Jessica A. Turner

The paper examines associations between childhood trauma and cortical thickness in bipolar disorder subgroups, finding nominal but uncorrected region-specific links.

This study identifies three psychosis neurosubtypes using imaging data, showing distinct connectivity patterns and supporting their biological validity through relative alignment.

The paper identifies sex-dominant neurosubtypes in psychosis using fMRI data and shows their unique dysconnectivity patterns and cognitive implications.

The paper examines effective connectivity in emotion regulation and reward networks in individuals with early psychosis and self-injury, finding altered connectivity patterns associated with NSSI and SA.

This paper develops a model using resting-state fMRI to identify functional connectivity patterns in schizophrenia, finding a robust cerebellothalamic disruption signature.

The paper develops a normative template for multimodal brain MRI fusion using ICA, showing it guides new dataset analyses and correlates with age.

This study identifies a shared genetic factor underlying brain age gaps across 30 cohorts, finding 19 SNPs and associations with health traits.

This study identifies spatiotemporal complexity disruptions in the psychotic brain using resting-state fMRI, revealing altered information integration and network instability.

The paper identifies altered default mode network connectivity dynamics in schizophrenia using a novel iterative feature-removal clustering approach on resting-state fMRI data.

This study identifies small but consistent anatomical correlates of negative symptom dimensions in schizophrenia, linking them to prefrontal cortical thickness and limbic subcortical volumes.

This paper demonstrates that large language models can effectively extract metadata from neuroimaging publications with performance comparable to human annotators.

The paper introduces a voxel-centric framework using information theory to analyze multi-way brain network interactions, revealing reduced Hamming weights in auditory, visual, and motor networks in schizophrenia.

This study examines time-varying spatial propagation of brain networks in fMRI data and links propagative patterns to schizophrenia.

The paper presents a WM intrinsic connectivity network template derived from 100,000 fMRI scans, showing distinct WM and GM networks with task-related activity and differences in schizophrenia.

This paper develops a method to detect linked features across neuroimaging modalities and identifies markers related to age, sex, cognition, and schizophrenia in large datasets.

This paper introduces a template-driven method to generate dynamic functional network connectivity features from neuroimaging data, showing improved prediction of medication response in mood disorders.

This paper creates a whole-brain functional atlas from resting-state fMRI data to standardize terminology and improve consistency across neuroimaging studies.

This paper introduces dynamic fusion, a method to link brain structure and function over time, showing it captures dynamic functional processes and detects nuanced structural components.

This study compares AD and SZ using rs-FNC with confounder control and machine learning, finding shared and distinct functional abnormalities and classification accuracies of 85% and 68%.

This study investigates how structural brain alterations in schizophrenia relate to network architecture, finding hub susceptibility and shared epicenters across disease stages and disorders.

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.

This study examines subcortical brain alterations in CNV carriers across multiple neuropsychiatric disorders, finding shared and distinct patterns of structural changes.

This paper introduces COINSTAC Vaults to enhance collaborative neuroimaging research through federated analysis and reproducibility by hosting standardized datasets.

This study uses federated analysis in COINSTAC to examine functional network connectivity in adolescent brains linked to smoking and alcohol consumption, finding hypoconnectivity and reduced synchronization.

The paper identifies a shared cortical morphology dimension across six psychiatric conditions using MRI data from 28,546 participants.

The paper develops a reliable network template and methods to estimate individualized functional networks from resting-state fMRI data across 100k+ individuals, showing how data length and spatial resolution affect network estimation.

This study shows that intrinsic connectivity networks in fMRI are continuously present and overlapping, not just during discrete events, and that analyzing all time points provides more comprehensive insights into functional patterns and schizophrenia symptoms.

The paper identifies ten genes associated with ADHD-related grey matter brain networks in a sample of 899 individuals.

This paper introduces multi-model order ICA to evaluate brain functional network connectivity across multiple spatial scales and finds that FNC between model orders 25 and 50 best predicts schizophrenia vs. healthy controls.