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A growing body of research indicates that structural plasticity mechanisms are crucial for learning and memory consolidation. Starting from a simple phenomenological model, we exploit a mean-field approach to develop a theoretical framework of learning through this kind of plasticity, capable of taking into account several features of the connectivity and pattern of activity of biological neural networks, including probability distributions of neuron firing rates, selectivity of the responses of single neurons to multiple stimuli, probabilistic connection rules, and noisy stimuli. More importantly, it describes the effects of stabilization, pruning, and reorganization of synaptic connections. This framework is used to compute the values of some relevant quantities used to characterize the learning and memory capabilities of the neuronal network in training and testing procedures as the number of training patterns and other model parameters vary. The results are then compared with those obtained through simulations with firing-rate-based neuronal network models.

Machine Learning (ML) is nowadays an essential tool in the analysis of Magnetic Resonance Imaging (MRI) data, in particular in the identification of brain correlates in neurological and neurodevelopmental disorders. ML requires datasets of appropriate size for training, which in neuroimaging are typically obtained collecting data from multiple acquisition centers. However, analyzing large multicentric datasets can introduce bias due to differences between acquisition centers. ComBat harmonization is commonly used to address batch effects, but it can lead to data leakage when the entire dataset is used to estimate model parameters. In this study, structural and functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) collection were used to classify subjects with Autism Spectrum Disorders (ASD) compared to Typical Developing controls (TD). We compared the classical approach (external harmonization) in which harmonization is performed before train/test split, with an harmonization calculated only on the train set (internal harmonization), and with the dataset with no harmonization. The results showed that harmonization using the whole dataset achieved higher discrimination performance, while non-harmonized data and harmonization using only the train set showed similar results, for both structural and connectivity features. We also showed that the higher performances of the external harmonization are not due to larger size of the sample for the estimation of the model and hence these improved performance with the entire dataset may be ascribed to data leakage. In order to prevent this leakage, it is recommended to define the harmonization model solely using the train set., (© 2023. The Author(s).)

The cyclic peptide hormone somatostatin regulates physiological processes involved in growth and metabolism, through its binding to G-protein coupled somatostatin receptors. The isoform 2 (SSTR2) is of particular relevance for the therapy of neuroendocrine tumours for which different analogues to somatostatin are currently in clinical use. We present an extensive and systematic computational study on the dynamics of SSTR2 in three different states: active agonist-bound, inactive antagonist-bound and apo inactive. We exploited the recent burst of SSTR2 experimental structures to perform μs-long multi-copy molecular dynamics simulations to sample conformational changes of the receptor and rationalize its binding to different ligands (the agonists somatostatin and octreotide, and the antagonist CYN154806). Our findings suggest that the apo form is more flexible compared to the holo ones, and confirm that the extracellular loop 2 closes upon the agonist octreotide but not upon the antagonist CYN154806. Based on interaction fingerprint analyses and free energy calculations, we found that all peptides similarly interact with residues buried into the binding pocket. Conversely, specific patterns of interactions are found with residues located in the external portion of the pocket, at the basis of the extracellular loops, particularly distinguishing the agonists from the antagonist. This study will help in the design of new somatostatin-based compounds for theranostics of neuroendocrine tumours., (© 2023. The Author(s).)

Working Memory (WM) is a cognitive mechanism that enables temporary holding and manipulation of information in the human brain. This mechanism is mainly characterized by a neuronal activity during which neuron populations are able to maintain an enhanced spiking activity after being triggered by a short external cue. In this study, we implement, using the NEST simulator, a spiking neural network model in which the WM activity is sustained by a mechanism of short-term synaptic facilitation related to presynaptic calcium kinetics. The model, which is characterized by leaky integrate-and-fire neurons with exponential postsynaptic currents, is able to autonomously show an activity regime in which the memory information can be stored in a synaptic form as a result of synaptic facilitation, with spiking activity functional to facilitation maintenance. The network is able to simultaneously keep multiple memories by showing an alternated synchronous activity which preserves the synaptic facilitation within the neuron populations holding memory information. The results shown in this study confirm that a WM mechanism can be sustained by synaptic facilitation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tiddia, Golosio, Fanti and Paolucci.)

Spiking neural network models are increasingly establishing themselves as an effective tool for simulating the dynamics of neuronal populations and for understanding the relationship between these dynamics and brain function. Furthermore, the continuous development of parallel computing technologies and the growing availability of computational resources are leading to an era of large-scale simulations capable of describing regions of the brain of ever larger dimensions at increasing detail. Recently, the possibility to use MPI-based parallel codes on GPU-equipped clusters to run such complex simulations has emerged, opening up novel paths to further speed-ups. NEST GPU is a GPU library written in CUDA-C/C++ for large-scale simulations of spiking neural networks, which was recently extended with a novel algorithm for remote spike communication through MPI on a GPU cluster. In this work we evaluate its performance on the simulation of a multi-area model of macaque vision-related cortex, made up of about 4 million neurons and 24 billion synapses and representing 32 mm 2 surface area of the macaque cortex. The outcome of the simulations is compared against that obtained using the well-known CPU-based spiking neural network simulator NEST on a high-performance computing cluster. The results show not only an optimal match with the NEST statistical measures of the neural activity in terms of three informative distributions, but also remarkable achievements in terms of simulation time per second of biological activity. Indeed, NEST GPU was able to simulate a second of biological time of the full-scale macaque cortex model in its metastable state 3.1× faster than NEST using 32 compute nodes equipped with an NVIDIA V100 GPU each. Using the same configuration, the ground state of the full-scale macaque cortex model was simulated 2.4× faster than NEST., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Tiddia, Golosio, Albers, Senk, Simula, Pronold, Fanti, Pastorelli, Paolucci and van Albada.)

Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 10 4 , and so permits real-time VCT studies for X-ray breast imaging.

The brain exhibits capabilities of fast incremental learning from few noisy examples, as well as the ability to associate similar memories in autonomously-created categories and to combine contextual hints with sensory perceptions. Together with sleep, these mechanisms are thought to be key components of many high-level cognitive functions. Yet, little is known about the underlying processes and the specific roles of different brain states. In this work, we exploited the combination of context and perception in a thalamo-cortical model based on a soft winner-take-all circuit of excitatory and inhibitory spiking neurons. After calibrating this model to express awake and deep-sleep states with features comparable with biological measures, we demonstrate the model capability of fast incremental learning from few examples, its resilience when proposed with noisy perceptions and contextual signals, and an improvement in visual classification after sleep due to induced synaptic homeostasis and association of similar memories., Competing Interests: The authors have declared that no competing interests exist.

Over the past decade there has been a growing interest in the development of parallel hardware systems for simulating large-scale networks of spiking neurons. Compared to other highly-parallel systems, GPU-accelerated solutions have the advantage of a relatively low cost and a great versatility, thanks also to the possibility of using the CUDA-C/C++ programming languages. NeuronGPU is a GPU library for large-scale simulations of spiking neural network models, written in the C++ and CUDA-C++ programming languages, based on a novel spike-delivery algorithm. This library includes simple LIF (leaky-integrate-and-fire) neuron models as well as several multisynapse AdEx (adaptive-exponential-integrate-and-fire) neuron models with current or conductance based synapses, different types of spike generators, tools for recording spikes, state variables and parameters, and it supports user-definable models. The numerical solution of the differential equations of the dynamics of the AdEx models is performed through a parallel implementation, written in CUDA-C++, of the fifth-order Runge-Kutta method with adaptive step-size control. In this work we evaluate the performance of this library on the simulation of a cortical microcircuit model, based on LIF neurons and current-based synapses, and on balanced networks of excitatory and inhibitory neurons, using AdEx or Izhikevich neuron models and conductance-based or current-based synapses. On these models, we will show that the proposed library achieves state-of-the-art performance in terms of simulation time per second of biological activity. In particular, using a single NVIDIA GeForce RTX 2080 Ti GPU board, the full-scale cortical-microcircuit model, which includes about 77,000 neurons and 3 · 10 8 connections, can be simulated at a speed very close to real time, while the simulation time of a balanced network of 1,000,000 AdEx neurons with 1,000 connections per neuron was about 70 s per second of biological activity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Golosio, Tiddia, De Luca, Pastorelli, Simula and Paolucci.)

Breast Computed Tomography (bCT) is a three-dimensional imaging technique that is raising interest among radiologists as a viable alternative to mammographic planar imaging. In X-rays imaging it would be desirable to maximize the capability of discriminating different tissues, described by the Contrast to Noise Ratio (CNR), while minimizing the dose (i.e. the radiological risk). Both dose and CNR are functions of the X-ray energy. This work aims at experimentally investigating the optimal energy that, at fixed dose, maximizes the CNR between glandular and adipose tissues. Acquisitions of both tissue-equivalent phantoms and actual breast specimens have been performed with the bCT system implemented within the Syrma-3D collaboration at the Syrmep beamline of the Elettra synchrotron (Trieste). The experimental data have been also compared with analytical simulations and the results are in agreement. The CNR is maximized at energies around 26-28 keV. These results are in line with the outcomes of a previously presented simulation study which determined an optimal energy of 28 keV for a large set of breast phantoms with different diameters and glandular fractions. Finally, a study on photon starvation has been carried out to investigate how far the dose can be reduced still having suitable images for diagnostics.

This study relates to the INFN project SYRMA-3D for in vivo phase-contrast breast computed tomography using the SYRMEP synchrotron radiation beamline at the ELETTRA facility in Trieste, Italy. This peculiar imaging technique uses a novel dosimetric approach with respect to the standard clinical procedure. In this study, optimization of the acquisition procedure was evaluated in terms of dose delivered to the breast. An offline dose monitoring method was also investigated using radiochromic film dosimetry. Various irradiation geometries have been investigated for scanning the prone patient's pendant breast, simulated by a 14 cm-diameter polymethylmethacrylate cylindrical phantom containing pieces of calibrated radiochromic film type XR-QA2. Films were inserted mid-plane in the phantom, as well as wrapped around its external surface, and irradiated at 38 keV, with an air kerma value that would produce an estimated mean glandular dose of 5 mGy for a 14 cm-diameter 50% glandular breast. Axial scans were performed over a full rotation or over 180°. The results point out that a scheme adopting a stepped rotation irradiation represents the best geometry to optimize the dose distribution to the breast. The feasibility of using a piece of calibrated radiochromic film wrapped around a suitable holder around the breast to monitor the scan dose offline is demonstrated.

A quantitative characterization of the soft tissues composing the human breast is achieved by means of a monochromatic CT phase-contrast imaging system, through accurate measurements of their attenuation coefficients within the energy range of interest for breast CT clinical examinations. Quantitative measurements of linear attenuation coefficients are performed on tomographic reconstructions of surgical samples, using monochromatic x-ray beams from a synchrotron source and a free space propagation setup. An online calibration is performed on the obtained reconstructions, in order to reassess the validity of the standard calibration procedure of the CT scanner. Three types of healthy tissues (adipose, glandular, and skin) and malignant tumors, when present, are considered from each sample. The measured attenuation coefficients are in very good agreement with the outcomes of similar studies available in the literature, although they span an energy range that was mostly neglected in the previous studies. No globally significant differences are observed between healthy and malignant dense tissues, although the number of considered samples does not appear sufficient to address the issue of a quantitative differentiation of tumors. The study assesses the viability of the proposed methodology for the measurement of linear attenuation coefficients, and provides a denser sampling of attenuation data in the energy range useful to breast CT.

Breast computed tomography (BCT) is an emerging application of X-ray tomography in radiological practice. A few clinical prototypes are under evaluation in hospitals and new systems are under development aiming at improving spatial and contrast resolution and reducing delivered dose. At the same time, synchrotron-radiation phase-contrast mammography has been demonstrated to offer substantial advantages when compared with conventional mammography. At Elettra, the Italian synchrotron radiation facility, a clinical program of phase-contrast BCT based on the free-space propagation approach is under development. In this paper, full-volume breast samples imaged with a beam energy of 32 keV delivering a mean glandular dose of 5 mGy are presented. The whole acquisition setup mimics a clinical study in order to evaluate its feasibility in terms of acquisition time and image quality. Acquisitions are performed using a high-resolution CdTe photon-counting detector and the projection data are processed via a phase-retrieval algorithm. Tomographic reconstructions are compared with conventional mammographic images acquired prior to surgery and with histologic examinations. Results indicate that BCT with monochromatic beam and free-space propagation phase-contrast imaging provide relevant three-dimensional insights of breast morphology at clinically acceptable doses and scan times.

A program devoted to performing the first in vivo synchrotron radiation (SR) breast computed tomography (BCT) is ongoing at the Elettra facility. Using the high spatial coherence of SR, phase-contrast (PhC) imaging techniques can be used. The latest high-resolution BCT acquisitions of breast specimens, obtained with the propagation-based PhC approach, are herein presented as part of the SYRMA-3D collaboration effort toward the clinical exam. Images are acquired with a 60 - μ m   pixel dead-time-free single-photon-counting CdTe detector. The samples are imaged at 32 and 38 keV in a continuous rotating mode, delivering 5 to 20 mGy of mean glandular dose. Contrast-to-noise ratio (CNR) and spatial resolution performances are evaluated for both PhC and phase-retrieved images, showing that by applying the phase-retrieval algorithm a five-time CNR increase can be obtained with a minor loss in spatial resolution across soft tissue interfaces. It is shown that, despite having a poorer CNR, PhC images can provide a sharper visualization of microcalcifications, thus being complementary to phase-retrieved images. Furthermore, the first full-volume scan of a mastectomy sample ( 9 × 9 × 3    cm 3 ) is reported. This investigation into surgical specimens indicates that SR BCT in terms of CNR, spatial resolution, scan duration, and scan volume is feasible.

The occurrence of sleep passed through the evolutionary sieve and is widespread in animal species. Sleep is known to be beneficial to cognitive and mnemonic tasks, while chronic sleep deprivation is detrimental. Despite the importance of the phenomenon, a complete understanding of its functions and underlying mechanisms is still lacking. In this paper, we show interesting effects of deep-sleep-like slow oscillation activity on a simplified thalamo-cortical model which is trained to encode, retrieve and classify images of handwritten digits. During slow oscillations, spike-timing-dependent-plasticity (STDP) produces a differential homeostatic process. It is characterized by both a specific unsupervised enhancement of connections among groups of neurons associated to instances of the same class (digit) and a simultaneous down-regulation of stronger synapses created by the training. This hierarchical organization of post-sleep internal representations favours higher performances in retrieval and classification tasks. The mechanism is based on the interaction between top-down cortico-thalamic predictions and bottom-up thalamo-cortical projections during deep-sleep-like slow oscillations. Indeed, when learned patterns are replayed during sleep, cortico-thalamo-cortical connections favour the activation of other neurons coding for similar thalamic inputs, promoting their association. Such mechanism hints at possible applications to artificial learning systems.

X-ray phase imaging has the potential to dramatically improve soft tissue contrast sensitivity, which is a crucial requirement in many diagnostic applications such as breast imaging. In this context, a program devoted to perform in vivo phase-contrast synchrotron radiation breast computed tomography is ongoing at the Elettra facility (Trieste, Italy). The used phase-contrast technique is the propagation-based configuration, which requires a spatially coherent source and a sufficient object-to-detector distance. In this work the effect of this distance on image quality is quantitatively investigated scanning a large breast surgical specimen at three object-to-detector distances (1.6, 3, 9 m) and comparing the images both before and after applying the phase-retrieval procedure. The sample is imaged at 30 keV with a [Formula: see text] pixel pitch CdTe single-photon-counting detector, positioned at a fixed distance of 31.6 m from the source. The detector fluence is kept constant for all acquisitions. The study shows that, at the largest distance, a 20-fold SNR increase can be obtained by applying the phase-retrieval procedure. Moreover, it is shown that, for phase-retrieved images, changing the object-to-detector distance does not affect spatial resolution while boosting SNR (four-fold increase going from the shortest to the largest distance). The experimental results are supported by a theoretical model proposed by other authors, whose salient results are presented in this paper.

Large-area CdTe single-photon-counting detectors are becoming more and more attractive in view of low-dose imaging applications due to their high efficiency, low intrinsic noise and absence of a scintillating screen which affects spatial resolution. At present, however, since the dimensions of a single sensor are small (typically a few cm 2 ), multi-module architectures are needed to obtain a large field of view. This requires coping with inter-module gaps and with close-to-edge pixels, which generally show a non-optimal behavior. Moreover, high-Z detectors often show gain variations in time due to charge trapping: this effect is detrimental especially in computed tomography (CT) applications where a single tomographic image requires hundreds of projections continuously acquired in several seconds. This work has been carried out at the SYRMEP beamline of the Elettra synchrotron radiation facility (Trieste, Italy), in the framework of the SYRMA-3D project, which aims to perform the world's first breast-CT clinical study with synchrotron radiation. An ad hoc data pre-processing procedure has been developed for the PIXIRAD-8 CdTe single-photon-counting detector, comprising an array of eight 30.7 mm × 24.8 mm modules tiling a 246 mm × 25 mm sensitive area, which covers the full synchrotron radiation beam. The procedure consists of five building blocks, namely dynamic flat-fielding, gap seaming, dynamic ring removal, projection despeckling and around-gap equalization. Each block is discussed and compared, when existing, with conventional approaches. The effectiveness of the pre-processing is demonstrated for phase-contrast CT images of a human breast specimen. The dynamic nature of the proposed procedure, which provides corrections dependent upon the projection index, allows the effective removal of time-dependent artifacts, preserving the main image features including phase effects.

Communicative interactions involve a kind of procedural knowledge that is used by the human brain for processing verbal and nonverbal inputs and for language production. Although considerable work has been done on modeling human language abilities, it has been difficult to bring them together to a comprehensive tabula rasa system compatible with current knowledge of how verbal information is processed in the brain. This work presents a cognitive system, entirely based on a large-scale neural architecture, which was developed to shed light on the procedural knowledge involved in language elaboration. The main component of this system is the central executive, which is a supervising system that coordinates the other components of the working memory. In our model, the central executive is a neural network that takes as input the neural activation states of the short-term memory and yields as output mental actions, which control the flow of information among the working memory components through neural gating mechanisms. The proposed system is capable of learning to communicate through natural language starting from tabula rasa, without any a priori knowledge of the structure of phrases, meaning of words, role of the different classes of words, only by interacting with a human through a text-based interface, using an open-ended incremental learning process. It is able to learn nouns, verbs, adjectives, pronouns and other word classes, and to use them in expressive language. The model was validated on a corpus of 1587 input sentences, based on literature on early language assessment, at the level of about 4-years old child, and produced 521 output sentences, expressing a broad range of language processing functionalities.

Multislice computed tomography (MSCT) is a valuable tool for lung cancer detection, thanks to its ability to identify noncalcified nodules of small size (from about 3 mm). Due to the large number of images generated by MSCT, there is much interest in developing computer-aided detection (CAD) systems that could assist radiologists in the lung nodule detection task. A complete multistage CAD system, including lung boundary segmentation, regions of interest (ROIs) selection, feature extraction, and false positive reduction is presented. The selection of ROIs is based on a multithreshold surface-triangulation approach. Surface triangulation is performed at different threshold values, varying from a minimum to a maximum value in a wide range. At a given threshold value, a ROI is defined as the volume inside a connected component of the triangulated isosurface. The evolution of a ROI as a function of the threshold can be represented by a treelike structure. A multithreshold ROI is defined as a path on this tree, which starts from a terminal ROI and ends on the root ROI. For each ROI, the volume, surface area, roundness, density, and moments of inertia are computed as functions of the threshold and used as input to a classification system based on artificial neural networks. The method is suitable to detect different types of nodules, including juxta-pleural nodules and nodules connected to blood vessels. A training set of 109 low-dose MSCT scans made available by the Pisa center of the Italung-CT trial and annotated by expert radiologists was used for the algorithm design and optimization. The system performance was tested on an independent set of 23 low-dose MSCT scans coming from the Pisa Italung-CT center and on 83 scans made available by the Lung Image Database Consortium (LIDC) annotated by four expert radiologists. On the Italung-CT test set, for nodules having a diameter greater than or equal to 3 mm, the system achieved 84% and 71% sensitivity at false positive/scan rates of 10 and 4, respectively. For nodules having a diameter greater than or equal to 4 mm, the sensitivities were 97% and 80% at false positive/scan rates of 10 and 4, respectively. On the LIDC data set, the system achieved a 79% sensitivity at a false positive/scan rate of 4 in the detection of nodules with a diameter greater than or equal to 3 mm that have been annotated by all four radiologists.

A computer-aided detection (CAD) system for the selection of lung nodules in computer tomography (CT) images is presented. The system is based on region growing (RG) algorithms and a new active contour model (ACM), implementing a local convex hull, able to draw the correct contour of the lung parenchyma and to include the pleural nodules. The CAD consists of three steps: (1) the lung parenchymal volume is segmented by means of a RG algorithm; the pleural nodules are included through the new ACM technique; (2) a RG algorithm is iteratively applied to the previously segmented volume in order to detect the candidate nodules; (3) a double-threshold cut and a neural network are applied to reduce the false positives (FPs). After having set the parameters on a clinical CT, the system works on whole scans, without the need for any manual selection. The CT database was recorded at the Pisa center of the ITALUNG-CT trial, the first Italian randomized controlled trial for the screening of the lung cancer. The detection rate of the system is 88.5% with 6.6 FPs/CT on 15 CT scans (about 4700 sectional images) with 26 nodules: 15 internal and 11 pleural. A reduction to 2.47 FPs/CT is achieved at 80% efficiency.

Information about Cd distribution inside single municipal solid waste and biomass fly ash particles is fundamental since it affects its leachability. The internal 2D distributions of the main and trace elements in such highly inhomogeneous matrixes were successfully determined by means of the combined synchrotron radiation induced micro X-ray fluorescence (micro-SRXRF) and tomography (micro-SRXRFT) techniques. Scanning micro-SRXRF measurements show Cd elemental distribution within single fly ash particles to be inhomogeneous, but no information can be obtained about its internal distribution. During micro-SRXRFT analysis, single fly ash particles are successively measured by a rotational-translational scan in a VH=2 x 5 microm2 microbeam. The 2D internal elemental distribution images, obtained by the modified simultaneous algebraic reconstruction technique algorithm, provide the size and the location of Cd-containing areas together with the location of other measurable elements. Results showed Cd concentration to be higher in the core of the fly ash particles analyzed rather than on the surface of the particles. Moreover, in both ashes, Ca-containing matrixes are found to be the main Cd-bearing phases. A possible mechanism for Cd adsorption on the fly ash particles is proposed based on the obtained results.