Katharina Glomb

Using an Ordinary Differential Equation model to separate Rest and Task signals in fMRI

This study uses an ODE model to separate rest and task signals in fMRI data, showing task-specific dynamics are subsets of rest dynamics and improving reaction time predictions.

Connectome spectrum electromagnetic tomography: a method to reconstruct electrical brain source-networks at high-spatial resolution

This paper introduces CSET, a method that reconstructs electrical brain source-networks with high spatial resolution using diffusion MRI and EEG data.

The connectome spectrum as a canonical basis for a sparse representation of fast brain activity

This paper introduces the connectome spectrum as a method to represent brain activity using structural connectivity, showing it provides a more compact and informative representation of fast brain dynamics.

Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

The paper identifies functional harmonic modes as a governing principle for brain organization, showing they can reconstruct task-related activity and delineate cortical areas from resting-state fMRI data.

Connectome spectral analysis to track EEG task dynamics on a subsecond scale

This paper introduces a method using network harmonics to track fast EEG dynamics during a face detection task with subsecond resolution.

Using structural connectivity to augment community structure in EEG functional connectivity

The paper uses white matter structural connectivity to improve EEG functional connectivity by smoothing signals in a graph space derived from structural data, increasing resemblance to fMRI connectivity.

Stereotypical Modulations In Dynamic Functional Connectivity Explained By Changes In BOLD Variance

This study examines how fluctuations in functional connectivity and BOLD variance relate to dynamic changes in resting state networks, finding that these fluctuations are not random but reflect non-stationary processes.

Resting State Networks in empirical and simulated dynamic functional connectivity

This study uses tensor decomposition and a mean field model to show stationary dynamics can explain resting state networks in both empirical and simulated data.