ALT Frontal Temporal Dementia (FTD) is the second most common dementia and usually affects individuals younger than 60 years old. FTD is difficult to diagnose, since there is no single exam that determines the disease, but instead many costly or painful exams that together link the disease. Some of the symptoms of the disease may be confounding with others such as Alzheimer’s Disease (AD). While the usual search for biomarkers focuses on individual patterns, the present proposal is to use a holistic approach. Vibrational spectroscopy provides a snapshot of the entire sample in a label-free way. In particular, infrared and Raman spectroscopy among other techniques will be used in a multimodal way to identify a vibrational fingerprint for Frontal Temporal Dementia on biofluid markers. Saliva and plasma will be the main targets for this study. Data will be analyzed by using Machine Learning approaches.