Ru Kong

The paper identifies a convergent brain circuit modulated by neuromodulation techniques that reduces autonomic arousal in various conditions.

The paper evaluates how different censoring strategies affect cortical parcellations and TMS targets in rs-fMRI, finding that lenient censoring yields better results than strict censoring.

This paper develops a tree-based algorithm to identify personalized TMS targets for depression and anxiety by optimizing functional connectivity and scalp proximity.

This study compares brain age models and direct models for predicting Alzheimer's Disease outcomes, finding that direct models perform better despite brain age models having larger training data.

The paper shows longer scans improve prediction accuracy in brain-wide association studies while reducing costs, with 30-minute scans being the most cost-effective.

The paper examines how stable and changing brain network features predict cognitive development in adolescents using rs-fMRI and cognitive data from the ABCD Study.

The paper introduces the Network Correspondence Toolbox (NCT) to evaluate spatial correspondence between novel neuroimaging results and existing brain atlases, enabling standardized reporting and comparison across studies.

This paper develops a geometrically constrained framework to map brain regions across species and shows geometry influences regional organization through patterning molecules.

This paper adapts meta-matching frameworks to translate phenotypic prediction models from large to small MRI datasets, showing significant performance improvements over classical methods.

The paper introduces multilayer meta-matching to improve phenotypic prediction in small datasets by translating models from multiple large datasets, outperforming classical methods and transfer learning.

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.

The paper estimates the whole-cortex excitation-inhibition ratio using fMRI and shows it declines during youth, with lower ratios linked to better cognitive performance in children.

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

The paper develops homotopic local-global parcellations of the human cerebral cortex using resting-state and task-fMRI, showing improved homogeneity and lateralization compared to existing parcellations.

This study compares the ability of anatomical, diffusion, and functional MRI to predict individual behavioral measures, finding that functional connectivity provides the best predictions, especially for cognition.

This study shows that proportional intracranial volume correction affects the predictive accuracy and generalizability of brain-behavior models across anatomical features and populations.

The paper develops individual-specific areal-level parcellations using a Bayesian model and shows they improve functional connectivity prediction of behavior compared to group-level parcellations.

This study develops a spatially-heterogeneous circuit model to simulate brain activity dynamics, showing that anatomical and functional gradients are essential for generating realistic static and dynamic functional connectivity patterns.

This study investigates the heritability of individualized cortical network topography using twin and family data, finding genetic influences on network size and spatial organization.

The paper uses functional connectivity MRI to map the detailed organization of the human cerebellum in two individuals, revealing multiple networks and hierarchical structures.

This study shows that global signal fluctuation and functional connectivity in resting-state fMRI decrease throughout the day, contrary to expectations based on circadian arousal patterns.

This study identifies convergent molecular, cellular, and neural signatures of major depressive disorder across multiple datasets and modalities.

This study compares deep neural networks and kernel regression in predicting behavior and demographics from resting-state functional connectivity data, finding comparable performance with kernel regression being more computationally efficient.

The paper identifies three latent components of psychopathology linked to whole-brain resting-state functional connectivity across healthy and psychiatric populations.

This study finds that global signal regression enhances the association between resting-state functional connectivity and behavioral measures in young healthy adults.

The paper improves behavioral prediction by removing common resting-state fMRI signals from individual-specific subspaces, resulting in an 11.7% average improvement across 58 behavioral measures.

This study introduces a hierarchical Bayesian model to estimate individual-specific cortical networks and shows that their topography can predict human behavior with moderate accuracy.

This paper introduces a gradient-weighted Markov Random Field model for cortical parcellation that combines local and global approaches, resulting in more homogeneous and biologically meaningful parcels.