Desmond J. Oathes

This study identifies early functional connectivity changes in MDD patients treated with antidepressants or placebo, revealing common and distinct treatment effects.

This study maps TMS-induced discomfort across the whole brain using TMS-fMRI and shows it accounts for 12-25% of TMS-evoked responses.

This study identifies subtypes of functional connectivity development in youth using fMRI data, revealing spatial patterns linked to cognitive performance and gene expression.

This study introduces an interpretable predictive model to identify functional connectivity patterns predictive of cognitive traits in youth, showing improved accuracy and fine-grained characterization.

This paper demonstrates that real-time brain decoding using personalized functional networks enhances the effectiveness of transcranial magnetic stimulation for working memory.

This paper identifies two neurophysiology-informed symptom dimensions in MDD using contrastive functional connectivity analysis of fMRI data.

This paper introduces a self-supervised deep learning framework to compute individualized multi-scale hierarchical functional networks from fMRI data and shows their association with biological phenotypes.

This study shows that targeting cortical areas with high resting state functional connectivity to the sgACC with TMS leads to stronger sgACC responses in depressed patients.

pNet is an open-source toolbox for creating personalized functional networks from fMRI data, offering multiple interfaces and methods to enhance reliability and reproducibility.

This study evaluates head motion correction methods in diffusion MRI, showing both Eddy and SHORELine perform well but are affected by denoising and sampling schemes.

This paper develops a self-supervised deep learning method to compute personalized brain functional networks from fMRI data, showing it can identify canonical networks and predict individual differences in behavior and schizophrenia status.

This paper develops a self-supervised deep learning method to compute transcranial magnetic stimulation electric fields more efficiently than traditional finite-element methods.

This study shows that transcranial magnetic stimulation (TMS) applied to the ventrolateral prefrontal cortex (vlPFC) modulates amygdala activity via cortical-subcortical connections, suggesting potential therapeutic applications for affective disorders.

The paper develops a connectivity-based electric-field modelling approach to identify individualized TMS targets for depression treatment by combining rsFC and e-field simulations.

QSIPrep is a software platform for preprocessing diffusion MRI data that supports multiple sampling schemes and ensures reproducibility through standardized workflows and quality measures.

The paper uses network control theory to show that structural brain networks mature during adolescence to reduce the energy cost of activating the frontoparietal system, supporting executive function development.

This study shows how functional topography of brain networks changes with age and relates to executive function and brain organization in youth.

This study examines how structure-function coupling in brain networks develops during youth, finding it aligns with cortical hierarchies and supports cognitive improvements.

This study evaluates DTI, NODDI, and MAPL in youth to assess their associations with age and motion, finding that multi-shell models offer advantages for neurodevelopmental studies despite motion sensitivity.

This study uses combined fMRI and TMS to map causal brain networks involving the sgACC and amygdala, showing individualized targeting captures causal circuitry in humans.