pwc-archive/papers-with-abstracts huggingface dataset:
On the validity of fMRI studies with subject-level data processed through different pipelines
Authors: Elodie Germani, Xavier Rolland, Pierre Maurel, Camille Maumet
Date: 2024-02-20 00:00:00

In recent years, the lack of reproducibility of research findings has become an important source of concerns in many scientific fields, including functional Magnetic Resonance Imaging (fMRI). The low statistical power often observed in fMRI studies was identified as one of the leading causes of irreproducibility. The development of data sharing opens up new opportunities to achieve larger sample sizes by reusing existing data. FMRI studies proceed by first preparing subject-level data using a given analysis pipeline and then combining those into a group analysis. Historically the subject-level analysis pipeline was identical for all subjects. As practices evolve towards more data reuse, researchers might want to directly combine subject-level data thata were processed using different pipelines. Here, we investigate the impact of combining subject-level data processed with different pipelines in between-group fMRI studies. We used the HCP Young-Adult dataset (N=1,080 participants) processed with 24 different pipelines. We then performed between-group analyses comparing subject data processed with different pipelines. We worked under the null hypothesis of no differences between subjects and compared the estimated false-positive rates obtained with the nominal rates. We showed that the analytical variability induced by the parameters explored in this dataset increases the false positive rates of studies combining data from different pipelines. We conclude that different processed subject data cannot be combined without taking into account the processing applied on these data.
Generative Adversarial Collaborations: A practical guide for conference organizers and participating scientists
Authors: Gunnar Blohm, Benjamin Peters, Ralf Haefner, Leyla Isik, Nikolaus Kriegeskorte, Jennifer S. Lieberman, Carlos R. Ponce, Gemma Roig, Megan A. K. Peters
Date: 2024-02-19 00:00:00

Generative adversarial collaborations (GACs) are a form of formal teamwork between groups of scientists with diverging views. The goal of GACs is to identify and ultimately resolve the most important challenges, controversies, and exciting theoretical and empirical debates in a given research field. A GAC team would develop specific, agreed-upon avenues to resolve debates in order to move a field of research forward in a collaborative way. Such adversarial collaborations have many benefits and opportunities but also come with challenges. Here, we use our experience from (1) creating and running the GAC program for the Cognitive Computational Neuroscience (CCN) conference and (2) implementing and leading GACs on particular scientific problems to provide a practical guide for future GAC program organizers and leaders of individual GACs.
Self-organized clustering, prediction, and superposition of long-term cognitive decline from short-term individual cognitive test scores in Alzheimer's disease
Authors: Hiroyuki Sato, Keisuke Suzuki, Atsushi Hashizume, Ryoichi Hanazawa, Masanao Sasaki, Akihiro Hirakawa, the Japanese Alzheimer's Disease Neuroimaging Initiative, the Alzheimer's Disease Neuroimaging Initiative
Date: 2024-02-19 00:00:00
Tasks: Self-Organized Clustering

Progressive cognitive decline spanning across decades is characteristic of Alzheimer's disease (AD). Various predictive models have been designed to realize its early onset and study the long-term trajectories of cognitive test scores across populations of interest. Research efforts have been geared towards superimposing patients' cognitive test scores with the long-term trajectory denoting gradual cognitive decline, while considering the heterogeneity of AD. Multiple trajectories representing cognitive assessment for the long-term have been developed based on various parameters, highlighting the importance of classifying several groups based on disease progression patterns. In this study, a novel method capable of self-organized prediction, classification, and the overlay of long-term cognitive trajectories based on short-term individual data was developed, based on statistical and differential equation modeling. We validated the predictive accuracy of the proposed method for the long-term trajectory of cognitive test score results on two cohorts: the Alzheimer's Disease Neuroimaging Initiative (ADNI) study and the Japanese ADNI study. We also presented two practical illustrations of the simultaneous evaluation of risk factor associated with both the onset and the longitudinal progression of AD, and an innovative randomized controlled trial design for AD that standardizes the heterogeneity of patients enrolled in a clinical trial. These resources would improve the power of statistical hypothesis testing and help evaluate the therapeutic effect. The application of predicting the trajectory of longitudinal disease progression goes beyond AD, and is especially relevant for progressive and neurodegenerative disorders.
Gradients of brain organization: Smooth sailing from methods development to user community
Authors: Jessica Royer, Casey Paquola, Sofie L. Valk, Matthias Kirschner, Seok-Jun Hong, Bo-yong Park, Richard A. I. Bethlehem, Robert Leech, B. T. Thomas Yeo, Elizabeth Jefferies, Jonathan Smallwood, Daniel Margulies, Boris C. Bernhardt
Date: 2024-02-16 00:00:00

Multimodal neuroimaging grants a powerful in vivo window into the structure and function of the human brain. Recent methodological and conceptual advances have enabled investigations of the interplay between large-scale spatial trends, or gradients, in brain structure and function, offering a framework to unify principles of brain organization across multiple scales. Strong community enthusiasm for these techniques has been instrumental in their widespread adoption and implementation to answer key questions in neuroscience. Following a brief review of current literature on this framework, this perspective paper will highlight how pragmatic steps aiming to make gradient methods more accessible to the community propelled these techniques to the forefront of neuroscientific inquiry. More specifically, we will emphasize how interest for gradient methods was catalyzed by data sharing, open-source software development, as well as the organization of dedicated workshops led by a diverse team of early career researchers. To this end, we argue that the growing excitement for brain gradients is the result of coordinated and consistent efforts to build an inclusive community and can serve as a case in point for future innovations and conceptual advances in neuroinformatics. We close this perspective paper by discussing challenges for the continuous refinement of neuroscientific theory, methodological innovation, and real-world translation to maintain our collective progress towards integrated models of brain organization.
How causal inference concepts can guide research into the effects of climate on infectious diseases
Authors: Laura Andrea Barrero Guevara, Sarah C Kramer, Tobias Kurth, Matthieu Domenech de Cellès
Date: 2024-02-19 00:00:00
Tasks: Causal Inference

A pressing question resulting from global warming is how infectious diseases will be affected by climate change. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proven difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here, we show how concepts from causal inference -- the sub-field of statistics aiming at inferring causality from data -- can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.
Modeling the mechanisms of antibody mixtures in viral infections: the cases of sequential homologous and heterologous dengue infections
Authors: Charlotte Dugourd-Camus, Claudia P. Ferreira, Mostafa Adimy
Date: 2024-02-19 00:00:00

Antibodies play an essential role in the immune response to viral infections, vaccination, or antibody therapy. Nevertheless, they can be either protective or harmful during the immune response. Moreover, competition or cooperation between mixed antibodies can enhance or reduce this protective or harmful effect. Using the laws of chemical reactions, we propose a new approach to modeling the antigen-antibody complex activity. The resulting expression covers not only purely competitive or purely independent binding but also synergistic binding which, depending on the antibodies, can promote either neutralization or enhancement of viral activity. We then integrate this expression of viral activity in a within-host model and investigate the existence of steady-states and their asymptotic stability. We complete our study with numerical simulations to illustrate different scenarios: firstly, where both antibodies are neutralizing, and secondly, where one antibody is neutralizing and the other enhancing. The results indicate that efficient viral neutralization is associated with purely independent antibody binding, whereas strong viral activity enhancement is expected in the case of purely competitive antibody binding. Finally, data collected during a secondary dengue infection were used to validate the model. The data set includes sequential measurements of virus and antibody titers during viremia in patients. Data fitting shows that the two antibodies are in strong competition, as the synergistic binding is low. This contributes to the high levels of virus titers and may explain the Antibody-Dependent Enhancement phenomenon. Other applications of the model may be considered, such as the efficacy of vaccines and antibody-based therapies.
Early feasibility of an embedded bi-directional brain-computer interface for ambulation
Authors: Jeffrey Lim, Po T. Wang, Wonjoon Sohn, Claudia Serrano-Amenos, Mina Ibrahim, Derrick Lin, Shravan Thaploo, Susan J. Shaw, Michelle Armacost, Hui Gong, Brian Lee, Darrin Lee, Richard A. Andersen, Payam Heydari, Charles Y. Liu, Zoran Nenadic, An H. Do
Date: 2024-02-19 00:00:00
Tasks: Brain Computer Interface

Current treatments for paraplegia induced by spinal cord injury (SCI) are often limited by the severity of the injury. The accompanying loss of sensory and motor functions often results in reliance on wheelchairs, which in turn causes reduced quality of life and increased risk of co-morbidities. While brain-computer interfaces (BCIs) for ambulation have shown promise in restoring or replacing lower extremity motor functions, none so far have simultaneously implemented sensory feedback functions. Additionally, many existing BCIs for ambulation rely on bulky external hardware that make them ill-suited for non-research settings. Here, we present an embedded bi-directional BCI (BDBCI), that restores motor function by enabling neural control over a robotic gait exoskeleton (RGE) and delivers sensory feedback via direct cortical electrical stimulation (DCES) in response to RGE leg swing. A first demonstration with this system was performed with a single subject implanted with electrocorticography electrodes, achieving an average lag-optimized cross-correlation of 0.80$\pm$0.08 between cues and decoded states over 5 runs.
Ethnic Disparities of Female Infiltrating Duct and Lobular Breast Cancer Survival by Cancer Stage: Findings From SEER 2006-2010
Authors: Ishmael Nii Amartei Amartey
Date: 2024-02-16 00:00:00
Tasks: Epidemiology

Breast Cancer is a major disease affecting women's health in the United States with incidence and prevalence dominant among younger women and the Black race. We analyzed the association between breast cancer characteristics with age and race and how survival months and age differ in racial groups. Using the Surveillance, Epidemiology, and End Results (SEER) datasets we performed a logistic regression to examine significant predictors that affect survival month. There were 3414 whites, 291 Blacks, and 320 Others (American Indian/AK Native, Asian/Pacific Islander) in the sample. We found significant associations between racial groups and ages with significant differences in age between Blacks and Whites, and Whites and Others. Patients with a breast cancer tumor in grades 1 and 2 have higher survival months (by 1.49% and 0.49%) respectively.
Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases
Authors: Josué Pagán, José L. Risco-Martín, José M. Moya, José L. Ayala
Date: 2024-02-15 00:00:00
Tasks: Model Selection, Prediction

Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40 minutes, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises.
Binding SNOMED-CT Terms to Archetype Elements: Establishing a Baseline of Results
Authors: Idoia Berges, Jesús Bermúdez, Arantza Illarramendi
Date: 2024-01-26 00:00:00

Introduction: This article is part of the Focus Theme of METHODS of Information in Medicine on "Managing Interoperability and Complexity in Health Systems". Background: The proliferation of archetypes as a means to represent information of Electronic Health Records has raised the need of binding terminological codes - such as SNOMED CT codes - to their elements, in order to identify them univocally. However, the large size of the terminologies makes it difficult to perform this task manually. Objectives: To establish a baseline of results for the aforementioned problem by using off-the-shelf string comparison-based techniques against which results from more complex techniques could be evaluated. Methods: Nine Typed Comparison METHODS were evaluated for binding using a set of 487 archetype elements. Their recall was calculated and Friedman and Nemenyi tests were applied in order to assess whether any of the methods outperformed the others. Results: Using the qGrams method along with the 'Text' information piece of archetype elements outperforms the other methods if a level of confidence of 90% is considered. A recall of 25.26% is obtained if just one SNOMED CT term is retrieved for each archetype element. This recall rises to 50.51% and 75.56% if 10 and 100 elements are retrieved respectively, that being a reduction of more than 99.99% on the SNOMED CT code set. Conclusions: The baseline has been established following the above-mentioned results. Moreover, it has been observed that although string comparison-based methods do not outperform more sophisticated techniques, they still can be an alternative for providing a reduced set of candidate terms for each archetype element from which the ultimate term can be chosen later in the more-than-likely manual supervision task.
The Price of Information
Authors: Sebastian Jaimungal, Xiaofei Shi
Date: 2024-02-19 00:00:00

When an investor is faced with the option to purchase additional information regarding an asset price, how much should she pay? To address this question, we solve for the indifference price of information in a setting where a trader maximizes her expected utility of terminal wealth over a finite time horizon. If she does not purchase the information, then she solves a partial information stochastic control problem, while, if she does purchase the information, then she pays a cost and receives partial information about the asset's trajectory. We further demonstrate that when the investor can purchase the information at any stopping time prior to the end of the trading horizon, she chooses to do so at a deterministic time.
Stackelberg reinsurance and premium decisions with MV criterion and irreversibility
Authors: Zongxia Liang, Xiaodong Luo
Date: 2024-02-18 00:00:00
Tasks: Decision Making

We study a reinsurance Stackelberg game in which both the insurer and the reinsurer adopt the mean-variance (abbr. MV) criterion in their decision-making and the reinsurance is irreversible. We apply a unified singular control framework where irreversible reinsurance contracts can be signed in both discrete and continuous times. The results theoretically illustrate that, rather than continuous-time contracts or a bunch of discrete-time contracts, a single once-for-all reinsurance contract is preferred. Moreover, the Stackelberg game turns out to be centering on the signing time of the single contract. The insurer signs the contract if the premium rate is lower than a time-dependent threshold and the reinsurer designs a premium that triggers the signing of the contract at his preferred time. Further, we find that reinsurance preference, discount and reversion have a decreasing dominance in the reinsurer's decision-making, which is not seen for the insurer.
Denoised Monte Carlo for option pricing and Greeks estimation
Authors: Andrzej Daniluk, Evgeny Lakshtanov, Rafal Muchorski
Date: 2024-02-19 00:00:00

We present a novel technique of Monte Carlo error reduction that finds direct application in option pricing and Greeks estimation. The method is applicable to any LSV modelling framework and concerns a broad class of payoffs, including path-dependent and multi-asset cases. Most importantly, it allows to reduce the Monte Carlo error even by an order of magnitude, which is shown in several numerical examples.
Stylized Facts of High-Frequency Bitcoin Time Series
Authors: Yaoyue Tang, Karina Arias-Calluari, M. N. Najafi, Michael S. Harré, Fernando Alonso-Marroquin
Date: 2024-02-19 00:00:00
Tasks: Time Series

This paper analyses the high-frequency intraday Bitcoin dataset from 2019 to 2022. During this time frame, the Bitcoin market index exhibited two distinct periods, 2019-20 and 2021-22, characterized by an abrupt change in volatility. The Bitcoin price returns for both periods can be described by an anomalous diffusion process, transitioning from subdiffusion for short intervals to weak superdiffusion over longer time intervals. The characteristic features related to this anomalous behavior studied in the present paper include heavy tails, which can be described using a $q$-Gaussian distribution and correlations. When we sample the autocorrelation of absolute returns, we observe a power-law relationship, indicating time dependence in both periods initially. The ensemble autocorrelation of the returns decays rapidly. We fitted the autocorrelation with a power law to capture the decay and found that the second period experienced a slightly higher decay rate. The further study involves the analysis of endogenous effects within the Bitcoin time series, which are examined through detrending analysis. We found that both periods are multifractal and present self-similarity in the detrended probability density function (PDF). The Hurst exponent over short time intervals shifts from less than 0.5 ($\sim$ 0.42) in Period 1 to closer to 0.5 in Period 2 ($\sim$ 0.49), indicating that the market has gained efficiency over time.
Reinforcement Learning for Optimal Execution when Liquidity is Time-Varying
Authors: Andrea Macrì, Fabrizio Lillo
Date: 2024-02-19 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning

Optimal execution is an important problem faced by any trader. Most solutions are based on the assumption of constant market impact, while liquidity is known to be dynamic. Moreover, models with time-varying liquidity typically assume that it is observable, despite the fact that, in reality, it is latent and hard to measure in real time. In this paper we show that the use of Double Deep Q-learning, a form of Reinforcement Learning based on neural networks, is able to learn optimal trading policies when liquidity is time-varying. Specifically, we consider an Almgren-Chriss framework with temporary and permanent impact parameters following several deterministic and stochastic dynamics. Using extensive numerical experiments, we show that the trained algorithm learns the optimal policy when the analytical solution is available, and overcomes benchmarks and approximated solutions when the solution is not available.
Estimating the age-conditioned average treatment effects curves: An application for assessing load-management strategies in the NBA
Authors: Shinpei Nakamura-Sakai, Laura Forastiere, Brian Macdonald
Date: 2024-02-17 00:00:00
Tasks: Management, Sports Understanding

In the realm of competitive sports, understanding the performance dynamics of athletes, represented by the age curve (showing progression, peak, and decline), is vital. Our research introduces a novel framework for quantifying age-specific treatment effects, enhancing the granularity of performance trajectory analysis. Firstly, we propose a methodology for estimating the age curve using game-level data, diverging from traditional season-level data approaches, and tackling its inherent complexities with a meta-learner framework that leverages advanced machine learning models. This approach uncovers intricate non-linear patterns missed by existing methods. Secondly, our framework enables the identification of causal effects, allowing for a detailed examination of age curves under various conditions. By defining the Age-Conditioned Treatment Effect (ACTE), we facilitate the exploration of causal relationships regarding treatment impacts at specific ages. Finally, applying this methodology to study the effects of rest days on performance metrics, particularly across different ages, offers valuable insights into load management strategies' effectiveness. Our findings underscore the importance of tailored rest periods, highlighting their positive impact on athlete performance and suggesting a reevaluation of current management practices for optimizing athlete performance.