The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …

Medical image reconstruction aims to acquire high-quality medical images for clinical usage
at minimal cost and risk to the patients. Deep learning and its applications in medical …

Reconstructing visual experiences from human brain activity offers a unique way to
understand how the brain represents the world, and to interpret the connection between …

Decoding visual stimuli from brain recordings aims to deepen our understanding of the
human visual system and build a solid foundation for bridging human and computer vision …

Understanding how the brain encodes external stimuli and how these stimuli can be
decoded from the measured brain activities are long-standing and challenging questions in …

Recently, a new paradigm of building general-purpose language models (eg, Google's Bert
and OpenAI's GPT-2) in Natural Language Processing (NLP) for text feature extraction, a …

Although distinct categories are reliably decoded from fMRI brain responses, it has proved
more difficult to distinguish visually similar inputs, such as different faces. Here, we apply a …

One of the most useful metaphors for driving scientific and engineering progress has been
that of the “machine”. Much controversy exists about the applicability of this concept in the …

Missing data imputation (MDI) is the task of replacing missing values in a dataset with
alternative, predicted ones. Because of the widespread presence of missing data, it is a …

Highlights•The architecture of the cerebral vasculature sculpts the hemodynamics
response.•The BOLD signal is correlated with increases in gamma-band power in the LFP …