Daniel C. Alexander

The paper links infant gut microbiome composition and behavioral responses to maternal unpredictability, showing how microbial metabolism influences behavioral outcomes.

The paper examines how different gene dosage disorders affect the human brain using multimodal neuroimaging, revealing diverse cortical changes and a shared vulnerability axis aligned with psychiatric disorders.

The paper identifies a tau-first subtype of Alzheimer's disease through analysis of post mortem and in vivo data, showing higher soluble TREM2 levels in tau-first cases.

This paper introduces a Bayesian segmentation algorithm for thalamic nuclei using diffusion MRI and an improved histological atlas, showing improved accuracy in identifying nuclei and detecting Alzheimer's disease involvement.

pySuStaIn provides a Python implementation of the SuStaIn algorithm to infer disease subtypes and stages from cross-sectional data.

This paper shows that training data distribution affects the accuracy and precision of microstructural parameter estimates using supervised machine learning in quantitative MRI.

This study compares two deep learning strategies for qMRI signal model fitting, finding that strategy 2 is more practical but may yield less accurate parameter estimation.

This paper predicts Alzheimer's disease progression using a minimal recurrent neural network, showing better performance than baseline methods when handling missing data with model-based imputation.

This paper introduces a unified signal readout approach for multi-parametric spinal cord MRI, combined with MP-PCA denoising, improving the quality and consistency of microstructural metrics.

This paper introduces a Bayesian multi-task learning model for longitudinal imaging biomarkers, showing improved trajectory inference and disease effect detection in Alzheimer's disease data.

The paper applies multivariate mechanistic models to a clinical trial in multiple sclerosis to investigate pathways linking simvastatin to clinical outcomes.

The paper investigates microstructural cortex development in neonates with congenital heart disease and finds impaired oxygen delivery correlates with abnormal microstructure.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

This paper introduces KDEMM for disease progression modeling, showing it outperforms GMMs in simulating cognitive data and aligns better with clinical phenotypes in Alzheimer's subtypes.

This study identifies the sequence of grey matter atrophy progression in multiple sclerosis using longitudinal MRI data and shows its association with disability accumulation in relapsing-remitting MS.

This study uses an event-based model to analyze cognitive decline in adults with Down syndrome, showing similar patterns to Alzheimer's disease and identifying useful measures for tracking decline stages.

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.

This study shows that deep grey matter volume loss predicts faster disability progression in multiple sclerosis, with specific patterns in different disease subtypes.

The paper describes an automated processing and quality control pipeline for brain imaging data from the first 10,000 UK Biobank participants.