Background The choice of preprocessing pipeline introduces variability in neuroimaging
analyses that affects the reproducibility of scientific findings. Features derived from structural …

Traditional neuroimage analysis pipelines involve computationally intensive, time-
consuming optimization steps, and thus, do not scale well to large cohort studies with …

Multimodal magnetic resonance imaging (MRI) has accelerated human neuroscience by
fostering the analysis of brain microstructure, geometry, function, and connectivity across …

A large range of sophisticated brain image analysis tools have been developed by the
neuroscience community, greatly advancing the field of human brain mapping. Here we …

Numerous studies have explored the relationship between cortical structure and brain
development, cognitive function, and functional connectivity. The highly convoluted cortical …

Accurate and reliable measures of cortical thickness from magnetic resonance imaging are
an important biomarker to study neurodegenerative and neurological disorders …

Subject motion can introduce noise into neuroimaging data and result in biased estimations
of brain structure. In-scanner motion can compromise data quality in a number of ways and …

With the proliferation of multi-site neuroimaging studies, there is a greater need for handling
non-biological variance introduced by differences in MRI scanners and acquisition …

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

Mapping brain structure in relation to neurological development, function, plasticity, and
disease is widely considered to be one of the most essential challenges for opening new …