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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Vydané v:	Scientific reports Ročník 10; číslo 1; s. 11237
Hlavní autori:	Torso, M., Bozzali, M., Cercignani, M., Jenkinson, M., Chance, S. A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London Nature Publishing Group UK 08.07.2020 Nature Publishing Group
Predmet:	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
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-020-68118-8