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Multidimensional morphometric 3D MRI analyses for detecting brain abnormalities in children: Impact of control population

Authors

  • Marko Wilke,

    Corresponding author
    1. Department of Pediatric Neurology and Developmental Medicine, Children's Hospital, University of Tübingen, Germany
    2. Experimental Pediatric Neuroimaging, Children's Hospital and Department of Neuroradiology, University of Tübingen, Germany
    3. Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
    4. Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
    • Correspondence to: Marko Wilke, Department of Pediatric Neurology and Developmental Medicine, Children's Hospital, University of Tübingen, Hoppe-Seyler-Str. 1, 72076 Tübingen, Germany. E-mail: Marko.Wilke@med.uni-tuebingen.de

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  • Douglas F. Rose,

    1. Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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  • Scott K. Holland,

    1. Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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  • James L. Leach

    1. Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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Abstract

Automated morphometric approaches are used to detect epileptogenic structural abnormalities in 3D MR images in adults, using the variance of a control population to obtain z-score maps in an individual patient. Due to the substantial changes the developing human brain undergoes, performing such analyses in children is challenging. This study investigated six features derived from high-resolution T1 datasets in four groups: normal children (1.5T or 3T data), normal clinical scans (3T data), and patients with structural brain lesions (3T data), with each n = 10. Normative control data were obtained from the NIH study on normal brain development (n = 401). We show that control group size substantially influences the captured variance, directly impacting the patient's z-scores. Interestingly, matching on gender does not seem to be beneficial, which was unexpected. Using data obtained at higher field scanners produces slightly different base rates of suprathreshold voxels, as does using clinically derived normal studies, suggesting a subtle but systematic effect of both factors. Two approaches for controlling suprathreshold voxels in a multidimensional approach (combining features and requiring a minimum cluster size) were shown to be substantial and effective in reducing this number. Finally, specific strengths and limitations of such an approach could be demonstrated in individual cases. Hum Brain Mapp 35:3199–3215, 2014. © 2013 Wiley Periodicals, Inc.

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