Janine Diane Bijsterbosch

HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders

HARMONY harmonizes neuroimaging data from multiple cohorts to study brain-behavior associations in anxious misery disorders, showing increased statistical power in pooled analyses.

From topography to connectome: Towards an integrated understanding of the resting brain

The paper develops a deep-learning model to translate spatial topography maps into individualized connectomes, confirming its accuracy and ability to retain brain-cognition associations.

Connectome Principal Component Drives Cross-Dataset Replication and Clinical Prediction in Symptom Lesion Network Mapping

This study shows that symptom lesion network mapping (sLNM) results are biased toward the first principal component of the normative connectome, leading to spurious convergence and non-specific clinical predictions, which can be corrected by controlling for PC1.

Profiles of Aging Based on Cognition, Affect, and Brain Reserve

The paper identifies aging profiles using cognitive, affective, and brain reserve measures from UK Biobank data, revealing distinct patterns of emotional wellbeing and cognitive decline.

Investigating Neural Correlates of Emotional Regulation as a Function of Age, Race, and Socioeconomic Status

The paper examines age-related changes in emotional regulation neural correlates across race and socioeconomic status using UK Biobank MRI data, finding age-related decreases in amygdala activation but SES-related increases.

The neuroimaging correlates of depression established across six large-scale datasets

This study identifies neuroimaging correlates of depression across six large-scale datasets, finding gray matter reductions in specific brain regions and unexpected results in subcortical and functional connectivity analyses.

Automatic Individual Cortical Parcellation for the Human Connectome Project

This paper introduces an automated individual cortical parcellation method using a novel weakly supervised classifier, improving performance over previous models and identifying new atypical language-related brain areas.

Opaque Ontology: Neuroimaging Classification of ICD-10 Diagnostic Groups in the UK Biobank

The paper evaluates the accuracy of classifying ICD-10 diagnostic groups using neuroimaging and socio-demographic features from the UK Biobank, finding limited success compared to age classification.

Multiscale Modes of Functional Brain Connectivity

This paper introduces mPFMs, a new method for multiscale functional brain connectivity analysis that improves prediction of UK Biobank traits compared to standard techniques.

Higher amplitudes of visual networks are associated with trait but not state- depression.

The paper identifies higher visual network amplitudes in individuals with trait depression compared to controls, suggesting altered visual processing in those predisposed to depression.

Associations between COVID-19 and putative markers of neuroinflammation: A diffusion basis spectrum imaging study.

This study investigates associations between COVID-19 and neuroinflammation markers using DBSI in UK Biobank data, finding increased vasogenic edema in specific brain regions.

Evaluating functional brain organization in individuals and identifying contributions to network overlap

This paper evaluates individual differences in brain network organization and identifies spatial overlap as indicative of linear additive coupling.

Gamma-burst cortical activity in awake behaving macaques

The paper investigates the role of NMDA receptor antagonism in generating gamma-burst patterns during anesthesia and finds that delta-band oscillations are critical for anesthesia.

Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of Behavior

This study compares parcellation and gradient methods for predicting behavior from resting-state functional connectivity in two datasets, finding that individual-specific hard parcellations perform best in HCP and principal gradients perform similarly in ABCD.

Population modeling with machine learning can enhance measures of mental health - Open-Data Replication

This paper replicates and improves mental health trait predictions using fMRI data through data manipulations like feature expansion and phenotype balancing.

Hierarchical modelling of functional brain networks in population and individuals from big fMRI data

This paper introduces sPROFUMO, a hierarchical model for estimating functional brain networks in individuals and populations from large fMRI datasets, showing improved performance over traditional methods.

Mental health explains individual deviations from normative range in cognition-associated brain states

The paper links individual variations in mental health to deviations in dynamic brain states associated with cognitive ability using UK Biobank data.

Modelling Subject Variability in the Spatial and Temporal Characteristics of Functional Modes

The paper extends probabilistic functional modes to model subject variability in fMRI data, showing spatial maps capture most cross-subject variability and functional coupling reflects cognitive state.

The relationship between spatial configuration and functional connectivity of brain regions revisited

The paper revisits the relationship between spatial configuration and functional connectivity, showing spatial overlap biases connectivity estimates and introducing an improved method to address this bias.

Using Temporal ICA to Selectively Remove Global Noise While Preserving Global Signal in Functional MRI Data

This paper introduces temporal ICA to remove global structured noise from fMRI data while preserving global neural signals, improving upon traditional global signal regression methods.

The relationship between spatial configuration and functional connectivity of brain regions

This study examines how the spatial arrangement of brain regions influences functional connectivity and behavioral predictions.