10 research outputs found
Explainable Artificial Intelligence in Paediatric: Challenges for the Future
No full textNo description supplied</p
DataSheet1.docx
No full text<p>Recent tract-based analyses provided evidence for the exploitability of 3D-SHORE microstructural descriptors derived from diffusion MRI (dMRI) in revealing white matter (WM) plasticity. In this work, we focused on the main open issues left: (1) the comparative analysis with respect to classical tensor-derived indices, i.e., Fractional Anisotropy (FA) and Mean Diffusivity (MD); and (2) the ability to detect plasticity processes in gray matter (GM). Although signal modeling in GM is still largely unexplored, we investigated their sensibility to stroke-induced microstructural modifications occurring in the contralateral hemisphere. A more complete picture could provide hints for investigating the interplay of GM and WM modulations. Ten stroke patients and ten age/gender-matched healthy controls were enrolled in the study and underwent diffusion spectrum imaging (DSI). Acquisitions at three and two time points (tp) were performed on patients and controls, respectively. For all subjects and acquisitions, FA and MD were computed along with 3D-SHORE-based indices [Generalized Fractional Anisotropy (GFA), Propagator Anisotropy (PA), Return To the Axis Probability (RTAP), Return To the Plane Probability (RTPP), and Mean Square Displacement (MSD)]. Tract-based analysis involving the cortical, subcortical and transcallosal motor networks and region-based analysis in GM were successively performed, focusing on the contralateral hemisphere to the stroke. Reproducibility of all the indices on both WM and GM was quantitatively proved on controls. For tract-based, longitudinal group analyses revealed the highest significant differences across the subcortical and transcallosal networks for all the indices. The optimal regression model for predicting the clinical motor outcome at tp3 included GFA, PA, RTPP, and MSD in the subcortical network in combination with the main clinical information at baseline. Region-based analysis in the contralateral GM highlighted the ability of anisotropy indices in discriminating between groups mainly at tp1, while diffusivity indices appeared to be altered at tp2. 3D-SHORE indices proved to be suitable in probing plasticity in both WM and GM, further confirming their viability as a novel family of biomarkers in ischemic stroke in WM and revealing their potential exploitability in GM. Their combination with tensor-derived indices can provide more detailed insights of the different tissue modulations related to stroke pathology.</p
Characterizing the Contribution of Dependent Features in XAI Methods
No full textExplainable Artificial Intelligence (XAI) provides tools to help understanding how AI models work and reach a particular decision or outcome. It helps to increase the interpretability of models and makes them more trustworthy and transparent. In this context, many XAI methods have been proposed to make black-box and complex models more digestible from a human perspective. However, one of the main issues that XAI methods have to face especially when dealing with a high number of features is the presence of multicollinearity, which casts shadows on the robustness of the XAI outcomes, such as the ranking of informative features. Most of the current XAI methods either do not consider the collinearity or assume the features are independent which, in general, is not necessarily true. Here, we propose a simple, yet useful, proxy that modifies the outcome of any XAI feature ranking method allowing to account for the dependency among the features, and to reveal their impact on the outcome. The proposed method was applied to SHAP, as an example of XAI method which assume that the features are independent. For this purpose, several models were exploited for a well-known classification task (males versus females) using nine cardiac phenotypes extracted from cardiac magnetic resonance imaging as features. Principal component analysis and biological plausibility were employed to validate the proposed method. Our results showed that the proposed proxy could lead to a more robust list of informative features compared to the original SHAP in presence of collinearity.</p
The significant IDPs categorized by modality.
No full textED: effect direction whether it is positive (+) or negative (-).</p
The causal association of TL and brain IDPs using the IVW method.
No full textThe y-axis represents the −log10(p−values) of the association. The color of each IDP indicates the MRI modality and the triangle shape indicates whether the identified association (IVW β value) is positive (△) or negative (▽). The black horizontal line indicates the FDR-adjusted significance threshold (P = 0.004409). The triangles with black border highlight the 193 IDPs that were significantly associated with TL using the IVW method as well as the complementary MR analyses. WM: white matter; FA: fractional anisotropy; MO: diffusion tensor mode; OD: orientation dispersion; ICVF: intracellular volume fraction; ISOVF: isotropic volume fraction; tfMRI: task fMRI; rfMRI: resting-state fMRI; QC: quality control.</p
Visual representation of the significant IDPs among the seven most prevalent measures.
No full textFor the six diffusion indices (top six rows) the tracts that are significantly associated with TL are highlighted. The last row shows the cortical regions with a significant effect of TL on gray-white matter intensity contrast. Different colors within a diffusion measure relate to IDPs extracted from two different methods: tract-based spatial statistics (solid colors) and probabilistic tractography (color gradients). The plots were generated by BrainPainter [10] and FSL [11].</p
List of the SNPs used in the MR analysis.
No full textrsID, ID of the SNP; Chr, chromosome; Pos, position of the SNP in the genome; EA, effect allele; OA, other allele; EAF, effect allele frequency; Beta, beta value of the SNP in GWAS; SE, standard error.</p
