Neuroimaging and Clinical Biomarkers in the Familial and Sporadic FTD Spectrum – from the Presymptomatic to the Symptomatic Disease Stage

Frontotemporal dementia (FTD) is the second most common type of early-onset dementia, in which symptoms commonly become apparent before the age of 65. The clinical spectrum is heterogeneous, ranging from a predominant behavioural manifestation (behavioural variant FTD; bvFTD) to progressive language deterioration (primary progressive aphasia; PPA) and parkinsonism. 10-30% of FTD cases have an autosomal dominant pattern of inheritance. Mutations in the progranulin (GRN) and microtubule-associated protein tau (MAPT) genes, and a repeat expansion in chromosome 9 open reading frame 72 (C9orf72) are the three most common causes. Familial FTD allows the identification of pathogenic mutation carriers in their presymptomatic phase – a critical time-window for treatment as the pathological damage is at its minimum and potentially still reversible. However, with promising avenues opening for disease-modifying therapies in clinical trials, we currently lack robust biomarkers for (familial) FTD. These biomarkers will be essential for improving diagnostic accuracy, staging and prognosis, onset prediction, and monitoring disease progression and treatment response. In this thesis, we have therefore investigated potential neuroimaging and neuropsychological biomarkers in the familial and sporadic FTD spectrum

Qualitative Assessment of Verbal Fluency Performance in Frontotemporal Dementia

Background/Aims: Verbal fluency is impaired in patients with frontotemporal dementia (FTD) and primary progressive aphasia (PPA). This study explored qualitative differences in verbal fluency (clustering of words, switching between strategies) between FTD and PPA variants. Methods: Twenty-nine patients with behavioral variant FTD (bvFTD) and 50 with PPA (13 nonfluent/agrammatic, 14 semantic, and 23 logopenic) performed a semantic and letter fluency task. Clustering (number of multiword strings) and switching (number of transitions between clustered and nonclustered words) were recorded by two independent raters. Between-group differences, associations with memory, language, and executive functioning, and longitudinal change (subsample) in clustering and switching were examined. Results: Interrater reliability was high (median 0.98). PPA patients generated (a) smaller (number of) clusters on semantic and letter fluency than bvFTD patients (p < 0.05). Semantic variant patients used more switches than nonfluent/agrammatic or logopenic variant patients (p < 0.05). Clustering in semantic fluency was significantly associated with memory and language (range standardized regression coefficients 0.24-0.38). Switching in letter fluency was associated with executive functioning (0.32-0.35). Conclusion: Clustering and switching in verbal fluency differed between patients with subtypes of FTD and PPA. Qualitative aspects of verbal fluency provide additional information on verbal ability and executive control which can be used for clinically diagnostic purposes

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

Qualitative Assessment of Longitudinal Changes in Phenocopy Frontotemporal Dementia

Phenocopy frontotemporal dementia (phFTD) shares core characteristics with behavioral variant frontotemporal dementia (bvFTD), yet without associated cognitive deficits and brain abnormalities on conventional magnetic resonance imaging (MRI), and without progression. Using advanced MRI techniques, we previously observed subtle structural and functional brain changes in phFTD similar to bvFTD. The aim of the current study was to follow these as well as cognition in phFTD over time, by means of a descriptive case series. Cognition, gray matter (GM) volume and white matter (WM) microstructure, and perfusion of 6 phFTD patients were qualitatively compared longitudinally (3-years follow-up), and cross-sectionally with baseline data from 9 bvFTD patients and 17 controls. For functional brain changes, arterial spin labeling (ASL) was performed to assess GM perfusion. For structural brain changes, diffusion tensor imaging was performed to assess WM microstructure and T1w imaging to assess GM volume. MRI acquisition was performed at 3T (General Electric, US). Clinical profiles of phFTD cases at follow-up are described. At follow-up phFTD patients showed clinical symptomatology similar to bvFTD, but had a relatively stable clinical profile. Longitudinal qualitative comparisons in phFTD showed some deterioration of language and memory function, a stable pattern of structural brain abnormalities and increased perfusion over time. Additionally, both baseline and follow-up cognitive scores and structural values in phFTD were generally in between those of controls and bvFTD. Although a descriptive case series does not allow for strong conclusions, these observations in a unique longitudinal phFTD patient cohort are suggestive of the notion that phFTD and bvFTD may belong to the same disease spectrum. They may also provide a basis for further longitudinal studies in phFTD, specifically exploring the structural vs. functional brain changes. Such studies are essential for improved insight, accurate diagnosis, and appropriate treatment of phFTD

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

Cerebral blood flow in presymptomatic MAPT and GRN mutation carriers: A longitudinal arterial spin labeling study

Objective Frontotemporal dementia (FTD) is characterized by behavioral disturbances and language problems. Familial forms can be caused by genetic defects in microtubule-associated protein tau (MAPT), progranulin (GRN), and C9orf72. In light of upcoming clinical trials with potential disease-modifying agents, the development of sensitive biomarkers to evaluate such agents in the earliest stage of FTD is crucial. In the current longitudinal study we used arterial spin labeling MRI (ASL) in presymptomatic carriers of MAPT and GRN mutations to investigate early changes in cerebral blood flow (CBF). Methods Healthy first-degree relatives of patients with a MAPT or GRN mutation underwent ASL at baseline and follow-up after two years. We investigated cross-sectional and longitudinal differences in CBF between mutation carriers (n = 34) and controls without a mutation (n = 31). Results GRN mutation carriers showed significant frontoparietal hypoperfusion compared with controls at follow-up, whereas we found no cross-sectional group differences in the total study group or the MAPT subgroup. Longitudinal analyses revealed a significantly stronger decrease in CBF in frontal, temporal, parietal, and subcortical areas in the total group of mutation carriers and the GRN subgroup, with the strongest decrease in two mutation carriers who converted to clinical FTD during follow-up. Interpretation We demonstrated longitudinal alterations in CBF in presymptomatic FTD independent of grey matter atrophy, with the strongest decrease in individuals that developed symptoms during follow-up. Therefore, ASL could have the potential to serve as a sensitive biomarker of disease progression in the presymptomatic stage of FTD in future clinical 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

Longitudinal cognitive biomarkers predicting symptom onset in presymptomatic frontotemporal dementia

Introduction: We performed 4-year follow-up neuropsychological assessment to investigate cognitive decline and the prognostic abilities from presymptomatic to symptomatic familial frontotemporal dementia (FTD). Methods: Presymptomatic MAPT (n = 15) and GRN mutation carriers (n = 31), and healthy controls (n = 39) underwent neuropsychological assessment every 2 years. Eight mutation carriers (5 MAPT, 3 GRN) became symptomatic. We investigated cognitive decline with multilevel regression modeling; the prognostic performance was assessed with ROC analyses and stepwise logistic regression. Results: MAPT converters declined on language, attention, executive function, social cognition, and memory, and GRN converters declined on attention and executive function (p < 0.05). Cognitive decline in ScreeLing phonology (p = 0.046) and letter fluency (p = 0.046) were predictive for conversion to non-fluent variant PPA, and decline on categorical fluency (p = 0.025) for an underlying MAPT mutation. Discussion: Using longitudinal neuropsychological assessment, we detected a mutation-specific pattern of cognitive decline, potentially suggesting prognostic value of neuropsychological trajectories in conversion to symptomatic FTD