Neurology

AmyloDeep: pLM-based ensemble model for predicting amyloid propensity from the amino acid sequence

Amyloids are predominantly β-sheet-rich, stable protein structures that can maintain their presence in the human body for multiple years. Amyloid protein aggregates contribute to the development of multiple neurodegenerative diseases, such as Alzheimer’s, Parkinson’s, and Huntington’s, and are involved in different vital functions, such as memory formation and immune system function. Here, we used...

A Shared Neural Marker Predicts Creative Performance Across Distinct Problem-Solving Tasks

Creativity is essential for innovation, yet the brain mechanisms supporting its moment-to-moment variability remain unclear. We hypothesize that creativity depends on dynamic fluctuations in neural flexibility, which determine the potential to generate creative solutions. Here, we identify a shared neural marker of “creativity potential” that predicts upcoming performance across distinct problem-s...

Prompt-Driven Target Identification: A Multi-Omics and Network Biology Case Study of PARP1 Using Swalife PromptStudio

Artificial intelligence–assisted scientific prompting is reshaping how biological targets can be rapidly identified and contextualized. In this work, ...

Spinal Circuit Mechanisms Constrain Therapeutic Windows for ALS Intervention

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disease characterized by progressive breakdown of neural circuits which leads to moto...

Distinct brain mechanisms support trust violations, belief integration, and bias in human-AI teams

This study provides an integrated electrophysiological and behavioral account of the neuro-cognitive markers underlying trust evolution during human i...

A lightweight, physics-based, sensor-fusion filter for real-time EEG denoising and improved downstream AI classification

Physiological time-series data, like electroencephalography (EEG), are vulnerable to motion, ocular, and muscle artifacts that hinder real-time infere...

Separable neural population representations are constructed from mixed single neuron selectivity in the mouse early visual system

Both sensory and non-sensory brain regions receive mixed inputs from single neurons which require decomposition and integration before proceeding thro...

Disease-specific tau polymorphs define unique protein interaction networks across proteinopathies

Tau protein aggregates exhibit distinct conformations across tauopathies, but their disease-specific protein interactions remain poorly understood. He...

Claustrum volume in humans – lifespan trajectory and effect of age, hemisphere, and sex

The human claustrum is a bilateral, thin, irregularly shaped gray matter structure located between the striatum and insula. While previous research de...

Enhancing fMRI decoded neurofeedback with co-adaptive training: simulation and proof-of-principle evidence

A significant challenge for neurofeedback training research and related clinical applications, is participants’ difficulty in learning to induce speci...

Interpretable Machine Learning Identifies an Emergent Absence Seizure Mechanism

Absence epilepsy is a generalized seizure disorder marked by widespread spike-and-wave oscillations and sudden lapses in consciousness. Although no co...

Variational autoencoder for interpretable seizure onset phases detection

In this study, we describe a deep learning framework for automated seizure annotation in stereo electroencephalography (SEEG) data of patients with fo...

Fast segmentation with the NextBrain histological atlas

Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histologica...

Shared texture-like representations, not global form, underlie deep neural network alignment with human visual processing

Deep neural networks (DNNs) are a leading computational framework for understanding neural visual processing. A standard approach for evaluating their...

Frequency-Aware Interpretable Deep Learning Framework for Alzheimer’s Disease Classification Using rs-fMRI

Gaining insight into the spectral and temporal alterations in brain connectivity associated with Alzheimer’s disease (AD) may offer pathways toward mo...

High-level Prediction of Continuous Speech During Mind-Wandering

Abundant evidence shows that when listening to speech or reading text, we continuously make predictions about upcoming words. Does this process stop w...

Deep Generative Optimization of mRNA Codon Sequences for Enhanced mRNA Translation and Therapeutic Efficacy

Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a...

Next generation neural mass model with dopamine modulation mediated by D1-type receptors

Neuromodulation is a complex process in which chemical substances modulate brain activity, allowing its rich repertoire of behaviors. Among these subs...

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

Alzheimer’s disease and related dementias (ADRD) involve overlapping neurodegenerative and vascular pathologies—such as amyloid-β (Aβ), tau, cerebral ...

Using Diffusion Transformers to Generate Synthetic Diffusion Scalar Maps for Data Augmentation

Generation of high-quality synthetic brain MRI data could be beneficial for advancing neuroimaging research, particularly when access to large-scale, ...