Re-awakening the brain: Forcing transitions in disorders of consciousness by external < i > in silico < /i > perturbation
by Paulina Clara Dagnino, Anira Escrichs, Ane L ópez-González, Olivia Gosseries, Jitka Annen, Yonatan Sanz Perl, Morten L. Kringelbach, Steven Laureys, Gustavo Deco
A fundamental challenge in neuroscience is accurately defining brain states and predicting how and where to perturb the brain to force a transition. Here, we investigated resting-state fMRI data of patients suffering from disorders of consciousness (DoC) after coma (minimally conscious and unresponsive wakefulness states) and healthy controls. We applied model-free and model-based approaches to help elucidate the underlying brain mechanisms of patients with ...
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: Paulina Clara Dagnino Source Type: research
A systematic analysis of regression models for protein engineering
by Richard Michael, Jacob K æstel-Hansen, Peter Mørch Groth, Simon Bartels, Jesper Salomon, Pengfei Tian, Nikos S. Hatzakis, Wouter Boomsma
To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field topredict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance ca...
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: Richard Michael Source Type: research
A mathematical model for the role of dopamine-D2 self-regulation in the production of ultradian rhythms
by An Qi Zhang, Martin R. Ralph, Adam R. Stinchcombe
Many self-motivated and goal-directed behaviours display highly flexible, approximately 4 hour ultradian (shorter than a day) oscillations. Despite lacking direct correspondence to physical cycles in the environment, these ultradian rhythms may be involved in optimizing functional interactions with the environment and reflect intrinsic neural dynamics. Current evidence supports a role of mesostriatal dopamine (DA) in the expression and propagation of ultradian rhythmicity, however, the biochemical processes underpinning these oscillations remain to be identified. Here, ...
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: An Qi Zhang Source Type: research
Visual social information use in collective foraging
by David Mezey, Dominik Deffner, Ralf H. J. M. Kurvers, Pawel Romanczuk
Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanist...
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: David Mezey Source Type: research
Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment
by Sebastien Benzekry, Michalis Mastri, Chiara Nicol ò, John M. L. Ebos
Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predi...
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: Sebastien Benzekry Source Type: research
Epidemiological and health economic implications of symptom propagation in respiratory pathogens: A mathematical modelling investigation
ConclusionsOverall, the preferred intervention type depended on the combination of the strength of symptom propagation and uptake. Given the importance of determining robust public health responses, we highlight the need to gather further data on symptom propagation, with our modelling framework acting as a template for future analysis. (Source: PLoS Computational Biology)
Source: PLoS Computational Biology - May 3, 2024 Category: Biology Authors: Phoebe Asplin Source Type: research
The quality and complexity of pairwise maximum entropy models for large cortical populations
by Valdemar Karg ård Olsen, Jonathan R. Whitlock, Yasser Roudi
We investigate the ability of the pairwise maximum entropy (PME) model to describe the spiking activity of large populations of neurons recorded from the visual, auditory, motor, and somatosensory cortices. To quantify this performance, we use (1) Kullback-Leibler (KL) divergences, (2) the extent to which the pairwise model predicts third-order correlations, and (3) its ability to predict the probability that multiple neurons are simultaneously active. We compare these with the performance of a model with independent neurons and study the relationship between...
Source: PLoS Computational Biology - May 2, 2024 Category: Biology Authors: Valdemar Karg ård Olsen Source Type: research
Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample test
by Keiji Ota, Laurence T. Maloney
Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value –step by step–in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian comp...
Source: PLoS Computational Biology - May 1, 2024 Category: Biology Authors: Keiji Ota Source Type: research
Synergistic epistasis among cancer drivers can rescue early tumors from the accumulation of deleterious passengers
by Carla Alejandre, Jorge Calle-Espinosa, Jaime Iranzo
Epistasis among driver mutations is pervasive and explains relevant features of cancer, such as differential therapy response and convergence towards well-characterized molecular subtypes. Furthermore, a growing body of evidence suggests that tumor development could be hampered by the accumulation of slightly deleterious passenger mutations. In this work, we combined empirical epistasis networks, computer simulations, and mathematical models to explore how synergistic interactions among driver mutations affect cancer progression under the burden of slightly deleteriou...
Source: PLoS Computational Biology - April 30, 2024 Category: Biology Authors: Carla Alejandre Source Type: research
UNNT: A novel Utility for comparing Neural Net and Tree-based models
by Vineeth Gutta, Satish Ranganathan Ganakammal, Sara Jones, Matthew Beyers, Sunita Chandrasekaran
The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages ov...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Vineeth Gutta Source Type: research
Bayesian workflow for time-varying transmission in stratified compartmental infectious disease transmission models
We present a Bayesian workflow for such models, including four features: (1) an adjustment for incomplete case ascertainment, (2) an adequate sampling distribution of laboratory-confirmed cases, (3) a flexible, time-varying transmission rate, and (4) a stratification by age group. Within the workflow, we benchmarked the performance of various implementations of two of these features (2 and 3). For the second feature, we used SARS-CoV-2 data from the canton of Geneva (Switzerland) and found that a quasi-Poisson distribution is the most suitable sampling distribution for describing the overdispersion in the observed laborato...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Judith A. Bouman Source Type: research
Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains
by Christian Donner, Julian Bartram, Philipp Hornauer, Taehoon Kim, Damian Roqueiro, Andreas Hierlemann, Guillaume Obozinski, Manuel Schr öter
Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Christian Donner Source Type: research
Recurrent neural networks that learn multi-step visual routines with reinforcement learning
We report that networks learn elemental operations, such as contour grouping and visual search, and execute sequences of operat ions, solely based on the characteristics of the visual stimuli and the reward structure of a task. After training was completed, the activity of the units of the neural network elicited by behaviorally relevant image items was stronger than that elicited by irrelevant ones, just as has been observe d in the visual cortex of monkeys solving the same tasks. Relevant information that needed to be exchanged between subroutines was maintained as a focus of enhanced activity and passed on to the subseq...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Sami Mollard Source Type: research
Informing policy via dynamic models: Cholera in Haiti
by Jesse Wheeler, AnnaElaine Rosengart, Zhuoxun Jiang, Kevin Tan, Noah Treutle, Edward L. Ionides
Public health decisions must be made about when and how to implement interventions to control an infectious disease epidemic. These decisions should be informed by data on the epidemic as well as current understanding about the transmission dynamics. Such decisions can be posed as statistical questions about scientifically motivated dynamic models. Thus, we encounter the methodological task of building credible, data-informed decisions based on stochastic, partially observed, nonlinear dynamic models. This necessitates addres...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Jesse Wheeler Source Type: research
Wagers for work: Decomposing the costs of cognitive effort
by Sarah L. Master, Clayton E. Curtis, Peter Dayan
Some aspects of cognition are more taxing than others. Accordingly, many people will avoid cognitively demanding tasks in favor of simpler alternatives. Which components of these tasks are costly, and how much, remains unknown. Here, we use a novel task design in which subjects request wages for completing cognitive tasks and a computational modeling procedure that decomposes their wages into the costs driving them. Using working memory as a test case, our approach revealed that gating new information into memory and protecting against interference are costly. Critically,...
Source: PLoS Computational Biology - April 29, 2024 Category: Biology Authors: Sarah L. Master Source Type: research
