Frontiers in Neuroinformatics (Dec 2018)

An Automated Pipeline for the Analysis of PET Data on the Cortical Surface

  • Arnaud Marcoux,
  • Arnaud Marcoux,
  • Arnaud Marcoux,
  • Arnaud Marcoux,
  • Arnaud Marcoux,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Ninon Burgos,
  • Anne Bertrand,
  • Anne Bertrand,
  • Anne Bertrand,
  • Anne Bertrand,
  • Anne Bertrand,
  • Anne Bertrand,
  • Marc Teichmann,
  • Marc Teichmann,
  • Marc Teichmann,
  • Marc Teichmann,
  • Marc Teichmann,
  • Alexandre Routier,
  • Alexandre Routier,
  • Alexandre Routier,
  • Alexandre Routier,
  • Alexandre Routier,
  • Junhao Wen,
  • Junhao Wen,
  • Junhao Wen,
  • Junhao Wen,
  • Junhao Wen,
  • Jorge Samper-González,
  • Jorge Samper-González,
  • Jorge Samper-González,
  • Jorge Samper-González,
  • Jorge Samper-González,
  • Simona Bottani,
  • Simona Bottani,
  • Simona Bottani,
  • Simona Bottani,
  • Simona Bottani,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Stanley Durrleman,
  • Marie-Odile Habert,
  • Marie-Odile Habert,
  • Marie-Odile Habert,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • Olivier Colliot,
  • for the Alzheimer's Disease Neuroimaging Initiative

DOI
https://doi.org/10.3389/fninf.2018.00094
Journal volume & issue
Vol. 12

Abstract

Read online

We present a fully automatic pipeline for the analysis of PET data on the cortical surface. Our pipeline combines tools from FreeSurfer and PETPVC, and consists of (i) co-registration of PET and T1-w MRI (T1) images, (ii) intensity normalization, (iii) partial volume correction, (iv) robust projection of the PET signal onto the subject's cortical surface, (v) spatial normalization to a template, and (vi) atlas statistics. We evaluated the performance of the proposed workflow by performing group comparisons and showed that the approach was able to identify the areas of hypometabolism characteristic of different dementia syndromes: Alzheimer's disease (AD) and both the semantic and logopenic variants of primary progressive aphasia. We also showed that these results were comparable to those obtained with a standard volume-based approach. We then performed individual classifications and showed that vertices can be used as features to differentiate cognitively normal and AD subjects. This pipeline is integrated into Clinica, an open-source software platform for neuroscience studies available at www.clinica.run.

Keywords