Data‐driven staging of genetic frontotemporal dementia using multi‐modal MRI. Issue 6 (3rd February 2022)

• Record Type: Journal Article
• Authors: McCarthy, Jillian; Borroni, Barbara; Sanchez‐Valle, Raquel; Moreno, Fermin; Laforce, Robert; Graff, Caroline; Synofzik, Matthis; Galimberti, Daniela; Rowe, James B.; Masellis, Mario; Tartaglia, Maria Carmela; Finger, Elizabeth; Vandenberghe, Rik; de Mendonça, Alexandre; Tagliavini, Fabrizio; Santana, Isabel; Butler, Chris; Gerhard, Alex; Danek, Adrian; Levin, Johannes; Otto, Markus; Frisoni, Giovanni; Ghidoni, Roberta; Sorbi, Sandro; Jiskoot, Lize C.; Seelaar, Harro; van Swieten, John C.; Rohrer, Jonathan D.; Iturria‐Medina, Yasser; Ducharme, Simon
• Other Names: Afonso Sónia investigator.; Almeida Maria Rosario investigator.; Anderl‐Straub Sarah investigator.; Andersson Christin investigator.; Antonell Anna investigator.; Archetti Silvana investigator.; Arighi Andrea investigator.; Balasa Mircea investigator.; Barandiaran Myriam investigator.; Bargalló Nuria investigator.; Bartha Robart investigator.; Bender Benjamin investigator.; Benussi Alberto investigator.; Benussi Luisa investigator.; Bessi Valentina investigator.; Binetti Giuliano investigator.; Black Sandra investigator.; Bocchetta Martina investigator.; Borrego‐Ecija Sergi investigator.; Bras Jose investigator.; Bruffaerts Rose investigator.; Cañada Marta investigator.; Cantoni Valentina investigator.; Caroppo Paola investigator.; Cash David investigator.; Castelo‐Branco Miguel investigator.; Convery Rhian investigator.; Cope Thomas investigator.; Cosseddu Maura investigator.; de Arriba María investigator.; Di Fede Giuseppe investigator.; Díaz Zigor investigator.; Díez Alina investigator.; Duro Diana investigator.; Fenoglio Chiara investigator.; Ferrari Camilla investigator.; Ferreira Carlos investigator.; Ferreira Catarina B. investigator.; Flanagan Toby investigator.; Fox Nick investigator.; Freedman Morris investigator.; Fumagalli Giorgio investigator.; Gabilondo Alazne investigator.; Gasparotti Roberto investigator.; Gauthier Serge investigator.; Gazzina Stefano investigator.; Giaccone Giorgio investigator.; Gorostidi Ana investigator.; Greaves Caroline investigator.; Guerreiro Rita investigator.; Heller Carolin investigator.; Hoegen Tobias investigator.; Indakoetxea Begoña investigator.; Jelic Vesna investigator.; Karnath Hans‐Otto investigator.; Keren Ron investigator.; Langheinrich Tobias investigator.; Leitão Maria João investigator.; Lladó Albert investigator.; Lombardi Gemma investigator.; Loosli Sandra investigator.; Maruta Carolina investigator.; Mead Simon investigator.; Meeter Lieke investigator.; Miltenberger Gabriel investigator.; van Minkelen Rick investigator.; Mitchell Sara investigator.; Moore Katrina M investigator.; Nacmias Benedetta investigator.; Neason Mollie investigator.; Nicholas Jennifer investigator.; Öijerstedt Linn investigator.; Olives Jaume investigator.; Ourselin Sebastien investigator.; Padovani Alessandro investigator.; Panman Jessica investigator.; Papma Janne investigator.; Peakman Georgia investigator.; Piaceri Irene investigator.; Pievani Michela investigator.; Pijnenburg Yolande investigator.; Polito Cristina investigator.; Premi Enrico investigator.; Prioni Sara investigator.; Prix Catharina investigator.; Rademakers Rosa investigator.; Redaelli Veronica investigator.; Rittman Tim investigator.; Rogaeva Ekaterina investigator.; Rosa‐Neto Pedro investigator.; Rossi Giacomina investigator.; Rossor Martin investigator.; Santiago Beatriz investigator.; Scarpini Elio investigator.; Schönecker Sonja investigator.; Semler Elisa investigator.; Shafei Rachelle investigator.; Shoesmith Christen investigator.; Tábuas‐Pereira Miguel investigator.; Tainta Mikel investigator.; Taipa Ricardo investigator.; Tang‐Wai David investigator.; Thomas David L investigator.; Thompson Paul investigator.; Thonberg Hakan investigator.; Timberlake Carolyn investigator.; Tiraboschi Pietro investigator.; Todd Emily investigator.; Vandamme Philip investigator.; Vandenbulcke Mathieu investigator.; Veldsman Michele investigator.; Verdelho Ana investigator.; Villanua Jorge investigator.; Warren Jason investigator.; Wilke Carlo investigator.; Woollacott Ione investigator.; Wlasich Elisabeth investigator.; Zetterberg Henrik investigator.; Zulaica Miren investigator.;
• Is Part Of: Human brain mapping. Volume 43:Issue 6(2022)
• Page Start: 1821
• Page End: 1835
• Publication Date: 2022-02-03
• Subjects: disease progression -- frontotemporal dementia -- magnetic resonance imaging -- unsupervised machine learning; Brain mapping -- Periodicals; 611.81
• DOI: 10.1002/hbm.25727
• Languages: English
• ISSNs: 1065-9471

Abstract

Abstract: Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high‐dimensional large‐scale population datasets to obtain individual scores of disease stage. We used cross‐sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting‐state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI‐obtained disease scores to the estimated years to onset (age—mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre‐dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data‐driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics. Abstract : Unifying methods to stage genetic frontotemporal dementia during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. We applied an unsupervised machine learning algorithm [the contrastive trajectory inference (cTI)] to multi‐modal MRI from presymptomatic and symptomatic carriers of FTD‐causing mutations to obtain individual scores of disease stage. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. This study provides a proof of concept that cTI can identify data‐driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics.