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Background: Frontotemporal lobar degeneration (FTLD) is associated with diverse clinical phenotypes underlain by multiple disease pathologies and genetic mutations. As such, traditional structural MRI analyses lack sensitivity and specificity for discriminating FTLD syndromes. Here, we use data‐driven methods to extract a concise set of MRI‐derived shape morphometric features and examine the discriminatory capability of their unique combinations in four FTLD clinical phenotypes. Method: 190 patients with sporadic or familial FTLD spectrum disorders (i.e., behavioral variant (bvFTD, n = 107), non‐fluent variant primary progressive aphasia (nfvPPA, n = 27), semantic variant primary progressive aphasia (svPPA, n = 12) and progressive supranuclear palsy (PSP, n = 44)) and 27 controls without pathogenic mutations from the ALLFTD cohort were evaluated. MRI data were preprocessed with FreeSurfer software and four cortical measures were extracted and indexed to a normalized surface atlas: cortical thickness (CT), surface area (SA), surface curvature (SC) and jacobian white matter surface metric distortion (JW). For all phenotypes, each shape feature was contrasted with controls using linear models adjusted for age and gender. Principal component analysis (PCA) was then applied to each individual structural measure and the discriminatory power based on individual and combined measures was assessed using logistic regression and 10‐fold cross‐validation. Result: Figure 1 displays FDR‐corrected T‐scores of differences from controls in each morphometric feature in bvFTD. Results reveal complementary patterns of CT, SA, SC and JW for each phenotype, with greatest differences observed in CT across groups. In Figure 2, we display the features derived from the PCA, weighted by eigenvector coefficients, in bvFTD. In Figures 3‐6, receiver operating characteristic curves for individual and combined measures alongside areas under the curve (AUCs) are shown for each phenotype. In bvFTD, nfvPPA and PSP, the superior model included CT and SC at AUCs of 88.3, 87, and 78.6, respectively. For svPPA, the superior model included CT, SC and JW at an AUC of 85.6. Conclusion: Integrating additional MRI‐derived surface morphometric features improved classification performance in all FTLD phenotypes. The principal component analysis‐based approach indicated distinct brain regions contribute to discrimination for each shape feature, suggesting they may reflect unique aspects of neurodegeneration across groups. [ABSTRACT FROM AUTHOR]

Background: The A/T/N framework allows for the assessment of pathology-specific markers of MRI-derived structural atrophy and hypometabolism on 18FDG-PET. However, how these measures relate to each other locally and distantly across pathology-defined A/T/N groups is currently unclear. Objective: To determine the regions of association between atrophy and hypometabolism in A/T/N groups both within and across time points. Methods: We examined multivariate multimodal neuroimaging relationships between MRI and 18FDG-PET among suspected non-Alzheimer's disease pathology (SNAP) (A–T/N+; n = 14), Amyloid Only (A+T–N–; n = 24) and Probable AD (A+T+N+; n = 77) groups. Sparse canonical correlation analyses were employed to model spatially disjointed regions of association between MRI and 18FDG-PET data. These relationships were assessed at three combinations of time points –cross-sectionally, between baseline visits and between month 12 (M-12) follow-up visits, as well as longitudinally between baseline and M-12 follow-up. Results: In the SNAP group, spatially overlapping relationships between atrophy and hypometabolism were apparent in the bilateral temporal lobes when both modalities were assessed at the M-12 timepoint. Amyloid-Only subjects showed spatially discordant distributed atrophy-hypometabolism relationships at all time points assessed. In Probable AD subjects, local correlations were evident in the bilateral temporal lobes when both modalities were assessed at baseline and at M-12. Across groups, hypometabolism at baseline correlated with non-local, or distant, atrophy at M-12. Conclusion: These results support the view that local concordance of atrophy and hypometabolism is the result of a tau-mediated process driving neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is a complex disorder influenced by many factors, but it is unclear how each factor contributes to disease progression. An in‐depth examination of these factors may yield an accurate estimate of time‐to‐conversion to AD for patients at various disease stages. Recent advances in deep learning have enabled researchers to predict patient's disease onset time by exploring the influencing factors in AD progression. Method: We used 543 subjects with 63 features from 3 data modalities from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The following modalities were used: 1) MRI, 2) genetic and 3) DTC (Demographic, cognitive Tests and Cerebrospinal fluid). The 21 most important features were automatically selected for the three modalities. We used a Deep Learning‐based survival analysis model that extends the classic Cox regression model to predict the subjects' disease onset time. Here we re‐define the non‐AD‐progression as "survivor", and AD‐progression as "non‐survivor". The subjects were divided into two groups: progressive subjects (non‐survivor), who were either healthy or diagnosed with Mild Cognitive Impairment (MCI) at initial clinical visit and later developed AD, and non‐progressive subjects ("survivor"), who were either healthy or MCI at initial visit but did not develop AD later. We used 10 random sub‐samples, selecting 80% of the subjects for training and 20% for testing each time; 20% of training data was used for internal validation. Result: Figure 1 shows the estimated survival rates over 10 years. Both groups had a high survival chance at the start. The progressive group's survival chance dropped much faster and fell below 20% by the end of the period. The non‐progressive group's survival chance remained around 50%. Feature importance analysis is displayed in Figure 2. Eight of the top ten most important features are from the cognitive tests, demonstrating their importance in survival analysis. Amygdala and Hippocampus regions, as well as age, are also notable features. Conclusion: Our study demonstrated that using powerful predictive models on multi‐modal data can improve prediction of time‐to‐conversion. This not only leads to a better understanding of AD, but also provides essential tools for practitioners who wish to follow their patients' disease progression. [ABSTRACT FROM AUTHOR]

Background: In recent years, many convolutional neural networks (CNN) have been proposed for the classification of Alzheimer's disease. Due to memory constraints, many of the proposed CNNs work at a 2D slice-level or 3D patch-level.Objective: Here, we propose a subject-level 3D CNN that can extract the neurodegenerative patterns of the whole brain MRI and converted into a probabilistic Dementia score.Methods: We propose an efficient and lightweight subject-level 3D CNN featuring dilated convolutions. We trained our network on the ADNI data on stable Dementia of the Alzheimer's type (sDAT) from stable normal controls (sNC). To comprehensively evaluate the generalizability of our proposed network, we performed four independent tests which includes testing on images from other ADNI individuals at various stages of the dementia, images acquired from other sites (AIBL), images acquired using different protocols (OASIS), and longitudinal images acquired over a short period of time (MIRIAD).Results: We achieved a 5-fold cross-validated balanced accuracy of 88%in differentiating sDAT from sNC, and an overall specificity of 79.5%and sensitivity 79.7%on the entire set of 7,902 independent test images.Conclusion: Independent testing is essential for estimating the generalization ability of the network to unseen data, but is often lacking in studies using CNN for DAT classification. This makes it difficult to compare the performances achieved using different architectures. Our comprehensive evaluation highlighting the competitive performance of our network and potential promise for generalization. [ABSTRACT FROM AUTHOR]

Methods: Alzheimer's disease and Frontotemporal dementia are the first and third most common forms of dementia. Due to their similar clinical symptoms, they are easily misdiagnosed as each other even with sophisticated clinical guidelines. For disease-specific intervention and treatment, it is essential to develop a computer-aided system to improve the accuracy of their differential diagnosis. Recent advances in deep learning have delivered some of the best performance for medical image recognition tasks. However, its application to the differential diagnosis of AD and FTD pathology has not been explored. Approach: In this study, we proposed a novel deep learning based framework to distinguish between brain images of normal aging individuals and subjects with AD and FTD. Specifically, we combined the multi-scale and multi-type MRI-base image features with Generative Adversarial Network data augmentation technique to improve the differential diagnosis accuracy. Results: Each of the multi-scale, multitype, and data augmentation methods improved the ability for differential diagnosis for both AD and FTD. A 10-fold cross validation experiment performed on a large sample of 1,954 images using the proposed framework achieved a high overall accuracy of 88.28%. Conclusions: The salient contributions of this study are three-fold: (1) our experiments demonstrate that the combination of multiple structural features extracted at different scales with our proposed deep neural network yields superior performance than individual features; (2) we show that the use of Generative Adversarial Network for data augmentation could further improve the discriminant ability of the network regarding challenging tasks such as differentiating dementia sub-types; (3) and finally, we show that ensemble classifier strategy could make the network more robust and stable. [ABSTRACT FROM AUTHOR]

18F‐fluorodeoxyglucose positron emission tomography (FDG‐PET) enables in‐vivo capture of the topographic metabolism patterns in the brain. These images have shown great promise in revealing the altered metabolism patterns in Alzheimer's disease (AD). The AD pathology is progressive, and leads to structural and functional alterations that lie on a continuum. There is a need to quantify the altered metabolism patterns that exist on a continuum into a simple measure. This work proposes a 3D convolutional neural network with residual connections that generates a probability score useful for interpreting the FDG‐PET images along the continuum of AD. This network is trained and tested on images of stable normal control and stable Dementia of the Alzheimer's type (sDAT) subjects, achieving an AUC of 0.976 via repeated fivefold cross‐validation. An independent test set consisting of images in between the two extreme ends of the DAT spectrum is used to further test the generalization performance of the network. Classification performance of 0.811 AUC is achieved in the task of predicting conversion of mild cognitive impairment to DAT for conversion time of 0–3 years. The saliency and class activation maps, which highlight the regions of the brain that are most important to the classification task, implicate many known regions affected by DAT including the posterior cingulate cortex, precuneus, and hippocampus. [ABSTRACT FROM AUTHOR]

Background: White matter signal abnormalities (WMSA) are frequently observed on magnetic resonance imaging (MRI) scans of people with frontotemporal dementia (FTD). We hypothesized that carriers of mutations in progranulin (GRN+), chromosome 9 open reading frame 72 (C9orf72+) and microtubule associated protein tau (MAPT+) have increased rates of progression of WMSA compared to non‐carrier individuals prior to onset of dementia, and that different mutations may have different rates of WMSA accumulation. Method: Participants were recruited through the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) Study. We selected participants with Clinical Dementia Rating scale (CDR) scores of 0 and 0.5. If a participant developed FTD during the follow‐up, we only analyzed observations collected before the conversion. WMSA were defined as hyperintensities on FLAIR MRI. Total WMSA volumes were normalized to the respective intracranial volumes. We used a linear mixed effects model, with random intercepts and slopes, to compare the baseline burden and the longitudinal progression of total WMSA volumes between mutation carriers and controls. The model was adjusted for baseline age, sex, education, baseline Montreal Cognitive Assessment (MoCA) scores, and MRI acquisition sites. Result: We analyzed MRI data from 199 participants who had at least two MRI visits (46 C9orf72+, 17 GRN+, 35 MAPT+, 101 non‐carrier controls; up to five years of follow‐up). We did not find significant group differences in baseline demographic features, except that MAPT+ was younger than non‐carrier controls (average age: C9orf72+ = 47, GRN+ = 56, MAPT+ = 40, controls = 47). Baseline WMSA volumes were not significantly different between mutation carriers and controls. Longitudinally, the rates of WMSA volume increase in C9orf72+ and MAPT+ were comparable to controls; whereas, GRN+ had higher rates of WMSA volume increases compared to controls (p = 0.01). Conclusion: WMSA may accumulate at higher rates in GRN+ prior to the onset of FTD, which is consistent with the frequent presence of white matter diseases observed in symptomatic FTD patients with GRN mutations. Future work is warranted to investigate the association between white matter structural changes and cognitive/psychiatric changes in FTD mutation carriers. [ABSTRACT FROM AUTHOR]