Kevin M. Aquino

This paper establishes a method to non-invasively study alpha oscillations using simultaneous EEG and layer-fMRI, showing depth-dependent correlations between alpha power and BOLD signals.

The paper examines white matter volume deviations in psychiatric disorders using normative modeling, finding heterogeneous patterns with some aggregation in autism and schizophrenia.

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

This paper systematically compares interpretable features of fMRI dynamics to identify effective signatures for neuropsychiatric disorder classification.

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 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 uses in-silico perturbation of a whole-brain model to identify critical brain areas involved in schizophrenia by analyzing resting-state fMRI data.

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

This study develops a model to capture wiring cost and topological complexity in human brain connectivity but finds models struggle to capture spatial embedding.

This paper develops a neural-mass model of the mouse cortex to understand brain dynamics using bifurcation diagrams and shows strong fits to resting-state fMRI data in simple dynamical regimes.

This paper shows that fMRI preprocessing choices significantly affect dynamical models of brain activity, highlighting the importance of data quality in model accuracy.

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 shows that regional transcriptional heterogeneity in excitatory and inhibitory receptor gene expression improves the accuracy of models of brain functional connectivity and supports complex neuronal dynamics.

This study identifies genetic influences on hub connectivity in the human connectome using diffusion-weighted imaging in twins and transcriptomic data.

The paper introduces DiCER, an alternative to global signal regression for removing signal deflections in fMRI data, showing improved performance in reducing artifacts and improving functional connectivity analysis.

The paper examines how impulsivity and compulsivity dimensions in OCD and GD correlate with CSTC circuit connectivity, finding continuous distributions and shared connectivity patterns across disorders.

The paper investigates how attention divides between modalities and spatial locations, revealing two distinct alpha sources with different functional roles in attention.

This paper develops methods for group analysis of high spatial resolution UHF fMRI data from higher-order cognitive tasks, showing quadratic modulation in prefrontal cortex and linear in visual areas.