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Cited by 62Dementia and Cognitive Impairment ResearchFunctional Brain Connectivity StudiesAlzheimer's disease research and treatments

Multiparametric MRI to distinguish early onset Alzheimer's disease and behavioural variant of frontotemporal dementia

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Canu, Elisa, Agosta, Federica, Mandic-Stojmenovic, Gorana, Stojković, Tanja, Stefanova, Elka, Inuggi, Alberto, Imperiale, Francesca, Copetti, Massimiliano +2 more·2017·NeuroImage: Clinical
DOI 10.1016/j.nicl.2017.05.018PMC PMC5458769
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Abstract

This prospective study explored whether an approach combining structural [cortical thickness and white matter (WM) microstructure] and resting state functional MRI can aid differentiation between 62 early onset Alzheimer's disease (EOAD) and 27 behavioural variant of frontotemporal dementia (bvFTD) patients. Random forest and receiver operator characteristic curve analyses assessed the ability of MRI in classifying the two clinical syndromes. All patients showed a distributed pattern of brain alterations relative to controls. Compared to bvFTD, EOAD patients showed bilateral inferior parietal cortical thinning and decreased default mode network functional connectivity. Compared to EOAD, bvFTD patients showed bilateral orbitofrontal and temporal cortical thinning, and WM damage of the corpus callosum, bilateral uncinate fasciculus, and left superior longitudinal fasciculus. Random forest analysis revealed that left inferior parietal cortical thickness (accuracy 0.78, specificity 0.76, sensitivity 0.83) and WM integrity of the right uncinate fasciculus (accuracy 0.81, specificity 0.96, sensitivity 0.43) were the best predictors of clinical diagnosis. The combination of cortical thickness and DT MRI measures was able to distinguish patients with EOAD and bvFTD with accuracy 0.82, specificity 0.76, and sensitivity 0.96. The diagnostic ability of MRI models was confirmed in a subsample of patients with biomarker-based clinical diagnosis. Multiparametric MRI is useful to identify brain alterations which are specific to EOAD and bvFTD. A severe cortical involvement is suggestive of EOAD, while a prominent WM damage is indicative of bvFTD.

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  • 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 [14]

    The Abstract and Discussion describe random-forest classification of individual patients with bvFTD versus early-onset Alzheimer’s disease using MRI features. They do not report individual-level classification of bvFTD versus cognitively normal subjects or other psychiatric and neurologic disorders. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Machine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating well-characterized cases of bvFTD from Alzheimer's disease.

    “Machine-learning algorithms using MRI volumetry to develop diagnostic classifiers fare well against controls but achieve maximal performance of 82% in separating wellcharacterized cases of bvFTD from Alzheimer's disease (Moller et al. , 2016; Canu et al. , 2017; McCarthy et al. , 2018).”

    From Recommendations to distinguish behavioural variant frontotemporal dementia from psychiatric disorders (2020) · cites this paper as Canu et al. , 2017

    The Results (Discrimination analysis, Table 2; page unavailable) report 82% accuracy for one random-forest model separating early-onset Alzheimer’s disease from bvFTD, but 82% was not the maximum: another model reached 84% in the total sample, and a model reached 97% in the biomarker-supported subsample. The paper used cortical-thickness and white-matter measures, rather than MRI volumetry alone, and does not report classifier performance against controls. (LLM, conf=0.89)

    Experimental — accuracy not yet measured

  • 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 [14]

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Authors
Canu, E., Agosta, F., Mandic-Stojmenovic, G., Stojković, T., Stefanova, E., Inuggi, A., Imperiale, F., Copetti, M., Kostic, V. S., & Filippi, M.
Affiliations
  • IRCCS Ospedale San Raffaele (IT)
  • University of Belgrade (RS)
  • Italian Institute of Technology (IT)
  • Casa Sollievo della Sofferenza (IT)
Corresponding author
Filippi, Massimo
ORCID
Venue
NeuroImage: Clinical
Publisher
Elsevier BV
Published
2017
Volume
15
Pages
428-438
ISSN
2213-1582 (ISSN-L)
DOI
10.1016/j.nicl.2017.05.018
PMC
PMC5458769
OpenAlex
W2619323525
Cited by
62
References
86
Citations by year
2026: 2 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 8 · 2021: 10 · 2020: 5 · 2019: 13 · 2018: 10
FWCI
6.29
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage Clinical (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
Funders
Ministry of Education and Science (175090)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
early-onset Alzheimer's disease, behavioral variant frontotemporal dementia, differential diagnosis, cortical thickness, white matter microstructure, resting-state functional connectivity, default mode network, inferior parietal cortex, uncinate fasciculus, corpus callosum, superior longitudinal fasciculus, orbitofrontal cortex, cortical thinning, diffusion MRI, random forest classification
MeSH terms
Aged, Alzheimer Disease, Cerebral Cortex, Diagnosis, Differential, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prospective Studies, Age of Onset, Frontotemporal Dementia, Functional Neuroimaging, White Matter

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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