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Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI.

ABSTRACT: Studies into cortical thickness in psychiatric diseases based on T1-weighted MRI frequently report on aberrations in the cerebral cortex. Due to limitations in image resolution for studies conducted at conventional MRI field strengths (e.g. 3 Tesla (T)) this information cannot be used to establish which of the cortical layers may be implicated. Here we propose a new analysis method that computes one high-resolution average cortical profile per brain region extracting myeloarchitectural information from T1-weighted MRI scans that are routinely acquired at a conventional field strength. To assess this new method, we acquired standard T1-weighted scans at 3 T and compared them with state-of-the-art ultra-high resolution T1-weighted scans optimised for intracortical myelin contrast acquired at 7 T. Average cortical profiles were computed for seven different brain regions. Besides a qualitative comparison between the 3 T scans, 7 T scans, and results from literature, we tested if the results from dynamic time warping-based clustering are similar for the cortical profiles computed from 7 T and 3 T data. In addition, we quantitatively compared cortical profiles computed for V1, V2 and V7 for both 7 T and 3 T data using a priori information on their relative myelin concentration. Although qualitative comparisons show that at an individual level average profiles computed for 7 T have more pronounced features than 3 T profiles the results from the quantitative analyses suggest that average cortical profiles computed from T1-weighted scans acquired at 3 T indeed contain myeloarchitectural information similar to profiles computed from the scans acquired at 7 T. The proposed method therefore provides a step forward to study cortical myeloarchitecture in vivo at conventional magnetic field strength both in health and disease.

SUBMITTER: Ferguson B

PROVIDER: S-EPMC5984962 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI.

Ferguson Bart B Petridou Natalia N Fracasso Alessio A van den Heuvel Martijn P MP Brouwer Rachel M RM Hulshoff Pol Hilleke E HE Kahn René S RS Mandl René C W RCW

Neuroinformatics 20180401 2

Studies into cortical thickness in psychiatric diseases based on T1-weighted MRI frequently report on aberrations in the cerebral cortex. Due to limitations in image resolution for studies conducted at conventional MRI field strengths (e.g. 3 Tesla (T)) this information cannot be used to establish which of the cortical layers may be implicated. Here we propose a new analysis method that computes one high-resolution average cortical profile per brain region extracting myeloarchitectural informati ...[more]

PMID: 29352389

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Project description:PurposePhosphorus (31 P) metabolites are emerging liver disease biomarkers. Of particular interest are phosphomonoester and phosphodiester (PDE) "peaks" that comprise multiple overlapping resonances in 31 P spectra. This study investigates the effect of improved spectral resolution at 7 Tesla (T) on quantifying hepatic metabolites in cirrhosis.MethodsFive volunteers were scanned to determine metabolite T1 s. Ten volunteers and 11 patients with liver cirrhosis were scanned at 7T. Liver spectra were acquired in 28 min using a 16-channel 31 P array and 3D chemical shift imaging. Concentrations were calculated using γ-adenosine-triphosphate (γ-ATP) = 2.65 mmol/L wet tissue.ResultsT1 means ± standard deviations: phosphatidylcholine 1.05 ± 0.28 s, nicotinamide-adenine-dinucleotide (NAD+ ) 2.0 ± 1.0 s, uridine-diphosphoglucose (UDPG) 3.3 ± 1.4 s. Concentrations in healthy volunteers: α-ATP 2.74 ± 0.11 mmol/L wet tissue, inorganic phosphate 2.23 ± 0.20 mmol/L wet tissue, glycerophosphocholine 2.34 ± 0.46 mmol/L wet tissue, glycerophosphoethanolamine 1.50 ± 0.28 mmol/L wet tissue, phosphocholine 1.06 ± 0.16 mmol/L wet tissue, phosphoethanolamine 0.77 ± 0.14 mmol/L wet tissue, NAD+ 2.37 ± 0.14 mmol/L wet tissue, UDPG 2.00 ± 0.22 mmol/L wet tissue, phosphatidylcholine 1.38 ± 0.31 mmol/L wet tissue. Inorganic phosphate and phosphatidylcholine concentrations were significantly lower in patients; glycerophosphoethanolamine concentrations were significantly higher (P < 0.05).ConclusionWe report human in vivo hepatic T1 s for phosphatidylcholine, NAD+ , and UDPG for the first time at 7T. Our protocol allows high signal-to-noise, repeatable measurement of metabolite concentrations in human liver. The splitting of PDE into its constituent peaks at 7T may allow more insight into changes in metabolism. Magn Reson Med 78:2095-2105, 2017. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Project description:BackgroundParkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs).Methods155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models.ResultsOur VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high.ConclusionThe SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.

Dataset Information

Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI.

Publications

Detailed T1-Weighted Profiles from the Human Cortex Measured in Vivo at 3 Tesla MRI.

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