Magnetic resonance imaging and computed tomography from multiple batches (eg sites,
scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to …

The comprehension of the glymphatic system, a postulated mechanism responsible for the
removal of interstitial solutes within the central nervous system (CNS), has witnessed …

Data harmonization is an important method for combining or transforming data. To date
however, articles about data harmonization are field-specific and highly technical, making it …

Magnetic resonance spectroscopy (MRS) is a non-invasive neuroimaging technique used to
measure brain chemistry in vivo and has been used to study the healthy brain as well as …

Background The growth in multi-center neuroimaging studies generated a need for methods
that mitigate the differences in hardware and acquisition protocols across sites ie, scanner …

Although many studies have demonstrated structural brain abnormalities associated with
auditory verbal hallucinations (AVH) in schizophrenia, the results remain inconsistent …

Deep learning has become a leading subset of machine learning and has been successfully
employed in diverse areas, ranging from natural language processing to medical image …

The accumulation of multisite large-sample MRI datasets collected during large brain
research projects in the last decade has provided critical resources for understanding the …

Abstract Changes in the brain's default mode network (DMN) in the resting state are closely
related to autism spectrum disorder (ASD). Module segmentation can effectively elucidate …

Abstract Background and Hypothesis Machine learning approaches using structural
magnetic resonance imaging (MRI) can be informative for disease classification; however …