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Your search keyword '"De Mendonça, Alexandre"' showing total 2 results
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2 results on '"De Mendonça, Alexandre"'

1. Cognitive composites for genetic frontotemporal dementia: GENFI-Cog

Author

Poos, Jackie M, Moore, Katrina M, Seelaar, Harro, Vandamme, Philip, Vandenbulcke, Mathieu, Ferreira, Catarina B, Miltenberger, Gabriel, Maruta, Carolina, Verdelho, Ana, Afonso, Sónia, Taipa, Ricardo, Caroppo, Paola, Di Fede, Giuseppe, Pijnenburg, Yolande A L, Giaccone, Giorgio, Prioni, Sara, Redaelli, Veronica, Rossi, Giacomina, Duro, Diana, Almeida, Maria Rosario, Castelo-Branco, Miguel, Leitão, Maria João, Tabuas-Pereira, Miguel, Santiago, Beatriz, Moreno, Fermin, Gauthier, Serge, Rosa-Neto, Pedro, Veldsman, Michele, Thompson, Paul, Langheinrich, Tobias, Prix, Catharina, Hoegen, Tobias, Wlasich, Elisabeth, Loosli, Sandra, Schonecker, Sonja, Sanchez-Valle, Raquel, Anderl-Straub, Sarah, Lombardi, Jolina, Bargalló, Nuria, Benussi, Alberto, Cantoni, Valentina, Bertoux, Maxime, Bertrand, Anne, Brice, Alexis, Camuzat, Agnès, Colliot, Olivier, Borroni, Barbara, Sayah, Sabrina, Funkiewiez, Aurélie, Rinaldi, Daisy, Lombardi, Gemma, Nacmias, Benedetta, Saracino, Dario, Bessi, Valentina, Ferrari, Camilla, Cañada, Marta, Deramecourt, Vincent, Laforce, Robert, Kuchcinski, Gregory, Lebouvier, Thibaud, Ourselin, Sebastien, Polito, Cristina, Rollin, Adeline, Masellis, Mario, Tartaglia, Carmela, Graff, Caroline, Galimberti, Daniela, Nicholas, Jennifer, Rowe, James B, Finger, Elizabeth, Synofzik, Matthis, Vandenberghe, Rik, de Mendonça, Alexandre, Tiraboschi, Pietro, Santana, Isabel, Ducharme, Simon, Butler, Chris, Gerhard, Alexander, Russell, Lucy L, Levin, Johannes, Danek, Adrian, Otto, Markus, Le Ber, Isabel, Pasquier, Florence, van Swieten, John C, Rohrer, Jonathan D, Initiative, Genetic FTD, Bouzigues, Arabella, Rossor, Martin N, Peakman, Georgia, Fox, Nick C, Warren, Jason D, Bocchetta, Martina, Swift, Imogen J, Shafei, Rachelle, Heller, Carolin, Todd, Emily, Cash, David, Woollacott, Ione, Zetterberg, Henrik, Convery, Rhian S, Nelson, Annabel, Guerreiro, Rita, Bras, Jose, Thomas, David L, Mead, Simon, Meeter, Lieke, Panman, Jessica, van Minkelen, Rick, Barandiaran, Myriam, Indakoetxea, Begoña, Jiskoot, Lize C, Gabilondo, Alazne, Tainta, Mikel, Gorostidi, Ana, Zulaica, Miren, Díez, Alina, Villanua, Jorge, Borrego-Ecija, Sergi, Olives, Jaume, Lladó, Albert, Balasa, Mircea, van der Ende, Emma, Antonell, Anna, Bargallo, Nuria, Premi, Enrico, Gazzina, Stefano, Gasparotti, Roberto, Archetti, Silvana, Black, Sandra, Mitchell, Sara, Rogaeva, Ekaterina, Freedman, Morris, van den Berg, Esther, Keren, Ron, Tang-Wai, David, Thonberg, Hakan, Öijerstedt, Linn, Andersson, Christin, Jelic, Vesna, Arighi, Andrea, Fenoglio, Chiara, Scarpini, Elio, Fumagalli, Giorgio, Papma, Janne M, Cope, Thomas, Timberlake, Carolyn, Rittman, Timothy, Shoesmith, Christen, Bartha, Robart, Rademakers, Rosa, Wilke, Carlo, Karnarth, Hans-Otto, Bender, Benjamin, Bruffaerts, Rose, Neurology, Amsterdam Neuroscience - Neurodegeneration, Medical Research Council, Clinical Genetics, Rowe, James [0000-0001-7216-8679], Rohrer, Jonathan D [0000-0002-6155-8417], and Apollo - University of Cambridge Repository

Subjects
PROGRESSION, Neuropsychological Tests, Composite score, Genetic FTD Initiative (GENFI), diagnosis [Frontotemporal Dementia], Cognition, CRITERIA, Attention, genetics [Frontotemporal Dementia], 11 Medical and Health Sciences, Language, Executive function, Frontotemporal dementia, Memory, Neuropsychology, Social cognition, Humans, Mutation, Prodromal Symptoms, Sample Size, tau Proteins, Frontotemporal Dementia, CARRIERS, Neurology, Psychology, Life Sciences & Biomedicine, CLINICAL-TRIALS, RC321-571, medicine.medical_specialty, Cognitive Neuroscience, Clinical Neurology, genetics [Mutation], Neurosciences. Biological psychiatry. Neuropsychiatry, Physical medicine and rehabilitation, Cog, mental disorders, medicine, ddc:610, RC346-429, Science & Technology, Research, Neurosciences, medicine.disease, genetics [tau Proteins], Neurosciences & Neurology, Neurology (clinical), Neurology. Diseases of the nervous system
Abstract

Funder: Alzheimer's Research UK, Funder: Alzheimer's Society, Funder: Brain Research UK, Funder: the Wolfson Foundation, Funder: NIHR UCL/H Biomedical Research Centre, Funder: Leonard Wolfson Experimental Neurology Centre Clinical Research Facility, Funder: UK Dementia Research Institute, Funder: UK Medical Research Council, Funder: Bluefield project, Funder: the Association for Frontotemporal Dementias Research Grant 2009, BACKGROUND: Clinical endpoints for upcoming therapeutic trials in frontotemporal dementia (FTD) are increasingly urgent. Cognitive composite scores are often used as endpoints but are lacking in genetic FTD. We aimed to create cognitive composite scores for genetic frontotemporal dementia (FTD) as well as recommendations for recruitment and duration in clinical trial design. METHODS: A standardized neuropsychological test battery covering six cognitive domains was completed by 69 C9orf72, 41 GRN, and 28 MAPT mutation carriers with CDR�� plus NACC-FTLD ��� 0.5 and 275 controls. Logistic regression was used to identify the combination of tests that distinguished best between each mutation carrier group and controls. The composite scores were calculated from the weighted averages of test scores in the models based on the regression coefficients. Sample size estimates were calculated for individual cognitive tests and composites in a theoretical trial aimed at preventing progression from a prodromal stage (CDR�� plus NACC-FTLD 0.5) to a fully symptomatic stage (CDR�� plus NACC-FTLD ��� 1). Time-to-event analysis was performed to determine how quickly mutation carriers progressed from CDR�� plus NACC-FTLD = 0.5 to ��� 1 (and therefore how long a trial would need to be). RESULTS: The results from the logistic regression analyses resulted in different composite scores for each mutation carrier group (i.e. C9orf72, GRN, and MAPT). The estimated sample size to detect a treatment effect was lower for composite scores than for most individual tests. A Kaplan-Meier curve showed that after 3 years, ~ 50% of individuals had converted from CDR�� plus NACC-FTLD 0.5 to ��� 1, which means that the estimated effect size needs to be halved in sample size calculations as only half of the mutation carriers would be expected to progress from CDR�� plus NACC FTLD 0.5 to ��� 1 without treatment over that time period. DISCUSSION: We created gene-specific cognitive composite scores for C9orf72, GRN, and MAPT mutation carriers, which resulted in substantially lower estimated sample sizes to detect a treatment effect than the individual cognitive tests. The GENFI-Cog composites have potential as cognitive endpoints for upcoming clinical trials. The results from this study provide recommendations for estimating sample size and trial duration.

Published
2022
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2. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

Author

Santana Isabel, Guerreiro Manuela, Rodrigues Ana, Silva Dina, Maroco João, and de Mendonça Alexandre

Subjects
Medicine, Biology (General), QH301-705.5, Science (General), Q1-390
Abstract

Abstract Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing.

Published
2011
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