414 research outputs found
A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments
In this study we propose a deformation-based framework to jointly model the
influence of aging and Alzheimer's disease (AD) on the brain morphological
evolution. Our approach combines a spatio-temporal description of both
processes into a generative model. A reference morphology is deformed along
specific trajectories to match subject specific morphologies. It is used to
define two imaging progression markers: 1) a morphological age and 2) a disease
score. These markers can be computed locally in any brain region. The approach
is evaluated on brain structural magnetic resonance images (MRI) from the ADNI
database. The generative model is first estimated on a control population,
then, for each subject, the markers are computed for each acquisition. The
longitudinal evolution of these markers is then studied in relation with the
clinical diagnosis of the subjects and used to generate possible morphological
evolution. In the model, the morphological changes associated with normal aging
are mainly found around the ventricles, while the Alzheimer's disease specific
changes are more located in the temporal lobe and the hippocampal area. The
statistical analysis of these markers highlights differences between clinical
conditions even though the inter-subject variability is quiet high. In this
context, the model can be used to generate plausible morphological trajectories
associated with the disease. Our method gives two interpretable scalar imaging
biomarkers assessing the effects of aging and disease on brain morphology at
the individual and population level. These markers confirm an acceleration of
apparent aging for Alzheimer's subjects and can help discriminate clinical
conditions even in prodromal stages. More generally, the joint modeling of
normal and pathological evolutions shows promising results to describe
age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres
Modeling and inference of spatio-temporal protein dynamics across brain networks
Models of misfolded proteins (MP) aim at discovering the bio-mechanical
propagation properties of neurological diseases (ND) by identifying plausible
associated dynamical systems. Solving these systems along the full disease
trajectory is usually challenging, due to the lack of a well defined time axis
for the pathology. This issue is addressed by disease progression models (DPM)
where long-term progression trajectories are estimated via time
reparametrization of individual observations. However, due to their loose
assumptions on the dynamics, DPM do not provide insights on the bio-mechanical
properties of MP propagation. Here we propose a unified model of
spatio-temporal protein dynamics based on the joint estimation of long-term MP
dynamics and time reparameterization of individuals observations. The model is
expressed within a Gaussian Process (GP) regression setting, where constraints
on the MP dynamics are imposed through non--linear dynamical systems. We use
stochastic variational inference on both GP and dynamical system parameters for
scalable inference and uncertainty quantification of the trajectories.
Experiments on simulated data show that our model accurately recovers
prescribed rates along graph dynamics and precisely reconstructs the underlying
progression. When applied to brain imaging data our model allows the
bio-mechanical interpretation of amyloid deposition in Alzheimer's disease,
leading to plausible simulations of MP propagation, and achieving accurate
predictions of individual MP deposition in unseen data
Disease Progression Modeling and Prediction through Random Effect Gaussian Processes and Time Transformation
The development of statistical approaches for the joint modelling of the
temporal changes of imaging, biochemical, and clinical biomarkers is of
paramount importance for improving the understanding of neurodegenerative
disorders, and for providing a reference for the prediction and quantification
of the pathology in unseen individuals. Nonetheless, the use of disease
progression models for probabilistic predictions still requires investigation,
for example for accounting for missing observations in clinical data, and for
accurate uncertainty quantification. We tackle this problem by proposing a
novel Gaussian process-based method for the joint modeling of imaging and
clinical biomarker progressions from time series of individual observations.
The model is formulated to account for individual random effects and time
reparameterization, allowing non-parametric estimates of the biomarker
evolution, as well as high flexibility in specifying correlation structure, and
time transformation models. Thanks to the Bayesian formulation, the model
naturally accounts for missing data, and allows for uncertainty quantification
in the estimate of evolutions, as well as for probabilistic prediction of
disease staging in unseen patients. The experimental results show that the
proposed model provides a biologically plausible description of the evolution
of Alzheimer's pathology across the whole disease time-span as well as
remarkable predictive performance when tested on a large clinical cohort with
missing observations.Comment: 13 pages, 2 figure
Free-rider Attacks on Model Aggregation in Federated Learning
Free-rider attacks against federated learning consist in dissimulating
participation to the federated learning process with the goal of obtaining the
final aggregated model without actually contributing with any data. This kind
of attacks is critical in sensitive applications of federated learning, where
data is scarce and the model has high commercial value. We introduce here the
first theoretical and experimental analysis of free-rider attacks on federated
learning schemes based on iterative parameters aggregation, such as FedAvg or
FedProx, and provide formal guarantees for these attacks to converge to the
aggregated models of the fair participants. We first show that a
straightforward implementation of this attack can be simply achieved by not
updating the local parameters during the iterative federated optimization. As
this attack can be detected by adopting simple countermeasures at the server
level, we subsequently study more complex disguising schemes based on
stochastic updates of the free-rider parameters. We demonstrate the proposed
strategies on a number of experimental scenarios, in both iid and non-iid
settings. We conclude by providing recommendations to avoid free-rider attacks
in real world applications of federated learning, especially in sensitive
domains where security of data and models is critical
Processi per la fabbricazione di cristalli fotonici bidimensionali
Questo lavoro di tesi ha lo scopo di studiare e mettere a punto i processi necessari alla fabbricazione di cristalli fotonici bidimensionali su substrati di silicio ed orientazione cristallografica
Experimental and Numerical Comparison of Internal Insulation Systems for Building Refurbishment
AbstractIn order to increase the knowledge of both local builders and designers about the energy efficiency of internal insulation a test chamber has been realized at the Edilmaster site. The test system features two isothermal chambers separated by the specimen under test. The controlled temperatures guarantee a constant temperature difference in order to measure the heat flux with thermofluximeter techniques. In this paper the uninsulated system is a brick wall of about 50cm thickness which represents a typical solution found in Trieste. Two internal insulation systems, with wooden and metallic studs, have been tested. The measurements highlighted the different thermal behavior between the area covered by the insulation material and the thermal bridges due to studs. The experimental results have been compared with solutions obtained with numerical and analytical methods
Sequential Informed Federated Unlearning: Efficient and Provable Client Unlearning in Federated Optimization
The aim of Machine Unlearning (MU) is to provide theoretical guarantees on
the removal of the contribution of a given data point from a training
procedure. Federated Unlearning (FU) consists in extending MU to unlearn a
given client's contribution from a federated training routine. Current FU
approaches are generally not scalable, and do not come with sound theoretical
quantification of the effectiveness of unlearning. In this work we present
Informed Federated Unlearning (IFU), a novel efficient and quantifiable FU
approach. Upon unlearning request from a given client, IFU identifies the
optimal FL iteration from which FL has to be reinitialized, with unlearning
guarantees obtained through a randomized perturbation mechanism. The theory of
IFU is also extended to account for sequential unlearning requests.
Experimental results on different tasks and dataset show that IFU leads to more
efficient unlearning procedures as compared to basic re-training and
state-of-the-art FU approaches
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
literature on image registration is generally based on the assumption that
images are usually accessible to the researcher, from which the spatial
transformation is subsequently estimated. This common assumption may not be met
in current practical applications, since the sensitive nature of medical images
may ultimately require their analysis under privacy constraints, preventing to
share the image content in clear form. 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 first propose to optimize the underlying image registration
operations using gradient approximations. We further revisit the use of
homomorphic encryption and use a packing method to allow the encryption and
multiplication of large matrices more efficiently. We demonstrate our privacy
preserving framework in linear and non-linear registration problems, evaluating
its accuracy and scalability with respect to standard image registration. Our
results show that privacy preserving image registration is feasible and can be
adopted in sensitive medical imaging applications
Longitudinal Analysis of Image Time Series with Diffeomorphic Deformations: A Computational Framework Based on Stationary Velocity Fields
International audienceWe propose and detail a deformation-based morphometry computational framework, called Longitudinal Log-Demons Framework (LLDF), to estimate the longitudinal brain deformations from image data series, transport them in a common space and perform statistical group-wise analyses. It is based on freely available software and tools, and consists of three main steps: (i) Pre-processing, (ii) Position correction, and (iii) Non-linear deformation analysis. It is based on the LCC log-Demons non-linear symmetric diffeomorphic registration algorithm with an additional modulation of the similarity term using a confidence mask to increase the robustness with respect to brain boundary intensity artifacts. The pipeline is exemplified on the longitudinal Open Access Series of Imaging Studies (OASIS) database and all the parameters values are given so that the study can be reproduced. We investigate the group-wise differences between the patients with Alzheimer's disease and the healthy control group, and show that the proposed pipeline increases the sensitivity with no decrease in the specificity of the statistical study done on the longitudinal deformations
- …