Assessing network degeneration and phenotypic heterogeneity in genetic frontotemporal lobar degeneration by decoding FDG-PET

Nick Corriveau-Lecavalier, Leland R. Barnard, Scott A. Przybelski, Venkatsampath Gogineni, Hugo Botha, Jonathan Graff-Radford, Vijay K. Ramanan, Leah K. Forsberg, Julie A. Fields, Mary M. Machulda, Rosa Rademakers, Ralitza H. Gavrilova, Maria I. Lapid, Bradley F. Boeve, David S. Knopman, Val J. Lowe, Ronald C. Petersen, Clifford R. Jack, Kejal Kantarci, David T. Jones

Research output: Contribution to journalArticlepeer-review

Abstract

Genetic mutations causative of frontotemporal lobar degeneration (FTLD) are highly predictive of a specific proteinopathy, but there exists substantial inter-individual variability in their patterns of network degeneration and clinical manifestations. We collected clinical and 18Fluorodeoxyglucose-positron emission tomography (FDG-PET) data from 39 patients with genetic FTLD, including 11 carrying the C9orf72 hexanucleotide expansion, 16 carrying a MAPT mutation and 12 carrying a GRN mutation. We performed a spectral covariance decomposition analysis between FDG-PET images to yield unbiased latent patterns reflective of whole brain patterns of metabolism (“eigenbrains” or EBs). We then conducted linear discriminant analyses (LDAs) to perform EB-based predictions of genetic mutation and predominant clinical phenotype (i.e., behavior/personality, language, asymptomatic). Five EBs were significant and explained 58.52 % of the covariance between FDG-PET images. EBs indicative of hypometabolism in left frontotemporal and temporo-parietal areas distinguished GRN mutation carriers from other genetic mutations and were associated with predominant language phenotypes. EBs indicative of hypometabolism in prefrontal and temporopolar areas with a right hemispheric predominance were mostly associated with predominant behavioral phenotypes and distinguished MAPT mutation carriers from other genetic mutations. The LDAs yielded accuracies of 79.5 % and 76.9 % in predicting genetic status and predominant clinical phenotype, respectively. A small number of EBs explained a high proportion of covariance in patterns of network degeneration across FTLD-related genetic mutations. These EBs contained biological information relevant to the variability in the pathophysiological and clinical aspects of genetic FTLD, and for offering valuable guidance in complex clinical decision-making, such as decisions related to genetic testing.

Original languageEnglish (US)
Article number103559
JournalNeuroImage: Clinical
Volume41
DOIs
StatePublished - Jan 2024

Keywords

  • Clinical neurology
  • FDG-PET
  • Frontotemporal dementia
  • Frontotemporal lobar degeneration
  • Machine learning

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

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