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J Neurol. 2019 Jan;266(1):212-222. doi: 10.1007/s00415-018-9130-2. Epub 2018 Nov 22.

Structural network topology relates to tissue properties in multiple sclerosis.

Author information

1
Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands. s.kiljan@vumc.nl.
2
Department of Anatomy and Neurosciences, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
3
Department of Neurology, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands.
4
Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, MS Center Amsterdam, Amsterdam UMC, Location VU University Medical Center, Amsterdam, The Netherlands.
5
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, USA.

Abstract

OBJECTIVE:

Abnormalities in segregative and integrative properties of brain networks have been observed in multiple sclerosis (MS) and are related to clinical functioning. This study aims to investigate the micro-scale correlates of macro-scale network measures of segregation and integration in MS.

METHODS:

Eight MS patients underwent post-mortem in situ whole-brain diffusion tensor (DT) imaging and subsequent brain dissection. Macro-scale structural network topology was derived from DT data using graph theory. Clustering coefficient and mean white matter (WM) fiber length were measures of nodal segregation and integration. Thirty-three tissue blocks were collected from five cortical brain regions. Using immunohistochemistry micro-scale tissue properties were evaluated, including, neuronal size, neuronal density, axonal density and total cell density. Nodal network properties and tissue properties were correlated.

RESULTS:

A negative correlation between clustering coefficient and WM fiber length was found. Higher clustering coefficient was associated with smaller neuronal size and lower axonal density, and vice versa for fiber length. Higher whole-brain WM lesion load was associated with higher whole-brain clustering, shorter whole-brain fiber length, lower neuronal size and axonal density.

CONCLUSION:

Structural network properties on MRI associate with neuronal size and axonal density, suggesting that macro-scale network measures may grasp cortical neuroaxonal degeneration in MS.

KEYWORDS:

Axonal density; Histopathology; Integration; Neuronal size; Post-mortem MRI; Segregation

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