Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.

BACKGROUND: Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse. METHODS: We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area. RESULTS: Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images. CONCLUSION: We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.

Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data.

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.

Atypical White Matter Hyperintensities Markedly Impact Plasma Neurofilament Light Chain Variability in GRN Patients.

GRN mutations, causing frontotemporal dementia, can be associated with atypical white matter hyperintensities (WMH). We hypothesized that the presence of WMH may impact neurofilament light chain (NfL) levels, markers of neuroaxonal damage. We analyzed plasma NfL in 20 GRN patients and studied their association to visually-scored WMH burden. The 12 patients displaying atypical WMH had significantly higher NfL levels (98.4±34.9 pg/mL) than those without WMH (47.2±29.4 pg/mL, p = 0.003), independently from age, disease duration and Fazekas-Schmidt grade. NfL correlated with WMH burden (rho = 0.55, p = 0.01). This study prompts considering WMH burden as a variability factor when evaluating NfL levels in GRN patients.

Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse.

A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine.

Pilot study of repeated blood-brain barrier disruption in patients with mild Alzheimer's disease with an implantable ultrasound device.

BACKGROUND: Temporary disruption of the blood-brain barrier (BBB) using pulsed ultrasound leads to the clearance of both amyloid and tau from the brain, increased neurogenesis, and mitigation of cognitive decline in pre-clinical models of Alzheimer's disease (AD) while also increasing BBB penetration of therapeutic antibodies. The goal of this pilot clinical trial was to investigate the safety and efficacy of this approach in patients with mild AD using an implantable ultrasound device. METHODS: An implantable, 1-MHz ultrasound device (SonoCloud-1) was implanted under local anesthesia in the skull (extradural) of 10 mild AD patients to target the left supra-marginal gyrus. Over 3.5 months, seven ultrasound sessions in combination with intravenous infusion of microbubbles were performed twice per month to temporarily disrupt the BBB. 18F-florbetapir and 18F-fluorodeoxyglucose positron emission tomography (PET) imaging were performed on a combined PET/MRI scanner at inclusion and at 4 and 8 months after the initiation of sonications to monitor the brain metabolism and amyloid levels along with cognitive evaluations. The evolution of cognitive and neuroimaging features was compared to that of a matched sample of control participants taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS: A total of 63 BBB opening procedures were performed in nine subjects. The procedure was well-tolerated. A non-significant decrease in amyloid accumulation at 4 months of - 6.6% (SD = 7.2%) on 18F-florbetapir PET imaging in the sonicated gray matter targeted by the ultrasound transducer was observed compared to baseline in six subjects that completed treatments and who had evaluable imaging scans. No differences in the longitudinal change in the glucose metabolism were observed compared to the neighboring or contralateral regions or to the change observed in the same region in ADNI participants. No significant effect on cognition evolution was observed in comparison with the ADNI participants as expected due to the small sample size and duration of the trial. CONCLUSIONS: These results demonstrate the safety of ultrasound-based BBB disruption and the potential of this technology to be used as a therapy for AD patients. Research of this technique in a larger clinical trial with a device designed to sonicate larger volumes of tissue and in combination with disease-modifying drugs may further enhance the effects observed. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03119961.

Validation of an automatic tool for the rapid measurement of brain atrophy and white matter hyperintensity: QyScore®.

OBJECTIVES: QyScore® is an imaging analysis tool certified in Europe (CE marked) and the US (FDA cleared) for the automatic volumetry of grey and white matter (GM and WM respectively), hippocampus (HP), amygdala (AM), and white matter hyperintensity (WMH). Here we compare QyScore® performances with the consensus of expert neuroradiologists. METHODS: Dice similarity coefficient (DSC) and the relative volume difference (RVD) for GM, WM volumes were calculated on 50 3DT1 images. DSC and the F1 metrics were calculated for WMH on 130 3DT1 and FLAIR images. For each index, we identified thresholds of reliability based on current literature review results. We hypothesized that DSC/F1 scores obtained using QyScore® markers would be higher than the threshold. In contrast, RVD scores would be lower. Regression analysis and Bland-Altman plots were obtained to evaluate QyScore® performance in comparison to the consensus of three expert neuroradiologists. RESULTS: The lower bound of the DSC/F1 confidence intervals was higher than the threshold for the GM, WM, HP, AM, and WMH, and the higher bounds of the RVD confidence interval were below the threshold for the WM, GM, HP, and AM. QyScore®, compared with the consensus of three expert neuroradiologists, provides reliable performance for the automatic segmentation of the GM and WM volumes, and HP and AM volumes, as well as WMH volumes. CONCLUSIONS: QyScore® represents a reliable medical device in comparison with the consensus of expert neuroradiologists. Therefore, QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases. KEY POINTS: • QyScore® provides reliable automatic segmentation of brain structures in comparison with the consensus of three expert neuroradiologists. • QyScore® automatic segmentation could be performed on MRI images using different vendors and protocols of acquisition. In addition, the fast segmentation process saves time over manual and semi-automatic methods. • QyScore® could be implemented in clinical trials and clinical routine to support the diagnosis and longitudinal monitoring of neurological diseases.

Automatic segmentation of white matter hyperintensities: validation and comparison with state-of-the-art methods on both Multiple Sclerosis and elderly subjects.

Different types of white matter hyperintensities (WMH) can be observed through MRI in the brain and spinal cord, especially Multiple Sclerosis (MS) lesions for patients suffering from MS and age-related WMH for subjects with cognitive disorders and/or elderly people. To better diagnose and monitor the disease progression, the quantitative evaluation of WMH load has proven to be useful for clinical routine and trials. Since manual delineation for WMH segmentation is highly time-consuming and suffers from intra and inter observer variability, several methods have been proposed to automatically segment either MS lesions or age-related WMH, but none is validated on both WMH types. Here, we aim at proposing the White matter Hyperintensities Automatic Segmentation Algorithm adapted to 3D T2-FLAIR datasets (WHASA-3D), a fast and robust automatic segmentation tool designed to be implemented in clinical practice for the detection of both MS lesions and age-related WMH in the brain, using both 3D T1-weighted and T2-FLAIR images. In order to increase its robustness for MS lesions, WHASA-3D expands the original WHASA method, which relies on the coupling of non-linear diffusion framework and watershed parcellation, where regions considered as WMH are selected based on intensity and location characteristics, and finally refined with geodesic dilation. The previous validation was performed on 2D T2-FLAIR and subjects with cognitive disorders and elderly subjects. 60 subjects from a heterogeneous database of dementia patients, multiple sclerosis patients and elderly subjects with multiple MRI scanners and a wide range of lesion loads were used to evaluate WHASA and WHASA-3D through volume and spatial agreement in comparison with consensus reference segmentations. In addition, a direct comparison on the MS database with six available supervised and unsupervised state-of-the-art WMH segmentation methods (LST-LGA and LPA, Lesion-TOADS, lesionBrain, BIANCA and nicMSlesions) with default and optimised settings (when feasible) was conducted. WHASA-3D confirmed an improved performance with respect to WHASA, achieving a better spatial overlap (Dice) (0.67 vs 0.63), a reduced absolute volume error (AVE) (3.11 vs 6.2 mL) and an increased volume agreement (intraclass correlation coefficient, ICC) (0.96 vs 0.78). Compared to available state-of-the-art algorithms on the MS database, WHASA-3D outperformed both unsupervised and supervised methods when used with their default settings, showing the highest volume agreement (ICC = 0.95) as well as the highest average Dice (0.58). Optimising and/or retraining LST-LGA, BIANCA and nicMSlesions, using a subset of the MS database as training set, resulted in improved performances on the remaining testing set (average Dice: LST-LGA default/optimized = 0.41/0.51, BIANCA default/optimized = 0.22/0.39, nicMSlesions default/optimized = 0.17/0.63, WHASA-3D = 0.58). Evaluation and comparison results suggest that WHASA-3D is a reliable and easy-to-use method for the automated segmentation of white matter hyperintensities, for both MS lesions and age-related WMH. Further validation on larger datasets would be useful to confirm these first findings.

Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse.

Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.

Fatal Measles Inclusion-Body Encephalitis in Adult with Untreated AIDS, France.

We report a fatal case of measles inclusion-body encephalitis occurring in a woman from Romania with AIDS. After an extensive but unsuccessful diagnostic evaluation, a pan-pathogen shotgun metagenomic approach revealed a measles virus infection. We identified no mutations previously associated with neurovirulence.

Comparison and validation of seven white matter hyperintensities segmentation software in elderly patients.

BACKGROUND: Manual segmentation is currently the gold standard to assess white matter hyperintensities (WMH), but it is time consuming and subject to intra and inter-operator variability. PURPOSE: To compare automatic methods to segment white matter hyperintensities (WMH) in the elderly in order to assist radiologist and researchers in selecting the most relevant method for application on clinical or research data. MATERIAL AND METHODS: We studied a research dataset composed of 147 patients, including 97 patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) 2 database and 50 patients from ADNI 3 and a clinical routine dataset comprising 60 patients referred for cognitive impairment at the Pitié-Salpêtrière hospital (imaged using four different MRI machines). We used manual segmentation as the gold standard reference. Both manual and automatic segmentations were performed using FLAIR MRI. We compared seven freely available methods that produce segmentation mask and are usable by a radiologist without a strong knowledge of computer programming: LGA (Schmidt et al., 2012), LPA (Schmidt, 2017), BIANCA (Griffanti et al., 2016), UBO detector (Jiang et al., 2018), W2MHS (Ithapu et al., 2014), nicMSlesion (with and without retraining) (Valverde et al., 2019, 2017). The primary outcome for assessing segmentation accuracy was the Dice similarity coefficient (DSC) between the manual and the automatic segmentation software. Secondary outcomes included five other metrics. RESULTS: A deep learning approach, NicMSlesion, retrained on data from the research dataset ADNI, performed best on this research dataset (DSC: 0.595) and its DSC was significantly higher than that of all others. However, it ranked fifth on the clinical routine dataset and its performance severely dropped on data with artifacts. On the clinical routine dataset, the three top-ranked methods were LPA, SLS and BIANCA. Their performance did not differ significantly but was significantly higher than that of other methods. CONCLUSION: This work provides an objective comparison of methods for WMH segmentation. Results can be used by radiologists to select a tool.

Retrospective Observational Study of Brain MRI Findings in Patients with Acute SARS-CoV-2 Infection and Neurologic Manifestations.

Background This study provides a detailed imaging assessment in a large series of patients infected with coronavirus disease 2019 (COVID-19) and presenting with neurologic manifestations. Purpose To review the MRI findings associated with acute neurologic manifestations in patients with COVID-19. Materials and Methods This was a cross-sectional study conducted between March 23 and May 7, 2020, at the Pitié-Salpêtrière Hospital, a reference center for COVID-19 in the Paris area. Adult patients were included if they had a diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection with acute neurologic manifestations and referral for brain MRI. Patients with a prior history of neurologic disease were excluded. The characteristics and frequency of different MRI features were investigated. The findings were analyzed separately in patients in intensive care units (ICUs) and other departments (non-ICU). Results During the inclusion period, 1176 patients suspected of having COVID-19 were hospitalized. Of 308 patients with acute neurologic symptoms, 73 met the inclusion criteria and were included (23.7%): thirty-five patients were in the ICU (47.9%) and 38 were not (52.1%). The mean age was 58.5 years ± 15.6 [standard deviation], with a male predominance (65.8% vs 34.2%). Forty-three patients had abnormal MRI findings 2-4 weeks after symptom onset (58.9%), including 17 with acute ischemic infarct (23.3%), one with a deep venous thrombosis (1.4%), eight with multiple microhemorrhages (11.3%), 22 with perfusion abnormalities (47.7%), and three with restricted diffusion foci within the corpus callosum consistent with cytotoxic lesions of the corpus callosum (4.1%). Multifocal white matter-enhancing lesions were seen in four patients in the ICU (5%). Basal ganglia abnormalities were seen in four other patients (5%). Cerebrospinal fluid analyses were negative for SARS-CoV-2 in all patients tested (n = 39). Conclusion In addition to cerebrovascular lesions, perfusion abnormalities, cytotoxic lesions of the corpus callosum, and intensive care unit-related complications, we identified two patterns including white matter-enhancing lesions and basal ganglia abnormalities that could be related to severe acute respiratory syndrome coronavirus 2 infection. © RSNA, 2020 Online supplemental material is available for this article.

Fatal Encephalitis Caused by Cristoli Virus, an Emerging Orthobunyavirus, France.

We report the discovery of a new orthobunyavirus, Cristoli virus, by means of shotgun metagenomics. The virus was identified in an immunodepressed patient with fatal encephalitis. Full-length genome sequencing revealed high-level expression of a virulence factor, possibly explaining the severity of the infection. The patient's recent history suggests circulation in France.

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort.

BACKGROUND: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets. OBJECTIVE: Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic. METHODS: We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria. RESULTS: Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM. CONCLUSION: In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis.

Frequency and Incidence of Carney Complex Manifestations: A Prospective Multicenter Study With a Three-Year Follow-Up.

INTRODUCTION: Carney Complex (CNC) is a rare multiple endocrine and nonendocrine neoplasia syndrome. Manifestations and genotype-phenotype correlations have been described by retrospective studies, but no prospective study evaluating the occurrence of the different manifestations has been available so far. METHODS: This multicenter national prospective study included patients with CNC, primary pigmented nodular adrenal disease (PPNAD), or a pathogenic PRKAR1A mutation; after a full initial workup, participants were followed for 3 years with annual standardized evaluation. RESULTS: The cohort included 70 patients (50 female/20 male, mean age 35.4 ± 16.7 years, 81% carrying PRKAR1A mutation). The initial investigations allowed identification of several manifestations. At the end of the 3-year follow-up, the newly diagnosed manifestations of the disease were subclinical acromegaly in 6 patients, bilateral testicular calcifications in 1 patient, and cardiac myxomas in 2 patients. Recurrences of cardiac myxomas were diagnosed in 4 patients during the 3-year follow-up study period. Asymptomatic abnormalities of the corticotroph and somatotroph axis that did not meet criteria of PPNAD and acromegaly were observed in 11.4% and 30% of the patients, respectively. Patients carrying the PRKAR1A c.709-7del6 mutation had a mild phenotype. CONCLUSION: This study underlines the importance of a systematic follow-up of the CNC manifestations, especially a biannual screening for cardiac myxoma. By contrast, regular screening for the other manifestations after a first extensive workup could be spread out, leading to a lighter and more acceptable follow-up schedule for patients. These are important results for recommendations for long-term management of CNC patients.

Testing the therapeutic effects of transcranial direct current stimulation (tDCS) in semantic dementia: a double blind, sham controlled, randomized clinical trial.

BACKGROUND: Semantic dementia is a neurodegenerative disease that primarily affects the left anterior temporal lobe, resulting in a gradual loss of conceptual knowledge. There is currently no validated treatment. Transcranial stimulation has provided evidence for long-lasting language effects presumably linked to stimulation-induced neuroplasticity in post-stroke aphasia. However, studies evaluating its effects in neurodegenerative diseases such as semantic dementia are still rare and evidence from double-blind, prospective, therapeutic trials is required. OBJECTIVE: The primary objective of the present clinical trial (STIM-SD) is to evaluate the therapeutic efficacy of a multiday transcranial direct current stimulation (tDCS) regime on language impairment in patients with semantic dementia. The study also explores the time course of potential tDCS-driven improvements and uses imaging biomarkers that could reflect stimulation-induced neuroplasticity. METHODS: This is a double-blind, sham-controlled, randomized study using transcranial Direct Current Stimulation (tDCS) applied daily for 10 days, and language/semantic and imaging assessments at four time points: baseline, 3 days, 2 weeks and 4 months after 10 stimulation sessions. Language/semantic assessments will be carried out at these same 4 time points. Fluorodeoxyglucose positron emission tomography (FDG-PET), resting-state functional magnetic resonance imaging (rs-fMRI), T1-weighted images and white matter diffusion tensor imaging (DTI) will be applied at baseline and at the 2-week time point. According to the principle of inter-hemispheric inhibition between left (language-related) and right homotopic regions we will use two stimulation modalities - left-anodal and right-cathodal tDCS over the anterior temporal lobes. Accordingly, the patient population (n = 60) will be subdivided into three subgroups: left-anodal tDCS (n = 20), right-cathodal tDCS (n = 20) and sham tDCS (n = 20). The stimulation will be sustained for 20 min at an intensity of 1.59 mA. It will be delivered through 25cm2-round stimulation electrodes (current density of 0.06 mA/cm2) placed over the left and right anterior temporal lobes for anodal and cathodal stimulation, respectively. A group of healthy participants (n = 20) matched by age, gender and education will also be recruited and tested to provide normative values for the language/semantic tasks and imaging measures. DISCUSSION: The aim of this study is to assess the efficacy of tDCS for language/semantic disorders in semantic dementia. A potential treatment would be easily applicable, inexpensive, and renewable when therapeutic effects disappear due to disease progression. TRIAL REGISTRATION: ClinicalTrials.gov NCT03481933. Registered on March 2018.

Recovery from cortical blindness with mepivacaïne.

We report the case of a patient suffering from cortical blindness following bilateral occipital stroke, who recovered normal vision in his right visual field following injection of the local anesthetic mepivacaïne. The effect was transient but reproducible, allowing the patient to lead a normal life. Effect duration increased after adjunction of paroxetine. We provide anatomical and functional brain imaging correlates of this improvement, showing particularly how functional connectivity is restored between intact perilesional cortex and distant brain regions. This serendipitous finding may potentially benefit patients suffering from visual but also nonvisual handicap following brain lesions.

Impact of simultaneous measurement of central blood pressure with the SphygmoCor Xcel during MRI acquisition to better estimate aortic distensibility.

OBJECTIVES: Aortic distensibility estimation of local aortic stiffness is based on local aortic strains and central pulse pressure (cPP) measurements. Most MRI studies used either brachial PP (bPP) despite differences with cPP, or direct cPP estimates obtained after MRI examination, assuming no major pressure variations. We evaluated the feasibility of assessment of cPP with a specific device fitted with a 6 m long hose (study1) and looked at the influence of using such cPP within the magnet instead of bPP on aortic distensibility in a control population (study 2). METHODS: Brachial and central pressures values were recorded with the SphygmoCor XCEL system fitted with 2 and 6 m long tubing randomly assigned on arms. A 6 m long tubing was used in the second study to measure aortic distensibility with MRI. Aortic distensibility was calculated using either bPP (bAD) or cPP (cAD). RESULTS: Study1, performed on 38 patients (mean age: 43 ±â€Š17 years), showed no statistical difference between bPP and cPP measured with 2 or 6 m long tubing (0.41 ±â€Š4.45 and 0.78 ±â€Š3.18 mmHg, respectively, both P = ns). In study 2, cAD provided statistically higher values than bAD (1.87 ±â€Š1.43 10 ·â€ŠmmHg, P < 0.001) especially in younger individuals (3.28 ±â€Š0.86 10 ·â€ŠmmHg). The correlation between age and aortic distensibility was stronger with cAD (r = -0.92; P < 0,001) than with bAD (r = -0.88; P < 0.001). CONCLUSION: cPP can be estimated with reasonable accuracy during MRI acquisition using a 6 m long tube. Using either cPP or bPP greatly influences aortic distensibility values, especially in young individuals in whom an accurate detection of early or accelerated vascular aging can be of major importance.

Structural magnetic resonance imaging in frontotemporal lobar dementia.

Frontotemporal lobar dementia (FTLD) is a heterogeneous group of neurodegenerative diseases. FTLD encompass: 1) behavioral forms, sometimes associated with amyotrophic lateral sclerosis; 2) linguistic forms (semantic and non-fluent primary progressive aphasia); 3) atypical parkinsonian syndromes (progressive supranuclear palsy and corticobasal syndrome). Standard brain MRI allows for strengthening the clinical suspicion of FTLD, by showing a pattern of atrophy in relation with the patient's clinical symptoms: frontotemporal anterior atrophy in behavioral forms; temporopolar or inferior left frontal atrophy in the linguistic forms; mesencephalic or corticosubcortical hemispheric atrophy in forms with atypical pakinsonism. MRI is now part of the diagnostic criteria of some FTLD (behavioral FTLD, primary progressive aphasia). Genetic forms are common in FTLD, especially in behavioral FTLD. The three main mutations (C9ORF72, GRN and MAPT) are associated with different imaging patterns, which can thus orient the clinician towards a particular mutation in a patient with a familial form of FTLD.

White matter lesions in FTLD: distinct phenotypes characterize GRN and C9ORF72 mutations.

Frontotemporal lobar degeneration (FTLD) has a high frequency of genetic forms; the 2 most common are GRN (progranulin) and C9ORF72 mutations. Recently, our group reported extensive white matter (WM) lesions in 4 patients with FTLD caused by GRN mutation, in the absence of noteworthy cardiovascular risk factors,(1) in line with other studies in GRN mutation carriers.(2,3) Here we compared the characteristics of frontal WM lesions in patients with behavioral variant of FTLD (bv-FTLD) caused by GRN and C9ORF72 mutations.