Reconstruction of the temporal correlation network of all-cause mortality fluctuation across Italian regions: the importance of temperature and among-nodes flux

All-cause mortality is a very coarse grain, albeit very reliable, index to check the health implications of lifestyle determinants, systemic threats and socio-demographic factors. In this work we adopt a statistical-mechanics approach to the analysis of temporal fluctuations of all-cause mortality, focusing on the correlation structure of this index across different regions of Italy. The correlation network among the 20 Italian regions was reconstructed using temperature oscillations and travellers' flux (as a function of distance and region's attractiveness, based on GDP), allowing for a separation between infective and non-infective death causes. The proposed approach allows monitoring of emerging systemic threats in terms of anomalies of correlation network structure.Comment: For (Python + Pandas + Jax) code, see https://github.com/GuidoGigante/All-cause-mortality-fluctuation-across-Italian-regions

Inferring Synaptic Structure in presence of Neural Interaction Time Scales

Biological networks display a variety of activity patterns reflecting a web of interactions that is complex both in space and time. Yet inference methods have mainly focused on reconstructing, from the network's activity, the spatial structure, by assuming equilibrium conditions or, more recently, a probabilistic dynamics with a single arbitrary time-step. Here we show that, under this latter assumption, the inference procedure fails to reconstruct the synaptic matrix of a network of integrate-and-fire neurons when the chosen time scale of interaction does not closely match the synaptic delay or when no single time scale for the interaction can be identified; such failure, moreover, exposes a distinctive bias of the inference method that can lead to infer as inhibitory the excitatory synapses with interaction time scales longer than the model's time-step. We therefore introduce a new two-step method, that first infers through cross-correlation profiles the delay-structure of the network and then reconstructs the synaptic matrix, and successfully test it on networks with different topologies and in different activity regimes. Although step one is able to accurately recover the delay-structure of the network, thus getting rid of any \textit{a priori} guess about the time scales of the interaction, the inference method introduces nonetheless an arbitrary time scale, the time-bin $dt$ used to binarize the spike trains. We therefore analytically and numerically study how the choice of $dt$ affects the inference in our network model, finding that the relationship between the inferred couplings and the real synaptic efficacies, albeit being quadratic in both cases, depends critically on $dt$ for the excitatory synapses only, whilst being basically independent of it for the inhibitory ones

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametri-zation of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.Instituto de Física de Líquidos y Sistemas Biológico

Evaluation of estimated glomerular filtration rate and clinical variables in systemic sclerosis patients

Objectives: The most important renal complication of systemic sclerosis (SSc) is scleroderma renal crisis (SRC). Many patients demonstrate less severe renal complications, most likely associated with reduced renal blood flow and a consequent reduction in glomerular filtration rate (GFR). The mechanism of this slowly progressive form of chronic renal disease is unclear. The aim of this study was to evaluate GFR by the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and the 7-variable Modification of Diet and Renal Disease (MDRD) equations in SSc patients and to correlate estimated GFR (eGFR) with clinical variables of the disease. Methods: 105 unselected and consecutive patients with SSc were enrolled. Serum creatinine was measured in all patients and GFR was estimated by 7-variable MDRD and CKD-EPI equations. Nailfold videocapillaroscopy was performed in all patients. Results: The mean value of eGFR evaluated by both 7-variable MDRD and CKD-EPI was significantly different (p < 0.0001) in the three capillaroscopic groups and correlated negatively with the severity of capillaroscopic damage (early: 95 ± 16 mL/min and 101 ± 12 mL/min, active: 86 ± 25 mL/min and 95 ± 17 mL/min, late: 76 ± 21 mL/min and 82 ± 21 mL/min). The mean value of eGFR evaluated by 7-variable MDRD (97 ± 23 mL/min vs. 74 ± 15 mL/min, p < 0.0001) and CKD-EPI (0.83 ± 0.20 mL/min vs. 0.68 ± 0.10 mL/min, p < 0.0001) was significantly higher in SSc patients without history of digital ulcers than in those with. Conclusion: We can conclude that in SSc patients without renal involvement, eGFR decreases with the progression of digital vascular damag

Rest tremor in Parkinson's disease: body distribution and time of appearance

Objective To assess body distribution and timing of appearance of rest tremor in Parkinson's disease. Methods Information was obtained by a computerized database containing historical information collected at the first visit and data collected during the subsequent follow-up visits. Information on rest tremor developed during the follow-up could be therefore obtained by our own observation in a proportion of patients. Results Among 289 patients, rest tremor was reported at disease onset in 65.4% of cases and detected at last follow-up examination in 74.4% of patients. Analysis of patients who did not report rest tremor at disease onset indicated that 26% of such patients (9% in the overall population) manifested rest tremor over the disease course. Rest tremor spread to new sites in 39% of patients who manifested rest tremor at disease onset. Regardless of tremor presentation at disease onset or during the follow-up, upper limb was the most frequent tremor localization. Over the follow-up, rest tremor developed faster in the upper limb than in other body sites. The risk of developing rest tremor during the follow-up was not affected by sex, side of motor symptom onset and site of tremor presentation. However, age of disease onset > 63 years was associated with an increased risk of rest tremor spread. Conclusions This study provides new information about body distribution and timing of rest tremor appearance during the course of early stages of Parkinson's disease that may help clinicians in patients' counselling

Density-based clustering: A 'landscape view' of multi-channel neural data for inference and dynamic complexity analysis

Two, partially interwoven, hot topics in the analysis and statistical modeling of neural data, are the development of efficient and informative representations of the time series derived from multiple neural recordings, and the extraction of information about the connectivity structure of the underlying neural network from the recorded neural activities. In the present paper we show that state-space clustering can provide an easy and effective option for reducing the dimensionality of multiple neural time series, that it can improve inference of synaptic couplings from neural activities, and that it can also allow the construction of a compact representation of the multi-dimensional dynamics, that easily lends itself to complexity measures. We apply a variant of the 'mean-shift' algorithm to perform state-space clustering, and validate it on an Hopfield network in the glassy phase, in which metastable states are largely uncorrelated from memories embedded in the synaptic matrix. In this context, we show that the neural states identified as clusters' centroids offer a parsimonious parametri-zation of the synaptic matrix, which allows a significant improvement in inferring the synaptic couplings from the neural activities. Moving to the more realistic case of a multi-modular spiking network, with spike-frequency adaptation inducing history-dependent effects, we propose a procedure inspired by Boltzmann learning, but extending its domain of application, to learn inter-module synaptic couplings so that the spiking network reproduces a prescribed pattern of spatial correlations; we then illustrate, in the spiking network, how clustering is effective in extracting relevant features of the network's state-space landscape. Finally, we show that the knowledge of the cluster structure allows casting the multi-dimensional neural dynamics in the form of a symbolic dynamics of transitions between clusters; as an illustration of the potential of such reduction, we define and analyze a measure of complexity of the neural time series.Instituto de Física de Líquidos y Sistemas Biológico

Learning selective top-down control enhances performance in a visual categorization task.

We model the putative neuronal and synaptic mechanisms involved in learning a visual categorization task, taking inspiration from single-cell recordings in inferior temporal cortex (ITC). Our working hypothesis is that learning the categorization task involves both bottom-up, ITC to prefrontal cortex (PFC), and top-down (PFC to ITC) synaptic plasticity and that the latter enhances the selectivity of the ITC neurons encoding the task-relevant features of the stimuli, thereby improving the signal-to-noise ratio. We test this hypothesis by modeling both areas and their connections with spiking neurons and plastic synapses, ITC acting as a feature-selective layer and PFC as a category coding layer. This minimal model gives interesting clues as to properties and function of the selective feedback signal from PFC to ITC that help solving a categorization task. In particular, we show that, when the stimuli are very noisy because of a large number of nonrelevant features, the feedback structure helps getting better categorization performance and decreasing the reaction time. It also affects the speed and stability of the learning process and sharpens tuning curves of ITC neurons. Furthermore, the model predicts a modulation of neural activities during error trials, by which the differential selectivity of ITC neurons to task-relevant and task-irrelevant features diminishes or is even reversed, and modulations in the time course of neural activities that appear when, after learning, corrupted versions of the stimuli are input to the network

Renal parenchymal resistance in patients with biopsy proven glomerulonephritis: correlation with histological findings

Renal Doppler ultrasound is increasingly used in nephrology for the evaluation of renovascular disease, allograft dysfunction, and chronic nephropathies. We compared intrarenal hemodynamic parameters to biopsy findings of glomerular sclerosis, tubular atrophy, interstitial fibrosis, crescents, arteriolosclerosis, and clinical variables in 100 patients. A positive correlation exists between renal function and percentage of glomerular sclerosis (P &lt;0.01, r = 0.26), conversely a negative correlation exists between glomerular filtrate rate and percentage of glomerular sclerosis(P &lt;0.0001, r = -0.35). The percentage of glomerular sclerosis correlate positively with pulsatile index (PI) (P &lt;0.05, r = 0.21) and renal resistive index (RI) (P &lt;0.05, r = 0.20). The percentage of crescents correlates positively with PI(P &lt;0.05, r = 0.21) and RI (P &lt;0.05, r = 0.20). Classifying arteriolosclerosis in four groups according to a severity scale, from absence to severe, PI (P &lt;0.05) and RI (P &lt;0.01) were significantly different. In the post hoc analysis, the median values of PI and RI are significantly different in patients with severe arteriolosclerosis than others. Ultrasound examination is a non-invasive diagnostic technique used on patients with suspected or established renal disease. Our study shows a close correlation between kidney function, ultrasound parameters, and histological findings. Measurement of renal parenchymal resistance by ultrasound could be used in association with biopsy and glomerular function for the evaluation of renal damage in patients with glomerulonephritis

A Fluctuation-Driven Mechanism for Slow Decision Processes in Reverberant Networks

The spike activity of cells in some cortical areas has been found to be correlated with reaction times and behavioral responses during two-choice decision tasks. These experimental findings have motivated the study of biologically plausible winner-take-all network models, in which strong recurrent excitation and feedback inhibition allow the network to form a categorical choice upon stimulation. Choice formation corresponds in these models to the transition from the spontaneous state of the network to a state where neurons selective for one of the choices fire at a high rate and inhibit the activity of the other neurons. This transition has been traditionally induced by an increase in the external input that destabilizes the spontaneous state of the network and forces its relaxation to a decision state. Here we explore a different mechanism by which the system can undergo such transitions while keeping the spontaneous state stable, based on an escape induced by finite-size noise from the spontaneous state. This decision mechanism naturally arises for low stimulus strengths and leads to exponentially distributed decision times when the amount of noise in the system is small. Furthermore, we show using numerical simulations that mean decision times follow in this regime an exponential dependence on the amplitude of noise. The escape mechanism provides thus a dynamical basis for the wide range and variability of decision times observed experimentally