Aleksandr Volkov

Personalized Prediction of Differences in fMRI Connectivity Estimates Across Preprocessing Pipelines

The paper predicts how preprocessing affects fMRI connectivity estimates using prior scans, showing personalized models outperform average predictions.

Temporal Structure in Resting-State fMRI under Marginal and Cross-Spectral Surrogate Controls

The paper evaluates temporal complexity measures in resting-state fMRI to identify disease-related changes, finding that real data show greater outliers than randomized controls.

Evaluating State-Transition Models in Short Resting-State fMRI with Continuous-Dynamics Controls

This study compares hidden Markov models (HMMs) and vector autoregressive (VAR) models in resting-state fMRI to determine if apparent state transitions reflect discrete dynamics or continuous activity, finding that VAR often outperforms HMMs in continuous cases.

Dementia Severity Is Associated with Resting-State fMRI Network Responses to Nuisance Regression

The paper examines how dementia severity affects resting-state fMRI network responses to preprocessing, finding that greater severity correlates with reduced temporal concentration after nuisance regression.

Temporal Variability in Brain Connectivity for Estimating Two-Year Clinical Severity in Mild Cognitive Impairment

The paper examines if temporal variability in brain connectivity improves prediction of two-year clinical severity in mild cognitive impairment compared to static connectivity measures.