Alex Fornito

This study links childhood ADHD to neurophysiological aging by showing ADHD brain profiles correlate with aging markers and testosterone levels in adulthood.

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.

The paper presents a dataset of EEG, TMS-EEG, and behavioural data from 123 healthy adults to study the neural correlates of working memory.

The paper presents the MIND model, showing that competing induction and exclusion programs shape the sensorimotor-to-association axis in the neocortex across species.

This study shows that repeated suprathreshold paired-pulse TMS at short inter-stimulus intervals increases corticospinal excitability over time, suggesting potential for modulating motor system activity.

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.

This study evaluates denoising strategies for resting-state fMRI, finding that multi-echo pipelines generally outperform single-echo ones but no single method is optimal for all tasks.

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.

The paper benchmarks overlapping community detection methods on human connectomes, finding OSLOM best identifies overlapping structures and reveals hierarchical cortical organization.

This study investigates how non-reinforced and reinforced preference changes are maintained over time using functional MRI, finding distinct neural connectivity patterns in different brain networks.

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.

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

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.

The paper examines task-related changes in aperiodic EEG activity and finds they correlate with working memory capacity independently of ERPs and alpha oscillations.

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 shows that generative network models fail to capture the spatial embedding of long-range brain connectivity, highlighting issues with current model evaluation methods.

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

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.

This study investigates how auditory and somatosensory inputs contribute to TMS-evoked potentials (TEPs) in different cortical regions and finds that later TEPs reflect sensory input while earlier peaks reflect cortical stimulation.

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