Andrew Zalesky

The paper examines frontolimbic functional connectivity development from birth to emerging adulthood, finding that fronto-hippocampus and fronto-amygdala connectivity differentiate with distinct developmental timing and sensitivity to adversity and cognitive ability.

97% of 184 high-impact biomedical AI studies compare cross-validated models with tests assuming fold independence; in 69% of re-analyzable studies a significant comparison does not survive correction.

This study examines polysynaptic signal propagation in neural mass models across different dynamical regimes and benchmarks results against empirical data.

PaSTA provides a fast and reliable method to assess the statistical significance of spatial associations between brain maps, improving false positive control and power in heterogeneous data.

The paper evaluates neural oscillatory mechanisms for communication in the human brain using MEG data, finding that power and phase coherence in specific frequency bands correlate with communication patterns.

This study optimizes in vitro neuronal microenvironments to reduce phototoxicity by testing culture conditions and imaging protocols over 33 days.

The paper identifies MRI-derived markers in the VTA linked to depression diagnosis and symptom severity, showing distinct patterns of neuroinflammation and microstructural changes.

This study maps the coupling between cortical geometry and white matter tract connectivity, showing how these aspects jointly shape brain function.

The paper introduces a deep learning model to generate individual connectomes from personal attributes and shows it can accurately replicate empirical connectomes from the UK Biobank.

This study identifies neuroimaging biomarkers of resilience and vulnerability to chronic stress in rats using machine learning and functional connectivity analysis.

TRAMFIX evaluates the reproducibility of fixel-based analysis across multiple MRI scanners and finds high reliability for whole-brain and tract-level metrics, with some variability at the fixel level.

The paper identifies nine neuroimaging endophenotypes linked to genetic loci and shows they improve prediction of systemic diseases and mortality.

This study benchmarks 239 pairwise interaction statistics for functional connectivity mapping, finding substantial variation in their topological, neurobiological, and behavioral properties.

This paper introduces EWC, a method to infer discrete neural communication from continuous time series data, showing it is faster and effective at capturing directional signaling.

This study evaluates the accuracy of reconstructing cortical functional maps using subject-specific geometric and connectome eigenmodes, finding that spatially smooth eigenmodes best explain functional data.

The paper examines white matter volume deviations in psychiatric disorders using normative modeling, finding heterogeneous patterns with some aggregation in autism and schizophrenia.

The paper shows two hippocampal integration dimensions persist across adulthood, relate to memory and dopamine distribution, and decline with age.

This study identifies and addresses spurious correlations in surface-based fMRI data caused by uneven vertex spacing in processing pipelines.

This study shows that high-precision neurofeedback-guided meditation training improves disengagement from mental activity and enhances emotional well-being in novice meditators.

This study identifies 393 genomic loci associated with biological age gaps in nine human organ systems using UK Biobank data, revealing organ-specific and pleiotropic genetic effects.

The paper uses GWAS summary statistics to analyze the genetic architecture of 2024 multi-organ AI endophenotypes, revealing genetic correlations and causal relationships across diseases.

The paper re-evaluates the comparative explanatory power of geometric and connectome eigenmodes in predicting brain activity, finding that connectome eigenmodes can achieve comparable accuracy when improved connectome mapping is used.

This paper introduces a generative model of the connectome that simulates dynamic axon growth, producing brain-like network structures and revealing how axonal guidance principles shape macroscopic connectomes.

The paper provides a large-scale connectome resource for 40,000 UK Biobank participants, including structural and functional brain networks with multiple parcellations and preprocessing techniques.

This study examines structural brain heterogeneity in schizophrenia using multimodal MRI data from the ENIGMA dataset, finding higher frontotemporal heterogeneity and left parahippocampal homogeneity in patients.

The paper investigates interhemispheric functional connectivity during non-dominant hand movement in right-handed subjects using gPPI and GLM analyses, finding increased connectivity in motor-related regions during non-dominant hand movement.

The paper uses intracranial EEG data to show that structural connectivity models can predict the propagation of electrical brain stimulation across the cortex.

This pilot study uses 7 Tesla fMRI to show that focused attention meditation reduces Default-mode network activity and increases mindfulness scores.

The paper uses VR-based real-time imaging to study cortical functional connectivity dynamics during behavioral transitions in a mouse model of autism, finding altered FC patterns in motor areas associated with motor clumsiness.

This paper develops a hierarchical Bayesian model to account for site effects in neuroimaging data, showing improved predictive performance compared to harmonization techniques.