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: | Oliver Bruton |
| Institution: | Carl von Ossietzky Universität Oldenburg |
| Department: | Department of Psychology |
| Country: | |
| Proposed Analysis: | In a previous study (Li, ..., Hildebrandt, in prep.) we identified AD progression trajectories based on ADAS-Cog, MMSE, and CDR scores for individuals who suffered from mild cognitive impairement at baseline. Growth curve mixture models suggested three classes of progression: slow, moderate and fast. The most challenging prediction in clinical context is identifying those patients who will have a fast progression. Here, we aim at tuning different classifiers to improve prediction accuracy of the underrepresented fast AD progression class based on different bundles of bio- and early behavioral markers of AD as well as genetic risk factors. Besides under- and oversampling techniques, we will explore cost efficient learning in linear discriminant analyses, naive bayes and support vector machines and tree based methods as an approach to improve prediction accuracy of AD progression described in Li et al. The following features should be used for classification: • Genetic risk factors, focusing especially on the APOE polymorphism; • Time varying structural brain characteristics including cortical thinning at baseline and its development over time, hippocampus atrophy, white matter / grey matter density change and white matter lesions; • Time varying resting state connectivity and dispersion entropy in the fronto-parietal, the salience and the default mode network, • Neuropsychological functioning and neuropsychiatric symptoms, including everyday cognition (ECOG), emotion (NPI-Q), depression at baseline and depression development over time (GDS), as well as instrumental activities of daily living (IDAL) • Socio-demographic factors and physical fitness, more specifically education, gender, medical history, age at first symptom onset and vital signs over time, including pulse rate, temperature, respiration rate, and blood pressure, that indicate the state of a patient's essential bodily functions. |
| Additional Investigators | |
| Investigator's Name: | Andrea Hildebrandt |
| Proposed Analysis: | In a previous study (Li, ..., Hildebrandt, in prep.) we identified AD progression trajectories based on ADAS-Cog, MMSE, and CDR scores for individuals who suffered from mild cognitive impairement at baseline. Growth curve mixture models suggested three classes of progression: slow, moderate and fast. The most challenging prediction in clinical context is identifying those patients who will have a fast progression. Here, we aim at tuning different classifiers to improve prediction accuracy of the underrepresented fast AD progression class based on different bundles of bio- and early behavioral markers of AD as well as genetic risk factors. Besides under- and oversampling techniques, we will explore cost efficient learning in linear discriminant analyses, naive bayes and support vector machines and tree based methods as an approach to improve prediction accuracy of AD progression described in Li et al. The following features should be used for classification: • Genetic risk factors, focusing especially on the APOE polymorphism; • Time varying structural brain characteristics including cortical thinning at baseline and its development over time, hippocampus atrophy, white matter / grey matter density change and white matter lesions; • Time varying resting state connectivity and dispersion entropy in the fronto-parietal, the salience and the default mode network, • Neuropsychological functioning and neuropsychiatric symptoms, including everyday cognition (ECOG), emotion (NPI-Q), depression at baseline and depression development over time (GDS), as well as instrumental activities of daily living (IDAL) • Socio-demographic factors and physical fitness, more specifically education, gender, medical history, age at first symptom onset and vital signs over time, including pulse rate, temperature, respiration rate, and blood pressure, that indicate the state of a patient's essential bodily functions. |
| Investigator's Name: | Sumbul Jafri |
| Proposed Analysis: | In a previous study (Li, ..., Hildebrandt, in prep.) we identified AD progression trajectories based on ADAS-Cog, MMSE, and CDR scores for individuals who suffered from mild cognitive impairement at baseline. Growth curve mixture models suggested three classes of progression: slow, moderate and fast. The most challenging prediction in clinical context is identifying those patients who will have a fast progression. Here, we aim at tuning different classifiers to improve prediction accuracy of the underrepresented fast AD progression class based on different bundles of bio- and early behavioral markers of AD as well as genetic risk factors. Besides under- and oversampling techniques, we will explore cost efficient learning in linear discriminant analyses, naive bayes and support vector machines and tree based methods as an approach to improve prediction accuracy of AD progression described in Li et al. The following features should be used for classification: • Genetic risk factors, focusing especially on the APOE polymorphism; • Time varying structural brain characteristics including cortical thinning at baseline and its development over time, hippocampus atrophy, white matter / grey matter density change and white matter lesions; • Time varying resting state connectivity and dispersion entropy in the fronto-parietal, the salience and the default mode network, • Neuropsychological functioning and neuropsychiatric symptoms, including everyday cognition (ECOG), emotion (NPI-Q), depression at baseline and depression development over time (GDS), as well as instrumental activities of daily living (IDAL) • Socio-demographic factors and physical fitness, more specifically education, gender, medical history, age at first symptom onset and vital signs over time, including pulse rate, temperature, respiration rate, and blood pressure, that indicate the state of a patient's essential bodily functions. |
| Investigator's Name: | Raxide Eugenia Andrade Leon |
| Proposed Analysis: | In a previous study (Li, ..., Hildebrandt, in prep.) we identified AD progression trajectories based on ADAS-Cog, MMSE, and CDR scores for individuals who suffered from mild cognitive impairement at baseline. Growth curve mixture models suggested three classes of progression: slow, moderate and fast. The most challenging prediction in clinical context is identifying those patients who will have a fast progression. Here, we aim at tuning different classifiers to improve prediction accuracy of the underrepresented fast AD progression class based on different bundles of bio- and early behavioral markers of AD as well as genetic risk factors. Besides under- and oversampling techniques, we will explore cost efficient learning in linear discriminant analyses, naive bayes and support vector machines and tree based methods as an approach to improve prediction accuracy of AD progression described in Li et al. The following features should be used for classification: • Genetic risk factors, focusing especially on the APOE polymorphism; • Time varying structural brain characteristics including cortical thinning at baseline and its development over time, hippocampus atrophy, white matter / grey matter density change and white matter lesions; • Time varying resting state connectivity and dispersion entropy in the fronto-parietal, the salience and the default mode network, • Neuropsychological functioning and neuropsychiatric symptoms, including everyday cognition (ECOG), emotion (NPI-Q), depression at baseline and depression development over time (GDS), as well as instrumental activities of daily living (IDAL) • Socio-demographic factors and physical fitness, more specifically education, gender, medical history, age at first symptom onset and vital signs over time, including pulse rate, temperature, respiration rate, and blood pressure, that indicate the state of a patient's essential bodily functions. |
