James Christopher S. Pang

This study shows that the development of long-range cortical connections leads to the emergence of hierarchical functional modes in the human cortex, shifting from geometrically driven patterns to sensory-association organization.

This paper develops a geometric model of cortical connectomes that outperforms existing approaches in reproducing topological and topographical properties across multiple species.

The paper tests if cortical geometry encodes sensory function by modeling multisensory integration from geometric eigenmodes, finding they align with sensory domains but require additional modes for full hierarchy.

This paper develops a computational framework to model how regional cortical heterogeneities influence macroscopic wave dynamics in human and non-human primate brains, showing that myelin and excitation-inhibition balance significantly affect wave speed.

The paper compares 10 functional coupling measures in rs-fMRI to assess their motion sensitivity and predictive utility for behavioral outcomes.

This paper introduces a framework for geometry-constrained coupling between neural manifolds, showing how inter-manifold interactions reorganize dynamics and produce critical transitions in brain activity.

This paper introduces a chromatic dynamic causal model that links structural valency to spectral properties of neural fluctuations, showing it outperforms standard models and reveals conserved valency-exponent mappings across species.

This paper introduces a laterality index to quantify hemispheric asymmetry in cortical maps using geometric eigenmodes, validated on fMRI and tau-PET data from Alzheimer's disease patients.

This study evaluates the accuracy of reconstructing cortical functional maps using subject-specific geometric and connectome eigenmodes, finding that spatially smooth eigenmodes best explain functional data.

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

This paper introduces eigenstrapping, a method to generate surrogate brain maps that preserve spatial autocorrelation while breaking map correlations, improving false positive control in brain map analysis.

This study identifies and addresses spurious correlations in surface-based fMRI data caused by uneven vertex spacing in processing pipelines.

This study evaluates rs-fMRI denoising pipelines for motion correction and behavioural prediction, finding no single method consistently excels at both tasks across datasets.

The paper responds to criticisms of a previous study showing that geometric eigenmodes of the human cortex better reconstruct fMRI activity maps than connectome-derived eigenmodes.

This study examines how geometric eigenmodes of the brain change over adolescence and their links to mental health and wellbeing.

This paper introduces mode-based morphometry (MBM), a multiscale method for analyzing neuroanatomical variability, showing it is as valid or more reliable than traditional techniques for cortical thickness maps.

The paper addresses concerns about eigenmode reconstructions in a previous study, demonstrating how specificity is achieved through appropriate benchmarks.

This paper shows that brain function can be understood through geometric resonant modes rather than connectivity, revealing brain-wide dynamics in task-evoked activity.

This study evaluates how different diffusion MRI processing pipelines affect the identification of connectome hubs in humans, finding significant variability in hub location and connectivity based on processing choices.

This study shows that human brain dynamics differ from chimpanzees in dynamic range distribution, supporting faster integration in transmodal systems.

This paper characterizes individualized cortical shape asymmetries using MRI data, finding they are more informative for individual identification than size-based measures.

The paper develops a 3D hemodynamic model to predict BOLD responses across cortical depth, showing it matches experimental data with fewer assumptions than prior 2D models.

This study investigates stochastic synchronization in the human connectome, showing that noise can enhance synchronization in neural networks under specific conditions.