Biology subjects

Costagli, M.

Publications and source records attributed to Costagli, M..

3 recordsLinked to original sources

bioRxiv2022

Dissecting brain grey and white matter microstructure: a novel clinical diffusion MRI protocol

Soma and Neurite Density Image (SANDI) is an advanced diffusion magnetic resonance imaging (dMRI) signal model devised to probe in vivo microstructural information from both grey and white matter. However, this model requires multi-shell acquisitions that include b-values that are at least 6 times higher than those used in clinical practice. Here we present a 10-minute acquisition protocol that enables to acquire the necessary images for SANDI modelling on a clinical 3T scanner. We demonstrate the feasibility and assess the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI and other state-of-the-art models on five healthy subjects and we present its potential clinical impact on five subjects affected by multiple sclerosis with relapsing-remitting course. Our results suggest that SANDI is a repeatable, reproducible, feasible, and practical method to characterize both white and grey matter tissues in both the healthy brain and in neurological diseases.

neuroscience↗

bioRxiv2021

Modeling Brain Connectivity Dynamics in Functional Magnetic Resonance Imaging via Particle Filtering

Interest in the studying of functional connections in the brain has grown considerably in the last decades, as many studies have pointed out that these interactions can play a role as markers of neurological diseases. Most studies in this field treat the brain network as a system of connections stationary in time, but dynamic features of brain connectivity can provide useful information, both on physiology and pathological conditions of the brain. In this paper, we propose the application of a computational methodology, named Particle Filter (PF), to study non-stationarities in brain connectivity in functional Magnetic Resonance Imaging (fMRI). The PF algorithm estimates time-varying hidden parameters of a first-order linear time-varying Vector Autoregressive model (VAR) through a Sequential Monte Carlo strategy. On simulated time series, the PF approach effectively detected and enabled to follow time-varying hidden parameters and it captured causal relationships among signals. The method was also applied to real fMRI data, acquired in presence of periodic tactile or visual stimulations, in different sessions. On these data, the PF estimates were consistent with current knowledge on brain functioning. Most importantly, the approach enabled to detect statistically significant modulations in the cause-effect relationship between brain areas, which correlated with the underlying visual stimulation pattern presented during the acquisition.

neuroscience↗

bioRxiv2020

Temporal binding of Visual and Motor cortical oscillations revealed by BOLD response in Humans

The mechanisms coordinating action and perception over time are poorly understood. The sensory cortex needs to prepare for upcoming changes contingent on action, and this requires temporally precise communication that takes into account the variable delays between sensory and motor processing. Several theorists1,2 have proposed synchronization of the endogenous oscillatory activity observed in most regions of the brain3 as the basis for an efficient and flexible communication protocol between distal brain areas2,4, a concept known as "communication through coherence". Synchronization of endogenous oscillations5,6 occurs after a salient sensory stimulus, such as a flash or a sound7-11, and after a voluntary action12-18, and this impacts directly on perception, causing performance to oscillate rhythmically over time. Here we introduce a novel fMRI paradigm to probe the neural sources of oscillations, based on the concept of perturbative signals, which overcomes the low temporal resolution of BOLD signals. The assumption is that a synchronized endogenous rhythm will modulate cortical excitability rhythmically, which should be reflected in the BOLD responses to brief stimuli presented at different phases of the oscillation cycle. We record rhythmic oscillations of V1 BOLD synchronized by a simple voluntary action, in phase with behaviourally measured oscillations in visual sensitivity in the theta range. The functional connectivity between V1 and M1 also oscillates at the same rhythm. By demonstrating oscillatory temporal coupling between primary motor and sensory cortices, our results strongly implicate communication through coherence to achieve precise coordination and to encode sensory-motor timing.

neuroscience↗