Automated detection of gray matter malformations using optimized voxel-based morphometry: a systematic approach

• Published in: NeuroImage (Orlando, Fla.) Fla.), 2003-09, Vol.20 (1), p.330-343
• Main Authors: Wilke, M, Kassubek, J, Ziyeh, S, Schulze-Bonhage, A, Huppertz, H.J
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Automatic Data Processing; Brain; Cerebral Cortex - abnormalities; Cerebral Cortex - growth & development; Cerebral Cortex - pathology; Data processing; Databases, Factual; Female; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Patients; Reference Values
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/S1053-8119(03)00296-9

Malformations of cortical development (MCD) are a recognized cause of epilepsy. Their special significance lies in the fact that, once detected and delineated, they are amenable to surgical removal. However, diagnosis from high-resolution MRI is still difficult, time-consuming, and highly dependent on individual expertise. We have recently proposed a simple procedure to detect cortical dysplasias, using automated procedures available within SPM99 (Wellcome Department, University College London, UK). Here, we aimed to systematically determine the best combination of processing parameters, using an optimized voxel-based morphometry approach. We included 20 patients with a known MCD and compared them to a normal database of 53 healthy, age- and gender-matched controls. The approaches taken during spatial normalization and a number of other parameters were systematically altered in order to find the best combination of parameters. Overall, 99 different approaches were evaluated in different ways. As far as possible, automatic processing and evaluation steps were used. With the number of candidate regions for MCD limited to five per patient, the best approaches resulted in the correct identification of up to 16 of 20 malformations. However, a number of approaches failed to perform well. The reasons for these failures and the implications this has for other studies are discussed. We conclude that voxel-based morphometry is able to detect cortical malformations with a high degree of accuracy. However, specific problems seem to arise when using an optimized protocol for voxel-based morphometry, indicating that this protocol may not be optimal for all voxel-based studies on brain morphology. Our approach, involving systematic alterations of parameters and evaluation, may be useful for other studies.