Health Condition 1: F02- Dementia in other diseases classified elsewhere
Conditions
Interventions
Sponsors
Eligibility
Inclusion criteria
Inclusion criteria: 1. Patients must be age 45 or older. 2. Patients must have a history of progressive memory impairment. A. MoCA score B. Patient must fall into either of the 3 categories as per DSM-5 criteria - MCI, AD, FTD 4. Patients must have a caregiver who is willing to accompany the subject to the hospital.
Exclusion criteria
Exclusion criteria: 1. Pregnant women - Women of childbearing potential will be screened by history for the possibility of pregnancy. 2. Any other cause of dementia apart from the ones mentioned in the inclusion criteria (for eg.: drug-induced dementia, pseudodementia, etc.) 3. History of delirium, recent head injury, stroke or epilepsy. 4. Any medical contraindication to the procedures performed in the study, or any current severe medical or psychiatric illness other than the diseases included above. 5. Behavioral symptoms that would preclude the gathering of data for the study, or advanced disease such that subjects cannot provide assent based on the following: Modified MINI Scale (MMS) Clinical Dementia Rating Scale(CDR) score (if greater than or equal to 3) MoCA less than 26
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Build a Machine Learning model to distinguish between Alzheimers Disease (AD) and Frontotemporal Dementia (FTD) and identify connectivity patterns indicative of Mild Cognitive Impairment (MCI) using resting state functional MRI (rs-fMRI)Timepoint: 1 year data collection 3 months optimization of ML | — |
Secondary
| Measure | Time frame |
|---|---|
| Assess the efficacy of machine learning models to differentiate between AD and FTD as well as to detect structural signs of MCI using structural MRI and DTI features.Timepoint: 6 months | — |
Countries
India
Contacts
BrainSight Technology Private Limited