Biology subjects

Gili, T.

Publications and source records attributed to Gili, T..

3 recordsLinked to original sources

bioRxiv2024

Linearizing and forecasting: a reservoir computing route to digital twins of the brain

Exploring the dynamics of a complex system, such as the human brain, poses significant challenges due to inherent uncertainties and limited data. In this study, we enhance the capabilities of noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework, demonstrating their effectiveness in creating autonomous in silico replicas - digital-twins - of brain activity. Our findings reveal that the poles of the Laplace transform of high-dimensional inferred lRNNs are directly linked to the spectral properties of observed systems and to the kernels of auto-regressive models. Applying this theoretical framework to resting-state fMRI, we successfully predict and decompose BOLD signals into spatiotemporal modes of a low-dimensional latent state space confined around a single equilibrium point. lRNNs provide an interpretable proxy for clustering among subjects and different brain areas. This adaptable digital-twin framework not only enables virtual experiments but also offers computational efficiency for real-time learning, highlighting its potential for personalized medicine and intervention strategies.

neuroscience↗

bioRxiv2024

Partial Correlation as a Tool for Mapping Functional-Structural Correspondence in Human Brain Connectivity

Brain structure-function coupling has been studied in health and disease by many different researchers in recent years. Most of the studies have estimated functional connectivity matrices as correlation coefficients between different brain areas, despite well-known disadvantages compared to partial correlation connectivity matrices. Indeed, partial correlation represents a more sensible model for structural connectivity since, under a Gaussian approximation, it accounts only for direct dependencies between brain areas. Motivated by this and following previous results by different authors, we investigate structure-function coupling using partial correlation matrices of functional magnetic resonance imaging (fMRI) brain activity time series under various regularization (a.k.a. noise-cleaning) algorithms. We find that, across different algorithms and conditions, partial correlation provides a higher match with structural connectivity retrieved from Density Weighted Imaging data than standard correlation, and this occurs at both subject and population levels. Importantly, we also show that regularization and thresholding are crucial for this match to emerge. Finally, we assess neuro-genetic associations in relation to structure-function coupling, which presents promising opportunities to further advance research in the field of network neuroscience, particularly concerning brain disorders.

neuroscience↗

bioRxiv2022

Mesoscopic patterns of functional connectivity alterations in autism by contrast subgraphs

Despite the breakthrough achievements in understanding structural and functional connectivity alterations that underlie autism spectrum disorder (ASD), the exact nature and type of such alterations are not yet clear due to conflicting reports of hyper-connectivity, hypo-connectivity, and -in some cases- combinations of both. In this work, we approach the debate about hyper- vs hypoconnectivity in ASD using a novel network comparison technique designed to capture mesoscopic-scale differential structures. In particular, we build on recent algorithmic advances in the sparsification of functional connectivity matrices, in the extraction of contrast subgraphs, and in the computation of statistically significant maximal frequent itemsets, and develop a method to identify mesoscale structural subgraphs that are maximally dense and different in terms of connectivity levels between the different sets of networks. We apply our method to analyse brain networks of typically developed individuals and ASD patients across different developmental phases and find a set of altered cortical-subcortical circuits between healthy subjects and patients affected by ASD. Specifically, our analysis highlights in ASD patients a significantly larger number of functional connections among regions of the occipital cortex and between the left precuneus and the superior parietal gyrus. At the same time, reduced connectivity characterised the superior frontal gyrus and the temporal lobe regions. More importantly, we can simultaneously detect regions of the brain that show hyper and hypo-connectivity in ASD in children and adolescents, recapitulating within a single framework multiple previous separate observations.

neuroscience↗