Specific Regions Of Interest Research Articles

Cerebellar engagement in the attachment behavioral system

Brain structural bases of individual differences in attachment are not yet fully clarified. Given the evidence of relevant cerebellar contribution to cognitive, affective, and social functions, the present research was aimed at investigating potential associations between attachment dimensions (through the Attachment Style Questionnaire, ASQ) and cerebellar macro- and micro-structural measures (Volumetric and Diffusion Tensor Imaging data). In a sample of 79 healthy subjects, cerebellar and neocortical volumetric data were correlated with ASQ scores at the voxel level within specific Regions Of Interest. Also, correlations between ASQ scores and age, years of education, anxiety and depression levels were performed to control for the effects of sociodemographic and psychological variables on neuroimaging results. Positive associations between scores of the Preoccupation with Relationships (ASQ subscale associated to insecure/anxious attachment) and cortical volume were found in the cerebellum (right lobule VI and left Crus 2) and neocortex (right medial OrbitoFrontal Cortex, OFC) regions. Cerebellar contribution to the attachment behavioral system reflects the more general cerebellar engagement in the regulation of emotional and social behaviors. Cerebellar properties of timing, prediction, and learning well integrate with OFC processing, supporting the regulation of attachment experiences. Cerebellar areas might be rightfully included in the attachment behavioral system.

Numerical solution of the Fokker–Planck equation using physics-based mixture models

The Fokker–Planck equation governs the uncertainty propagation of dynamical systems driven by stochastic processes. The solution of the Fokker–Planck equation is a time-varying Probability Density Function (PDF) that is usually of high dimension with unbounded support. There are also properties that must be conserved in time for the joint and any marginal solution PDF (i.e., a time-varying PDF is a non-negative function that must integrate to unity at any given time.) Satisfying these properties poses a significant challenge to the numerical solution of the Fokker–Planck equation. Another challenge is to capture the tail behavior of the solution PDF with unbounded support, required to predict the probability of rare events like a system failure. Satisfying the conditions of a proper PDF with unbounded support limits the application of traditional grid-based numerical methods, like finite difference and finite element methods. This paper develops a novel numerical method based on physics-based mixture models for the transient and steady-state solutions of the Fokker–Planck equation. In the proposed numerical method, the trial function space consists of the convex combinations of some parametric PDFs (i.e., mixture models) that trivially satisfy the necessary conditions of the solution. Estimating the unknown parameters of the mixture model is via Bayesian inference while considering the constraints on model parameters. Bayesian inference facilitates integrating data on the responses of dynamical systems (e.g., from simulations) with the governing Fokker–Planck equation to estimate the unknown parameters. Since the solution PDF is not observable, combining it with observable data on system responses is far from trivial based on current numerical methods. The formulation of Bayesian inference also introduces a weighting function to reduce the estimation error in specific regions of interest like the tail of the solution PDF. To reduce the computational demand, the paper develops an importance sampling algorithm that generates a small set of collocation points at which the residual of the Fokker–Planck equation is evaluated. The performance of the proposed numerical method is demonstrated with several benchmark problems. The obtained results are compared with analytical solutions when available and otherwise with simulations.

Deep learning models of cognitive processes constrained by human brain connectomes

Decoding cognitive processes from recordings of brain activity has been an active topic in neuroscience research for decades. Traditional decoding studies focused on pattern classification in specific regions of interest and averaging brain activity over many trials. Recently, brain decoding with graph neural networks has been shown to scale at fine temporal resolution and on the full brain, achieving state-of-the-art performance on the human connectome project benchmark. The reason behind this success is likely the strong inductive connectome prior that enables the integration of distributed patterns of brain activity. Yet, the nature of such inductive bias is still poorly understood. In this work, we investigate the impact of the inclusion of multiple path lengths (through high-order graph convolution), the homogeneity of brain parcels (graph nodes), and the type of interactions (graph edges). We evaluate the decoding models on a large population of 1200 participants, under 21 different experimental conditions, acquired from the Human Connectome Project database.Our findings reveal that the optimal choice for large-scale cognitive decoding is to propagate neural dynamics within empirical functional connectomes and integrate brain dynamics using high-order graph convolutions. In this setting, the model exhibits high decoding accuracy and robustness against adversarial attacks on the graph architecture, including randomization in functional connectomes and lesions in targeted brain regions and networks. The trained model relies on biologically meaningful features for the prediction of cognitive states and generates task-specific graph representations resembling task-evoked activation maps. These results demonstrate that a full-brain integrative model is critical for the large-scale brain decoding. Our study establishes principles of how to effectively leverage human connectome constraints in deep graph neural networks, providing new avenues to study the neural substrates of human cognition at scale.

Neurophysiological Examination of the Effects of Behavioral Antecedents on Physical Balance in Older Individuals

Falls in older individuals can be caused by balance disorders, influenced by predictive factors based on self-efficacy and outcome expectation. This study investigated the relationship between predictive factors related to regional neural functional activity and postural control. We included 16 older men (average age, 76.4±5.8 years) and evaluated their balancing ability and fall-related selfefficacy using the Japanese version of Mini-Balance Evaluation Systems Test (J-Mini-BESTest) and the Japanese version of the Falls Efficacy Scale (JFES), respectively. We performed an electroencephalogram before, during, and after postural perturbations. The cortical activity in the right Inferior Parietal Lobe (IPL) and Supplementary Motor Area (SMA) was analyzed using current density in the specific regions of interest. Foot Response Values (FRV) were used to evaluate physical responses during postural perturbations. The neural activity values in the IPL after postural perturbations indicated a significant positive correlation with JFES and J-Mini-BESTest scores when prior information was provided to participants. The neural activity values in the SMA before postural perturbations showed a significant positive correlation with J-Mini-BESTest score and a significant negative correlation with FRV. Furthermore, during postural perturbations, subjects with prior information exhibited significant positive neural correlations with neural activity between the SMA and IPL. These results suggest that neural activity in these brain regions influence balancing ability and predictive factors. Prior knowledge of a postural perturbation’s timing could be a compensatory factor promoting the activation of predictive factors.

LIVER SEGMENTATION IN CT SCAN FOR OPTIMIZED METHOD

In a medical side liver segmentation is a major problem. It is a much more challenging task among other segmentation than a liver segment. So we have to introduce automatic segmentation of liver in CT images and MRI has become significance in medical area, so we are focusing the managing on domain from MRI to CT volumes on the example of 3D and 2D liver segmentation. Here, we are implemented to fuzzy clustering method and cuckoo optimized method to used in this method. In additional that the purpose of this method is to used for liver boundary calculation and segment the values using fuzzy C-means and fuzzy clustering using Support Vector Machine (SVM) for classification). The spatial liver boundary by constraints are named with different method such as the user can choose on particular region of interest and contour method until more accurate results to be obtained.

In Vitro Cell Behavior and Antibiotic Activity under Sustained Release of Doxycycline-Loaded Poly(lactic-co-glycolic acid) Microspheres.

The state-of-the-art sustained drug delivery systems are related to features to improve pharmacological transport through a controlled ratio between drug release and the desired therapeutic effect. Microspheres of biodegradable polymers, such as poly(lactic-co-glycolic acid) (PLGA), play an important role in these approaches, directing the release in a specific region of interest. In this way, the encapsulation of doxycycline (DOX) as a microbial agent turns the PLGA microspheres into a potential device for the treatment of topic oral diseases. Thus, this work aimed to produce DOX-loaded PLGA microspheres and see how they interfered with mesenchymal stem cell viability and in the sustained release in antimicrobial assays. Scanning electron microscopy showed the spherical microstructured pattern, revealing assorted sized distribution, with major diameters ranging 1–3 µm. The encapsulation efficiency presented a mean of 80% in both methods to obtain the microspheres (sonication and magnetic rotation). The DOX release test revealed a gradual and continuous profile of 30–40% between 120 and 168 h. Mesenchymal stem cells cultured in PLGA with or without DOX at several concentrations revealed no effect on the cell metabolic activity. Striking morphology changes were observed by confocal microscopy after 1 to 3 days under culture. The live/dead assay indicated that when microsphere densities were increased (from 10 to 100 µg/mL) cultured cells presented an internalized pattern of microspheres in both groups of PLGA containing DOX or not, while slight cell death signals were identified nearby microsphere clusters. Microbiological assays performed by the agar diffusion test pointed out that an inhibition zone was identified in Staphylococcus aureus (S. aureus) cultures at earlier times of DOX release. Despite the well-known use of PLGA as a drug delivery vehicle, when synthesized with DOX, it presents both characteristics of the desired treatment to prevent healthy tissue damage while avoiding bacterial growth in a microenvironment with anatomical features, such as grooves, projections, and other tough conditions that favor the development of oral diseases.

Motor Network Reorganization Induced in Chronic Stroke Patients with the Use of a Contralesionally-Controlled Brain Computer Interface

ABSTRACT Upper extremity weakness in chronic stroke remains a problem not fully addressed by current therapies. Brain–computer interfaces (BCIs) engaging the unaffected hemisphere are a promising therapy that are entering clinical application, but the mechanism underlying recovery is not well understood. We used resting state functional MRI to assess the impact a contralesionally driven EEG BCI therapy had on motor system functional organization. Patients used a therapeutic BCI for 12 weeks at home. We acquired resting-state fMRI scans and motor function data before and after the therapy period. Changes in functional connectivity (FC) strength between motor network regions of interest (ROIs) and the topographic extent of FC to specific ROIs were analyzed. Most patients achieved clinically significant improvement. Motor FC strength and topographic extent decreased following BCI therapy. Motor recovery correlated with reductions in motor FC strength across the entire motor network. These findings suggest BCI-mediated interventions may reverse pathologic strengthening of dysfunctional network interactions.

MeConcord: a new metric to quantitatively characterize DNA methylation heterogeneity across reads and CpG sites

MotivationIntermediately methylated regions occupy a significant fraction of the human genome and are closely associated with epigenetic regulations or cell-type deconvolution of bulk data. However, these regions show distinct methylation patterns, corresponding to different biological mechanisms. Although there have been some metrics developed for investigating these regions, the high noise sensitivity limits the utility for distinguishing distinct methylation patterns.ResultsWe proposed a method named MeConcord to measure local methylation concordance across reads and CpG sites, respectively. MeConcord showed the most stable performance in distinguishing distinct methylation patterns (‘identical’, ‘uniform’ and ‘disordered’) compared with other metrics. Applying MeConcord to the whole genome data across 25 cell lines or primary cells or tissues, we found that distinct methylation patterns were associated with different genomic characteristics, such as CTCF binding or imprinted genes. Further, we showed the differences of CpG island hypermethylation patterns between senescence and tumorigenesis by using MeConcord. MeConcord is a powerful method to study local read-level methylation patterns for both the whole genome and specific regions of interest.Availability and implementationMeConcord is available at https://github.com/WangLabTHU/MeConcord.Supplementary information Supplementary data are available at Bioinformatics online.

CARD26: Effects of the Artificial Pulse of the HeartMate 3 on Left Ventricular Flow Patterns

Purpose of study: In order to reduce the risk of thrombotic events in left ventricular assist device patients, the HeartMate 3 is equipped with an artificial pulse (AP) setting. However, experimental results concerning the AP’s impact on left ventricular (LV) flow patterns and timing within the cardiac contraction are limited. This study aims to analyze LV flow patterns occurring during and after the AP, which is synchronized to the simulated cardiac function inside the mock loop setup. Methods: The in vitro measurements were triggered using a system capable of detecting AP timing and were performed for selected timings within the cardiac cycle. A pulsatile cardiovascular mock loop was developed with a simplified LV model and the flow patterns were imaged via Particle Image Velocimetry. The measurements were performed with stroke volumes of 0ml, 38ml, 49ml, and 57ml, with pump speeds configured to achieve 80mmHg mean aortic pressure and a total cardiac output of 5l/min for all settings. Parameters such as kinetic energy (KE) were computed in specific regions of interest, especially in the LV apex, as they indicate LV washout. Results: The overall influence of the AP on the intraventricular flow appears small (2.5% increase in KEmean for entire flow field), however short durations of large KE increases were observed in the LV apex with the AP turned on (KE mean: +4%; KEpeak: +24%), showing a dependence on the timing of the AP (KEpeak increased by up to 39% for specific timings). This increase can be observed when a counter-clockwise vortex ring of the mitral jet travelled further towards the apex, which was seen for specific timings of the AP. Vortices arising next to the valves could also improve washout there. Conclusion: While the AP produced only minor effects in the overall flow fields, synchronization of the AP with the cardiac cycle could potentially lead to increased LV washout, thus leading to better clinical outcomes.

Forecasting large-scale circulation regimes using deformable convolutional neural networks and global spatiotemporal climate data

Classifying the state of the atmosphere into a finite number of large-scale circulation regimes is a popular way of investigating teleconnections, the predictability of severe weather events, and climate change. Here, we investigate a supervised machine learning approach based on deformable convolutional neural networks (deCNNs) and transfer learning to forecast the North Atlantic-European weather regimes during extended boreal winter for 1–15 days into the future. We apply state-of-the-art interpretation techniques from the machine learning literature to attribute particular regions of interest or potential teleconnections relevant for any given weather cluster prediction or regime transition. We demonstrate superior forecasting performance relative to several classical meteorological benchmarks, as well as logistic regression and random forests. Due to its wider field of view, we also observe deCNN achieving considerably better performance than regular convolutional neural networks at lead times beyond 5–6 days. Finally, we find transfer learning to be of paramount importance, similar to previous data-driven atmospheric forecasting studies.

Atypical Functional Connectivity During Unfamiliar Music Listening in Children With Autism.

BackgroundAtypical processing of unfamiliar, but less so familiar, stimuli has been described in Autism Spectrum Disorder (ASD), in particular in relation to face processing. We examined the construct of familiarity in ASD using familiar and unfamiliar songs, to investigate the link between familiarity and autism symptoms, such as repetitive behavior.MethodsForty-eight children, 24 with ASD (21 males, mean age = 9.96 years ± 1.54) and 24 typically developing (TD) controls (21 males, mean age = 10.17 ± 1.90) completed a music familiarity task using individually identified familiar compared to unfamiliar songs, while magnetoencephalography (MEG) was recorded. Each song was presented for 30 s. We used both amplitude envelope correlation (AEC) and the weighted phase lag index (wPLI) to assess functional connectivity between specific regions of interest (ROI) and non-ROI parcels, as well as at the whole brain level, to understand what is preserved and what is impaired in familiar music listening in this population.ResultsIncreased wPLI synchronization for familiar vs. unfamiliar music was found for typically developing children in the gamma frequency. There were no significant differences within the ASD group for this comparison. During the processing of unfamiliar music, we demonstrated left lateralized increased theta and beta band connectivity in children with ASD compared to controls. An interaction effect found greater alpha band connectivity in the TD group compared to ASD to unfamiliar music only, anchored in the left insula.ConclusionOur results revealed atypical processing of unfamiliar songs in children with ASD, consistent with previous studies in other modalities reporting that processing novelty is a challenge for ASD. Relatively typical processing of familiar stimuli may represent a strength and may be of interest to strength-based intervention planning.

Testing the effect of bioturbation and species abundance upon discrete-depth individual foraminifera analysis

Abstract. We used a single foraminifera enabled, holistic hydroclimate-to-sediment transient modelling approach to fundamentally evaluate the efficacy of discrete-depth individual foraminifera analysis (IFA) for reconstructing past sea surface temperature (SST) variability from deep-sea sediment archives, a method that has been used, amongst other applications, for reconstructing El Niño–Southern Oscillation (ENSO). The computer model environment allows us to strictly control for variables such as SST, foraminifera species abundance response to SST, as well as depositional processes such as sediment accumulation rate (SAR) and bioturbation depth (BD) and subsequent laboratory processes such as sample size and machine error. Examining a number of best-case scenarios, we find that IFA-derived reconstructions of past SST variability are sensitive to all of the aforementioned variables. Running 100 ensembles for each scenario, we find that the influence of bioturbation upon IFA-derived SST reconstructions, combined with typical samples sizes employed in the field, produces noisy SST reconstructions with poor correlation to the original SST distribution in the water. This noise is especially apparent for values near the tails of the SST distribution, which is the distribution region of particular interest in the case of, e.g. ENSO. The noise is further increased in the case of increasing machine error, decreasing SAR and decreasing sample size. We also find poor agreement between ensembles, underscoring the need for replication studies in the field to confirm findings at particular sites and time periods. Furthermore, we show that a species abundance response to SST could in theory bias IFA-derived SST reconstructions, which can have consequences when comparing IFA-derived SST distributions from markedly different mean climate states. We provide a number of idealised simulations spanning a number of SAR, sample size, machine error and species abundance scenarios, which can help assist researchers in the field to determine under which conditions they could expect to retrieve significant results.

Multivariate, region-based genetic analyses of facets of reproductive aging in White and Black women.

BackgroundAge at final menstrual period (FMP) and the accompanying hormone trajectories across the menopause transition do not occur in isolation, but likely share molecular pathways. Understanding the genetics underlying the endocrinology of the menopause transition may be enhanced by jointly analyzing multiple interrelated traits.MethodsIn a sample of 347 White and 164 Black women from the Study of Women's Health Across the Nation (SWAN), we investigated pleiotropic effects of 54 candidate genetic regions of interest (ROI) on 5 menopausal traits (age at FMP and premenopausal and postmenopausal levels of follicle stimulation hormone and estradiol) using multivariate kernel regression (Multi‐SKAT). A backward elimination procedure was used to identify which subset of traits were most strongly associated with a specific ROI.ResultsIn White women, the 20 kb ROI around rs10734411 was significantly associated with the multivariate distribution of age at FMP, premenopausal estradiol, and postmenopausal estradiol (omnibus p‐value = .00004). This association did not replicate in the smaller sample of Black women.ConclusionThis study using a region‐based, multiple‐trait approach suggests a shared genetic basis among multiple facets of reproductive aging.

Automated Teller Machine Authentication Using Biometric

This paper presents a novel method of a secured card-less Automated Teller Machine (ATM) authentication based on the three bio-metrics measures. It would help in the identification and authorization of individuals and would provide robust security enhancement. Moreover, it would assist in providing identification in ways that cannot be impersonated. To the best of our knowledge, this method of Biometric_ fusion way is the first ATM security algorithm that utilizes a fusion of three biometric features of an individual such as Fingerprint, Face, and Retina simultaneously for recognition and authentication. These biometric images have been collected as input data for each module in this system, like a fingerprint, a face, and a retina module. A database is created by converting these images to YIQ color space, which is helpful in normalizing the brightness levels of the image hence mainly (Y component’s) luminance. Then, it attempt to enhance Cellular Automata Segmentation has been carried out to segment the particular regions of interest from these database images. After obtaining segmentation results, the featured extraction method is carried out from these critical segments of biometric photos. The Enhanced Discrete Wavelet Transform technique (DWT Mexican Hat Wavelet) was used to extract the features. Fusion of extracted features of all three biometrics features have been used to bring in the multimodal classification approach to get fusion vectors. Once fusion vectors ware formulated, the feature level fusion technique is incorporated based on the extracted feature vectors. These features have been applied to the machine learning algorithm to identify and authorization of multimodal biometrics for ATM security. In the proposed approach, we attempt at useing an enhanced Deep Convolutional Neural Network (DCNN). A hybrid optimization algorithm has been selected based on the effectiveness of the features. The proposed approach results were compared with existing algorithms based on the classification accuracy to prove the effectiveness of our algorithm. Moreover, comparative results of the proposed method stand as a proof of more promising outcomes by combining the three biometric features.

The influence of the manufacturing process conditions on the in-vitro bio-performance of Titanium alloys

The prolonged hospitalization due to the premature failure of the implant is one of the key aspect to be addressed by the new category of prosthetic devices, which must ensure a perfect fit with the surrounding bones and a good and fast healing process. Such strict requirements can be satisfied by processing Titanium (Ti) alloys using innovative manufacturing processes, like the SuperPlastic Forming (SPF) and the Single Point Incremental Forming (SPIF). Nevertheless, even if the biocompatibility of the Ti alloys is quite well assessed, the manufacturing conditions might affect the final characteristics of the implant. In fact, the adoption of lubricants used to reduce friction between the blank and the rotating tool (SPIF) or the graphite-based sprays to avoid sticking between the blank and the tools (SPF) should change into cytotoxic the produced prosthesis. In addition, the adoption of working temperatures higher than the room one, especially in the case of the SPF, represents a possible critical aspect due to the high reactivity of Ti alloys. In this work, some results of the Proof-of-Concept (P.o.C.) project FORMAE-BIO are presented. In particular, the biocompatibility and cytotoxicity performance of prosthetic devices produced via SPF and SPIF were investigated: a benchmark geometry (truncated pyramid) was manufactured via both the abovementioned processes, so that square samples could be extracted from specific regions of interest and subjected to in-vitro tests: results, gathered from different time points, showed that if the surfaces are not properly cleaned after the manufacturing, residuals of lubricants may gradually be released making the material cytotoxic or, alternatively, remaining adherent to the surface and hindering the cells’ viability.

Spatial trans-epithelial electrical resistance (S-TEER) integrated in organs-on-chips.

Transepithelial/transendothelial electrical resistance (TEER) is a label-free assay that is commonly used to assess tissue barrier integrity. TEER measurement systems have been embedded in organ-on-a-chip devices to provide live readouts of barrier functionality. Yet, these systems commonly provide the impedance values which correspond to the highest level of permeability throughout the chip and cannot provide localized information on specific regions of interest. This work introduces a system that provides this essential information: a spatial-TEER (S-TEER) organ-on-a-chip platform, which incorporates moving (scanning) electrodes that can measure electrical resistance at any desired location along the chip. We demonstrate the system's capacity to obtain localized measurements of permeability in selected regions of a cell sample. We show how, in a layer with non-uniform levels of cell coverage, permeability is higher in areas with lower cell density-suggesting that the system can be used to monitor local cellular growth in vitro. To demonstrate the applicability of the chip in studies of barrier function, we characterize tissue response to TNF-α and to EGTA, agents known to harm tissue barrier integrity.

Thermography signs of lung inflammation

The purpose of the study was to find out if infrared thermography of the thorax is the method to select the patients with lung inflammationMaterial, methods: Thermograms were accumulated and processed in the «TVision» cloud storage («Dignosis», Russia). Special regions of interest (ROI) were automatically created: 1. on the front and back of the thorax roughly in the projection of the upper lobe (ULP) and the lower lobe (LLP) of the lung; 2.e lines on the front surface of the thorax. Two types of temperature gradients were calculated: between ULP and LLP (by subtraction mean temperature in LLP from mean temperature in ULP) (ΔT1); between both ULP and both LLP on the back of the thorax (ΔT2). Approximation confidence value for the polynomial trend line (R²) along the marked lines on the front surface of the thorax also calculated. Totally 489 thermograms, were analyzed, included 337 from healthy patients (group 1) and 152 from patients with confirmed diagnosis of lung inflammation (group 2)Results: R² value was higher in the group 1 compare to group 2 (0.58 ± 0.16 vs 0.3 ± 0.2, p < 0.05). ΔT1 value was negative only in patients from group 2, as well as ΔT1 value greater than 0.4 °C.Conclusion: three independent thermographic criteria suitable for detecting lung inflammation were found, so infrared thermography is the valuable method for screening this pathology.

A novel method for measuring bowel motility and velocity with dynamic magnetic resonance imaging in two and three dimensions.

Increasingly, dynamic magnetic resonance imaging (MRI) has potential as a noninvasive and accessible tool for diagnosing and monitoring gastrointestinal motility in healthy and diseased bowel. However, current MRI methods of measuring bowel motility have limitations: requiring bowel preparation or long acquisition times; providing mainly surrogate measures of motion; and estimating bowel-wall movement in just two dimensions. In this proof-of-concept study we apply a method that provides a quantitative measure of motion within the bowel, in both two and three dimensions, using existing, vendor-implemented MRI pulse sequences with minimal bowel preparation. This method uses a minimised cost function to fit linear vectors in the spatial and temporal domains. It is sensitised to the spatial scale of the bowel and aims to address issues relating to the low signal-to-noise in high-temporal resolution dynamic MRI scans, previously compensated for by performing thick-slice (10-mm) two-dimensional (2D) coronal scans. We applied both 2D and three-dimensional (3D) scanning protocols in two healthy volunteers. For 2D scanning, analysis yielded bi-modal velocity peaks, with a mean antegrade motion of 5.5 mm/s and an additional peak at ~9 mm/s corresponding to longitudinal peristalsis, as supported by intraoperative data from the literature. Furthermore, 3D scans indicated a mean forward motion of 4.7 mm/s, and degrees of antegrade and retrograde motion were also established. These measures show promise for the noninvasive assessment of bowel motility, and have the potential to be tuned to particular regions of interest and behaviours within the bowel.

Regional Brain Volume, Brain Reserve and MMSE Performance in Healthy Aging From the NEUROAGE Cohort: Contributions of Sex, Education, and Depression Symptoms.

Objective: The aim of this study was twofold. First, to investigate the relationship between age, gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volumes, brain reserve (BR), and specific regions of interest (ROIs) with global cognitive function in healthy older adults participating in a longitudinal study on aging in the island country of Cyprus. Second, to assess the contribution of important demographic and psychosocial factors on brain volume. Specifically, the effects of sex and years of education and the association between depression symptoms on brain volume were also explored in this Mediterranean cohort.Methods: Eighty-seven healthy older adults (males = 37, females = 50) scoring ≥24 on the Mini-Mental State Examination (MMSE) were included, with a mean age of 72.75 years and a mean educational level of 10.48 years. The Geriatric Depression Scale was used to assess depression. T1-weighted magnetic resonance images were used to calculate global and regional volumes.Results: Age was negatively correlated with GM, WM, BR, MMSE scores, and ROIs, including the hippocampus, amygdala, entorhinal cortex, prefrontal cortex, anterior cingulate gyrus, and positively with CSF. Higher MMSE scores positively correlated with GM volume. Women exhibited greater levels of depression than men. Depression was also negatively correlated with GM volume and MMSE scores. Men had greater ventricular size than women and participants with higher education had greater ventricular expansion than those with fewer years in education.Conclusions: The reported structural changes provide evidence on the overlap between age-related brain changes and healthy cognitive aging and suggest that these age changes affect certain regions. Furthermore, sex, depressive symptomatology, and education are significant predictors of the aging brain. Brain reserve and higher education accommodate these changes and works against the development of clinical symptoms.

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.