Publikation

A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis

Wissenschaftlicher Artikel/Review - 08.01.2016

PubMed
DOI

Zitation
Müller H, Kassubek J, Filippi M, Ludolph A, Prudlo J, Govind V, Bede P, Abrahams S, Grosskreutz J, Turner M. A large-scale multicentre cerebral diffusion tensor imaging study in amyotrophic lateral sclerosis. J Neurol Neurosurg Psychiatr 2016; 87:570-9.
Art
Wissenschaftlicher Artikel/Review (Englisch)
Zeitschrift
J Neurol Neurosurg Psychiatr 2016; 87
Veröffentlichungsdatum
08.01.2016
eISSN (Online)
1468-330X
Seiten
570-9
Kurzbeschreibung/Zielsetzung

OBJECTIVE
Damage to the cerebral tissue structural connectivity associated with amyotrophic lateral sclerosis (ALS), which extends beyond the motor pathways, can be visualised by diffusion tensor imaging (DTI). The effective translation of DTI metrics as biomarker requires its application across multiple MRI scanners and patient cohorts. A multicentre study was undertaken to assess structural connectivity in ALS within a large sample size.

METHODS
442 DTI data sets from patients with ALS (N=253) and controls (N=189) were collected for this retrospective study, from eight international ALS-specialist clinic sites. Equipment and DTI protocols varied across the centres. Fractional anisotropy (FA) maps of the control participants were used to establish correction matrices to pool data, and correction algorithms were applied to the FA maps of the control and ALS patient groups.

RESULTS
Analysis of data pooled from all centres, using whole-brain-based statistical analysis of FA maps, confirmed the most significant alterations in the corticospinal tracts, and captured additional significant white matter tract changes in the frontal lobe, brainstem and hippocampal regions of the ALS group that coincided with postmortem neuropathological stages. Stratification of the ALS group for disease severity (ALS functional rating scale) confirmed these findings.

INTERPRETATION
This large-scale study overcomes the challenges associated with processing and analysis of multiplatform, multicentre DTI data, and effectively demonstrates the anatomical fingerprint patterns of changes in a DTI metric that reflect distinct ALS disease stages. This success paves the way for the use of DTI-based metrics as read-out in natural history, prognostic stratification and multisite disease-modifying studies in ALS.