Publications by Ilaria Ricchi | EPFL Graph Search

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Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patt ...

The cerebellum is thought to help detect and correct errors between intended and executed commands(1,2) and is critical for social behaviours, cognition and emotion 3 Computations for motor control must be performed quicklyto correct errors in real time an ...

The Graph Laplacian Mixture Model (GLMM) allows to infer multiple underlying graph structures from multivariate time series data. Given its effectiveness in identifying brain states from brain activity measured by functional magnetic resonance imaging (fMR ...

The lumbosacral spinal cord contains neural circuits crucial for locomotion, organized into rostrocaudal levels with distinct somatosensory and motor neuron pools that project to and from the muscles of the lower limbs. However, the specific spinal levels ...

In recent years, there has been a growing interest in investigating spontaneous brain activity, with a newfound focus on understanding the resting state activity of the spinal cord due to its importance in sensorimotor interactions. Previous studies have p ...

The amyloid, tau, neurodegeneration (ATN) framework provides a biological staging model of Alzheimer's disease (AD) using magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), or positron emission tomography (PET) biomarkers. MRI, being non-invasive ...

Ilaria Ricchi

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Dynamics of functional network organization through graph mixture learning

Structured cerebellar connectivity supports resilient pattern separation

Revealing Brain and Spinal Cord Functional Networks by Block-Structured Graph Laplacian Mixture Models

Towards personalized mapping through lumbosacral spinal cord task fMRI

Characterization of the resting-state functional dynamics in the lumbosacral spinal cord

The complementary role of automated brain volumetry to stratify ADNI participants within the ATN framework