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Principal Investigator  
Principal Investigator's Name: Eduardo Castro
Institution: IBM Research
Department: Healthcare and Life Sciences
Country:
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences
Additional Investigators  
Investigator's Name: Guillermo Cecchi
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences
Investigator's Name: Pablo Polosecki
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences
Investigator's Name: Shreyas Fadnavis
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) and clinical measures using multivariate and multimodal predictive models to a) detect brain-based longitudinal patterns of Alzheimer's disease trajectories, and b) detect clinical heterogeneity within Alzheimer's and brain markers associated to these differences
Investigator's Name: Avner Abrami
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that it is a good predictor of cognitive impairment, as measured by available clinical assessments such as the Mini-Mental State Examination
Investigator's Name: Jenna Reinen
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that it is a good predictor of cognitive impairment, as measured by available clinical assessments such as the Mini-Mental State Examination
Investigator's Name: Amit Dhurandhar
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that, when coupled with age, it becomes a good predictor of cognitive impairment as measured by available clinical assessments such as the Mini-Mental State Examination.
Investigator's Name: Matias Aiskovich
Proposed Analysis: We will analyze ADNI's MRI data (functional, structural, diffusion-weighted) along with metabolic imaging data (PET) using multivariate and multimodal predictive models to detect a progression invariant marker of Alzheimer’s disease. We will validate such marker by verifying that, when coupled with age, it becomes a good predictor of cognitive impairment as measured by available clinical assessments such as the Mini-Mental State Examination.