Maxwell A. Bertolero

The paper identifies three conserved spatiomolecular gradients in the adult human brain that align with developmental axes and predict functional territories.

XCP-D provides a standardized, reproducible post-processing pipeline for fMRI data, supporting multiple pre-processing pipelines and BIDS best practices.

The paper challenges the assumption that nonlinear models are necessary for whole-brain dynamics by showing linear models achieve better fit, simplicity, and interpretability in resting state fMRI and iEEG data.

This study identifies white matter fiber covariance networks in youth, showing their development aligns with executive function and hierarchical cortical maturation patterns.

The paper investigates how functional connectivity patterns in brain networks predict symptoms of borderline personality disorder in adolescents and young adults using fMRI data.

This study tracks cortical activity propagations in youth, finding increased top-down propagations with cognitive control demands and development.

The paper defines personalized functional networks in children to predict cognitive differences, finding that fronto-parietal network representation correlates with general cognition.

The paper shows that external inputs affect BOLD signal non-stationarity and presents a method to estimate these inputs using in silico models and fMRI data.

CuBIDS streamlines reproducible curation of large BIDS datasets by validating metadata, managing variability, and preparing data for preprocessing pipelines.

The paper shows that intrinsic brain activity decreases along a sensorimotor-association cortical axis during human development, with reduced activity in disadvantaged youth.

The paper introduces CoCoA, a statistical framework to estimate conditional correlations and association sizes, demonstrating its effectiveness in neurocognitive data analysis.

The paper examines how individual differences in functional brain network topography relate to psychopathology dimensions in youth using machine learning and fMRI data.

The paper analyzes multi-scale functional brain networks in youths to identify developmental patterns of network re-organization and their cognitive implications.

This study uses pharmacological fMRI to show a reduction in the excitation:inhibition ratio in association cortex during adolescence, linked to psychopathology.

This study evaluates how different functional connectivity measures in resting-state fMRI data are affected by motion artifacts and finds that partial correlation is most robust.

A curated collection of processed ABCD neuroimaging derivatives plus utilities for access, sharing and analysis, letting researchers exchange derivatives they cannot share through the central archive.

FlywheelTools provides software for reproducible data curation and manipulation on the Flywheel platform, addressing challenges in converting neuroimaging data to BIDS format.

The paper uses machine learning to analyze functional topography in youth brains, finding sex differences linked to X-linked genes and specific brain networks.

In five top neuroscience journals, papers with name-inferred first and last authors of color are under-cited, mostly by White-led teams, and the gap is growing.

This study investigates how brain activity and connectivity reorganize during learning, finding distinct patterns in early and later stages of value learning.

This paper shows deep neural networks outperform linear models in predicting behavior from brain connectivity, especially in connector hubs, and demonstrates their interpretability using multislice network models.

The paper identifies hub and community structures in brain networks driven by long-distance functional connections, showing primary sensory cortices as more hub-like and community structures spanning multiple levels.

This paper introduces a multi-layer network method to analyze brain network modularity across individuals and time, revealing scale-specific variability linked to cognitive performance.

This paper introduces a dynamical systems framework to estimate both intrinsic brain activity and external stimuli, showing task-related changes and non-stationarities in cortical dynamics.

The paper examines community structure in functional brain networks during rest and tasks, finding non-assortative communities during tasks and their correlation with cognitive performance.