The detection of pathological changes in neurodegenerative diseases that occur before clinical onset would be of great value for identifying suitable subjects and assessing drug ecacy in trials aimed at preventing or slowing onset. Using MRI derived volumetric information, researchers have been able to detect significant di↵erences between patients in the presymptomatic phase of neurodegenerative diseases and healthy controls. However, volumetric studies provide only a summary representation of complex morphological changes. Shape analysis has already been successfully applied to model pathological features in neu-rodegeneration and represents a valuable instrument to model presymp-tomatic anatomical changes occurring in specific brain regions. In this study we propose a computational framework to model group-wise spatio-temporal shape di↵erences, and to statistically evaluate the e↵ects of time and pathological components on the modeled variability. The proposed approach leverages the geodesic regression framework based on varifolds, and models the spatio-temporal shape variability via dimensionality reduction of the subject-specific " residual " transformations normalised in a common reference frame through parallel transport. The proposed approach is applied to patients with genetic variants of fronto-temporal dementia, and shows that shape di↵erences in the posterior part of the thalamus can be observed several years before the appearance of clinical symptoms.