scholarly journals MRI-Visible Perivascular Space (PVS) Changes with Long-Duration Spaceflight

2021 ◽  
Author(s):  
Kathleen Hupfeld ◽  
Sutton Richmond ◽  
Heather McGregor ◽  
Daniel Schwartz ◽  
Madison Luther ◽  
...  
Keyword(s):  
Morphological Changes ◽  
Space Station ◽  
Ventricular Volume ◽  
Perivascular Spaces ◽  
Median Length ◽  
Long Duration ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
And Shifts ◽  
Brain Homeostasis

Humans are exposed to extreme environmental stressors during spaceflight and return with alterations in brain structure and shifts in intracranial fluids. To date, no studies have evaluated the effects of spaceflight on perivascular spaces (PVSs) within the brain, which are believed to facilitate fluid drainage and brain homeostasis. Here, we examined how the number and morphology of magnetic resonance imaging (MRI)-visible PVSs are affected by spaceflight, including prior spaceflight experience. Fifteen astronauts underwent six T1-weighted 3T MRI scans, twice prior to launch and four times following their return to Earth after ~6-month missions to the International Space Station. White matter MRI-visible PVS number and morphology were calculated using an established automated segmentation algorithm. We found that novice astronauts showed an increase in total PVS volume from pre- to post-flight, whereas experienced crewmembers did not (adjusted for age, sex, and time between landing and first MRI scan). Moreover, experienced astronauts exhibited a significant correlation between more previous flight days and greater PVS median length at baseline, suggesting that experienced astronauts exhibit holdover effects from prior spaceflight(s). There was also a significant positive correlation between pre- to post-flight increases in PVS median length and increases in right lateral ventricular volume. The presence of spaceflight associated neuro-ocular syndrome (SANS) was not associated with PVS number or morphology. Together, these findings demonstrate that spaceflight is associated with PVS morphological changes, and specifically that spaceflight experience is an important factor in determining PVS characteristics.

scholarly journals 3D Segmentation of Perivascular Spaces on T1-Weighted 3 Tesla MR Images With a Convolutional Autoencoder and a U-Shaped Neural Network

2021 ◽  
Vol 15 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

We implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

scholarly journals The Impact of 6 and 12 Months in Space on Human Brain Structure and Intracranial Fluid Shifts

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Kathleen E Hupfeld ◽  
Heather R McGregor ◽  
Jessica K Lee ◽  
Nichole E Beltran ◽  
Igor S Kofman ◽  
...  
Keyword(s):  
Brain Structure ◽  
Ventricular Volume ◽  
Brain Health ◽  
Reference Dataset ◽  
Long Duration ◽  
Fluid Shifts ◽  
Mri Scans ◽  
Brain Changes ◽  
The Impact ◽  
Insight Into

Abstract As plans develop for Mars missions, it is important to understand how long-duration spaceflight impacts brain health. Here we report how 12-month (n = 2 astronauts) versus 6-month (n = 10 astronauts) missions impact brain structure and fluid shifts. We collected MRI scans once before flight and four times after flight. Astronauts served as their own controls; we evaluated pre- to postflight changes and return toward preflight levels across the 4 postflight points. We also provide data to illustrate typical brain changes over 7 years in a reference dataset. Twelve months in space generally resulted in larger changes across multiple brain areas compared with 6-month missions and aging, particularly for fluid shifts. The majority of changes returned to preflight levels by 6 months after flight. Ventricular volume substantially increased for 1 of the 12-month astronauts (left: +25%, right: +23%) and the 6-month astronauts (left: 17 ± 12%, right: 24 ± 6%) and exhibited little recovery at 6 months. Several changes correlated with past flight experience; those with less time between subsequent missions had larger preflight ventricles and smaller ventricular volume increases with flight. This suggests that spaceflight-induced ventricular changes may endure for long periods after flight. These results provide insight into brain changes that occur with long-duration spaceflight and demonstrate the need for closer study of fluid shifts.

Spaceflight-induced changes in white matter hyperintensity burden in astronauts

Neurology ◽  
2017 ◽  
Vol 89 (21) ◽  
pp. 2187-2191 ◽  
Author(s):  
Noam Alperin ◽  
Ahmet M. Bagci ◽  
Sang H. Lee
Keyword(s):  
White Matter ◽  
Brain Tissue ◽  
Space Shuttle ◽  
Brain Mri ◽  
Space Station ◽  
Automated Analysis ◽  
White Matter Hyperintensity ◽  
Long Duration ◽  
Mri Scans ◽  
Fluid Attenuated Inversion Recovery

Objective:To assess the effect of weightlessness and the respective roles of CSF and vascular fluid on changes in white matter hyperintensity (WMH) burden in astronauts.Methods:We analyzed prespaceflight and postspaceflight brain MRI scans from 17 astronauts, 10 who flew a long-duration mission on the International Space Station (ISS) and 7 who flew a short-duration mission on the Space Shuttle. Automated analysis methods were used to determine preflight to postflight changes in periventricular and deep WMH, CSF, and brain tissue volumes in fluid-attenuated inversion recovery and high-resolution 3-dimensional T1-weighted imaging. Differences between cohorts and associations between individual measures were assessed. The short-term reversibility of the identified preflight to postflight changes was tested in a subcohort of 5 long-duration astronauts who had a second postflight MRI scan 1 month after the first postflight scan.Results:Significant preflight to postflight changes were measured only in the long-duration cohort and included only the periventricular WMH and ventricular CSF volumes. Changes in deep WMH and brain tissue volumes were not significant in either cohort. The increase in periventricular WMH volume was significantly associated with an increase in ventricular CSF volume (ρ = 0.63, p = 0.008). A partial reversal of these increases was observed in the long-duration subcohort with a 1-month follow-up scan.Conclusions:Long-duration exposure to microgravity is associated with an increase in periventricular WMH in astronauts. This increase was linked to an increase in ventricular CSF volume documented in ISS astronauts. There was no associated change in or abnormal levels of WMH volumes in deep white matter as reported in U-2 high-altitude pilots.

scholarly journals Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

2021 ◽  
Vol 11 ◽  
Author(s):  
Angela Lombardi ◽  
Alfonso Monaco ◽  
Giacinto Donvito ◽  
Nicola Amoroso ◽  
Roberto Bellotti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Morphological Changes ◽  
Structure Characterization ◽  
Feature Importance ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Brain Age ◽  
The Brain ◽  
Age Prediction

Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

scholarly journals 3D segmentation of perivascular spaces on T1-weighted 3 Tesla MR images with a convolutional autoencoder and a U-shaped neural network

2020 ◽  
Author(s):  
Philippe Boutinaud ◽  
Ami Tsuchida ◽  
Alexandre Laurent ◽  
Filipa Adonias ◽  
Zahra Hanifehlou ◽  
...  
Keyword(s):  
Learning Performance ◽  
Similar Distribution ◽  
Perivascular Spaces ◽  
Visual Rating ◽  
Independent Expert ◽  
Convolutional Autoencoder ◽  
Mri Scans ◽  
Experienced Operator ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

AbstractWe implemented a deep learning (DL) algorithm for the 3-dimensional segmentation of perivascular spaces (PVSs) in deep white matter (DWM) and basal ganglia (BG). This algorithm is based on an autoencoder and a U-shaped network (U-net), and was trained and tested using T1-weighted magnetic resonance imaging (MRI) data from a large database of 1,832 healthy young adults. An important feature of this approach is the ability to learn from relatively sparse data, which gives the present algorithm a major advantage over other DL algorithms. Here, we trained the algorithm with 40 T1-weighted MRI datasets in which all “visible” PVSs were manually annotated by an experienced operator. After learning, performance was assessed using another set of 10 MRI scans from the same database in which PVSs were also traced by the same operator and were checked by consensus with another experienced operator. The Sorensen-Dice coefficients for PVS voxel detection in DWM (resp. BG) were 0.51 (resp. 0.66), and 0.64 (resp. 0.71) for PVS cluster detection (volume threshold of 0.5 within a range of 0 to 1). Dice values above 0.90 could be reached for detecting PVSs larger than 10 mm3 and 0.95 for PVSs larger than 15 mm3. We then applied the trained algorithm to the rest of the database (1,782 individuals). The individual PVS load provided by the algorithm showed a high agreement with a semi-quantitative visual rating done by an independent expert rater, both for DWM and for BG. Finally, we applied the trained algorithm to an age-matched sample from another MRI database acquired using a different scanner. We obtained a very similar distribution of PVS load, demonstrating the interoperability of this algorithm.

scholarly journals Regional medical practice variation in high-cost healthcare services

2021 ◽  
Author(s):  
Michael Berger ◽  
Thomas Czypionka
Keyword(s):  
Medical Practice ◽  
Regional Variation ◽  
Multivariate Regression ◽  
Healthcare Services ◽  
Practice Variation ◽  
District Level ◽  
Healthcare Data ◽  
Regional Medical ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

AbstractMagnetic resonance imaging (MRI) is a popular yet cost-intensive diagnostic measure whose strengths compared to other medical imaging technologies have led to increased application. But the benefits of aggressive testing are doubtful. The comparatively high MRI usage in Austria in combination with substantial regional variation has hence become a concern for its policy makers. We use a set of routine healthcare data on outpatient MRI service consumption of Austrian patients between Q3-2015 and Q2-2016 on the district level to investigate the extent of medical practice variation in a two-step statistical analysis combining multivariate regression models and Blinder–Oaxaca decomposition. District-level MRI exam rates per 1.000 inhabitants range from 52.38 to 128.69. Controlling for a set of regional characteristics in a multivariate regression model, we identify payer autonomy in regulating access to MRI scans as the biggest contributor to regional variation. Nevertheless, the statistical decomposition highlights that more than 70% of the regional variation remains unexplained by differences between the observable district characteristics. In the absence of epidemiological explanations, the substantial regional medical practice variation calls the efficiency of resource deployment into question.

scholarly journals A detailed analysis of anatomical plausibility of crossed and uncrossed streamline rendition of the dentato-rubro-thalamic tract (DRT(T)) in a commercial stereotactic planning system

2021 ◽  
Author(s):  
Volker A. Coenen ◽  
Bastian E. Sajonz ◽  
Peter C. Reinacher ◽  
Christoph P. Kaller ◽  
Horst Urbach ◽  
...  
Keyword(s):  
Surgical Planning ◽  
Ground Truth ◽  
Planning System ◽  
Single Subject ◽  
Fiber Structure ◽  
Native Space ◽  
Volume Of Interest ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
The Individual

Abstract Background An increasing number of neurosurgeons use display of the dentato-rubro-thalamic tract (DRT) based on diffusion weighted imaging (dMRI) as basis for their routine planning of stimulation or lesioning approaches in stereotactic tremor surgery. An evaluation of the anatomical validity of the display of the DRT with respect to modern stereotactic planning systems and across different tracking environments has not been performed. Methods Distinct dMRI and anatomical magnetic resonance imaging (MRI) data of high and low quality from 9 subjects were used. Six subjects had repeated MRI scans and therefore entered the analysis twice. Standardized DICOM structure templates for volume of interest definition were applied in native space for all investigations. For tracking BrainLab Elements (BrainLab, Munich, Germany), two tensor deterministic tracking (FT2), MRtrix IFOD2 (https://www.mrtrix.org), and a global tracking (GT) approach were used to compare the display of the uncrossed (DRTu) and crossed (DRTx) fiber structure after transformation into MNI space. The resulting streamlines were investigated for congruence, reproducibility, anatomical validity, and penetration of anatomical way point structures. Results In general, the DRTu can be depicted with good quality (as judged by waypoints). FT2 (surgical) and GT (neuroscientific) show high congruence. While GT shows partly reproducible results for DRTx, the crossed pathway cannot be reliably reconstructed with the other (iFOD2 and FT2) algorithms. Conclusion Since a direct anatomical comparison is difficult in the individual subjects, we chose a comparison with two research tracking environments as the best possible “ground truth.” FT2 is useful especially because of its manual editing possibilities of cutting erroneous fibers on the single subject level. An uncertainty of 2 mm as mean displacement of DRTu is expectable and should be respected when using this approach for surgical planning. Tractographic renditions of the DRTx on the single subject level seem to be still illusive.

scholarly journals Brain atrophy and clinical characteristics predicting SDMT performance in multiple sclerosis: A 10-year follow-up study

2021 ◽  
Vol 7 (1) ◽  
pp. 205521732199239
Author(s):  
Cecilie Jacobsen ◽  
Robert Zivadinov ◽  
Kjell-Morten Myhr ◽  
Turi O Dalaker ◽  
Ingvild Dalen ◽  
...  
Keyword(s):  
Clinical Data ◽  
Grey Matter Volume ◽  
Lesion Volume ◽  
Information Processing Speed ◽  
Disability Status ◽  
Mri Scans ◽  
Patients At Risk ◽  
Magnetic Resonance Imaging Mri ◽  
Specific Volumes

Objectives To identify Magnetic Resonance Imaging (MRI), clinical and demographic biomarkers predictive of worsening information processing speed (IPS) as measured by Symbol Digit Modalities Test (SDMT). Methods Demographic, clinical data and 1.5 T MRI scans were collected in 76 patients at time of inclusion, and after 5 and 10 years. Global and tissue-specific volumes were calculated at each time point. For the primary outcome of analysis, SDMT was used. Results Worsening SDMT at 5-year follow-up was predicted by baseline age, Expanded Disability Status Scale (EDSS), SDMT, whole brain volume (WBV) and T2 lesion volume (LV), explaining 30.2% of the variance of SDMT. At 10-year follow-up, age, EDSS, grey matter volume (GMV) and T1 LV explained 39.4% of the variance of SDMT change. Conclusion This longitudinal study shows that baseline MRI-markers, demographic and clinical data can help predict worsening IPS. Identification of patients at risk of IPS decline is of importance as follow-up, treatment and rehabilitation can be optimized.

scholarly journals Cross-sectional analysis of follow-up chest MRI and chest CT scans in patients previously affected by COVID-19

2021 ◽  
Author(s):  
Martina Pecoraro ◽  
Stefano Cipollari ◽  
Livia Marchitelli ◽  
Emanuele Messina ◽  
Maurizio Del Monte ◽  
...  
Keyword(s):  
Chest Ct ◽  
Ct Scans ◽  
Imaging Biomarker ◽  
Cross Sectional ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri ◽  
Sectional Analysis ◽  
Ct And Mri ◽  
Interlobular Septal Thickening

Abstract Purpose The aim of the study was to prospectively evaluate the agreement between chest magnetic resonance imaging (MRI) and computed tomography (CT) and to assess the diagnostic performance of chest MRI relative to that of CT during the follow-up of patients recovered from coronavirus disease 2019. Materials and methods Fifty-two patients underwent both follow-up chest CT and MRI scans, evaluated for ground-glass opacities (GGOs), consolidation, interlobular septal thickening, fibrosis, pleural indentation, vessel enlargement, bronchiolar ectasia, and changes compared to prior CT scans. DWI/ADC was evaluated for signal abnormalities suspicious for inflammation. Agreement between CT and MRI was assessed with Cohen’s k and weighted k. Measures of diagnostic accuracy of MRI were calculated. Results The agreement between CT and MRI was almost perfect for consolidation (k = 1.00) and change from prior CT (k = 0.857); substantial for predominant pattern (k = 0.764) and interlobular septal thickening (k = 0.734); and poor for GGOs (k = 0.339), fibrosis (k = 0.224), pleural indentation (k = 0.231), and vessel enlargement (k = 0.339). Meanwhile, the sensitivity of MRI was high for GGOs (1.00), interlobular septal thickening (1.00), and consolidation (1.00) but poor for fibrotic changes (0.18), pleural indentation (0.23), and vessel enlargement (0.50) and the specificity was overall high. DWI was positive in 46.0% of cases. Conclusions The agreement between MRI and CT was overall good. MRI was very sensitive for GGOs, consolidation and interlobular septal thickening and overall specific for most findings. DWI could be a reputable imaging biomarker of inflammatory activity.

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