Eswar Damaraju

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 study uses a Siamese CNN framework to predict individual identity from resting-state fMRI data, achieving 88% accuracy in subject discrimination using spatial network pairs.

The paper introduces a novel tri-clustering method for dynamic functional network connectivity to identify schizophrenia effects across subgroups in resting fMRI data.

The paper introduces a toolbox to automatically label and order brain activity maps with high accuracy using a pretrained model and integration of existing techniques.

This paper proposes a classification-based method to determine the optimal number of states in dynamic functional connectivity analysis, showing improved classification accuracy compared to traditional methods.

This paper introduces a statelet-based approach to analyze dynamic functional network connectivity, finding reduced modularity and lower connection consistency in schizophrenia patients compared to healthy controls.

This paper introduces a new method for estimating connectivity that captures both static and dynamic frequency profiles, showing it can detect group differences in resting fMRI data between schizophrenia patients and controls.

This study analyzes connectivity dynamics in fMRI data across wakefulness and sleep stages using ICA and sliding window techniques, showing improved classification of sleep states and motion effects with shorter windows.

This study identifies 45 structural-functional component maps across 9 functional domains using independent component analysis in two cohorts, showing varying S-F coherence in different brain regions.

This study analyzes how cerebrovascular reactivity affects static and dynamic functional network connectivity using fMRI, finding weaker connectivity during vasodilation and incomplete recovery after CO2 inhalation.

This study analyzes age-related changes in brain structure and function across 5967 individuals from 13 to 72 years old, revealing nonlinear patterns in gray matter volume, structural network correlation, and functional network connectivity.

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

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.

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.

This study evaluates the replicability of dynamic functional network connectivity patterns across 7500 fMRI datasets, finding robust and reproducible connectivity states.