Andrew Zalesky

The paper compares inference levels in fMRI, finding that network and whole-brain analyses have higher power than cluster-level analyses for detecting widespread effects.

This study shows that neuroimaging studies in clinical populations require hundreds, not thousands, of participants to achieve reproducible results due to larger effect sizes.

The paper shows that adding models of indirect neural communication to structural brain networks better aligns their modular organization with functional systems.

This paper introduces Connectome Spatial Smoothing (CSS) to improve the reliability and sensitivity of structural connectomes by applying spatial smoothing to tractography data.

This study finds that adult brain age is more influenced by early life factors like birth weight and polygenic scores than by longitudinal brain decline.

The paper investigates the reliability of feature weights in machine learning models predicting cognition from functional connectivity and finds poor reliability despite modest prediction accuracy.

This study investigates how structural brain connections constrain fast neural dynamics using MEG and diffusion MRI data, finding a significant association between structural connectivity and neuronal avalanche spread.

This study investigates how intracranial brain stimulation affects network switching in fMRI data, finding reduced switching rates and more consistent patterns after stimulation.

The paper shows that dopamine transporter regulates network connectivity differences between substantia nigra and ventral tegmental area dopaminergic neurons.

The paper validates that rsfMRI temporal complexity is reproducible and correlates with fluid intelligence across multiple brain networks.

The paper reveals the topographic organization of the human subcortex using resting-state fMRI data from 1000 adults, identifying four scales of organization and 27 new regions, and introduces a new subcortical atlas.

The paper maps individual deviations from normative brain structure in schizophrenia patients and finds high heterogeneity, with most patients showing infra-normal deviations in at least one region.

The paper examines how brain age gaps relate to psychopathology dimensions in youth, finding significant associations with psychosis, obsessive-compulsive, and general psychopathology.

This study shows that network communication models enhance the prediction of behavior and strengthen structure-function coupling in the human structural connectome.

The paper links anatomical and functional brain features of the default mode network to pediatric concussion recovery and shows they predict persistent symptoms.

The paper infers neural signalling directionality from undirected structural connectomes using send-receive communication asymmetry and shows it aligns with effective connectivity directionality.

The paper introduces a normalized participation coefficient to reduce module size bias in network analysis, demonstrating improved identification of high-participation nodes in various networks.

This study links brain connectivity patterns with persistent symptoms in children after mild traumatic brain injury using resting-state fMRI and behavioral assessments.

The paper examines the relationship between functional connectivity of corticostriatal circuits and subclinical psychosis-like experiences in a community sample.

The paper models brain network switching using multilayer networks and finds it correlates with cognitive performance and sleep.

This study compares instantaneous phase synchrony analysis and correlation-based sliding window analysis for time-resolved fMRI connectivity, finding they are comparable with optimal parameters.

The paper identifies a dimensional biotype in medication-naïve adults with ADHD using brain-behavior patterns from functional connectivity and symptom correlations.

The paper examines how brain network architectures change between resting state and task-driven cognitive reasoning, finding that increased complexity leads to merging of resting state modules and selective connectivity changes.

The paper shows that neural decoding of visual stimuli is higher during periods of high global network efficiency compared to low efficiency.