arXiv subjects

Francesco Saverio Pavone

Publications and source records attributed to Francesco Saverio Pavone.

15 recordsLinked to original sources

arXiv2024

A transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies

Histopathological examination of surgical biopsies, such as in glioma and glioblastoma resection, is hindered in current clinical practice by the long times required for the laboratory analysis and pathological screening, typically taking several days or even weeks to be completed. We propose here a transportable, high-density, spectral-scanning based hyperspectral imaging setup, named HyperProbe1, that can provide in situ, fast biochemical analysis and mapping of fresh surgical tissue samples, right after excision, and without the need of fixing or staining. HyperProbe1 is based on spectral scanning via supercontinuum laser illumination filtered with acousto-optic tuneable filters. Such methodology allows the user to select any number and type of wavelength bands in the visible and near-infrared range between 510 and 900 nm (up to 79), and to reconstruct 3D hypercubes composed of high-resolution, widefield images of the surgical samples, where each pixel is associated with a complete spectrum. The system is applied on 11 fresh surgical biopsies of glioma from routine patients, including different grades of tumour classification. Quantitative analysis of the composition of the tissue is performed via fast spectral unmixing to reconstruct mapping of major biomarkers. We also provided a preliminary attempt to infer tumour classification based on differences of composition in the samples, suggesting the possibility to use lipid content and differential cytochrome-c-oxidase concentrations to distinguish between lower and higher grade gliomas. A proof-of-concept of the performances of HyperProbe1 for quantitative, biochemical mapping of surgical biopsies is demonstrated, paving the way for improving current post-surgical, histopathological practice via non-destructive, in situ streamlined screening of fresh tissue samples in a matter of minutes after excision.

physics.med-ph↗

arXiv2023

Brain-wide functional imaging to highlight differences between the diurnal and nocturnal neuronal activity in zebrafish larvae

Most living organisms show highly conserved physiological changes following a 24-hour cycle which goes by the name of circadian rhythm. Among experimental models, the effects of light-dark cycle have been recently investigated in the larval zebrafish. Owing to its small size and transparency, this vertebrate enables optical access to the entire brain. Indeed, the combination of this organism with light-sheet imaging grants high spatio-temporal resolution volumetric recording of neuronal activity. This imaging technique, in its multiphoton variant, allows functional investigations without unwanted visual stimulation. Here, we employed a custom two-photon light-sheet microscope to study brain-wide differences in neuronal activity between diurnal and nocturnal periods in larval zebrafish assessed at the transition between day and night. We describe for the first time an activity increase in the low frequency domain of the pretectum and a frequency-localized activity decrease of the anterior rhombencephalic turning region during the nocturnal period. Moreover, our data confirm a nocturnal reduction in habenular activity. Furthermore, brain-wide detrended fluctuation analysis revealed a nocturnal decrease in the self-affinity of the neuronal signals in parts of the dorsal thalamus and the medulla oblongata and an increase in the pretectum. Our data show that brain-wide nonlinear light-sheet imaging represents a useful tool to investigate circadian rhythm effects on neuronal activity.

q-bio.NC↗

arXiv2023

Comparing apples to apples -- Using a modular and adaptable analysis pipeline to compare slow cerebral rhythms across heterogeneous datasets

Neuroscience is moving towards a more integrative discipline, where understanding brain function requires consolidating the accumulated evidence seen across experiments, species, and measurement techniques. A remaining challenge on that path is integrating such heterogeneous data into analysis workflows such that consistent and comparable conclusions can be distilled as an experimental basis for models and theories. Here, we propose a solution in the context of slow wave activity ($<1$ Hz), which occurs during unconscious brain states like sleep and general anesthesia, and is observed across diverse experimental approaches. We address the issue of integrating and comparing heterogeneous data by conceptualizing a general pipeline design that is adaptable to a variety of inputs and applications. Furthermore, we present the Collaborative Brain Wave Analysis Pipeline (Cobrawap) as a concrete, reusable software implementation to perform broad, detailed, and rigorous comparisons of slow wave characteristics across multiple, openly available ECoG and calcium imaging datasets.

q-bio.NC↗

arXiv2022

Four-channel radio-frequency signal generator programmed by an open-source Arduino-based control system via single or quad Serial Peripheral Interface

Radio-frequency (RF) signal generators are standard laboratory equipment and a wide-range of open-source and commercial devices exists to address their many applications. Nonetheless, only few expensive and proprietary solutions can be re-configured within a wide frequency band and triggered on a micro-second timescale. Such specifications are required for applications that use variable radio-frequencies to generate programmed mixed signals, to control processes or states and to precisely steer laser beams using acousto-optical devices, tasks often needed in industrial manufacturing, atomic and molecular physics or microscopy. Here we present an open-source low-cost Arduino-based control system that can store up to millions of commands received from a computer and then perform reliable high-speed programming of an arbitrary device under its control (DUC) via a single- or quad-wire Serial Peripheral Interface. The software architecture operates as a real-time state machine, making it easily extensible and adaptable to any DUC. Each configuration change can be triggered either externally or internally, reaching ~1 MHz rates when using a Teensy 4.1 Arduino-compatible board. Leveraging this flexible system, we developed a programmable four-channel RF signal generator, based on an Analog Devices 9959 Evaluation board, and we demonstrated its capability and validated its performance.

physics.ins-det↗

arXiv2022

Latency correction in sparse neuronal spike trains

Background: In neurophysiological data, latency refers to a global shift of spikes from one spike train to the next, either caused by response onset fluctuations or by finite propagation speed. Such systematic shifts in spike timing lead to a spurious decrease in synchrony which needs to be corrected. New Method: We propose a new algorithm of multivariate latency correction suitable for sparse data for which the relevant information is not primarily in the rate but in the timing of each individual spike. The algorithm is designed to correct systematic delays while maintaining all other kinds of noisy disturbances. It consists of two steps, spike matching and distance minimization between the matched spikes using simulated annealing. Results: We show its effectiveness on simulated and real data: cortical propagation patterns recorded via calcium imaging from mice before and after stroke. Using simulations of these data we also establish criteria that can be evaluated beforehand in order to anticipate whether our algorithm is likely to yield a considerable improvement for a given dataset. Comparison with Existing Method(s): Existing methods of latency correction rely on adjusting peaks in rate profiles, an approach that is not feasible for spike trains with low firing in which the timing of individual spikes contains essential information. Conclusions: For any given dataset the criterion for applicability of the algorithm can be evaluated quickly and in case of a positive outcome the latency correction can be applied easily since the source codes of the algorithm are publicly available.

physics.data-an↗

arXiv2021

Fast whole-brain imaging of seizures in zebrafish larvae by two-photon light-sheet microscopy

Light-sheet fluorescence microscopy (LSFM) enables real-time whole-brain functional imaging in zebrafish larvae. Conventional one photon LSFM can however induce undesirable visual stimulation due to the use of visible excitation light. The use of two-photon (2P) excitation, employing near-infrared invisible light, provides unbiased investigation of neuronal circuit dynamics. However, due to the low efficiency of the 2P absorption process, the imaging speed of this technique is typically limited by the signal-to-noise-ratio. Here, we describe a 2P LSFM setup designed for non-invasive imaging that enables quintuplicating state-of-the-art volumetric acquisition rate of the larval zebrafish brain (5 Hz) while keeping low the laser intensity on the specimen. We applied our system to the study of pharmacologically-induced acute seizures, characterizing the spatial-temporal dynamics of pathological activity and describing for the first time the appearance of caudo-rostral ictal waves (CRIWs).

q-bio.QM↗

arXiv2020

Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data

Semantic segmentation of neuronal structures in 3D high-resolution fluorescence microscopy imaging of the human brain cortex can take advantage of bidimensional CNNs, which yield good results in neuron localization but lead to inaccurate surface reconstruction. 3D CNNs, on the other hand, would require manually annotated volumetric data on a large scale and hence considerable human effort. Semi-supervised alternative strategies which make use only of sparse annotations suffer from longer training times and achieved models tend to have increased capacity compared to 2D CNNs, needing more ground truth data to attain similar results. To overcome these issues we propose a two-phase strategy for training native 3D CNN models on sparse 2D annotations where missing labels are inferred by a 2D CNN model and combined with manual annotations in a weighted manner during loss calculation.

eess.IV↗

arXiv2020

Effects of excitation light polarization on fluorescence emission in two-photon light-sheet microscopy

Light-sheet microscopy (LSM) is a powerful imaging technique that uses a planar illumination oriented orthogonally to the detection axis. Two-photon (2P) LSM is a variant of LSM that exploits the 2P absorption effect for sample excitation. The light polarization state plays a significant, and often overlooked, role in 2P absorption processes. The scope of this work is to test whether using different polarization states for excitation light can affect the detected signal levels in 2P LSM imaging of typical biological samples with a spatially unordered dye population. Supported by a theoretical model, we compared the fluorescence signals obtained using different polarization states with various fluorophores (fluorescein, EGFP and GCaMP6s) and different samples (liquid solution and fixed or living zebrafish larvae). In all conditions, in agreement with our theoretical expectations, linear polarization oriented parallel to the detection plane provided the largest signal levels, while perpendicularly-oriented polarization gave low fluorescence signal with the biological samples, but a large signal for the fluorescein solution. Finally, circular polarization generally provided lower signal levels. These results highlight the importance of controlling the light polarization state in 2P LSM of biological samples. Furthermore, this characterization represents a useful guide to choose the best light polarization state when maximization of signal levels is needed, e.g. in high-speed 2P LSM.

physics.bio-ph↗

arXiv2020

Reconstruction scheme for excitatory and inhibitory dynamics with quenched disorder: application to zebrafish imaging

An inverse procedure is developed and tested to recover functional and structural information from global signals of brains activity. The method assumes a leaky-integrate and fire model with excitatory and inhibitory neurons, coupled via a directed network. Neurons are endowed with a heterogenous current value, which sets their associated dynamical regime. By making use of a heterogenous mean-field approximation, the method seeks to reconstructing from global activity patterns the distribution of in-coming degrees, for both excitatory and inhibitory neurons, as well as the distribution of the assigned currents. The proposed inverse scheme is first validated against synthetic data. Then, time-lapse acquisitions of a zebrafish larva recorded with a two-photon light sheet microscope are used as an input to the reconstruction algorithm. A power law distribution of the in-coming connectivity of the excitatory neurons is found. Local degree distributions are also computed by segmenting the whole brain in sub-regions traced from annotated atlas.

q-bio.NC↗

arXiv2020

Analysis and Model of Cortical Slow Waves Acquired with Optical Techniques

Slow waves (SWs) are spatio-temporal patterns of cortical activity that occur both during natural sleep and anesthesia and are preserved across species. Even though electrophysiological recordings have been largely used to characterize brain states, they are limited in the spatial resolution and cannot target specific neuronal population. Recently, large-scale optical imaging techniques coupled with functional indicators overcame these restrictions, and new pipelines of analysis and novel approaches of SWs modelling are needed to extract relevant features of the spatio-temporal dynamics of SWs from these highly spatially resolved data-sets. Here we combined wide-field fluorescence microscopy and a transgenic mouse model expressing a calcium indicator (GCaMP6f) in excitatory neurons to study SW propagation over the meso-scale under ketamine anesthesia. We developed a versatile analysis pipeline to identify and quantify the spatio-temporal propagation of the SWs. Moreover, we designed a computational simulator based on a simple theoretical model, which takes into account the statistics of neuronal activity, the response of fluorescence proteins and the slow waves dynamics. The simulator was capable of synthesizing artificial signals that could reliably reproduce several features of the SWs observed in vivo, thus enabling a calibration tool for the analysis pipeline. Comparison of experimental and simulated data shows the robustness of the analysis tools and its potential to uncover mechanistic insights of the Slow Wave Activity (SWA).

q-bio.NC↗

arXiv2019

Inferring network structure and local dynamics from neuronal patterns with quenched disorder

An inverse procedure is proposed and tested which aims at recovering the a priori unknown functional and structural information from global signals of living brains activity. To this end we consider a Leaky-Integrate and Fire (LIF) model with short term plasticity neurons, coupled via a directed network. Neurons are assigned a specific current value, which is heterogenous across the sample, and sets the firing regime in which the neuron is operating in. The aim of the method is to recover the distribution of incoming network degrees, as well as the distribution of the assigned currents, from global field measurements. The proposed approach to the inverse problem implements the reductionist Heterogenous Mean-Field approximation. This amounts in turn to organizing the neurons in different classes, depending on their associated degree and current. When tested again synthetic data, the method returns accurate estimates of the sought distributions, while managing to reproduce and interpolate almost exactly the time series of the supplied global field. Finally, we also applied the proposed technique to longitudinal wide-field fluorescence microscopy data of cortical functionality in groups of awake Thy1-GCaMP6f mice. Mice are induced a photothrombotic stroke in the primary motor cortex and their recovery monitored in time. An all-to-all LIF model which accommodates for currents heterogeneity allows to adequately explain the recorded patterns of activation. Altered distributions in neuron excitability are in particular detected, compatible with the phenomenon of hyperexcitability in the penumbra region after stroke.

cond-mat.dis-nn↗

arXiv2018

Piezoelectric Barium Titanate Nanostimulators for the Treatment of Glioblastoma Multiforme

Major obstacles to the successful treatment of gliolastoma multiforme are mostly related to the acquired resistance to chemotherapy drugs and, after surgery, to the cancer recurrence in correspondence of residual microscopic foci. As innovative anticancer approach, low-intensity electric stimulation represents a physical treatment able to reduce multidrug resistance of cancer and to induce remarkable anti-proliferative effects by interfering with Ca2+ and K+ homeostasis and by affecting the organization of the mitotic spindles. However, to preserve healthy cells, it is utterly important to direct the electric stimuli only to malignant cells. In this work, we propose a nanotechnological approach based on ultrasound-sensitive piezoelectric nanoparticles to remotely deliver electric stimulations to glioblastoma cells. Barium titanate nanoparticles (BTNPs) have been functionalized with an antibody against the transferrin receptor (TfR) in order to obtain the dual targeting of blood-brain barrier and of glioblastoma cells. The remote ultrasound-mediated piezo-stimulation allowed to significantly reduce in vitro the proliferation of glioblastoma cells and, when combined with a sub-toxic concentration of temozolomide, induced an increased sensitivity to the chemotherapy treatment and remarkable anti-proliferative and pro-apoptotic effects.

physics.bio-ph↗

arXiv2015

Cell identification in whole-brain multiview images of neural activation

We present a scalable method for brain cell identification in multiview confocal light sheet microscopy images. Our algorithmic pipeline includes a hierarchical registration approach and a novel multiview version of semantic deconvolution that simultaneously enhance visibility of fluorescent cell bodies, equalize their contrast, and fuses adjacent views into a single 3D images on which cell identification is performed with mean shift. We present empirical results on a whole-brain image of an adult Arc-dVenus mouse acquired at 4micron resolution. Based on an annotated test volume containing 3278 cells, our algorithm achieves an $F_1$ measure of 0.89.

cs.CV↗

arXiv2015

A versatile clearing agent for multi-modal brain imaging

Extensive mapping of neuronal connections in the central nervous system requires high-throughput um-scale imaging of large volumes. In recent years, different approaches have been developed to overcome the limitations due to tissue light scattering. These methods are generally developed to improve the performance of a specific imaging modality, thus limiting comprehensive neuroanatomical exploration by multimodal optical techniques. Here, we introduce a versatile brain clearing agent (2,2'-thiodiethanol; TDE) suitable for various applications and imaging techniques. TDE is cost-efficient, water-soluble and low-viscous and, more importantly, it preserves fluorescence, is compatible with immunostaining and does not cause deformations at sub-cellular level. We demonstrate the effectiveness of this method in different applications: in fixed samples by imaging a whole mouse hippocampus with serial two-photon tomography; in combination with CLARITY by reconstructing an entire mouse brain with light sheet microscopy and in translational research by imaging immunostained human dysplastic brain tissue.

q-bio.NC↗

arXiv2014

Topology-driven instabilities: the theory of pattern formation on directed networks

The theory of pattern formation in reaction-diffusion systems is extended to the case of a directed network. Due to the structure of the network Laplacian of the scrutinised system, the dispersion relation has both real and imaginary parts, at variance with the conventional case for a symmetric network. It is found that the homogeneous fixed point can become unstable due to the topology of the network, resulting in a new class of instabilities which cannot be induced on undirected graphs. Results from a linear stability analysis allow the instability region to be analytically traced. Numerical simulations show that the instability can lead to travelling waves, or quasi-stationary patterns, depending on the characteristics of the underlying graph. The results presented here could impact on the diverse range of disciplines where directed networks are found, such as neuroscience, computer networks and traffic systems.

nlin.PS↗