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14.12.2018 | Magnetic Resonance

Altered structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features

verfasst von: Apurva Shah, Shweta Prasad, Bharti Rastogi, Santosh Dash, Jitender Saini, Pramod Kumar Pal, Madhura Ingalhalikar

Erschienen in: European Radiology | Ausgabe 6/2019

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Abstract

Objectives

To investigate the structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features (MSA-C), a distinct subtype of MSA, characterized by predominant cerebellar symptoms.

Methods

Twenty-three patients with MSA-C and 25 age- and gender-matched healthy controls were recruited for the study. Disease severity was quantified using the Unified Multiple System Atrophy Rating Scale (UMSARS). Diffusion MRI images were acquired and used to compute the structural connectomes (SCs) using probabilistic fiber tracking. The motor network with 12 brain regions and 26 cerebellar regions was extracted and was compared between the groups using analysis of variance at a global (network-wide), nodal (at each node), and edge (at each connection) levels, and was corrected for multiple comparisons. In addition, the acquired connectivity measures were correlated with duration of illness, total Unified MSA Rating Scale (UMSARS), and the motor component score.

Results

Significantly lower global network metrics—global density, transitivity, clustering coefficient, and characteristic path length—were observed in MSA-C (corrected p < 0.05). Reduced nodal strength was observed in the bilateral ventral diencephalon, the left thalamus, and several cerebellar regions. Network-based statistics revealed significant abnormal edge-wise connectivity in 40 connections (corrected p < 0.01), with majority of deficits observed in the cerebellum. Finally, significant negative correlations were observed between UMSARS scores and thalamic and cerebellar connectivity (p < 0.05) as well as between duration of illness and cerebellar connectivity.

Conclusions

Abnormal connectivity of the basal ganglia and cerebellar network may be causally implicated for the motor features observed in MSA-C.

Key Points

• Structural connectivity of the motor subnetwork was explored in patients with multiple system atrophy with cerebellar features (MSA-C) using probabilistic tractography.

• The motor subnetwork in MSA-C has significant alterations in both basal ganglia and cerebellar connectivity, with a higher extent of abnormality in the cerebellum.

• These findings may be causally implicated for the motor features of cerebellar dysfunction and parkinsonism observed in MSA-C.

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Fanciulli A, Wenning GK (2015) Multiple-system atrophy. N Engl J Med 372:249–263CrossRefPubMed

Gilman S, Wenning GK, Low PA et al (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71:670–676CrossRefPubMedPubMedCentral

Köllensperger M, Geser F, Ndayisaba JP et al (2010) Presentation, diagnosis, and management of multiple system atrophy in Europe: final analysis of the European multiple system atrophy registry. Mov Disord 25:2604–2612CrossRefPubMed

May S, Gilman S, Sowell BB et al (2007) Potential outcome measures and trial design issues for multiple system atrophy. Mov Disord 22:2371–2377CrossRefPubMed

Yabe I, Soma H, Takei A, Fujiki N, Yanagihara T, Sasaki H (2006) MSA-C is the predominant clinical phenotype of MSA in Japan: analysis of 142 patients with probable MSA. J Neurol Sci 249:115–121CrossRefPubMed

Ciolli L, Krismer F, Nicoletti F, Wenning GK (2014) An update on the cerebellar subtype of multiple system atrophy. Cerebellum Ataxias 1:14CrossRefPubMedPubMedCentral

Chandran V, Stoessl AJ (2014) Imaging in multiple system atrophy. Neurol Clin Neurosci 2:178–187CrossRef

Brooks DJ, Seppi K, Neuroimaging Working Group on MSA (2009) Proposed neuroimaging criteria for the diagnosis of multiple system atrophy. Mov Disord 24:949–964CrossRefPubMed

Watanabe H, Saito Y, Terao S et al (2002) Progression and prognosis in multiple system atrophy: an analysis of 230 Japanese patients. Brain 125:1070–1083CrossRefPubMed

10.

Schrag A, Kingsley D, Phatouros C et al (1998) Clinical usefulness of magnetic resonance imaging in multiple system atrophy. J Neurol Neurosurg Psychiatry 65:65–71CrossRefPubMedPubMedCentral

11.

Schulz JB, Skalej M, Wedekind D et al (1999) Magnetic resonance imaging-based volumetry differentiates idiopathic Parkinson’s syndrome from multiple system atrophy and progressive supranuclear palsy. Ann Neurol 45:65–74CrossRefPubMed

12.

Shiga K, Yamada K, Yoshikawa K, Mizuno T, Nishimura T, Nakagawa M (2005) Local tissue anisotropy decreases in cerebellopetal fibers and pyramidal tract in multiple system atrophy. J Neurol 252:589–596CrossRefPubMed

13.

Oishi K, Konishi J, Mori S et al (2009) Reduced fractional anisotropy in early-stage cerebellar variant of multiple system atrophy. J Neuroimaging 19:127–131CrossRefPubMed

14.

Yang H, Wang X, Liao W, Zhou G, Li L, Ouyang L (2015) Application of diffusion tensor imaging in multiple system atrophy: the involvement of pontine transverse and longitudinal fibers. Int J Neurosci 125:18–24CrossRefPubMed

15.

Dash SK, Stezin A, Takalkar T et al (2018) Abnormalities of white and grey matter in early multiple system atrophy: comparison of parkinsonian and cerebellar variants. Eur Radiol. https://doi.org/10.1007/s00330-018-5594-9

16.

Gilman S, Markel DS, Koeppe RA et al (1988) Cerebellar and brainstem hypometabolism in olivopontocerebellar atrophy detected with positron emission tomography. Ann Neurol 23:223–230CrossRefPubMed

17.

Wang PS, Yeh CL, Lu CF, Wu HM, Soong BW, Wu YT (2017) The involvement of supratentorial white matter in multiple system atrophy: a diffusion tensor imaging tractography study. Acta Neurol Belg 117:213–220CrossRefPubMed

18.

Lu CF, Soong BW, Wu HM, Teng S, Wang PS, Wu YT (2013) Disrupted cerebellar connectivity reduces whole-brain network efficiency in multiple system atrophy. Mov Disord 28:362–369CrossRefPubMed

19.

Barbagallo G, Caligiuri ME, Arabia G et al (2017) Structural connectivity differences in motor network between tremor-dominant and nontremor Parkinson’s disease. Hum Brain Mapp 38:4716–4729CrossRefPubMedPubMedCentral

20.

Lewis MM, Du G, Sen S et al (2011) Differential involvement of striato- and cerebello-thalamo-cortical pathways in tremor- and akinetic/rigid-predominant Parkinson’s disease. Neuroscience 177:230–239CrossRefPubMed

21.

Wenning GK, Tison F, Seppi K et al (2004) Development and validation of the Unified Multiple System Atrophy Rating Scale (UMSARS). Mov Disord 19:1391–1402CrossRefPubMed

22.

Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219CrossRefPubMed

23.

Smith SM (2002) Fast robust automated brain extraction. Hum Brain Mapp 17:143–155CrossRefPubMedPubMedCentral

24.

Desikan RS, Segonne F, Fischl B et al (2006) An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31:968–980CrossRefPubMed

25.

Fischl B (2012) FreeSurfer. Neuroimage 62:774–781CrossRefPubMed

26.

Ou Y, Sotiras A, Paragios N, Davatzikos C (2011) DRAMMS: deformable registration via attribute matching and mutual-saliency weighting. Med Image Anal 15:622–639CrossRefPubMed

27.

Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34:144–155CrossRefPubMed

28.

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069CrossRefPubMed

29.

Zalesky A, Fornito A, Bullmore ET (2010) Network-based statistic: identifying differences in brain networks. Neuroimage 53:1197–1207CrossRefPubMed

30.

Bostan AC, Strick PL (2018) The basal ganglia and the cerebellum: nodes in an integrated network. Nat Rev Neurosci. https://doi.org/10.1038/s41583-018-0002-7

31.

Roostaei T, Nazeri A, Sahraian MA, Minagar A (2014) The human cerebellum: a review of physiologic neuroanatomy. Neurol Clin 32:859–869CrossRefPubMed

32.

Alexander AL, Lee JE, Wu YC, Field AS (2006) Comparison of diffusion tensor imaging measurements at 3.0 T versus 1.5 T with and without parallel imaging. Neuroimaging Clin N Am 16:299–309 xiCrossRefPubMed

33.

Bloy L, Ingalhalikar M, Batmanghelich NK, Schultz RT, Roberts TP, Verma R (2012) An integrated framework for high angular resolution diffusion imaging-based investigation of structural connectivity. Brain Connect 2:69–79CrossRefPubMedPubMedCentral

34.

Rosskopf J, Gorges M, Müller HP, Pinkhardt EH, Ludolph AC, Kassubek J (2018) Hyperconnective and hypoconnective cortical and subcortical functional networks in multiple system atrophy. Parkinsonism Relat Disord 49:75–80CrossRefPubMed

35.

Jones DK, Knösche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. Neuroimage 73:239–254CrossRefPubMed

36.

Toselli B, Tortora D, Severino M et al (2017) Improvement in white matter tract reconstruction with constrained spherical deconvolution and track density mapping in low angular resolution data: a pediatric study and literature review. Front Pediatr 5:182CrossRefPubMedPubMedCentral

Titel: Altered structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features
verfasst von: Apurva Shah
Shweta Prasad
Bharti Rastogi
Santosh Dash
Jitender Saini
Pramod Kumar Pal
Madhura Ingalhalikar
Publikationsdatum: 14.12.2018
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2019
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-018-5874-4

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Altered structural connectivity of the motor subnetwork in multiple system atrophy with cerebellar features