TY - JOUR
T1 - Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes
AU - Torso, M.
AU - Bozzali, M.
AU - Cercignani, M.
AU - Jenkinson, M.
AU - Chance, S. A.
N1 - Funding Information:
Data collection and sharing for this project was funded by the Frontotemporal Lobar Degeneration Neuroimaging Initiative (National Institutes of Health Grant R01 AG032306). The study is coordinated through the University of California, San Francisco, Memory and Aging Center. FTLDNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. Study Funded by Alzheimer’s Research UK (Network Pilot Award to MT, and Pilot Project Grant ARUK-PPG2017A-19 to SC). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z) from which M.J received funding. M.J. is also supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC).
Publisher Copyright:
© 2020, The Author(s).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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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U2 - 10.1038/s41598-020-68118-8
DO - 10.1038/s41598-020-68118-8
M3 - Article
C2 - 32641807
AN - SCOPUS:85087713527
VL - 10
JO - Scientific Reports
JF - Scientific Reports
SN - 2045-2322
IS - 1
M1 - 11237
ER -