Preictal dysfunctions of inhibitory interneurons paradoxically lead to their rebound hyperactivity and to low-voltage-fast onset seizures in Dravet syndrome.

Epilepsies have numerous specific mechanisms. The understanding of neural dynamics leading to seizures is important for disclosing pathological mechanisms and developing therapeutic approaches. We investigated electrographic activities and neural dynamics leading to convulsive seizures in patients and mouse models of Dravet syndrome (DS), a developmental and epileptic encephalopathy in which hypoexcitability of GABAergic neurons is considered to be the main dysfunction. We analyzed EEGs from DS patients carrying a SCN1A pathogenic variant, as well as epidural electrocorticograms, hippocampal local field potentials, and hippocampal single-unit neuronal activities in Scn1a+/- and Scn1aRH/+ DS mice. Strikingly, most seizures had low-voltage-fast onset in both patients and mice, which is thought to be generated by hyperactivity of GABAergic interneurons, the opposite of the main pathological mechanism of DS. Analyzing single-unit recordings, we observed that temporal disorganization of the firing of putative interneurons in the period immediately before the seizure (preictal) precedes the increase of their activity at seizure onset, together with the entire neuronal network. Moreover, we found early signatures of the preictal period in the spectral features of hippocampal and cortical field potential of Scn1a mice and of patients' EEG, which are consistent with the dysfunctions that we observed in single neurons and that allowed seizure prediction. Therefore, the perturbed preictal activity of interneurons leads to their hyperactivity at the onset of generalized seizures, which have low-voltage-fast features that are similar to those observed in other epilepsies and are triggered by hyperactivity of GABAergic neurons. Preictal spectral features may be used as predictive seizure biomarkers.

A 13- to 17-year Retrospective Evaluation of the Clinical Performances of Anterior and Posterior Lithium Disilicate Restorations onto Teeth and Implants.

This retrospective study aimed at evaluating the clinical outcomes of lithium disilicate prostheses onto teeth and implants. A total of 860 restorations were delivered to 312 patients, including crowns, veneers and onlays. Patients with uncontrolled gingival inflammation and/or periodontitis were excluded, whilst subjects with occlusal parafunctions were included. The retrospective observational period ranged between 13 to 17 years. The mechanical and esthetic performances of the restorations were rated according to the modified CDA criteria. The recorded data were analyzed statistically. In total, 26 mechanical complications were noticed: 17 ceramic chippings, 5 core fractures and 4 losses of retention. Mechanical complications occurred predominantly in posterior areas; monolithic prostheses showed the lowest percentage of structural problems. The clinical scores of layered and monolithic restorations were fully satisfactory according to the modified CDA rating. The cumulative survival and success rates ranged between 95.46-100% and 93.75-100% respectively up to 17 years of follow-up. Although patient selection and the rigorous application of validated clinical protocols were considered paramount, the use of lithium disilicate prostheses onto teeth and implants was reported to be a viable and reliable treatment option in the long-term.

Artificial intelligence applied to MRI data to tackle key challenges in multiple sclerosis.

Artificial intelligence (AI) is the branch of science aiming at creating algorithms able to carry out tasks that typically require human intelligence. In medicine, there has been a tremendous increase in AI applications thanks to increasingly powerful computers and the emergence of big data repositories. Multiple sclerosis (MS) is a chronic autoimmune condition affecting the central nervous system with a complex pathogenesis, a challenging diagnostic process strongly relying on magnetic resonance imaging (MRI) and a high and largely unexplained variability across patients. Therefore, AI applications in MS have the great potential of helping us better support the diagnosis, find markers for prognosis to eventually design more powerful randomised clinical trials and improve patient management in clinical practice and eventually understand the mechanisms of the disease. This topical review aims to summarise the recent advances in AI applied to MRI data in MS to illustrate its achievements, limitations and future directions.

Privacy preserving image registration.

Image registration is a key task in medical imaging applications, allowing to represent medical images in a common spatial reference frame. Current approaches to image registration are generally based on the assumption that the content of the images is usually accessible in clear form, from which the spatial transformation is subsequently estimated. This common assumption may not be met in practical applications, since the sensitive nature of medical images may ultimately require their analysis under privacy constraints, preventing to openly share the image content. In this work, we formulate the problem of image registration under a privacy preserving regime, where images are assumed to be confidential and cannot be disclosed in clear. We derive our privacy preserving image registration framework by extending classical registration paradigms to account for advanced cryptographic tools, such as secure multi-party computation and homomorphic encryption, that enable the execution of operations without leaking the underlying data. To overcome the problem of performance and scalability of cryptographic tools in high dimensions, we propose several techniques to optimize the image registration operations by using gradient approximations, and by revisiting the use of homomorphic encryption trough packing, to allow the efficient encryption and multiplication of large matrices. We focus on registration methods of increasing complexity, including rigid, affine, and non-linear registration based on cubic splines or diffeomorphisms parameterized by time-varying velocity fields. In all these settings, we demonstrate how the registration problem can be naturally adapted for accounting to privacy-preserving operations, and illustrate the effectiveness of PPIR on a variety of registration tasks.

A retrospective Italian Society of Veterinary Oncology (SIONCOV) study of 56 cats with appendicular osteosarcoma.

Osteosarcoma is the most common malignant primary bone cancer, but it is infrequently reported in cats. Feline appendicular osteosarcoma typically exhibits good prognosis when treated with surgery alone. A retrospective multi-institutional study was conducted to identify possible prognostic factors. Cats diagnosed with appendicular osteosarcoma were included if initial staging and follow-up information were available. Data including signalment, tumour characteristics, treatment modalities, and survival outcomes were collected and analysed. Fifty-six cats were included; the femur was the most frequently affected bone. Eight cats had distant metastasis at admission and an additional 9 developed metastatic disease during follow-up, resulting in an overall metastatic rate of 30%. Forty-nine (87.5%) cats underwent surgery, and 4 also received adjuvant chemotherapy. Among operated cats, median time to local progression (TTLP), time to distant progression and tumour-specific survival (TSS) were not reached. One- and 2-year survival rates were 66% and 55%, respectively. Seven (12.5%) cats received no treatment; 1- and 2-year survival rates were 25% and 0%, respectively. Operated cats had significantly longer TTLP (P < .001) and TSS (P = .001) compared with non-operated cats. Among operated cats, young age negatively impacted local tumour progression, while the presence of distant metastasis at diagnosis was associated with a higher risk of tumour-related death. This study reaffirms the good prognosis for cats with appendicular osteosarcoma undergoing surgery, but sheds light on some additional factors to consider. Accurate initial staging is recommended, as the metastatic rate may exceed many previous estimations. Surgery substantially extends survival time, whereas the role of chemotherapy remains uncertain.

Evaluation of Prosthetic Marginal Fit and Implant Survival Rates for Conventional and Digital Workflows in Full-Arch Immediate Loading Rehabilitations: A Retrospective Clinical Study.

Digital impression provides several advantages in implant prosthodontics; however, its use in full-arch rehabilitations, especially immediately after surgery, has yet to be validated. The aim of this study was to retrospectively analyse the fit of immediate full-arch prostheses, fabricated using conventional or digital impressions. Patients requiring a full-arch immediate loading rehabilitation were divided into three groups: T1 (digital impression taken immediately after surgery), T2 (Preoperative digital impression, guided surgery-prefabricated temporary bridge) and C (conventional impression taken immediately after surgery). Immediate temporary prostheses were delivered within 24 h after surgery. X-rays were obtained at the time of prosthesis delivery and at the 2-year follow-up. Primary outcomes were cumulative survival rate (CSR) and prosthesis fit. Secondary outcomes were marginal bone level (MBL) and patient satisfaction. One hundred and fifty patients were treated from 2018 to 2020, with 50 in each group. Seven implants failed during the observation period. The CSR was 99% for T1, 98% for T2 and 99.5% for C. A statistically significant difference in prosthesis fit was found among T1 and T2 vs. C. A statistically significant difference was found in the MBL between T1 and C. The outcomes of the present study suggest that digital impression is a viable alternative to conventional protocols for the realisation of full-arch immediate loading prostheses.

First pilot case-control interventional study using autologous extracellular vesicles to treat chronic venous ulcers unresponsive to conventional treatments.

Current therapeutic approaches for chronic venous ulcers (CVUs) still require evidence of effectiveness. Diverse sources of extracellular vesicles (EVs) have been proposed for tissue regeneration, however the lack of potency tests, to predict in-vivo effectiveness, and a reliable scalability have delayed their clinical application. This study aimed to investigate whether autologous serum-derived EVs (s-EVs), recovered from patients with CVUs, may be a proper therapeutic approach to improve the healing process. A pilot case-control interventional study (CS2/1095/0090491) has been designed and s-EVs recovered from patients. Patient eligibility included two or more distinct chronic lesions in the same limb with 11 months as median persistence of active ulcer before enrollment. Patients were treated three times a week, for 2 weeks. Qualitative CVU analysis demonstrated that s-EVs-treated lesions displayed a higher percentage of granulation tissue compared to the control group (Sham) (s-EVs 3 out of 5: 75-100 % vs Sham: none), further confirmed at day 30. s-EVs-treated lesions also displayed higher sloughy tissue reduction at the end of treatment even increased at day 30. Additionally, s-EV treatment led to a median surface reduction of 151 mm2 compared to 84 mm2 in the Sham group, difference even more evident at day 30 (s-EVs 385 mm2vs Sham 106 mm2p = 0.004). Consistent with the enrichment of transforming growth factor-ß1 in s-EVs, histological analyses showed a regenerative tissue with an increase in microvascular proliferation areas. This study first demonstrates the clinical effectiveness of autologous s-EVs in promoting the healing process of CVUs unresponsive to conventional treatments.

MC-RVAE: Multi-channel recurrent variational autoencoder for multimodal Alzheimer's disease progression modelling.

The progression of neurodegenerative diseases, such as Alzheimer's Disease, is the result of complex mechanisms interacting across multiple spatial and temporal scales. Understanding and predicting the longitudinal course of the disease requires harnessing the variability across different data modalities and time, which is extremely challenging. In this paper, we propose a model based on recurrent variational autoencoders that is able to capture cross-channel interactions between different modalities and model temporal information. These are achieved thanks to its multi-channel architecture and its shared latent variational space, parametrized with a recurrent neural network. We evaluate our model on both synthetic and real longitudinal datasets, the latter including imaging and non-imaging data, with N=897 subjects. Results show that our multi-channel recurrent variational autoencoder outperforms a set of baselines (KNN, random forest, and group factor analysis) for the task of reconstructing missing modalities, reducing the mean absolute error by 5% (w.r.t. the best baseline) for both subcortical volumes and cortical thickness. Our model is robust to missing features within each modality and is able to generate realistic synthetic imaging biomarkers trajectories from cognitive scores.

Investigating the role of the strong field ligands in [FeFe] hydrogenase: spectroscopic and functional characterization of a semi-synthetic mono-cyanide active site.

Artificial maturation of hydrogenases provides a path towards generating new semi-synthetic enzymes with novel catalytic properties. Here enzymes featuring a synthetic asymmetric mono-cyanide cofactor have been prepared using two different hydrogenase scaffolds. Their structure and reactivity was investigated in order to elucidate the design rationale behind the native di-cyanide cofactor, and by extension the second coordination sphere of the active-site pocket. Surprisingly, the choice of host enzyme was found to have a dramatic impact on reactivity. Moreover, the study shows that synthetic manipulations of the active-site can significantly increase inhibitor tolerance, as compared to native [FeFe] hydrogenase, while retaining the enzyme's native capacity for reversible catalysis.

Convalescent or standard plasma versus standard of care in the treatment of COVID-19 patients with respiratory impairment: short and long-term effects. A three-arm randomized controlled clinical trial.

BACKGROUND: The efficacy of early treatment with convalescent plasma in patients with COVID-19 is debated. Nothing is known about the potential effect of other plasma components other than anti-SARS-CoV-2 antibodies. METHODS: To determine whether convalescent or standard plasma would improve outcomes for adults in early phase of Covid19 respiratory impairment we designed this randomized, three-arms, clinical trial (PLACO COVID) blinded on interventional arms that was conducted from June 2020 to August 2021. It was a multicentric trial at 19 Italian hospitals. We enrolled 180 hospitalized adult patients with COVID-19 pneumonia within 5 days from the onset of respiratory distress. Patients were randomly assigned in a 1:1:1 ratio to standard of care (n = 60) or standard of care + three units of standard plasma (n = 60) or standard of care + three units of high-titre convalescent plasma (n = 60) administered on days 1, 3, 5 after randomization. Primary outcome was 30-days mortality. Secondary outcomes were: incidence of mechanical ventilation or death at day 30, 6-month mortality, proportion of days with mechanical ventilation on total length of hospital stay, IgG anti-SARS-CoV-2 seroconversion, viral clearance from plasma and respiratory tract samples, and variations in Sequential Organ Failure Assessment score. The trial was analysed according to the intention-to-treat principle. RESULTS: 180 patients (133/180 [73.9%] males, mean age 66.6 years [IQR 57-73]) were enrolled a median of 8 days from onset of symptoms. At enrollment, 88.9% of patients showed moderate/severe respiratory failure. 30-days mortality was 20% in Control arm, 23% in Convalescent (risk ratio [RR] 1.13; 95% confidence interval [CI], 0.61-2.13, P = 0.694) and 25% in Standard plasma (RR 1.23; 95%CI, 0.63-2.37, P = 0.544). Time to viral clearance from respiratory tract was 21 days for Convalescent, 28 for Standard plasma and 23 in Control arm but differences were not statistically significant. No differences for other secondary endpoints were seen in the three arms. Serious adverse events were reported in 1.7%, 3.3% and 5% of patients in Control, Standard and Convalescent plasma arms respectively. CONCLUSIONS: Neither high-titer Convalescent nor Standard plasma improve outcomes of COVID-19 patients with acute respiratory failure. Trial Registration Clinicaltrials.gov Identifier: NCT04428021. First posted: 11/06/2020.

Light-Driven [FeFe] Hydrogenase Based H2 Production in E. coli: A Model Reaction for Exploring E. coli Based Semiartificial Photosynthetic Systems.

Biohybrid technologies like semiartificial photosynthesis are attracting increased attention, as they enable the combination of highly efficient synthetic light-harvesters with the self-healing and outstanding performance of biocatalysis. However, such systems are intrinsically complex, with multiple interacting components. Herein, we explore a whole-cell photocatalytic system for hydrogen (H2) gas production as a model system for semiartificial photosynthesis. The employed whole-cell photocatalytic system is based on Escherichia coli cells heterologously expressing a highly efficient, but oxygen-sensitive, [FeFe] hydrogenase. The system is driven by the organic photosensitizer eosin Y under broad-spectrum white light illumination. The direct involvement of the [FeFe] hydrogenase in the catalytic reaction is verified spectroscopically. We also observe that E. coli provides protection against O2 damage, underscoring the suitability of this host organism for oxygen-sensitive enzymes in the development of (photo) catalytic biohybrid systems. Moreover, the study shows how factorial experimental design combined with analysis of variance (ANOVA) can be employed to identify relevant variables, as well as their interconnectivity, on both overall catalytic performance and O2 tolerance.

Polymer Dots as Photoactive Membrane Vesicles for [FeFe]-Hydrogenase Self-Assembly and Solar-Driven Hydrogen Evolution.

A semiartificial photosynthesis approach that utilizes enzymes for solar fuel production relies on efficient photosensitizers that should match the enzyme activity and enable long-term stability. Polymer dots (Pdots) are biocompatible photosensitizers that are stable at pH 7 and have a readily modifiable surface morphology. Therefore, Pdots can be considered potential photosensitizers to drive such enzyme-based systems for solar fuel formation. This work introduces and unveils in detail the interaction within the biohybrid assembly composed of binary Pdots and the HydA1 [FeFe]-hydrogenase from Chlamydomonas reinhardtii. The direct attachment of hydrogenase on the surface of toroid-shaped Pdots was confirmed by agarose gel electrophoresis, cryogenic transmission electron microscopy (Cryo-TEM), and cryogenic electron tomography (Cryo-ET). Ultrafast transient spectroscopic techniques were used to characterize photoinduced excitation and dissociation into charges within Pdots. The study reveals that implementation of a donor-acceptor architecture for heterojunction Pdots leads to efficient subpicosecond charge separation and thus enhances hydrogen evolution (88 460 µmolH2·gH2ase-1·h-1). Adsorption of [FeFe]-hydrogenase onto Pdots resulted in a stable biohybrid assembly, where hydrogen production persisted for days, reaching a TON of 37 500 ± 1290 in the presence of a redox mediator. This work represents an example of a homogeneous biohybrid system combining polymer nanoparticles and an enzyme. Detailed spectroscopic studies provide a mechanistic understanding of light harvesting, charge separation, and transport studied, which is essential for building semiartificial photosynthetic systems with efficiencies beyond natural and artificial systems.

Hydride state accumulation in native [FeFe]-hydrogenase with the physiological reductant H2 supports its catalytic relevance.

Small molecules in solution may interfere with mechanistic investigations, as they can affect the stability of catalytic states and produce off-cycle states that can be mistaken for catalytically relevant species. Here we show that the hydride state (Hhyd), a proposed central intermediate in the catalytic cycle of [FeFe]-hydrogenase, can be formed in wild-type [FeFe]-hydrogenases treated with H2 in absence of other, non-biological, reductants. Moreover, we reveal a new state with unclear role in catalysis induced by common low pH buffers.

SimulAD: a dynamical model for personalized simulation and disease staging in Alzheimer's disease.

SimulAD is a computational disease progression model (DPM) originally developed on the ADNI database to simulate the evolution of clinical and imaging markers characteristic of AD, and to quantify the disease severity (DS) of a subject. In this work, we assessed the validity of this estimated DS, as well as the generalization of the DPM., by applying SimulAD on a new cohort from the Geneva Memory Center (GMC). The differences between the estimated DS of healthy, mild cognitive impairment and AD dementia groups were statistically significant (p-values < 0.05; d ≥ 0.8). DS correlated with MMSE (ρ = -0.55), hippocampal atrophy (ρ = -0.62), glucose hypometabolism (ρ = -0.67), amyloid burden (ρ = 0.31) and tau deposition (ρ = 0.62) (p-values < 0.01). Based on the dynamics estimated on the ADNI cohort, we simulated a DPM for the subjects of the GMC cohort. The difference between the temporal evolution of similar biomarkers simulated on the ADNI and GMC cohorts remained below 10%. This study illustrates the robustness and good generalization of SimulAD, highlighting its potential for clinical and pharmaceutical studies.

Stability of the H-cluster under whole-cell conditions-formation of an Htrans-like state and its reactivity towards oxygen.

Hydrogenases are metalloenzymes that catalyze the reversible oxidation of molecular hydrogen into protons and electrons. For this purpose, [FeFe]-hydrogenases utilize a hexanuclear iron cofactor, the H-cluster. This biologically unique cofactor provides the enzyme with outstanding catalytic activities, but it is also highly oxygen sensitive. Under in vitro conditions, oxygen stable forms of the H-cluster denoted Htrans and Hinact can be generated via treatment with sulfide under oxidizing conditions. Herein, we show that an Htrans-like species forms spontaneously under intracellular conditions on a time scale of hours, concurrent with the cells ceasing H2 production. Addition of cysteine or sulfide during the maturation promotes the formation of this H-cluster state. Moreover, it is found that formation of the observed Htrans-like species is influenced by both steric factors and proton transfer, underscoring the importance of outer coordination sphere effects on H-cluster reactivity.

Predicting Visual Fields From Optical Coherence Tomography via an Ensemble of Deep Representation Learners.

PURPOSE: To develop and validate a deep learning method of predicting visual function from spectral domain optical coherence tomography (SD-OCT)-derived retinal nerve fiber layer thickness (RNFLT) measurements and corresponding SD-OCT images. DESIGN: Development and evaluation of diagnostic technology. METHODS: Two deep learning ensemble models to predict pointwise VF sensitivity from SD-OCT images (model 1: RNFLT profile only; model 2: RNFLT profile plus SD-OCT image) and 2 reference models were developed. All models were tested in an independent test-retest data set comprising 2181 SD-OCT/VF pairs; the median of ∼10 VFs per eye was taken as the best available estimate (BAE) of the true VF. The performance of single VFs predicting the BAE VF was also evaluated. The training data set comprised 954 eyes of 220 healthy and 332 glaucomatous participants, and the test data set, 144 eyes of 72 glaucomatous participants. The main outcome measures included the pointwise prediction mean error (ME), mean absolute error (MAE), and correlation of predictions with the BAE VF sensitivity. RESULTS: The median mean deviation was -4.17 dB (-14.22 to 0.88). Model 2 had excellent accuracy (ME 0.5 dB, SD 0.8) and overall performance (MAE 2.3 dB, SD 3.1), and significantly (paired t test) outperformed the other methods. For single VFs predicting the BAE VF, the pointwise MAE was 1.5 dB (SD 0.7). The association between SD-OCT and single VF predictions of the BAE pointwise VF sensitivities was R2 = 0.78 and R2 = 0.88, respectively. CONCLUSIONS: Our method outperformed standard statistical and deep learning approaches. Predictions of BAEs from OCT images approached the accuracy of single real VF estimates of the BAE.

Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas.

Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation.

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.

Revealing the Timeline of Structural MRI Changes in Premanifest to Manifest Huntington Disease.

BACKGROUND AND OBJECTIVES: Longitudinal measurements of brain atrophy using structural MRI (sMRI) can provide powerful markers for tracking disease progression in neurodegenerative diseases. In this study, we use a disease progression model to learn individual-level disease times and hence reveal a new timeline of sMRI changes in Huntington disease (HD). METHODS: We use data from the 2 largest cohort imaging studies in HD-284 participants from TRACK-HD (100 control, 104 premanifest, and 80 manifest) and 159 participants from PREDICT-HD (36 control and 128 premanifest)-to train and test the model. We longitudinally register T1-weighted sMRI scans from 3 consecutive time points to reduce intraindividual variability and calculate regional brain volumes using an automated segmentation tool with rigorous manual quality control. RESULTS: Our model reveals, for the first time, the relative magnitude and timescale of subcortical and cortical atrophy changes in HD. We find that the largest (∼20% average change in magnitude) and earliest (∼2 years before average abnormality) changes occur in the subcortex (pallidum, putamen, and caudate), followed by a cascade of changes across other subcortical and cortical regions over a period of ∼11 years. We also show that sMRI, when combined with our disease progression model, provides improved prediction of onset over the current best method (root mean square error = 4.5 years and maximum error = 7.9 years vs root mean square error = 6.6 years and maximum error = 18.2 years). DISCUSSION: Our findings support the use of disease progression modeling to reveal new information from sMRI, which can potentially inform imaging marker selection for clinical trials.

Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data.

In this study, we investigate SimulAD, a novel quantitative instrument for the development of intervention strategies for disease-modifying drugs in Alzheimer's disease. SimulAD is based on the modeling of the spatio-temporal dynamics governing the joint evolution of imaging and clinical biomarkers along the history of the disease, and allows the simulation of the effect of intervention time and drug dosage on the biomarkers' progression. When applied to multi-modal imaging and clinical data from the Alzheimer's Disease Neuroimaging Initiative the method enables to generate hypothetical scenarios of amyloid lowering interventions. The results quantify the crucial role of intervention time, and provide a theoretical justification for testing amyloid modifying drugs in the pre-clinical stage. Our experimental simulations are compatible with the outcomes observed in past clinical trials, and suggest that anti-amyloid treatments should be administered at least 7 years earlier than what is currently being done in order to obtain statistically powered improvement of clinical endpoints.