Background Once considered mere noise, fMRI-based functional connectivity has become a
major neuroscience tool in part due to early studies demonstrating its reliability. These …

The neuroimaging community has witnessed a paradigm shift in biomarker discovery from
using traditional univariate brain map** approaches to multivariate predictive models …

Functional interactions between brain regions can be viewed as a network, enabling
neuroscientists to investigate brain function through network science. Here, we …

There is significant interest in the development and application of deep neural networks
(DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their …

Psychiatric disorders are complex, involving heterogeneous symptomatology and
neurobiology that rarely involves the disruption of single, isolated brain structures. In an …

Most neuroimaging studies of brain function analyze data in normalized space to identify
regions of common activation across participants. These studies treat interindividual …

When fields lack consensus standard methods and accessible ground truths, reproducibility
can be more of an ideal than a reality. Such has been the case for functional neuroimaging …

Background Higuchi's fractal dimension (FD) captures brain dynamics complexity and may
be a promising method to analyze resting‐state functional magnetic resonance imaging …

Attention is central to many aspects of cognition, but there is no singular neural measure of a
person's overall attentional functioning across tasks. Here, using original data from 92 …

Identifying biomarkers that predict mental states with large effect sizes and high test-retest
reliability is a growing priority for fMRI research. We examined a well-established …