Using diffusion tensor imaging to detect cortical changes in fronto-temporal dementia subtypes.
Aged
Case-Control Studies
Cerebral Cortex
/ diagnostic imaging
Cohort Studies
Diagnosis, Differential
Diffusion Tensor Imaging
Feasibility Studies
Female
Frontotemporal Dementia
/ diagnosis
Gray Matter
/ diagnostic imaging
Humans
Image Interpretation, Computer-Assisted
/ methods
Machine Learning
Male
Middle Aged
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
08 07 2020
08 07 2020
Historique:
received:
17
01
2020
accepted:
21
05
2020
entrez:
10
7
2020
pubmed:
10
7
2020
medline:
22
12
2020
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 32641807
doi: 10.1038/s41598-020-68118-8
pii: 10.1038/s41598-020-68118-8
pmc: PMC7343779
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
11237Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NIA NIH HHS
ID : R01 AG032306
Pays : United States
Organisme : Wellcome Trust
ID : 203139/Z/16/Z
Pays : United Kingdom
Organisme : Department of Health
Pays : United Kingdom
Références
Vieira, R. T. et al. Epidemiology of early-onset dementia: a review of the literature. Clin. Pract. Epidemiol. Mental Health: CP & EMH 9, 88 (2013).
doi: 10.2174/1745017901309010088
Kersaitis, C., Halliday, G. M. & Kril, J. J. Regional and cellular pathology in frontotemporal dementia: relationship to stage of disease in cases with and without Pick bodies. Acta Neuropathol. 108(6), 515–523 (2004).
doi: 10.1007/s00401-004-0917-0
Bang, J., Spina, S. & Miller, B. L. Frontotemporal dementia. Lancet 386(10004), 1672–1682 (2015).
doi: 10.1016/S0140-6736(15)00461-4
Maruyama, M. et al. Imaging of tau pathology in a tauopathy mouse model and in Alzheimer patients compared to normal controls. Neuron 79(6), 1094–1108 (2013).
doi: 10.1016/j.neuron.2013.07.037
Smailagic, N. et al. 18 F‐FDG PET for the early diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database of Syst. Rev. (1) (2015).
Sheikh-Bahaei, N., Sajjadi, S. A. & Pierce, A. L. Current role for biomarkers in clinical diagnosis of Alzheimer disease and frontotemporal dementia. Curr. Treat. Options Neurol. 19(12), 46 (2017).
doi: 10.1007/s11940-017-0484-z
Iaccarino, L., Sala, A., Caminiti, S. P. & Perani, D. The emerging role of PET imaging in dementia. F1000Research, 6 (2017).
Rivero-Santana, A. et al. Cerebrospinal fluid biomarkers for the differential diagnosis between Alzheimer’s disease and frontotemporal lobar degeneration: systematic review, HSROC analysis, and confounding factors. J. Alzheimer’s Dis. 55(2), 625–644 (2017).
doi: 10.3233/JAD-160366
Chance, S. A. et al. Microanatomical correlates of cognitive ability and decline: normal ageing, MCI, and Alzheimer’s disease. Cereb. Cortex 21(8), 1870–1878 (2011).
doi: 10.1093/cercor/bhq264
van Veluw, S. J. et al. Prefrontal cortex cytoarchitecture in normal aging and Alzheimer’s disease: a relationship with IQ. Brain Struct. Funct. 217(4), 797–808 (2012).
doi: 10.1007/s00429-012-0381-x
Chance, S. A., Casanova, M. F., Switala, A. E., Crow, T. J. & Esiri, M. M. Minicolumn thinning in temporal lobe association cortex but not primary auditory cortex in normal human ageing. Acta Neuropathol. 111(5), 459–464 (2006).
doi: 10.1007/s00401-005-0014-z
McKavanagh, R. et al. Relating diffusion tensor imaging measurements to microstructural quantities in the cerebral cortex in multiple sclerosis. Hum. Brain Mapp. 40(15), 4417–4431 (2019).
doi: 10.1002/hbm.24711
Zhang, Y. et al. White matter damage in frontotemporal dementia and Alzheimer’s disease measured by diffusion MRI. Brain 132(9), 2579–2592 (2009).
doi: 10.1093/brain/awp071
Ahmed, M. R. et al. Neuroimaging and machine learning for dementia diagnosis: recent advancements and future prospects. IEEE Rev. Biomed. Eng. 12, 19–33 (2018).
doi: 10.1109/RBME.2018.2886237
Pellegrini, E. et al. Machine learning of neuroimaging for assisted diagnosis of cognitive impairment and dementia: a systematic review. Alzheimer’s Dementia: Diagn. Assess. Dis. Monitor. 10, 519–535 (2018).
Rascovsky, K. et al. Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134(9), 2456–2477 (2011).
doi: 10.1093/brain/awr179
Gorno-Tempini, M. L. et al. Classification of primary progressive aphasia and its variants. Neurology 76(11), 1006–1014 (2011).
doi: 10.1212/WNL.0b013e31821103e6
Baum, G. L. et al. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. Neuroimage 173, 275–286 (2018).
doi: 10.1016/j.neuroimage.2018.02.041
von Economo, C. F. & Koskinas, G. N. Die cytoarchitektonik der hirnrinde des erwachsenen menschen. J. Springer (1925).
Reuter, M. et al. Head motion during MRI acquisition reduces gray matter volume and thickness estimates. Neuroimage 107, 107–115 (2015).
doi: 10.1016/j.neuroimage.2014.12.006
Benjamini, Y. & Yekutieli, D. The control of the false discovery rate in multiple testing under dependency. Ann. Stat. 29(4), 1165–1188 (2001).
doi: 10.1214/aos/1013699998
Bron, E. E. et al. Multiparametric computer-aided differential diagnosis of Alzheimer’s disease and frontotemporal dementia using structural and advanced MRI. Eur. Radiol. 27(8), 3372–3382 (2017).
doi: 10.1007/s00330-016-4691-x
Koikkalainen, J. et al. Differential diagnosis of neurodegenerative diseases using structural MRI data. NeuroImage: Clin. 11, 435–449 (2016).
doi: 10.1016/j.nicl.2016.02.019
Feis, R. A. et al. Single-subject classification of presymptomatic frontotemporal dementia mutation carriers using multimodal MRI. NeuroImage: Clin. 20, 188–196 (2018).
doi: 10.1016/j.nicl.2018.07.014
Mion, M. et al. What the left and right anterior fusiform gyri tell us about semantic memory. Brain 133(11), 3256–3268 (2010).
doi: 10.1093/brain/awq272
Chadwick, M. J. et al. Semantic representations in the temporal pole predict false memories. Proc. Natl. Acad. Sci. 113(36), 10180–10185 (2016).
doi: 10.1073/pnas.1610686113
Irish, M., Hodges, J. R. & Piguet, O. Right anterior temporal lobe dysfunction underlies theory of mind impairments in semantic dementia. Brain 137(4), 1241–1253 (2014).
doi: 10.1093/brain/awu003
Chan, D. et al. Patterns of temporal lobe atrophy in semantic dementia and Alzheimer’s disease. Ann. Neurol. 49(4), 433–442 (2001).
doi: 10.1002/ana.92
Craig, A. D. & Craig, A. D. How do you feel-now? The anterior insula and human awareness. Nat. Rev. Neurosci. 10(1) (2009).
Singer, T., Critchley, H. D. & Preuschoff, K. A common role of insula in feelings, empathy and uncertainty. Trends Cognit. Sci. 13(8), 334–340 (2009).
doi: 10.1016/j.tics.2009.05.001
Mandelli, M. L. et al. Two insular regions are differentially involved in behavioral variant FTD and nonfluent/agrammatic variant PPA. Cortex 74, 149–157 (2016).
doi: 10.1016/j.cortex.2015.10.012
Kitada, R., Johnsrude, I. S., Kochiyama, T. & Lederman, S. J. Brain networks involved in haptic and visual identification of facial expressions of emotion: an fMRI study. Neuroimage 49(2), 1677–1689 (2010).
doi: 10.1016/j.neuroimage.2009.09.014
Moretti, R. & Signori, R. Neural correlates for apathy: frontal-prefrontal and parietal cortical-subcortical circuits. Front. Aging Neurosci. 8, 289 (2016).
pubmed: 28018207
pmcid: 5145860
Young, J. J., Lavakumar, M., Tampi, D., Balachandran, S. & Tampi, R. R. Frontotemporal dementia: latest evidence and clinical implications. Therap. Adv. Psychopharmacol. 8(1), 33–48 (2018).
doi: 10.1177/2045125317739818
Mandelli, M. L. et al. Healthy brain connectivity predicts atrophy progression in non-fluent variant of primary progressive aphasia. Brain 139(10), 2778–2791 (2016).
doi: 10.1093/brain/aww195
Japee, S., Holiday, K., Satyshur, M. D., Mukai, I. & Ungerleider, L. G. A role of right middle frontal gyrus in reorienting of attention: a case study. Front. Syst. Neurosci. 9, 23 (2015).
doi: 10.3389/fnsys.2015.00023
Rohrer, J. D. et al. Mapping the progression of progranulin-associated frontotemporal lobar degeneration. Nat. Rev. Neurol. 4(8), 455 (2008).
doi: 10.1038/ncpneuro0869
Omer, T. et al. Neuroimaging patterns along the ALS-FTD spectrum: a multiparametric imaging study. Amyotroph. Lateral Scler. Frontotemporal Degener. 18(7–8), 611–623 (2017).
doi: 10.1080/21678421.2017.1332077