Frontotemporal dementia (FTD) is a neurodegenerative disease with a strong genetic basis. Understanding the structural brain changes during pre-symptomatic stages may allow for earlier diagnosis of patients suffering from FTD; therefore, we investigated asymptomatic members of FTD families with mutations in C9orf72 and granulin (GRN) genes. Clinically asymptomatic subjects from families with C9orf72 mutation (15 mutation carriers, C9orf72+; and 23 non-carriers, C9orf72−) and GRN mutations (9 mutation carriers, GRN+; and 15 non-carriers, GRN−) underwent structural neuroimaging (MRI). Cortical thickness and subcortical gray matter volumes were calculated using FreeSurfer. Group differences were evaluated, correcting for age, sex and years to mean age of disease onset within the subject's family. Mean age of C9orf72+ and C9orf72− were 42.6 ± 11.3 and 49.7 ± 15.5 years, respectively; while GRN+ and GRN− groups were 50.1 ± 8.7 and 53.2 ± 11.2 years respectively. The C9orf72+ group exhibited cortical thinning in the temporal, parietal and frontal regions, as well as reduced volumes of bilateral thalamus and left caudate compared to the entire group of mutation non-carriers (NC: C9orf72− and GRN− combined). In contrast, the GRN+ group did not show any significant differences compared to NC. C9orf72 mutation carriers demonstrate a pattern of reduced gray matter on MRI prior to symptom onset compared to GRN mutation carriers. These findings suggest that the preclinical course of FTD differs depending on the genetic basis and that the choice of neuroimaging biomarkers for FTD may need to take into account the specific genes involved in causing the disease.

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thickness best predicts conversion to AD, compared to whole cortex analysis, looking for an optimal aggregation did not provide significantly better results. Nevertheless, such approach may be more robust to image quality issues, which is to be further confirmed on other datasets. A similar analysis will be performed on longitudinal data in order to determine which single or composite region would provide the best effect size when measuring change in cortical thickness over time.Background: Morphological studies analyzing subcortical brain changes caused by neurodegenerative dementias or aging require the delineation or segmentation of subcortical structure boundaries from the structural Magnetic Resonance Imaging (MRI) images. As manual segmentation becomes tedious and extremely time consuming beyond small image datasets, automated techniques are needed in practice for subcortical MRI segmentation. Methods: In this work, we propose a novel method for subcortical segmentation following the well established multi-template fusion framework. Our proposed method employs the popular Large Deformation Diffeomorphic Metric Mapping (LDDMM) algorithm for propagating the manual labels from the template image onto the target image. The accuracy and efficiency of this label propagation is improved by considering a tight bounding boxes around the subcortical structures based on the initial segmentations obtained using Freesurfer (FS). The most important aspect of the proposed method is the use of a robust template selection strategy to choose templates that are anatomically similar to the target image while discarding the ones that are too dissimilar before the fusion step. The robust template selection involves ranking the propagated manual template segmentations based on their Hausdorff surface distance to the initial FS segmentation. Only the top ranked propagated template segmentations are fused to obtain the desired target segmentation. Results: We developed a manual template library consisting 74 MRI images taken from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. In each of the template images, six subcortical structures, amygdala, caudate, hippocampus, putamen, thalamus and ventricles were delineated by a manual operator. The proposed method was validated using the ADNI template library in a leave-one-out cross-validation framework. The segmentation accuracy was measured using the dice overlap measure between the manual and automated segmentations. Our method obtained excellent (80-90%) dice scores for all the six subcortical structures on an average. Further, independent validation on the benchmark Harmonized Protocol (HarP) database with "ground truth" hippocampus labels also yielded (> 80%) mean dice scores. Conclusions: An accurate and robust automated subcortical segmentation method has been developed. The proposed method can be used to facilitate large scale studies on analyzing morphological changes of the subcortical structures in the brain using large image databases.Background: Accumulati...

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