White matter damage in frontotemporal lobar degeneration spectrum
Abstract
White matter (WM) tract damage was assessed in patients with the behavioral variant frontotemporal dementia (bvFTD) and the 3 primary progressive aphasia (PPA) variants and compared with the corresponding brain atrophy patterns. Thirteen bvFTD and 20 PPA patients were studied. Tract-based spatial statistics and voxel-based morphometry were used. Patients with bvFTD showed widespread diffusion tensor magnetic resonance imaging (DT MRI) abnormalities affecting most of the WM bilaterally. In PPA patients, WM damage was more focal and varied across the 3 syndromes: left frontotemporoparietal in nonfluent, left frontotemporal in semantic, and left frontoparietal in logopenic patients. In each syndrome, DT MRI changes extended beyond the topography of gray matter loss. Left uncinate damage was the best predictor of frontotemporal lobar degeneration diagnosis versus controls. DT MRI measures of the anterior corpus callosum and left superior longitudinal fasciculus differentiated bvFTD from nonfluent cases. The best predictors of semantic PPA compared with both bvFTD and nonfluent cases were diffusivity abnormalities of the left uncinate and inferior longitudinal fasciculus. This study provides insights into the similarities and differences of WM damage in bvFTD and PPA variants. DT MRI metrics hold promise to serve as early markers of WM integrity loss that only at a later stage may be detectable by volumetric measures.
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The study's group-level statistical findings can be compared with previous research.
“Moreover, our group-level statistical findings can be compared with previous research (Agosta et al., 2012; Bharti et al., 2017; Iaccarino et al., 2015; Popal et al., 2020; Tovar-Moll et al., 2014; Upadhyay et al., 2016; Whitwell et al., 2009).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Agosta et al., 2012
The multimodal approach helps to provide information on specific functional and structural alterations capturing differential patterns in FTD.
“However, the multimodal approach helps to provide information on specific functional and structural alterations capturing differential patterns in FTD (Agosta et al., 2012; Nguyen et al., 2013; Reyes et al., 2018; Whitwell, 2019).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Agosta et al., 2012
Raw structural connectivity models showed alterations in the uncinate fasciculus, superior longitudinal fasciculus, corpus callosum, dentatorubrothalamic tract, and inferior fronto-occipital fasciculus.
“we found alterations in the uncinate fasciculus (Agosta et al., 2012; Daianu et al., 2016; Iaccarino et al., 2015; Nguyen et al., 2013), superior longitudinal fasciculus (Agosta et al., 2012; Daianu et al., 2016), corpus callosum (Tovar-Moll et al., 2014), dentatorubrothalamic tract (Whitwell et al., 2011a), and inferior fronto-occipital fasciculus (Meijboom et al., 2017).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Agosta et al., 2012
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- Authors
- Agosta, F., Scola, E., Canu, E., Marcone, A., Magnani, G., Sarro, L., Copetti, M., Caso, F., Cerami, C., Comi, G., Cappa, S. F., Falini, A., & Filippi, M.
- Affiliations
- Vita-Salute San Raffaele University (IT)
- IRCCS Ospedale San Raffaele (IT)
- Casa Sollievo della Sofferenza (IT)
- ORCID
- Venue
- Cerebral Cortex
- Publisher
- Oxford University Press (OUP)
- Published
- 2012-10-10
- Volume
- 22
- Issue
- 12
- Pages
- 2705-2714
- ISSN
- 1047-3211 (ISSN-L)
- 1460-2199
- OpenAlex
- W2097764378
- Cited by
- 180
- References
- 51
- Citations by year
- 2026: 12 · 2025: 8 · 2024: 5 · 2023: 9 · 2022: 15 · 2021: 11 · 2020: 12 · 2019: 13 · 2018: 12 · 2017: 15 · 2016: 12 · 2015: 15 · 2014: 19 · 2013: 13 · 2012: 9
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cerebral Cortex (publishedVersion)
- PubMed (publishedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- white matter injury, frontotemporal lobar degeneration, behavioral variant frontotemporal dementia, primary progressive aphasia, non-fluent aphasia, semantic dementia, logopenic primary progressive aphasia, uncinate fasciculus, inferior longitudinal fasciculus, corpus callosum, superior longitudinal fasciculus, diffusion tensor imaging, white matter integrity, brain atrophy patterns, early biomarkers
- MeSH terms
- Aged, Atrophy, Brain, Diagnosis, Differential, Female, Humans, Male, Middle Aged, Nerve Fibers, Myelinated, Sensitivity and Specificity, Reproducibility of Results, Aphasia, Primary Progressive, Diffusion Tensor Imaging, Frontotemporal Lobar Degeneration
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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