Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 608-616 |
| Number of pages | 9 |
| Journal | Journal of Neurology, Neurosurgery and Psychiatry |
| Volume | 92 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1 Jun 2021 |
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MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia. / Manera, Ana L.; Dadar, Mahsa; van Swieten, John Cornelis et al.
In: Journal of Neurology, Neurosurgery and Psychiatry, Vol. 92, No. 6, 01.06.2021, p. 608-616.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
AU - Manera, Ana L.
AU - Dadar, Mahsa
AU - van Swieten, John Cornelis
AU - Borroni, Barbara
AU - Sanchez-Valle, Raquel
AU - Moreno, Fermin
AU - Laforce, Robert
AU - Graff, Caroline
AU - Synofzik, Matthis
AU - Galimberti, Daniela
AU - Rowe, James Benedict
AU - Masellis, Mario
AU - Tartaglia, Maria Carmela
AU - Finger, Elizabeth
AU - Vandenberghe, Rik
AU - de Mendonca, Alexandre
AU - Tagliavini, Fabrizio
AU - Santana, Isabel
AU - Butler, Christopher R.
AU - Gerhard, Alex
AU - Danek, Adrian
AU - Levin, Johannes
AU - Otto, Markus
AU - Frisoni, Giovanni
AU - Ghidoni, Roberta
AU - Sorbi, Sandro
AU - Rohrer, Jonathan Daniel
AU - Ducharme, Simon
AU - Louis Collins, D.
AU - Rosen, Howard
AU - Dickerson, Bradford C.
AU - Domoto-Reilly, Kimoko
AU - Knopman, David
AU - Boeve, Bradley F.
AU - Boxer, Adam L.
AU - Kornak, John
AU - Miller, Bruce L.
AU - Seeley, William W.
AU - Gorno-Tempini, Maria-Luisa
AU - McGinnis, Scott
AU - Mandelli, Maria Luisa
AU - Afonso, S. nia
AU - Almeida, Maria Rosario
AU - Anderl-Straub, Sarah
AU - Andersson, Christin
AU - Antonell, Anna
AU - Archetti, Silvana
AU - Arighi, Andrea
AU - Balasa, Mircea
AU - Barandiaran, Myriam
AU - Bargalló, Nuria
AU - Bartha, Robart
AU - Bender, Benjamin
AU - Benussi, Alberto
AU - Benussi, Luisa
AU - Bessi, Valentina
AU - Binetti, Giuliano
AU - Black, Sandra
AU - Bocchetta, Martina
AU - Borrego-Ecija, Sergi
AU - Bras, Jose
AU - Bruffaerts, Rose
AU - Caroppo, Paola
AU - Cash, David
AU - Castelo-Branco, Miguel
AU - Convery, Rhian
AU - Cope, Thomas
AU - Cosseddu, Maura
AU - de Arriba, María
AU - di Fede, Giuseppe
AU - Díaz, Zigor
AU - Duro, Diana
AU - Fenoglio, Chiara
AU - Ferrari, Camilla
AU - Ferreira, Carlos
AU - Ferreira, Catarina B.
AU - Flanagan, Toby
AU - Fox, Nick
AU - Freedman, Morris
AU - Fumagalli, Giorgio
AU - Gabilondo, Alazne
AU - Gasparotti, Roberto
AU - Gauthier, Serge
AU - Gazzina, Stefano
AU - Giaccone, Giorgio
AU - Gorostidi, Ana
AU - Greaves, Caroline
AU - Guerreiro, Rita
AU - Heller, Carolin
AU - Hoegen, Tobias
AU - Indakoetxea, Begoña
AU - Jelic, Vesna
AU - Jiskoot, Lize
AU - Karnath, Hans-Otto
AU - Keren, Ron
AU - Leitão, Maria João
AU - Lladó, Albert
AU - Lombardi, Gemma
AU - Loosli, Sandra
AU - Maruta, Carolina
AU - Mead, Simon
AU - Meeter, Lieke
AU - Miltenberger, Gabriel
AU - van Minkelen, Rick
AU - Mitchell, Sara
AU - Moore, Katrina M.
AU - Nacmias, Benedetta
AU - Neason, Mollie
AU - Nicholas, Jennifer
AU - Öijerstedt, Linn
AU - Olives, Jaume
AU - Ourselin, Sebastien
AU - Padovani, Alessandro
AU - Panman, Jessica
AU - Papma, Janne
AU - Peakman, Georgia
AU - Piaceri, Irene
AU - Pievani, Michela
AU - Pijnenburg, Yolande
AU - Polito, Cristina
AU - Premi, Enrico
AU - Prioni, Sara
AU - Prix, Catharina
AU - Rademakers, Rosa
AU - Redaelli, Veronica
AU - Rittman, Tim
AU - Rogaeva, Ekaterina
AU - Rosa-Neto, Pedro
AU - Rossi, Giacomina
AU - Rossor, Martin
AU - Santiago, Beatriz
AU - Scarpini, Elio
AU - Schönecker, Sonja
AU - Semler, Elisa
AU - Shafei, Rachelle
AU - Shoesmith, Christen
AU - Tábuas-Pereira, Miguel
AU - Tainta, Mikel
AU - Taipa, Ricardo
AU - Tang-Wai, David
AU - Thomas, David L.
AU - Thonberg, Hakan
AU - Timberlake, Carolyn
AU - Tiraboschi, Pietro
AU - Todd, Emily
AU - Vandamme, Philip
AU - Vandenbulcke, Mathieu
AU - Veldsman, Michele
AU - Verdelho, Ana
AU - FTLDNI Investigators, GENFI Consortium
AU - Villanua, Jorge
AU - Warren, Jason
AU - Wilke, Carlo
AU - Woollacott, Ione
AU - Wlasich, Elisabeth
AU - Zetterberg, Henrik
AU - Zulaica, Miren
N1 - Funding Information: Funding 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. Brain scan acquisition at the McConnell Brain Imaging was supported by the Brain Canada Foundation with support from Health Canada and the Canada Foundation for Innovation (CFI Project 34874). This work was supported by Italian Ministry of Health (CoEN015 and Ricerca Corrente). Funding Information: of Health Research (MOP-111169), les Fonds de Research Santé Quebec Pfizer Innovation fund, and an NSERC CREATE grant (4140438-2012). We would like to acknowledge funding from the Famille Louise & André Charron. Dr Ducharme receives salary funding from the Fonds de Recherche du Québec-Santé. Dr Collins is co-founder of True Positive Medical Devices. Dr Ducharme is the co-founder of Arctic Fox AI. Funding Information: Competing interests Dr Collins receives funding from the Canadian Institutes Publisher Copyright: © Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/6/1
Y1 - 2021/6/1
N2 - Introduction Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. Methods A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. Results Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. Conclusion Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.
AB - Introduction Structural brain imaging is paramount for the diagnosis of behavioural variant of frontotemporal dementia (bvFTD), but it has low sensitivity leading to erroneous or late diagnosis. Methods A total of 515 subjects from two different bvFTD cohorts (training and independent validation cohorts) were used to perform voxel-wise morphometric analysis to identify regions with significant differences between bvFTD and controls. A random forest classifier was used to individually predict bvFTD from deformation-based morphometry differences in isolation and together with semantic fluency. Tenfold cross validation was used to assess the performance of the classifier within the training cohort. A second held-out cohort of genetically confirmed bvFTD cases was used for additional validation. Results Average 10-fold cross-validation accuracy was 89% (82% sensitivity, 93% specificity) using only MRI and 94% (89% sensitivity, 98% specificity) with the addition of semantic fluency. In the separate validation cohort of definite bvFTD, accuracy was 88% (81% sensitivity, 92% specificity) with MRI and 91% (79% sensitivity, 96% specificity) with added semantic fluency scores. Conclusion Our results show that structural MRI and semantic fluency can accurately predict bvFTD at the individual subject level within a completely independent validation cohort coming from a different and independent database.
UR - http://www.scopus.com/inward/record.url?scp=85102678202&partnerID=8YFLogxK
U2 - 10.1136/jnnp-2020-324106
DO - 10.1136/jnnp-2020-324106
M3 - Article
C2 - 33722819
SN - 0022-3050
VL - 92
SP - 608
EP - 616
JO - Journal of Neurology, Neurosurgery and Psychiatry
JF - Journal of Neurology, Neurosurgery and Psychiatry
IS - 6
ER -