pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Modal Latent Variables for Cross-Individual Primary Visual Cortex Modeling and Analysis
Authors: Yu Zhu, Bo Lei, Chunfeng Song, Wanli Ouyang, Shan Yu, Tiejun Huang
Date: 2024-12-19 00:00:00
Tasks: Disentanglement

Elucidating the functional mechanisms of the primary visual cortex (V1) remains a fundamental challenge in systems neuroscience. Current computational models face two critical limitations, namely the challenge of cross-modal integration between partial neural recordings and complex visual stimuli, and the inherent variability in neural characteristics across individuals, including differences in neuron populations and firing patterns. To address these challenges, we present a multi-modal identifiable variational autoencoder (miVAE) that employs a two-level disentanglement strategy to map neural activity and visual stimuli into a unified latent space. This framework enables robust identification of cross-modal correlations through refined latent space modeling. We complement this with a novel score-based attribution analysis that traces latent variables back to their origins in the source data space. Evaluation on a large-scale mouse V1 dataset demonstrates that our method achieves state-of-the-art performance in cross-individual latent representation and alignment, without requiring subject-specific fine-tuning, and exhibits improved performance with increasing data size. Significantly, our attribution algorithm successfully identifies distinct neuronal subpopulations characterized by unique temporal patterns and stimulus discrimination properties, while simultaneously revealing stimulus regions that show specific sensitivity to edge features and luminance variations. This scalable framework offers promising applications not only for advancing V1 research but also for broader investigations in neuroscience.
Diverging network architecture of the $\textit{C. elegans}$ connectome and signaling network
Authors: Sophie Dvali, Caio Seguin, Richard Betzel, Andrew M. Leifer
Date: 2024-12-19 00:00:00
Tasks: Anatomy

The connectome describes the complete set of synaptic contacts through which neurons communicate. While the architecture of the $\textit{C. elegans}$ connectome has been extensively characterized, much less is known about the organization of causal signaling networks arising from functional interactions between neurons. Understanding how effective communication pathways relate to or diverge from the underlying structure is a central question in neuroscience. Here, we analyze the modular architecture of the $\textit{C. elegans}$ signal propagation network, measured via calcium imaging and optogenetics, and compare it to the underlying anatomical wiring measured by electron microscopy. Compared to the connectome, we find that signaling modules are not aligned with the modular boundaries of the anatomical network, highlighting an instance where function deviates from structure. An exception to this is the pharynx which is delineated into a separate community in both anatomy and signaling. We analyze the cellular compositions of the signaling architecture and find that its modules are enriched for specific cell types and functions, suggesting that the network modules are neurobiologically relevant. Lastly, we identify a "rich club" of hub neurons in the signaling network. The membership of the signaling rich club differs from the rich club detected in the anatomical network, challenging the view that structural hubs occupy positions of influence in functional (signaling) networks. Our results provide new insight into the interplay between brain structure, in the form of a complete synaptic-level connectome, and brain function, in the form of a system-wide causal signal propagation atlas.
Starting a Synthetic Biological Intelligence Lab from Scratch
Authors: Md Sayed Tanveer, Dhruvik Patel, Hunter E. Schweiger, Kwaku Dad Abu-Bonsrah, Brad Watmuff, Azin Azadi, Sergey Pryshchep, Karthikeyan Narayanan, Christopher Puleo, Kannathal Natarajan, Mohammed A. Mostajo-Radji, Brett J. Kagan, Ge Wang
Date: 2024-12-18 00:00:00

With the recent advancements in artificial intelligence, researchers and industries are deploying gigantic models trained on billions of samples. While training these models consumes a huge amount of energy, human brains produce similar outputs (along with other capabilities) with massively lower data and energy requirements. For this reason, more researchers are increasingly considering alternatives. One of these alternatives is known as synthetic biological intelligence, which involves training \textit{in vitro} neurons for goal-directed tasks. This multidisciplinary field requires knowledge of tissue engineering, bio-materials, digital signal processing, computer programming, neuroscience, and even artificial intelligence. The multidisciplinary requirements make starting synthetic biological intelligence research highly non-trivial and time-consuming. Generally, most labs either specialize in the biological aspects or the computational ones. Here, we propose how a lab focusing on computational aspects, including machine learning and device interfacing, can start working on synthetic biological intelligence, including organoid intelligence. We will also discuss computational aspects, which can be helpful for labs that focus on biological research. To facilitate synthetic biological intelligence research, we will describe such a general process step by step, including risks and precautions that could lead to substantial delay or additional cost.
Who Saves us From Risk? Altruists Promote Cooperation in a Public Investment Game
Authors: Shen Zhang, Xueyi Shen, Ruida Zhu, Zilu Liang, Chao Liu
Date: 2024-12-18 00:00:00

Providing commons in the risky world is crucial for human survival, however, suffers more from the "free-riding" problem. Here, we proposed a solution that limits the access of the resource to an agent and tested its efficiency with a novel public investment game. On each trial, all group members invest in an agent responsible for a gamble and distribution. Resources are distributed evenly between non-agent group members after the agent extracts for him/herself. In 3 laboratory experiments (n = 704), we found that, as an agent, many participants extracted fewer resources than others. This altruistic distribution strategy improved cooperation levels. Furthermore, this cooperation-promoting effect could be spread to selfish agents who are in the same group as the altruistic agent and was replicated in a one-shot setting. We proposed that, when the commons are under risk, this distribution solution serves as a better alternative to the well-documented sanction solutions.
Efficacy of Temporal Interference Electrical Stimulation for Spinal Cord Injury Rehabilitation: A Case Series
Authors: Ruidong Cheng, Yuling Shao, Xi Li, Li Zhang, Zehao Sheng, Chenyang Li, Xu Xie, Huilin Mou, Weidong Chen, Shaomin Zhang, Yuchen Xu, Minmin Wang
Date: 2024-12-16 00:00:00

Spinal cord injury (SCI) is a debilitating condition that often results in significant motor and sensory deficits, impacting the quality of life. Current rehabilitation methods, including physical therapy and electrical stimulation, offer variable outcomes and often require invasive procedures. Temporal interference (TI) stimulation has emerged as a novel, non-invasive neuromodulation technique capable of targeting deep neural structures with precision, providing a promising alternative for SCI rehabilitation. This study explores the efficacy of TI stimulation as a non-invasive approach for improving motor and sensory function in patients with incomplete SCI. Three male patients with incomplete cervical SCI (AIS D) participated in a two-week intervention consisting of 14 sessions of TI stimulation targeting their injury sites. TI stimulation was delivered using frequencies of 1000 Hz and 1040 Hz, with assessments conducted pre- and post-intervention, including motor and sensory evaluations, functional scales, and imaging studies.All participants demonstrated significant improvements in neurological function, motor strength, sensory perception, and functional independence. Neurological levels of injury shifted upward in all cases, with one patient improving from C5 to C7. Graded Redefined Assessment of Strength, Sensibility and Prehension (GRASSP) results shows additional strength, prehension and sensory outcomes obtained for the arm and hand functions of participants. Motor scores (UEMS and LEMS) increased, sensory scores for light touch and pin prick improved, and functional assessments, such as the Berg Balance Scale (BBS) and Barthel Index (BI), showed marked gains. Pain scores also decreased in two participants, highlighting additional therapeutic benefits.
Pop-out vs. Glue: A Study on the pre-attentive and focused attention stages in Visual Search tasks
Authors: Hendrik Beukelman, Wilder C. Rodrigues
Date: 2024-12-14 00:00:00

This study explores visual search asymmetry and the detection process between parallel and serial search strategies, building upon Treisman's Feature Integration Theory [3]. Our experiment examines how easy it is to locate an oblique line among vertical distractors versus a vertical line among oblique distractors, a framework previously validated by Treisman & Gormican (1988) [4] and Gupta et al. (2015) [1]. We hypothesised that an oblique target among vertical lines would produce a perceptual 'pop-out' effect, allowing for faster, parallel search, while the reverse condition would require serial search strategy. Seventy-eight participants from Utrecht University engaged in trials with varied target-distractor orientations and number of items. We measured reaction times and found a significant effect of target type on search speed: oblique targets were identified more quickly, reflecting 'pop-out' behaviour, while vertical targets demanded focused attention ('glue phase'). Our results align with past findings, supporting our hypothesis on search asymmetry and its dependency on distinct visual features. Future research could benefit from eye-tracking and neural network analysis, particularly for identifying the neural processing of visual features in both parallel and serial search conditions.
Generative Modeling of Neural Dynamics via Latent Stochastic Differential Equations
Authors: Ahmed ElGazzar, Marcel van Gerven
Date: 2024-12-01 00:00:00
Tasks: Variational Inference

We propose a probabilistic framework for developing computational models of biological neural systems. In this framework, physiological recordings are viewed as discrete-time partial observations of an underlying continuous-time stochastic dynamical system which implements computations through its state evolution. To model this dynamical system, we employ a system of coupled stochastic differential equations with differentiable drift and diffusion functions and use variational inference to infer its states and parameters. This formulation enables seamless integration of existing mathematical models in the literature, neural networks, or a hybrid of both to learn and compare different models. We demonstrate this in our framework by developing a generative model that combines coupled oscillators with neural networks to capture latent population dynamics from single-cell recordings. Evaluation across three neuroscience datasets spanning different species, brain regions, and behavioral tasks show that these hybrid models achieve competitive performance in predicting stimulus-evoked neural and behavioral responses compared to sophisticated black-box approaches while requiring an order of magnitude fewer parameters, providing uncertainty estimates, and offering a natural language for interpretation.
Why does time feel the way it does? Towards a principled account of temporal experience
Authors: Renzo Comolatti, Matteo Grasso, Giulio Tononi
Date: 2024-11-30 00:00:00
Tasks: Philosophy

Time flows, or at least the time of our experience does. Can we provide an objective account of why experience, confined to the short window of the conscious present, encompasses a succession of moments that slip away from now to then--an account of why time feels flowing? Integrated Information Theory (IIT) aims to account for both the presence and quality of consciousness in objective, physical terms. Given a substrate's architecture and current state, the formalism of IIT allows one to unfold the cause-effect power of the substrate, yielding a cause-effect structure. According to IIT, this accounts in full for the presence and quality of experience, without any additional ingredients. In previous work, we showed how unfolding the cause-effect structure of non-directed grids, like those found in many posterior cortical areas, can account for the way space feels--namely, extended. Here we show that unfolding the cause-effect structure of directed grids can account for how time feels--namely, flowing. First, we argue that the conscious present is experienced as flowing because it is composed of phenomenal distinctions (moments) that are directed, and these distinctions are related in a way that satisfies directed inclusion, connection, and fusion. We then show that directed grids, which we conjecture constitute the substrate of temporal experience, yield a cause-effect structure that accounts for these and other properties of temporal experience. In this account, the experienced present does not correspond to a process unrolling in "clock time," but to a cause-effect structure specified by a system in its current state: time is a structure, not a process. We conclude by outlining similarities and differences between the experience of time and space, and some implications for the neuroscience, psychophysics, and philosophy of time.
The Syncytial Mesh Model: A Biophysical Framework for Scale-Dependent Coherence in the Brain
Authors: Andreu Ballus Santacana
Date: 2024-11-29 00:00:00
Tasks: Functional Connectivity

The brain-mesh model introduces a novel three-layered architecture that integrates local and macro-regional connectivity with an underlying, mesh-inspired network layer. This foundational mesh layer, based on metallic mesh structures, spans the entire brain and generates interference patterns, noise, and resonance effects that modulate both local and global neural dynamics. The fused model goes beyond traditional connectivity frameworks by providing a unified explanation for phenomena such as brain-wide phase gradients, stable low-frequency resonance frequencies, and long-range plasticity effects, which are often difficult to explain cohesively within existing models. In addition to accounting for classical neurobiological observations, such as phase synchrony, functional connectivity fluctuations, and local Hebbian plasticity, the model offers novel insights into less understood phenomena. Specifically, it predicts connectivity-independent phase gradients across non-synaptic regions, harmonic resonance peaks consistent across individuals, and diffuse plasticity driven by global interference patterns, all of which are challenging to explain under current frameworks. These unique predictions align with partial empirical observations, such as traveling wave dynamics, consistent low-frequency oscillations, and task-induced connectivity shifts, underscoring the model's relevance. Additionally, the brain-mesh model generates testable hypotheses that distinguish it from traditional approaches. This provides a promising framework for future experimental validation and opens new avenues for understanding global brain function.
Insecticide treated bed net use and elimination of malaria in Sub-Saharan African countries: Assessing the Global Technical Strategy (GTS) using an evolutionary game approach
Authors: Laxmi, Tamer Oraby, Michael G Tyshenko, Ina Danquah, Samit Bhattacharyya
Date: 2024-12-17 00:00:00

The WHO 2021 malaria report revealed that its Global Technical Strategy (GTS) 2020 milestones for morbidity and mortality, based on the 2015 baseline, have not been achieved globally -- the world is off-track by 42% and, can be extended up to 91% in 2030. Most of the Sub-Saharan African (SSA) countries failed to achieve GTS 2020 -- only 4 out of 40 highest burden countries met the goals. By fitting evolutionary game modeling to the malaria case and Insecticide-Treated Nets (ITN) usages data, we identify factors contributing to the GTS 2020 failures of 38 SSA countries. We use optimized projection of our model to evaluate further the potential achievement of GTS 2025 and 2030 objectives and discuss strategies for attaining goals in situations where these milestones seem unattainable. Our findings categorize all 38 countries based on the possibility to achieve their future milestone, either through increased campaigns, or by economic assistance, or through enhancing the efficacy of ITNs at minimal expense.
Prey-taxis vs an external signal: short-wave asymptotic and stability analysis
Authors: Andrey Morgulis, Karrar Malal
Date: 2024-12-16 00:00:00

We consider two models of predator-prey community with prey-taxis, one relies on Patlak-Keller-Segel law (Lee et al, 2009), the other one employs the Cattaneo model of heat transfer following Dolak and Hillen (2003). Thus, the former one uses the prey density gradient for directing the predators flux, and the latter one -- for directing the vector density of sources of the predators flux. We assume the predators to be also capable of responding to an external signal in the same manner. Additionally, we assume that some scaling makes the dimensionless prey diffusivity and the wavelength of the external signal small quantities of the same order. In the case of Cattaneo's model, we additionally assume the resistivity to varying the predators flux to be as high as the reciprocal of the prey diffusivity. With these assumptions we construct the complete asymptotic expansion of the short-wave solution. We use this asymptotic to examine the effect of the short-wave signal on the formation of spatiotemporal patterns. We do so by comparing the stability of equilibria with no signal to that of the quasi-equilibria, by which we mean the simplest patterns directly forced by the external signal. Historically, such an approach goes back to Kapitza's theory for upside-down pendulum, and it had been already applied to examining other predator-prey systems and/or other limits (Morgulis and Ilin, 2020, Morgulis, 2023). This time the overall conclusion is that the external signal is likely not capable of creating the instability domain in the parametric space from nothing but it can substantially widen the one that is non-empty with no signal. The details, however, essentially depend on the speed at which the external signal propagates and on the system kinetics, at least when the signal amplitude is small
Modeling therapy sequence for advanced cancer: A microsimulation approach leveraging Electronic Health Record data
Authors: Elizabeth A. Handorf, J. Robert Beck, Daniel M. Geynisman
Date: 2024-12-17 00:00:00

Many patients with advanced cancers undergo multiple lines of treatment. We develop methods for estimating quality-adjusted outcomes and cost-effectiveness of therapy sequences, informed by patient-level longitudinal data from Electronic Health Records (EHRs). We develop microsimulation models with a discrete-time health-state transition framework and propose two methods: one using multi-state models to estimate transition probabilities, and one using observed patient trajectories through the health states. We use bootstrap resampling to estimate standard errors. We create synthetic EHR-like datasets to evaluate these methods where within-patient transition times depend on covariates and a copula generator, and compare with Markov cohort models. We demonstrate these methods in two treatment sequences for advanced bladder cancer (cisplatin or carboplatin-based therapy followed by immunotherapy), incorporating external information on costs, utilities, and expected adverse event. Both methods produced well-calibrated overall survivals, although the trajectory approach was often superior. The multi-state model approach generated lower standard errors but was biased when compared to known results from the synthetic datasets. The observed trajectory approach mostly produced confidence intervals that covered known values. In the bladder cancer example, both methods result in a Net Monetary Benefit (NMB)>0 for the cisplatin-based treatment sequence with a willingness to pay of $100,000 per quality-adjusted life year. Both microsimulation methods produce well-calibrated results and offer superior performance to a homogeneous Markov cohort approach when studying therapy sequence. Where available, patient level EHR-based data should be considered to inform cost-effectiveness models.
MitraClip Device Automated Localization in 3D Transesophageal Echocardiography via Deep Learning
Authors: Riccardo Munafò, Simone Saitta, Luca Vicentini, Davide Tondi, Veronica Ruozzi, Francesco Sturla, Giacomo Ingallina, Andrea Guidotti, Eustachio Agricola, Emiliano Votta
Date: 2024-12-19 00:00:00
Tasks: Segmentation

The MitraClip is the most widely percutaneous treatment for mitral regurgitation, typically performed under the real-time guidance of 3D transesophagel echocardiography (TEE). However, artifacts and low image contrast in echocardiography hinder accurate clip visualization. This study presents an automated pipeline for clip detection from 3D TEE images. An Attention UNet was employed to segment the device, while a DenseNet classifier predicted its configuration among ten possible states, ranging from fully closed to fully open. Based on the predicted configuration, a template model derived from computer-aided design (CAD) was automatically registered to refine the segmentation and enable quantitative characterization of the device. The pipeline was trained and validated on 196 3D TEE images acquired using a heart simulator, with ground-truth annotations refined through CAD-based templates. The Attention UNet achieved an average surface distance of 0.76 mm and 95% Hausdorff distance of 2.44 mm for segmentation, while the DenseNet achieved an average weighted F1-score of 0.75 for classification. Post-refinement, segmentation accuracy improved, with average surface distance and 95% Hausdorff distance reduced to 0.75 mm and 2.05 mm, respectively. This pipeline enhanced clip visualization, providing fast and accurate detection with quantitative feedback, potentially improving procedural efficiency and reducing adverse outcomes.
Soft Modes as a Predictive Framework for Low Dimensional Biological Systems across Scales
Authors: Christopher Joel Russo, Kabir Husain, Arvind Murugan
Date: 2024-12-18 00:00:00

All biological systems are subject to perturbations: due to thermal fluctuations, external environments, or mutations. Yet, while biological systems are composed of thousands of interacting components, recent high-throughput experiments show that their response to perturbations is surprisingly low-dimensional: confined to only a few stereotyped changes out of the many possible. Here, we explore a unifying dynamical systems framework - soft modes - to explain and analyze low-dimensionality in biology, from molecules to eco-systems. We argue that this one framework of soft modes makes non-trivial predictions that generalize classic ideas from developmental biology to disparate systems, namely: phenocopying, dual buffering, and global epistasis. While some of these predictions have been borne out in experiments, we discuss how soft modes allow for a surprisingly far-reaching and unifying framework in which to analyze data from protein biophysics to microbial ecology.