Investigating premanifest synucleinopathy: structural connectome of brainstem nuclei in REM sleep behavior disorder
Maria Guadalupe Garcia Gomar1,2, Laura Lewis1,2, Lawrence Wald1,2, Bruce Rosen1,2, Aleksandar Videnovic2,3, and Marta Bianciardi1,2

1Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 2Harvard Medical School, Boston, MA, United States, 3Neurology, Massachusetts General Hospital, Boston, MA, United States


REM-sleep-behavior-disorder (RBD) is characterized by the absence of muscle-atonia during REM-sleep. RBD is strongly associated with presymptomatic-manifestations of neurodegenerative-synucleinopathies. Thus, it allows the investigation of early/premanifest neurodegenerative-stages when treatment can be most effective in delaying the development of full-blown-disease. Changes in brainstem-nuclei-connectivity are expected in RBD/premanifest-synucleinopathy based on animal- and ex-vivo-human-studies. Yet, their investigation in living-humans is understudied. Through high-spatial-resolution 7Tesla-MRI and a recently-developed probabilistic-brainstem-nuclei-atlas, we built a brainstem-based structural-connectome in living RBD-patients and age-matched controls. Interestingly, in RBD-patients we detected structural-connectivity-changes within the brainstem, with the striatum and cerebellum in line with the pathophysiology of RBD in animal-models.


Rapid-eye-movement (REM) sleep behavior disorder (RBD) is a sleep disorder characterized by the absence of muscular atonia during REM sleep. RBD patients have up to a 90% risk of developing a neurodegenerative synucleinopathy (including Parkinson’s disease, multiple system atrophy and dementia with Lewy bodies) after 14 years from RBD-diagnosis [1]. Thus RBD allows the investigation of early, premanifest neurodegenerative stages when treatment can be most effective in delaying the development of full-blown disease [2]. Changes in the microstructure and connectivity of brainstem nuclei are expected in RBD/premanifest-synucleinopathy based on (Figure 1A-C): (i) ex-vivo human staging models of synucleinopathy progression [3]; (ii) in-vivo human lesion studies of non-idiopathic-RBD [4]; (iii) animal studies of idiopathic-RBD [4]. Nevertheless, these brainstem changes underlying RBD/premanifest-synucleinopathy are currently understudied in living humans.


To investigate the presence of structural connectivity changes of brainstem nuclei in RBD by the use of high spatial-resolution diffusion-tensor-imaging (DTI) at 7 Tesla as well as a recently developed probabilistic structural atlas of brainstem nuclei of the arousal and motor systems in Montreal-Neurological-Institute (MNI) space [5-6].


Data acquisition: Five patients with idiopathic-RBD (age 69.6 ± 1.12) and two healthy subjects (age 64.5 ± 1.5) underwent 3 Tesla and 7 Tesla-MRI under IRB-approval. 7 Tesla spin-echo DTI: with parameters n. slices/diffusion-weighting gradients/echo-time/repetition-time/phase-encoding direction/bandwith/partial-Fourier/n. diffusion-directions/b-value: 82/unipolar/66.8 ms/7.4 s/“anterior/posterior”/“1456 Hz/pixel”/“6/8”/60/2500 s/mm2, seven interspersed “b0” images (T2-weighted, non-diffusion weighted, b-value = 0 s/mm2), acquisition-time: 8′53′′. To perform distortion-correction we also acquired seven “b0” images with opposite phase-encoding direction. To aid coregistration of DTI to MNI space, a 3 Tesla T1-weighted MEMPRAGE MRI was acquired with repetition-time/echo-times/inversions-time/flip-angle/field-of-view/matrix/bandwidth/GRAPPA-factor/acquisition-time: 2.51 s/[1.6, 3.5, 5.3, 7.2] ms/1.5 s/7°/256×256×176 mm3/256×256×176/“651 Hz/pixel”/2/6′34′′.

Data analysis: a) Preprocessing: We computed the root-mean-square MEMPRAGE image across echo-times, rotated it to standard-orientation (“RPI”), cropped the most inferior slices containing the spinal-cord (in order to aid its coregistration to MNI-space) and bias-field corrected it (SPM8); we then parcellated the resulting image with Freesurfer (7). DTIs were rotated to standard-orientation, de-noised (8), motion and distortion-corrected (FSL, topup/eddy). We then computed the diffusion tensor, tensor-invariants (e.g. fractional anisotropy, FA) and S0 (T2-weighted) image from the preprocessed DTI (FSL, dtifit). To map the Freesurfer parcellation to native DTI-space, we computed an affine boundary-based transformation (FSL, FLIRT-BBR) between the preprocessed MEMPRAGE image and single-subject S0 images. To map the brainstem nuclei atlas to native DTI-space, we computed the bivariate high-dimensional diffeomorphic transformations (ANTs) between IIT-MNI FA/S0 templates (9) and single-subject FA/S0 images. b) Definition of seed and target regions for DTI-based connectivity analysis: As seed regions, we used the structural probabilistic atlas labels [5-6] of eight brainstem nuclei relevant for premanifest synucleinopathy (Figure 1D) mapped from IIT-MNI-space to native-space (using the coregistration transformations explained above). As target regions, we used the probabilistic atlas labels of 16 brainstem nuclei of [5-6], as well as the 82 cortical/subcortical bilateral regions obtained in each subject from the MEMPRAGE Freesurfer-parcellation (mapped to native space as explained above). c) Single-subject DTI-based connectivity analysis: We performed deterministic tractography using MRtrix3. We propagated 10,000 streamlines from each seed-mask, and computed a “structural-connectivity-index” (range: [0 1]) for each pair of seed-target masks (= fraction of streamlines propagated from the seed reaching the target mask). d) Group DTI-based connectivity analysis: We averaged across subjects the structural-connectivity-index of brainstem nuclei with target-regions to yield a group structural connectome of these nuclei. We displayed this connectome using a 2D circular diagram [10]. e) As a validation of the DTI-based connectome obtained in controls, we derived a prediction model of expected structural-connectivity pathways of these nuclei based on animal literature [11-12].


The structural connectome of eight brainstem nuclei demonstrated an overall decreased connectivity within the brainstem and with the striatum in RBD compared to controls (Figure 2). Interestingly, the substantia nigra (SN) subregion 1 (compatible with pars-reticulata, SNR) showed decreased connectivity with the pedunculotegmental nucleus (PTg) in RBD compared to controls (see Figure 3). The prediction connectivity model of SNR is shown in Figure 4.


The observed decreased connectivity of the SN-subregion1 with the PTg in RBD patients (premanifest-synucleinopathy), is in line with animal RBD-studies [3] (Figure 1B) and with models of synucleinopathy progression [4] (Figure 1A). Interestingly, the observed decrease in connectivity of the SN-subregion1 with the striatum and cerebellum is also observed in manifest-synucleinopathy [13]. The prediction model of the SNR connectivity showed that the connectivity observed in controls is in line with animal work [11-12].


The structural connectome of brainstem pathways in living humans is a promising tool to better understand and assess premanifest synucleinopathy stages.


MGH-Claflin-Distinguished-Scholar; NIH-NIBIB-K01EB019474; NIH-NIBIB-P41EB015896.


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Figure 1. The brainstem clue in premanifest synucleinopathy. Changes in the brainstem nuclei microstructure and connectivity are expected in premanifest synucleinopathy based on: A. Staging models of synucleinopathy progression [3], which predict that neuro-degeneration occurs in the brainstem (black/dark-red) at earlier stages than in other brain areas; B. Animal studies [4] of premanifest synucleinopathy (idiopathic RBD), which show a network dysfunction of several brainstem nuclei; C. Lesion studies of non-idiopathic RBD [3], showing brainstem lesions (specifically in the midbrain and pons). D. List of brainstem nuclei relevant for premanifest synucleinopathy/RBD (the top eight nuclei (*) were investigated in this study).

Figure 2. Structural connectome of eight brainstem nuclei relevant for premanifest-synucleinopathy/RBD in: Left) RBD patients; Right) controls. The connectome of these brainstem nuclei (list of nuclei shown in Figure 1D) demonstrated an overall decreased connectivity within the brainstem and with the striatum (e.g. left/right pallidum) in RBD compared to controls. Note that the mean connectivity index across subjects was displayed. For display purposes, we used a connectivity-index threshold of 0.1, and displayed only the links between eight brainstem seeds (left SN-subregion1 -compatible with SNR, left SN-subregion2 -compatible with SNC, left PTg, left PMnR, CLi, DR, MnR, RMg and 71 target-regions.

Figure 3. A closer look at the structural connectivity of substantia-nigra(SN)-subregion1 (compatible with pars-reticulata, SNR). Upper row) The structural connectome of left SN-subregion1 demonstrated decreased connectivity with the pedunculotegmental-nucleus (PTg, black-arrow —link not visible because connectivity-index < threshold), striatum (e.g. pallidum) and cerebellum in RBD compared to elder-controls. Note that the mean connectivity-index of left-SN-subregion1 across subjects with 71 target regions is displayed (connectivity-index threshold= 0.05). Lower row) The probability of streamlines (blue-lightblue) connecting left-SN-subregion1 (red) to left-PTg (green) decreased in RBD compared to controls (mean streamline-probability across subjects in stereotactic-space is displayed, overlaid on a FA-template in IIT-MNI-space [5]).

Figure 4. Prediction model of the structural connectivity pathways of substantia-nigra pars-reticulata (SNR, indicated in the connectome as SN-subregion1) based on animal and human literature [11-12]. Noticeably, several connectivity pathways based on this model are also visible in the structural DTI-based connectome obtained in controls (Figure 3, “Elder controls”), such as the connectivity with the ipsilateral pedunculotegmental nucleus (PTg), substantia nigra pars compacta (indicated as SN-subregion2), subthalamic nucleus (STh-subregions), striatum (caudate, putamen, pallidum) and thalamus.

Proc. Intl. Soc. Mag. Reson. Med. 27 (2019)