pwc-archive/papers-with-abstracts huggingface dataset:
Shifts in Brain Dynamics and Drivers of Consciousness State Transitions
Authors: Joseph Bodenheimer, Paul Bogdan, Sérgio Pequito, Arian Ashourvan
Date: 2024-07-09 00:00:00

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics - particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions towards conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.
Beyond acoustics -- capacity limitations of linguistic levels
Authors: Jérémy Giroud, Benjamin Morillon
Date: 2024-07-09 00:00:00

Speech is a multiplexed signal displaying levels of complexity, organizational principles and perceptual units of analysis at distinct timescales. This critical acoustic signal for human communication is thus characterized at distinct representational and temporal scales, related to distinct linguistic features, from acoustic to supra-lexical. This chapter presents an overview of experimental work devoted to the characterization of the speech signal at different timescales, beyond its acoustic properties. The functional relevance of these different levels of analysis for speech processing is discussed. We advocate that studying speech perception through the prism of multi-time scale representations effectively integrates work from various research areas into a coherent picture and contributes significantly to increase our knowledge on the topic. Finally, we discuss how these experimental results fit with neural data and current dynamical models of speech perception.
Neural population dynamics in songbird RA and HVC during learned motor-vocal behavior
Authors: Pablo Tostado-Marcos, Ezequiel M. Arneodo, Lauren Ostrowski, Daril E. Brown II, Xavier A. Perez, Adam Kadwory, Lauren L. Stanwicks, Abdullah Alothmanm, Timothy Q. Gentner, Vikash Gilja
Date: 2024-07-08 00:00:00
Tasks: State Space Models

Complex, learned motor behaviors involve the coordination of large-scale neural activity across multiple brain regions, but our understanding of the population-level dynamics within different regions tied to the same behavior remains limited. Here, we investigate the neural population dynamics underlying learned vocal production in awake-singing songbirds. We use Neuropixels probes to record the simultaneous extracellular activity of populations of neurons in two regions of the vocal motor pathway. In line with observations made in non-human primates during limb-based motor tasks, we show that the population-level activity in both the premotor nucleus HVC and the motor nucleus RA is organized on low-dimensional neural manifolds upon which coordinated neural activity is well described by temporally structured trajectories during singing behavior. Both the HVC and RA latent trajectories provide relevant information to predict vocal sequence transitions between song syllables. However, the dynamics of these latent trajectories differ between regions. Our state-space models suggest a unique and continuous-over-time correspondence between the latent space of RA and vocal output, whereas the corresponding relationship for HVC exhibits a higher degree of neural variability. We then demonstrate that comparable high-fidelity reconstruction of continuous vocal outputs can be achieved from HVC and RA neural latents and spiking activity. Unlike those that use spiking activity, however, decoding models using neural latents generalize to novel sub-populations in each region, consistent with the existence of preserved manifolds that confine vocal-motor activity in HVC and RA.
Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics
Authors: Nghi Nguyen, Tao Hou, Enrico Amico, Jingyi Zheng, Huajun Huang, Alan D. Kaplan, Giovanni Petri, Joaquín Goñi, Ralph Kaufmann, Yize Zhao, Duy Duong-Tran, Li Shen
Date: 2024-07-06 00:00:00

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities' insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning.
Reverse Engineering the Fly Brain Using FlyCircuit Database
Authors: Yu-Tai Ching, Chin-Ping Cho, Fu-Kai Tang, Yi-Chiun Chang, Chang-Chieh Cheng, Guan-Wei He, Ann-Shyn Chang, Chaochun Chuang
Date: 2024-07-05 00:00:00
Tasks: Clustering

A method to reverse engineering of a fly brain using the {\it FlyCircuit} database is presented. This method was designed based on the assumption that similar neurons could serve identical functions. We thus cluster the neurons based on the similarity between neurons. The procedures are to partition the neurons in the database into groups, and then assemble the groups into potential modules. Some of the modules correspond to known neuropils, including Medulla were obtained. The same clustering algorithm was applied to analyze Medulla's structure. Another possible application of the clustering result is to study the brain-wide neuron connectome by looking at the connectivity between groups of neurons.
Efficient optimization of ODE neuron models using gradient descent
Authors: Ilenna Simone Jones, Konrad Paul Kording
Date: 2024-07-04 00:00:00

Neuroscientists fit morphologically and biophysically detailed neuron simulations to physiological data, often using evolutionary algorithms. However, such gradient-free approaches are computationally expensive, making convergence slow when neuron models have many parameters. Here we introduce a gradient-based algorithm using differentiable ODE solvers that scales well to high-dimensional problems. GPUs make parallel simulations fast and gradient calculations make optimization efficient. We verify the utility of our approach optimizing neuron models with active dendrites with heterogeneously distributed ion channel densities. We find that individually stimulating and recording all dendritic compartments makes such model parameters identifiable. Identification breaks down gracefully as fewer stimulation and recording sites are given. Differentiable neuron models, which should be added to popular neuron simulation packages, promise a new era of optimizable neuron models with many free parameters, a key feature of real neurons.
Cerebral cortex inspired representation of neural field network
Authors: Anil Kumar Sharma, Asha Sharma
Date: 2024-07-03 00:00:00
Tasks: Form

Evolution and its intelligence element present thrill and challenges in its exploration. Yet, how species have memory, retrieve them and maintain continuity are the fundamental questions. Most of the phenomenon can only be hypothesised by researchers and validating them through experiments is a big challenge. Taking brain as an ideal intelligent machine and modelling it opens new dimensions for computational algorithm. This paper presents a hypothesis to resemble memory creation in cerebral cortex. The regions of cerebral cortex are implicit to be specific for specific function and constitute neural field that is single dimension and have vector form. The neural field throughout cortex connects with each other to form a network. These networks associate with survival instincts, emotions and rewards to constitute a memory of the exposed environment or say learning. Graphical tool NURBS with multidimensional control points are implicitly used in representing these networks as a set of cubic equations. Learning through data is a primary block of intelligent system, the paper attempts to convert the data in lower dimension patterns rather than existing absolute form for real time intelligent systems.
Automated Chronotyping from a Daily Calendar using Machine Learning
Authors: Pratiik Kaushik, Koorosh Askari, Saksham Gupta, Rahul Mohan, Kris Skrinak, Royan Kamyar, Benjamin Smarr
Date: 2024-07-09 00:00:00
Tasks: Miscellaneous

Chronotype compares individuals' circadian phase to others. It contextualizes mental health risk assessments and detection of social jet lag, which can hamper mental health and cognitive performance. Existing ways of determining chronotypes, such as Dim Light Melatonin Onset (DLMO) or the Morningness-Eveningness Questionnaire (MEQ), are limited by being discrete in time and time-intensive to update, meaning they rarely capture real-world variability across time. Chronotyping users based on a daily planner app might augment existing methods to enable assessment continuously and at scale. This paper reports the construction of a supervised binary classifier that attempts to demonstrate the feasibility of this approach. 1,460 registered users from the Owaves app opted in by filling out the MEQ survey between July 14, 2022, and May 1, 2023. 142 met the eligibility criteria. We used multimodal app data from individuals identified as morning and evening types from MEQ data, basing the classifier on app time series data. This included daily timing for 8 main lifestyle activity types: exercise, sleep, social interactions, meal times, relaxation, work, play, and miscellaneous, as defined in the app. The timing of activities showed substantial change across time, as well as heterogeneity by activity type. Our novel chronotyping classifier was able to predict the morningness and eveningness of its users with an ROC AUC of 0.70. Our findings demonstrate the feasibility of chronotype classification from multimodal, real-world app data, while highlighting fundamental challenges to applying discrete and fixed labels to complex, dynamic, multimodal behaviors. Our findings suggest a potential for real-time monitoring of shifts in chronotype specific to different causes (i.e. types of activity), which could feasibly be used to support future, prospective mental health support research.
Data-centric challenges with the application and adoption of artificial intelligence for drug discovery
Authors: Ghita Ghislat, Saiveth Hernandez-Hernandez, Chayanit Piyawajanusorn, Pedro J. Ballester
Date: 2024-07-06 00:00:00
Tasks: Drug Discovery, Uncertainty Quantification

Introduction: Artificial intelligence (AI) is exhibiting tremendous potential to reduce the massive costs and long timescales of drug discovery. There are however important challenges currently limiting the impact and scope of AI models. Areas covered: In this perspective, the authors discuss a range of data issues (bias, inconsistency, skewness, irrelevance, small size, high dimensionality), how they challenge AI models, and which issue-specific mitigations have been effective. Next, they point out the challenges faced by uncertainty quantification techniques aimed at enhancing and trusting the predictions from these AI models. They also discuss how conceptual errors, unrealistic benchmarks and performance misestimation can confound the evaluation of models and thus their development. Lastly, the authors explain how human bias, whether from AI experts or drug discovery experts, constitutes another challenge that can be alleviated by gaining more prospective experience. Expert opinion: AI models are often developed to excel on retrospective benchmarks unlikely to anticipate their prospective performance. As a result, only a few of these models are ever reported to have prospective value (e.g. by discovering potent and innovative drug leads for a therapeutic target). The authors have discussed what can go wrong in practice with AI for drug discovery. We hope that this will help inform the decisions of editors, funders investors and researchers working in this area.
Quantifying "just-right" APC inactivation for colorectal cancer initiation
Authors: Meritxell Brunet Guasch, Nathalie Feeley, Ignacio Soriano, Steve Thorn, Ian Tomlinson, Michael D. Nicholson, Tibor Antal
Date: 2024-07-09 00:00:00

Dysregulation of the tumour suppressor gene Adenomatous Polyposis Coli (APC) is a canonical step in colorectal cancer development. Curiously, most colorectal tumours carry biallelic mutations that result in only partial loss of APC function, suggesting that a "just-right" level of APC inactivation, and hence Wnt signalling, provides the optimal conditions for tumorigenesis. Mutational processes act variably across the APC gene, which could contribute to the bias against complete APC inactivation. Thus the selective consequences of partial APC loss are unclear. Here we propose a mathematical model to quantify the tumorigenic effect of biallelic APC genotypes, controlling for somatic mutational processes. Analysing sequence data from >2500 colorectal cancers, we find that APC genotypes resulting in partial protein function confer about 50 times higher probability of progressing to cancer compared to complete APC inactivation. The optimal inactivation level varies with anatomical location and additional mutations of Wnt pathway regulators. We use this context dependency to assess the regulatory effect of secondary Wnt drivers in combination with APC in vivo, and provide evidence that mutant AMER1 combines with APC genotypes that lead to relatively low Wnt. The fitness landscape of APC inactivation is consistent across microsatellite unstable and POLE-deficient colorectal cancers and tumours in patients with Familial Adenomatous Polyposis, suggesting a general "just-right" optimum, and pointing to Wnt hyperactivation as a potential cancer vulnerability.
A state-space catch-at-length assessment model for redfish on the Eastern Grand Bank of Newfoundland reveals large uncertainties in data and stock dynamics
Authors: Noel G. Cadigan, Andrea M. Perreault, Hoang Nguyen, Jiaying Chen, Andres Beita-Jimenez, Natalie Fuller, Krista Ransier
Date: 2024-07-05 00:00:00
Tasks: Management, Survey

We developed a state-space age-structured catch-at-length (ACL) assessment model for redfish in NAFO Divisions 3LN. The model was developed to address limitations in the surplus production model that was previously used to assess this stock. The ACL model included temporal variations in recruitment, growth, and mortality rates, which were limitations identified for the surplus production model. Our ACL model revealed some important discrepancies in survey and fishery length compositions. Our model also required large population dynamics process errors to achieve good fits to survey indices and catch estimates, which also demonstrated that additional understanding of these data and other model assumptions is required. As such, we do not propose the ACL model to provide management advice for 3LN redfish, but we do provide research recommendations that should provide a better basis to model the 3LN redfish stock dynamics. Recommendations include implementing sampling programs to determine redfish species/ecotypes in commercial and research survey catches and improving biological sampling for maturity and age.
Protein-environment-sensitive computational epitope accessibility analysis from antibody dose-response data
Authors: Dominik Tschimmel, Momina Saeed, Maria Milani, Steffen Waldherr, Tim Hucho
Date: 2024-07-08 00:00:00
Tasks: Specificity

Antibodies are widely used in life-sciences and medical therapy. Yet, broadly applicable methods are missing to determine, in the biological system of choice, antibody specificity and its quantitative contribution to e.g. immunofluorescence stainings. Thereby, antibody-based data often needs to be seen with caution. Here, we present a simple-to-use approach to characterize and quantify antibody binding properties directly in the system of choice. We determine an epitope accessibility distribution in the system of interest based on a computational analysis of antibody-dilution immunofluorescence stainings. This allows the selection of specific antibodies, the choice of a dilution to maximize signal-specificity, and an improvement of signal quantification. It further expands the scope of antibody-based imaging to detect changes of the subcellular nano-environment and allows for antibody multiplexing.
The Emergent Aging Model: Aging as an Emergent Property of Biological Systems
Authors: Hong Qin
Date: 2024-07-07 00:00:00

Based on the study of cellular aging using the single-cell model organism of budding yeast and corroborated by other studies, we propose the Emergent Aging Model (EAM). EAM hypothesizes that aging is an emergent property of complex biological systems, exemplified by biological networks such as gene networks. An emergent property refers to traits that a system has at the system level but which its low-level components do not. EAM is based on a quantitative definition of aging using the mortality rate. A biological entity with a constant mortality rate is considered non-aging which is equivalent to a first-order chemical reaction. Aging can be quantitatively defined as an increasing mortality rate over time, corresponding to an organism's increasing chance of dying over time. EAM posits that biological aging can arise at the system level of an organism, even if the system is composed of only non-aging components. EAM is consistent with the observation that aging is largely stochastic, influenced by numerous genes and epigenetic factors, with no single gene or factor known as the bona fide cause of aging. A parsimonious version of EAM can predict the Gompertz model of biological aging, the Strehler-Mildvan correlation, and the trade-off between initial reproductive fitness (asexual reproductive fitness) and late-life survival. EAM has been applied to experimental results of the replicative lifespan of the budding yeast and can potentially offer new insights into the aging process of other biological species.
Selection of single cell clustering methodologies through rank aggregation of multiple performance measures
Authors: Owen Visser, Somnath Datta
Date: 2024-07-03 00:00:00
Tasks: Clustering

As single-cell gene expression data analysis continues to grow, the need for reliable clustering methods has become increasingly important. The prevalence of heuristic means for method choice could lead to inaccurate reports if comprehensive evaluation of the methods is omitted. Typical comparisons of methods fail to address the complexity presented by the data, transformations, or internal parameters. Previous work in the field of microarray data provided measures to evaluate the stability characteristic of clustering algorithms. Additional work on aggregation in the same era presented a way to compare multiple methodologies using several performance measures. In this paper, we provide adaptations to these measures and employ two aggregation schemes to create ranked lists of method and parameter choices for six unique datasets. Our findings demonstrate that an ensemble of validation measures, combined with ranking based on measures' dataset specific preferences, provides an objective way to select clustering methodologies, taking into account characteristic evaluation from each measure.
A reaction-diffusion model for relapsing-remitting multiple sclerosis with a treatment term
Authors: Romina Travaglini
Date: 2024-07-09 00:00:00

We present a mathematical study for the development of multiple sclerosis based on a reaction-diffusion system. The model describes interactions among different populations of human cells, motion of immune cells stimulated by cytokines, consumption of myelin sheath due to anomalously activated lymphocytes and its restoration by oligodendrocytes. Successively, we introduce a therapy term representing injection of low-dose IL-2 interleukine. A natural step is then to study the system, investigating the formation of spatial patterns by means of a Turing instability analysis of the problem. In particular, we get spatial patterns oscillating in time that may reproduce brain lesions characteristic of the early stage of the pathology, in both non-treatment and treatment scenarios.
Disk-stacking models are consistent with Fibonacci and non-Fibonacci structure in sunflowers
Authors: Jonathan Swinton
Date: 2024-07-08 00:00:00

This paper investigates a model of plant organ placement motivated by the appearance of large Fibonacci numbers in phyllotaxis, and provides the first large-scale empirical validation of this model. Specifically it evaluates the ability of Schwendener disk-stacking models to generate parastichy patterns seen in a large dataset of sunflower seedheads. We find that features of this data that the models can account for include a predominance of Fibonacci counts, usually in a pair of left and right counts on a single seedhead, a smaller but detectable frequency of Lucas and double Fibonacci numbers, a comparable frequency of Fibonacci numbers plus or minus one, and occurrences of pairs of roughly equal but non-Fibonacci counts in a `columnar' structure. A further observation in the dataset was an occasional lack of rotational symmetry in the parastichy spirals, and this paper demonstrates those in the model for the first time. Schwendener disk-stacking models allow Fibonacci structure by ensuring that a parameter of the model corresponding to the speed of plant growth is kept small enough. While many other models can exhibit Fibonacci structure, usually by specifying a rotation parameter to an extremely high precision, no other model has accounted for further, non-Fibonacci, features in the observed data. The Schwendener model produces these naturally in the region of parameter space just beyond where the Fibonacci structure breaks down, without any further parameter fitting. We also introduce stochasticity into the model and show that it while it can be responsible for the appearance of columnar structure, the disordered dynamics of the deterministic system near the critical region can also generate this structure.