Analysis of Parkinson\u27s Disease Data

In this paper, we investigate the diagnostic data from patients suffering with Parkinson\u27s disease (PD) and design classification/prediction model to simplify the diagnosis. The main aim of this research is to open possibilities to be able to apply deep learning algorithms to help better understand and diagnose the disease. To our knowledge, the capabilities of deep learning algorithms have not yet been completely utilized in the field of Parkinson\u27s research and we believe that by having an in-depth understanding of data, we can create a platform to apply different algorithms to automate the Parkinson\u27s Disease diagnosis to certain extent. We use Parkinson\u27s Progression Markers Initiative (PPMI) dataset provided by Michael J. Fox Foundation to perform our analysis

Methodological considerations for neuroimaging in deep brain stimulation of the subthalamic nucleus in Parkinson’s disease patients

Deep brain stimulation (DBS) of the subthalamic nucleus is a neurosurgical intervention for Parkinson’s disease patients who no longer appropriately respond to drug treatments. A small fraction of patients will fail to respond to DBS, develop psychiatric and cognitive side-effects, or incur surgery-related complications such as infections and hemorrhagic events. In these cases, DBS may require recalibration, reimplantation, or removal. These negative responses to treatment can partly be attributed to suboptimal pre-operative planning procedures via direct targeting through low-field and low-resolution magnetic resonance imaging (MRI). One solution for increasing the success and efficacy of DBS is to optimize preoperative planning procedures via sophisticated neuroimaging techniques such as high-resolution MRI and higher field strengths to improve visualization of DBS targets and vasculature. We discuss targeting approaches, MRI acquisition, parameters, and post-acquisition analyses. Additionally, we highlight a number of approaches including the use of ultra-high field (UHF) MRI to overcome limitations of standard settings. There is a trade-off between spatial resolution, motion artifacts, and acquisition time, which could potentially be dissolved through the use of UHF-MRI. Image registration, correction, and post-processing techniques may require combined expertise of traditional radiologists, clinicians, and fundamental researchers. The optimization of pre-operative planning with MRI can therefore be best achieved through direct collaboration between researchers and clinicians

Automated segmentation of the substantia nigra, subthalamic nucleus and red nucleus in 7 T data at young and old age

With recent developments in MR acquisition at 7 T, smaller brainstem structures such as the red nuclei, substantia nigra and subthalamic nuclei can be imaged with good contrast and resolution. These structures have important roles both in the study of the healthy brain and in diseases such as Parkinson's disease, but few methods have been described to automatically segment them. In this paper, we extend a method that we have previously proposed for segmentation of the striatum and globus pallidus to segment these deeper and smaller structures. We modify the method to allow more direct control over segmentation smoothness by using a Markov random field prior. We investigate segmentation performance in three age groups and show that the method produces consistent results that correspond well with manual segmentations. We perform a vertex-based analysis to identify changes with age in the shape of the structures and present results suggesting that the method may be at least as effective as manual delineation in capturing differences between subject

Imaging the subthalamic nucleus in Parkinson’s disease

This thesis is comprised of a set of work that aims to visualize and quantify the anatomy, structural variability, and connectivity of the subthalamic nucleus (STN) with optimized neuroimaging methods. The study populations include both healthy cohorts and individuals living with Parkinson's disease (PD). PD was chosen specifically due to the involvement of the STN in the pathophysiology of the disease. Optimized neuroimaging methods were primarily obtained using ultra-high field (UHF) magnetic resonance imaging (MRI). An additional component of this thesis was to determine to what extent UHF-MRI can be used in a clinical setting, specifically for pre-operative planning of deep brain stimulation (DBS) of the STN for patients with advanced PD. The thesis collectively demonstrates that i, MRI research, and clinical applications must account for the different anatomical and structural changes that occur in the STN with both age and PD. ii, Anatomical connections involved in preparatory motor control, response inhibition, and decision-making may be compromised in PD. iii. The accuracy of visualizing and quantifying the STN strongly depends on the type of MR contrast and voxel size. iv, MRI at a field strength of 3 Tesla (T) can under certain circumstances be optimized to produce results similar to that of 7 T at the expense of increased acquisition time

Segmentation of Substantia Nigra, Subthalamic and Red Nuclei with a Multi-Modal Quantitative 7T MRI High Resolution Template

Synopsis: The segmentation of brain substructures is very useful in the characterization of alterations involved in multiple diseases. From 200 7T brain MRI scan including MP2RAGE and MGRE used to generate quantitative T1 maps (qT1), R2* and QSM volumes, a pipeline was developed to create a high-resolution multi-modal template at (400 µm)3 based on these multiple quantitative imaging modalities. Preliminary results show that multi-modality allows for a more precise parcellation of the SN, RN and STH substructures.Introduction: The importance of high spatial resolution in-vivo 7T MRI for deep grey nuclei (DGN) segmentation has been highlighted in recent studies. The parcellation of substructures would enable easier small alteration characterization, such as in Parkinson's disease mainly affecting the Substantia Nigra (SN) [1], [2], a region adjacent to Red (RN) and Subthalamic (STH) nuclei. Multiple atlases for these areas have been proposed, such as the CIT168 atlas [3], that offers a probabilistic subdivision of SN into two parts performed from a T1-w/T2-w multi-modal (MM) template, an atlas of Zona Incerta [4] and adjacent structures that includes the SN, RN and STH, and more recently the 7TAMIbrainDGN high-resolution (500 µm)3 DGN atlas [5]. These methods rely on the creation of templates that improve CNR and SNR via the use of super-resolution [6]. Others authors [7], [8] have shown the relevance of quantitative imaging (QSM [9] in particular) and multi-modal clustering of SN, RN and STH, for automatic segmentation by template-to-subjects co-registrations.In this study, we introduce the 7TAMIbrainqT1_R2*_QSM_400 MM template, an improved version of 7TAMIbrainT1w_30 [5], that is built using a larger number of subjects (30 to 200), with a higher super-resolution target (400 µm)3. Moreover, we also propose an automatic parcellation process of the SN using multi-class clustering on the QSM template, with a segmentation on the subject space optimized by the use of an atlas-based MM co-registration.Methods: 200 healthy subjects and patients with Parkinson's disease, Epilepsy, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, and Alzheimer were included. The data were acquired at 7T (Magnetom investigational device, Siemens, Erlangen, Germany) using a 1Tx/32Rx head coil (Nova Medical, Inc., Wilmington, MA USA). MP2RAGE were acquired for T1-weighted (T1-w) volume and quantitative T1 maps (qT1) (TA = 10.12 min; TR = 5000 ms; TE = 3.13 ms; inversion times TI1/ TI2 = 900/2750 ms; flip angles α1/α2 = 6/5; acceleration factor GRAPPA = 3; FOV = 240 mm; voxel size = (600 µm)3 isotropic; 256 sagittal partitions (partial Fourier 6/8)). For R2*/QSM, a transverse 3D multi-gradient echo (MGRE) sequence was applied (TR = 28ms/TE1 = 2.82 ms/dTE = 4.36 ms, 600 µm isotropic resolution, Tacq = 12.2min, BW=347Hz/pix, matrix size 320x260x256, acceleration factor 2, elliptical scanning). Phase image combination was ensured using the first-echo individual coil images as a reference. Magnitude and phase DICOM images were sent to a DICOM node for post-processing. QSM reconstruction involved a field inhomogeneity calculation and unwrapping step from the multiple echoes, a brain extraction step, an estimation of the internal field and the final MEDI reconstruction as in [10]. Template construction: For the creation of the multi-modal template, we relied on an iterative SyGN pipeline [11] (using antsMultivariateTemplateCreation2.sh) pooling on qT1, R2* and QSM.T1w volumes were initially co-registered on the 7TAMIbrainT1w_30 template, with the parameters described in [5] and upsampled at an isotropic voxel size of (400 µm)3 cropped in the matrix of size (96)3 centered on the Red Nuclei. The QSM and R2* volumes were co-registered to the (T1w, qT1) volumes rigidly. All volumes were left/right flipped to generate by averaging a symmetric multi-contrast template (Figure 1c). A final stage of the SyGN process with 5 iterations was performed, using equal weights for the 3 modalities. The 7TAMIbrainqT1_400, 7TAMIbrainR2*_400, 7TAMIbrainQSM_400 obtained are displayed using a look-up table optimized to improve the contrast range in the SN (colored in Figure 1d, and grayscale in Figure 2a).Segmentation of SN/RN/STH: We used a finite mixture modeling approach [8] (Atropos from the ants toolkit) for the segmentation of the 7TAMIbrainQSM_400 template into four regions, RN, STH, and two parts for SN, corresponding to SNr (pars reticulata) and SNc (pars compacta). We used the CIT168 atlas as the prior probability mapping with a spatial regularization using Markov random fields (smoothingFactor=0.1, radius = 1x1x1). Two distinct registrations methods were then used, a SyN registration from the QSM template to a QSM subject (Figure 2d) and a multi-modal SyN registration on QSM, R2* and qT1 using equals weights to compute the similarity metric. (Figure 2e).Results and Discussions: The creation and usage of a multi-modal template allows for a more accurate segmentation of the SN, RN and STH (figure 2e), as opposed to a T1w based segmentation that show high uncertainties at the boundaries of these regions (Figure 2b). Fine SN substructures and details are strongly highlighted on the MM template, in particular on R2* and QSM.Conclusions: In this study, a new pipeline for the generation of a multimodal template at 7T at (400 µm)3 based on quantitative T1, R2* and QSM was presented. We also highlighted the preliminary impact and complementarity of all contrasts in helping to identify the two sub-parts SNc and SNr in the template space and their best deformation to obtain more accurate segmentation of neighboring structures such as RN, STH in the QSM subject space.Acknowledgements: This work was supported by France Life Imaging, grant ANR-11-INSB-0006, A*midex. The authors sincerely thank L. Pini, C. Costes, P. Viout and V. Gimenez for data acquisition and study logistic.Figure 1 – Multimodal template construction, subject volumes (a), 7TAMIbrain_T1w_30 template (b), reslicing and flipping in the subject space (c) and Multi-Modal template qT1, R2* and QSM (d)Figure 2 – Segmentation of substructures in the subtantia nigra, parcellation in the template space (a), segmentation in the subject space using a co-registration to the T1w volume (b), qT1 (c), QSM (d) and using the multi-modality (e)References:[1]S. Lehéricy et al., ‘7 tesla magnetic resonance imaging: A closer look at substantia nigra anatomy in Parkinson’s disease: 7T MRI in PD’, Mov Disord., vol. 29, no. 13, pp. 1574–1581, Nov. 2014, doi: 10.1002/mds.26043.[2]B. R. Isaacs et al., ‘3 versus 7 Tesla magnetic resonance imaging for parcellations of subcortical brain structures in clinical settings’, PLoS ONE, vol. 15, no. 11, p. e0236208, Nov. 2020, doi: 10.1371/journal.pone.0236208.[3]W. M. 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