Measurement of brain atrophy in subcortical vascular disease: a comparison of different approaches and the impact of ischaemic lesions.
Measurement of brain atrophy has been proposed as a surrogate marker in MS and degenerative dementias. Although cerebral small vessel disease predominantly affects white and subcortical grey matter, recent data suggest that whole brain atrophy is also a good indicator of clinical and cognitive status in this disease. Automated methods to measure atrophy are available that are accurate and reproducible in disease-free brains. However, optimal methods in small vessel disease have not been established and the impact of ischaemic lesions on different techniques has not been explored systematically. In this study, three contrasting techniques -- Statistical Parametric Mapping 5 (SPM5), SIENAX and BrainVisa -- were applied to measure cross-sectional atrophy (brain parenchymal fraction or BPF) in a large (n=143) two-centre cohort of patients with CADASIL, a genetic model of small vessel disease. All three techniques showed similar sensitivity to trends in BPF associated with age and lesion load. No single technique was particularly vulnerable to error as a result of lesions. Provided major errors in registration were excluded by visual inspection, manual correction of segmentations had a negligible impact with mean errors of 0.41% for SIENAX and 0.46% for BrainVisa. BPF correlated strongly with global cognitive function and physical disability, independent of the technique used. Correlation coefficients with the Minimental State Examination score were: BrainVisa 0.58, SIENAX 0.58, SPM5 0.60 (for all, p<0.001). These results suggest that all three methods can be applied reliably in patients with ischaemic lesions. Choice of analysis approach for this kind of clinical question will be determined by factors other than their robustness and precision, such as a desire to explore subtle localised changes using extensions of these processing tools.
O'Sullivan
Mike
M
Neurologische Klinik, Klinikum Grobetahadern, Ludwig Maximilians University, Munich, Germany. research@mikeosullivan.org
Jouvent
Eric
E
Saemann
Philipp G
PG
Mangin
Jean-Francois
JF
Viswanathan
Anand
A
Gschwendtner
Andreas
A
Bracoud
Luc
L
Pachai
Chahin
C
Chabriat
Hugues
H
Dichgans
Martin
M
D000328:Adult; D000368:Aged; D000465:Algorithms; D001284:Atrophy / Q000175:diagnosis; D002545:Brain Ischemia / Q000473:pathology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity