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Franceschiello Benedetta

Franceschiello Benedetta

Professeur-e HES Associé-e

Main skills

MRI

Brain imaging (EEG, MRI)

Neuroscience

Eye-movements

Vision Rehabilitation

Applied Mathematics

Computational Modelling

  • Contact

  • Teaching

  • Publications

  • Conferences

Main contract

Professeur-e HES Associé-e

Desktop: ENP.23.N403

HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
Rue de l'Industrie 23, 1950 Sion, CH
HEI - VS
Faculty
Technique et IT
Main Degree Programme
Systèmes industriels
BSc HES-SO en Ingénierie des sciences du vivant - HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
  • Algèbre linéaire 1 et 2
BSc HES-SO en Systèmes industriels - HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
  • Algèbre linéaire 1 et 2
BSc HES-SO en Energie et techniques environnementales - HES-SO Valais-Wallis - Haute Ecole d'Ingénierie
  • Lineare Algebra

2024

A psychophysically-tuned computational model of human primary visual cortex produces geometric optical illusions
Scientific paper ArODES

Chrysa Retsa, Ana Hernando Ariza, Nathanael W. Noordanus, Lorenzo Ruffoni, Micah M. Murray, Benedetta Franceschiello

Current Research in Neurobiology,  2024, 7, 100140

Link to the publication

Summary:

Geometric optical illusions (GOIs) are mismatches between physical stimuli and perception. GOIs provide an access point to study the interplay between sensation and perception, Yet, there is relatively scant quantitative investigation of the extent to which different GOIs rely on similar or distinct perceptual mechanisms, which themselves are driven by specific physical properties. We addressed this knowledge gap with a combination of psychophysics and computational modelling. First, 30 healthy adults reported quantitatively their perceptual biases with three GOIs, whose physical properties parametrically varied on a trial-by-trial basis. A given physical property, when considered in isolation, had different effects on perceptual biases depending on the GOI (e.g. the spacing of stimuli affected one GOI, but not another). For a given GOI, there were oftentimes interactions between the effects of different physical properties. Next, we used these psychophysical results to tune a computational model of primary visual cortex that combines parameters of orientation selectivity, receptive-field size, intra-cortical connectivity, and long-range interactions. We showed that similar biases generated in-silico mirror those observed in human behavior when receptive field size, bandwidth and shape (rounded or elongated) are tuned, as well as parameters encoding the strength of the long-range intra-regional interactions between receptive fields. Collectively, our results suggest that different physical properties are not operating independently, but rather synergistically, to generate a GOI. Such results provide a roadmap whereby computational modelling, informed by human psychophysics, can reveal likely mechanistic underpinnings of perception.

On the determination of Lagrange multipliers for a weighted LASSO problem using geometric and convex analysis techniques
Scientific paper ArODES

Gianluca Giacchi, Bastien Milani, Benedetta Franceschiello

Applied Mathematics & Optimization,  2024, 89, 2

Link to the publication

Summary:

Compressed Sensing (CS) encompasses a broad array of theoretical and applied techniques for recovering signals, given partial knowledge of their coefficients, cf. Candés (C. R. Acad. Sci. Paris, Ser. I 346, 589–592 (2008)), Candés et al. (IEEE Trans. Inf. Theo (2006)), Donoho (IEEE Trans. Inf. Theo. 52(4), (2006)), Donoho et al. (IEEE Trans. Inf. Theo. 52(1), (2006)). Its applications span various fields, including mathematics, physics, engineering, and several medical sciences, cf. Adcock and Hansen (Compressive Imaging: Structure, Sampling, Learning, p. 2021), Berk et al. (2019 13th International conference on Sampling Theory and Applications (SampTA) pp. 1-5. IEEE (2019)), Brady et al. (Opt. Express 17(15), 13040–13049 (2009)), Chan (Terahertz imaging with compressive sensing. Rice University, USA (2010)), Correa et al. (2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 7789–7793 (2014, May) IEEE), Gao et al. (Nature 516(7529), 74–77 (2014)), Liu and Kang (Opt. Express 18(21), 22010–22019 (2010)), McEwen and Wiaux (Mon. Notices Royal Astron. Soc. 413(2), 1318–1332 (2011)), Marim et al. (Opt. Lett. 35(6), 871–873 (2010)), Yu and Wang (Phys. Med. Biol. 54(9), 2791 (2009)), Yu and Wang (Phys. Med. Biol. 54(9), 2791 (2009)). Motivated by our interest in the mathematics behind Magnetic Resonance Imaging (MRI) and CS, we employ convex analysis techniques to analytically determine equivalents of Lagrange multipliers for optimization problems with inequality constraints, specifically a weighted LASSO with voxel-wise weighting. We investigate this problem under assumptions on the fidelity term ||Ax-b||2,2, either concerning the sign of its gradient or orthogonality-like conditions of its matrix. To be more precise, we either require the sign of each coordinate of 2(Ax-b)T A to be fixed within a rectangular neighborhood of the origin, with the side lengths of the rectangle dependent on the constraints, or we assume AT A to be diagonal. The objective of this work is to explore the relationship between Lagrange multipliers and the constraints of a weighted variant of LASSO, specifically in the mentioned cases where this relationship can be computed explicitly. As they scale the regularization terms of the weighted LASSO, Lagrange multipliers serve as tuning parameters for the weighted LASSO, prompting the question of their potential effective use as tuning parameters in applications like MR image reconstruction and denoising. This work represents an initial step in this direction.

2023

Reliability characterization of MRI measurements for analyses of brain networks on a single human
Scientific paper ArODES

Céline Provins, Hélène Lajous, Elodie Savary, Eleonora Fornari, Benedetta Franceschiello, Yasser Aleman-Gomez, William H. Thompson, Ileana Jelescu, Patric Hagmann, Oscar Esteban

Scientific reports,  2023

Link to the publication

Summary:

Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and interpretation of the skyrocketing production of scientific literature requires caution. We will characterize relevant sources of variability and assess the reliability of structural and functional networks extracted from MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome Phantom’. Two comprehensive MRI protocols will be executed across three different devices (48, 12, and 12 sessions, respectively) while recording a wealth of physiological signals to help model corresponding spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives. By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will deliver reliability margins of brain networks that inform future research and contribute to the standardization of ‘connectivity measurement’.

Reliability characterization of MRI measurements for analyses of brain networks on a single human
Scientific paper ArODES

Céline Provins, Hélène Lajous, Elodie Savary, Eleonora Fornari, Benedetta Franceschiello, Yasser Aleman-Gomez, William H. Thompson, Ileana Jelescu, Patric Hagmann, Oscar Esteban

Springer Nature,  2023

Link to the publication

Summary:

Network-based approaches are widely adopted to model functional and structural ‘connectivity’ of the living brain, extracted noninvasively with magnetic resonance imaging (MRI). However, these analyses —on functional and structural networks— render unreliable at the finer temporal, spatial, and brain-parcellation scales. Consequently, the clinical translation of these analyses has yet to materialize meaningfully, and interpretation of the skyrocketing production of scientific literature requires caution. We will characterize relevant sources of variability and assess the reliability of structural and functional networks extracted from MRI with the repeated acquisition of a single, healthy individual, whom we regard as the ‘Human Connectome Phantom’. Two comprehensive MRI protocols will be executed across three different devices (48, 12, and 12 sessions, respectively) while recording a wealth of physiological signals to help model corresponding spurious effects on brain networks. To maximize reuse, e.g., as a benchmark reference, a baseline for machine learning models, or a source of prior knowledge, we will openly share all data and their derivatives. By systematically assessing spurious sources of variability throughout the neuroimaging workflow, we will deliver reliability margins of brain networks that inform future research and contribute to the standardization of ‘connectivity measurement’

A cortical inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations
Book chapter

Franceschiello Benedetta

,  Lecture Notes in Computer Science. 2023,  Cham : Springer

Link to the publication

2022

Computational models in Electroencephalography
Scientific paper

Franceschiello Benedetta

Brain Topography, 2022

Link to the publication

The physics of higher-order interactions in complex systems
Scientific paper

Franceschiello Benedetta

Nature Physics, 2022

Link to the publication

Topological Features of Electroencephalography are Reference-Invariant
Scientific paper

Franceschiello Benedetta

Brain Topography, 2022

Link to the publication

A Roadmap for Computational Modelling of M/EEG
Scientific paper

Franceschiello Benedetta

Brain Topography, 2022

Link to the publication

2021

A neuro-mathematical model for size and context related illusions
Book chapter

Franceschiello Benedetta

,  Lecture Notes in Morphogenesis. 2021,  Cham : Springer

Link to the publication

Convolutional neural network on eye tracking trajectories in neglect
Scientific paper

Franceschiello Benedetta

Computer Methods and Programs in Biomedicine, 2021

Link to the publication

Summary:

• We identify signs of visuo-spatial neglect through an automated analysis of saccadic eye trajectories using a series of machine learning classifiers.

• We provide a standardized pre-processing pipeline adaptable to other task-based eye-tracker measurements.

• Patient-wise, we benchmark the predictions form a 1D convolutional neural network with standardized paper-and-pencil test results.

• We evaluate white matter tracts by using Diffusion Tensor Imaging (DTI) and find a clear correlation with the microstructure of the third branch of the superior longitudinal fasciculus.

• Machine learning methods can efficiently and non-invasively characterize left spatial neglect.

2020

A computational model for grid maps in neural populations
Scientific paper

Franceschiello Benedetta

Journal of Computational Neuroscience, 2020

Link to the publication

Visual illusions via neural dynamics: Wilson-Cowan-type models and the efficient representation principle
Scientific paper

Franceschiello Benedetta

Journal of Neurophysiology, 2020

Link to the publication

Cortical-inspired Wilson-Cowan-type equations for orientation-dependent contrast perception modelling
Scientific paper

Franceschiello Benedetta

JMIV, 2020

Link to the publication

3-Dimensional Magnetic Resonance Imaging of the Freely Moving Human Eye,
Scientific paper

Franceschiello Benedetta

Progress in Neurobiology, 2020

Link to the publication

2019

Geometrical Optical Illusion via Sub-Riemannian Geodesics in the Roto-Translation Group
Scientific paper

Franceschiello Benedetta

Differential geometry and its applications, 2019

Link to the publication

Electroencephalography
Scientific paper

Franceschiello Benedetta

Current Biology, 2019

Link to the publication

2017

Modelling of Poggendorff Illusion via Sub-Riemannian Geodesics in the Roto-Translation Group
Book chapter

Franceschiello Benedetta

,  Lecture Notes in Computer Science. 2017,  Cham : Springer

Link to the publication

Mathematical Models of Visual Perception for the Analysis of Geometrical Optical Illusions
Book chapter

Franceschiello Benedetta

,  Mathematical and Theoretical Neuroscience. 2017,  Cham : Springer INdAM Series

Link to the publication

A Neuromathematical Model for Geometrical Optical Illusions
Scientific paper

Franceschiello Benedetta

JMIV, 2017

Link to the publication

Cortical based mathematical models of geometric optical illusions
Doctoral thesis

Franceschiello Benedetta

2017,  Paris, France : Université Pierre et Marie Curie

Link to the publication

2015

Sub-Riemannian mean curvature flow for image processing
Scientific paper

Franceschiello Benedetta

SIAM, 2015

Link to the publication

2022

ISMRM
Conference

Franceschiello Benedetta

International Society for Magnetic Resonance Imaging in Medicine, 27.01.2022 - 27.01.2023, London, Toronto

Link to the conference

2019

Organisation for Human Brain Mapping
Conference

Franceschiello Benedetta

OHBM, 08.06.2019 - 22.07.2023, Rome, Glasgow, Montreal

Link to the conference

ARVO
Conference

Franceschiello Benedetta

The Association for Research Vision and Ophthalmology, 27.01.2019 - 27.01.2023, Vancouver, New Orleans

Link to the conference

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