Structural and Clinical Correlates of a Periventricular Gradient of Neuroinflammation in Multiple Sclerosis

Emilie Poirion, Matteo Tonietto, François-Xavier Lejeune, Vito A G Ricigliano, Marine Boudot de la Motte, Charline Benoit, Géraldine Bera, Bertrand Kuhnast, Michel Bottlaender, Benedetta Bodini, Bruno Stankoff, Emilie Poirion, Matteo Tonietto, François-Xavier Lejeune, Vito A G Ricigliano, Marine Boudot de la Motte, Charline Benoit, Géraldine Bera, Bertrand Kuhnast, Michel Bottlaender, Benedetta Bodini, Bruno Stankoff

Abstract

Objectives: To explore in vivo innate immune cell activation as a function of the distance from ventricular CSF in patients with multiple sclerosis (MS) using [18F]-DPA714 PET and to investigate its relationship with periventricular microstructural damage, evaluated by magnetization transfer ratio (MTR), and with trajectories of disability worsening.

Methods: Thirty-seven patients with MS and 19 healthy controls underwent MRI and [18F]-DPA714 TSPO dynamic PET, from which individual maps of voxels characterized by innate immune cell activation (DPA+) were generated. White matter (WM) was divided in 3-mm-thick concentric rings radiating from the ventricular surface toward the cortex, and the percentage of DPA+ voxels and mean MTR were extracted from each ring. Two-year trajectories of disability worsening were collected to identify patients with and without recent disability worsening.

Results: The percentage of DPA+ voxels was higher in patients compared to controls in the periventricular WM (p = 6.10e-6) and declined with increasing distance from ventricular surface, with a steeper gradient in patients compared to controls (p = 0.001). This gradient was found in both periventricular lesions and normal-appearing WM. In the total WM, it correlated with a gradient of microstructural tissue damage measured by MTR (r s = -0.65, p = 1.0e-3). Compared to clinically stable patients, patients with disability worsening were characterized by a higher percentage of DPA+ voxels in the periventricular normal-appearing WM (p = 0.025).

Conclusions: Our results demonstrate that in MS the innate immune cell activation predominates in periventricular regions and is associated with microstructural damage and disability worsening. This could result from the diffusion of proinflammatory CSF-derived factors into surrounding tissues.

Copyright © 2021 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.

Figures

Figure 1. Processing Steps to Generate 3-mm-Thick…
Figure 1. Processing Steps to Generate 3-mm-Thick Ring From CSF to Adjacent White Matter
(A–C) Axial, coronal, and sagittal views of a T1-weighted images segmented with a multiatlas segmentation approach generating masks for the white matter, cortex, and ventricles (red, green, and yellow, respectively). (D–F) Axial, coronal, and sagittal views of the corresponding distance map from the ventricular surfaces toward the cortex, calculated in the white matter mask and divided into 3-mm-thick rings.
Figure 2. Illustrative Example of [ 18…
Figure 2. Illustrative Example of [18F]-DPA714 DVR Maps and the Corresponding Individual Map of DPA+ Voxels
(A) [18F ]-DPA714 distribution volume ratio (DVR) map of a representative patient with multiple sclerosis (MS) (45-year-old female patient with secondary progressive MS, disease duration 22 years, Expanded Disability Status Scale [EDSS] score at baseline 6, EDSS step change in the 2 years preceding baseline 1.5). Map was obtained using Logan graphical analysis with reference region extracted with a supervised clustering approach. (B) Corresponding individual map of voxels characterized by a significant activation of innate immune cells (DPA+) (yellow) obtained by thresholding the DVR map of panel A (see text for details about the thresholding technique used).
Figure 3. Periventricular Gradient of Innate Immune…
Figure 3. Periventricular Gradient of Innate Immune Cell Activation in the WM of Patients With MS
Boxplots represent the percentage of voxels characterized by a significant activation of innate immune cells (DPA+) in (A) the total white matter (WM), (B) the normal-appearing WM, and (C) T2 lesions in patients with multiple sclerosis (MS) (red) and in the WM of healthy controls (blue), calculated in 3-mm-thick concentric rings radiating from the ventricular CSF toward the cortex. Solid lines represent the mixed-effect model fits obtained at the population level for both groups.
Figure 4. Relationship Between Innate Immune Cell…
Figure 4. Relationship Between Innate Immune Cell Activation and MTR in the Periventricular WM
(A) Boxplots represent the mean magnetization transfer ratio (MTR) in the white matter (WM) of healthy controls (blue) and patients with multiple sclerosis (MS) (red) calculated in 3-mm-thick concentric rings radiating from the ventricular CSF toward the cortex. Solid lines represent the mixed-effect model fits obtained at the population level for both healthy controls and patients with MS. In WM, both (B) the intercepts and (C) the slopes of the percentage of voxels characterized by a significant activation of innate immune cells (DPA+) and mean MTR values were inversely correlated with each other.
Figure 5. Periventricular Innate Immune Cells Activation…
Figure 5. Periventricular Innate Immune Cells Activation Associates With Clinical Trajectories of Disability Worsening
Boxplots represent the percentage of voxels characterized by a significant activation of innate immune cells (DPA+) in (A) the total white matter, (B) the normal-appearing white matter, and (C) T2 lesions of clinically stable patients (green) and clinically worsening patients (pink) calculated in 3-mm-thick concentric rings radiating from the ventricular CSF toward the cortex. Solid lines represent the mixed-effect model fits obtained at the population level for both groups.

References

    1. Lassmann H. Pathogenic mechanisms associated with different clinical courses of multiple sclerosis. Front Immunol 2019;9:3116.
    1. Magliozzi R, Howell OW, Reeves C, et al. . A gradient of neuronal loss and meningeal inflammation in multiple sclerosis. Ann Neurol 2010;68:477–493.
    1. Magliozzi R, Howell OW, Nicholas R, et al. . Inflammatory intrathecal profiles and cortical damage in multiple sclerosis. Ann Neurol 2018;83:739–755.
    1. Jehna M, Pirpamer L, Khalil M, et al. . Periventricular lesions correlate with cortical thinning in multiple sclerosis: cortical thinning and periventricular lesions in MS. Ann Neurol 2015;78:530–539.
    1. Brown JW, Chowdhury A, Kanber B, et al. . Magnetisation transfer ratio abnormalities in primary and secondary progressive multiple sclerosis. Mult Scler J 2020;26:679–687.
    1. Brown JWL, Pardini M, Brownlee WJ, et al. . An abnormal periventricular magnetization transfer ratio gradient occurs early in multiple sclerosis. Brain 2017;140:387–398.
    1. Liu Z, Pardini M, Ö Yaldizli, et al. . Magnetization transfer ratio measures in normal-appearing white matter show periventricular gradient abnormalities in multiple sclerosis. Brain 2015;138:1239–1246.
    1. Pardini M, Sudre CH, Prados F, et al. . Relationship of grey and white matter abnormalities with distance from the surface of the brain in multiple sclerosis. J Neurol Neurosurg Psychiatry 2016;87:1212–1217.
    1. Fadda G, Brown RA, Magliozzi R, et al. . A surface-in gradient of thalamic damage evolves in pediatric multiple sclerosis. Ann Neurol 2019;85:340–351.
    1. Pardini M, Petracca M, Harel A, et al. . The relationship between cortical lesions and periventricular NAWM abnormalities suggests a shared mechanism of injury in primary-progressive MS. Neuroimage Clin 2017;16:111–115.
    1. Airas L, Nylund M, Rissanen E. Evaluation of microglial activation in multiple sclerosis patients using positron emission tomography. Front Neurol 2018;9:181.
    1. Banati R, Newcombe J, Gunn RN, et al. . The peripheral benzodiazepine binding site in the brain in multiple sclerosis. quantitative in vivo imaging of microglia as a measure of disease activity. Brain 2000;123:2321–2337.
    1. Stankoff B, Poirion E, Tonietto M, Bodini B. Exploring the heterogeneity of MS lesions using positron emission tomography: a reappraisal of their contribution to disability. Brain Pathol Zurich Switz 2018;28:723–734.
    1. Datta G, Colasanti A, Kalk N, et al. . 11C-PBR28 and 18F-PBR111 detect white matter inflammatory heterogeneity in multiple sclerosis. J Nucl Med 2017;58:1477–1482.
    1. Politis M, Giannetti P, Su P, et al. . Increased PK11195 PET binding in the cortex of patients with MS correlates with disability. Neurology 2012;79:523–530.
    1. Rissanen E, Tuisku J, Rokka J, et al. . In vivo detection of diffuse inflammation in secondary progressive multiple sclerosis using PET imaging and the radioligand 11C-PK11195. J Nucl Med 2014;55:939–944.
    1. Herranz E, Giannì C, Louapre C, et al. . The neuroinflammatory component of gray matter pathology in multiple sclerosis. Ann Neurol 2016;80:776–790.
    1. James ML, Fulton RR, Vercoullie J, et al. . DPA-714, a new translocator protein-specific ligand: synthesis, radiofluorination, and pharmacologic characterization. J Nucl Med 2008;49:814–822.
    1. Rizzo G, Veronese M, Tonietto M, et al. . Generalization of endothelial modelling of TSPO PET imaging: considerations on tracer affinities. J Cereb Blood Flow Metab 2019;39:874–885.
    1. Polman CH, Reingold SC, Banwell B, et al. . Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302.
    1. Owen DRJ, Gunn RN, Rabiner EA, et al. . Mixed-affinity binding in humans with 18-kDa translocator protein ligands. J Nucl Med 2011;52:24–32.
    1. Kurtzke JF. Rating neurologic impairment in multiple sclerosis: an Expanded Disability Status Scale (EDSS). Neurology 1983;33:1444–1452.
    1. Weinshenker BG, Issa M, Baskerville J. Meta-analysis of the placebo-treated groups in clinical trials of progressive MS. Neurology 1996;46:1613–1619.
    1. García-Lorenzo D, Lavisse S, Leroy C, et al. . Validation of an automatic reference region extraction for the quantification of [18F] DPA-714 in dynamic brain PET studies. J Cereb Blood Flow Metab 2017;38:333–346.
    1. Bodini B, Poirion E, Tonietto M, et al. . Individual mapping of innate immune cell activation is a candidate marker of patient-specific trajectories of disability worsening in multiple sclerosis. J Nucl Med 2020;61:1043–1049.
    1. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med Image Anal 2001;5:143–156.
    1. Chard DT, Jackson JS, Miller DH, Wheeler‐Kingshott CAM. Reducing the impact of white matter lesions on automated measures of brain gray and white matter volumes. J Magn Reson Imaging 2010;32:223–228.
    1. Wang H, Suh JW, Das SR, Pluta JB, Craige C, Yushkevich PA. Multi-atlas segmentation with joint label fusion. IEEE Trans Pattern Anal Mach Intell 2013;35:611–623.
    1. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 2008;12:26–41.
    1. Lavisse S, García-Lorenzo D, Peyronneau M-A, et al. . Optimized quantification of translocator protein radioligand 18F-DPA-714 uptake in the brain of genotyped healthy volunteers. J Nucl Med 2015;56:1048–1054.
    1. Adams CWM, Abdulla YH, Torres EM, Poston RN. Periventricular lesions in multiple sclerosis: their perivenous origin and relationship to granular ependymitis. Neuropathol Appl Neurobiol 1987;13:141–152.
    1. Machado-Santos J, Saji E, Tröscher AR, et al. . The compartmentalized inflammatory response in the multiple sclerosis brain is composed of tissue-resident CD8+ T lymphocytes and B cells. Brain J Neurol 2018;141:2066–2082.
    1. Zrzavy T, Hametner S, Wimmer I, Butovsky O, Weiner HL, Lassmann H. Loss of “homeostatic” microglia and patterns of their activation in active multiple sclerosis. Brain J Neurol 2017;140:1900–1913.
    1. Lee NJ, Ha S-K, Sati P, et al. . Spatiotemporal distribution of fibrinogen in marmoset and human inflammatory demyelination. Brain 2018;141:1637–1649.
    1. Petersen MA, Ryu JK, Akassoglou K. Fibrinogen in neurological diseases: mechanisms, imaging and therapeutics. Nat Rev Neurosci 2018;19:283–301.
    1. Alcázar A, Regidor I, Masjuan J, Salinas M, Alvarez-Cermeño JC. Axonal damage induced by cerebrospinal fluid from patients with relapsing-remitting multiple sclerosis. J Neuroimmunol 2000;104:58–67.
    1. Vidaurre OG, Haines JD, Katz Sand I, et al. . Cerebrospinal fluid ceramides from patients with multiple sclerosis impair neuronal bioenergetics. Brain J Neurol 2014;137:2271–2286.
    1. Chiou B, Lucassen E, Sather M, Kallianpur A, Connor J. Semaphorin4A and H-ferritin utilize Tim-1 on human oligodendrocytes: a novel neuro-immune axis. Glia 2018;66:1317–1330.
    1. Küry P, Nath A, Créange A, et al. . Human endogenous retroviruses in neurological diseases. Trends Mol Med 2018;24:379–394.
    1. Bevan RJ, Evans R, Griffiths L, et al. . Meningeal inflammation and cortical demyelination in acute multiple sclerosis. Ann Neurol 2018;84:829–842.
    1. Howell OW, Reeves CA, Nicholas R, et al. . Meningeal inflammation is widespread and linked to cortical pathology in multiple sclerosis. Brain 2011;134:2755–2771.
    1. Magliozzi R, Howell O, Vora A, et al. . Meningeal B-cell follicles in secondary progressive multiple sclerosis associate with early onset of disease and severe cortical pathology. Brain 2007;130:1089–1104.
    1. Engelhardt B, Carare RO, Bechmann I, Flügel A, Laman JD, Weller RO. Vascular, glial, and lymphatic immune gateways of the central nervous system. Acta Neuropathol (Berl) 2016;132:317–338.
    1. Kooij G, Kopplin K, Blasig R, et al. . Disturbed function of the blood-cerebrospinal fluid barrier aggravates neuro-inflammation. Acta Neuropathol (Berl) 2014;128:267–277.
    1. Lassmann H, van Horssen J, Mahad D. Progressive multiple sclerosis: pathology and pathogenesis. Nat Rev Neurol 2012;8:647–656.
    1. Lavisse S, Guillermier M, Hérard A-S, et al. . Reactive astrocytes overexpress TSPO and are detected by TSPO positron emission tomography imaging. J Neurosci 2012;32:10809–10818.
    1. Nutma E, Stephenson JA, Gorter RP, et al. . A quantitative neuropathological assessment of translocator protein expression in multiple sclerosis. Brain 2019;142:3440–3455.
    1. Wimberley C, Lavisse S, Brulon V, et al. . Impact of endothelial 18-kDa translocator protein on the quantification of 18F-DPA-714. J Nucl Med 2018;59:307–314.
    1. Chechneva OV, Deng W. Mitochondrial translocator protein (TSPO), astrocytes and neuroinflammation. Neural Regen Res 2016;11:1056–1057.
    1. Moll NM, Rietsch AM, Thomas S, et al. . Multiple sclerosis normal-appearing white matter: pathology-imaging correlations. Ann Neurol 2011;70:764–773.

Source: PubMed

Sponsorit ja yhteistyökumppanit

Sairaudet

Huumeiden interventiot

3
Tilaa