Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia

Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomarkers, such as regional volumes, as inputs. However, regional aggregates are a crude summary of the underlying high-resolution images, potentially limiting the accuracy of EBM. Therefore, we propose a novel method that exploits high-dimensional voxel-wise imaging biomarkers: n-dimensional discriminative EBM (nDEBM). nDEBM is based on an insight that mixture modeling, which is a key element of conventional EBMs, can be replaced by a more scalable semi-supervised support vector machine (SVM) approach. This SVM is used to estimate the degree of abnormality of each region which is then used to obtain subject-specific disease progression patterns. These patterns are in turn used for estimating the mean ordering by fitting a generalized Mallows model. In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions. SImBioTE trains variational auto-encoders (VAE) in different brain regions independently to simulate images at varying stages of disease progression. We also validate nDEBM clinically using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In both experiments, nDEBM using high-dimensional features gave better performance than state-of-the-art EBM methods using regional volume biomarkers. This suggests that nDEBM is a promising approach for disease progression modeling.Comment: IPMI 201

TADPOLE Challenge: Accurate Alzheimer's disease prediction through crowdsourced forecasting of future data

The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time of forecasting (it was acquired afterwards), and that it focuses on the challenging problem of cohort selection for clinical trials by identifying fast progressors. The submission phase of TADPOLE was open until 15 November 2017; since then data has been acquired until April 2019 from 219 subjects with 223 clinical visits and 150 Magnetic Resonance Imaging (MRI) scans, which was used for the evaluation of the participants' predictions. Thirty-three teams participated with a total of 92 submissions. No single submission was best at predicting all three outcomes. For diagnosis prediction, the best forecast (team Frog), which was based on gradient boosting, obtained a multiclass area under the receiver-operating curve (MAUC) of 0.931, while for ventricle prediction the best forecast (team EMC1), which was based on disease progression modelling and spline regression, obtained mean absolute error of 0.41% of total intracranial volume (ICV). For ADAS-Cog 13, no forecast was considerably better than the benchmark mixed effects model (BenchmarkME), provided to participants before the submission deadline. Further analysis can help understand which input features and algorithms are most suitable for Alzheimer's disease prediction and for aiding patient stratification in clinical trials.Comment: 10 pages, 1 figure, 4 tables. arXiv admin note: substantial text overlap with arXiv:1805.0390

Modelling the cascade of biomarker changes in GRN-related frontotemporal dementia

OBJECTIVE: Progranulin-related frontotemporal dementia (FTD-GRN) is a fast progressive disease. Modelling the cascade of multimodal biomarker changes aids in understanding the aetiology of this disease and enables monitoring of individual mutation carriers. In this cross-sectional study, we estimated the temporal cascade of biomarker changes for FTD-GRN, in a data-driven way. METHODS: We included 56 presymptomatic and 35 symptomatic GRN mutation carriers, and 35 healthy non-carriers. Selected biomarkers were neurofilament light chain (NfL), grey matter volume, white matter microstructure and cognitive domains. We used discriminative event-based modelling to infer the cascade of biomarker changes in FTD-GRN and estimated individual disease severity through cross-validation. We derived the biomarker cascades in non-fluent variant primary progressive aphasia (nfvPPA) and behavioural variant FTD (bvFTD) to understand the differences between these phenotypes. RESULTS: Language functioning and NfL were the earliest abnormal biomarkers in FTD-GRN. White matter tracts were affected before grey matter volume, and the left hemisphere degenerated before the right. Based on individual disease severities, presymptomatic carriers could be delineated from symptomatic carriers with a sensitivity of 100% and specificity of 96.1%. The estimated disease severity strongly correlated with functional severity in nfvPPA, but not in bvFTD. In addition, the biomarker cascade in bvFTD showed more uncertainty than nfvPPA. CONCLUSION: Degeneration of axons and language deficits are indicated to be the earliest biomarkers in FTD-GRN, with bvFTD being more heterogeneous in disease progression than nfvPPA. Our data-driven model could help identify presymptomatic GRN mutation carriers at risk of conversion to the clinical stage

Modeling the brain morphology distribution in the general aging population

<p>Both normal aging and neurodegenerative diseases such as Alzheimer's disease cause morphological changes of the brain. To better distinguish between normal and abnormal cases, it is necessary to model changes in brain morphology owing to normal aging. To this end, we developed a method for analyzing and visualizing these changes for the entire brain morphology distribution in the general aging population. The method is applied to 1000 subjects from a large population imaging study in the elderly, from which 900 were used to train the model and 100 were used for testing. The results of the 100 test subjects show that the model generalizes to subjects outside the model population. Smooth percentile curves showing the brain morphology changes as a function of age and spatiotemporal atlases derived from the model population are publicly available via an interactive web application at agingbrain.bigr.nl.</p

Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease

Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. // Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. // Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2EBM = 0.866; R2DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. // Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain

Binocular summation and other forms of non-dominant eye contribution in individuals with strabismic amblyopia during habitual viewing

YesAdults with amblyopia ('lazy eye'), long-standing strabismus (ocular misalignment) or both typically do not experience visual symptoms because the signal from weaker eye is given less weight than the signal from its fellow. Here we examine the contribution of the weaker eye of individuals with strabismus and amblyopia with both eyes open and with the deviating eye in its anomalous motor position. The task consisted of a blue-on-yellow detection task along a horizontal line across the central 50 degrees of the visual field. We compare the results obtained in ten individuals with strabismic amblyopia with ten visual normals. At each field location in each participant, we examined how the sensitivity exhibited under binocular conditions compared with sensitivity from four predictions, (i) a model of binocular summation, (ii) the average of the monocular sensitivities, (iii) dominant-eye sensitivity or (iv) non-dominant-eye sensitivity. The proportion of field locations for which the binocular summation model provided the best description of binocular sensitivity was similar in normals (50.6%) and amblyopes (48.2%). Average monocular sensitivity matched binocular sensitivity in 14.1% of amblyopes' field locations compared to 8.8% of normals'. Dominant-eye sensitivity explained sensitivity at 27.1% of field locations in amblyopes but 21.2% in normals. Non-dominant-eye sensitivity explained sensitivity at 10.6% of field locations in amblyopes but 19.4% in normals. Binocular summation provided the best description of the sensitivity profile in 6/10 amblyopes compared to 7/10 of normals. In three amblyopes, dominant-eye sensitivity most closely reflected binocular sensitivity (compared to two normals) and in the remaining amblyope, binocular sensitivity approximated to an average of the monocular sensitivities. Our results suggest a strong positive contribution in habitual viewing from the non-dominant eye in strabismic amblyopes. This is consistent with evidence from other sources that binocular mechanisms are frequently intact in strabismic and amblyopic individuals

A data-driven disease progression model of fluid biomarkers in genetic frontotemporal dementia

Several CSF and blood biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), astrogliosis (glial fibrillary acidic protein (GFAP)), and complement activation (C1q, C3b). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging and help identify mutation carriers with prodromal or early-stage FTD, which is especially important as pharmaceutical trials emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross-sectional data from the Genetic Frontotemporal dementia Initiative (GENFI), a longitudinal cohort study. 275 presymptomatic and 127 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non-carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of sample collection ('converters'). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event-based modelling (DEBM) and for each genetic subgroup using co-initialised DEBM. These models estimate probabilistic biomarker abnormalities in a data-driven way and do not rely on prior diagnostic information or biomarker cut-off points. Using cross-validation, subjects were subsequently assigned a disease stage based on their position along the disease progression timeline. CSF NPTX2 was the first biomarker to become abnormal, followed by blood and CSF NfL, blood pNfH, blood GFAP, and finally CSF C3b and C1q. Biomarker orderings did not differ significantly between genetic subgroups, but more uncertainty was noted in the C9orf72 and MAPT groups than for GRN. Estimated disease stages could distinguish symptomatic from presymptomatic carriers and non-carriers with areas under the curve (AUC) of 0.84 (95% confidence interval 0.80-0.89) and 0.90 (0.86-0.94) respectively. The AUC to distinguish converters from non-converting presymptomatic carriers was 0.85 (0.75-0.95). Our data-driven model of genetic FTD revealed that NPTX2 and NfL are the earliest to change among the selected biomarkers. Further research should investigate their utility as candidate selection tools for pharmaceutical trials. The model's ability to accurately estimate individual disease stages could improve patient stratification and track the efficacy of therapeutic interventions

Structural Insights into Viral Determinants of Nematode Mediated Grapevine fanleaf virus Transmission

Many animal and plant viruses rely on vectors for their transmission from host to host. Grapevine fanleaf virus (GFLV), a picorna-like virus from plants, is transmitted specifically by the ectoparasitic nematode Xiphinema index. The icosahedral capsid of GFLV, which consists of 60 identical coat protein subunits (CP), carries the determinants of this specificity. Here, we provide novel insight into GFLV transmission by nematodes through a comparative structural and functional analysis of two GFLV variants. We isolated a mutant GFLV strain (GFLV-TD) poorly transmissible by nematodes, and showed that the transmission defect is due to a glycine to aspartate mutation at position 297 (Gly297Asp) in the CP. We next determined the crystal structures of the wild-type GFLV strain F13 at 3.0 Å and of GFLV-TD at 2.7 Å resolution. The Gly297Asp mutation mapped to an exposed loop at the outer surface of the capsid and did not affect the conformation of the assembled capsid, nor of individual CP molecules. The loop is part of a positively charged pocket that includes a previously identified determinant of transmission. We propose that this pocket is a ligand-binding site with essential function in GFLV transmission by X. index. Our data suggest that perturbation of the electrostatic landscape of this pocket affects the interaction of the virion with specific receptors of the nematode's feeding apparatus, and thereby severely diminishes its transmission efficiency. These data provide a first structural insight into the interactions between a plant virus and a nematode vector

A Limited Role for Suppression in the Central Field of Individuals with Strabismic Amblyopia.

yesBackground: Although their eyes are pointing in different directions, people with long-standing strabismic amblyopia typically do not experience double-vision or indeed any visual symptoms arising from their condition. It is generally believed that the phenomenon of suppression plays a major role in dealing with the consequences of amblyopia and strabismus, by preventing images from the weaker/deviating eye from reaching conscious awareness. Suppression is thus a highly sophisticated coping mechanism. Although suppression has been studied for over 100 years the literature is equivocal in relation to the extent of the retina that is suppressed, though the method used to investigate suppression is crucial to the outcome. There is growing evidence that some measurement methods lead to artefactual claims that suppression exists when it does not. Methodology/Results: Here we present the results of an experiment conducted with a new method to examine the prevalence, depth and extent of suppression in ten individuals with strabismic amblyopia. Seven subjects (70%) showed no evidence whatsoever for suppression and in the three individuals who did (30%), the depth and extent of suppression was small. Conclusions: Suppression may play a much smaller role in dealing with the negative consequences of strabismic amblyopia than previously thought. Whereas recent claims of this nature have been made only in those with micro-strabismus our results show extremely limited evidence for suppression across the central visual field in strabismic amblyopes more generally. Instead of suppressing the image from the weaker/deviating eye, we suggest the visual system of individuals with strabismic amblyopia may act to maximise the possibilities for binocular co-operation. This is consistent with recent evidence from strabismic and amblyopic individuals that their binocular mechanisms are intact, and that, just as in visual normals, performance with two eyes is better than with the better eye alone in these individuals

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guessing. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as patient-specific biomarker trends. The submission system remains open via the website https://tadpole.grand-challenge.org, while code for submissions is being collated by TADPOLE SHARE: https://tadpole-share.github.io/. Our work suggests that current prediction algorithms are accurate for biomarkers related to clinical diagnosis and ventricle volume, opening up the possibility of cohort refinement in clinical trials for Alzheimer's disease