Format

Send to

Choose Destination
Brain. 2017 Dec 1;140(12):3329-3345. doi: 10.1093/brain/awx254.

Clinicopathological correlations in behavioural variant frontotemporal dementia.

Author information

1
Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
2
University of California Davis, Davis, CA, USA.
3
Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
4
Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA.
5
Department of Neurology, University of Minnesota, Minneapolis, MN, USA.
6
Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Canada.
7
Department of Neurology, Sungkyunkwan University School of Medicine, Samsung Medical Center, Seoul, South Korea.
8
Department of Neurology and Alzheimer Center, Neuroscience Campus Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
9
University of Washington School of Medicine, Seattle, WA, USA.
10
Department of Pathology and Laboratory Medicine, University of California, San Francisco, San Francisco, CA, USA.
11
Neurogenetics program, Department of Neurology, and Semel Institute for Neuroscience and Human Behaviour, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
12
Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, University of Pennsylvania, Philadelphia, PA, USA.

Abstract

Accurately predicting the underlying neuropathological diagnosis in patients with behavioural variant frontotemporal dementia (bvFTD) poses a daunting challenge for clinicians but will be critical for the success of disease-modifying therapies. We sought to improve pathological prediction by exploring clinicopathological correlations in a large bvFTD cohort. Among 438 patients in whom bvFTD was either the top or an alternative possible clinical diagnosis, 117 had available autopsy data, including 98 with a primary pathological diagnosis of frontotemporal lobar degeneration (FTLD), 15 with Alzheimer's disease, and four with amyotrophic lateral sclerosis who lacked neurodegenerative disease-related pathology outside of the motor system. Patients with FTLD were distributed between FTLD-tau (34 patients: 10 corticobasal degeneration, nine progressive supranuclear palsy, eight Pick's disease, three frontotemporal dementia with parkinsonism associated with chromosome 17, three unclassifiable tauopathy, and one argyrophilic grain disease); FTLD-TDP (55 patients: nine type A including one with motor neuron disease, 27 type B including 21 with motor neuron disease, eight type C with right temporal lobe presentations, and 11 unclassifiable including eight with motor neuron disease), FTLD-FUS (eight patients), and one patient with FTLD-ubiquitin proteasome system positive inclusions (FTLD-UPS) that stained negatively for tau, TDP-43, and FUS. Alzheimer's disease was uncommon (6%) among patients whose only top diagnosis during follow-up was bvFTD. Seventy-nine per cent of FTLD-tau, 86% of FTLD-TDP, and 88% of FTLD-FUS met at least 'possible' bvFTD diagnostic criteria at first presentation. The frequency of the six core bvFTD diagnostic features was similar in FTLD-tau and FTLD-TDP, suggesting that these features alone cannot be used to separate patients by major molecular class. Voxel-based morphometry revealed that nearly all pathological subgroups and even individual patients share atrophy in anterior cingulate, frontoinsula, striatum, and amygdala, indicating that degeneration of these regions is intimately linked to the behavioural syndrome produced by these diverse aetiologies. In addition to these unifying features, symptom profiles also differed among pathological subtypes, suggesting distinct anatomical vulnerabilities and informing a clinician's prediction of pathological diagnosis. Data-driven classification into one of the 10 most common pathological diagnoses was most accurate (up to 60.2%) when using a combination of known predictive factors (genetic mutations, motor features, or striking atrophy patterns) and the results of a discriminant function analysis that incorporated clinical, neuroimaging, and neuropsychological data.

KEYWORDS:

Alzheimer’s disease; Pick’s disease; corticobasal degeneration; frontotemporal dementia; frontotemporal lobar degeneration

PMID:
29053860
PMCID:
PMC5841140
[Available on 2018-12-01]
DOI:
10.1093/brain/awx254
[Indexed for MEDLINE]

MeSH terms, Grant support

MeSH terms

Grant support

Supplemental Content

Full text links

Icon for Silverchair Information Systems
Loading ...
Support Center