NeuroImage
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The glymphatic pathway is a system which facilitates continuous cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange and plays a key role in removing waste products from the rodent brain. Dysfunction of the glymphatic pathway may be implicated in the pathophysiology of Alzheimer's disease. Intriguingly, the glymphatic system is most active during deep wave sleep general anesthesia. ⋯ We use 3D visualization computational tools to visualize the OMT defined network of CSF-ISF flow channels in relation to anatomical and vascular key landmarks from the live rodent brain. The resulting OMT model of the glymphatic transport network agrees largely with the current understanding of the glymphatic transport patterns defined by dynamic contrast-enhanced MRI revealing key CSF transport pathways along the ventral surface of the brain with a trajectory towards the pineal gland, cerebellum, hypothalamus and olfactory bulb. In addition, the OMT analysis also revealed some interesting previously unnoticed behaviors regarding CSF transport involving parenchymal streamlines moving from ventral reservoirs towards the surface of the brain, olfactory bulb and large central veins.
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With increasing age, cognitive control processes steadily decline. Prior research suggests that healthy older adults have a generally intact performance monitoring system, but show specific deficits in error awareness, i.e., the ability to detect committed errors. We examined the neural processing of errors across the adult lifespan (69 participants; age range 20-72 years) by analysing the error (-related) negativity (Ne/ERN) and the error positivity (Pe) using an adapted version of the Go/Nogo task. ⋯ Structural path models suggested that through those age-related changes in Pe amplitude, an indirect effect on the performance was observed. Our results confirm and extend previous extreme-group based findings about specific deficits in error detection associated with higher age using age as a continuous predictor. Age-related reductions in Pe amplitude, associated with more undetected errors, are independent of early error processing, as evidenced by the preserved Ne/ERN.
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The MR signal from gray matter has been long known to present small differences in intensity that have been attributed to variations in cortical myelin content. Previous studies have shown that the T1-, T2-weighted signal and their ratio are sensitive to these variations. ⋯ The resulting intensity maps correspond well to known regional myeloarchitectural differences between cortical regions. These results confirm that widely available MR sequences contain signal that may be used to reliably detect subtle differences in the composition of gray matter with a segmentation approach.
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The spinal cord white and gray matter can be affected by various pathologies such as multiple sclerosis, amyotrophic lateral sclerosis or trauma. Being able to precisely segment the white and gray matter could help with MR image analysis and hence be useful in further understanding these pathologies, and helping with diagnosis/prognosis and drug development. Up to date, white/gray matter segmentation has mostly been done manually, which is time consuming, induces a bias related to the rater and prevents large-scale multi-center studies. ⋯ When compared to the classical template registration framework, the proposed framework that accounts for gray matter shape significantly improved the quality of the registration (comparing Dice coefficient in gray matter: p=9.5×10-6). While further validation is needed to show the benefits of the new registration framework in large cohorts and in a variety of patients, this study provides a fully-integrated tool for quantitative assessment of white/gray matter morphometry and template-based analysis. All the proposed methods are implemented in the Spinal Cord Toolbox (SCT), an open-source software for processing spinal cord multi-parametric MRI data.