A Comparison of Magnetic Resonance Imaging and Neuropsychological Examination in the Diagnostic Distinction of Alzheimer's Disease and Behavioral Variant Frontotemporal Dementia
Open accessAbstract
The clinical distinction between Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) remains challenging and largely dependent on the experience of the clinician. This study investigates whether objective machine learning algorithms using supportive neuroimaging and neuropsychological clinical features can aid the distinction between both diseases. Retrospective neuroimaging and neuropsychological data of 166 participants (54 AD; 55 bvFTD; 57 healthy controls) was analyzed via a Naïve Bayes classification model. A subgroup of patients (n = 22) had pathologically-confirmed diagnoses. Results show that a combination of gray matter atrophy and neuropsychological features allowed a correct classification of 61.47% of cases at clinical presentation. More importantly, there was a clear dissociation between imaging and neuropsychological features, with the latter having the greater diagnostic accuracy (respectively 51.38 vs. 62.39%). These findings indicate that, at presentation, machine learning classification of bvFTD and AD is mostly based on cognitive and not imaging features. This clearly highlights the urgent need to develop better biomarkers for both diseases, but also emphasizes the value of machine learning in determining the predictive diagnostic features in neurodegeneration.
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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 [16]
The Discussion reports that this study distinguished AD from bvFTD with approximately 51.4% accuracy using cortical volume and 62.4% using neuropsychological scores. It also reports high sensitivity and specificity in an earlier study, but that study distinguished AD from the broader FTLD group, not bvFTD specifically. The source therefore supports a moderate result for bvFTD, not the claim that prior studies generally achieved moderate to high accuracy for that distinction. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMachine 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 [16]
The Discussion (page unavailable) reports MRI-feature-based machine-learning classification of AD and bvFTD, including a three-way analysis with healthy controls. The paper does not report classifying bvFTD against other psychiatric or neurologic disorders, so that extension is unsupported by this source. (LLM, conf=0.87)
Experimental — accuracy not yet measured
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
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- Authors
- Wang, J., Redmond, S. J., Bertoux, M., Hodges, J. R., & Hornberger, M.
- Affiliations
- UNSW Sydney (AU)
- University of East Anglia (GB)
- Corresponding author
- Hornberger, Michael
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2016-06-16
- Volume
- 8
- Pages
- 119-119
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC4909756
- OpenAlex
- W2436762990
- Cited by
- 35
- References
- 46
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 8 · 2023: 3 · 2022: 2 · 2021: 4 · 2020: 2 · 2019: 4 · 2018: 4 · 2017: 1 · 2016: 2
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- ePrints Soton (University of Southampton) (acceptedVersion)
- UEA Digital Repository (University of East Anglia) (acceptedVersion)
- Funders
- University of New South Wales
- Medical Research Council
- Australian Research Council (CE11000102)
- National Health and Medical Research Council (1037746)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Social Sciences › Psychology › Clinical Psychology › Historical and Modern Theater Studies
- Keywords
- Alzheimer's disease, behavioral variant frontotemporal dementia, differential diagnosis, machine learning classification, Naive Bayes classifier, gray matter atrophy, neuropsychological characteristics, neuroimaging features, diagnostic biomarkers, neurodegeneration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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