title: Automated MRI parcellation of the frontal lobe.
abstract: Examination of associations between specific disorders and physical properties of functionally relevant frontal lobe sub-regions is a fundamental goal in neuropsychiatry. Here, we present and evaluate automated methods of frontal lobe parcellation with the programs FreeSurfer(FS) and TOADS-CRUISE(T-C), based on the manual method described in Ranta et al. [2009]: Psychiatry Res 172:147-154 in which sulcal-gyral landmarks were used to manually delimit functionally relevant regions within the frontal lobe: i.e., primary motor cortex, anterior cingulate, deep white matter, premotor cortex regions (supplementary motor complex, frontal eye field, and lateral premotor cortex) and prefrontal cortex (PFC) regions (medial PFC, dorsolateral PFC, inferior PFC, lateral orbitofrontal cortex [OFC] and medial OFC). Dice's coefficient, a measure of overlap, and percent volume difference were used to measure the reliability between manual and automated delineations for each frontal lobe region. For FS, mean Dice's coefficient for all regions was 0.75 and percent volume difference was 21.2%. For T-C the mean Dice's coefficient was 0.77 and the mean percent volume difference for all regions was 20.2%. These results, along with a high degree of agreement between the two automated methods (mean Dice's coefficient = 0.81, percent volume difference = 12.4%) and a proof-of-principle group difference analysis that highlights the consistency and sensitivity of the automated methods, indicate that the automated methods are valid techniques for parcellation of the frontal lobe into functionally relevant sub-regions. Thus, the methodology has the potential to increase efficiency, statistical power and reproducibility for population analyses of neuropsychiatric disorders with hypothesized frontal lobe contributions.
authors.affiliation: Kennedy Krieger Institute, Laboratory for Neurocognitive Imaging and Research, Baltimore, Maryland.
authors.forename: Marin E
authors.initials: ME
authors.lastname: Ranta
authors.forename: Min
authors.initials: M
authors.lastname: Chen
authors.forename: Deana
authors.initials: D
authors.lastname: Crocetti
authors.forename: Jerry L
authors.initials: JL
authors.lastname: Prince
authors.forename: Krish
authors.initials: K
authors.lastname: Subramaniam
authors.forename: Bruce
authors.initials: B
authors.lastname: Fischl
authors.forename: Walter E
authors.initials: WE
authors.lastname: Kaufmann
authors.forename: Stewart H
authors.initials: SH
authors.lastname: Mostofsky
mesh_terms: D000293:Adolescent; D001931:Brain Mapping; D002648:Child; D001330:Electronic Data Processing; D005260:Female; D005625:Frontal Lobe / Q000033:anatomy & histology* / Q000254:growth & development; D007839:Functional Laterality; D006801:Humans; D007091:Image Processing, Computer-Assisted; D007361:Intelligence Tests; D008279:Magnetic Resonance Imaging; D008297:Male; D012737:Sex Factors
keywords: automation; children; cortex; prefrontal; premotor; segmentation
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