MRI data-driven algorithm for the diagnosis of behavioural variant frontotemporal dementia
Creators: name: Manera, Ana L
name: Dadar, Mahsa
name: Van Swieten, John Cornelis
name: Borroni, Barbara
name: Sanchez-Valle, Raquel
name: Moreno, Fermin
name: Laforce Jr, Robert
name: Graff, Caroline
name: Synofzik, Matthis
name: Galimberti, Daniela
name: Rowe, James Benedict
name: Masellis, Mario
name: Tartaglia, Maria Carmela
name: Finger, Elizabeth
name: Vandenberghe, Rik
name: De Mendonca, Alexandre
name: Tagliavini, Fabrizio
name: Santana, Isabel
name: Butler, Christopher R
name: Gerhard, Alex
name: Danek, Adrian
name: Levin, Johannes
name: Otto, Markus
name: Frisoni, Giovanni
name: Ghidoni, Roberta
name: Sorbi, Sandro
name: Rohrer, Jonathan Daniel
name: Ducharme, Simon
name: Collins, D Louis
name: FTLDNI Investigators
name: Rosen, Howard
name: Dickerson, Bradford C.
name: Domoto-Reilly, Kimoko
name: Knopman, David
name: Boeve, Bradley F.
name: Boxer, Adam L.
name: Kornak, John
name: Miller, Bruce L.
name: Seeley, William W.
name: Gorno-Tempini, Maria-Luisa
name: McGinnis, Scott
name: Mandelli, Maria Luisa
name: GENFI Consortium
name: Afonso, Sónia
name: Almeida, Maria Rosario
name: Anderl-Straub, Sarah
name: Andersson, Christin
name: Antonell, Anna
name: Archetti, Silvana
name: Arighi, Andrea
name: Balasa, Mircea
name: Barandiaran, Myriam
name: Bargalló, Nuria
name: Bartha, Robart
name: Bender, Benjamin
name: Benussi, Alberto
name: Benussi, Luisa
name: Bessi, Valentina
name: Binetti, Giuliano
name: Black, Sandra
name: Bocchetta, Martina
name: Borrego-Ecija, Sergi
name: Bras, Jose
name: Bruffaerts, Rose
name: Caroppo, Paola
name: Cash, David
name: Castelo-Branco, Miguel
name: Convery, Rhian
name: Cope, Thomas
name: Cosseddu, Maura
name: Arriba, María De
name: Fede, Giuseppe Di
name: Díaz, Zigor
name: Duro, Diana
name: Fenoglio, Chiara
name: Ferrari, Camilla
name: Ferreira, Carlos
name: Ferreira, Catarina B.
name: Flanagan, Toby
name: Fox, Nick
name: Freedman, Morris
name: Fumagalli, Giorgio
name: Gabilondo, Alazne
name: Gasparotti, Roberto
name: Gauthier, Serge
name: Gazzina, Stefano
name: Giaccone, Giorgio
name: Gorostidi, Ana
name: Greaves, Caroline
name: Guerreiro, Rita
name: Heller, Carolin
name: Hoegen, Tobias
name: Indakoetxea, Begoña
name: Jelic, Vesna
name: Jiskoot, Lize
name: Karnath, Hans-Otto
name: Keren, Ron
name: Leitão, Maria João
name: Lladó, Albert
name: Lombardi, Gemma
name: Loosli, Sandra
name: Maruta, Carolina
name: Mead, Simon
name: Meeter, Lieke
name: Miltenberger, Gabriel
name: Minkelen, Rick Van
name: Mitchell, Sara
name: Moore, Katrina M
name: Nacmias, Benedetta
name: Neason, Mollie
name: Nicholas, Jennifer
name: Öijerstedt, Linn
name: Olives, Jaume
name: Ourselin, Sebastien
name: Padovani, Alessandro
name: Panman, Jessica
name: Papma, Janne
name: Peakman, Georgia
name: Piaceri, Irene
name: Pievani, Michela
name: Pijnenburg, Yolande
name: Polito, Cristina
name: Premi, Enrico
name: Prioni, Sara
name: Prix, Catharina
name: Rademakers, Rosa
name: Redaelli, Veronica
name: Rittman, Tim
name: Rogaeva, Ekaterina
name: Rosa-Neto, Pedro
name: Rossi, Giacomina
name: Rossor, Martin
name: Santiago, Beatriz
name: Scarpini, Elio
name: Schönecker, Sonja
name: Semler, Elisa
name: Shafei, Rachelle
name: Shoesmith, Christen
name: Tábuas-Pereira, Miguel
name: Tainta, Mikel
name: Taipa, Ricardo
name: Tang-Wai, David
name: Thomas, David L
name: Thonberg, Hakan
name: Timberlake, Carolyn
name: Tiraboschi, Pietro
name: Todd, Emily
name: Vandamme, Philip
name: Vandenbulcke, Mathieu
name: Veldsman, Michele
name: Verdelho, Ana
name: Villanua, Jorge
name: Warren, Jason
name: Wilke, Carlo
name: Woollacott, Ione
name: Wlasich, Elisabeth
name: Zetterberg, Henrik
name: Zulaica, Miren
Publisher: name: Apollo - University of Cambridge Repository
Year: 2021
Subjects: Cognitive neurology

description: 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.