Recently, deep learning models have achieved superior performance for mapping functional
brain networks from functional magnetic resonance imaging (fMRI) data compared with …

A novel self-supervised deep learning (DL) method is developed to compute personalized
brain functional networks (FNs) for characterizing brain functional neuroanatomy based on …

Resting-state magnetoencephalography (MEG) data show complex but structured
spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is …

With the increasing prevalence of digital multimedia devices and the growing reliance on
compression and wireless data transmission, evaluating image quality remains a persistent …

Abstract Representation learning is a core component in data-driven modeling of various
complex phenomena. Learning a contextually informative representation can especially …

Positive feedbacks between permafrost degradation and the release of soil carbon into the
atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and …

Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate
previously inaccessible trajectories of early-life prenatal and neonatal brain development …

Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for
atypical pattern detection in brain imaging. During training, these models learn to capture …

Functional magnetic resonance imaging (fMRI) data is collected in millions of noisy,
redundant dimensions. To understand how different brains process the same stimulus, we …

Clustering plays a crucial role in the field of data mining, where deep non-negative matrix
factorization (NMF) has attracted significant attention due to its effective data representation …