Varanasi, S. | bioRxiv Science

bioRxiv2026

A Drosophila screen identifies domino as a link between chromatin regulation and synaptic organization

While heterozygous gene-disrupting variants in dosage-sensitive genes are strongly implicated in autism spectrum disorder (ASD), their effects on behavior in vivo remain poorly understood. To address this, we conducted a targeted behavioral screen in Drosophila using high-confidence ASD risk genes. This screen identified 48 lines with altered sleep, activity, or social behavior, including many genes not previously known to regulate these behaviors. The chromatin remodeler domino (dom) emerged as a compelling hit. Heterozygous mutants showed altered social spacing and male-biased changes in sleep and activity. RNA-sequencing revealed changes in gene expression and splicing associated with synaptic pathways. Consistent with these molecular changes, immunofluorescence revealed increased presynaptic activity in a brain region associated with sleep and sensory processing. Together, these findings show that partial loss of ASD risk genes is sufficient to alter behavior and identify dom as a link between transcriptional regulation, synaptic organization and behavior.

neuroscience↗

bioRxiv2025

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language models (LLMs). In particular, several studies have investigated the metabolic costs of sentence formation using neuroimaging techniques such as positron emission tomography, functional magnetic resonance imaging, electroencephalography (EEG), and imagined speech reconstruction (ISR). In this study, EEG data corresponding to imagined English-language speech phonemes were used for ISR, in combination with an LLM trained on an abridged autobiography. The LLM-generated text responses guided the synthesis of EEG data from relevant phonemes, which were then used to estimate corresponding metabolic activity, and the changes in simulated neurometabolic and electrical parameters were visually represented. Notably, introducing pseudorandom variance significantly (p < 0.001) enhanced the models ability to reflect biological variability. Future directions include expanding the ISR system with lightweight or locally run LLMs, incorporating training data from larger and more diverse populations, and utilizing truly random variability sources. Further optimization for broader hardware compatibility and implementation--such as neural phantoms, emotional context integration, or human-computer interaction platforms--offer promising pathways for advancement. Overall, this work establishes a foundation for the next generation of biologically inspired, modular, and adaptable ISR systems for both research and practical applications.

neuroscience↗