Publications by authors named "Stephane Lehéricy"

215 Publications

Identification of a Brain Network Underlying the Execution of Freely Chosen Movements.

Cereb Cortex 2021 Jul 27. Epub 2021 Jul 27.

Faculté de Médecine, INSERM U 1127, CNRS UMR 7225, UM 75, ICM, Sorbonne Université, Paris 75013, France.

Action selection refers to the decision regarding which action to perform in order to reach a desired goal, that is, the "what" component of intention. Whether the action is freely chosen or externally instructed involves different brain networks during the selection phase, but it is assumed that the way an action is selected should not influence the subsequent execution phase of the same movement. Here, we aim to test this hypothesis by investigating whether the modality of movement selection influences the brain networks involved during the execution phase of the movement. Twenty healthy volunteers performed a delayed response task in an event-related functional magnetic resonance imaging design to compare freely chosen and instructed unimanual or bimanual movements during the execution phase. Using activation analyses, we found that the pre-supplementary motor area (preSMA) and the parietal and cerebellar areas were more activated during the execution phase of freely chosen as compared to instructed movements. Connectivity analysis showed an increase of information flow between the right posterior parietal cortex and the cerebellum for freely chosen compared to instructed movements. We suggest that the parieto-cerebellar network is particularly engaged during freely chosen movement to monitor the congruence between the intentional content of our actions and their outcome.
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http://dx.doi.org/10.1093/cercor/bhab204DOI Listing
July 2021

Emerging Neuroimaging Biomarkers Across Disease Stage in Parkinson Disease: A Review.

JAMA Neurol 2021 Oct;78(10):1262-1272

Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville.

Importance: Imaging biomarkers in Parkinson disease (PD) are increasingly important for monitoring progression in clinical trials and also have the potential to improve clinical care and management. This Review addresses a critical need to make clear the temporal relevance for diagnostic and progression imaging biomarkers to be used by clinicians and researchers over the clinical course of PD. Magnetic resonance imaging (diffusion imaging, neuromelanin-sensitive imaging, iron-sensitive imaging, T1-weighted imaging), positron emission tomography/single-photon emission computed tomography dopaminergic, serotonergic, and cholinergic imaging as well as metabolic and cerebral blood flow network neuroimaging biomarkers in the preclinical, prodromal, early, and moderate to late stages are characterized.

Observations: If a clinical trial is being carried out in the preclinical and prodromal stages, potentially useful disease-state biomarkers include dopaminergic imaging of the striatum; metabolic imaging; free-water, neuromelanin-sensitive, and iron-sensitive imaging in the substantia nigra; and T1-weighted structural magnetic resonance imaging. Disease-state biomarkers that can distinguish atypical parkinsonisms are metabolic imaging, free-water imaging, and T1-weighted imaging; dopaminergic imaging and other molecular imaging track progression in prodromal patients, whereas other established progression biomarkers need to be evaluated in prodromal cohorts. Progression in early-stage PD can be monitored using dopaminergic imaging in the striatum, metabolic imaging, and free-water and neuromelanin-sensitive imaging in the posterior substantia nigra. Progression in patients with moderate to late-stage PD can be monitored using free-water imaging in the anterior substantia nigra, R2* of substantia nigra, and metabolic imaging. Cortical thickness and gyrification might also be useful markers or predictors of progression. Dopaminergic imaging and free-water imaging detect progression over 1 year, whereas other modalities detect progression over 18 months or longer. The reliability of progression biomarkers varies with disease stage, whereas disease-state biomarkers are relatively consistent in individuals with preclinical, prodromal, early, and moderate to late-stage PD.

Conclusions And Relevance: Imaging biomarkers for various stages of PD are readily available to be used as outcome measures in clinical trials and are potentially useful in multimodal combination with routine clinical assessment. This Review provides a critically important template for considering disease stage when implementing diagnostic and progression biomarkers in both clinical trials and clinical care settings.
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http://dx.doi.org/10.1001/jamaneurol.2021.1312DOI Listing
October 2021

The wide spectrum of COVID-19 neuropsychiatric complications within a multidisciplinary centre.

Brain Commun 2021 17;3(3):fcab135. Epub 2021 Jun 17.

Service d'Addictologie, Assistance Publique Hôpitaux de Paris, Sorbonne Université, Pitié-Salpêtrière, Paris 75013, France.

A variety of neuropsychiatric complications has been described in association with COVID-19 infection. Large scale studies presenting a wider picture of these complications and their relative frequency are lacking. The objective of our study was to describe the spectrum of neurological and psychiatric complications in patients with COVID-19 seen in a multidisciplinary hospital centre over 6 months. We conducted a retrospective, observational study of all patients showing neurological or psychiatric symptoms in the context of COVID-19 seen in the medical and university neuroscience department of Assistance Publique Hopitaux de Paris-Sorbonne University. We collected demographic data, comorbidities, symptoms and severity of COVID-19 infection, neurological and psychiatric symptoms, neurological and psychiatric examination data and, when available, results from CSF analysis, MRI, EEG and EMG. A total of 249 COVID-19 patients with a neurological or psychiatric manifestation were included in the database and 245 were included in the final analyses. One-hundred fourteen patients (47%) were admitted to the intensive care unit and 10 (4%) died. The most frequent neuropsychiatric complications diagnosed were encephalopathy (43%), critical illness polyneuropathy and myopathy (26%), isolated psychiatric disturbance (18%) and cerebrovascular disorders (16%). No patients showed CSF evidence of SARS-CoV-2. Encephalopathy was associated with older age and higher risk of death. Critical illness neuromyopathy was associated with an extended stay in the intensive care unit. The majority of these neuropsychiatric complications could be imputed to critical illness, intensive care and systemic inflammation, which contrasts with the paucity of more direct SARS-CoV-2-related complications or post-infection disorders.
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http://dx.doi.org/10.1093/braincomms/fcab135DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344449PMC
June 2021

Dying-back of ascending noradrenergic projections in Parkinson's disease.

Brain 2021 Oct;144(9):2562-2564

Institut du Cerveau-Paris Brain Institute (ICM), Centre de NeuroImagerie de Recherche (CENIR), Team 'Movement Investigations and Therapeutics' (MOV'IT), Sorbonne Université INSERM U1127, CNRS 7225, Paris, France.

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http://dx.doi.org/10.1093/brain/awab286DOI Listing
October 2021

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure.

Netw Neurosci 2021 27;5(2):358-372. Epub 2021 Apr 27.

NeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada.

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.
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http://dx.doi.org/10.1162/netn_a_00179DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8233108PMC
April 2021

Update on neuroimaging for categorization of Parkinson's disease and atypical parkinsonism.

Curr Opin Neurol 2021 08;34(4):514-524

Institut du Cerveau - ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Team 'Movement Investigations and Therapeutics' (MOV'IT).

Purpose Of Review: Differential diagnosis of Parkinsonism may be difficult. The objective of this review is to present the work of the last three years in the field of imaging for diagnostic categorization of parkinsonian syndromes focusing on progressive supranuclear palsy (PSP) and multiple system atrophy (MSA).

Recent Findings: Two main complementary approaches are being pursued. The first seeks to develop and validate manual qualitative or semi-quantitative imaging markers that can be easily used in clinical practice. The second is based on quantitative measurements of magnetic resonance imaging abnormalities integrated in a multimodal approach and in automatic categorization machine learning tools.

Summary: These two complementary approaches obtained high diagnostic around 90% and above in the classical Richardson form of PSP and probable MSA. Future work will determine if these techniques can improve diagnosis in other PSP variants and early forms of the diseases when all clinical criteria are not fully met.
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http://dx.doi.org/10.1097/WCO.0000000000000957DOI Listing
August 2021

The spatiotemporal changes in dopamine, neuromelanin and iron characterizing Parkinson's disease.

Brain 2021 May 12. Epub 2021 May 12.

Sorbonne Université, Institut du Cerveau - Paris Brain Institute - ICM, INSERM, CNRS, 75013 Paris, France.

In Parkinson's disease, there is a progressive reduction in striatal dopaminergic function, and loss of neuromelanin-containing dopaminergic neurons and increased iron deposition in the substantia nigra. We tested the hypothesis of a relationship between impairment of the dopaminergic system and changes in the iron metabolism. Based on imaging data of patients with prodromal and early clinical Parkinson's disease, we assessed the spatiotemporal ordering of such changes and relationships in the sensorimotor, associative and limbic territories of the nigrostriatal system. Patients with Parkinson's disease (disease duration < 4 years) or idiopathic REM sleep behaviour disorder (a prodromal form of Parkinson's disease) and healthy controls underwent longitudinal examination (baseline and 2-year follow-up). Neuromelanin and iron sensitive MRI and dopamine transporter single-photon emission tomography were performed to assess nigrostriatal levels of neuromelanin, iron, and dopamine. For all three functional territories of the nigrostriatal system, in the clinically most and least affected hemispheres separately, the following was performed: cross-sectional and longitudinal inter-group difference analysis of striatal dopamine and iron, and nigral neuromelanin and iron; in Parkinson's disease patients, exponential fitting analysis to assess the duration of the prodromal phase and the temporal ordering of changes in dopamine, neuromelanin or iron relative to controls; voxel-wise correlation analysis to investigate concomitant spatial changes in dopamine-iron, dopamine-neuromelanin and neuromelanin-iron in the substantia nigra pars compacta. The temporal ordering of dopaminergic changes followed the known spatial pattern of progression involving first the sensorimotor, then the associative and limbic striatal and nigral regions. Striatal dopaminergic denervation occurred first followed by abnormal iron metabolism and finally neuromelanin changes in the substantia nigra pars compacta, which followed the same spatial and temporal gradient observed in the striatum but shifted in time. In conclusion, dopaminergic striatal dysfunction and cell loss in the substantia nigra pars compacta are interrelated with increased nigral iron content.
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http://dx.doi.org/10.1093/brain/awab191DOI Listing
May 2021

Longitudinal Changes in Neuromelanin MRI Signal in Parkinson's Disease: A Progression Marker.

Mov Disord 2021 07 10;36(7):1592-1602. Epub 2021 Mar 10.

Paris Brain Institute- ICM, Center for NeuroImaging Research - CENIR, Paris, France.

Background: Development of reliable and accurate imaging biomarkers of dopaminergic cell neurodegeneration is necessary to facilitate therapeutic drug trials in Parkinson's disease (PD). Neuromelanin-sensitive MRI techniques have been effective in detecting neurodegeneration in the substantia nigra pars compacta (SNpc). The objective of the current study was to investigate longitudinal neuromelanin signal changes in the SNpc in PD patients.

Methods: In this prospective, longitudinal, observational case-control study, we included 140 PD patients and 64 healthy volunteers divided into 2 cohorts. Cohort I included 99 early PD patients (disease duration, 1.5 ± 1.0 years) and 41 healthy volunteers analyzed at baseline (V1), where 79 PD patients and 32 healthy volunteers were rescanned after 2.0 ± 0.2 years of follow-up (V2). Cohort II included 41 progressing PD patients (disease duration, 9.3 ± 3.7 years) and 23 healthy volunteers at V1, where 30 PD patients were rescanned after 2.4 ± 0.5 years of follow-up. Subjects were scanned at 3 T MRI using 3-dimensional T1-weighted and neuromelanin-sensitive imaging. Regions of interest were delineated manually to calculate SN volumes, volumes corrected by total intracranial volume, signal-to-noise ratio, and contrast-to-noise ratio.

Results: Results showed (1) significant reduction in volume and volume corrected by total intracranial volume between visits, greater in progressing PD than nonsignificant changes in healthy volunteers; (2) no significant effects of visit for signal intensity (signal-to-noise ratio); (3) significant interaction in volume between group and visit; (4) greater volume corrected by total intracranial volume at baseline in female patients and greater decrease in volume and increase in the contrast-to-noise ratio in progressing female PD patients compared with male patients; and (5) correlations between neuromelanin SN changes and disease severity and duration.

Conclusions: We observed a progressive and measurable decrease in neuromelanin-based SN signal and volume in PD, which might allow a direct noninvasive assessment of progression of SN loss and could represent a target biomarker for disease-modifying treatments. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.28531DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8359265PMC
July 2021

X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.

Front Neuroinform 2021 19;15:578369. Epub 2021 Feb 19.

Department of Electrical & Computer Engineering, PERFORM Center, Concordia University, Montreal, QC, Canada.

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients-Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7-15% improvement). This result was observed for both recording types (high-quality microphone and telephone).
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http://dx.doi.org/10.3389/fninf.2021.578369DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7935511PMC
February 2021

Inflammation-driven glial alterations in the cuprizone mouse model probed with diffusion-weighted magnetic resonance spectroscopy at 11.7 T.

NMR Biomed 2021 04 21;34(4):e4480. Epub 2021 Jan 21.

Center for Neuroimaging Research-CENIR, Paris Brain Institute (Institut du Cerveau-ICM), Paris, France.

Inflammation of brain tissue is a complex response of the immune system to the presence of toxic compounds or to cell injury, leading to a cascade of pathological processes that include glial cell activation. Noninvasive MRI markers of glial reactivity would be very useful for in vivo detection and monitoring of inflammation processes in the brain, as well as for evaluating the efficacy of personalized treatments. Due to their specific location in glial cells, myo-inositol (mIns) and choline compounds (tCho) seem to be the best candidates for probing glial-specific intra-cellular compartments. However, their concentrations quantified using conventional proton MRS are not specific for inflammation. In contrast, it has been recently suggested that mIns intra-cellular diffusion, measured using diffusion-weighted MRS (DW-MRS) in a mouse model of reactive astrocytes, could be a specific marker of astrocytic hypertrophy. In order to evaluate the specificity of both mIns and tCho diffusion to inflammation-driven glial alterations, we performed DW-MRS in a volume of interest containing the corpus callosum and surrounding tissue of cuprizone-fed mice after 6 weeks of intoxication, and evaluated the extent of astrocytic and microglial alterations using immunohistochemistry. Both mIns and tCho apparent diffusion coefficients were significantly elevated in cuprizone-fed mice compared with control mice, and histologic evaluation confirmed the presence of severe inflammation. Additionally, mIns and tCho diffusion showed, respectively, strong and moderate correlations with histological measures of astrocytic and microglial area fractions, confirming DW-MRS as a promising tool for specific detection of glial changes under pathological conditions.
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http://dx.doi.org/10.1002/nbm.4480DOI Listing
April 2021

Parkinson Disease Propagation Using MRI Biomarkers and Partial Least Squares Path Modeling.

Neurology 2021 01 4;96(3):e460-e471. Epub 2020 Dec 4.

From the Institut Cerveau Moelle (N.P., L.Y.-C., R.V., R.G., S.F.-V., S.L.), Centre de NeuroImagerie de Recherche; Sorbonne Université (N.P., L.Y.-C,, R.G., F.G., C.E., C.G., S.F.-V., I.A., M.V., S.L.), Paris 06, UMR S 1127, CNRS UMR 7225, Institut Cerveau Moelle, F-75013; Institut Cerveau Moelle Team Movement Investigation and Therapeutics (N.P., R.G., F.G., C.E., C.G., I.A., M.V., S.L.); Service de neuroradiologie (N.P., M.V., S.L.), APHP, Pitié-Salpêtrière; and Clinique des Mouvements Anormaux (C.E.), Département des Maladies du Système Nerveux, and Service des Pathologies du Sommeil (I.A.), Hôpital Pitié-Salpêtrière, APHP, Paris, France.

Objectives: The classic Braak neuropathologic staging model in Parkinson disease (PD) suggests that brain lesions progress from the medulla oblongata to the cortex. An alternative model in which neurodegeneration first occurs in the cortex has also been proposed. These 2 models may correspond to different patient phenotypes. To test these 2 models and to investigate whether they were influenced by the presence of REM sleep behavior disorder (RBD), we used multimodal MRI and partial least squares path modeling (PLS-PM) assuming that patients with RBD followed distinct neurodegeneration pattern.

Methods: Fifty-four patients with PD (34 with RBD) and 25 healthy volunteers were scanned with T1-weighted, diffusion tensor, and neuromelanin-sensitive imaging. Volume, signal, and mean, axial, and radial diffusivities were calculated in brainstem, basal forebrain, and cortical regions. PLS-PM, estimating a network of causal relationships between blocks of variables, was used to build and test an analytical model based on Braak staging. The overall quality of the model was assessed with goodness of fit coefficient (Gof).

Results: PLS-PM was run on patients with PD with RBD and without RBD separately. In PD with RBD, a brainstem-to-cortex model had significant Gof (0.71, = 0.01), whereas a cortex-to-brainstem model did not. In contrast, in patients with PD without RBD, the brainstem-to-cortex model was not significant (Gof = 0.64, = 0.27), and the cortex-to-brainstem model was highly significant (Gof = 0.72, = 0.008).

Conclusions: With the PLS-PM imaging-based model, the neurodegeneration pattern of patients with PD with RBD was consistent with the Braak brainstem-to-cortex model, whereas that of patients without RBD followed the cortex-to-brainstem model.
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http://dx.doi.org/10.1212/WNL.0000000000011155DOI Listing
January 2021

Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting.

Mov Disord 2021 02 2;36(2):460-470. Epub 2020 Nov 2.

Paris Brain Institute-ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UMR S 1127, CNRS UMR 7225, Paris, France.

Background: Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing.

Objective: The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes.

Methods: Three hundred twenty-two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA-P), and 23 with MSA of the cerebellar variant (MSA-C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner-dependent effects, we tested two types of normalizations using patient data or healthy control data.

Results: In the replication cohort, high accuracies were achieved using volumetry in the classification of PD-PSP, PD-MSA-C, PSP-MSA-C, and PD-atypical parkinsonism (balanced accuracies: 0.840-0.983, area under the receiver operating characteristic curves: 0.907-0.995). Performances were lower for the classification of PD-MSA-P, MSA-C-MSA-P (balanced accuracies: 0.765-0.784, area under the receiver operating characteristic curve: 0.839-0.871) and PD-PSP-MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI.

Conclusions: A machine learning approach based on volumetry enabled accurate classification of subjects with early-stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.28348DOI Listing
February 2021

Machine learning based white matter models with permeability: An experimental study in cuprizone treated in-vivo mouse model of axonal demyelination.

Neuroimage 2021 01 6;224:117425. Epub 2020 Oct 6.

Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.

The intra-axonal water exchange time (τ), a parameter associated with axonal permeability, could be an important biomarker for understanding and treating demyelinating pathologies such as Multiple Sclerosis. Diffusion-Weighted MRI (DW-MRI) is sensitive to changes in permeability; however, the parameter has so far remained elusive due to the lack of general biophysical models that incorporate it. Machine learning based computational models can potentially be used to estimate such parameters. Recently, for the first time, a theoretical framework using a random forest (RF) regressor suggests that this is a promising new approach for permeability estimation. In this study, we adopt such an approach and for the first time experimentally investigate it for demyelinating pathologies through direct comparison with histology. We construct a computational model using Monte Carlo simulations and an RF regressor in order to learn a mapping between features derived from DW-MRI signals and ground truth microstructure parameters. We test our model in simulations, and find strong correlations between the predicted and ground truth parameters (intra-axonal volume fraction f: R =0.99, τ: R =0.84, intrinsic diffusivity d: R =0.99). We then apply the model in-vivo, on a controlled cuprizone (CPZ) mouse model of demyelination, comparing the results from two cohorts of mice, CPZ (N=8) and healthy age-matched wild-type (WT, N=8). We find that the RF model estimates sensible microstructure parameters for both groups, matching values found in literature. Furthermore, we perform histology for both groups using electron microscopy (EM), measuring the thickness of the myelin sheath as a surrogate for exchange time. Histology results show that our RF model estimates are very strongly correlated with the EM measurements (ρ = 0.98 for f, ρ = 0.82 for τ). Finally, we find a statistically significant decrease in τ in all three regions of the corpus callosum (splenium/genu/body) of the CPZ cohort (<τ>=310ms/330ms/350ms) compared to the WT group (<τ>=370ms/370ms/380ms). This is in line with our expectations that τ is lower in regions where the myelin sheath is damaged, as axonal membranes become more permeable. Overall, these results demonstrate, for the first time experimentally and in vivo, that a computational model learned from simulations can reliably estimate microstructure parameters, including the axonal permeability .
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http://dx.doi.org/10.1016/j.neuroimage.2020.117425DOI Listing
January 2021

Spatiotemporal changes in substantia nigra neuromelanin content in Parkinson's disease.

Brain 2020 09;143(9):2757-2770

Institut du Cerveau - ICM,  INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France.

This study aimed to investigate the spatiotemporal changes in neuromelanin-sensitive MRI signal in the substantia nigra and their relation to clinical scores of disease severity in patients with early or progressing Parkinson's disease and patients with idiopathic rapid eye movement sleep behaviour disorder (iRBD) exempt of Parkinsonian signs compared to healthy control subjects. Longitudinal T1-weighted anatomical and neuromelanin-sensitive MRI was performed in two cohorts, including patients with iRBD, patients with early or progressing Parkinson's disease, and control subjects. Based on the aligned substantia nigra segmentations using a study-specific brain anatomical template, parametric maps of the probability of a voxel belonging to the substantia nigra were calculated for patients with various degrees of disease severity and controls. For each voxel in the substantia nigra, probability map of controls, correlations between signal-to-noise ratios on neuromelanin-sensitive MRI in patients with iRBD and Parkinson's disease and clinical scores of motor disability, cognition and mood/behaviour were calculated. Our results showed that in patients, compared to the healthy control subjects, the volume of the substantia nigra was progressively reduced for increasing disease severity. The neuromelanin signal changes appeared to start in the posterolateral motor areas of the substantia nigra and then progressed to more medial areas of this region. The ratio between the volume of the substantia nigra in patients with Parkinson's disease relative to the controls was best fitted by a mono-exponential decay. Based on this model, the pre-symptomatic phase of the disease started at 5.3 years before disease diagnosis, and 23.1% of the substantia nigra volume was lost at the time of diagnosis, which was in line with previous findings using post-mortem histology of the human substantia nigra and radiotracer studies of the human striatum. Voxel-wise patterns of correlation between neuromelanin-sensitive MRI signal-to-noise ratio and motor, cognitive and mood/behavioural clinical scores were localized in distinct regions of the substantia nigra. This localization reflected the functional organization of the nigrostriatal system observed in histological and electrophysiological studies in non-human primates (motor, cognitive and mood/behavioural domains). In conclusion, neuromelanin-sensitive MRI enabled us to assess voxel-wise modifications of substantia nigra's morphology in vivo in humans, including healthy controls, patients with iRBD and patients with Parkinson's disease, and identify their correlation with nigral function across all motor, cognitive and behavioural domains. This insight could help assess disease progression in drug trials of disease modification.
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http://dx.doi.org/10.1093/brain/awaa216DOI Listing
September 2020

The Role of Magnetic Resonance Imaging for the Diagnosis of Atypical Parkinsonism.

Front Neurol 2020 17;11:665. Epub 2020 Jul 17.

Institut du Cerveau et de la Moelle épinière-ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, UPMC Univ Paris 06, UMRS 1127, CNRS UMR 7225, Paris, France.

The diagnosis of Parkinson's disease and atypical Parkinsonism remains clinically difficult, especially at the early stage of the disease, since there is a significant overlap of symptoms. Multimodal MRI has significantly improved diagnostic accuracy and understanding of the pathophysiology of Parkinsonian disorders. Structural and quantitative MRI sequences provide biomarkers sensitive to different tissue properties that detect abnormalities specific to each disease and contribute to the diagnosis. Machine learning techniques using these MRI biomarkers can effectively differentiate atypical Parkinsonian syndromes. Such approaches could be implemented in a clinical environment and improve the management of Parkinsonian patients. This review presents different structural and quantitative MRI techniques, their contribution to the differential diagnosis of atypical Parkinsonian disorders and their interest for individual-level diagnosis.
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http://dx.doi.org/10.3389/fneur.2020.00665DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7380089PMC
July 2020

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

Radiology 2020 12 17;297(3):E313-E323. Epub 2020 Jul 17.

From the Sorbonne Université, Inserm, CNRS, Institut du Cerveau-Paris Brain Institute (ICM), F-75013 Paris, France (L.C., D.G., B.M., C.R., D.D., J.C.C., S.L., N.P.); Sorbonne Université, INSERM 75013 Paris, France (L.C., N.S., N.W., D.G., B.M., S. Burrel, D.B., A.D., C.R., D.S., D.D., E.M., M.R., T.S., V.D., J.C.C., S.L., N.P.); Paris Brain Institute - ICM, Movement Investigations and Therapeutics Team (MOV'IT), Paris, France (L.C., S.L., N.P.); ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France (L.C., D.G., S.L., N.P.); Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Service de Neuroradiologie (L.C., N.S., D.G., D.L., S. Belkacem, S.S., D.D., S.G., S.T., S.L., N.P.), Médecine Intensive Réanimation Neurologique (N.W.), Service de Neurochirurgie (B.M.), Service de Virologie, Centre d'Investigation Clinique Neurosciences (S. Burrel, D.B.), Service de Pneumologie, Médecine Intensive et Réanimation (A.D., E.M., T.S.), Urgences Cérébro-Vasculaires (C.R.), Département de Neurologie, Centre d'Investigation Clinique Neurosciences (C.D., J.C.C.), Département de Neuropathologie (D.S.), Department of Anesthesia, Critical Care and Peri-Operative Medicine (M.R., V.D.), Paris, France; Brain Liver Pitié-Salpêtrière Study Group, INSERM UMR S 938, Centre de Recherche Saint-Antoine, Maladies Métaboliques, biliaires et fibro-inflammatoire du foie, Institute of Cardiometabolism and Nutrition (N.W., S.D.); CNR Herpèsvirus (laboratoire associé HSV), SU-INSERM UMR_S 1136 Team 3 THERAVIR IPLESP (S. Burrel, D.B.); ICM, Stroke Network, STAR Team, Paris, France (C.R.); Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière,; ICM, INRIA, ARAMIS project-team, Paris, France (D.D., M.R.); Clinical Research Group ARPE, Sorbonne University, Paris, France (V.D.); INSERM UMR 1141, Paris France (V.D.); and Assistance Publique Hôpitaux de Paris, DMU ESPRIT, Paris, France (P.R.).

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 ( = 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
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http://dx.doi.org/10.1148/radiol.2020202422DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370354PMC
December 2020

MRI of neurodegeneration with brain iron accumulation.

Curr Opin Neurol 2020 08;33(4):462-473

Paris Brain Institute, Institut du Cerveau et de la Moelle épinière - ICM, INSERM U 1127, CNRS UMR 7225, Sorbonne Université, Team 'Movement Investigations and Therapeutics' (MOV'IT).

Purpose Of Review: The diagnosis of neurodegeneration with brain iron accumulation (NBIA) typically associates various extrapyramidal and pyramidal features, cognitive and psychiatric symptoms with bilateral hypointensities in the globus pallidus on iron-sensitive magnetic resonance images, reflecting the alteration of iron homeostasis in this area. This article details the contribution of MRI in the diagnosis by summarizing and comparing MRI patterns of the various NBIA subtypes.

Recent Findings: MRI almost always shows characteristic changes combining iron accumulation and additional neuroimaging abnormalities. Iron-sensitive MRI shows iron deposition in the basal ganglia, particularly in bilateral globus pallidus and substantia nigra. Other regions may be affected depending on the NBIA subtypes including the cerebellum and dentate nucleus, the midbrain, the striatum, the thalamus, and the cortex. Atrophy of the cerebellum, brainstem, corpus callosum and cortex, and white matter changes may be associated and worsen with disease duration. Iron deposition can be quantified using R2 or quantitative susceptibility mapping.

Summary: Recent MRI advances allow depicting differences between the various subtypes of NBIA, providing a useful analytical framework for clinicians. Standardization of protocols for image acquisition and analysis may help improving the detection of imaging changes associated with NBIA and the quantification of iron deposition.
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http://dx.doi.org/10.1097/WCO.0000000000000844DOI Listing
August 2020

Iron Imaging as a Diagnostic Tool for Parkinson's Disease: A Systematic Review and Meta-Analysis.

Front Neurol 2020 28;11:366. Epub 2020 May 28.

Institut du Cerveau et de la Moelle épinière (ICM), Centre de NeuroImagerie de Recherche (CENIR), ICM, Paris, France.

Parkinson's disease (PD) is a progressive neurodegenerative disease whose main neuropathological feature is the loss of dopaminergic neurons of the substantia nigra (SN). There is also an increase in iron content in the SN in postmortem and imaging studies using iron-sensitive MRI techniques. However, MRI results are variable across studies. We performed a systematic meta-analysis of SN iron imaging studies in PD to better understand the role of iron-sensitive MRI quantification to distinguish patients from healthy controls. We also studied the factors that may influence iron quantification and analyzed the correlations between demographic and clinical data and iron load. We searched PubMed and ScienceDirect databases (from January 1994 to December 2019) for studies that analyzed iron load in the SN of PD patients using T2, R2, susceptibility weighting imaging (SWI), or quantitative susceptibility mapping (QSM) and compared the values with healthy controls. Details for each study regarding participants, imaging methods, and results were extracted. The effect size and confidence interval (CI) of 95% were calculated for each study as well as the pooled weighted effect size for each marker over studies. Hence, the correlations between technical and clinical metrics with iron load were analyzed. Forty-six articles fulfilled the inclusion criteria including 27 for T2/R2 measures, 10 for SWI, and 17 for QSM (3,135 patients and 1,675 controls). Eight of the articles analyzed both R2 and QSM. A notable effect size was found in the SN in PD for R2 increase (effect size: 0.84, 95% CI: 0.60 to 1.08), for SWI measurements (1.14, 95% CI: 0.54 to 1.73), and for QSM increase (1.13, 95% CI: 0.86 to 1.39). Correlations between imaging measures and Unified Parkinson's Disease Rating Scale (UPDRS) scores were mostly observed for QSM. The consistent increase in MRI measures of iron content in PD across the literature using R2, SWI, or QSM techniques confirmed that these measurements provided reliable markers of iron content in PD. Several of these measurements correlated with the severity of motor symptoms. Lastly, QSM appeared more robust and reproducible than R2 and more suited to multicenter studies.
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http://dx.doi.org/10.3389/fneur.2020.00366DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7270360PMC
May 2020

In vivo diffusion-weighted MRS using semi-LASER in the human brain at 3 T: Methodological aspects and clinical feasibility.

NMR Biomed 2021 05 13;34(5):e4206. Epub 2020 Jan 13.

Centre de NeuroImagerie de Recherche (CENIR), Institut du Cerveau et de la Moelle épinère (ICM), Paris, France.

Diffusion-weighted (DW-) MRS investigates non-invasively microstructural properties of tissue by probing metabolite diffusion in vivo. Despite the growing interest in DW-MRS for clinical applications, little has been published on the reproducibility of this technique. In this study, we explored the optimization of a single-voxel DW-semi-LASER sequence for clinical applications at 3 T, and evaluated the reproducibility of the method under different experimental conditions. DW-MRS measurements were carried out in 10 healthy participants and repeated across three sessions. Metabolite apparent diffusion coefficients (ADCs) were calculated from mono-exponential fits (ADC ) up to b = 3300 s/mm , and from the diffusional kurtosis approach (ADC ) up to b = 7300 s/mm . The inter-subject variabilities of ADCs of N-acetylaspartate + N-acetylaspartylglutamate (tNAA), creatine + phosphocreatine, choline containing compounds, and myo-inositol were calculated in the posterior cingulate cortex (PCC) and in the corona radiata (CR). We explored the effect of physiological motion on the DW-MRS signal and the importance of cardiac gating and peak thresholding to account for signal amplitude fluctuations. Additionally, we investigated the dependence of the intra-subject variability on the acquisition scheme using a bootstrapping resampling method. Coefficients of variation were lower in PCC than CR, likely due to the different sensitivities to motion artifacts of the two regions. Finally, we computed coefficients of repeatability for ADC and performed power calculations needed for designing clinical studies. The power calculation for ADC of tNAA showed that in the PCC seven subjects per group are sufficient to detect a difference of 5% between two groups with an acquisition time of 4 min, suggesting that ADC of tNAA is a suitable marker for disease-related intracellular alteration even in small case-control studies. In the CR, further work is needed to evaluate the voxel size and location that minimize the motion artifacts and variability of the ADC measurements.
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http://dx.doi.org/10.1002/nbm.4206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7354897PMC
May 2021

Non-invasive ultrasonic modulation of visual evoked response by GABA delivery through the blood brain barrier.

J Control Release 2020 02 6;318:223-231. Epub 2019 Dec 6.

Physics for Medicine Paris, Inserm, ESPCI Paris, CNRS, PSL Research University, Université Paris Diderot, Sorbonne Paris Cité, Paris, France. Electronic address:

GABA is an inhibitory neurotransmitter that is maintained outside the brain by the blood brain barrier in normal condition. In this paper we demonstrate the feasibility of modulating brain activity in the visual cortex of non-human primates by transiently permeabilizing the blood brain barrier (BBB) using focused ultrasound (FUS) coupled with ultrasound contrast agents (UCA), followed by intra-venous injection of GABA. The visual evoked potentials exhibited a significant and GABA-dose-depend decrease in activity. The effect of the sonication only (with and without UCA) was also investigated and was shown to decrease the activity 8.7 times less than the GABA-induced inhibition enabled by BBB permeabilization. Finally, the UCA harmonic response was monitored during sonication to estimate the level of stable cavitation (a signature of the effectiveness of BBB permeabilization) and to avoid damage due to inertial cavitation (the sonication was automatically shut down when this condition was detected). Our results extend the promise of the exploration and treatment of the brain using non-invasive, controllable, repeatable, and reversible neuromodulation.
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http://dx.doi.org/10.1016/j.jconrel.2019.12.006DOI Listing
February 2020

Multimodal Magnetic Resonance Imaging Quantification of Brain Changes in Progressive Supranuclear Palsy.

Mov Disord 2020 01 11;35(1):161-170. Epub 2019 Nov 11.

Institut du Cerveau et de la Moelle-ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France.

Background: Progressive supranuclear palsy (PSP) is a neurodegenerative clinically heterogeneous disorder, formal diagnosis being based on postmortem histological brain examination.

Objective: We aimed to perform a precise in vivo staging of neurodegeneration in PSP using quantitative multimodal MRI. The ability of MRI biomarkers to differentiate PSP from PD was also evaluated.

Methods: Eleven PSP patients were compared to 26 age-matched healthy controls and 51 PD patients. Images were acquired at 3 Tesla (three-dimensional T -weighted, diffusion tensor, and neuromelanin-sensitive images) and 7 Tesla (three-dimensional-T * images). Regions of interest included the cortical areas, hippocampus, amygdala, basal ganglia, basal forebrain, brainstem nuclei, dentate nucleus, and cerebellum. Volumes, mean diffusivity, and fractional anisotropy were measured. In each region, a threshold value for group categorization was calculated, and four grades of change (0-3) were determined.

Results: PSP patients showed extensive volume decreases and diffusion changes in the midbrain, SN, STN, globus pallidus, basal forebrain, locus coeruleus, pedunculopontine nucleus, and dentate nucleus, in close agreement with the degrees of impairment in histological analyses. The predictive factors for the separation of PSP and healthy controls were, in descending order, the neuromelanin-based SN volume; midbrain fractional anisotropy; volumes of the midbrain, globus pallidus, and putamen; and fractional anisotropy in the locus coeruleus. The best predictors for separating PSP from PD were the neuromelanin-based volume in the SN, fractional anisotropy in the pons, volumes of the midbrain and globus pallidus, and fractional anisotropy in the basal forebrain.

Conclusions: These results suggest that it is possible to evaluate brain neurodegeneration in PSP noninvasively, even in small brainstem nuclei, in close agreement with previously published histological data. © 2019 International Parkinson and Movement Disorder Society.
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http://dx.doi.org/10.1002/mds.27877DOI Listing
January 2020

MRI monitoring of temperature and displacement for transcranial focus ultrasound applications.

Neuroimage 2020 01 6;204:116236. Epub 2019 Oct 6.

IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France; Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France. Electronic address:

Background: Transcranial focus ultrasound applications applied under MRI-guidance benefit from unrivaled monitoring capabilities, allowing the recording of real-time anatomical information and biomarkers like the temperature rise and/or displacement induced by the acoustic radiation force. Having both of these measurements could allow for better targeting of brain structures, with improved therapy monitoring and safety.

Method: We investigated the use of a novel MRI-pulse sequence described previously in Bour et al., (2017) to quantify both the displacement and temperature changes under various ultrasound sonication conditions and in different regions of the brain. The method was evaluated in vivo in a non-human primate under anesthesia using a single-element transducer (f = 850 kHz) in a setting that could mimic clinical applications. Acquisition was performed at 3 T on a clinical imaging system using a modified single-shot gradient echo EPI sequence integrating a bipolar motion-sensitive encoding gradient. Four slices were acquired sequentially perpendicularly or axially to the direction of the ultrasound beam with a 1-Hz update frequency and an isotropic spatial resolution of 2-mm. A total of twenty-four acquisitions were performed in three different sets of experiments. Measurement uncertainty of the sequence was investigated under different acoustic power deposition and in different regions of the brain. Acoustic simulation and thermal modeling were performed and compared to experimental data.

Results: The sequence simultaneously provides relevant information about the focal spot location and visualization of heating of brain structures: 1) The sequence localized the acoustic focus both along as well as perpendicular to the ultrasound direction. Tissue displacements ranged from 1 to 2 μm. 2) Thermal rise was only observed at the vicinity of the skull. Temperature increase ranged between 1 and 2 °C and was observed delayed relative the sonication due to thermal diffusion. 3) The fast frame rate imaging was able to highlight magnetic susceptibility artifacts related to breathing, for the most caudal slices. We demonstrated that respiratory triggering successfully restored the sensitivity of the method (from 0.7 μm to 0.2 μm). 4) These results were corroborated by acoustic simulations.

Conclusions: The current rapid, multi-slice acquisition and real-time implementation of temperature and displacement visualization may be useful in clinical practices. It may help defining operational safety margins, improving therapy precision and efficacy. Simulations were in good agreement with experimental data and may thus be used prior treatment for procedure planning.
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http://dx.doi.org/10.1016/j.neuroimage.2019.116236DOI Listing
January 2020

Clinical Integration of Automated Processing for Brain Quantitative Susceptibility Mapping: Multi-Site Reproducibility and Single-Site Robustness.

J Neuroimaging 2019 11 4;29(6):689-698. Epub 2019 Aug 4.

Department of Radiology, Weill Medical College of Cornell University, New York, NY.

Background And Purpose: Quantitative susceptibility mapping (QSM) of the brain has become highly reproducible and has applications in an expanding array of diseases. To translate QSM from bench to bedside, it is important to automate its reconstruction immediately after data acquisition. In this work, a server system that automatically reconstructs QSM and exchange images with the scanner using the DICOM standard is demonstrated using a multi-site, multi-vendor reproducibility study and a large, single-site, multi-scanner image quality review study in a clinical environment.

Methods: A single healthy subject was scanned with a 3D multi-echo gradient echo sequence at nine sites around the world using scanners from three manufacturers. A high-resolution (HiRes, .5 × .5 × 1 mm reconstructed) and standard-resolution (StdRes, .5 × .5 × 3 mm ) protocol was performed. ROI analysis of various white matter and gray matter regions was performed to investigate reproducibility across sites. At one institution, a retrospective multi-scanner image quality review was carried out of all clinical QSM images acquired consecutively in 1 month.

Results: Reconstruction times using a GPU were 29 ± 22 seconds (StdRes) and 55 ± 39 seconds (HiRes). ROI standard deviation across sites was below 24 ppb (StdRes) and 17 ppb (HiRes). Correlations between ROI averages across sites were on average .92 (StdRes) and .96 (HiRes). Image quality review of 873 consecutive patients revealed diagnostic or excellent image quality in 96% of patients.

Conclusion: Online QSM reconstruction for a variety of sites and scanner platforms with low cross-site ROI standard deviation is demonstrated. Image quality review revealed diagnostic or excellent image quality in 96% of 873 patients.
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http://dx.doi.org/10.1111/jon.12658DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6814493PMC
November 2019

Early alteration of the locus coeruleus in phenotypic variants of Alzheimer's disease.

Ann Clin Transl Neurol 2019 07 23;6(7):1345-1351. Epub 2019 Jun 23.

Unit of Neurology of Memory and Language, Université Paris Descartes, Sorbonne Paris Cité, GHU Paris Psychiatry and Neurosciences, Hôpital Sainte Anne, Paris, France.

Neuropathological studies showed early locus coeruleus (LC) neuronal loss associated with tauopathy in Alzheimer's Disease (AD). We used the LC signal intensity (LC-I) on 3T MRI to assess the LC integrity in AD (n = 37) and controls (n = 17). The LC-I was decreased in AD regardless of typical (amnesic) and atypical presentation (logopenic aphasia/visuo-spatial deficit), from the prodromal stage, and independently of the amyloid load measured by PiB-PET. The LC-I was correlated with memory performance of typical AD. This supports the pathophysiological model in which the LC plays a critical role in AD and may thus be a potential therapeutic target.
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http://dx.doi.org/10.1002/acn3.50818DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6649639PMC
July 2019

Acute Diffusivity Biomarkers for Prediction of Motor and Language Outcome in Mild-to-Severe Stroke Patients.

Stroke 2019 08 5;50(8):2050-2056. Epub 2019 Jul 5.

From the Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013, Paris, France (E.M., S.M., R.V., S.L., Y.S., C.R.).

Background and Purpose- Early severity of stroke symptoms-especially in mild-to-severe stroke patients-are imperfect predictors of long-term motor and aphasia outcome. Motor function and language processing heavily rely on the preservation of important white matter fasciculi in the brain. Axial diffusivity (AD) from the diffusion tensor imaging model has repeatedly shown to accurately reflect acute axonal damage and is thus optimal to probe the integrity of important white matter bundles and their relationship with long-term outcome. Our aim was to investigate the independent prognostic value of the AD of white matter tracts in the motor and language network evaluated at 24 hours poststroke for motor and aphasia outcome at 3 months poststroke. Methods- Seventeen (motor cohort) and 28 (aphasia cohort) thrombolyzed patients with initial mild-to-severe stroke underwent a diffusion tensor imaging sequence at 24 hours poststroke. Motor and language outcome were evaluated at 3 months poststroke with a composite motor score and the aphasia handicap scale. We first used stepwise regression to determine which classic (age, initial motor or aphasia severity, and lesion volume) and imaging (ratio of affected/unaffected AD of motor and language fasciculi) factors were related to outcome. Second, to determine the specificity of our a priori choices of fasciculi, we performed voxel-based analyses to determine if the same, additional, or altogether new regions were associated with long-term outcome. Results- The ratio of AD in the corticospinal tract was the sole predictor of long-term motor outcome, and the ratio of AD in the arcuate fasciculus-along with age and initial aphasia severity-was an independent predictor of 3-month aphasia outcome. White matter regions overlapping with these fasciculi naturally emerged in the corresponding voxel-based analyses. Conclusions- AD of the corticospinal tract and arcuate fasciculus are effective biomarkers of long-term motor and aphasia outcome, respectively.
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http://dx.doi.org/10.1161/STROKEAHA.119.024946DOI Listing
August 2019

In vivo H MRS detection of cystathionine in human brain tumors.

Magn Reson Med 2019 10 26;82(4):1259-1265. Epub 2019 May 26.

Center for Magnetic Resonance Research and Department of Radiology, University of Minnesota, Minneapolis, Minnesota.

Purpose: To report the technical aspects of noninvasive detection of cystathionine in human brain glioma with edited MRS, and to investigate possible further acquisition improvements for robust quantification of this metabolite.

Methods: In vivo H MR spectra were acquired at 3 T in 15 participants with an isocitrate dehydrogenase-mutated glioma using a MEGA-PRESS (MEscher GArwood point resolved spectroscopy) sequence previously employed for 2-hydroxyglutarate detection (T = 2 s, T = 68 ms). The editing pulse was applied at 1.9 ppm for the edit-on condition and at 7.5 ppm for the edit-off condition. To evaluate the editing efficiency, spectra were acquired in 1 participant by placing the editing pulse for the edit-on condition at 1.9, 2.03, and 2.16 ppm. Cystathionine concentration was quantified using LCModel and a simulated basis set. To confirm chemical shifts and J-coupling values of cystathionine, the H NMR cystathionine spectrum was measured using a high-resolution 500 MHz spectrometer.

Results: In 12 gliomas, cystathionine was observed in the in vivo edited MR spectra at 2.72 and 3.85 ppm and quantified. The signal intensity of the cystathionine resonance at 2.72 ppm increased 1.7 and 2.13 times when the editing pulse was moved to 2.03 and 2.16 ppm, respectively. Cystathionine was not detectable in normal brain tissue.

Conclusion: Cystathionine can be detected in vivo by edited MRS using the same protocol as for 2-hydroxyglutarate detection. This finding may enable a more accurate, noninvasive investigation of cellular metabolism in glioma.
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http://dx.doi.org/10.1002/mrm.27810DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626581PMC
October 2019

Multivariate prediction of functional outcome using lesion topography characterized by acute diffusion tensor imaging.

Neuroimage Clin 2019 10;23:101821. Epub 2019 Apr 10.

Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, F-75013 Paris, France; ICM Team Movement Investigation and Therapeutics, France; AP-HP, Urgences Cérébro-Vasculaires, Hôpital Pitié-Salpêtrière, Paris, France. Electronic address:

The relationship between stroke topography and functional outcome has largely been studied with binary manual lesion segmentations. However, stroke topography may be better characterized by continuous variables capable of reflecting the severity of ischemia, which may be more pertinent for long-term outcome. Diffusion Tensor Imaging (DTI) constitutes a powerful means of quantifying the degree of acute ischemia and its potential relation to functional outcome. Our aim was to investigate whether using more clinically pertinent imaging parameters with powerful machine learning techniques could improve prediction models and thus provide valuable insight on critical brain areas important for long-term outcome. Eighty-seven thrombolyzed patients underwent a DTI sequence at 24 h post-stroke. Functional outcome was evaluated at 3 months post-stroke with the modified Rankin Score and was dichotomized into good (mRS ≤ 2) and poor (mRS > 2) outcome. We used support vector machines (SVM) to classify patients into good vs. poor outcome and evaluate the accuracy of different models built with fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity asymmetry maps, and lesion segmentations in combination with lesion volume, age, recanalization status, and thrombectomy treatment. SVM classifiers built with axial diffusivity maps yielded the best accuracy of all imaging parameters (median [IQR] accuracy = 82.8 [79.3-86.2]%), compared to that of lesion segmentations (76.7 [73.3-82.8]%) when predicting 3-month functional outcome. The analysis revealed a strong contribution of clinical variables, notably - in descending order - lesion volume, thrombectomy treatment, and recanalization status, in addition to the deep white matter at the crossroads of major white matter tracts, represented by brain regions where model weights were highest. Axial diffusivity is a more appropriate imaging marker to characterize stroke topography for predicting long-term outcome than binary lesion segmentations.
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http://dx.doi.org/10.1016/j.nicl.2019.101821DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6462821PMC
April 2020

Neuroimaging biomarkers for clinical trials in atypical parkinsonian disorders: Proposal for a Neuroimaging Biomarker Utility System.

Alzheimers Dement (Amst) 2019 Dec 2;11:301-309. Epub 2019 Apr 2.

Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom.

Introduction: Therapeutic strategies targeting protein aggregations are ready for clinical trials in atypical parkinsonian disorders. Therefore, there is an urgent need for neuroimaging biomarkers to help with the early detection of neurodegenerative processes, the early differentiation of the underlying pathology, and the objective assessment of disease progression. However, there currently is not yet a consensus in the field on how to describe utility of biomarkers for clinical trials in atypical parkinsonian disorders.

Methods: To promote standardized use of neuroimaging biomarkers for clinical trials, we aimed to develop a conceptual framework to characterize in more detail the kind of neuroimaging biomarkers needed in atypical parkinsonian disorders, identify the current challenges in ascribing utility of these biomarkers, and propose criteria for a system that may guide future studies.

Results: As a consensus outcome, we describe the main challenges in ascribing utility of neuroimaging biomarkers in atypical parkinsonian disorders, and we propose a conceptual framework that includes a graded system for the description of utility of a specific neuroimaging measure. We included separate categories for the ability to accurately identify an intention-to-treat patient population early in the disease (Early), to accurately detect a specific underlying pathology (Specific), and the ability to monitor disease progression (Progression).

Discussion: We suggest that the advancement of standardized neuroimaging in the field of atypical parkinsonian disorders will be furthered by a well-defined reference frame for the utility of biomarkers. The proposed utility system allows a detailed and graded description of the respective strengths of neuroimaging biomarkers in the currently most relevant areas of application in clinical trials.
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http://dx.doi.org/10.1016/j.dadm.2019.01.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6446052PMC
December 2019
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