Alessandro Crimi

This study examines how acute ischemic stroke disrupts functional connectivity gradients in the brain, showing that correcting for hemodynamic lags improves gradient accuracy and that behavioral impairments correlate with specific network deviations.

This paper analyzes brain connectivity using EEG and fNIRS data through a multilayer network model, showing that combining modalities provides a richer understanding of brain function than single-modality approaches.

This study examines structure-function coupling in brain networks using EEG and fNIRS during rest and motor tasks, revealing differences in coupling patterns across brain states and regions.

This study uses machine learning to predict post-surgical structural rearrangements in brain tumors by analyzing preoperative functional and diffusion MRI data.

This paper develops a deep learning model for glioma grading using a tumor microenvironment analysis protocol, achieving 0.85 accuracy on a small, imbalanced dataset with single-stained biopsies.

The paper introduces a causality detection framework using reservoir computing for neuroimaging data, showing improved accuracy and reduced false inferences compared to Granger causality in brain network analysis.

The paper shows that congenitally blind individuals maintain connectivity between visual and sensorimotor cortices, with eye movements driving visual cortex activity and sensorimotor activity, despite lacking visual experience and oculomotor control.

This paper uses quantum annealers to detect communities in brain connectomes, finding they achieve higher modularity than classical methods.

This paper uses neural radiance fields to generate 3D reconstructions from 2D MRI slices, improving accuracy and reducing motion artifacts in clinical settings.

This study analyzes resting-state brain connectivity using EEG and fNIRS signals, finding that fNIRS can reflect both short- and long-range neural activity organization.

This paper predicts Amyloid-β concentrations in Alzheimer's disease using a machine learning autoregressive model constrained by structural connectivity, achieving lower prediction error than existing methods.

The paper applies the Rosenbluth sampling method to calculate the energy spectrum and estimate the number of folds for hydrophobic-polar model proteins.

The paper examines how genetic risk factors and gene expression influence longitudinal changes in brain connectivity and dementia progression in Alzheimer's disease.

This paper proposes a constrained autoregressive model to estimate effective brain connectivity by integrating structural and functional data, showing improved performance in community detection and autism classification.

The paper identifies 38 genes regulating a-synuclein cell-to-cell transfer using an siRNA screen and shows genetic overlap with Parkinson's disease.

This paper develops a deep learning model for malaria parasite detection and synthetic image generation in microscopy images, achieving high classification accuracy and creating realistic images indistinguishable from real data.

The paper introduces vMSOT with AOI987 to quantify Aβ deposition in AD mouse models, revealing strain-specific patterns.

This paper introduces BiGen, a structural equation model that integrates genetic, neuroimaging, and clinical data to analyze complex brain diseases like Alzheimer's and glioblastoma.

The paper conducts a GWAS to analyze changes in brain connectivity over disease progression in Alzheimer's, identifying genetic associations with structural connectivity changes.

This paper introduces a method to identify multivariate brain network differences between groups using sparse connectivity analysis, applied to human and mouse connectomes.

This paper uses dynamic functional connectivity analysis and machine learning to classify schizophrenia and autism spectrum disorder and investigates their potential overlap.

This paper constructs speech networks from pre-operative neuroimaging data to identify differences in graph metrics between glioma patients with and without aphasia, aiding surgical decision-making.