Association of Changes in Cerebral and Hypothalamic Structure With Sleep Dysfunction in Patients With Genetic Frontotemporal Dementia.

Background And Objectives: Sleep dysfunction is common in patients with neurodegenerative disorders; however, its neural underpinnings remain poorly characterized in genetic frontotemporal dementia (FTD). Hypothalamic nuclei important for sleep regulation may be related to this dysfunction. Thus, we examined changes in hypothalamic structure across the lifespan in patients with genetic FTD and whether these changes related to sleep dysfunction.

A (sub)field guide to quality control in hippocampal subfield segmentation on high-resolution T-weighted MRI.

Inquiries into properties of brain structure and function have progressed due to developments in magnetic resonance imaging (MRI). To sustain progress in investigating and quantifying neuroanatomical details in vivo, the reliability and validity of brain measurements are paramount. Quality control (QC) is a set of procedures for mitigating errors and ensuring the validity and reliability of brain measurements.

A next-generation, histological atlas of the human brain and its application to automated brain MRI segmentation.

Magnetic resonance imaging (MRI) is the standard tool to image the human brain In this domain, digital brain atlases are essential for subject-specific segmentation of anatomical regions of interest (ROIs) and spatial comparison of neuroanatomy from different subjects in a common coordinate frame. High-resolution, digital atlases derived from histology (e.g.

High-resolution segmentations of the hypothalamus and its subregions for training of segmentation models.

Segmentation of brain structures on magnetic resonance imaging (MRI) is a highly relevant neuroimaging topic, as it is a prerequisite for different analyses such as volumetry or shape analysis. Automated segmentation facilitates the study of brain structures in larger cohorts when compared with manual segmentation, which is time-consuming. However, the development of most automated methods relies on large and manually annotated datasets, which limits the generalizability of these methods.