Neuroimaging signatures of frontotemporal dementia genetics

C9ORF72, tau, progranulin and sporadics

Jennifer Lynn Whitwell, 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 Carl Petersen, Rosa V Rademakers, Clifford R Jr. Jack, Keith Anthony Josephs

Research output: Contribution to journalArticle

244 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)794-806
Number of pages13
JournalBrain
Volume135
Issue number3
DOIs
StatePublished - Mar 2012

Fingerprint

Frontotemporal Dementia
Neuroimaging
Mutation
Atrophy
Atlases
Logistic Models
Principal Component Analysis
Signature
Cerebellum
Genes
Occipital Lobe
Parietal Lobe
Microtubule-Associated Proteins
Amyotrophic Lateral Sclerosis
Temporal Lobe
Dementia
Analysis of Variance

Keywords

  • C9ORF72
  • frontotemporal dementia
  • magnetic resonance imaging
  • progranulin
  • tau

ASJC Scopus subject areas

  • Clinical Neurology
  • Arts and Humanities (miscellaneous)

Cite this

Neuroimaging signatures of frontotemporal dementia genetics : C9ORF72, tau, progranulin and sporadics. / Whitwell, Jennifer Lynn; Weigand, Stephen D.; Boeve, Bradley F; Senjem, Matthew L.; Gunter, Jeffrey L.; Dejesus-Hernandez, Mariely; Rutherford, Nicola J.; Baker, Matthew; Knopman, David S; Wszolek, Zbigniew K; Parisi, Joseph E; Dickson, Dennis W; Petersen, Ronald Carl; Rademakers, Rosa V; Jack, Clifford R Jr.; Josephs, Keith Anthony.

In: Brain, Vol. 135, No. 3, 03.2012, p. 794-806.

Research output: Contribution to journalArticle

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AU - Senjem, Matthew L.

AU - Gunter, Jeffrey L.

AU - Dejesus-Hernandez, Mariely

AU - Rutherford, Nicola J.

AU - Baker, Matthew

AU - Knopman, David S

AU - Wszolek, Zbigniew K

AU - Parisi, Joseph E

AU - Dickson, Dennis W

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N2 - 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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