Neurology

Model-based EEG phenotyping uncovers distinct neurocomputational mechanisms underlying learning impairments across psychopathologies

Major depressive disorder (MDD), bipolar disorder (BP), and schizophrenia (SCZ) involve learning impairments with poorly understood mechanisms. Understanding both the similarities and differences in these mechanisms is important to guide the development of new, targeted interventions. 255 participants diagnosed with MDD (n=54), BP (n=47), SCZ (n=67) or without any diagnoses (CTRL; n=87) performed ...

Biophysical validation of explainable AI for functional brain imaging: bridging cellular mechanisms and network dynamics

Deep neural networks have revolutionized functional neuroimaging analysis but remain “black boxes,” concealing which brain mechanisms and regions drive their predictions—a critical limitation for clinical neuroscience. Here we develop and validate an explainable AI (xAI) framework to test whether feature attribution techniques can reliably recover brain regions affected by excitation/inhibition (E...

Generative cerebral vasculature visualization using spatial transcriptomic data

The brain is sustained by an intricate vascular network that provides a continuous supply of nutrients and oxygen essential for its function. Understa...

An Application for Automated Drosophila Locomotor Assay with Integrated Device Design and Computer Vision Tracking

Drosophila has long served as a powerful model for investigating locomotor behavior, and geotaxis assays have generated valuable insights into genetic...

PyDecNef: An open-source framework for fMRI-based decoded neurofeedback

Real time fMRI research has suffered from inaccessible analysis pipelines, hindering collaboration and reproducibility. Here we present PyDecNef, a Py...

A Neurodevelopment-Inspired Deep Spiking Neural Network for Auditory Spatial Attention Detection using Single Trial EEG

This study presents a deep probabilistic spiking neural network designed to extract discriminative spatiotemporal features from EEG signals associated...

Comparison of Brain Age Algorithms in Bipolar Disorder

Advances in computational methods have accelerated the application of machine learning to analyze large complex biological data. By applying machine l...

“Rapid prototyping of flexible biodegradable ECoG arrays for high-resolution cortical mapping and real-time seizure classification ”

Neural interfaces are essential tools for diagnosing and managing neurological disorders, yet conventional electrocorticography (ECoG) devices are lim...

An EEG-based Multi-Scale Hybrid Attention and Squeeze Network for objective taste assessment

Taste perception is central to flavor experiences. Electroencephalography (EEG) signals carry rich information about taste perception. Because these n...

Data-Driven Symbolic Higher-Order Epistasis Discovery with Kolmogorov-Arnold Networks

Many human diseases are polygenic conditions that arise from a complex interplay of interactions between multiple genes at different loci, but current...

Deep Learning-Augmented Stimulated Raman Imaging for Cell-Type-Specific Metabolic Profiling in Live Neuronal Co-Cultures

Neuronal metabolism is fundamental to brain functions and diseases, yet its spatial and temporal dynamics and interactions remain poorly understood. H...

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging

Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain difference...

ProCyon: A multimodal foundation model for protein phenotypes

Characterizing human proteins remains a major challenge: approximately 29% of human proteins lack experimentally validated functions and even well-ann...

Multi-cohort, cross-species urinary proteomics reveals signatures of LRRK2 dysfunction in Parkinson’s disease

Pathogenic mutations in Leucine-rich repeat kinase 2 (LRRK2) are the predominant genetic cause of Parkinson’s disease (PD) and often increase kinase a...

Multiplex imaging combined to machine learning enable automated profiling of cortical malformations: applications in tuberous sclerosis complex

Malformations of cortical development such as tuberous sclerosis complex arise within a heterogeneous cellular landscape that conventional histopathol...

Topological basal ganglia model with dopamine-modulated spike-timing-dependent plasticity reproduces reinforcement learning, discriminatory learning, and neuropsychiatric disorders

The basal ganglia (BG) are central to action selection and reinforcement learning, yet how the topological organization of the BG circuit with dopamin...

MORPH2DIAG: Automated Structural MRI Preprocessing and Tissue Segmentation for Interpretable Machine and Deep Learning-Based Neuroanatomical Classification

Structural MRI provides a noninvasive window into brain morphology, yet the reproducibility and interpretability of morphometric analyses remain limit...

Non-decision time-informed collapsing threshold diffusion model: A joint modeling framework with identifiable time-dependent parameters

Over the past sixty years, evidence accumulation models have emerged as a dominant framework for explaining the neural and behavioral aspects of the p...

Increased levels of HAPLN2, which anchors dense extracellular matrix, in the hippocampus of APOE4 targeted replacement mice

Hyaluronan and proteoglycan link protein 2 (HAPLN2) / Brain link protein-1 (Bral1) is important for the binding of chondroitin sulfate proteoglycans (...

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on...