Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes

Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and semantic variant PPA. To better understand the FTD s...

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Bibliographic Details
Published in:Scientific reports Vol. 10; no. 1; p. 11237
Main Authors: Torso, M., Bozzali, M., Cercignani, M., Jenkinson, M., Chance, S. A.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 08.07.2020
Nature Publishing Group
Subjects:
631/378/1689/132
631/378/2612
Accuracy
Aged
Aphasia
Case-Control Studies
Cerebral Cortex - diagnostic imaging
Classification
Cohort Studies
Dementia
Dementia disorders
Diagnosis, Differential
Differential diagnosis
Diffusion Tensor Imaging
Feasibility Studies
Female
Frontotemporal dementia
Frontotemporal Dementia - diagnosis
Gray Matter - diagnostic imaging
Humanities and Social Sciences
Humans
Image Interpretation, Computer-Assisted - methods
Learning algorithms
Machine Learning
Magnetic resonance imaging
Male
Middle Aged
multidisciplinary
Science
Science (multidisciplinary)
Substantia grisea
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Fronto-temporal dementia (FTD) is a common type of presenile dementia, characterized by a heterogeneous clinical presentation that includes three main subtypes: behavioural-variant FTD, non-fluent/agrammatic variant primary progressive aphasia and semantic variant PPA. To better understand the FTD subtypes and develop more specific treatments, correct diagnosis is essential. This study aimed to test the discrimination power of a novel set of cortical Diffusion Tensor Imaging measures (DTI), on FTD subtypes. A total of 96 subjects with FTD and 84 healthy subjects (HS) were included in the study. A “selection cohort” was used to determine the set of features (measurements) and to use them to select the “best” machine learning classifier from a range of seven main models. The selected classifier was trained on a “training cohort” and tested on a third cohort (“test cohort”). The classifier was used to assess the classification power for binary (HS vs. FTD), and multiclass (HS and FTD subtypes) classification problems. In the binary classification, one of the new DTI features obtained the highest accuracy (85%) as a single feature, and when it was combined with other DTI features and two other common clinical measures (grey matter fraction and MMSE), obtained an accuracy of 88%. The new DTI features can distinguish between HS and FTD subgroups with an accuracy of 76%. These results suggest that DTI measures could support differential diagnosis in a clinical setting, potentially improve efficacy of new innovative drug treatments through effective patient selection, stratification and measurement of outcomes.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-68118-8