Bayesian inference

Bayesian in situ point spread function modeling enables high-fidelity three-dimensional nanoscopy in deep tissue

This paper introduces Bayesian in situ PSF modeling to improve 3D nanoscopy in deep tissue by accounting for optical distortions and uncertainties in PSF estimation.

A clinically annotated proteomic and histological resource of 1,000 colorectal cancers

Deep proteomics of 997 treatment-naïve colorectal cancers defines six subgroups, splitting chromosomally unstable tumors into three and revealing a rare adverse subgroup, with H&E slide benchmarks.

A Bayesian approach to volumetric analysis in rare neurodegenerative disorders

This paper applies Bayesian hierarchical modeling to assess regional brain atrophy in rare neurodegenerative disorders, finding significant volume loss in specific brain regions across disease groups.

Multi-Trajectory Pseudotime Inference via Permutation Factorizations

Factorizing multiple monotone pseudotimes into one shared donor permutation plus cluster-specific warps (HiP-BIND) yields Alzheimer's donor orderings agreeing better with Braak, ADNC and CERAD staging than single-trajectory B-BIND.

An information-borrowing Bayesian method for inferring high-dimensional population-level brain connectivity from small samples

The paper introduces a Bayesian method for estimating population-level brain connectivity from small samples, showing improved accuracy over existing methods in simulations and real data.

Prion Protein Deficiency Results in Synaptic, Neural Network and Behavioral Alterations

Lifelong prion protein (PrP) loss in mice lowers excitatory synaptic proteins, makes network bursts in mature cortical cultures rarer and neuronal activity more correlated, and increases adult dimming-disk escapes.

Normative Modeling of Molecular-Enriched Functional Connectivity for Detecting Deviations from Healthy Brain Aging

This paper develops normative models to detect deviations in molecular-enriched functional connectivity associated with amyloid burden in healthy aging brains.

A Bayesian framework reveals heterogeneous and stochastic decision-making in NK cell cytotoxicity

This study develops a Bayesian framework to analyze NK cell cytotoxicity and identifies distinct mechanisms underlying variability in killing responses.

TCF25 emerges as a cytoplasmic regulator of GPRASP2 stability

TCF25 is mostly cytoplasmic with a perinuclear membrane-proximal pool, stabilises GPRASP2 against proteasomal degradation, and its C-terminal residues 604-664 are required for their association.

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Drone imagery from 356 flights in 19 Genomes to Fields maize environments; vegetation-index functional principal components complement genomic kernels, predicting yield of held-out hybrids at r up to 0.501.

SmartHisto: Bayesian Active Learning for Histology Images

SmartHisto reduces annotation effort for histopathology images using Bayesian active learning, achieving higher mean IoU than competing methods.

Two-State Effective Connectivity Predicts the Effect of Anterior Cingulate Cortex Excitatory / Inhibitory Balance on Posterior Cingulate Cortex

The paper develops a network-level measure combining MRI spectroscopy ratios and effective connectivity to show that Glx/GABA ratio shapes posterior cingulate cortex connectivity.

Seeing Through Touch: Visual Reliability Shapes Tactile Guidance in 360° Virtual Reality

This study investigates how visual and tactile cues are combined in 360° virtual reality, finding that degraded vision increases reliance on tactile guidance and alters multisensory integration.

Functional brain network correlates of pubertal timing and depressive symptoms in preadolescence

This study examines how pubertal timing relates to resting-state functional connectivity in preadolescents and its potential link to depressive symptoms.

Kalman-filter Force Inference: an estimation framework for cellular forces from temporal evolution of epithelial morphogenesis

This paper introduces Kalman-filter Force Inference (KFI) to estimate cellular forces from time-lapse imaging of epithelial deformation, without assuming static force balance.

Uncertainty-aware quantitative analysis of high-throughput live cell migration data

The paper introduces cellmig, a Bayesian tool for quantifying cell migration velocity with uncertainty, improving reproducibility and biological insight in high-throughput assays.

Bayesian-enhanced closed-loop optimization of ultrasound protocols for targeted and precise neuromodulation

This paper develops BEACUN, a Bayesian-enhanced adaptive control method for optimizing LIFU neuromodulation protocols in rats, achieving faster convergence than conventional methods.

A Hierarchical Bayesian Agent Based Model for Binary Spatio-Temporal Spread: Theory, PDE Scaling Limit, and an Application to Predator Prey Cycles

The paper develops a hierarchical Bayesian agent-based model for binary spatio-temporal data and shows its scaling to a PDE, applying it to predator-prey cycles.

Bayesian generative modeling reveals a multi-modal hierarchical architecture in the mouse functional connectome

The paper uses Bayesian generative modeling to show the mouse brain's functional connectome has multiple co-dominant organizational configurations rather than a single structure.

High frequency spike inference with particle Gibbs sampling

This paper introduces a Bayesian method using particle Gibbs sampling to infer high-frequency neuronal spikes and quantify their uncertainty, showing improved performance on benchmark datasets.

Brain dynamics of attentional, default-mode and limbic networks are disrupted at rest in Post-COVID-19 Syndrome

This paper identifies altered dynamic brain states in Post-COVID-19 Syndrome using resting-state fMRI, showing reduced attentional states and increased limbic-DMN states linked to cognitive impairment and inflammation.

Hierarchical Bayesian Modelling Improves Microstructural Parameter Mapping in Diffusion and Exchange MRI Data

This paper introduces a hierarchical Bayesian modelling approach for diffusion and exchange MRI data, showing improved accuracy and noise resilience compared to traditional least-squares methods.

Nonergodicity and Simpson's paradox in neurocognitive dynamics of cognitive control

The paper investigates nonergodicity and Simpson's paradox in cognitive control dynamics using brain imaging and behavioral data from over 4,000 children, revealing brain-behavior associations that reverse between between-subjects and within-subjects analyses.

AutoNeuro: An Open-Source fMRI Toolbox for Real-Time Neuroadaptive Task Design

AutoNeuro enables real-time fMRI with adaptive task design by integrating acquisition, preprocessing, and Bayesian optimization for closed-loop neuroimaging.

Identity-state expression and switching in co-conscious dissociative identity disorder: a non-traumatic proof-of-concept fMRI study

This study uses fMRI to investigate neural differences between identity-states in DID without trauma provocation, finding distinct brain activity patterns during state switching.

Convergent and selective representations of pain, appetitive processes, aversive processes, and cognitive control in the insula

The paper identifies convergent and selective functional zones in the insula using fMRI data, revealing a hierarchical convergence architecture across pain, appetitive, aversive, and cognitive control domains.

AlphaFold as a Prior: Experimental StructureDetermination Conditioned on a Pretrained Neural Network

ROCKET refines AlphaFold2 predictions against crystallographic, cryo-EM and cryo-ET data by gradient descent on MSA cluster profiles, capturing loop flips and domain shifts without retraining.

A Neural Population Code for Value in Human Orbitofrontal Cortex.

The paper investigates the neural code for value in the human orbitofrontal cortex and finds that a probabilistic, non-linear population code represents both value and its uncertainty.

Testing hypotheses about correlations between brain activation patterns

This paper develops a method to test hypotheses about the correlation between brain activation patterns in fMRI studies, showing that a subject-wise bootstrap on the maximum-likelihood group estimate performs best.

An alternative patterning mechanism for vertebrate tooth complexity

This study identifies an alternative epithelial patterning mechanism for vertebrate tooth complexity in squamates, showing that cusp formation does not rely on enamel knots but on a broad signaling field.