Profiles of white matter tract pathology in frontotemporal dementia
Open accessAbstract
Despite considerable interest in improving clinical and neurobiological characterisation of frontotemporal dementia and in defining the role of brain network disintegration in its pathogenesis, information about white matter pathway alterations in frontotemporal dementia remains limited. Here we investigated white matter tract damage using an unbiased, template‐based diffusion tensor imaging (DTI) protocol in a cohort of 27 patients with the behavioral variant of frontotemporal dementia (bvFTD) representing both major genetic and sporadic forms, in relation both to healthy individuals and to patients with Alzheimer's disease. Widespread white matter tract pathology was identified in the bvFTD group compared with both healthy controls and Alzheimer's disease group, with prominent involvement of uncinate fasciculus, cingulum bundle and corpus callosum. Relatively discrete and distinctive white matter profiles were associated with genetic subgroups of bvFTD associated with MAPT and C9ORF72 mutations. Comparing diffusivity metrics, optimal overall separation of the bvFTD group from the healthy control group was signalled using radial diffusivity, whereas optimal overall separation of the bvFTD group from the Alzheimer's disease group was signalled using fractional anisotropy. Comparing white matter changes with regional grey matter atrophy (delineated using voxel based morphometry) in the bvFTD cohort revealed co‐localisation between modalities particularly in the anterior temporal lobe, however white matter changes extended widely beyond the zones of grey matter atrophy. Our findings demonstrate a distributed signature of white matter alterations that is likely to be core to the pathophysiology of bvFTD and further suggest that this signature is modulated by underlying molecular pathologies. Hum Brain Mapp 35:4163–4179, 2014. © 2014 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
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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 [10]
The Results (Comparison of Diffusivity Metrics; page unavailable) report sensitivity and specificity ranging from moderate to high when distinguishing bvFTD from healthy controls or patients with Alzheimer’s disease. However, the paper reports its own diffusion-metric ROC analysis, not accuracy achieved by prior machine-learning studies. (LLM, conf=0.83)
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 [10]
The Methods section, “Sensitivity and Specificity of Diffusion Metrics in Predicting Group Membership” (page unavailable), describes ROC analysis of MRI-derived diffusivity metrics to classify individual participants as bvFTD, healthy controls, or Alzheimer’s disease. It does not describe a machine-learning technique or classification against other psychiatric or neurologic disorders. (LLM, conf=0.87)
Experimental — accuracy not yet measuredPrevious 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 [10]
The cingulum is affected in many neurological conditions, including multiple sclerosis, Parkinson's disease, behavioural variant frontotemporal dementia, Mild Cognitive Impairment, and Alzheimer's disease.
“The cingulum is affected in many neurological conditions, including multiple sclerosis, Parkinson’s disease, the behavioural variant of frontemporal dementia (Mahoney et al., 2014), Mild Cognitive Impairment (MCI), and Alzheimer’s disease (AD).”
From The cingulum bundle: Anatomy, function, and dysfunction (2018) · cites this paper as Mahoney et al., 2014
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Marian, O. C., Matis, S., Dobson‐Stone, C., Kim, W. S., Kwok, J. B., Piguet, O., Halliday, G. M., Landín-Romero, R., & Don, A. S. (2025) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.70131 · Cited by 2 · see citing works
- Authors
- Mahoney, C. J., Ridgway, G. R., Malone, I. B., Downey, L. E., Beck, J., Kinnunen, K. M., Schmitz, N., Golden, H. L., Rohrer, J. D., Schott, J. M., Rossor, M. N., Ourselin, S., Mead, S., Fox, N. C., & Warren, J. D.
- Affiliations
- UCL Queen Square Institute of Neurology (GB)
- Wellcome Centre for Human Neuroimaging (GB)
- MRC Prion Unit UCL Institute of Neurology University College London London United Kingdom
- Dementia Research Centre UCL Institute of Neurology University College London London United Kingdom
- MRC Prion Unit (GB)
- Corresponding author
- Mahoney, Colin J.
- ORCID
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2014-02-07
- Volume
- 35
- Issue
- 8
- Pages
- 4163-4179
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC4312919
- OpenAlex
- W2129981926
- Cited by
- 124
- References
- 65
- Citations by year
- 2026: 5 · 2025: 6 · 2024: 5 · 2023: 5 · 2022: 9 · 2021: 10 · 2020: 10 · 2019: 16 · 2018: 16 · 2017: 11 · 2016: 11 · 2015: 13 · 2014: 7
- FWCI
- 10.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Brain Mapping (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Research Portal (King's College London) (submittedVersion, cc-by)
- Funders
- Department of Health's NIHR Queen Square Dementia BRU Funding Scheme
- Medical Research Council, UK
- Wellcome Trust, MRC Programme Grant
- NIHR
- Wellcome Trust Senior Clinical Fellowship (091673/Z/10/Z)
- 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
- frontotemporal dementia, behavioral variant frontotemporal dementia, uncinate fasciculus, cingulum, corpus callosum, MAPT mutations, C9orf72 mutation, Alzheimer's disease, radial diffusivity, fractional anisotropy, gray matter atrophy, anterior temporal lobe, genetic subgroups
- MeSH terms
- C9orf72 Protein, Alzheimer Disease, Atrophy, Brain, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Mutation, Nerve Fibers, Myelinated, Proteins, Sensitivity and Specificity, Cohort Studies, tau Proteins, Anisotropy, Diffusion Tensor Imaging, Frontotemporal Dementia, Genotyping Techniques, White Matter, Gray Matter
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