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Early and accurate mild cognitive impairment (MCI) detection within a heterogeneous, nonclinical population is needed to improve care for persons at risk of developing dementia. Magnetic resonance imaging (MRI)-based classification may aid early diagnosis of MCI, but has only been applied within clinical cohorts. We aimed to determine the generalizability of MRI-based classification probability scores to detect MCI on an individual basis within a general population. To determine classification probability scores, an AD, mild-AD, and moderate-AD detection model were created with anatomical and diffusion MRI measures calculated from a clinical Alzheimer's Disease (AD) cohort and subsequently applied to a population-based cohort with 48 MCI and 617 normal aging subjects. Each model's ability to detect MCI was quantified using area under the receiver operating characteristic curve (AUC) and compared with an MCI detection model trained and applied to the population-based cohort. The AD-model and mild-AD identified MCI from controls better than chance level (AUC = 0.600, p = 0.025; AUC = 0.619, p = 0.008). In contrast, the moderate-AD-model was not able to separate MCI from normal aging (AUC = 0.567, p = 0.147). The MCI-model was able to separate MCI from controls better than chance (p = 0.014) with mean AUC values comparable with the AD-model (AUC = 0.611, p = 1.0). Within our population-based cohort, classification models detected MCI better than chance. Nevertheless, classification performance rates were moderate and may be insufficient to facilitate robust MRI-based MCI detection on an individual basis. Our data indicate that multiparametric MRI-based classification algorithms, that are effective in clinical cohorts, may not straightforwardly translate to applications in a general population., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

In genetic frontotemporal dementia, cross-sectional studies have identified profiles of presymptomatic neuroanatomical loss for C9orf72 repeat expansion, MAPT, and GRN mutations. In this study, we characterize longitudinal gray matter (GM) and white matter (WM) brain changes in presymptomatic frontotemporal dementia. We included healthy carriers of C9orf72 repeat expansion (n = 12), MAPT (n = 15), GRN (n = 33) mutations, and related noncarriers (n = 53), that underwent magnetic resonance imaging at baseline and 2-year follow-up. We analyzed cross-sectional baseline, follow-up, and longitudinal GM and WM changes using voxel-based morphometry and cortical thickness analysis in SPM and tract-based spatial statistics in FSL. Compared with noncarriers, C9orf72 repeat expansion carriers showed lower GM volume in the cerebellum and insula, and WM differences in the anterior thalamic radiation, at baseline and follow-up. MAPT mutation carriers showed emerging GM temporal lobe changes and longitudinal WM degeneration of the uncinate fasciculus. GRN mutation carriers did not show presymptomatic neurodegeneration. This study shows distinct presymptomatic cross-sectional and longitudinal patterns of GM and WM changes across C9orf72 repeat expansion, MAPT, and GRN mutation carriers compared with noncarriers., (Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Multimodal MRI-based classification may aid early frontotemporal dementia (FTD) diagnosis. Recently, presymptomatic FTD mutation carriers, who have a high risk of developing FTD, were separated beyond chance level from controls using MRI-based classification. However, it is currently unknown how these scores from classification models progress as mutation carriers approach symptom onset. In this longitudinal study, we investigated multimodal MRI-based classification scores between presymptomatic FTD mutation carriers and controls. Furthermore, we contrasted carriers that converted during follow-up ('converters') and non-converting carriers ('non-converters').Methods: We acquired anatomical MRI, diffusion tensor imaging and resting-state functional MRI in 55 presymptomatic FTD mutation carriers and 48 healthy controls at baseline, and at 2, 4, and 6 years of follow-up as available. At each time point, FTD classification scores were calculated using a behavioural variant FTD classification model. Classification scores were tested in a mixed-effects model for mean differences and differences over time.Results: Presymptomatic mutation carriers did not have higher classification score increase over time than controls (p=0.15), although carriers had higher FTD classification scores than controls on average (p=0.032). However, converters (n=6) showed a stronger classification score increase over time than non-converters (p<0.001).Conclusions: Our findings imply that presymptomatic FTD mutation carriers may remain similar to controls in terms of MRI-based classification scores until they are close to symptom onset. This proof-of-concept study shows the promise of longitudinal MRI data acquisition in combination with machine learning to contribute to early FTD diagnosis. [ABSTRACT FROM AUTHOR]

Background/objective: Overlapping clinical symptoms often complicate differential diagnosis between patients with Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD). Magnetic resonance imaging (MRI) reveals disease specific structural and functional differences that aid in differentiating AD from bvFTD patients. However, the benefit of combining structural and functional connectivity measures to-on a subject-basis-differentiate these dementia-types is not yet known.Methods: Anatomical, diffusion tensor (DTI), and resting-state functional MRI (rs-fMRI) of 30 patients with early stage AD, 23 with bvFTD, and 35 control subjects were collected and used to calculate measures of structural and functional tissue status. All measures were used separately or selectively combined as predictors for training an elastic net regression classifier. Each classifier's ability to accurately distinguish dementia-types was quantified by calculating the area under the receiver operating characteristic curves (AUC).Results: Highest AUC values for AD and bvFTD discrimination were obtained when mean diffusivity, full correlations between rs-fMRI-derived independent components, and fractional anisotropy (FA) were combined (0.811). Similarly, combining gray matter density (GMD), FA, and rs-fMRI correlations resulted in highest AUC of 0.922 for control and bvFTD classifications. This, however, was not observed for control and AD differentiations. Classifications with GMD (0.940) and a GMD and DTI combination (0.941) resulted in similar AUC values (p = 0.41).Conclusion: Combining functional and structural connectivity measures improve dementia-type differentiations and may contribute to more accurate and substantiated differential diagnosis of AD and bvFTD patients. Imaging protocols for differential diagnosis may benefit from also including DTI and rs-fMRI. [ABSTRACT FROM AUTHOR]