Adding source encircling increased source detection (98.1 %, 100 %, and 99.5 %, all p<0.01 vs. local detection only), reducing required steps (9 [6;12], 8 [6;12], and 9 [6;13], all p<0.05). In some cases (11.9 %, 17.1 %, and 10.5 % of procedures), the algorithm encircled regions >15 mm from the source. Conclusion: Moving mapping catheters upstream improves source detection efficiency, even in the presence of severe fibrosis. Encircling sources may help find regions of interest in fewer steps.

Tensor-based signal decomposition methods offer a promising avenue for signal decomposition of short, non-stationary and non-linear input signals. A novel Tensor-based Singular Spectrum Decomposition (TSSD) framework is presented that extends Singular Spectrum Decomposition (SSD) to tensors for univariate signals using two tensor decomposition techniques, namely, Multilinear Singular Value Decomposition (MLSVD) and Canonical Polyadic Decomposition (CPD). Results indicate improved performance under the influence of noise and in the presence of sizeable trends. Experiments on real-life data on EEG signals from epileptic seizures further show the strong practical relevance of TSSD as a tool for exploratory signal analysis that helps unveil underlying system(s) in signals.

Introduction: Repetitive focal and rotational activation patterns are currently used as additional ablation targets for atrial fibrillation (AF). However, there is no evidence that all these detected targets are actual sources of AF. In this paper, we present an approach that detects and ranks AF activation patterns not only based on the degree of pattern repetitiveness but also on the extent to which they are able to entrain their vicinity. This new technique might enable selecting the site with the highest probability of being a source for AF. Methods: We retrospectively analyzed high-density bi-atrial sequential mapping in ablation-naive persistent AF patients (n = 13, PentaRay catheter, 30s recordings). Repetitive focal and rotational activation patterns were detected based on local activation time annotation of unipolar electrograms. The spatial stability was determined as local repetitive pattern duration. The entrainment capability was defined as the average time a directionally coherent repetitive activation pattern was observed in adjacent recordings. Results: A total of 459 recordings were analyzed (35 +/- 5 per patient). We detected 131 repetitive focal (10 +/- 4 per patient) and 56 rotational activation patterns (4 +/- 3 per patient) in total. Focal patterns were more repetitive than rotational patterns (median [IQR] 0.7 [0.4-1.3] seconds vs. 0.5 [0.4-0.6] seconds, p < 0.001 Mann-Whitney U test). By applying a 90th percentile threshold to both local and directionally coherent adjacent repetitiveness, we identified 10 sites (9 focal and 1 rotational) in 7 patients as the most probable sources. The majority of these sites were in the upper right atrium or left pulmonary vein region. Notably, in 6 patients (46 %), no probable sources were detected using this threshold. Conclusion: This study introduces a novel technique to select the repetitive focal or rotational pattern with the highest probability of being a source. We observed that only a minority of repetitive focal or rotational patterns seem to be able to entrain their vicinity and thereby are likely to serve as sources of AF.

Objective: A growing body of research focuses on the automated diagnosis of acute myocardial infarction (AMI) using electrocardiogram (ECG) recordings. Several methods rely on differences between the ECG at baseline (no AMI) and during AMI condition. However, this approach may not sufficiently account for the progress of AMI, and it can underestimate the effect of false positives in a continuous monitoring setting. This in turn may hinder the adoption of automated methods for AMI diagnosis in the clinical practice. In this study, we propose a new automated method for the dynamic assessment of AMI condition. This method accounts for the dynamic nature underlying AMI events and the need for a low false positives incidence. Using a reduced 3-lead ECG system, we developed a novel set of parameters able to capture changes over time in the distribution properties of ECG -derived features. These parameters are used to train and validate a deep learning model in order to perform dynamic assessment of AMI condition. Conclusion: Results suggest that the proposed method is able to capture the dynamic evolution of AMI with a false positive rate below 1%. Significance: Thanks to the reduced number of leads, the proposed method could be used to assess AMI condition in long-term, remote and home monitoring, and intensive care unit (ICU) environments.

ABSTRACTIn this research, we present an alternative methodology to search for ring-like structures in the sky with unusually large temperature gradients, namely, Hawking points (HPs), in the Cosmic Microwave Background (CMB), which are possible observational effects associated with Conformal Cyclic Cosmology (CCC). To assess the performance of our method, we constructed an artificial data set of HP, according to CCC, and we were able to retrieve $95 {{\ \rm per\ cent}}$ of ring-like anomalies from it. Furthermore, we scanned the Planck CMB sky map and compared it to simulations according to ΛCDM, where we applied robust statistical tests to assess the existence of HP. Even though no significant ring-like structures were observed, we report the largest excess of HP candidates found at α =1 per cent significance level for the analysed sky maps (CMB at 70GHz, SEVEM, SMICA, and Commander-Ruler), and we stress the need to continue the theoretical and experimental research in this direction.

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.

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.