Publications by Jonas Richiardi

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Biometric authentication can be cast as a signal processing and statistical pattern recognition problem. As such, it relies on models of signal representations that can be used to discriminate between classes. One of the assumptions typically made by the p ...

We present NIMOSEF, a novel unified framework that leverages neural implicit functions for joint segmentation, reconstruction, and displacement field estimation in cardiac magnetic resonance imaging (CMRI). By leveraging on a shared implicit representation for joint segmentation and motion estimation our approach improves spatio-temporal consistency with respect to conventional grid-based convolutional neural networks and implicit segmentation functions. NIMOSEF employs an auto-decoder architecture to learn

Background: Patients suffering from functional neurological disorder (FND) experience disabling neurological symptoms not caused by an underlying classical neurological disease (such as stroke or multiple sclerosis). The diagnosis is made based on reliable ...

Brain mapping using magnetic resonance imaging (MRI) is traditionally performed using voxel-wise statistical hypothesis testing. Such mass-univariate approach ignores subtle spatial interactions. The searchlight method, in contrast, uses a multivariate pre ...

A critical step before data-sharing of human neuroimaging is removing facial features to protect individuals' privacy. However, not only does this process redact identifiable information about individuals, but it also removes non-identifiable information. ...

Multiple sclerosis (MS), a variable and diffuse disease affecting white and gray matter, is known to cause functional connectivity anomalies in patients. However, related studies published to-date are post hoc; our hypothesis was that such alterations could discriminate between patients and healthy controls in a predictive setting, laying the groundwork for imaging-based prognosis. Using functional magnetic resonance imaging resting state data of 22 minimally disabled MS patients and 14 controls, we develop

Background: Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast “connectometry” approach to assess the structural and functional ...

The integration of structural and functional brain connectivity provides a holistic view of the brain’s organization, but its application in Graph Neural Network (GNN) models for predicting “brain age” is understudied, and a systematic benchmark of optimal data fusion strategies is currently lacking. We systematically benchmark the performances of early and late fusion multimodal architectures against single-modality models for brain age prediction using structural and functional connectomes, using five dif

Resting state functional connectivity is defined as correlations in brain activity measured by functional magnetic resonance imaging without any stimulation paradigm. Such connectivity is dynamic, even over the course of minutes, and the development of too ...

Functional connectivity analysis of fMRI data can reveal synchronized activity between anatomically distinct brain regions. Here, we exploit the characteristic connectivity graphs of task and resting epochs to perform classification between these conditions. Our approach is based on ensembles of decision trees, which combine powerful discriminative ability with interpretability of results. This makes it possible to extract discriminative graphs that represent a subset of the connections that distinguish bes

Functional connectivity (FC) as measured by correlation between NM BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-st ...

Currently, one of the gold-standard methods to obtain brain myelin water fraction (MWF) maps is the multi-echo spin-echo sequence. To overcome some of its limitations (e.g. long acquisition times), a data-driven approach for deriving MWF maps is proposed here. A general linear model (GLM) and a conditional generative adversarial network (cGAN) were trained to learn the reference MWF from T1 and T2 maps acquired in a healthy cohort. While GLM-derived maps exhibited MWF overestimation, especially in WM tissue

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Jonas Richiardi

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Probabilistic models for multi-classifier biometric authentication using quality measures

NIMOSEF: Neural Implicit Motion and Segmentation Functions

Multi-centre classification of functional neurological disorders based on resting-state functional connectivity

Connectivity Searchlight: A Novel Approach For Mri Information Mapping Using Multivariate Connectivity

Removing Facial Features From Structural Mri Images Biases Visual Quality Assessment

Classifying minimally disabled multiple sclerosis patients from resting state functional connectivity

Multicontrast connectometry: A new tool to assess cerebellum alterations in early relapsing‐remitting multiple sclerosis

Late and Early Fusion Graph Neural Network Architectures for Integrative Modeling of Multimodal Brain Connectivity Graphs

Functional Connectivity Eigennetworks Reveal Different Brain Dynamics In Multiple Sclerosis Patients

Brain Decoding Of Fmri Connectivity Graphs Using Decision Tree Ensembles

Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Deriving Brain Myelin Water Fraction Maps from Relaxometry: a Data-Driven Approach

Domain shift, domain adaptation, and generalization: A focus on MRI

Accuracy versus reproducibility: comprehensive assessment of four automated methods for multiple sclerosis lesion segmentation

Improving clinico-radiological correlation in multiple sclerosis with automated tract and topology annotations

Differential diagnosis of multiple sclerosis with machine learning-based central vein sign recognition