Zaixu Cui

The paper identifies two distinct ADHD biotypes using brain imaging and transcriptomic data, showing different developmental, molecular, and treatment responses.

TLS-Sim provides a toolkit for individualized transcranial light stimulation modeling using deep-learning-accelerated Monte Carlo simulations to improve efficiency and accuracy.

The paper quantifies spatiotemporal variability in functional parcellation and connectivity across development, finding distinct developmental trajectories and associations with brain structure and cognition.

This paper identifies a tripartite organization of the human ventromedial prefrontal cortex using multimodal analyses and shows its reproducibility and cross-species correspondence.

The paper identifies a spatiotemporal continuum of structural connectivity development in youth, showing a shift from sensorimotor to association connectivity around age 15 and its links to cognition and psychopathology.

This paper introduces BrainRNN, a brain-like recurrent neural network architecture, and shows that structural constraints lead to functional organization similar to the human cortex.

This study identifies subtypes of functional connectivity development in youth using fMRI data, revealing spatial patterns linked to cognitive performance and gene expression.

This study examines how spatiotemporal brain activity propagations change from childhood to early adulthood, finding that certain propagation patterns become more prevalent with age and correlate with cognitive performance.

This study identifies structural connectivity deviations in ADHD youths as biomarkers predicting symptom progression and treatment response.

The paper maps personalized functional topography in the neonatal human brain using neuroimaging data and links it to neurocognitive outcomes in early childhood.

This study investigates how transcranial light stimulation affects the neurovascular unit in the human brain, showing increased cerebral blood flow and reduced cortical excitability.

The paper identifies robust sex differences in personalized functional brain network topography in youth, linked to X-linked genes and brain regions involved in psychiatric disorders.

This paper introduces a self-supervised deep learning framework to compute individualized multi-scale hierarchical functional networks from fMRI data and shows their association with biological phenotypes.

The paper identifies a connectional axis of functional connectivity variability across the human connectome, showing its structural correlates, developmental changes, and link to cognitive performance.

The paper analyzes functional and structural MRI data from 33,250 individuals to map lifespan changes in the functional connectome, identifying key developmental stages and differences in neurological disorders.

This study uses an ontology-guided machine learning approach to identify core functional brain networks for cognition, finding superior predictive performance for cognitive tasks compared to traditional methods.

This paper develops a method to construct morphological white matter networks from MRI data, showing they are reliable, heritable, and predictive of various brain features and diseases.

The paper investigates how an individual's exposome influences functional brain network organization and cognitive functioning in youth, finding that exposome factors predict cognitive performance better than neuroimaging data.

This study identifies two distinct ADHD biotypes based on brain morphology, showing different treatment responses and genetic correlates.

The paper uses graph neural networks to predict functional connectivity from structural connectivity in human brain networks, finding strong group-common structure-function coupling with some individual-specific effects.

The paper shows that the frontoparietal control network's topographic organization supports flexible behavior by switching between unified and segregated functional states based on memory needs.

This paper develops a self-supervised deep learning method to compute personalized brain functional networks from fMRI data, showing it can identify canonical networks and predict individual differences in behavior and schizophrenia status.

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

This study links individual mobility patterns to mood, sleep, and brain connectivity by identifying unique mobility footprints.

The paper examines how individual differences in functional brain network topography relate to psychopathology dimensions in youth using machine learning and fMRI data.

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

This study examines how cerebral blood flow and fMRI fluctuations are coupled in youth, finding age-related decreases, sex differences, and links to executive function.

This study uses pharmacological fMRI to show a reduction in the excitation:inhibition ratio in association cortex during adolescence, linked to psychopathology.

The paper develops a theory of efficient coding in brain networks and shows that structural connectivity balances information fidelity and compression, predicting behavior and explaining biological organization.

This study identifies altered brain network dynamics and associated transcriptomic signatures in autism spectrum disorder using resting-state fMRI and postmortem gene expression data from the ABIDE dataset.