Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

Magnetic resonance imaging (MRI) has transformed our understanding of the human brain
through well-replicated map** of abilities to specific structures (for example, lesion …

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic
oscillations have been linked to numerous physiological, cognitive, behavioral and disease …

We propose and empirically support a parsimonious account of intrinsic, brain-wide
spatiotemporal organization arising from traveling waves linked to arousal. We hypothesize …

Despite advances in data augmentation and transfer learning, convolutional neural
networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans …

Background: Analysis of the human connectome using functional magnetic resonance
imaging (fMRI) started in the mid-1990s and attracted increasing attention in attempts to …

Addressing the multiscale organization of the brain, which is fundamental to the dynamic
repertoire of the organ, remains challenging. In principle, it should be possible to model …

Preprocessing of functional magnetic resonance imaging (fMRI) involves numerous steps to
clean and standardize the data before statistical analysis. Generally, researchers create ad …

Abstract The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing,
nationwide study of the effects of environmental influences on behavioral and brain …