Taylor Salo

The paper examines sex differences in functional brain connectivity across youth, finding that males show greater network integration while females show greater network segregation along the sensorimotor-association axis.

This study examines insula functional connectivity development across 562 individuals, showing spatially heterogeneous changes aligned with an antero-posterior axis.

The paper introduces a multimodal MRI dataset to evaluate myelin-sensitive imaging methods across test-retest acquisitions and metrics.

Resting-state fMRI intrinsic timescale lengthens with age in association, not sensorimotor, cortex during ages 8-22, following the sensorimotor-association axis in two cohorts; it is largely age-stable in young adults.

The paper shows that white matter tracts are positioned within the cortical hierarchy to support specific cognitive functions and connect regions with greater cognitive diversity.

The PMADS Project tracks neurobiological and clinical changes in high-risk individuals across the perinatal period to identify biomarkers linking brain plasticity to PMADs risk.

The paper introduces a longitudinal data resource combining multimodal imaging and cognitive phenotyping to study brain development and EF variation in adolescents.

The paper presents a curated dataset of processed diffusion MRI data from the ABCD Study, showing that specific diffusion metrics are sensitive to adolescent white matter development and robust to image quality variation.

The paper introduces an open data resource combining neuroimaging and behavioral measures to study mood and sleep in adolescents and young adults.

This study identifies subtypes of functional connectivity development in youth using fMRI data, revealing spatial patterns linked to cognitive performance and gene expression.

The paper describes improvements in diffusion MRI processing for the HBCD study, introducing new software and observing developmental patterns in brain data.

This study introduces an interpretable predictive model to identify functional connectivity patterns predictive of cognitive traits in youth, showing improved accuracy and fine-grained characterization.

This study examines changes in hippocampal functional connectivity in students undergoing active vs. lecture-based physics learning using fMRI.

The paper introduces Reproducible Brain Charts (RBC), an open data resource integrating brain development data from 6,346 youth, showing the benefits of harmonized phenotypes and reproducible processing for studying psychopathology.

The paper uses fMRI to compare brain state dynamics between active and lecture-based physics learning, finding distinct neural patterns associated with each approach.

The paper extends fMRIPrep to handle MRI data from infancy to old age, demonstrating high-quality preprocessing across a wide age range.

This paper introduces a self-supervised deep learning framework to compute individualized multi-scale hierarchical functional networks from fMRI data and shows their association with biological phenotypes.

The paper examines how contraceptive and hormone-related functional brain networks vary across the menstrual cycle using fMRI and hormone data from two longitudinal datasets, finding individual-specific and generalizable patterns influenced by hormonal contraceptives.

The paper shows thalamocortical connections develop along a sensorimotor-association gradient in youth, linking structural connectivity to cortical plasticity and environmental sensitivity.

The paper examines how functional connectivity develops along the sensorimotor-association axis during childhood to adolescence, finding that sensorimotor connectivity increases while association connectivity decreases, reinforcing cortical hierarchy.

The DIVA Study investigates intra-individual variability in brain and behavior across the menstrual cycle in premenopausal women using multimodal neuroimaging and physiological data.

This paper develops and evaluates methods for decoding functional connectivity gradients in fMRI data using meta-analytic techniques and k-means segmentation.

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

The paper introduces BABS, a Python package for reproducible and scalable neuroimaging data processing using BIDS Apps and HPC systems.

This study identifies two graded functional organization axes in the left inferior frontal gyrus using resting-state and task-based connectivity analyses.

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

The paper conducts a meta-analysis to identify neural systems associated with RDoC social constructs using fMRI data.

The paper investigates how STEM anxiety profiles correlate with brain connectivity during physics tasks using fMRI and latent profile analysis.

This study identifies convergent structural brain alterations in PTSD using meta-analysis and examines their functional connectivity across brain networks.