pwc-archive/papers-with-abstracts huggingface dataset:
Escape time in bistable neuronal populations driven by colored synaptic noise
Authors: Gianni Valerio Vinci, Maurizio Mattia
Date: 2024-04-08 00:00:00
Tasks: valid

Local networks of neurons are nonlinear systems driven by synaptic currents elicited by its own spiking activity and the input received from other brain areas. Synaptic currents are well approximated by correlated Gaussian noise. Besides, the population dynamics of neuronal networks is often found to be multistable, allowing the noise source to induce state transitions. State changes in neuronal systems underlies the way information is encoded and transformed. The characterization of the escape time from metastable states is then a cornerstone to understand how information is processed in the brain. The effects of correlated input forcing bistable systems have been studied for over half a century, nonetheless most results are perturbative or valid only when a separation of time scales is present. Here, we present a novel and exact result holding when the correlation time of the noise source is identical to that of the neural population, hence solving in a very general setting the mean escape time problem.
A probabilistic model of relapse in drug addiction
Authors: Sayun Mao, Tom Chou, Maria D'Orsogna
Date: 2024-04-06 00:00:00
Tasks: model

More than 60% of individuals recovering from substance use disorder relapse within one year. Some will resume drug consumption even after decades of abstinence. The cognitive and psychological mechanisms that lead to relapse are not completely understood, but stressful life experiences and external stimuli that are associated with past drug-taking are known to play a primary role. Stressors and cues elicit memories of drug-induced euphoria and the expectation of relief from current anxiety, igniting an intense craving to use again; positive experiences and supportive environments may mitigate relapse. We present a mathematical model of relapse in drug addiction that draws on known psychiatric concepts such as the "positive activation; negative activation" paradigm and the "peak-end" rule to construct a relapse rate that depends on external factors (intensity and timing of life events) and individual traits (mental responses to these events). We analyze which combinations and ordering of stressors, cues, and positive events lead to the largest relapse probability and propose interventions to minimize the likelihood of relapse. We find that the best protective factor is exposure to a mild, yet continuous, source of contentment, rather than large, episodic jolts of happiness.
A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations
Authors: Yijie Li, Wei zhang, Ye Wu, Li Yin, Ce Zhu, Yuqian Chen, Suheyla Cetin-Karayumak, Kang Ik K Cho, Leo R. Zekelman, Jarrett Rushmore, Yogesh Rathi, Nikos Makris, Lauren J. O'Donnell, Fan Zhang
Date: 2024-04-06 00:00:00
Tasks: Diffusion  MRI

The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.
Analyzing heterogeneity in Alzheimer Disease using multimodal normative modeling on imaging-based ATN biomarkers
Authors: Sayantan Kumar, Tom Earnest, Braden Yang, Deydeep Kothapalli, Andrew J. Aschenbrenner, Jason Hassenstab, Chengie Xiong, Beau Ances, John Morris, Tammie L. S. Benzinger, Brian A. Gordon, Philip Payne, Aristeidis Sotiras
Date: 2024-04-04 00:00:00

INTRODUCTION: Previous studies have applied normative modeling on a single neuroimaging modality to investigate Alzheimer Disease (AD) heterogeneity. We employed a deep learning-based multimodal normative framework to analyze individual-level variation across ATN (amyloid-tau-neurodegeneration) imaging biomarkers. METHODS: We selected cross-sectional discovery (n = 665) and replication cohorts (n = 430) with available T1-weighted MRI, amyloid and tau PET. Normative modeling estimated individual-level abnormal deviations in amyloid-positive individuals compared to amyloid-negative controls. Regional abnormality patterns were mapped at different clinical group levels to assess intra-group heterogeneity. An individual-level disease severity index (DSI) was calculated using both the spatial extent and magnitude of abnormal deviations across ATN. RESULTS: Greater intra-group heterogeneity in ATN abnormality patterns was observed in more severe clinical stages of AD. Higher DSI was associated with worse cognitive function and increased risk of disease progression. DISCUSSION: Subject-specific abnormality maps across ATN reveal the heterogeneous impact of AD on the brain.
Quantum Tonality: A Mathemusical Playground
Authors: Peter beim Graben, Thomas Noll
Date: 2024-04-03 00:00:00

We adopt some basic ideas on quantum-theoretical modeling of tonal attraction and develop them further in an alternative direction. Fitting Gaussian Mixture Models (GMM) to the Krumhansl-Kessler (KK) probe tone profiles for static attraction opens the possibility to investigate the underlying wave function as the stationary ground state of an anharmonic quantum oscillator with a schematic Hamiltonian involving a perturbation potential. We numerically verify the fulfilment of the associated stationary Schr\"odinger equation and also inspect its excited states as a solution basis for the corresponding time-dependent Schr\"odinger equation. With their help we calculate the temporal evolution of any initial state. As an example, we study the dynamics of transpositions of the stationary KK wave function across Regener's line of fifths. This offers potential models for dynamic tonal attraction and also for the behavior of deflected key profiles within the Hamiltonian dynamics of a given tonality.
Mapping the Mind-Brain Duality to a Digital-Analog Perceptual Duality
Authors: Ehud Ahissar, Daniel Polani, Merav Ahissar
Date: 2024-02-20 00:00:00

Could the abstract ideas of our minds originate from neuronal interactions within our brains? To address this question, we examine interactions within 'brain-world' (BW) and 'brain-brain' (BB) domains, which represent the brain's physical interactions with its environment and the mental interactions between brains, respectively. BW interactions are characterized as analog - dynamic and continuous, whereas BB interactions are digital - non-dynamic and discrete. This distinction allows BB interactions to facilitate effective, albeit information-limited, communication through categorization. We review existing data showing that cascades of neural circuits can convert between analog and digital signals, thereby linking physical and mental processes. Importantly, we argue that these circuits cannot fully reduce one domain to the other, suggesting that the mind-brain duality can be mapped to the BB-BW duality. Such mapping suggests that the mind's foundation is inherently social, offering a novel explanation for the physical-mental gap while acknowledging the coexistence of the physical body and the non-physical mind.
A Primer on Gibsonian Information
Authors: Bradly Alicea, Daniela Cialfi, Avery Lim, Jesse Parent
Date: 2024-01-31 00:00:00

Across the scientific literature, information measurement in the nervous system is posed as a problem of information processing internal to the brain by constructs such as neuronal populations, sensory surprise, or cognitive models. Application of information theory in the nervous system has focused on measuring phenomena such as capacity and integration. Yet the ecological perspective suggests that information is a product of active perception and interactions with the environment. Here, we propose Gibsonian Information (GI), relevant to both the study of cognitive agents and single cell systems that exhibit cognitive behaviors. We propose a formal model of GI that characterizes how agents extract environmental information in a dynamic fashion. GI demonstrates how sensory information guides information processing within individual nervous system representations of motion and continuous multisensory integration, as well as representations that guide collective behaviors. GI is useful for understanding first-order sensory inputs in terms of agent interactions with naturalistic contexts and simple internal representations and can be extended to cybernetic or symbolic representations. Statistical affordances, or clustered information that is spatiotemporally dependent perceptual input, facilitate extraction of GI from the environment. As a quantitative accounting of perceptual information, GI provides a means to measure a generalized indicator of nervous system input and can be characterized by three scenarios: disjoint distributions, contingent action, and coherent movement. By applying this framework to a variety of specific contexts, including a four-channel model of multisensory embodiment, we demonstrate how GI is essential to understanding the full scope of cognitive information processing.
Astrocyte control bursting mode of spiking neuron network with memristor-implemented plasticity
Authors: Sergey V. Stasenko, Alexey N. Mikhaylov, Alexander A. Fedotov, Vladimir A. Smirnov, Victor B. Kazantsev
Date: 2023-11-30 00:00:00

A mathematical model of a spiking neuron network accompanied by astrocytes is considered. The network is composed of excitatory and inhibitory neurons with synaptic connections supplied by a memristor-based model of plasticity. Another mechanism for changing the synaptic connections involves astrocytic regulations using the concept of tripartite synapses. In the absence of memristor-based plasticity, the connections between these neurons drive the network dynamics into a burst mode, as observed in many experimental neurobiological studies when investigating living networks in neuronal cultures. The memristive plasticity implementing synaptic plasticity in inhibitory synapses results in a shift in network dynamics towards an asynchronous mode. Next,it is found that accounting for astrocytic regulation in glutamatergic excitatory synapses enable the restoration of 'normal' burst dynamics. The conditions and parameters of such astrocytic regulation's impact on burst dynamics established.
Effect of the social environment on olfaction and social skills in WT and mouse model of autism
Authors: Caroline Gora, Ana Dudas, Lucas Court, Anil Annamneedi, Gaëlle Lefort, Thiago-Seike Picoreti-Nakahara, Nicolas Azzopardi, Adrien Acquistapace, Anne-Lyse Lainé, Anne-Charlotte Trouillet, Lucile Drobecq, Emmanuel Pecnard, Benoit Piegu, Pascale Crépieux, Pablo Chamero, Lucie P. Pellissier
Date: 2023-11-29 00:00:00

Autism spectrum disorders are complex, polygenic and heterogenous neurodevelopmental conditions, imposing a substantial economic burden. Genetics are influenced by the environment, specifically the social experience during the critical neurodevelopmental period. Despite efficacy of early behavior interventions targeted specific behaviors in some autistic children, there is no sustainable treatment for the two core symptoms: deficits in social interaction and communication, and stereotyped or restrained behaviors or interests. In this study, we investigated the impact of the social environment on both wild-type (WT) and Shank3 knockout (KO) mice, a mouse model that reproduces core autism-like symptoms. Our findings revealed that WT mice raised in an enriched social environment maintained social interest towards new conspecifics across multiple trials. Additionally, we observed that 2 hours or chronic social isolation induced social deficits or enhanced social interaction and olfactory neuron responses in WT animals, respectively. Notably, chronic social isolation restored both social novelty and olfactory deficits, and normalized self-grooming behavior in Shank3 KO mice. These results novel insights for the implementation of behavioral intervention and inclusive classrooms programs for children with ASD.
Effectiveness of Self-Assessment Software to Evaluate Preclinical Operative Procedures
Authors: Qi Dai, Ryan Davis, Houlin Hong, Ying Gu
Date: 2024-04-08 00:00:00

Objectives: To assess the effectiveness of digital scanning techniques for self-assessment and of preparations and restorations in preclinical dental education when compared to traditional faculty grading. Methods: Forty-four separate Class I (#30-O), Class II (#30-MO) preparations, and class II amalgam restorations (#31-MO) were generated respectively under preclinical assessment setting. Calibrated faculty evaluated the preparations and restorations using a standard rubric from preclinical operative class. The same teeth were scanned using Planmeca PlanScan intraoral scanner and graded using the Romexis E4D Compare Software. Each tooth was compared against a corresponding gold standard tooth with tolerance intervals ranging from 100{\mu}m to 500{\mu}m. These scores were compared to traditional faculty grades using a linear mixed model to estimate the mean differences at 95% confidence interval for each tolerance level. Results: The average Compare Software grade of Class I preparation at 300{\mu}m tolerance had the smallest mean difference of 1.64 points on a 100 points scale compared to the average faculty grade. Class II preparation at 400{\mu}m tolerance had the smallest mean difference of 0.41 points. Finally, Class II Restoration at 300{\mu}m tolerance had the smallest mean difference at 0.20 points. Conclusion: In this study, tolerance levels that best correlated the Compare Software grades with the faculty grades were determined for three operative procedures: class I preparation, class II preparation and class II restoration. This Compare Software can be used as a useful adjunct method for more objective grading. It also can be used by students as a great self-assessment tool.
A hybrid discrete-continuum modelling approach for the interactions of the immune system with oncolytic viral infections
Authors: David Morselli, Marcello E. Delitala, Adrianne L. Jenner, Federico Frascoli
Date: 2024-04-09 00:00:00

Oncolytic virotherapy, utilizing genetically modified viruses to combat cancer and trigger anti-cancer immune responses, has garnered significant attention in recent years. In our previous work arXiv:2305.12386, we developed a stochastic agent-based model elucidating the spatial dynamics of infected and uninfected cells within solid tumours. Building upon this foundation, we present a novel stochastic agent-based model to describe the intricate interplay between the virus and the immune system; the agents' dynamics are coupled with a balance equation for the concentration of the chemoattractant that guides the movement of immune cells. We formally derive the continuum limit of the model and carry out a systematic quantitative comparison between this system of PDEs and the individual-based model in two spatial dimensions. Furthermore, we describe the traveling waves of the three populations, with the uninfected proliferative cells trying to escape from the infected cells while immune cells infiltrate the tumour. Simulations show a good agreement between agent-based approaches and numerical results for the continuum model. Some parameter ranges give rise to oscillations of cell number in both models, in line with the behaviour of the corresponding nonspatial model, which presents Hopf bifurcations. Nevertheless, in some situations the behaviours of the two models may differ significantly, suggesting that stochasticity plays a key role in the dynamics. Our results highlight that a too rapid immune response, before the infection is well-established, appears to decrease the efficacy of the therapy and thus some care is needed when oncolytic virotherapy is combined with immunotherapy. This further suggests the importance of clinically improving the modulation of the immune response according to the tumour's characteristics and to the immune capabilities of the patients.
Convolutional Neural Network Transformer (CNNT) for Fluorescence Microscopy image Denoising with Improved Generalization and Fast Adaptation
Authors: Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yosuke Mukoyama, Jiji Chen, Alberto Rissone, Rosa Puertollano, Harshad Vishwasrao, Hari Shroff, Christian A. Combs, Hui Xue
Date: 2024-04-06 00:00:00
Tasks: Denoising, Image Denoising

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically with tens to hundreds of image pairs) it can then be used to enhance new images that are like the training data. In this study, we proposed a novel imaging-transformer based model, Convolutional Neural Network Transformer (CNNT), to outperform the CNN networks for image denoising. In our scheme we have trained a single CNNT based backbone model from pairwise high-low SNR images for one type of fluorescence microscope (instance structured illumination, iSim). Fast adaption to new applications was achieved by fine-tuning the backbone on only 5-10 sample pairs per new experiment. Results show the CNNT backbone and fine-tuning scheme significantly reduces the training time and improves the image quality, outperformed training separate models using CNN approaches such as - RCAN and Noise2Fast. Here we show three examples of the efficacy of this approach on denoising wide-field, two-photon and confocal fluorescence data. In the confocal experiment, which is a 5 by 5 tiled acquisition, the fine-tuned CNNT model reduces the scan time form one hour to eight minutes, with improved quality.
A model for membrane degradation using a gelatin invadopodia assay
Authors: Giorgia Ciavolella, Nathalie Ferrand, Michèle Sabbah, Benoît Perthame, Roberto Natalini
Date: 2024-02-08 00:00:00
Tasks: parameter estimation

One of the most crucial and lethal characteristics of solid tumors is represented by the increased ability of cancer cells to migrate and invade other organs during the so-called metastatic spread. This is allowed thanks to the production of matrix metalloproteinases (MMPs), enzymes capable of degrading a type of collagen abundant in the basal membrane separating the epithelial tissue from the connective one. In this work, we employ a synergistic experimental and mathematical modelling approach to explore the invasion process of tumor cells. A athematical model composed of reaction-diffusion equations describing the evolution of the tumor cells density on a gelatin substrate, MMPs enzymes concentration and the degradation of the gelatin is proposed. This is completed with a calibration strategy. We perform a sensitivity analysis and explore a parameter estimation technique both on synthetic and experimental data in order to find the optimal parameters that describe the in vitro experiments. A comparison between numerical and experimental solutions ends the work.
A Comparison of Cryptocurrency Volatility-benchmarking New and Mature Asset Classes
Authors: Alessio Brini, Jimmie Lenz
Date: 2024-04-07 00:00:00
Tasks: Benchmarking

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies' future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.
The PEAL Method: a mathematical framework to streamline securitization structuring
Authors: Andrea Pinto, Antonio Scala
Date: 2024-04-08 00:00:00

Securitization is a financial process where the cash flows of income-generating assets are sold to institutional investors as securities, liquidating illiquid assets. This practice presents persistent challenges due to the absence of a comprehensive mathematical framework for structuring asset-backed securities. While existing literature provides technical analysis of credit risk modeling, there remains a need for a definitive framework detailing the allocation of the inbound cash flows to the outbound positions. To fill this gap, we introduce the PEAL Method: a 10-step mathematical framework to streamline the securitization structuring across all time periods. The PEAL Method offers a rigorous and versatile approach, allowing practitioners to structure various types of securitizations, including those with complex vertical positions. By employing standardized equations, it facilitates the delineation of payment priorities and enhances risk characterization for both the asset and the liability sides throughout the securitization life cycle. In addition to its technical contributions, the PEAL Method aims to elevate industry standards by addressing longstanding challenges in securitization. By providing detailed information to investors and enabling transparent risk profile comparisons, it promotes market transparency and enables stronger regulatory oversight. In summary, the PEAL Method represents a significant advancement in securitization literature, offering a standardized framework for precision and efficiency in structuring transactions. Its adoption has the potential to drive innovation and enhance risk management practices in the securitization market.
Generalized measure Black-Scholes equation: Towards option self-similar pricing
Authors: Nizar Riane, Claire David
Date: 2024-04-08 00:00:00
Tasks: valid

In this work, we give a generalized formulation of the Black-Scholes model. The novelty resides in considering the Black-Scholes model to be valid on 'average', but such that the pointwise option price dynamics depends on a measure representing the investors' 'uncertainty'. We make use of the theory of non-symmetric Dirichlet forms and the abstract theory of partial differential equations to establish well posedness of the problem. A detailed numerical analysis is given in the case of self-similar measures.