dimensionality reduction

Broadband electronic resonance coherent anti-Stokes/Stokes Raman scattering microscopy

This paper introduces BER-CARS/CSRS microscopy, which enables highly sensitive, label-free imaging of endogenous chromophores with broad spectral coverage, applied to living cells and tissues.

A cortical gradient of distance to criticality governs large-scale resting-state fMRI dynamics

The paper shows that cortical resting-state fMRI dynamics are organized along a gradient of distance to criticality, with consistent patterns across subjects.

The neural correlates of parallel and serial search

This study investigates the neural correlates of parallel and serial visual search modes using EEG, showing distinct neural patterns and attentional dynamics under identical visual conditions.

From Perception to Appraisal: Brain Responses to Natural and Built Features in Urban Environments

This study examines how urban and natural features in environments affect subjective experience and neural responses using EEG, finding that greenery correlates with positive perception and specific brain activity patterns.

Pre-Training for Large-Scale Functional Connectome Fingerprinting Supports Generalization and Transfer Learning in Functional Neuroimaging

The paper pre-trains a neural network on functional connectome data to enable generalization and transfer learning in fMRI, achieving high recognition accuracy across datasets and scan durations.

Ultrasensitive Detection of Alpha-Synuclein Oligomers in Human Plasma Using Optimized Nano-QuIC

This study develops an optimized Nano-QuIC platform to detect alpha-synuclein oligomers in human plasma with high sensitivity for early Parkinson's disease diagnosis.

CellColoc: A modular, open-source workflow for cell colocalization, segmentation, and feature extraction in microscopy images

Seeing clearly with CLARI-O: a window into cellular architecture, interactions, and morphology of organoid models.

This paper introduces CLARI-O, a tissue-clearing technique for 3D visualization of cortical organoids and assembloids, enabling structural and functional analysis of cellular interactions and integration in vitro and in vivo.

Brain folding as a Fourier series yields a developmental clock

This paper uses spherical harmonic expansions to model fetal brain development, showing that truncating the series at different harmonic degrees reveals developmental stages and predicts gestational age.

DCGAN-Based Synthetic MRI Augmentation for Data-centric Brain Tumor Segmentation

This paper uses DCGAN to generate synthetic MRI images for brain tumor segmentation, improving Dice coefficient and IoU metrics compared to a baseline model.

Spaceland: Histology-Guided Reconstruction of High-Resolution Whole-Organ 3D Molecular Atlases from Sparse Spatial Transcriptomics

Spaceland reconstructs high-resolution 3D molecular atlases from sparse spatial transcriptomics and histology, improving resolution and accuracy across multiple biological contexts.

A surface-intrinsic framework for topology-preserving hippocampal alignment and precision morphometry

This paper introduces a surface-intrinsic framework for hippocampal unfolding that improves alignment accuracy and morphology preservation compared to volumetric methods.

Individualized surface parcellation enhances characterization of resting-state brain dynamics and their alterations in schizophrenia

This study compares parcellation strategies in schizophrenia rs-fMRI, finding that individualized parcellation better captures dynamic brain dynamics and symptom associations.

Pseudotime graph diffusion for post hoc visualization of inferred single-cell trajectories

PGD improves visualization of single-cell trajectories by smoothing cell-level features along pseudotime, enhancing continuity and structure in complex biological processes.

Noise Optimization of Basic Signal Component Extraction for Cryogenic and On-Scalp Magnetoencephalography (MEG)

This paper introduces a novel preprocessing method for MEG data using Fosters inverse with signal space separation to improve noise reduction and source localization.

Multi-Source Neural Activity Indices for EEG/MEG Localization: A Two-Stage Spatial Filtering Framework and Extension to MNE-Python

This paper introduces a two-stage spatial filtering framework for EEG/MEG source localization, extending MNE-Python with a novel family of multi-source neural activity indices that do not require target source covariance matrix knowledge.

Neural manifold connectomics reveals multiregime functional connectivity

The paper shows that using low-dimensional temporal subspaces from fMRI data reveals distinct functional connectivity regimes that depend on cognitive state and individual identity.

Developmental tuning of functional manifold dimensionality across the human brain

The paper estimates intrinsic dimensionality in fMRI data across development, finding that task-relevant brain regions have higher-dimensional activity in adults, while infants show no such modulation.

Multiscale Symbolic Morpho-Barcoding Reveals Region-Specific and Scale-Dependent Neuronal Organization

This paper introduces MMB, a framework that encodes neuronal morphology into symbolic representations, revealing region-specific and scale-dependent principles of neuronal organization across the brain.

Three-Dimensional Photoacoustic Tomography with Ultrasound Localization Priors

This paper introduces 3D-PAULMprior, a method that improves 3D photoacoustic tomography by integrating ultrasound localization priors, enhancing resolution and accuracy in vascular imaging and oxygen saturation estimation.

A low-variance subspace underlies individual differences in resting state fMRI

This study identifies a low-dimensional subspace in resting state fMRI that captures reliable individual differences and can accurately identify participants.

AI segmentation requires accounting for brain size to maintain performance on developmental MRI cohorts

The paper evaluates SynthSeg's performance on developmental MRI scans and finds that rescaling to adult brain sizes improves segmentation accuracy in infants and children.

On the characterization and interpretation of phenotypic ellipse geometry, with examples from Canis and sigmodontine rodents.

This paper examines phenotypic ellipse geometry in Canis and sigmodontine rodents, showing that Vrel fails to distinguish between different geometric differences and advocating the use of three parameters to better characterize ellipse geometry.

Evolutionary adaptation proceeds through a small number of phenotypic modules

The paper shows that fitness variation across drug environments in yeast is organized by a low-dimensional phenotypic structure, revealing modular phenotypic modules that constrain adaptive mutations.

Whole human organ clearing and multimodal mapping

This paper introduces mCleLight, a method for clearing, labeling, and imaging whole human tissues, enabling multiscale analysis of organ architecture.

HiC2Self: self-supervised denoising for bulk and single-cell Hi-C contact maps

HiC2Self denoises Hi-C contact maps using self-supervised learning, reconstructing 3D chromatin structures from low-coverage data across bulk and single-cell samples.

Deep Learning Based Cross-Modality Histological Brain Section Registration in Multiple Species Using Synthetic Images

The paper develops a deep learning framework for cross-modality histological brain section registration across species using synthetic images, achieving accuracy comparable to alternative methods.

BioPhasor: Decoding Cellular State Tensors from Multi-Omics Phasor Dynamics for Quantum Ready Systems Biology

The paper introduces BioPhasor, a framework that models cellular states using phasor dynamics and multi-omics data, showing improved performance in cell-cycle and circadian analysis.

SpatialJEPA: JEPA-inspired graph-context distillation for spatially aware multiomics integration

SpatialJEPA transfers spatial context from spatial multiomics data to dissociated non-spatial data through graph-context distillation, improving alignment and recovery of spatially organized programs in mouse brain multiomics.

Learning Minimal Gene Programs for Disease-Aligned Representations

This paper introduces SLMC to select minimal gene programs for disease-aligned representations in single-cell RNA-seq data, finding greedy methods outperform LASSO with small gene budgets.