Alzheimer's disease (AD) is the most common type (> 60%) of dementia and can wreak
havoc on the psychological and physiological development of sufferers and their carers, as …

Methods for the analysis of neuroimaging data have advanced significantly since the
beginning of neuroscience as a scientific discipline. Today, sophisticated statistical …

Featured Application Enhancing Clinical Diagnosis through the Integration of Deep
Learning Techniques in Medical Image Recognition. This comprehensive review highlights …

Recent learning-based approaches have made astonishing advances in calibrated medical
imaging like computerized tomography (CT). Yet, they struggle to generalize in uncalibrated …

Background Clinical trials for upcoming disease‐modifying therapies of spinocerebellar
ataxias (SCA), a group of rare movement disorders, lack endpoints sensitive to early disease …

Deep learning segmentation algorithms can produce reproducible results in a matter of
seconds. However, their application to more complex datasets is uncertain and may fail in …

Head motion during MR acquisition reduces image quality and has been shown to bias
neuromorphometric analysis. The quantification of head motion, therefore, has both …

Abstract Low-field (< 1 T) magnetic resonance imaging (MRI) scanners remain in
widespread use in low-and middle-income countries (LMICs) and are commonly used for …

Contemporary biomechanical modeling of traumatic brain injury (TBI) focuses on either the
global brain as an organ or a representative tiny section of a single axon. In addition, while it …

The amygdala is a subcortical region in the mesiotemporal lobe that plays a key role in
emotional and sensory functions. Conventional neuroimaging experiments treat this …