Al-Naji, A. | Search bioRxiv

bioRxiv2026

A standardized naturalistic audio stimuli database with unsupervised labeling

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

neuroscience↗

bioRxiv2025

Morphometric Latent Factors in Autism and Their Association with Receptor Profiles and Behavior

The profound heterogeneity of Autism Spectrum Disorder (ASD) is a major barrier to developing targeted therapies. While dimensional subtyping using functional connectivity (FC) has advanced the field, the intrinsic instability of FC limits its power to identify stable trait-like biomarkers. This study provides a direct comparison of latent factors derived from both FC and a stable morphometric measure, Morphometric Inverse Divergence (MIND), within the same ASD cohort. We hypothesized that stable structural factors would provide a more behaviorally relevant and biologically grounded account of ASD heterogeneity. Our findings reveal an important dissociation between functional and morphometric features. Latent factors derived from morphometric similarity (MIND) significantly correlated with core ASD behavioral traits (SCQ, SRS), while functional factors showed no association. This dissociation was also found at the neural level. Specifically, we observed weaker structure-function correspondence in the ASD group compared to the healthy control group (HC). Moreover, the stable structural factors were linked to trait-like neurodevelopmental mechanisms: the association with the CB1 receptor (important for synaptic pruning) observed in the HC was notably missing in the ASD group. Conversely, the functional factors were associated with a state-like arousal system (the norepinephrine transporter, NET). Collectively, our results demonstrate that stable morphometric-based factors, rather than time-varying functional ones, are predictive of behavioral traits in ASD. This work validates MIND as a robust approach and suggests that the link between structural organization and its neurodevelopmental (CB1) underpinnings is a more powerful and stable target for developing biomarkers in autism.

neuroscience↗