Thomas P. DeRamus

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.

Individualized Spatial Network Predictions Using Siamese Convolutional Neural Networks: A Resting-State fMRI Study of over 11,000 Unaffected Individuals

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.

Modular and state-relevant connectivity in high-frequency resting-state BOLD fMRI data: An independent component analysis

The paper analyzes resting-state fMRI data across different frequency bins to assess functional network connectivity and finds distinct patterns at higher frequencies.

Ultra-high-order ICA: fine overlapping functional parcellations and spatiotemporal reconfiguration

This paper uses ultra-high-order ICA to identify fine overlapping functional dynamic parcellations of the brain, highlighting their evolution over time.

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.

Spatial dynamics within and between brain functional domains: A hierarchical approach to study time-varying brain function

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.