Overlap in frontotemporal atrophy between normal aging and patients with frontotemporal dementias
Abstract
Normal aging leads to frontocortical atrophy. The degree to which this complicates the use of frontotemporal atrophy as a diagnostic criterion for the frontotemporal dementias (FTDs) has not been reported. The present case-control study compared frontotemporal volumes delineated with semi-automatic brain region extraction [n=30 controls vs. 16 behavioral variant FTD (bvFTD) vs. 14 primary progressive aphasia]. Logistic regression identified those regions least helpful for distinguishing bvFTD and primary progressive aphasia from controls. Linear regression tested the correlation of duration of illness to atrophy severity. The control group showed high variance in volumes. Controls had right frontal lobe volumes that overlapped considerably with bvFTD volumes, but, as anticipated, the left anterior temporal volumes of interest showed 91% accuracy in distinguishing the aphasic subgroup from controls. Left-sided and not right-sided atrophy in the medial middle frontal region distinguished the bvFTD group from controls. The relegation of structural imaging to a supportive criterion for diagnosis is reasonable in the context of the range of atrophy due to normal aging. While volumetry identified left-sided atrophy as useful for identifying FTD cases, future studies should determine whether clinicians could make these distinctions on viewing routine diagnostic magnetic resonance imaging scans.
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Prior machine learning studies achieved moderate to high accuracy distinguishing bvFTD.
“In general, they achieved moderate to high accuracy distinguishing bvFTD.”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [9]
Machine learning techniques have been applied to distinguish bvFTD from cognitively normal controls, Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features.
“Recently, machine learning techniques have been applied to distinguish between bvFTD and Cognitively Normal Subjects (CNCs), Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI-based features. 9-17”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [9]
Previous studies classified bvFTD from a control group.
“Previous studies classified bvFTD from a control group. 9-13”
From MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia (2021) · cites this paper as [9]
Assessment of cortical atrophy by standard visual neuroradiological review is often insufficient in the initial stages of bvFTD to differentiate it from normal age-related volume loss.
“Assessment of cortical atrophy by standard visual neuroradiological review is often insufficient in the initial stages of bvFTD to differentiate it with normal age-related volume loss (Gregory et al. , 1999; Chow et al. , 2008; Vijverberg et al. , 2016 a ), which can lead to erroneous diagnoses.”
From Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders (2020) · cites this paper as Chow et al. , 2008
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McCarthy, J., Collins, D. L., & Ducharme, S. (2018) · NeuroImage: Clinical · doi:10.1016/j.nicl.2018.08.028 · Cited by 47 · see citing works
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Hu, W. T., Trojanowski, J. Q., & Shaw, L. M. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.04.012 · Cited by 42 · see citing works
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Nguyen, H.-D., Clément, M., Planche, V., Mansencal, B., & Coupé, P. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102636 · Cited by 35 · see citing works
MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
Manera, A. L., Dadar, M., Van Swieten, J. C., Borroni, B., Sanchez-Valle, R., Moreno, F., Laforce Jr, R., Graff, C., Synofzik, M., Galimberti, D., et al. (2021) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp-2020-324106 · Cited by 23 · see citing works
Di Benedetto, M., Carrara, F., Tafuri, B., Nigro, S., De Blasi, R., Falchi, F., Gennaro, C., Gigli, G., Logroscino, G., & Amato, G. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105937 · Cited by 14 · see citing works
- Authors
- Chow, T. W., Binns, M. A., Freedman, M., Stuss, D. T., Ramirez, J., Scott, C. J. M., & Black, S.
- Affiliations
- University of Toronto (CA)
- Rotman Research Institute (CA)
- Sunnybrook Health Science Centre (CA)
- Corresponding author
- Chow, Tiffany W.
- Freedman, Morris
- Stuss, Donald T.
- Black, Sandra
- Venue
- Alzheimer Disease & Associated Disorders
- Published
- 2008-10
- Volume
- 22
- Issue
- 4
- Pages
- 327-335
- ISSN
- 0893-0341 (ISSN-L)
- OpenAlex
- W1977167964
- Cited by
- 37
- References
- 0
- Citations by year
- 2026: 2 · 2024: 1 · 2023: 3 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 4 · 2018: 1 · 2017: 1 · 2016: 2 · 2015: 1 · 2014: 2 · 2013: 3
- FWCI
- 1.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Alzheimer Disease & Associated Disorders (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute on Aging (F32AG022802)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Clinical Psychology › Historical and Modern Theater Studies
- Health Sciences › Medicine › Neurology › Amyotrophic Lateral Sclerosis Research
- Keywords
- frontotemporal atrophy, frontotemporal dementia, normal aging, behavioral variant frontotemporal dementia, primary progressive aphasia, brain volumetry, diagnostic overlap, disease duration
- MeSH terms
- Aged, Aged, 80 and over, Aging, Atrophy, Dementia, Female, Frontal Lobe, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Neuropsychological Tests, ROC Curve, Case-Control Studies
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