4.7 Article

Frontotemporal dementia: Clinicopathological correlations

期刊

ANNALS OF NEUROLOGY
卷 59, 期 6, 页码 952-962

出版社

WILEY
DOI: 10.1002/ana.20873

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资金

  1. NIA NIH HHS [P01 AG09215, P01 AG017586, P01 AG17586, P01 AG009215, P30 AG010124, R01 AG015116, R01 AG15116, L30 AG024692, P30 AG10124, R01 AG022538-03, P01 AG019724, R01 AG022538, K08 AG20073, K08 AG020073, P01 AG19724] Funding Source: Medline
  2. NINDS NIH HHS [K08 NS41408, K08 NS041408, R01 NS044266, R01 NS44266] Funding Source: Medline

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Objective: Frontotemporal lobar degeneration (FTLD) is characterized by impairments in social, behavioral, and/or language function, but postmortem studies indicate that multiple neuropathological entities lead to FTLD. This study assessed whether specific clinical features predict the underlying pathology. Methods: A clinicopathological correlation was performed on 90 consecutive patients with a pathological diagnosis of frontotemporal dementia and was compared with an additional 24 cases accrued during the same time period with a clinical diagnosis of FTLD, but with pathology not typically associated with frontotemporal dementia. Results: Postmortem examination showed multiple pathologies including tauopathies (46%), FTLD with ubiquitin-positive inclusions (29%), and Alzheimer's disease (17%). The pathological groups manifested some distinct demographic, clinical, and neuropsychological features, although these attributes showed only a statistical association with the underlying pathology. FTLD with ubiquitin-positive inclusions was more likely to present with both social and language dysfunction, and motor neuron disease was more likely to emerge in these patients. Tauopathies were more commonly associated with an extrapyramidal disorder. Alzheimer's disease was associated with relatively greater deficits in memory and executive function. Interpretation: Clinical and neuropsychological features contribute to delineating the spectrum of pathology underlying a patient diagnosed with FTLD, but biomarkers are needed that, together with the clinical phenotype, can predict the underlying neuropathology.

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