There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Robert Laforce |
| Institution: | CHU de Québec - Université Laval |
| Department: | Département des Sciences Neurologiques |
| Country: | |
| Proposed Analysis: | This project follows a previously published project in the Canadian Medical Association Journal (see Bernier et al. 2017). We aimed to generate ready-to-use cognitive charts for follow-up of age-associated cognitive decline, analogous to pediatric growth charts based on key predictors (i.e. age and education) of incipient decline in the Mini-Mental State Examination (MMSE), to allow simple clinical follow-up of age-associated cognitive decline by first-line physicians using the MMSE. Our project remains similar: we want to generate cognitive charts, but using the Montreal Cognitive Assessment (MoCA). Indeed, the MoCA is deemed more sensitive regarding the screening of cognitive disorders in patients according to the literature. Using this data, we plan to create even more sensitive cognitive charts that will be able to track the decline of patients over time with the score on the MoCA. Using such a model will allow us to expand possibilities for clinical settings country-wide for longitudinal follow-up of patients both in primary and tertiary care through earlier management of disorders. To develop and test the model we will obtain access to various databases across the world containing normal controls and patients. Per the initial project, specific analyses for this new project will include: - Building a model to predict scores for the MoCA in healthy controls, taking into account age and education, using repeated-measures regression analyses. We will then transform this model into a simplified linear form for use in generating the cognitive charts. Our main purpose is not to provide a prediction model for the MoCA but to linearize its relationship to age and education, the 2 predominant factors of relevance to change in this test over time. - Testing the model on external databases to validate the generalizability of the model (participants from local research networks, international open databases, etc.). Appropriate legal and ethical methods will be undertaken to obtain those databases. - Comparing, when the model will have been validated in normal subjects, sensitivities and specificities based on the cognitive charts to those based on the most widely recognized Montreal Cognitive Assessment cut point (< 26) using both study populations (normal subjects and individuals with a diagnosis), as well as subgroups of age, education and baseline test score. Because our algorithm aimed at using multiple measures to monitor cognitive decline (as opposed to cut-offs), we compared its performance to that of the score of the last follow-up visit. Finally, we will construct a receiver operating characteristic (ROC) curve for the MoCA with 95% bootstrap confidence bands and the diagnostic accuracy of the cognitive charts, and highlighted the diagnostic accuracy of the suggested MoCA cut-off on the cognitive charts. - Creating a composite model from both the MMSE and MoCA scores and validating this model in the same manner described above, to verify if the model will be more accurate than the MMSE or MoCA alone in characterizing patients’ scores. |
| Additional Investigators | |
| Investigator's Name: | Patrick Bernier |
| Proposed Analysis: | This project follows a previously published project in the Canadian Medical Association Journal (see Bernier et al. 2017). We aimed to generate ready-to-use cognitive charts for follow-up of age-associated cognitive decline, analogous to pediatric growth charts based on key predictors (i.e. age and education) of incipient decline in the Mini-Mental State Examination (MMSE), to allow simple clinical follow-up of age-associated cognitive decline by first-line physicians using the MMSE. Our project remains similar: we want to generate cognitive charts, but using the Montreal Cognitive Assessment (MoCA). Indeed, the MoCA is deemed more sensitive regarding the screening of cognitive disorders in patients according to the literature. Using this data, we plan to create even more sensitive cognitive charts that will be able to track the decline of patients over time with the score on the MoCA. Using such a model will allow us to expand possibilities for clinical settings country-wide for longitudinal follow-up of patients both in primary and tertiary care through earlier management of disorders. To develop and test the model we will obtain access to various databases across the world containing normal controls and patients. Per the initial project, specific analyses for this new project will include: - Building a model to predict scores for the MoCA in healthy controls, taking into account age and education, using repeated-measures regression analyses. We will then transform this model into a simplified linear form for use in generating the cognitive charts. Our main purpose is not to provide a prediction model for the MoCA but to linearize its relationship to age and education, the 2 predominant factors of relevance to change in this test over time. - Testing the model on external databases to validate the generalizability of the model (participants from local research networks, international open databases, etc.). Appropriate legal and ethical methods will be undertaken to obtain those databases. - Comparing, when the model will have been validated in normal subjects, sensitivities and specificities based on the cognitive charts to those based on the most widely recognized Montreal Cognitive Assessment cut point (< 26) using both study populations (normal subjects and individuals with a diagnosis), as well as subgroups of age, education and baseline test score. Because our algorithm aimed at using multiple measures to monitor cognitive decline (as opposed to cut-offs), we compared its performance to that of the score of the last follow-up visit. Finally, we will construct a receiver operating characteristic (ROC) curve for the MoCA with 95% bootstrap confidence bands and the diagnostic accuracy of the cognitive charts, and highlighted the diagnostic accuracy of the suggested MoCA cut-off on the cognitive charts. - Creating a composite model from both the MMSE and MoCA scores and validating this model in the same manner described above, to verify if the model will be more accurate than the MMSE or MoCA alone in characterizing patients’ scores. |
| Investigator's Name: | Christian Gourdeau |
| Proposed Analysis: | This project follows a previously published project in the Canadian Medical Association Journal (see Bernier et al. 2017). We aimed to generate ready-to-use cognitive charts for follow-up of age-associated cognitive decline, analogous to pediatric growth charts based on key predictors (i.e. age and education) of incipient decline in the Mini-Mental State Examination (MMSE), to allow simple clinical follow-up of age-associated cognitive decline by first-line physicians using the MMSE. Our project remains similar: we want to generate cognitive charts, but using the Montreal Cognitive Assessment (MoCA). Indeed, the MoCA is deemed more sensitive regarding the screening of cognitive disorders in patients according to the literature. Using this data, we plan to create even more sensitive cognitive charts that will be able to track the decline of patients over time with the score on the MoCA. Using such a model will allow us to expand possibilities for clinical settings country-wide for longitudinal follow-up of patients both in primary and tertiary care through earlier management of disorders. To develop and test the model we will obtain access to various databases across the world containing normal controls and patients. Per the initial project, specific analyses for this new project will include: - Building a model to predict scores for the MoCA in healthy controls, taking into account age and education, using repeated-measures regression analyses. We will then transform this model into a simplified linear form for use in generating the cognitive charts. Our main purpose is not to provide a prediction model for the MoCA but to linearize its relationship to age and education, the 2 predominant factors of relevance to change in this test over time. - Testing the model on external databases to validate the generalizability of the model (participants from local research networks, international open databases, etc.). Appropriate legal and ethical methods will be undertaken to obtain those databases. - Comparing, when the model will have been validated in normal subjects, sensitivities and specificities based on the cognitive charts to those based on the most widely recognized Montreal Cognitive Assessment cut point (< 26) using both study populations (normal subjects and individuals with a diagnosis), as well as subgroups of age, education and baseline test score. Because our algorithm aimed at using multiple measures to monitor cognitive decline (as opposed to cut-offs), we compared its performance to that of the score of the last follow-up visit. Finally, we will construct a receiver operating characteristic (ROC) curve for the MoCA with 95% bootstrap confidence bands and the diagnostic accuracy of the cognitive charts, and highlighted the diagnostic accuracy of the suggested MoCA cut-off on the cognitive charts. - Creating a composite model from both the MMSE and MoCA scores and validating this model in the same manner described above, to verify if the model will be more accurate than the MMSE or MoCA alone in characterizing patients’ scores. |
| Investigator's Name: | David Bergeron |
| Proposed Analysis: | This project follows a previously published project in the Canadian Medical Association Journal (see Bernier et al. 2017). We aimed to generate ready-to-use cognitive charts for follow-up of age-associated cognitive decline, analogous to pediatric growth charts based on key predictors (i.e. age and education) of incipient decline in the Mini-Mental State Examination (MMSE), to allow simple clinical follow-up of age-associated cognitive decline by first-line physicians using the MMSE. Our project remains similar: we want to generate cognitive charts, but using the Montreal Cognitive Assessment (MoCA). Indeed, the MoCA is deemed more sensitive regarding the screening of cognitive disorders in patients according to the literature. Using this data, we plan to create even more sensitive cognitive charts that will be able to track the decline of patients over time with the score on the MoCA. Using such a model will allow us to expand possibilities for clinical settings country-wide for longitudinal follow-up of patients both in primary and tertiary care through earlier management of disorders. To develop and test the model we will obtain access to various databases across the world containing normal controls and patients. Per the initial project, specific analyses for this new project will include: - Building a model to predict scores for the MoCA in healthy controls, taking into account age and education, using repeated-measures regression analyses. We will then transform this model into a simplified linear form for use in generating the cognitive charts. Our main purpose is not to provide a prediction model for the MoCA but to linearize its relationship to age and education, the 2 predominant factors of relevance to change in this test over time. - Testing the model on external databases to validate the generalizability of the model (participants from local research networks, international open databases, etc.). Appropriate legal and ethical methods will be undertaken to obtain those databases. - Comparing, when the model will have been validated in normal subjects, sensitivities and specificities based on the cognitive charts to those based on the most widely recognized Montreal Cognitive Assessment cut point (< 26) using both study populations (normal subjects and individuals with a diagnosis), as well as subgroups of age, education and baseline test score. Because our algorithm aimed at using multiple measures to monitor cognitive decline (as opposed to cut-offs), we compared its performance to that of the score of the last follow-up visit. Finally, we will construct a receiver operating characteristic (ROC) curve for the MoCA with 95% bootstrap confidence bands and the diagnostic accuracy of the cognitive charts, and highlighted the diagnostic accuracy of the suggested MoCA cut-off on the cognitive charts. - Creating a composite model from both the MMSE and MoCA scores and validating this model in the same manner described above, to verify if the model will be more accurate than the MMSE or MoCA alone in characterizing patients’ scores. |
