John David Griffiths

This paper develops a mathematical model of calcium-dependent synaptic plasticity to unify the effects of transcranial focused ultrasound and transcranial magnetic stimulation.

The paper introduces a connectome-constrained Jansen-Rit model to infer cortical gain control and ensemble stability using variational free-energy inversion and sliding-window analysis.

This study investigates how corticothalamic circuits contribute to age-related changes in resting-state alpha oscillations using MEG data from the Cam-CAN dataset.

The paper uses computational modeling to explore how theta burst stimulation affects brain oscillations and plasticity through calcium-dependent mechanisms, revealing resonance with alpha rhythms and implications for personalized treatment.

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

This paper systematically compares four theoretical models of alpha rhythm generation in neural circuits, identifying common elements and differences, and classifying two dynamical regimes for alpha rhythms.

This paper applies a corticothalamic model to analyze sleep EEG data from multiple datasets, revealing consistent physiological patterns and drug-related effects during sleep stages.

This study investigates how functional connectivity in resting-state networks depends on structural connectivity, showing state-dependent interactions between lower and higher order networks.

This study evaluates the validity and reliability of HD-DOT TD-fNIRS in rapid succession data collection, finding consistent performance with less spatial detail than fMRI.

This paper investigates how inhibition levels affect fMRI resting-state negative correlations (NCs) using simulations and connectome-based modeling, showing that greater inhibition increases NC magnitude and spatial extent.

The paper uses electrophysiology and modeling to show that high-order brain regions have higher excitability due to recurrent feedback, revealing a gradient linked to cortical hierarchy.

The paper examines correlations between task and resting-state brain signatures of sleep, cognition, and depression in over 30,000 individuals, revealing opposing neural patterns associated with insomnia and depression.

This paper investigates how TMS-evoked responses are influenced by recurrent large-scale network dynamics and local reverberatory activity, using TMS-EEG and connectome-based modeling.

This study examines inter-individual variability in functional connectivity of TMS targets for MDD using E-field maps and identifies major functional networks involved.

The paper introduces a deep learning approach to estimate parameters in connectome-based neural mass models using fMRI data and demonstrates its effectiveness on synthetic and real resting-state fMRI data.

The paper models human cortical microcircuits to show reduced inhibition in depression impairs stimulus processing by increasing baseline activity and reducing signal-to-noise ratio.

The paper simulates EEG signals from human cortical microcircuits with reduced SST interneuron inhibition to identify distinct EEG biomarkers associated with depression.

This paper develops a connectome-based neural mass model to simulate state- and stimulation-dependent changes in rhythmic neural activity and connectivity, revealing how corticothalamic circuits modulate oscillatory patterns.

The paper models how hippocampal and medial temporal lobe activity influences oculomotor systems in macaques, showing subfield-specific effects and lesion impacts on signal propagation.

The paper investigates how structural and functional brain connectivity relate to cognition, finding distinct patterns in each modality that uniquely contribute to cognitive functions.

The paper examines the subject-specificity of the correlation between structural and functional connectivity across six human neuroimaging datasets, finding limited subject-specificity except for one dataset.