Michael Peter Milham

This study uses multimodal imaging and connectivity modeling to show that polysynaptic mechanisms are implicated in autism spectrum disorder, with reduced structure-function coupling in transmodal networks.

This study examines how socioeconomic status components and brain age relate to psychiatric symptoms in youth using RBA data from the Healthy Brain Network.

The paper uses variograms to show that function changes more rapidly in association cortex than in sensory and motor cortex, linked to intracortical myelin levels.

The paper introduces an open-access dataset combining EEG and fMRI data from naturalistic viewing tasks to study brain function and preprocessing methods.

This study uses real-world experience sampling to analyze how daily activities influence thought patterns, finding that socializing and environmental factors shape ongoing thought.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

The paper curates and provides analysis-ready diffusion MRI data for pediatric brain white-matter research, including automated quality control using a trained neural network.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.

Simulations and neuroimaging analyses argue that unreliable clinical and cognitive phenotypes, not only noisy fMRI, cap biomarker discovery; aggregating across raters or repeated measures beats enlarging samples.

The paper analyzes multi-scale functional brain networks in youths to identify developmental patterns of network re-organization and their cognitive implications.

This study identifies a structure-function substrate of cognitive imbalances in autism using multi-site neuroimaging data and dimensional analysis.

The paper introduces a framework to quantify and optimize measurement reliability for studying individual differences in neuroscience and clinical research.

This study identifies shared and distinct structural brain differences in children with autism and anxiety, using neuroimaging data from 82 participants.

This paper introduces a low-resource pipeline for estimating connectomes from MRI data, demonstrating its reliability across diverse datasets without manual intervention.

This study characterizes cortical thickness development in Japanese macaques using latent growth models and identifies how maternal diet and inflammation influence regional cortical maturation patterns.

The paper examines factors predicting successful MRI data acquisition in children with neurodevelopmental conditions after simulator training, finding age and sensorimotor atypicalities as key predictors.

The paper presents a task and models human decision-making in an information-selective symmetric reversal bandit task, finding participants use a belief state-based hybrid strategy.

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

This study compares the predictive power of brain structural and functional metrics for age, sex, and schizophrenia treatment outcomes using variance partitioning and permutation tests.

The paper combines connectome analysis and computational models to show autism has altered subcortico-cortical interactions linked to microcircuit and macroscale network anomalies.

The paper investigates sex differences in autism using resting-state fMRI data, finding significant effects in brain connectivity and replicating some findings in independent samples.

The paper identifies subtypes of school-aged children based on brain structure and environmental factors using Similarity Network Fusion, revealing distinct associations between environments and neurodevelopment.

This study compares two MRI sequences, MPRAGE and MPRAGE+PMC, finding that MPRAGE+PMC offers higher intra-sequence reliability but lower quality control metrics compared to MPRAGE.

The paper examines how concatenating fMRI data affects the reliability of functional connectomics, finding that concatenating multiple scans under the same state improves reliability compared to a single long scan.

The paper introduces PRIME-RE, a collaborative platform for sharing NHP neuroimaging resources, tools, and data.

The paper develops a transfer-learning U-Net model for brain extraction in non-human primates using human data, achieving better performance than traditional methods and popular MRI packages.

The paper introduces MindLogger, an open-source platform for creating mobile mental health assessments and interventions, demonstrating its use in a large-scale study.

This study examines the stability and similarity of the pediatric connectome across different states and tasks, finding correlations with social skills but not age.

This paper evaluates test-retest reliability of multilayer network measures in neuroimaging, identifying methodological factors affecting reliability and task-specific requirements.

This paper introduces a function-based method to align cortical organization across species, revealing evolutionary changes in brain regions associated with human cognition.