Meysam Hashemi

The paper shows that different biophysical parameters can produce equivalent bifurcation sequences in neuronal models, revealing degeneracy in neuronal dynamics.

The paper develops ADAM, a model linking dopamine clearance and structural connectivity to cognitive aging, finding elevated clearance timescales and a structure-age-dependent association with cognitive performance.

This paper develops a normative modeling framework to analyze static and dynamic functional connectivity across lifespan, revealing distinct age-related trajectories for each.

The paper identifies SK2/3 channels coupled with T-type Ca2+ channels as a mechanism gating spinal locomotor rhythm generation, showing their role in initiating and terminating locomotor activity.

This paper identifies a deformable attractor manifold that organizes human resting-state brain dynamics, showing it relates to cognitive variability and pathology.

This paper introduces Virtual Brain Twins (VBTs) as personalized digital brain models that integrate structural and functional data to improve understanding of brain function and support personalized treatment.

This paper introduces VBI, a toolkit for Bayesian inference on virtual brain models, demonstrating its accuracy and reliability through in-silico testing.

This paper develops Bayesian inference methods for Dynamic Causal Modelling using probabilistic programming languages to improve neural dynamics prediction.

This study investigates how social presence affects excitatory synaptic efficacy across brain scales in monkeys and humans, linking these changes to task performance improvements.

This paper compares parallel global optimization solvers for parameter estimation in whole-brain epilepsy models, showing improved scalability and performance with Bayesian optimization and uncertainty quantification.

This paper introduces SBI-VBMs, a method using deep neural networks to estimate generative parameters in brain disorders through probabilistic inference on virtual brain models.

This study uses deep learning to improve inference on spiking neuron networks, showing better performance in handling noise and missing data compared to traditional methods.

This study shows how local brain manipulations affect global brain dynamics using simulations and experimental data, revealing specific network changes and localization of effects.

This paper develops a causal inference framework to link structural connectivity and brain function in aging, showing increased structural modulation in poor cognitive performers.

This paper introduces a data-driven method to infer neural mass models and parameters from brain data, showing it can recover original parameters and produce realistic dynamics in synthetic and resting-state fMRI data.