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42/100

✓ Peer-reviewed alzhub usefulness score 42 out of 100. Lewy body dementia

Topic: Risk, prevention and care in population studies

Microstructural alterations in brain tissue play a crucial role in the pathophysiology of frontotemporal dementia (FTD). This study assessed brain white matter (WM) and grey-matter (GM) microstructure in FTD variants using neurite orientation dispersion and density imaging (NODDI) diffusion MRI model and developed an exploratory machine-learning algorithm to classify FTD subtypes according to diffusion MRI metrics. Brain MRI including multi-shell diffusion sequences and neuropsychological assessment were obtained in controls and participants with FTD: 35 behavioural variant of FTD (bvFTD), 20 semantic-variant primary progressive aphasia (svPPA), 14 nonfluent-variant primary progressive aphasia (nfvPPA), 9 semantic-bvFTD (sbvFTD). Fractional anisotropy (FA), mean diffusivity (MD), intracellular-volume fraction (ICVF), and orientation-dispersion index (ODI) were analysed using tract-based and GM-based spatial statistic at the voxel-wise level, with nonparametric and permutation-based methods. Support vector machine (SVM) models were trained on different combinations of diffusion MRI and neuropsychological features to classify FTD subtypes. FA and MD showed widespread WM alterations in all variants. ICVF showed reductions in both WM and GM (bilateral frontotemporal for bvFTD, left temporal-frontal for svPPA and nfvPPA and right temporal for sbvFTD). GM ODI reduction exhibited a similar but more diffuse pattern compared with ICVF. WM ODI alterations were also observed, with specific WM alterations in the corpus callosum and long-range WM tracts when comparing FTD syndromes. SVM algorithm, trained on mean FA, ICVF and ODI values from different brain lobes and neuropsychological scores, achieved 98.6% accuracy in classifying different clinical syndromes, outperforming standard diffusion tensor (DT) imaging-based models. NODDI capture subtle microstructural alterations in brain GM and WM, demonstrating advantages over standard DT imaging in capturing disease-relevant alterations. By integrating NODDI with cognitive data, machine-learning models can learn complex patterns and relationships facilitating the differentiation of FTD subtypes.

Key findings

GM ODI reduction exhibited a similar but more diffuse pattern compared with ICVF.

Why it matters

Using NODDI diffusion MRI, this study found widespread white matter changes across frontotemporal dementia variants and grey matter changes in specific regions, and built an exploratory machine-learning model to classify subtypes. It is about imaging characterization and classification, not about treating, curing or preventing the disease.

Study details

Effect size98.6%
Evidence strengthLow

How this study scored 42/100

Published in a leading specialty journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.

Component	Points	Why
Journal tier	9 / 10	Tier 2 — leading specialty journal (Brain Communications)
Journal's citation record	0 / 4	Brain Communications: journal h-index 67 (OpenAlex, 2026-10-29)
Study design	0 / 12	No study design recorded — scores zero, not guessed (not stated in the record — scored 0)
Strength of evidence	1 / 5	Rated “low” in our summary
Peer-review validation	10 / 10	Peer-reviewed. Published as an article in a named peer-reviewed journal.
How recent	4 / 5	2025 — within 3 year(s) of the newest study here
Sample size	0 / 4	Participant count not stated — scores zero (not stated in the record — scored 0)
Effect reported	2 / 3	Effect reported as a number, without an interval or test
Studied in people	0 / 3	Not stated whether this was studied in people — scores zero (not stated in the record — scored 0)
Randomised / blinded	0 / 2	No randomisation or blinding stated — scores zero (not stated in the record — scored 0)
Times cited	1 / 7	Cited 3 times (OpenAlex, 2026-10-10)
Citations per year	1 / 5	1.0 citations a year over 3 year(s) since 2025
Impact for its field and year	2 / 6	Cited 1.9× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	0 / 6	No other study in this library cites it
Still being cited	1 / 4	Cited 3 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	4 / 4	Last-listed author Massimo Filippi: h-index 156 (OpenAlex, whole career)
Institution's track record	1 / 3	University of Milan: institution h-index 1,023 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 9 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 41346467)
Free to read	2 / 2	Free to read
Total	42 / 100	—

What each of these 20 components measures, and what this score is not →

Read the original

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

Cite this study APA, Vancouver, BibTeX · download .bib or .ris

Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.

APA 7th

Pisano, S., Basaia, S., Agosta, F., Canu, E., Spinelli, E. G., Cecchetti, G., et al. (2025). Frontotemporal dementia characterization using neurite orientation dispersion and density imaging. Brain Communications. https://doi.org/10.1093/braincomms/fcaf442

Vancouver

Pisano S, Basaia S, Agosta F, Canu E, Spinelli EG, Cecchetti G, et al. Frontotemporal dementia characterization using neurite orientation dispersion and density imaging. Brain Communications. 2025. doi:10.1093/braincomms/fcaf442. PMID: 41346467.

BibTeX

@article{pisano2025-41346467,
  author  = {Pisano, Stefano and Basaia, Silvia and Agosta, Federica and Canu, Elisa and Spinelli, Edoardo Gioele and Cecchetti, Giordano and Ghirelli, Alma and Sibilla, Elisa and others},
  title   = {{Frontotemporal dementia characterization using neurite orientation dispersion and density imaging}},
  journal = {Brain Communications},
  year    = {2025},
  doi     = {10.1093/braincomms/fcaf442},
  pmid    = {41346467},
  url     = {https://doi.org/10.1093/braincomms/fcaf442}
}

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Referenced by this study and also in the alzhub library, so you can read them here.

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