Neurons and Cognition (q-bio.NC)

Abstract: Sensory perception (e.g. vision) relies on a hierarchy of cortical areas, in which neural activity propagates in both directions, to convey information not only about sensory inputs but also about cognitive states, expectations and predictions. At the macroscopic scale, neurophysiological experiments have described the corresponding neural signals as both forward and backward-travelling waves, sometimes with characteristic oscillatory signatures. It remains unclear, however, how such activity patterns relate to specific functional properties of the perceptual apparatus. Here, we present a mathematical framework, inspired by neural network models of predictive coding, to systematically investigate neural dynamics in a hierarchical perceptual system. We show that stability of the system can be systematically derived from the values of hyper-parameters controlling the different signals (related to bottom-up inputs, top-down prediction and error correction). Similarly, it is possible to determine in which direction, and at what speed neural activity propagates in the system. Different neural assemblies (reflecting distinct eigenvectors of the connectivity matrices) can simultaneously and independently display different properties in terms of stability, propagation speed or direction. We also derive continuous-limit versions of the system, both in time and in neural space. Finally, we analyze the possible influence of transmission delays between layers, and reveal the emergence of oscillations at biologically plausible frequencies.

Abstract: The amygdala is assumed to contribute to a bottom-up attentional bias during visual processing of emotional faces. Still, how its response to emotion interacts with top-down attention is not fully understood. It is also unclear if amygdala activity and scalp EEG respond to emotion and attention in a similar way. Therefore, we studied the interaction of emotion and attention during face processing in oscillatory gamma-band activity (GBA) in the amygdala and on the scalp. Amygdala signals were recorded via intracranial EEG (iEEG) in 9 patients with epilepsy. Scalp recordings were collected from 19 healthy participants. Three randomized blocks of angry, neutral, and happy faces were presented, and either negative, neutral, or positive expressions were denoted as targets. Both groups detected happy faces fastest and most accurately. In the amygdala, the earliest effect was observed around 170 ms in high GBA (105-117.5 Hz) when neutral faces served as targets. Here, GBA was higher for emotional than neutral faces. During attention to negative faces, low GBA (< 90 Hz) increased specifically for angry faces both in the amygdala and over posterior scalp regions, albeit earlier on the scalp (60 ms) than in the amygdala (210 ms). From 570 ms, amygdala high GBA (117.5-145 Hz) was also increased for both angry and neutral, compared to happy, faces. When positive faces were the targets, GBA did not differentiate between expressions. The present data reveal that attention-independent emotion detection in amygdala high GBA may only occur during a neutral focus of attention. Top-down threat vigilance coordinates widespread low GBA, biasing stimulus processing in favor of negative faces. These results are in line with a multi-pathway model of emotion processing and help specify the role of GBA in this process by revealing how attentional focus can tune timing and amplitude of emotional GBA responses.

Abstract: Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a well-known correlation-based measure of functional connectivity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.

Abstract: Childhood maltreatment may adversely affect brain development and consequently behavioral, emotional, and psychological patterns during adulthood. In this study, we propose an analytical pipeline for modeling the altered topological structure of brain white matter structure in maltreated and typically developing children. We perform topological data analysis (TDA) to assess the alteration in global topology of the brain white-matter structural covariance network for child participants. We use persistent homology, an algebraic technique in TDA, to analyze topological features in the brain covariance networks constructed from structural magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). We develop a novel framework for statistical inference based on the Wasserstein distance to assess the significance of the observed topological differences. Using these methods in comparing maltreated children to a typically developing sample, we find that maltreatment may increase homogeneity in white matter structures and thus induce higher correlations in the structural covariance; this is reflected in the topological profile. Our findings strongly demonstrates that TDA can be used as a baseline framework to model altered topological structures of the brain.