Adrian Preda

This paper introduces a chronnectogram-ICA framework to track continuously evolving and overlapping brain states in fMRI data, revealing new insights into schizophrenia's connectivity dynamics.

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

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

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.

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.

This study identifies distinct functional connectivity patterns in mental disorders using dFIPs derived from rs-fMRI data, revealing disorder-specific and shared neural signatures.

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.

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

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 paper introduces a multi-modal AI model for schizophrenia classification using imaging and genetic data, achieving 94.10% accuracy and identifying key brain regions and genes associated with the disorder.

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

The paper develops a method to estimate time-resolved spatial networks and applies it to study sex-specific schizophrenia disruptions and their genetic links.

This paper introduces a multi-scale spatially constrained ICA approach to analyze fMRI data, revealing consistent group differences and predictive patterns in schizophrenia.

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 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.

The paper uses a Bayesian optimization framework to analyze brain functional subdomains in schizophrenia, showing that more complex models better capture discriminatory information in specific subdomains.

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.

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.

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.

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.

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.

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.

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

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

This paper introduces a hierarchical approach to analyze spatial and temporal dynamics in brain function, revealing new insights into the chronnectome and identifying changes in schizophrenia patients.