Matteo Frigo

Matteo Frigo
Corsmed · R&D

PhD

About

19
Publications
6,043
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246
Citations
Additional affiliations
November 2017 - October 2020
National Institute for Research in Computer Science and Control
Position
  • PhD Student

Publications

Publications (19)
Article
Full-text available
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-...
Article
Full-text available
The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface....
Article
Background Systemic inflammation induces acute changes in mood, motivation and cognition that closely resemble those observed in depressed individuals. However, the mechanistic pathways linking peripheral inflammation to depression-like psychopathology via intermediate effects on brain function remain incompletely understood. Methods We combined d...
Article
Full-text available
As the global health crisis unfolded, many academic conferences moved online in 2020. This move has been hailed as a positive step towards inclusivity in its attenuation of economic, physical, and legal barriers and effectively enabled many individuals from groups that have traditionally been underrepresented to join and participate. A number of st...
Article
Full-text available
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode s...
Article
Full-text available
The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein,...
Preprint
The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein,...
Preprint
Full-text available
The move from in-person to online scientific conferences due to the global health crisis has been hailed as a positive step towards inclusivity in its attenuation of economic, physical and legal barriers. Yet pre-existing and new challenges to truly inclusive conference experiences remain unaddressed. While acknowledging the benefits of an online s...
Preprint
Full-text available
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode s...
Thesis
The human brain is a complex multiscale biomechanism composed of nearly a hundred billion neurons.Using diffusion magnetic resonance imaging (dMRI), in this thesis we contributed to probing in-vivo the microstructural composition of brain tissues with multi-compartment (MC) models, the trajectories of the axonal pathways connecting different cortic...
Preprint
Full-text available
State-of-the-art multi-compartment microstructural models of diffusion MRI (dMRI) in the human brain have limited capability to model multiple tissues at the same time. In particular, the available techniques that allow this multi-tissue modelling are based on multi-TE acquisitions. In this work we propose a novel multi-tissue formulation of classi...
Article
Objective: The use of non-invasive techniques for the estimation of structural brain networks (i.e. connectomes) opened the door to large-scale investigations on the functioning and the architecture of the brain, unveiling the link between neurological disorders and topological changes of the brain network. This study aims at assessing if and how...
Article
Full-text available
Characterizing the connection between brain structure and brain function is essential for understanding how behaviour emerges from the underlying anatomy. A number of studies have shown that the network structure of the white matter shapes functional connectivity. Therefore, it should be possible to predict, at least partially, functional connectiv...
Conference Paper
Full-text available
This paper highlights a systematic bias in white matter tissue microstructure modelling via diffusion MRI that is due to the common, yet inaccurate, assumption that all brain tissues have a similar T 2 response. We show that the concept of "sig-nal fraction" is more appropriate to describe what have always been referred to as "volume fraction". Thi...
Chapter
The architecture of the white matter is endowed with kissing and crossing bundles configurations. When these white matter tracts are reconstructed using diffusion MRI tractography, this systematically induces the reconstruction of many fiber tracts that are not coherent with the structure of the brain. The question on how to discriminate between tr...
Preprint
Full-text available
Purpose: Fiber tracking with diffusion weighted magnetic resonance imaging has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. Phantom studies provide concrete quantitative comparisons of methods relative to absolute groun...
Article
We will introduce a Monte Carlo type inference in the framework of Markov Switching models to analyse financial time series, namely the Gibbs Sampling. In particular we generalize the results obtained in Albert and Chib (1993), Di Persio and Vettori (2014) and Kim and Nelson (1999) to take into account the switching mean as well as the switching va...
Article
Full-text available
The present paper concerns a Maximum Likelihood analysis for the Markov switching approach to the forecasting problem of financial time series. In particular we model the volatility parameter characterizing time series of interest as a state variable of a suitable Markov chain. Latter formulation is based on the idea of describing abrupt changes in...

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