Judith M. Ford

No objective evidence of neuropsychological deficits in people with subjective cognitive changes following COVID-19 infection

This study finds no objective cognitive deficits in individuals with post-COVID-19 subjective cognitive complaints, but links them to higher depression/anxiety and biological markers like APOE-ε4 and sCD14.

Shared genetic architecture of brain age gap across 30 cohorts worldwide

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

Neuroanatomical Correlates of Negative Symptoms in Schizophrenia

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

An information theory framework for capturing multi-connectivity via spatial network encoding reveals reduced population count (Hamming weight) localized to auditory, visual, and motor networks

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.

Visualizing Functional Network Connectivity Differences Using an Explainable Machine-learning Method

The paper introduces a framework using SHAP to identify FNC biomarkers distinguishing between schizophrenia patients and controls, and young vs. old adults with high accuracy.

Static and Dynamic Cross-Network Functional Connectivity Shows Elevated Entropy in Schizophrenia Patients

This paper introduces a novel method to analyze static and dynamic functional connectivity in schizophrenia patients, finding elevated entropy in connectivity patterns compared to healthy controls.

Time-varying Spatial Propagation of Brain Networks in fMRI data

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

Evidence for white matter intrinsic connectivity networks at rest and during a task: a large-scale study and templates

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.

Networks extracted from nonlinear fMRI connectivity exhibit unique spatial variation and enhanced sensitivity to differences between individuals with schizophrenia and controls

This paper introduces a nonlinear method to estimate brain networks from fMRI data, showing it better distinguishes schizophrenia patients from controls than linear methods.

Direct linkage detection with multimodal IVA fusion reveals markers of age, sex, cognition, and schizophrenia in large neuroimaging studies

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.

Explicitly Nonlinear Connectivity-Matrix Independent Component Analysis in Resting fMRI Data

This paper introduces a method to identify linear and explicitly nonlinear dependencies in resting fMRI data and applies it to detect differences in schizophrenia.

Functionally Adaptive Structural Basis Sets of the Brain: A Dynamic Fusion Approach

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.

Genetic variants for head size share genes and pathways with cancer

This study identifies 67 genetic loci associated with head size, finding strong overlaps with cancer pathways and genes, suggesting shared genetic mechanisms between head growth and cancer risk.

Alterations in grey matter structure linked to frequency-specific cortico-subcortical connectivity in schizophrenia via multimodal data fusion

Canonical and Replicable Multi-Scale Intrinsic Connectivity Networks in 100k+ Resting-State fMRI Datasets

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.

Explicitly Nonlinear Dynamic Functional Network Connectivity In Resting-State fMRI Data

This study introduces a method to analyze explicitly nonlinear dynamic functional connectivity in resting-state fMRI data and finds significant group differences between schizophrenia patients and healthy controls.

Moving Beyond the 'CAP' of the Iceberg: Intrinsic Connectivity Networks in fMRI are Continuously Engaging and Overlapping

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.

Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: a combined EEG-fMRI study

This study combines EEG and fMRI to show that aperiodic EEG measures correlate with specific hemodynamic networks during rest, indicating a balance of top-down and bottom-up processing.

Nonlinear Functional Network Connectivity In Resting fMRI Data

This paper introduces a method to quantify nonlinear functional connectivity in resting-state fMRI data, showing distinct nonlinear patterns in schizophrenia patients compared to healthy controls.

Multi-Spatial Scale Dynamic Interactions between Functional Sources Reveal Sex-Specific Changes in Schizophrenia

This paper introduces msICA to analyze dynamic functional interactions across spatial scales in schizophrenia, finding sex-specific differences in connectivity patterns and their association with symptom scores.

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

The paper investigates dynamic functional connectivity (dFC) of the default mode network (DMN) in schizophrenia and links it to symptom severity using resting-state fMRI data from two datasets.

Alpha Event-related Desynchronization During Reward Processing in Schizophrenia

The paper examines alpha event-related desynchronization during reward processing in schizophrenia, finding altered alpha ERD magnitude between reward outcomes and associations with trait rumination.

Electrophysiological investigation of reward anticipation and outcome evaluation during slot machine play

This study uses EEG to examine reward anticipation and outcome processing during slot machine play, finding distinct neural responses to winning, near misses, and total misses.

Multiple overlapping dynamic patterns of the visual sensory network in schizophrenia

The paper analyzes dynamic functional connectivity in the visual sensory network of schizophrenia patients and healthy controls, finding disrupted connectivity patterns and a correlation between connectivity states and visual learning.

Theta phase synchrony is sensitive to corollary discharge abnormalities in early illness schizophrenia but not in the clinical high-risk syndrome

The paper investigates theta phase synchrony and N1 suppression in schizophrenia and clinical high-risk groups, finding theta inter-trial coherence suppression is more sensitive to corollary discharge abnormalities in early illness schizophrenia.

The genetic architecture of the human cerebral cortex

The paper identifies genetic loci associated with human cortical structure and finds genetic correlations with neuropsychiatric disorders.

Reward processing electrophysiology in schizophrenia: Effects of age and illness phase

The paper examines reward processing in schizophrenia using ERPs, finding altered later reward outcome evaluation in early-stage patients and a link between brain age and depressive symptoms.

Parallel Group ICA + ICA: Joint Estimation of Linked Functional Network Variability and Structural Covariation with Application to Schizophrenia

This paper introduces a method to fuse functional and structural MRI data by integrating temporal information, identifying linked variability and covariation in schizophrenia.

The spatial chronnectome reveals a dynamic interplay between functional segregation and integration

This paper introduces the spatial chronnectome, a method to analyze dynamic spatiotemporal brain network interactions, revealing transient network changes and cognitive relevance in schizophrenia.

Group ICA for Identifying Biomarkers in Schizophrenia: ‘Adaptive’ Networks via Spatially Constrained ICA Show More Sensitivity to Group Differences than Spatio-temporal Regression

This study compares GIG-ICA and STR for identifying brain functional networks in schizophrenia, finding GIG-ICA more sensitive to group differences and better at classification.