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

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply. This work therefore presents a new approach for WM tract segmentation: Neuro4Neuro, that is capable of direct extraction of WM tracts from diffusion tensor images using convolutional neural network (CNN). This 3D end-to-end method is trained to segment 25 WM tracts in aging individuals from a large population-based study (N=9752, 1.5T MRI). The proposed method showed good segmentation performance and high reproducibility, i.e., a high spatial agreement (Cohen's kappa, k = 0.72 ~ 0.83) and a low scan-rescan error in tract-specific diffusion measures (e.g., fractional anisotropy: error = 1% ~ 5%). The reproducibility of the proposed method was higher than that of a tractography-based segmentation algorithm, while being orders of magnitude faster (0.5s to segment one tract). In addition, we showed that the method successfully generalizes to diffusion scans from an external dementia dataset (N=58, 3T MRI). In two proof-of-principle experiments, we associated WM microstructure obtained using the proposed method with age in a normal elderly population, and with disease subtypes in a dementia cohort. In concordance with the literature, results showed a widespread reduction of microstructural organization with aging and substantial group-wise microstructure differences between dementia subtypes. In conclusion, we presented a highly reproducible and fast method for WM tract segmentation that has the potential of being used in large-scale studies and clinical practice.Comment: Preprint to be published in NeuroImag

Low-intensity cognitive-behaviour therapy interventions for obsessive-compulsive disorder compared to waiting list for therapist-led cognitive-behaviour therapy: 3-arm randomised controlled trial of clinical effectiveness

Background Obsessive-compulsive disorder (OCD) is prevalent and without adequate treatment usually follows a chronic course. “High-intensity” cognitive-behaviour therapy (CBT) from a specialist therapist is current “best practice.” However, access is difficult because of limited numbers of therapists and because of the disabling effects of OCD symptoms. There is a potential role for “low-intensity” interventions as part of a stepped care model. Low-intensity interventions (written or web-based materials with limited therapist support) can be provided remotely, which has the potential to increase access. However, current evidence concerning low-intensity interventions is insufficient. We aimed to determine the clinical effectiveness of 2 forms of low-intensity CBT prior to high-intensity CBT, in adults meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for OCD. Methods and findings This study was approved by the National Research Ethics Service Committee North West–Lancaster (reference number 11/NW/0276). All participants provided informed consent to take part in the trial. We conducted a 3-arm, multicentre randomised controlled trial in primary- and secondary-care United Kingdom mental health services. All patients were on a waiting list for therapist-led CBT (treatment as usual). Four hundred and seventy-three eligible patients were recruited and randomised. Patients had a median age of 33 years, and 60% were female. The majority were experiencing severe OCD. Patients received 1 of 2 low-intensity interventions: computerised CBT (cCBT; web-based CBT materials and limited telephone support) through “OCFighter” or guided self-help (written CBT materials with limited telephone or face-to-face support). Primary comparisons concerned OCD symptoms, measured using the Yale-Brown Obsessive Compulsive Scale–Observer-Rated (Y-BOCS-OR) at 3, 6, and 12 months. Secondary outcomes included health-related quality of life, depression, anxiety, and functioning. At 3 months, guided self-help demonstrated modest benefits over the waiting list in reducing OCD symptoms (adjusted mean difference = −1.91, 95% CI −3.27 to −0.55). These effects did not reach a prespecified level of “clinically significant benefit.” cCBT did not demonstrate significant benefit (adjusted mean difference = −0.71, 95% CI −2.12 to 0.70). At 12 months, neither guided self-help nor cCBT led to differences in OCD symptoms. Early access to low-intensity interventions led to significant reductions in uptake of high-intensity CBT over 12 months; 86% of the patients allocated to the waiting list for high-intensity CBT started treatment by the end of the trial, compared to 62% in supported cCBT and 57% in guided self-help. These reductions did not compromise longer-term patient outcomes. Data suggested small differences in satisfaction at 3 months, with patients more satisfied with guided self-help than supported cCBT. A significant issue in the interpretation of the results concerns the level of access to high-intensity CBT before the primary outcome assessment. Conclusions We have demonstrated that providing low-intensity interventions does not lead to clinically significant benefits but may reduce uptake of therapist-led CBT

Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: The CADDementia challenge

Algorithms for computer-aided diagnosis of dementia based on structural MRI have demonstrated high performance in the literature, but are difficult to compare as different data sets and methodology were used for evaluation. In addition, it is unclear how the algorithms would perform on previously unseen data, and thus, how they would perform in clinical practice when there is no real opportunity to adapt the algorithm to the data at hand. To address these comparability, generalizability and clinical applicability issues, we organized a grand challenge that aimed to objectively compare algorithms based on a clinically representative multi-center data set. Using clinical practice as the starting point, the goal was to reproduce the clinical diagnosis. Therefore, we evaluated algorithms for multi-class classification of three diagnostic groups: patients with probable Alzheimer's disease, patients with mild cognitive impairment and healthy controls. The diagnosis based on clinical criteria was used as reference standard, as it was the best available reference despite its known limitations. For evaluation, a previously unseen test set was used consisting of 354 T1-weighted MRI scans with the diagnoses blinded. Fifteen research teams participated with a total of 29 algorithms. The algorithms were trained on a small training set (n = 30) and optionally on data from other sources (e.g., the Alzheimer's Disease Neuroimaging Initiative, the Australian Imaging Biomarkers and Lifestyle flagship study of aging). The best performing algorithm yielded an accuracy of 63.0% and an area under the receiver-operating-characteristic curve (AUC) of 78.8%. In general, the best performances were achieved using feature extraction based on voxel-based morphometry or a combination of features that included volume, cortical thickness, shape and intensity. The challenge is open for new submissions via the web-based framework: http://caddementia.grand-challenge.org

Clinical value of cerebrospinal fluid neurofilament light chain in semantic dementia

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.Background: Semantic dementia (SD) is a neurodegenerative disorder characterised by progressive language problems falling within the clinicopathological spectrum of frontotemporal lobar degeneration (FTLD). The development of disease-modifying agents may be facilitated by the relative clinical and pathological homogeneity of SD, but we need robust monitoring biomarkers to measure their efficacy. In different FTLD subtypes, neurofilament light chain (NfL) is a promising marker, therefore we investigated the utility of cerebrospinal fluid (CSF) NfL in SD. Methods: This large retrospective multicentre study compared cross-sectional CSF NfL levels of 162 patients with SD with 65 controls. CSF NfL levels of patients were correlated with clinical parameters (including survival), neuropsychological test scores and regional grey matter atrophy (including longitudinal data in a subset). Results: CSF NfL levels were significantly higher in patients with SD (median: 2326 pg/mL, IQR: 1628-3593) than in controls (577 (446-766), p<0.001). Higher CSF NfL levels were moderately associated with naming impairment as measured by the Boston Naming Test (rs =-0.32, p=0.002) and with smaller grey matter volume of the parahippocampal gyri (rs =-0.31, p=0.004). However, cross-sectional CSF NfL levels were not associated with progression of grey matter atrophy and did not predict survival. Conclusion: CSF NfL is a promising biomarker in the diagnostic process of SD, although it has limited cross-sectional monitoring or prognostic abilities.This study was funded by a Memorabel grant from Deltaplan Dementie (The Netherlands Organisation for Health Research and Development, and Alzheimer Nederland grant number 7330598105), National Institutes of Health (Grants AG010124, AG032953, AG043503, NS088341, AG017586, AG052943, AG038490), the Wyncote Foundation, Dana Foundation, Brightfocus Foundation, Penn Institute on Aging, Pla estratègic de recerca i innovació en salut 2016-2020, Catalan Department of Health (grant number SLT002/16/00408), Italian Ministry of Health (Ricerca Corrente) and the German Federal Ministry of Education and Research (FTLDc 01GI1007A). MS was supported by the Else Kröner-Fresenius-Stiftung. CW was supported by the Vaillant Stiftunginfo:eu-repo/semantics/publishedVersio

Multiparametric computer-aided differential diagnosis of Alzheimer's disease and frontotemporal dementia using structural and advanced MRI

textabstractObjectives: To investigate the added diagnostic value of arterial spin labelling (ASL) and diffusion tensor imaging (DTI) to structural MRI for computer-aided classificat

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease

Effects of systematic partial volume errors on the estimation of gray matter cerebral blood flow with arterial spin labeling MRI

Objective: Partial volume (PV) correction is an important step in arterial spin labeling (ASL) MRI that is used to separate perfusion from structural effects when computing the mean gray matter (GM) perfusion. There are three main methods for performing this correction: (1) GM-threshold, which includes only voxels with GM volume above a preset threshold; (2) GM-weighted, which uses voxel-wise GM contribution combined with thresholding; and (3) PVC, which applies a spatial linear regression algorithm to estimate the flow contribution of each tissue at a given voxel. In all cases, GM volume is obtained using PV maps extracted from the segmentation of the T1-weighted (T1w) image. As such, PV maps contain errors due to the difference in readout type and spatial resolution between ASL and T1w images. Here, we estimated these errors and evaluated their effect on the performance of each PV correction method in computing GM cerebral blood flow (CBF). Materials and methods: Twenty-two volunteers underwent scanning using 2D echo planar imaging (EPI) and 3D spiral ASL. For each PV correction method, GM CBF was computed using PV maps simulated to contain estimated errors due to spatial resolution mismatch and geometric distortions which are caused by the mismatch in readout between ASL and T1w images. Results were analyzed to assess the effect of each error on the estimation of GM CBF from ASL data. Results: Geometric distortion had the largest effect on the 2D EPI data, whereas the 3D spiral was most affected by the resolution mismatch. The PVC method outperformed the GM-threshold even in the presence of combined errors from resolution mismatch and geometric distortions. The quantitative advantage of PVC was 16% without and 10% with the combined errors for both 2D and 3D ASL. Consistent with theoretical expectations, for error-free PV maps, the PVC method extracted the true GM CBF. In contrast, GM-weighted overestimated GM CBF by 5%, while GM-threshold underestimated it by 16%. The presence of PV map errors decreased the calculated GM CBF for all methods. Conclusion: The quality of PV maps presents no argument for the preferential use of the GM-threshold method over PVC in the clinical application of ASL