Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD), among the most prevalent neurodegenerative disorders, disrupt brain activity and connectivity, highlighting the need for tools that can effectively capture these alterations. Effective Connectivity Networks (ECNs), which model causal interactions between brain regions, offer a promising approach to characterizing AD and FTD related neural changes. In this study, we estimate ECNs from EEG traces using a state-of-the-art causal discovery method specifically designed for time-series data, to recover the causal structure of the interactions between brain areas. The recovered ECNs are integrated into a novel Graph Neural Network architecture (ECoGNet), where nodes represent brain regions and edge features encode causal relationships. Our method combines ECNs with features summarizing local brain dynamics to improve AD and FTD detection. Evaluated on a publicly available EEG dataset, the proposed approach demonstrates superior performance compared to models that either use non-causal connectivity networks or omit connectivity information entirely.

Attention guides our gaze to fixate the proper location of the scene and holds it in that location for the de-served amount of time given current processing demands, before shifting to the next one. As such, gaze deploy-ment crucially is a temporal process. Existing computational models have made significant strides in predicting spatial aspects of observer's visual scanpaths (where to look), while often putting on the background the tempo-ral facet of attention dynamics (when). In this paper we present TPP-Gaze, a novel and principled approach to model scanpath dynamics based on Neural Temporal Point Process (TPP), that Jointly learns the temporal dynamics of fixations position and duration, integrating deep learning methodologies with point process theory. We conduct ex-tensive experiments across five publicly available datasets. Our results show the overall superior performance of the proposed model compared to state-of-the-art approaches. Source code and trained models are publicly available at: https://github.com/phuselab/tppgaze.

This study investigates the potential of Graph Neural Networks (GNNs) for analyzing 3D facial morphology, leveraging facial landmarks as graph nodes to capture the intrinsic structure of 3D face scans. This research evaluates the effectiveness of three distinct approaches for defining graph vertices by associating them with: (1) a well-established set of anthropometric landmarks identified through tactile assessment, widely considered the gold standard in facial anthropometry; (2) automatically detected 3D facial keypoints estimated using advanced algorithms; and (3) geometry-based random point cloud sub-sampling via farthest point sampling (FPS). To evaluate the effectiveness of GNNs and facial landmarks in capturing and representing meaningful morphological patterns, the study employs two benchmark tasks: gender classification and age regression. Extensive experiments across various GNN architectures and three datasets — each presenting diverse and challenging conditions — demonstrate that semantically meaningful landmarks, whether anthropometric or automatically detected, consistently outperform non-semantic random samples in both tasks and across all datasets. These results highlight the crucial role of semantic contextualization in graph-based facial analysis. Notably, models utilizing automatically detected facial keypoints achieved performance comparable to those based on manually annotated anthropometric landmarks, offering a scalable and cost-effective alternative without compromising accuracy. These findings support the integration of automated GNN-based methodologies into a wide range of applications, including clinical diagnosis, forensic analysis, and biometric recognition. • Learning 3D facial features to classify gender and age. • Comparing anthropometric vs. automatic landmarks and FPS sub-sampling. • Characterizing graph nodes with local and global geometric features. • Comparing GCN, GAT, DynamicEdgeConv, and Transformer models.

We live in an era of privacy concerns, motivating a large research effort in face de-identification. As in other fields, we are observing a general movement from hand-crafted to deep learning methods, mainly involving generative models. Although these methods produce more natural de-identified images or videos, we claim that the mere evaluation of the de-identification is not sufficient, especially when it comes to processing the images/videos further. In this note, we take into account the issue of preserving privacy, facial expressions, and photo-reality simultaneously, proposing a general testing framework. The quantitative evaluation is applied to four open-source tools, producing a baseline for future de-identification methods.