Stephanie Noble

Dynamic social envirotypes demonstrate adolescent brain dysconnectivity is contextual

The paper identifies dynamic social envirotypes in adolescents and shows how brain connectivity patterns vary with social context, affecting mental health outcomes.

Traumatic Brain Injury Associated with Altered Corpus Callosum Microstructure in Females: Exploring the Roles of Menopause Timing and Hormone Therapy in UK Biobank

This study examines how traumatic brain injury (TBI) affects corpus callosum microstructure in females, with findings showing that menopause timing and hormone therapy duration influence these effects.

A low-variance subspace underlies individual differences in resting state fMRI

This study identifies a low-dimensional subspace in resting state fMRI that captures reliable individual differences and can accurately identify participants.

When Music Loses Its Pleasure: Hippocampal Cingulum White Matter as a Structural Mediator of Musical Reward Decline in Aging

This study identifies white matter integrity in the hippocampal cingulum and external capsule as predictors of declining musical reward sensitivity in older adults.

The Hidden Landscape of Missed Effects in Human Functional Neuroimaging

The paper identifies inflated effect sizes in functional neuroimaging studies and proposes a correction method to improve study planning and detection of brain effects.

The Incremental Cluster Threshold-Free Cluster Enhancement Algorithm for Functional Connectivity Analysis

The paper introduces IC-TFCE, an algorithm that improves the computational efficiency of threshold-free cluster enhancement for functional connectivity analysis, achieving significant speedups for large-scale analyses.

PRISME: A MATLAB Toolbox For Large Data-Driven Multimodal Power Benchmarking

PRISME is a MATLAB toolbox for neuroimaging power benchmarking that enables large-scale empirical power analysis across diverse data types and test types.

Trends in Self-citation Rates in High-impact Neurology, Neuroscience, and Psychiatry Journals

This study analyzes self-citation rates across 100,347 articles from 63 neuroscience journals between 2000-2020, revealing trends in self-citation by author position, country, gender, and field.

Overlooked features lead to divergent neurobiological interpretations of brain-based machine learning biomarkers

The paper shows that overlooked brain connectivity features can yield different neurobiological interpretations with similar prediction accuracy in neuroimaging studies.

When no answer is better than a wrong answer: a causal perspective on batch effects

This paper introduces causal methods to detect batch effects in multi-site data, showing they provide more accurate or 'no answer' results compared to non-causal methods.

Power and reproducibility in the external validation of brain-phenotype predictions

The paper evaluates the statistical power of external validation in brain-phenotype predictions and finds that sample size impacts power and effect size inflation.

Connectome caricatures: removing large-amplitude co-activation patterns in resting-state fMRI emphasizes individual differences

This paper introduces a method to remove task-like co-activation patterns from resting-state fMRI data, revealing individual differences and improving predictive modeling of brain-phenotype associations.

The effects of data leakage on connectome-based machine learning models

This study evaluates how data leakage affects machine learning models using neuroimaging data, finding that some leakage types significantly inflate performance while others reduce it or affect model interpretations.

Brain-phenotype predictions can survive across diverse real-world data

The paper tests predictive models' ability to generalize across diverse, unharmonized neuroimaging datasets and finds that cross-dataset training improves prediction robustness.

Spatial and temporal autocorrelation weave complexity in brain networks

The paper shows spatial and temporal autocorrelation explain network topology measures in rs-fMRI data and how they change with aging and drug treatment.

Leveling up: improving power in fMRI by moving beyond cluster-level inference

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.

Cross Atlas Remapping via Optimal Transport (CAROT): Creating connectomes for any atlas when raw data is not available

CAROT remaps connectomes between atlases without raw data, enabling reuse of processed data across different atlases.

Large-scale differences in functional organization of left- and right-handed individuals using whole-brain, data-driven analysis of connectivity

This study compares functional connectivity differences between left- and right-handed individuals across the whole brain, revealing non-region-specific patterns of connectivity.

The Instability of Functional Connectomes Across the First Year of Life

This study finds that functional connectomes in infants show lower uniqueness and stability compared to adults, with significant decreases in identification rates and test-retest reliability across the first year of life.

Eliminating accidental deviations to minimize generalization error and maximize replicability: applications in connectomics and genomics

This paper introduces a new statistic called discriminability to improve replicability by distinguishing accidental from systematic deviations in data, showing its effectiveness in both simulated and real datasets.

Task-Based Functional Connectomes Predict Cognitive Phenotypes Across Psychiatric Disease

The paper uses task-based functional MRI connectomes to predict cognitive phenotypes across psychiatric diseases and shows differences in connectomes by disease category and symptom severity.

The functional brain organization of an individual predicts measures of social abilities in autism spectrum disorder

This study uses connectome-based predictive modeling on fMRI data to identify functional brain networks that predict social ability measures in autism spectrum disorder.

How tasks change whole-brain functional organization to reveal brain-phenotype relationships

The paper shows that task-induced changes in functional connectivity predict phenotypes better than rest-based connectivity, and develops a method to analyze these changes.