Brainstem enlargement in pre-school children with autism: results from an inter-method agreement study of segmentation algorithms

Paolo Bosco, Alessia Giuliano, Jonathan Delafield-Butt, Filippo Muratori, Sara Calderoni, Alessandra Retico

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Abstract

The inter-method agreement between automated algorithms for brainstem segmentation is investigated, focusing on the potential involvement of this structure in Autism Spectrum Disorders (ASD). Inconsistencies highlighted in previous studies on brainstem in the population with ASD may in part be a result of poor agreement in the extraction of structural features between different methods. A sample of 76 children with ASD and 76 age-, gender- and intelligence-matched controls was considered. Volumetric analyses were performed using common tools for brain structures segmentation, namely FSL-FIRST, FreeSurfer (FS), and Advanced Normalization Tools (ANTs). For shape analysis SPHARM-MAT was employed. Inter-method agreement was quantified in terms of Pearson correlations between pairs of volumes obtained by the different methods. The degree of overlap between segmented masks was quantified in terms of the Dice index. Both Pearson correlations and Dice indices, showed poor agreement between FSL-FIRST and the other methods (ANTs and FS), which by contrast, yielded Pearson correlations greater than 0.93 and average Dice indices greater than 0.76 when compared with each other. As with volume, shape analyses exhibited discrepancies between segmentation methods, with particular differences noted between FSL-FIRST and the others (ANT and FS), with under- and over-segmentation in specific brainstem regions. These data suggest that research on brain structure alterations should cross-validate findings across multiple methods. We reliably detected an enlargement of brainstem volume in the whole sample and in the male cohort across multiple segmentation methods, a feature particularly driven by the subgroup of children with idiopathic intellectual disability associated with ASD.
Original languageEnglish
JournalHuman Brain Mapping
Early online date5 Sep 2018
DOIs
Publication statusE-pub ahead of print - 5 Sep 2018

Keywords

  • brainstem
  • autism spectrum disorders
  • segmentation
  • T1-weighted MRI
  • brainstem volume

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