Kevin M. Aquino

Cortical-layer EEG-fMRI at 7T: experimental setup and analysis pipeline to elucidate generating mechanisms of alpha oscillations

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.

Multiscale heterogeneity of white matter morphometry in psychiatric disorders

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

Geometric influences on the regional organization of the mammalian brain

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

Extracting interpretable signatures of whole-brain dynamics through systematic comparison

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

Reply to: Eigenmodes of the brain: revisiting connectomics and geometry

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.

Mode-based morphometry: A multiscale approach to mapping human neuroanatomy

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.

Reply to: Commentary on Pang et al. (2023) Nature

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

Geometric constraints on human brain function

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

Using in silico perturbational approach to identify critical areas in schizophrenia

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.

The individuality of shape asymmetries of the human cerebral cortex

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

Modeling spatial, developmental, physiological, and topological constraints on human brain connectivity

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

Extracting dynamical understanding from neural-mass models of mouse cortex

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.

On the intersection between data quality and dynamical modelling of large-scale fMRI signals

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

Cortical Depth-Dependent Modeling of Visual Hemodynamic Responses

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.

Dynamical consequences of regional heterogeneity in the brains transcriptional landscape

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.

Genetic influences on hub connectivity of the human connectome

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

Identifying and removing widespread signal deflections from fMRI data: Rethinking the global signal regression problem.

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.

Transdiagnostic variations in impulsivity and compulsivity in obsessive-compulsive disorder and gambling disorder correlate with effective connectivity in cortical-striatal-thalamic-cortical circuits.

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.

Two spatially distinct posterior alpha sources fulfill different functional roles in attention

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

Addressing challenges of high spatial resolution, UHF field fMRI for group analysis of higher-order cognitive tasks; an inter-sensory task directing attention between visual and somatosensory domains.

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.