The brain is a complex, multiscale dynamical system composed of many interacting regions.
Knowledge of the spatiotemporal organization of these interactions is critical for establishing …

Recent years have witnessed a rapid growth of interest in moving functional magnetic
resonance imaging (fMRI) beyond simple scan-length averages and into approaches that …

Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time
scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain …

Deep learning methods have recently made notable advances in the tasks of classification
and representation learning. These tasks are important for brain imaging and neuroscience …

Starting with a simple generative model and the assumption of statistical independence of
the underlying components, independent component analysis (ICA) decomposes a given …

A key challenge in functional neuroimaging is the meaningful combination of results across
subjects. Even in a sample of healthy participants, brain morphology and functional …

Matrix factorization models are the current dominant approach for resolving meaningful data-
driven features in neuroimaging data. Among them, independent component analysis (ICA) …

Independent component analysis (ICA) has been widely applied to identify intrinsic brain
networks from fMRI data. Group ICA computes group‐level components from all data and …

The sliding window correlation (SWC) analysis is a straightforward and common approach
for evaluating dynamic functional connectivity. Despite the fact that sliding window analyses …

Spatial smoothing of functional magnetic resonance imaging (fMRI) data can be performed
on volumetric images and on the extracted surface of the brain. Smoothing on the unfolded …