Three-Dimensional Whole-Brain Mapping

1992 ◽  
Vol 58 (1-4) ◽  
pp. 141-143 ◽  
Author(s):  
T.L. Hardy ◽  
L.R.D. Brynildson ◽  
J.G. Gray ◽  
D. Spurlock
Keyword(s):  
Brain Mapping ◽  
Three Dimensional ◽  
Whole Brain

Whole‐brain mapping of amylin‐induced neuronal activity in receptor activity–modifying protein 1/3 knockout mice

2021 ◽  
Author(s):  
Grethe Skovbjerg ◽  
Urmas Roostalu ◽  
Henrik H. Hansen ◽  
Thomas A. Lutz ◽  
Christelle Le Foll ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Brain Mapping ◽  
Knockout Mice ◽  
Receptor Activity ◽  
Whole Brain

Connectomics: comprehensive approaches for whole-brain mapping

Microscopy ◽  
2014 ◽  
Vol 64 (1) ◽  
pp. 57-67 ◽  
Author(s):  
Shinsuke Shibata ◽  
Yuji Komaki ◽  
Fumiko Seki ◽  
Michiko O. Inouye ◽  
Toshihiro Nagai ◽  
...  
Keyword(s):  
Brain Mapping ◽  
Whole Brain

scholarly journals T 2 prep three-dimensional spiral imaging with efficient whole brain coverage for myelin water quantification at 1.5 tesla

2012 ◽  
Vol 67 (3) ◽  
pp. 614-621 ◽  
Author(s):  
Thanh D. Nguyen ◽  
Cynthia Wisnieff ◽  
Mitchell A. Cooper ◽  
Dushyant Kumar ◽  
Ashish Raj ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Whole Brain ◽  
Spiral Imaging ◽  
Water Quantification

scholarly journals Whole Brain 3D MR Fingerprinting in Multiple Sclerosis: A Pilot Investigation

2021 ◽  
Author(s):  
Thomaz R. Mostardeiro ◽  
Ananya Panda ◽  
Norbert G. Campeau ◽  
Robert J. Witte ◽  
Yi Sui ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Three Dimensional ◽  
Acquisition Time ◽  
Imaging Method ◽  
Whole Brain ◽  
Normal Appearing White Matter ◽  
Pilot Investigation ◽  
Quantitative Properties ◽  
Perfect Discrimination ◽  
3.0T Mri

Abstract Background: MR fingerprinting (MRF) is a novel imaging method proposed for the diagnosis of Multiple Sclerosis (MS). This study aims to determine if MR Fingerprinting (MRF) relaxometry can differentiate frontal normal appearing white matter (F-NAWM) and splenium in patients diagnosed with MS as compared to controls and to characterize the relaxometry of demyelinating plaques relative to the time of diagnosis.Methods: Three-dimensional (3D) MRF data were acquired on a 3.0T MRI system resulting in isotropic voxels (1x1x1mm3) and a total acquisition time of 4min 38s. Data were collected on 18 subjects paired with 18 controls. Regions of interested were drawn over MRF-derived T1 relaxometry maps encompassing selected MS lesions, F-NAWM and splenium. T1 and T2 relaxometry features from those segmented areas were used to classify MS lesions from F-NAWM and splenium with T-distributed stochastic neighbor embedding algorithms (T-SNE). Partial least squares discriminant analysis (PLS-DA) was performed to discriminate NAWM and Splenium in MS compared with controls. Results: Mean out-of-fold machine learning prediction accuracy for discriminant results between MS patients and controls for F-NAWM was 65% and approached 90% for the splenium. There was significant positive correlation between time since diagnosis and MS lesions mean T2 (p=0.015), minimum T1 (p=0.03) and negative correlation with splenium uniformity (p=0.04). Perfect discrimination (AUC=1) was achieved between selected features from MS lesions and F-NAWM.Conclusions: 3D-MRF has the ability to differentiate between MS and controls based on relaxometry properties from the F-NAWM and splenium. Whole brain coverage allows the assessment of quantitative properties within lesions that provide chronological assessment of the time from MS diagnosis.

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