Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neu...
- Autores
- Russo, María Julieta; Campos, Jorge; Vázquez, Silvia; Sevlever, Gustavo; Allegri, Ricardo Francisco
- Año de publicación
- 2017
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.
Fil: Russo, María Julieta. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Campos, Jorge. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina
Fil: Vázquez, Silvia. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina
Fil: Sevlever, Gustavo. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Allegri, Ricardo Francisco. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina - Materia
-
ALZHEIMER'S DISEASE
DISEASE PROGRESSION
MEMORY
MILD COGNITIVE IMPAIRMENT
RECOGNITION DISCRIMINABILITY
SIGNAL DETECTION THEORY - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/40823
Ver los metadatos del registro completo
id |
CONICETDig_1773fa77a585a6c2534a405a342921fa |
---|---|
oai_identifier_str |
oai:ri.conicet.gov.ar:11336/40823 |
network_acronym_str |
CONICETDig |
repository_id_str |
3498 |
network_name_str |
CONICET Digital (CONICET) |
spelling |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging InitiativeRusso, María JulietaCampos, JorgeVázquez, SilviaSevlever, GustavoAllegri, Ricardo FranciscoALZHEIMER'S DISEASEDISEASE PROGRESSIONMEMORYMILD COGNITIVE IMPAIRMENTRECOGNITION DISCRIMINABILITYSIGNAL DETECTION THEORYhttps://purl.org/becyt/ford/3.2https://purl.org/becyt/ford/3Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.Fil: Russo, María Julieta. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Campos, Jorge. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; ArgentinaFil: Vázquez, Silvia. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; ArgentinaFil: Sevlever, Gustavo. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Allegri, Ricardo Francisco. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFrontiers Research Foundation2017-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/40823Russo, María Julieta; Campos, Jorge; Vázquez, Silvia; Sevlever, Gustavo; Allegri, Ricardo Francisco; Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative; Frontiers Research Foundation; Frontiers in Aging Neuroscience; 9; MAR; 3-2017; 1-71663-4365CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.3389/fnagi.2017.00046info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnagi.2017.00046/fullinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:48:28Zoai:ri.conicet.gov.ar:11336/40823instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 09:48:28.548CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
dc.title.none.fl_str_mv |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
title |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
spellingShingle |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative Russo, María Julieta ALZHEIMER'S DISEASE DISEASE PROGRESSION MEMORY MILD COGNITIVE IMPAIRMENT RECOGNITION DISCRIMINABILITY SIGNAL DETECTION THEORY |
title_short |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
title_full |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
title_fullStr |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
title_full_unstemmed |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
title_sort |
Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative |
dc.creator.none.fl_str_mv |
Russo, María Julieta Campos, Jorge Vázquez, Silvia Sevlever, Gustavo Allegri, Ricardo Francisco |
author |
Russo, María Julieta |
author_facet |
Russo, María Julieta Campos, Jorge Vázquez, Silvia Sevlever, Gustavo Allegri, Ricardo Francisco |
author_role |
author |
author2 |
Campos, Jorge Vázquez, Silvia Sevlever, Gustavo Allegri, Ricardo Francisco |
author2_role |
author author author author |
dc.subject.none.fl_str_mv |
ALZHEIMER'S DISEASE DISEASE PROGRESSION MEMORY MILD COGNITIVE IMPAIRMENT RECOGNITION DISCRIMINABILITY SIGNAL DETECTION THEORY |
topic |
ALZHEIMER'S DISEASE DISEASE PROGRESSION MEMORY MILD COGNITIVE IMPAIRMENT RECOGNITION DISCRIMINABILITY SIGNAL DETECTION THEORY |
purl_subject.fl_str_mv |
https://purl.org/becyt/ford/3.2 https://purl.org/becyt/ford/3 |
dc.description.none.fl_txt_mv |
Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI. Fil: Russo, María Julieta. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Campos, Jorge. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina Fil: Vázquez, Silvia. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina Fil: Sevlever, Gustavo. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Allegri, Ricardo Francisco. Fundación para la Lucha Contra las Enfermedades Neurológicas de la Infancia. Instituto de Investigaciones Neurológicas "Raúl Carrea"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina |
description |
Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-03 |
dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
format |
article |
status_str |
publishedVersion |
dc.identifier.none.fl_str_mv |
http://hdl.handle.net/11336/40823 Russo, María Julieta; Campos, Jorge; Vázquez, Silvia; Sevlever, Gustavo; Allegri, Ricardo Francisco; Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative; Frontiers Research Foundation; Frontiers in Aging Neuroscience; 9; MAR; 3-2017; 1-7 1663-4365 CONICET Digital CONICET |
url |
http://hdl.handle.net/11336/40823 |
identifier_str_mv |
Russo, María Julieta; Campos, Jorge; Vázquez, Silvia; Sevlever, Gustavo; Allegri, Ricardo Francisco; Adding Recognition Discriminability Index to the Delayed Recall Is Useful to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative; Frontiers Research Foundation; Frontiers in Aging Neuroscience; 9; MAR; 3-2017; 1-7 1663-4365 CONICET Digital CONICET |
dc.language.none.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.3389/fnagi.2017.00046 info:eu-repo/semantics/altIdentifier/url/https://www.frontiersin.org/articles/10.3389/fnagi.2017.00046/full |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
dc.format.none.fl_str_mv |
application/pdf application/pdf application/pdf application/pdf |
dc.publisher.none.fl_str_mv |
Frontiers Research Foundation |
publisher.none.fl_str_mv |
Frontiers Research Foundation |
dc.source.none.fl_str_mv |
reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
reponame_str |
CONICET Digital (CONICET) |
collection |
CONICET Digital (CONICET) |
instname_str |
Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.name.fl_str_mv |
CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
repository.mail.fl_str_mv |
dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
_version_ |
1842268925596794880 |
score |
13.13397 |