A high-resolution in vivo magnetic resonance imaging atlas of the human hypothalamic region.
Clemens NeudorferJurgen GermannGavin J B EliasRobert GramerAlexandre BoutetAndres M LozanoPublished in: Scientific data (2020)
The study of the hypothalamus and its topological changes provides valuable insights into underlying physiological and pathological processes. Owing to technological limitations, however, in vivo atlases detailing hypothalamic anatomy are currently lacking in the literature. In this work we aim to overcome this shortcoming by generating a high-resolution in vivo anatomical atlas of the human hypothalamic region. A minimum deformation averaging (MDA) pipeline was employed to produce a normalized, high-resolution template from multimodal magnetic resonance imaging (MRI) datasets. This template was used to delineate hypothalamic (n = 13) and extrahypothalamic (n = 12) gray and white matter structures. The reliability of the atlas was evaluated as a measure for voxel-wise volume overlap among raters. Clinical application was demonstrated by superimposing the atlas into datasets of patients diagnosed with a hypothalamic lesion (n = 1) or undergoing hypothalamic (n = 1) and forniceal (n = 1) deep brain stimulation (DBS). The present template serves as a substrate for segmentation of brain structures, specifically those featuring low contrast. Conversely, the segmented hypothalamic atlas may inform DBS programming procedures and may be employed in volumetric studies.
Keyphrases
- high resolution
- deep brain stimulation
- magnetic resonance imaging
- single cell
- white matter
- endothelial cells
- contrast enhanced
- rna seq
- end stage renal disease
- computed tomography
- chronic kidney disease
- systematic review
- magnetic resonance
- obsessive compulsive disorder
- mass spectrometry
- ejection fraction
- molecularly imprinted
- multiple sclerosis
- machine learning
- newly diagnosed
- tandem mass spectrometry
- peritoneal dialysis
- brain injury
- high speed
- diffusion weighted imaging
- prognostic factors
- convolutional neural network
- breast cancer cells
- case control