Looking for neuroimaging markers in frontotemporal lobar degeneration clinical trials: A multi-voxel pattern analysis study in Granulin disease

Enrico Premi, Franco Cauda, Tommaso Costa, M. Diano, Stefano Gazzina, Vera Gualeni, Antonella Alberici, Silvana Archetti, Mauro Magoni, Roberto Gasparotti, Alessandro Padovani, Barbara Borroni*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

20 Citations (Scopus)

Abstract

In light of future pharmacological interventions, neuroimaging markers able to assess the response to treatment would be crucial. In Granulin (GRN) disease, preclinical data will prompt pharmacological trials in the future. Two main points need to be assessed: 1) to identify target regions in different disease stages and 2) to determine the most accurate functional and structural neuroimaging index to be used. To this aim, we have taken advantage of the multivariate approach of multi-voxel pattern analysis (MVPA) to explore the information of brain activity patterns in a cohort of GRN Thr272fs carriers at different disease stages (14 frontotemporal dementia (FTD) patients and 17 asymptomatic carriers) and a group of 33 healthy controls. We studied structural changes by voxel-based morphometry (VBM), functional connectivity by assessing salience, default mode, fronto-parietal, dorsal attentional, executive networks, and local connectivity by regional homogeneity, amplitude of low frequency fluctuations (ALFF), fractional ALFF (fALFF), degree centrality, and voxel-mirrored homotopic connectivity. In FTD patients with GRN mutation, the most predictive measure was VBM structural analysis, while in asymptomatic carriers the best predictor marker was the local connectivity measure (fALFF). Altogether, all indexes demonstrated fronto-temporoparietal damage in GRN pathology, with widespread structural damage of fronto-parietal and temporal regions when disease is overt. MVPA could be of aid in identifying the most accurate neuroimaging marker for clinical trials. This approach was able to identify both the target region and the best neuroimaging approach, which would be specific in the different disease stages. Further studies are needed to simultaneously integrate multimodal indexes in a classifier able to trace the disease progression moving from preclinical to clinical stage of the disease.

Original languageEnglish
Pages (from-to)249-262
JournalJournal of Alzheimer's Disease
Volume51
Issue number1
DOIs
Publication statusPublished - 2016

Keywords

  • Degree centrality
  • fractional amplitude of low frequency fluctuation
  • frontotemporal dementia
  • granulin
  • multi-voxel pattern analysis
  • regional homogeneity
  • resting state fMRI
  • support vector machine learning
  • voxel-mirrored homotopic connectivity
  • STATE FUNCTIONAL CONNECTIVITY
  • ALZHEIMERS-DISEASE
  • NETWORK CONNECTIVITY
  • BEHAVIORAL VARIANT
  • BRAIN CONNECTIVITY
  • TISSUE PATHOLOGY
  • FMRI DATA
  • DEMENTIA
  • MORPHOMETRY
  • MUTATION

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