Event Abstract

Validation of new brain MRI biomarkers in Parkinson's Disease: the use of quantitative multi-parameter mapping

  • 1 GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Belgium
  • 2 University of Liège, Belgium
  • 3 Department of Neurology, University Hospital Liège, Belgium
  • 4 GIGA-In silico Medicine, University of Liège, Belgium

ABSTRACT DRAFT VALIDATION OF NEW BRAIN MRI BIOMARKERS IN PARKINSON’S DISEASE : THE USE OF QUANTITATIVE MULTI-PARAMETER MAPPING F. Depierreux1,2,4 , E. Parmentier1,2,4, K.A. Baquero1,2, E. Balteau2, C. Phillips3, P. Maquet2,4, G. Garraux1,2,4 1Movere Group, 2GIGA-CRC in vivo imaging, 3GIGA in silico medicine, University of Liège, Liège, Belgium, 4Department of Neurology, University Hospital Center (CHU), University of Liège, Liège, Belgium INTRODUCTION The accurate diagnosis of Parkinson’s disease (PD) and other related disorders (e.g. atypical Parkinson’s disease) remains a challenge as it remains based on clinical features (Antoniades and Barker, 2008; Pyatigorskaya et al., 2014). In idiopathic Parkinson’s disease, clinical brain magnetic resonance imaging (MRI) is usually normal, and there are no established MRI features positively supporting a diagnosis of PD(Michaeli et al., 2007). Recently, some advances have been made, both in structural and functional imaging to improve the capacity of MRI to detect changes in PD(Baudrexel et al., 2010; Pyatigorskaya et al., 2014). We have chosen to employ a multi-echo sequence to generate quantitative multi-parameter maps (MPM) and find an appropriate MRI biomarker of PD. Quantitative MRI reveals the physical properties of the tissues, that govern MRI contrasts(Callaghan et al., 2014). The following major parameters have been investigated: longitudinal relaxation rate (R1 = 1/T1), effective transverse relaxation rate (R2*= 1/T2*), magnetization transfer saturation (MT) and effective proton density (PD*). Our hypothesis posits that these sequences show differences between PD patients and controls, revealing changes in the microstructure of brain tissues due to the pathological process seen in the disease. MATERIAL AND METHODS 42 PD patients “on” medication and 42 healthy controls matched for age, gender and levels of education have been scanned (see details in table below). All participants had a structural MRI using a 3T head-only MRI-scanner (Magnetom Allegra, Siemens Medical Solutions, Erlangen, Germany) operated with an 8-channel head coil. Structural and quantitative maps of R1, R2*, effective proton density (PD*) and magnetization transfer saturation (MT) at 1 x 1 x 1 mm³ resolution were calculated from a multi-parameter protocol based on a 3D multi-echo fast low angle shot (FLASH) sequence. The VBQ toolbox implemented in SPM12(Callaghan et al., 2014; Helms et al., 2009; Weiskopf et al., 2013) was used for voxel-based analyses within the brain of the the MT, PD*, R1, R2* and grey matter maps, All MRI data were segmented using unified segmentation(Ashburner and Friston, 2005) after creation of the parametric maps from the raw data, then spatially normalized to a population-specific template using diffeomorphic registration(Ashburner, 2007) (DARTEL) and smoothed using a 6mm FWHM kernel. Analyses were restricted to regions known to be affected in Parkinson s’ disease basal ganglia and substantia nigra using an independent mask (available from https://www.nitric.org/projects/atag). We only report here results VBQ analysis for PD* and R2* maps, beside voxel based morphometry (VBM) analysis(Ashburner and Friston, 2000) for the grey matter maps. Group comparisons were performed voxel-by-voxel using SPM12 software. Statistical threshold was set at p<0.05, corrected for multiple comparisons using TFCE toolbox(Smith and Nichols, 2009) with 5000 permutations. RESULTS Using PD* maps, we found a significant decrease in PD patients as compared to controls in the left and right substantia nigra (figure A, left panel). On the other hand, R2* was significantly increased in PD patients as compared to controls into symmetrical clusters located in the Substantia Nigra (figure B, right panel). VBM analysis (figure C) showed the local grey matter volume was significantly decreased in PD patients, as compare to normal controls in the body of the right caudate nucleus. CONCLUSION Advanced quantitative MRI sequences such as MPM are able to show group differences in brain microstructure in brain regions known to be affected by neuropathological changes in Parkinson’s disease. We are currently conducting a validation study in a separate cohort and on a different scanner. SUMMARY FOR LAY PEOPLE Actuellement, le diagnostic de maladie de Parkinson (MP) s’appuie toujours sur des éléments essentiellement cliniques et les erreurs restent fréquentes, notamment avec des maladies voisines. Nous ne disposons pas encore d’un biomarqueur permettant un diagnostic de certitude et l’imagerie cérébrale de routine est réputée normale. Notre équipe a étudié la capacité de l’IRM cérébrale à détecter des anomalies spécifiques de la maladie par l’emploi de séquences quantitatives (« multi-parameter mapping »). En comparant un groupe de patients à un groupe contrôle, les analyses démontrent des anomalies de la microstructure cérébrale dans des régions connues pour être affectées par la MP.

Figure 1
Figure 2


We would like to thank the MoVeRe Team, Pr. P. Maquet, E. Balteau and C. Phillips for their kind advice on this manuscript.


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Keywords: Parkinson ’s disease, MRI - Magnetic resonance imaging, biomarker, diagnosis, Quantitative sequences, multi-parameter mapping

Conference: Belgian Brain Congress 2018 — Belgian Brain Council, LIEGE, Belgium, 19 Oct - 19 Oct, 2018.

Presentation Type: e-posters


Citation: Depierreux F, Parmentier E, Baquero KA, Balteau E, Phillips C, Maquet P and Garraux G (2019). Validation of new brain MRI biomarkers in Parkinson's Disease: the use of quantitative multi-parameter mapping. Front. Neurosci. Conference Abstract: Belgian Brain Congress 2018 — Belgian Brain Council. doi: 10.3389/conf.fnins.2018.95.00028

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Received: 10 Aug 2018; Published Online: 17 Jan 2019.

* Correspondence: MD. Frédérique Depierreux, GIGA-Cyclotron Research Center In Vivo Imaging, University of Liege, Liège, Belgium, fdepierreux@doct.uliege.be