Occupational differences in a Dutch sample of patients with primary progressive aphasia, behavioral variant frontotemporal dementia, and Alzheimer’s dementia

Background: Cognitive reserve is a potential mechanism to cope with brain damage as a result of dementia, which can be defined by indirect proxies, including education level, leisure time activities, and occupational attainment. In this study we explored the association between dementia diagnosis and type of occupation in a retrospective Dutch outpatient memory clinic sample of patients with primary progressive aphasia (PPA), behavioral variant frontotemporal dementia (bvFTD), and Alzheimer’s Dementia (AD). Methods: We included data from 427 patients (bvFTD n = 87, PPA n = 148, AD n = 192) and compared the frequency of occupations (11 categories) between patients and data from the Dutch census using Pearson Χ2 tests and we calculated odds ratios (OR) by means of multinomial logistic regression analyses. We also investigated patient group differences in age, sex, education, disease duration, and global cognition. Results: The frequency of teachers in patients with PPA was significantly higher than the frequency of teachers in patients with bvFTD [OR = 4.79, p =.007] and AD [OR = 2.04, p =.041]. The frequency of teachers in patients with PPA (16%) was also significantly higher than the frequency of teachers in the Dutch census [5.3%; OR = 3.27, p &lt;.001]. The frequency of teachers in both bvFTD and AD groups were not significantly different from the frequency of teachers in the Dutch census (p =.078 and p =.513, respectively). Conclusions: A potential explanation for our results is the so called “wear and tear” hypothesis, suggesting that teachers have a communication-wise demanding occupation–and therefore are at higher risk to develop PPA. Alternatively, teaching requires continuous communication, hence teachers are more sensitive to subtle changes in their speech and language abilities. Our findings broaden our understanding of the relationship between occupational activity and cognitive reserve in the development of dementia.</p

Clinical Value of Longitudinal Serum Neurofilament Light Chain in Prodromal Genetic Frontotemporal Dementia

BACKGROUND AND OBJECTIVES: Elevated serum neurofilament light chain (NfL) is used to identify carriers of genetic frontotemporal dementia (FTD) pathogenic variants approaching prodromal conversion. Yet, the magnitude and timeline of NfL increase are still unclear. Here, we investigated the predictive and early diagnostic value of longitudinal serum NfL for the prodromal conversion in genetic FTD. METHODS: In a longitudinal observational cohort study of genetic FTD pathogenic variant carriers, we examined the diagnostic accuracy and conversion risk associated with cross-sectional and longitudinal NfL. Time periods relative to prodromal conversion (&gt;3, 3-1.5, 1.5-0 years before; 0-1.5 years after) were compared with values of participants who did not convert. Next, we modeled longitudinal NfL and MRI volume trajectories to determine their timeline.RESULTS: We included 21 participants who converted (5 chromosome 9 open-reading frame 72 [C9orf72], 10 progranulin [GRN], 5 microtubule-associated protein tau [MAPT], and 1 TAR DNA-binding protein [TARDBP]) and 61 who did not (20 C9orf72, 30 GRN, and 11 MAPT). Participants who converted had higher NfL levels at all examined periods before prodromal conversion (median values 14.0-18.2 pg/mL; betas = 0.4-0.7, standard error [SE] = 0.1, p &lt; 0.046) than those who did not (6.5 pg/mL) and showed further increase 0-1.5 years after conversion (28.4 pg/mL; beta = 1.0, SE = 0.1, p &lt; 0.001). Annualized longitudinal NfL change was only significantly higher in participants who converted (vs. participants who did not) 0-1.5 years after conversion (beta = 1.2, SE = 0.3, p = 0.001). Diagnostic accuracy of cross-sectional NfL for prodromal conversion (vs. nonconversion) was good-to-excellent at time periods before conversion (area under the curve range: 0.72-0.92), improved 0-1.5 years after conversion (0.94-0.97), and outperformed annualized longitudinal change (0.76-0.84). NfL increase in participants who converted occurred earlier than frontotemporal MRI volume change and differed by genetic group and clinical phenotypes. Higher NfL corresponded to increased conversion risk (hazard ratio: cross-sectional = 6.7 [95% CI 3.3-13.7]; longitudinal = 13.0 [95% CI 4.0-42.8]; p &lt; 0.001), but conversion-free follow-up time varied greatly across participants. DISCUSSION: NfL increase discriminates individuals who convert to prodromal FTD from those who do not, preceding significant frontotemporal MRI volume loss. However, NfL alone is limited in predicting the exact timing of prodromal conversion. NfL levels also vary depending on underlying variant-carrying genes and clinical phenotypes. These findings help to guide participant recruitment for clinical trials targeting prodromal genetic FTD.</p

Addition of the FTD Module to the Neuropsychiatric Inventory improves classification of frontotemporal dementia spectrum disorders

Most neuropsychiatric symptoms (NPS) common in frontotemporal dementia (FTD) are currently not part of the Neuropsychiatric Inventory (NPI). We piloted an FTD Module that included eight extra items to be used in conjunction with the NPI. Caregivers of patients with behavioural variant FTD (n = 49), primary progressive aphasia (PPA; n = 52), Alzheimer's dementia (AD; n = 41), psychiatric disorders (n = 18), presymptomatic mutation carriers (n = 58) and controls (n = 58) completed the NPI and FTD Module. We investigated (concurrent and construct) validity, factor structure and internal consistency of the NPI and FTD Module. We performed group comparisons on item prevalence, mean item and total NPI and NPI with FTD Module scores, and multinomial logistic regression to determine its classification abilities. We extracted four components, together explaining 64.1% of the total variance, of which the largest indicated the underlying dimension 'frontal-behavioural symptoms'. Whilst apathy (original NPI) occurred most frequently in AD, logopenic and non-fluent variant PPA, the most common NPS in behavioural variant FTD and semantic variant PPA were loss of sympathy/empathy and poor response to social/emotional cues (part of FTD Module). Patients with primary psychiatric disorders and behavioural variant FTD showed the most severe behavioural problems on both the NPI as well as the NPI with FTD Module. The NPI with FTD Module correctly classified more FTD patients than the NPI alone. By quantifying common NPS in FTD the NPI with FTD Module has large diagnostic potential. Future studies should investigate whether it can also prove a useful addition to the NPI in therapeutic trials

An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.Comment: 11 pages, 5 figure

Resting state functional connectivity differences between behavioral variant frontotemporal dementia and Alzheimer's disease

Introduction: Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are the most common types of early-onset dementia. Early differentiation between both types of dementia may be challenging due to heterogeneity and overlap of symptoms. Here, we apply resting state functional magnetic resonance imaging (fMRI) to study functional brain connectivity differences between AD and bvFTD. Methods: We used resting state fMRI data of 31 AD patients, 25 bvFTD patients, and 29 controls from two centers specialized in dementia. We studied functional connectivity throughout the entire brain, applying two different analysis techniques, studying network-to-region and region-to-region connectivity. A general linear model approach was used to study group differences, while controlling for physiological noise, age, gender, study center, and regional gray matter volume. Results: Given gray matter differences, we observed decreased network-to-region connectivity in bvFTD between (a) lateral visual cortical network and lateral occipital and cuneal cortex, and (b) auditory system network and angular gyrus. In AD, we found decreased network-to-region connectivity between the dorsal visual stream network and lateral occipital and parietal opercular cortex. Region-to-region connectivity was decreased in bvFTD between superior temporal gyrus and cuneal, supracalcarine, intracalcarine cortex, and lingual gyrus. Conclusion: We showed that the pathophysiology

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

Modelling the cascade of biomarker changes in progranulin‐related frontotemporal dementia

AbstractBackgroundProgranulin related frontotemporal dementia (FTD‐GRN) is a fast progressive disorder, in which pathophysiological changes precede overt clinical symptoms in only a short time period. Modelling the cascade of multimodal biomarker changes aids in understanding the etiology of this disease, enables monitoring of individual mutation carriers, and would give input for disease‐modifying treatments. In this cross‐sectional study, we estimated the temporal cascade of biomarker changes for FTD‐GRN, in a data‐driven way.MethodWe included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non‐carriers. Of the symptomatic subjects, 17 had behavioural variant FTD (bvFTD), 16 presented as non‐fluent variant primary progressive aphasia (nfvPPA). The selected biomarkers for establishing the cascade of changes were neurofilament light chain, regional grey matter volumes, fractional anisotropy of white matter tracts, and cognitive domains. We used a data‐driven analysis called discriminative event‐based modelling (Venkatraghavan, NeuroImage, 2019) with a novel modification to its Gaussian Mixture Model (GMM) called Siamese GMM, to estimate the cascade of biomarker changes for FTD‐GRN. Using cross‐validation, we estimated disease severities of individual mutation carriers in the test set based on their progression along the biomarker cascade established on the training set.ResultNeurofilament light chain and white matter tracts were the earliest biomarkers to become abnormal in FTD‐GRN mutation carriers. Attention and executive functioning were also affected early on in the disease process. Based on the estimated individual disease severities, presymptomatic mutation carriers could be distinguished from symptomatic mutation carriers with a sensitivity of 95% and specificity of 100% in the cross‐validation experiment. There was a high correlation (r=0.94, p<0.001) between estimated disease severity and years since symptom onset in nfvPPA, but not in bvFTD (r=0.33, p=0.46).ConclusionIn this study, we unravelled the temporal cascade of multimodal biomarker changes for FTD‐GRN. Our results suggest that axonal degeneration is one of the first disease events in FTD‐GRN, which calls for designing disease modifying treatments that strengthens the axons. We also demonstrated a good delineation between symptomatic and presymptomatic carriers using the estimated disease severities, which suggest that our model could enable monitoring of individual mutation carriers

Data-driven staging of genetic frontotemporal dementia using multi-modal MRI

Frontotemporal dementia in genetic forms is highly heterogeneous and begins many years to prior symptom onset, complicating disease understanding and treatment development. Unifying methods to stage the disease during both the presymptomatic and symptomatic phases are needed for the development of clinical trials outcomes. Here we used the contrastive trajectory inference (cTI), an unsupervised machine learning algorithm that analyzes temporal patterns in high-dimensional large-scale population datasets to obtain individual scores of disease stage. We used cross-sectional MRI data (gray matter density, T1/T2 ratio as a proxy for myelin content, resting-state functional amplitude, gray matter fractional anisotropy, and mean diffusivity) from 383 gene carriers (269 presymptomatic and 115 symptomatic) and a control group of 253 noncarriers in the Genetic Frontotemporal Dementia Initiative. We compared the cTI-obtained disease scores to the estimated years to onset (age—mean age of onset in relatives), clinical, and neuropsychological test scores. The cTI based disease scores were correlated with all clinical and neuropsychological tests (measuring behavioral symptoms, attention, memory, language, and executive functions), with the highest contribution coming from mean diffusivity. Mean cTI scores were higher in the presymptomatic carriers than controls, indicating that the method may capture subtle pre-dementia cerebral changes, although this change was not replicated in a subset of subjects with complete data. This study provides a proof of concept that cTI can identify data-driven disease stages in a heterogeneous sample combining different mutations and disease stages of genetic FTD using only MRI metrics