Efficacy of a high concentration of nano silver fluoride in preventing caries: A randomized controlled trial.

Background: To investigate the effectiveness of a novel agent containing Nano Silver Fluoride 1500 (NSF 1500) and chitosan to inactivate carious lesions in children. Material and Methods: The study included eighty children. While both groups had fluoride dentifrice applied to their teeth, only the experimental group received treatment with the NSF 1500-ppm solution. The first and sixth-month interval examinations were conducted by two calibrated dentists (k = 0.85). Results: The NSF 1500 group had 69.2% of their teeth with arrested decay, while the control group had 24.1%. The difference was statistically significant (p 0.001), with a preventive fraction of 59.4%. The number needed to treat (NNT) was approximately two. The NSF 1500 formulation was more effective than toothbrushing alone with fluoridated dentifrice in preventing dental caries. Conclusions: The effectiveness of NSF 1500 is determined by the size and depth of the dental cavity. Its ability to arrest caries lesions was comparable to previously tested products, NSF 400 and NSF 600. Key words:Preventive dentistry, dental caries, nanoparticles.

EEG as a potential ground truth for the assessment of cognitive state in software development activities: A multimodal imaging study.

Cognitive human error and recent cognitive taxonomy on human error causes of software defects support the intuitive idea that, for instance, mental overload, attention slips, and working memory overload are important human causes for software bugs. In this paper, we approach the EEG as a reliable surrogate to MRI-based reference of the programmer's cognitive state to be used in situations where heavy imaging techniques are infeasible. The idea is to use EEG biomarkers to validate other less intrusive physiological measures, that can be easily recorded by wearable devices and useful in the assessment of the developer's cognitive state during software development tasks. Herein, our EEG study, with the support of fMRI, presents an extensive and systematic analysis by inspecting metrics and extracting relevant information about the most robust features, best EEG channels and the best hemodynamic time delay in the context of software development tasks. From the EEG-fMRI similarity analysis performed, we found significant correlations between a subset of EEG features and the Insula region of the brain, which has been reported as a region highly related to high cognitive tasks, such as software development tasks. We concluded that despite a clear inter-subject variability of the best EEG features and hemodynamic time delay used, the most robust and predominant EEG features, across all the subjects, are related to the Hjorth parameter Activity and Total Power features, from the EEG channels F4, FC4 and C4, and considering in most of the cases a hemodynamic time delay of 4 seconds used on the hemodynamic response function. These findings should be taken into account in future EEG-fMRI studies in the context of software debugging.

Emergent Endovascular Repair For Primary Aortoenteric Fistula.

We report the case of a 64-year-old male with significant cardiac comorbidities who reported three episodes of gastrointestinal bleeding. In the third episode, he presented massive hematemesis, anaemia and hypotension. Despite a standard upper endoscopy, a computed tomography (CT) showed an infrarenal abdominal aortic aneurysm and densification of the aortic fat cover. A primary aortoenteric fistula, with acute bleeding and haemodynamic instability, was assumed, and an emergent endovascular repair was performed. Subsequent CT scans and endoscopies demonstrated control of the enteric lesion. After five months, there was no evidence of infection or rebleeding.

Task-based functional MRI challenges in clinical neuroscience: Choice of the best head motion correction approach in multiple sclerosis.

Introduction: Functional MRI (fMRI) is commonly used for understanding brain organization and connectivity abnormalities in neurological conditions, and in particular in multiple sclerosis (MS). However, head motion degrades fMRI data quality and influences all image-derived metrics. Persistent controversies regarding the best correction strategy motivates a systematic comparison, including methods such as scrubbing and volume interpolation, to find optimal correction models, particularly in studies with clinical populations prone to characterize by high motion. Moreover, strategies for correction of motion effects gain more relevance in task-based designs, which are less explored compared to resting-state, have usually lower sample sizes, and may have a crucial role in describing the functioning of the brain and highlighting specific connectivity changes. Methods: We acquired fMRI data from 17 early MS patients and 14 matched healthy controls (HC) during performance of a visual task, characterized motion in both groups, and quantitatively compared the most used and easy to implement methods for correction of motion effects. We compared task-activation metrics obtained from: (i) models containing 6 or 24 motion parameters (MPs) as nuisance regressors; (ii) models containing nuisance regressors for 6 or 24 MPs and motion outliers (scrubbing) detected with Framewise Displacement or Derivative or root mean square VARiance over voxelS; and (iii) models with 6 or 24 MPs and motion outliers corrected through volume interpolation. To our knowledge, volume interpolation has not been systematically compared with scrubbing, nor investigated in task fMRI clinical studies in MS. Results: No differences in motion were found between groups, suggesting that recently diagnosed MS patients may not present problematic motion. In general, models with 6 MPs perform better than models with 24 MPs, suggesting the 6 MPs as the best trade-off between correction of motion effects and preservation of valuable information. Parsimonious models with 6 MPs and volume interpolation were the best combination for correcting motion in both groups, surpassing the scrubbing methods. A joint analysis regardless of the group further highlighted the value of volume interpolation. Discussion: Volume interpolation of motion outliers is an easy to implement technique, which may be an alternative to other methods and may improve the accuracy of fMRI analyses, crucially in clinical studies in MS and other neurological populations.

Effects and response of the Cerrado ground-layer to frost along the canopy cover gradient.

Frost effects on savanna plant communities have been considered as analogous to those from fire, both changing community structure and filtering species composition. However, while frost impacts have been well-studied for the woody component of savannas, it is still poorly explored for the ground-layer community. Here, we investigated effects of frost in the Cerrado along a gradient of tree cover, focusing on ground-layer plant species, near the southern limit of the Cerrado in Brazil. We aimed to elucidate if the pattern already described for the tree layer also extends to the ground layer in terms of mimicking the effects of fire on vegetation structure and composition. We assessed how damage severity differs across species and across the tree-cover gradient, and we examined the recovery process after frost in terms of richness and community structure along the canopy cover gradient. Frost caused immediate and widespread dieback of the perennial ground-layer, with greatest impact on community structure where tree cover was lowest. However, frost did not reduce the number of species, indicating community resilience to this natural disturbance. Although frost mimicked the effects of fire in some ways, in other ways it differed substantially from fire. Unlike fire, frost increases litter cover and decreases the proportion of bare soil, likely hindering crucial processes for recovery of plant populations, such as seed dispersal, seed germination and plant resprouting. This finding calls attention to the risk of misguided conclusions when the ground layer is neglected in ecological studies of tropical savannas and grasslands.

Temporal and spatial patterns of fire activity in three biomes of Brazil.

The trade-off between conservation of natural resources and agribusiness expansion is a constant challenge in Brazil. The fires used to promote agricultural expansion increased in the last decades. While studies linking annual fire occurrence and rainfall seasonality are common, the relationship between fires, land use, and land cover remains understudied. Here, we investigated the frequency of the fires and performed a trend analysis for monthly, seasonal, and annual fires in three different biomes: Cerrado, Pantanal, and Atlantic Forest. We used burned area and integrated models in distinct scales (interannual, intraseasonal, and monthly) using Probability Density Functions (PDFs). The best fitting was found for Generalized Extreme Values (GEV) distribution at all three biomes from the several PDFs tested. We found the most fire in the Pantanal (wetlands), followed by Cerrado (Brazilian Savanna) and Atlantic Forest (Semideciduous Forest). Our findings indicated that land use and land cover trends changed over the years. There was a strong correlation between fire and agricultural areas, with increasing trends pointing to land conversion to agricultural areas in all biomes. The high probability of fire indicates that expanding agricultural areas through the conversion of natural biomes impacts several natural ecosystems, transforming land cover and land use. This land conversion is promoting more fires each year.

A two-stage framework for neural processing of biological motion.

It remains to be understood how biological motion is hierarchically computed, from discrimination of local biological motion animacy to global dynamic body perception. Here, we addressed this functional separation of the correlates of the perception of local biological motion from perception of global motion of a body. We hypothesized that local biological motion processing can be isolated, by using a single dot motion perceptual decision paradigm featuring the biomechanical details of local realistic motion of a single joint. To ensure that we were indeed tackling processing of biological motion properties we used discrimination instead of detection task. We discovered using representational similarity analysis that two key early dorsal and two ventral stream regions (visual motion selective hMT+ and V3A, extrastriate body area EBA and a region within fusiform gyrus FFG) showed robust and separable signals related to encoding of local biological motion and global motion-mediated shape. These signals reflected two independent processing stages, as revealed by representational similarity analysis and deconvolution of fMRI responses to each motion pattern. This study showed that higher level pSTS encodes both classes of biological motion in a similar way, revealing a higher-level integrative stage, reflecting scale independent biological motion perception. Our results reveal a two-stage framework for neural computation of biological motion, with an independent contribution of dorsal and ventral regions for the initial stage.

Optimizing EEG Source Reconstruction with Concurrent fMRI-Derived Spatial Priors.

Reconstructing EEG sources involves a complex pipeline, with the inverse problem being the most challenging. Multiple inversion algorithms are being continuously developed, aiming to tackle the non-uniqueness of this problem, which has been shown to be partially circumvented by including prior information in the inverse models. Despite a few efforts, there are still current and persistent controversies regarding the inversion algorithm of choice and the optimal set of spatial priors to be included in the inversion models. The use of simultaneous EEG-fMRI data is one approach to tackle this problem. The spatial resolution of fMRI makes fMRI derived spatial priors very convenient for EEG reconstruction, however, only task activation maps and resting-state networks (RSNs) have been explored so far, overlooking the recent, but already accepted, notion that brain networks exhibit dynamic functional connectivity fluctuations. The lack of a systematic comparison between different source reconstruction algorithms, considering potentially more brain-informative priors such as fMRI, motivates the search for better reconstruction models. Using simultaneous EEG-fMRI data, here we compared four different inversion algorithms (minimum norm, MN; low resolution electromagnetic tomography, LORETA; empirical Bayes beamformer, EBB; and multiple sparse priors, MSP) under a Bayesian framework (as implemented in SPM), each with three different sets of priors consisting of: (1) those specific to the algorithm; (2) those specific to the algorithm plus fMRI task activation maps and RSNs; and (3) those specific to the algorithm plus fMRI task activation maps and RSNs and network modules of task-related dFC states estimated from the dFC fluctuations. The quality of the reconstructed EEG sources was quantified in terms of model-based metrics, namely the expectation of the posterior probability P(model|data) and variance explained of the inversion models, and the overlap/proportion of brain regions known to be involved in the visual perception tasks that the participants were submitted to, and RSN templates, with/within EEG source components. Model-based metrics suggested that model parsimony is preferred, with the combination MSP and priors specific to this algorithm exhibiting the best performance. However, optimal overlap/proportion values were found using EBB and priors specific to this algorithm and fMRI task activation maps and RSNs or MSP and considering all the priors (algorithm priors, fMRI task activation maps and RSNs and dFC state modules), respectively, indicating that fMRI spatial priors, including dFC state modules, might contain useful information to recover EEG source components reflecting neuronal activity of interest. Our main results show that providing fMRI spatial derived priors that reflect the dynamics of the brain might be useful to map neuronal activity more accurately from EEG-fMRI. Furthermore, this work paves the way towards a more informative selection of the optimal EEG source reconstruction approach, which may be critical in future studies.

Parametric fMRI analysis of videos of variable arousal levels reveals different dorsal vs ventral activation preferences between autism and controls.

Atypical sensory processing is now considered a ubiquitous feature of autism spectrum disorder (ASD) and is responsible for the atypical sensory-based behaviours seen in these individuals. Specifically, emotional arousal is a critical ASD target since it comprises emotion regulation and sensory processing, two core aspects of autism. So, in this project, we used task-based fMRI and a well-catalogued dataset of videos with variable arousal levels to characterize the sensory processing of emotional arousal content in ASD and typically developed controls. Our analysis revealed a difference in the secondary attention network where ASD individuals showed a clear yet lateralized preference to the dorsal attention network, whereas the neurotypical individuals preferred the ventral attention network.

Quantitative Assessment of the Impact of Geometric Distortions and Their Correction on fMRI Data Analyses.

Functional magnetic resonance imaging (fMRI) data is typically collected with gradient-echo echo-planar imaging (GE-EPI) sequences, which are particularly prone to the susceptibility artifact as a result of B0 field inhomogeneity. The component derived from in-plane spin dephasing induces pixel intensity variations and, more critically, geometric distortions. Despite the physical mechanisms underlying the susceptibility artifact being well established, a systematic investigation on the impact of the associated geometric distortions, and the direct comparison of different approaches to tackle them, on fMRI data analyses is missing. Here, we compared two different distortion correction approaches, by acquiring additional: (1) EPI data with reversed phase encoding direction (TOPUP), and (2) standard (and undistorted) GE data at two different echo times (GRE). We first characterized the geometric distortions and the correction approaches based on the estimated ΔB0 field offset and voxel shift maps, and then conducted three types of analyses on the distorted and corrected fMRI data: (1) registration into structural data, (2) identification of resting-state networks (RSNs), and (3) mapping of task-related brain regions of interest. GRE estimated the largest voxel shifts and more positively impacted the quality of the analyses, in terms of the (significantly lower) cost function of the registration, the (higher) spatial overlap between the RSNs and appropriate templates, and the (significantly higher) sensitivity of the task-related mapping based on the Z-score values of the associated activation maps, although also evident when considering TOPUP. fMRI data should thus be corrected for geometric distortions, with the choice of the approach having a modest, albeit positive, impact on the fMRI analyses.

The relationship between EEG and fMRI connectomes is reproducible across simultaneous EEG-fMRI studies from 1.5T to 7T.

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage). Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r ≈ 0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-ß frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship. This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

EEG Microstates Predict Concurrent fMRI Dynamic Functional Connectivity States.

Brain functional connectivity measured by resting-state fMRI varies over multiple time scales, and recurrent dynamic functional connectivity (dFC) states have been identified. These have been found to be associated with different cognitive and pathological states, with potential as disease biomarkers, but their neuronal underpinnings remain a matter of debate. A number of recurrent microstates have also been identified in resting-state EEG studies, which are thought to represent the quasi-simultaneous activity of large-scale functional networks reflecting time-varying brain states. Here, we hypothesized that fMRI-derived dFC states may be associated with these EEG microstates. To test this hypothesis, we quantitatively assessed the ability of EEG microstates to predict concurrent fMRI dFC states in simultaneous EEG-fMRI data collected from healthy subjects at rest. By training a random forests classifier, we found that the four canonical EEG microstates predicted fMRI dFC states with an accuracy of 90%, clearly outperforming alternative EEG features such as spectral power. Our results indicate that EEG microstates analysis yields robust signatures of fMRI dFC states, providing evidence of the electrophysiological underpinnings of dFC while also further supporting that EEG microstates reflect the dynamics of large-scale brain networks.

How much of the BOLD-fMRI signal can be approximated from simultaneous EEG data: relevance for the transfer and dissemination of neurofeedback interventions.

OBJECTIVE: fMRI-based neurofeedback (NF) interventions represent the method of choice for the neuromodulation of localized brain areas. Although we have already validated an fMRI-NF protocol targeting the facial expressions processing network (FEPN), its dissemination is hampered by the economical and logistical constraints of fMRI-NF interventions, which may be however surpassed by transferring it to EEG setups, due to their low cost and portability. One of the major challenges of this procedure is then to reconstruct the BOLD-fMRI signal measured at the FEPN using only EEG signals. Because these types of approaches have been poorly explored so far, here we systematically investigated the extent at which the BOLD-fMRI signal recorded from the FEPN during a fMRI-NF protocol could be reconstructed from the simultaneously recorded EEG signal. APPROACH: Several features from both scalp and source spaces (the latter estimated using continuous EEG source imaging) were extracted and used as predictors in a regression problem using random forests. Furthermore, three different approaches to deal with the hemodynamic delay of the BOLD signal were tested. The resulting models were compared with the only approach already proposed in the literature that uses spectral features and considers different time delays. MAIN RESULTS: The combination of linear and non-linear features (particularly the largest Lyapunov exponent and entropy measures) projected into the source space, spatially filtered by independent component analysis (ICA) and convolved with multiple HRF functions peaking at different latencies, increases significantly the reconstruction accuracy (defined as the correlation between the measured and approximated BOLD signal) from 20% (direct comparison with the method used in the current literature) to 56%. SIGNIFICANCE: With this pipeline, a more accurate reconstruction of the BOLD signal can be obtained, which will positively impact the transfer of fMRI-based neurofeedback interventions to EEG setups, and more importantly, their dissemination and efficacy in modulating the activity of the desired brain areas.

Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI.

Functional magnetic resonance imaging (fMRI) is the technique of choice for detecting large-scale functional brain networks and to investigate their dynamics. Because fMRI measures brain activity indirectly, electroencephalography (EEG) has been recently considered a feasible tool for detecting such networks, particularly the resting-state networks (RSNs). However, a truly unbiased validation of such claims is still missing, which can only be accomplished by using simultaneously acquired EEG and fMRI data, due to the spontaneous nature of the activity underlying the RSNs. Additionally, EEG is still poorly explored for the purpose of mapping task-specific networks, and no studies so far have been focused on investigating networks' dynamic functional connectivity (dFC) with EEG. Here, we started by validating RSNs derived from the continuous reconstruction of EEG sources by directly comparing them with those derived from simultaneous fMRI data of 10 healthy participants, and obtaining an average overlap (quantified by the Dice coefficient) of 0.4. We also showed the ability of EEG to map the facial expressions processing network (FEPN), highlighting regions near the posterior superior temporal sulcus, where the FEPN is anchored. Then, we measured the dFC using EEG for the first time in this context, estimated dFC brain states using dictionary learning, and compared such states with those obtained from the fMRI. We found a statistically significant match between fMRI and EEG dFC states, and determined the existence of two matched dFC states which contribution over time was associated with the brain activity at the FEPN, showing that the dynamics of FEPN can be captured by both fMRI and EEG. Our results push the limits of EEG toward being used as a brain imaging tool, while supporting the growing literature on EEG correlates of (dynamic) functional connectivity measured with fMRI, and providing novel insights into the coupling mechanisms underlying the two imaging techniques.

Flammability thresholds or flammability gradients? Determinants of fire across savanna-forest transitions.

Vegetation-fire feedbacks are important for determining the distribution of forest and savanna. To understand how vegetation structure controls these feedbacks, we quantified flammability across gradients of tree density from grassland to forest in the Brazilian Cerrado. We experimentally burned 102 plots, for which we measured vegetation structure, fuels, microclimate, ignition success and fire behavior. Tree density had strong negative effects on ignition success, rate of spread, fire-line intensity and flame height. Declining grass biomass was the principal cause of this decline in flammability as tree density increased, but increasing fuel moisture contributed. Although the response of flammability to tree cover often is portrayed as an abrupt, largely invariant threshold, we found the response to be gradual, with considerable variability driven largely by temporal changes in atmospheric humidity. Even when accounting for humidity, flammability at intermediate tree densities cannot be predicted reliably. Fire spread in savanna-forest mosaics is not as deterministic as often assumed, but may appear so where vegetation boundaries are already sharp. Where transitions are diffuse, fire spread is difficult to predict, but should become increasingly predictable over multiple fire cycles, as boundaries are progressively sharpened until flammability appears to respond in a threshold-like manner.

Efficacy and Brain Imaging Correlates of an Immersive Motor Imagery BCI-Driven VR System for Upper Limb Motor Rehabilitation: A Clinical Case Report.

To maximize brain plasticity after stroke, a plethora of rehabilitation strategies have been explored. These include the use of intensive motor training, motor-imagery (MI), and action-observation (AO). Growing evidence of the positive impact of virtual reality (VR) techniques on recovery following stroke has been shown. However, most VR tools are designed to exploit active movement, and hence patients with low level of motor control cannot fully benefit from them. Consequently, the idea of directly training the central nervous system has been promoted by utilizing MI with electroencephalography (EEG)-based brain-computer interfaces (BCIs). To date, detailed information on which VR strategies lead to successful functional recovery is still largely missing and very little is known on how to optimally integrate EEG-based BCIs and VR paradigms for stroke rehabilitation. The purpose of this study was to examine the efficacy of an EEG-based BCI-VR system using a MI paradigm for post-stroke upper limb rehabilitation on functional assessments, and related changes in MI ability and brain imaging. To achieve this, a 60 years old male chronic stroke patient was recruited. The patient underwent a 3-week intervention in a clinical environment, resulting in 10 BCI-VR training sessions. The patient was assessed before and after intervention, as well as on a one-month follow-up, in terms of clinical scales and brain imaging using functional MRI (fMRI). Consistent with prior research, we found important improvements in upper extremity scores (Fugl-Meyer) and identified increases in brain activation measured by fMRI that suggest neuroplastic changes in brain motor networks. This study expands on the current body of evidence, as more data are needed on the effect of this type of interventions not only on functional improvement but also on the effect of the intervention on plasticity through brain imaging.

Vascular training does matter in the outcomes of saphenous high ligation and stripping.

OBJECTIVE: Varicose vein (VV) surgery is frequently performed by surgeons without formal vascular training. We aimed to compare the outcomes of the procedure based on the background of the surgeon. METHODS: All patients registered with VV surgery between 2004 and 2016 in Portuguese public hospitals were included in the study. Intrahospital outcomes were assessed from this administrative database. A random multicenter sample of 315 patients submitted to saphenous high ligation and stripping (175 patients from six vascular surgery departments and 140 patients from five general surgery divisions) were further queried over the phone, whereby additional nonregistered outcomes were evaluated: preoperative venous ultrasound, impact on quality of life by the 14-item Chronic Venous Insufficiency Quality of Life Questionnaire, visual analogue scale evaluation (score of 1 to 5) of the aesthetic results and general satisfaction, work absence days, and time to return to physical activities. RESULTS: In 13 years, there were 153,382 patients submitted to VV surgery. Of these, 49% were operated on by general surgeons and 40% by vascular surgeons; in 11%, it was not possible to identify the specialty performing the operation. Twenty-three deaths were registered (no differences between groups). In the general surgery group, 14% of patients were hospitalized for more than one night compared with 3% in the vascular group (P < .001). Reintervention rate during the period analyzed was significantly higher in the general surgery group (13.5% vs 8.2%; P < .001). Rate of outpatient surgery was higher in the vascular surgery group (60% vs 36%; P < .001). Phone query revealed similar overall satisfaction and improvement in quality of life in both groups (4.2 vs 4.0 [P = .275] and 35% vs 36% [P = .745], respectively). However, patients operated on by general surgeons reported worse surgical scars (2.8 vs 2.1; P = .007), higher number of residual VVs (2.4 vs 1.7; P = .006), and higher number of days absent from work (40 vs 27 days; P = .005) and took longer to resume physical activities (60 vs 41 days; P = .001). CONCLUSIONS: Despite that the majority of VV surgery in Portugal is executed by general surgeons, this study highlights important advantages when it is performed by surgeons with vascular training.

Identification of epileptic brain states by dynamic functional connectivity analysis of simultaneous EEG-fMRI: a dictionary learning approach.

Most fMRI studies of the brain's intrinsic functional connectivity (FC) have assumed that this is static; however, it is now clear that it changes over time. This is particularly relevant in epilepsy, which is characterized by a continuous interchange between epileptic and normal brain states associated with the occurrence of epileptic activity. Interestingly, recurrent states of dynamic FC (dFC) have been found in fMRI data using unsupervised learning techniques, assuming either their sparse or non-sparse combination. Here, we propose an l1-norm regularized dictionary learning (l1-DL) approach for dFC state estimation, which allows an intermediate and flexible degree of sparsity in time, and demonstrate its application in the identification of epilepsy-related dFC states using simultaneous EEG-fMRI data. With this l1-DL approach, we aim to accommodate a potentially varying degree of sparsity upon the interchange between epileptic and non-epileptic dFC states. The simultaneous recording of the EEG is used to extract time courses representative of epileptic activity, which are incorporated into the fMRI dFC state analysis to inform the selection of epilepsy-related dFC states. We found that the proposed l1-DL method performed best at identifying epilepsy-related dFC states, when compared with two alternative methods of extreme sparsity (k-means clustering, maximum; and principal component analysis, minimum), as well as an l0-norm regularization framework (l0-DL), with a fixed amount of temporal sparsity. We further showed that epilepsy-related dFC states provide novel insights into the dynamics of epileptic networks, which go beyond the information provided by more conventional EEG-correlated fMRI analysis, and which were concordant with the clinical profile of each patient. In addition to its application in epilepsy, our study provides a new dFC state identification method of potential relevance for studying brain functional connectivity dynamics in general.

EEG-Informed fMRI: A Review of Data Analysis Methods.

The simultaneous acquisition of electroencephalography (EEG) with functional magnetic resonance imaging (fMRI) is a very promising non-invasive technique for the study of human brain function. Despite continuous improvements, it remains a challenging technique, and a standard methodology for data analysis is yet to be established. Here we review the methodologies that are currently available to address the challenges at each step of the data analysis pipeline. We start by surveying methods for pre-processing both EEG and fMRI data. On the EEG side, we focus on the correction for several MR-induced artifacts, particularly the gradient and pulse artifacts, as well as other sources of EEG artifacts. On the fMRI side, we consider image artifacts induced by the presence of EEG hardware inside the MR scanner, and the contamination of the fMRI signal by physiological noise of non-neuronal origin, including a review of several approaches to model and remove it. We then provide an overview of the approaches specifically employed for the integration of EEG and fMRI when using EEG to predict the blood oxygenation level dependent (BOLD) fMRI signal, the so-called EEG-informed fMRI integration strategy, the most commonly used strategy in EEG-fMRI research. Finally, we systematically review methods used for the extraction of EEG features reflecting neuronal phenomena of interest.

Laparoscopic treatment in Type IV Giant Paraesophagic Hernia and intestinal occlusion a case report.

INTRODUCTION: A Giant Hiatal Paraesophageal Hernia (GPEH) is a Hiatal Hernia (HH) that includes more than 30% of the stomach in the thorax. The gold standard form of repair today is the laparoscopic abdominal approach in elective scenarios. Laparoscopic HH repair advantages include, less postoperative pain, small incisions, reduced postoperative respiratory complications are reduced, shorter hospital stay. The objective of this paper is to describe a patient undergoing with upper intestinal obstruction and a GPEH Type IV, approached laparoscopically. CASE PRESENTATION: We received a female patient 59 years old, she came with symptoms abdominal pain, emesis of intestinal characteristics and obstipation, with an evolution of 5 days. She also referred dyspnea; she went to another institution where made a CAT scan finding a GPEH. We decided to realize the procedure laparoscopically. We follow the principal objectives, reducing the hernia, dissecting al de hernia sac excision, Hiatal reparation with no mesh, and Nissen type fundoplication without Collis Gastroplasty. The patient stayed for seven days for surveillance and when the leukocyte and LDH went to a regular rate patient was discharged. With no complications with normal intestinal function and nearly no pain. DISCUSSION: We present a GPEH case associated with upper intestinal obstruction, with clinical findings that suggested ischemia. The approach of the treatment was abdominal laparoscopy. CONCLUSION: In elective patients Laparoscopy is superior than abdominal approach. Randomized trials comparing laparoscopic versus open approach are needed to conclude that laparoscopic approach is superior to open approach, in potentially GPEH complicated patients.