Autism

Deep learning super-resolution of paediatric ultra-low-field MRI without paired high-field scans

Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI limit its use to high-income settings. Ultra-low-field MRI scanners offer a more affordable and energy-efficient alternative, but their reduced resolution and signal-to-noise ratio restrict research and clinical utility, prompting the nee...

Asymmetric neural dynamics of visuospatial attention in autism spectrum disorder

Background: Selective attention enables the prioritization of behaviorally relevant information in complex sensory environments. Despite substantial evidence for altered attention in autism spectrum disorder (ASD), the neurophysiological mechanisms underlying these differences remain poorly understood. Methods: Here, we integrate high-density electroencephalography (EEG), pupillometry, and behavio...

Learning residue-level context for modeling protein-protein interactions

Protein language models (PLMs) enable prediction of protein properties by learning residue-level features from sequence, yet most PLM-based approaches...

An interpretable machine learning framework for dog breed inference and ancestry decomposition

The over 300 currently recognized breeds of domesticated dogs are the culmination of centuries of intense artificial selection and recurrent populatio...

High-dimensional Characterization of Genome-Environment Fitness Landscapes in Klebsiella pneumoniae

Background Bacterial fitness is shaped by interactions between genome variation and environmental context, yet how these interactions determine its pr...

Identification of Heterogeneous Cortical Thickness Patterns Associated with Prenatal Gestational Diabetes Exposure: A SuStaIn-Based Subtyping Study

Importance: Prenatal exposure to gestational diabetes mellitus (GDM) has been associated with adverse metabolic, neurodevelopmental, and psychiatric o...

Protracted prediction: Neurodevelopment of reward processing in the adolescent cerebellum.

Adolescence is characterized by heightened reward sensitivity, novelty seeking, and risky decision-making. Prevailing neurodevelopmental frameworks ty...

Automated Segmentation of Cerebral Arteries on Three-Dimensional Rotational Angiography Using nnUNet v2: Prospective Validation with Quantitative Metrics and Expert Qualitative Assessment

Background: Three-dimensional visualization and quantitative analysis of cerebral arteries on 3DRA are central to endovascular treatment planning, dev...

FM-fMRI: Event Conditioned Flow Matching for Rest-to-Task fMRI Time-Series Synthesis

Task-based fMRI provides a direct readout of task-evoked neural dynamics, but it is expensive and difficult to acquire at scale, motivating rest-to-ta...

Multimodal MRI and Machine Learning Uncovers Distinct Progression Patterns in Friedreich Ataxia

Background Friedreich ataxia (FRDA) is a rare neurodegenerative disorder with substantial heterogeneity in clinical presentation and progression, comp...

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives

AI-driven chatbots have been utilized in healthcare to automate administrative tasks, improve patient education, and expand access to medical informat...

OpenSplice: the impact of half a million mutations on the alternative splicing of 600 human exons

Alternative splicing of mRNA precursors is an important step in gene regulation and a major mechanism by which genetic variants cause human disease. H...

Estimating bone marrow adiposity from head MRI and identifying its genetic architecture

Bone marrow adiposity changes radically through the lifespan, but this phenomenon is poorly characterised and understood in humans. Large datasets of ...

Genetic architecture of high-dimensional liver radiomic phenotypes and their role in common metabolic diseases

The liver plays a central role in systemic metabolism, yet large-scale genetic studies of quantitative liver imaging phenotypes remain limited. Here, ...

Synthetic Data Alone is Enough? Rethinking Data Scarcity in Pediatric Rare Disease Recognition

Children with rare genetic diseases often exhibit distinctive facial phenotypes, yet developing computer vision systems for early diagnosis remains ch...

Membrane proteomics of the Drosophila circadian neural network

Circadian behaviors are controlled by dedicated brain pacemaker neurons, whose activity oscillate during the day and the night. The Drosophila brain c...

Machine learning methodology using a masked neural network for robust genetic risk score calculation from noisy and missing data

Purpose: Genetic risk scores (GRSs) are summaries of genetic data that can improve prediction of disease risk and progression. GRSs are increasing ava...

Clonal haematopoiesis without identified genetic drivers: insights from analyses of 407,512 individuals

Clonal haematopoiesis (CH) becomes ubiquitous as humans age. The role of somatic driver mutations in its development has been studied widely, but litt...

Comprehensive interaction profiling and machine learning prediction of bacteriophage infectivity across clinically diverse Pseudomonas aeruginosa

The rise of antibiotic-resistant bacterial infections has driven renewed interest in bacteriophage therapy, where viruses that specifically kill bacte...

Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models

Functional magnetic resonance imaging (fMRI) data are inherently complex, characterized by high dimensionality, intricate inter-regional dependencies,...