Aurelia Viglione

Background: Network analysis is a promising approach for elucidating the dynamics of the transition from psychopathology to well-being. Recently, symptom connectivity strength has been proposed as a measure of plasticity – the capacity to change disease severity. Yet, empirical findings remain inconsistent. We propose that this inconsistency can be resolved by recognizing that the interpretation of connectivity strength varies along the recovery process from depression, whether at baseline or during clinical change. Methods: We analyzed 2,710 depressed patients from the STAR*D dataset, grouped by the magnitude of change in depressive score. Symptom network connectivity was estimated from QIDS-C items at three time points: (i) baseline, (ii) change – defined as when clinical change in depression score occurs, (iii) post-change - corresponding to when the maximum clinical change is achieved. Results: At baseline, connectivity strength predicts the maximum clinical change, inversely correlating with its magnitude (ρ = −0.95, p = 0.001). At the change time point, connectivity strength parallels clinical change (ρ = 0.92, p = 0.002). A direct and significant association between connectivity strength and depression severity emerges only at the change (ρ = 0.98, p = 0.0003) and post-change (ρ = 0.95, p = 0.001) time points. Conclusions: The interpretation of connectivity strength for predicting depression trajectories varies by timepoint: at baseline, it measures plasticity -- the capacity for change -- whereas during clinical change, it indicates the magnitude of change in symptom severity. This framework supports the reliability of this prognostic marker for designing personalized therapeutic interventions in psychiatry.

Emotional contagion (ECo) represents a fundamental form of empathy. In this study, we used pupillometry to quantify ECo by assessing pupil responses of a mouse watching another mouse receive a tail shock. Pupil dilation effectively measured both direct and vicarious emotional response thresholds at the individual level through psychometric curve analysis. The pupillary ECo response diminished when the observer could not see the demonstrator, suggesting a multisensory process involving vision. Viewing videos of tail-shocked mice was sufficient to elicit a pupil response in the observer. Whole-brain c-Fos mapping revealed a broad network of 88 brain regions activated during ECo, with all areas activated in the demonstrator also engaged in the observer. Additionally, in certain brain regions, correlated activation was detected between each observer-demonstrator pair, indicating that ECo promotes a shared neural state. These findings advance our understanding of the neural basis of empathy, with implications for analyzing neuropsychiatric disorder models.

Perineuronal nets (PNNs) surround specific neurons in the brain and are involved in various forms of plasticity and clinical conditions. However, our understanding of the PNN role in these phenomena is limited by the lack of highly quantitative maps of PNN distribution and association with specific cell types. Here, we present the first comprehensive atlas of PNN distribution (in Allen Brain Atlas coordinates) and colocalization with parvalbumin (PV) cells for over 600 regions of the adult mouse brain. Data analysis showed that PV expression is a good predictor of PNN aggregation. In the cortex, PNNs are dramatically enriched in layer 4 of all primary sensory areas in correlation with thalamocortical input density, and their distribution mirrors intracortical connectivity patterns. Gene expression analysis identified many PNN correlated genes. Strikingly, PNN anticorrelated transcripts were enriched in synaptic plasticity genes, generalizing PNN role as circuit stability factors. Overall, this atlas offers novel resources for understanding the organizational principles of the brain extracellular matrix.

Pupil dynamics alterations have been found in patients affected by a variety of neuropsychiatric conditions, including autism. Studies in mouse models have used pupillometry for phenotypic assessment and as a proxy for arousal. Both in mice and humans, pupillometry is non-invasive and allows for longitudinal experiments supporting temporal specificity, however its measure requires dedicated setups. Here, we introduce a Convolutional Neural Network that performs on-line pupillometry in both mice and humans in a web app format. This solution dramatically simplifies the usage of the tool for non-specialist and non-technical operators. Because a modern web browser is the only software requirement, this choice is of great interest given its easy deployment and set-up time reduction. The tested model performances indicate that the tool is sensitive enough to detect both spontaneous and evoked pupillary changes, and its output is comparable with state-of-the-art commercial devices.