arXiv subjects

Ivy Guo

Publications and source records attributed to Ivy Guo.

2 recordsLinked to original sources

arXiv2025

The Role of Affect and Priors in the Generation of Hallucinations in Early Psychosis

Background: Stress and negative affect play significant roles in developing psychosis. Bayesian analyses applied to the conditioned hallucinations (CH) task suggest that hallucinations arise when maladaptive prior beliefs outweigh sensory evidence. Prior weighting is linked to hallucination severity, yet the nature of these priors remains unclear. Negative affect may influence the strength of maladaptive priors. We hypothesized that, under stress, participants will show increased CH rates and prior weighting, with this effect more pronounced in patients. Methods: This study employs a modified CH task using valenced linguistic stimuli and stress and non-stress affective manipulations. The sample for this pilot study included those at risk for psychosis and patients with first episode psychosis (N=12) and healthy controls (N=15). The objective of this study was first to validate this affective version of the CH task and then to demonstrate an effect of affect on CH rates and prior weighting. Results: Replicating past results, patients had higher CH rates (b = 0.061, p < 0.001) and prior weighting (b = 0.097, p < 0.001) for session 1 compared to controls (n=15) across conditions. Further, runs with stress manipulations had higher prior weighting across patients and controls compared to runs with non-stress manipulations (b = 0.054, p = 0.033). Conclusions: This study validates this affective version of the CH task and provides preliminary evidence of a relationship between affective state and prior weighting. Future work will be aimed at confirming and extending these findings, with the objective of developing biomarkers of early psychosis. Key words: Schizophrenia, Affect, Priors, Computational Psychiatry, Clinical High Risk Population, Psychotic Symptoms

q-bio.NC↗

arXiv2025

Latent Space Data Fusion Outperforms Early Fusion in Multimodal Mental Health Digital Phenotyping Data

Background: Mental illnesses such as depression and anxiety require improved methods for early detection and personalized intervention. Traditional predictive models often rely on unimodal data or early fusion strategies that fail to capture the complex, multimodal nature of psychiatric data. Advanced integration techniques, such as intermediate (latent space) fusion, may offer better accuracy and clinical utility. Methods: Using data from the BRIGHTEN clinical trial, we evaluated intermediate (latent space) fusion for predicting daily depressive symptoms (PHQ-2 scores). We compared early fusion implemented with a Random Forest (RF) model and intermediate fusion implemented via a Combined Model (CM) using autoencoders and a neural network. The dataset included behavioral (smartphone-based), demographic, and clinical features. Experiments were conducted across multiple temporal splits and data stream combinations. Performance was evaluated using mean squared error (MSE) and coefficient of determination (R2). Results: The CM outperformed both RF and Linear Regression (LR) baselines across all setups, achieving lower MSE (0.4985 vs. 0.5305 with RF) and higher R2 (0.4695 vs. 0.4356). The RF model showed signs of overfitting, with a large gap between training and test performance, while the CM maintained consistent generalization. Performance was best when integrating all data modalities in the CM (in contradistinction to RF), underscoring the value of latent space fusion for capturing non-linear interactions in complex psychiatric datasets. Conclusion: Latent space fusion offers a robust alternative to traditional fusion methods for prediction with multimodal mental health data. Future work should explore model interpretability and individual-level prediction for clinical deployment.

cs.LG↗