This article reviews the evaluation and optimization of the preprocessing steps for blood-
oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This …

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 …

Schizophrenia (SZ) and bipolar disorder (BP) share significant overlap in clinical symptoms,
brain characteristics, and risk genes, and both are associated with dysconnectivity among …

This study proposes a novel approach to predict missing shear sonic log responses more
precisely and accurately using similarity patterns of various wells with similar geophysical …

Functional magnetic resonance imaging (fMRI) is a fairly new technique that has the
potential to characterize and classify brain disorders such as schizophrenia. It has the …

Objective In this paper, we develop a dynamic functional network connectivity (FNC)
analysis approach using correlations between windowed time-courses of different brain …

Behavioral studies have suggested that exaggerated reactivity to food cues, especially those
associated with high-calorie foods, may be a factor underlying obesity. This increased …

Objective: The “default mode” has been defined as a baseline condition of brain function
and is of interest because its component brain regions are believed to be abnormal in …

Multivariate analysis methods such as independent component analysis (ICA) have been
applied to the analysis of functional magnetic resonance imaging (fMRI) data to study brain …

Behavioral studies have suggested that food cues have stronger motivating effects in obese
than in normal-weight individuals, which may be a risk factor underlying obesity. Previous …