1,370 research outputs found
Cortical thickness, surface area and volume measures in Parkinson's disease, multiple system atrophy and progressive supranuclear palsy
OBJECTIVE
Parkinson's disease (PD), Multiple System Atrophy (MSA) and Progressive Supranuclear Palsy (PSP) are neurodegenerative diseases that can be difficult to distinguish clinically. The objective of the current study was to use surface-based analysis techniques to assess cortical thickness, surface area and grey matter volume to identify unique morphological patterns of cortical atrophy in PD, MSA and PSP and to relate these patterns of change to disease duration and clinical features.
METHODS
High resolution 3D T1-weighted MRI volumes were acquired from 14 PD patients, 18 MSA, 14 PSP and 19 healthy control participants. Cortical thickness, surface area and volume analyses were carried out using the automated surface-based analysis package FreeSurfer (version 5.1.0). Measures of disease severity and duration were assessed for correlation with cortical morphometric changes in each clinical group.
RESULTS
Results show that in PSP, widespread cortical thinning and volume loss occurs within the frontal lobe, particularly the superior frontal gyrus. In addition, PSP patients also displayed increased surface area in the pericalcarine. In comparison, PD and MSA did not display significant changes in cortical morphology.
CONCLUSION
These results demonstrate that patients with clinically established PSP exhibit distinct patterns of cortical atrophy, particularly affecting the frontal lobe. These results could be used in the future to develop a useful clinical application of MRI to distinguish PSP patients from PD and MSA patients
Pulse Sequence Resilient Fast Brain Segmentation
Accurate automatic segmentation of brain anatomy from
-weighted~(-w) magnetic resonance images~(MRI) has been a
computationally intensive bottleneck in neuroimaging pipelines, with
state-of-the-art results obtained by unsupervised intensity modeling-based
methods and multi-atlas registration and label fusion. With the advent of
powerful supervised convolutional neural networks~(CNN)-based learning
algorithms, it is now possible to produce a high quality brain segmentation
within seconds. However, the very supervised nature of these methods makes it
difficult to generalize them on data different from what they have been trained
on. Modern neuroimaging studies are necessarily multi-center initiatives with a
wide variety of acquisition protocols. Despite stringent protocol harmonization
practices, it is not possible to standardize the whole gamut of MRI imaging
parameters across scanners, field strengths, receive coils etc., that affect
image contrast. In this paper we propose a CNN-based segmentation algorithm
that, in addition to being highly accurate and fast, is also resilient to
variation in the input -w acquisition. Our approach relies on building
approximate forward models of -w pulse sequences that produce a typical
test image. We use the forward models to augment the training data with test
data specific training examples. These augmented data can be used to update
and/or build a more robust segmentation model that is more attuned to the test
data imaging properties. Our method generates highly accurate, state-of-the-art
segmentation results~(overall Dice overlap=0.94), within seconds and is
consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201
Evidence of distributed subpial T2* signal changes at 7T in multiple sclerosis : an histogram based approach
Subpial lesions are the most frequent type of cortical lesion in multiple sclerosis (MS), and are thought to be closely associated with poor clinical outcome. Neuropathological studies report that subpial lesions may come in two major types: they may appear as circumscribed, focal lesions, or extend across multiple adjacent gyri leading to a phenomenon termed “general subpial demyelination” [1]. The in vivo evaluation of diffuse subpial disease is challenging – signal changes may be subtle, and extend across large regions where signal inhomogeneities due to B1 and RF receive coil non-uniformities become more pronounced. Here, we investigate whether a histogram-based analysis of T2* signal intensity in the cortex, at 7T MRI, can show evidence of distributed subpial cortical changes in patients with MS, as described histopathologically. We hypothesized that this phenomenon would be associated with significantly increased T2* signal intensity in patients compared to age-matched controls.Center Algoritm
Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction
Deep learning for regression tasks on medical imaging data has shown
promising results. However, compared to other approaches, their power is
strongly linked to the dataset size. In this study, we evaluate
3D-convolutional neural networks (CNNs) and classical regression methods with
hand-crafted features for survival time regression of patients with high grade
brain tumors. The tested CNNs for regression showed promising but unstable
results. The best performing deep learning approach reached an accuracy of
51.5% on held-out samples of the training set. All tested deep learning
experiments were outperformed by a Support Vector Classifier (SVC) using 30
radiomic features. The investigated features included intensity, shape,
location and deep features. The submitted method to the BraTS 2018 survival
prediction challenge is an ensemble of SVCs, which reached a cross-validated
accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set,
and 42.9% on the testing set. The results suggest that more training data is
necessary for a stable performance of a CNN model for direct regression from
magnetic resonance images, and that non-imaging clinical patient information is
crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation
(BraTS) Challenge 2018, survival prediction tas
Automated hippocampal segmentation in 3D MRI using random undersampling with boosting algorithm
The automated identification of brain structure in Magnetic Resonance Imaging is very important both in neuroscience research and as a possible clinical diagnostic tool. In this study, a novel strategy for fully automated hippocampal segmentation in MRI is presented. It is based on a supervised algorithm, called RUSBoost, which combines data random undersampling with a boosting algorithm. RUSBoost is an algorithm specifically designed for imbalanced classification, suitable for large data sets because it uses random undersampling of the majority class. The RUSBoost performances were compared with those of ADABoost, Random Forest and the publicly available brain segmentation package, FreeSurfer. This study was conducted on a data set of 50 T1-weighted structural brain images. The RUSBoost-based segmentation tool achieved the best results with a Dice’s index of (Formula presented.) (Formula presented.) for the left (right) brain hemisphere. An independent data set of 50 T1-weighted structural brain scans was used for an independent validation of the fully trained strategies. Again the RUSBoost segmentations compared favorably with manual segmentations with the highest performances among the four tools. Moreover, the Pearson correlation coefficient between hippocampal volumes computed by manual and RUSBoost segmentations was 0.83 (0.82) for left (right) side, statistically significant, and higher than those computed by Adaboost, Random Forest and FreeSurfer. The proposed method may be suitable for accurate, robust and statistically significant segmentations of hippocampi
Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections
Nonlinear registration of 2D histological sections with corresponding slices
of MRI data is a critical step of 3D histology reconstruction. This task is
difficult due to the large differences in image contrast and resolution, as
well as the complex nonrigid distortions produced when sectioning the sample
and mounting it on the glass slide. It has been shown in brain MRI registration
that better spatial alignment across modalities can be obtained by synthesizing
one modality from the other and then using intra-modality registration metrics,
rather than by using mutual information (MI) as metric. However, such an
approach typically requires a database of aligned images from the two
modalities, which is very difficult to obtain for histology/MRI.
Here, we overcome this limitation with a probabilistic method that
simultaneously solves for registration and synthesis directly on the target
images, without any training data. In our model, the MRI slice is assumed to be
a contrast-warped, spatially deformed version of the histological section. We
use approximate Bayesian inference to iteratively refine the probabilistic
estimate of the synthesis and the registration, while accounting for each
other's uncertainty. Moreover, manually placed landmarks can be seamlessly
integrated in the framework for increased performance.
Experiments on a synthetic dataset show that, compared with MI, the proposed
method makes it possible to use a much more flexible deformation model in the
registration to improve its accuracy, without compromising robustness.
Moreover, our framework also exploits information in manually placed landmarks
more efficiently than MI, since landmarks inform both synthesis and
registration - as opposed to registration alone. Finally, we show qualitative
results on the public Allen atlas, in which the proposed method provides a
clear improvement over MI based registration
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Brain structural concomitants of resting state heart rate variability in the young and old: evidence from two independent samples
Previous research has shown associations between brain structure and resting state high-frequency heart rate variability (HF-HRV). Age affects both brain structure and HF-HRV. Therefore we sought to examine the relationship between brain structure and HF-HRV as a function of age. Data from two independent studies were used for the present analysis. Study 1 included 19 older adults (10 male, age range 62-78 years) and 19 younger adults (12 male, age range 19-37). Study 2 included 23 older adults (13 males; age range 55-75) and 27 younger adults (19 males; age range 18-34). The rootmean-
square of successive R-R-interval differences (RMSSD) from ECG recordings was used as timedomain measure of HF-HRV. MRI scans were performed on a 3.0-T Siemens Magnetom Trio scanner. Cortical reconstruction and volumetric segmentation were performed with the Freesurfer image analysis suite, including 12 regions as regions-of-interests (ROI). Zero-order and partial correlations were used to assess the correlation of RMSSD with cortical thickness in selected ROIs. Lateral
orbitofrontal cortex (OFC) cortical thickness was significantly associated with RMSSD. Further, both studies, in line with previous research, showed correlations between RMSSD and anterior cingulate cortex (ACC) cortical thickness. Meta-analysis on adjusted correlation coefficients from individual studies confirmed an association of RMSSD with the left rostral ACC and the left lateral OFC. Future longitudinal studies are necessary to trace individual trajectories in the association of HRV and brain
structure across aging
Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution
The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well-typically requiring T1-weighted images (e.g., MP-RAGE scans). This limitation prevents the analysis of millions of MRI scans acquired with large inter-slice spacing in clinical settings every year. In turn, the inability to quantitatively analyze these scans hinders the adoption of quantitative neuro imaging in healthcare, and also precludes research studies that could attain huge sample sizes and hence greatly improve our understanding of the human brain. Recent advances in convolutional neural networks (CNNs) are producing outstanding results in super-resolution and contrast synthesis of MRI. However, these approaches are very sensitive to the specific combination of contrast, resolution and orientation of the input images, and thus do not generalize to diverse clinical acquisition protocols - even within sites. In this article, we present SynthSR, a method to train a CNN that receives one or more scans with spaced slices, acquired with different contrast, resolution and orientation, and produces an isotropic scan of canonical contrast (typically a 1 mm MP-RAGE). The presented method does not require any preprocessing, beyond rigid coregistration of the input scans. Crucially, SynthSR trains on synthetic input images generated from 3D segmentations, and can thus be used to train CNNs for any combination of contrasts, resolutions and orientations without high-resolution real images of the input contrasts. We test the images generated with SynthSR in an array of common downstream analyses, and show that they can be reliably used for subcortical segmentation and volumetry, image registration (e.g., for tensor-based morphometry), and, if some image quality requirements are met, even cortical thickness morphometry. The source code is publicly available at https://github.com/BBillot/SynthSR
Isolation and Characterization of the Saccharomyces cerevisiae DPP1 Gene Encoding Diacylglycerol Pyrophosphate Phosphatase
Diacylglycerol pyrophosphate (DGPP) is involved in a putative novel lipid signaling pathway. DGPP phosphatase (DGPP phosphohydrolase) is a membrane-associated 34-kDa enzyme fromSaccharomyces cerevisiae which catalyzes the dephosphorylation of DGPP to yield phosphatidate (PA) and then catalyzes the dephosphorylation of PA to yield diacylglycerol. Amino acid sequence information derived from DGPP phosphatase was used to identify and isolate the DPP1(diacylglycerol pyrophosphatephosphatase) gene encoding the enzyme. Multicopy plasmids containing the DPP1 gene directed a 10-fold overexpression of DGPP phosphatase activity in S. cerevisiae. The heterologous expression of the S. cerevisiae DPP1 gene in Sf-9 insect cells resulted in a 500-fold overexpression of DGPP phosphatase activity over that expressed in wild-type S. cerevisiae. DGPP phosphatase possesses a Mg2+-independent PA phosphatase activity, and its expression correlated with the overexpression of DGPP phosphatase activity in S. cerevisiae and in insect cells. DGPP phosphatase was predicted to be an integral membrane protein with six transmembrane-spanning domains. The enzyme contains a novel phosphatase sequence motif found in a superfamily of phosphatases. Adpp1Δ mutant was constructed by deletion of the chromosomal copy of the DPP1 gene. The dpp1Δ mutant was viable and did not exhibit any obvious growth defects. The mutant was devoid of DGPP phosphatase activity and accumulated (4-fold) DGPP. Analysis of the mutant showed that the DPP1 gene was not responsible for all of the Mg2+-independent PA phosphatase activity in S. cerevisiae
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