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. 2012 Mar;135(Pt 3):794-806.
doi: 10.1093/brain/aws001.

Neuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics

Jennifer L Whitwell  1 , Stephen D Weigand, Bradley F Boeve, Matthew L Senjem, Jeffrey L Gunter, Mariely DeJesus-Hernandez, Nicola J Rutherford, Matthew Baker, David S Knopman, Zbigniew K Wszolek, Joseph E Parisi, Dennis W Dickson, Ronald C Petersen, Rosa Rademakers, Clifford R Jack Jr, Keith A Josephs
Affiliations

Neuroimaging signatures of frontotemporal dementia genetics: C9ORF72, tau, progranulin and sporadics

Jennifer L Whitwell et al. Brain. 2012 Mar.

Abstract

A major recent discovery was the identification of an expansion of a non-coding GGGGCC hexanucleotide repeat in the C9ORF72 gene in patients with frontotemporal dementia and amyotrophic lateral sclerosis. Mutations in two other genes are known to account for familial frontotemporal dementia: microtubule-associated protein tau and progranulin. Although imaging features have been previously reported in subjects with mutations in tau and progranulin, no imaging features have been published in C9ORF72. Furthermore, it remains unknown whether there are differences in atrophy patterns across these mutations, and whether regional differences could help differentiate C9ORF72 from the other two mutations at the single-subject level. We aimed to determine the regional pattern of brain atrophy associated with the C9ORF72 gene mutation, and to determine which regions best differentiate C9ORF72 from subjects with mutations in tau and progranulin, and from sporadic frontotemporal dementia. A total of 76 subjects, including 56 with a clinical diagnosis of behavioural variant frontotemporal dementia and a mutation in one of these genes (19 with C9ORF72 mutations, 25 with tau mutations and 12 with progranulin mutations) and 20 sporadic subjects with behavioural variant frontotemporal dementia (including 50% with amyotrophic lateral sclerosis), with magnetic resonance imaging were included in this study. Voxel-based morphometry was used to assess and compare patterns of grey matter atrophy. Atlas-based parcellation was performed utilizing the automated anatomical labelling atlas and Statistical Parametric Mapping software to compute volumes of 37 regions of interest. Hemispheric asymmetry was calculated. Penalized multinomial logistic regression was utilized to create a prediction model to discriminate among groups using regional volumes and asymmetry score. Principal component analysis assessed for variance within groups. C9ORF72 was associated with symmetric atrophy predominantly involving dorsolateral, medial and orbitofrontal lobes, with additional loss in anterior temporal lobes, parietal lobes, occipital lobes and cerebellum. In contrast, striking anteromedial temporal atrophy was associated with tau mutations and temporoparietal atrophy was associated with progranulin mutations. The sporadic group was associated with frontal and anterior temporal atrophy. A conservative penalized multinomial logistic regression model identified 14 variables that could accurately classify subjects, including frontal, temporal, parietal, occipital and cerebellum volume. The principal component analysis revealed similar degrees of heterogeneity within all disease groups. Patterns of atrophy therefore differed across subjects with C9ORF72, tau and progranulin mutations and sporadic frontotemporal dementia. Our analysis suggested that imaging has the potential to be useful to help differentiate C9ORF72 from these other groups at the single-subject level.

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Figures

Figure 1
Figure 1
Results of the voxel-based morphometry analysis of grey matter volume. (A) Patterns of grey matter loss in MAPT, GRN, C9ORF72 and sporadic FTD groups compared with controls, at P < 0.05 (corrected for multiple comparisons using family-wise error). (B) Differences in grey matter volume between the C9ORF72 group and the other disease groups, at P < 0.001 (uncorrected for multiple comparisons). Results are shown in 3D renderings of the brain.
Figure 2
Figure 2
Box plots of all variables selected in the penalized multinomial logistic regression model as best for differentiating disease groups. All regional grey matter volumes were divided by whole brain volume to correct for differences in global atrophy between subjects. This step was performed because we were interested in assessing the relative involvement of each region, without confounds of global severity. L = left; R = right; ROI = region of interest.
Figure 3
Figure 3
Box plots of variables that were not selected in the penalized multinomial logistic regression model as useful for differentiating disease groups, and hence not shown in Fig. 2. All regional grey matter volumes were divided by whole brain volume to correct for differences in global atrophy between subjects. This step was performed because we were interested in assessing the relative involvement of each region, without confounds of global severity. L = left; R = right; ROI = region of interest.
Figure 4
Figure 4
Coefficients for the 14 variables that were retained in the final penalized multinomial logistic regression for the MAPT, GRN, C9ORF72 and sporadic FTD groups. A negative coefficient means that lower values (i.e. more atrophy) on that variable increases the estimated probability of being in that group, holding all other variables constant. L = left; R = right.
Figure 5
Figure 5
Plots showing the estimated probability of MAPT, GRN, C9ORF72 and sporadic FTD according to volume for each of the 14 variables that were retained in the final model. In each panel, the adjustment covariates are set to their mean values. L = left; R = right; ROI = region of interest.
Figure 6
Figure 6
Plot of the cumulative proportion of variability explained by each principal component for the C9ORF72, MAPT, GRN and sporadic FTD groups.
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