Biology subjects

Dabiri, S.

Publications and source records attributed to Dabiri, S..

2 recordsLinked to original sources

bioRxiv2026

TRACR: an anterograde transneuronal tracing system for genetic access across synapses and longitudinal circuit analysis

Following neural signals as they converge onto and diverge from individual neurons is central to understanding circuit function and disease-related dysfunction. Existing anterograde transneuronal tracers are limited by cytotoxicity and incomplete genetic control over connected partners. To address these limitations, we adapted synthetic Notch designs to create TRanssynaptic Anterograde Circuit Readout (TRACR). Binding of the engineered ligand-receptor across synapses induces TRE-driven reporter transcription, enabling characterization of postsynaptic neurons. TRACR provides segregated genetic access to pre- and postsynaptic populations, and can be combined with markers, sensors, or effectors to expand circuit analysis. By applying TRACR at multiple synapses in the mouse visual system, we show that TRACR labels postsynaptic partners of sensory neurons, long-range projections and local inhibitory interneurons. TRACR signaling is reversible and fails to activate when synapses are absent or disrupted. Together, TRACR is an accessible, AAV-deliverable transneuronal reporting tool for longitudinal analysis of circuit assembly, degeneration, and repair. HIGHLIGHTSO_LITRACR adapts the synNotch system to signal across synapses for tracing postsynaptic targets. C_LIO_LITRACR identifies local and long-range postsynaptic targets in the mouse visual system. C_LIO_LITRACR activation requires intact synaptic connectivity rather than proximity. C_LIO_LITRACR signals are reversible, diminishing upon synapse loss and activating following assembly. C_LI

neuroscience↗

bioRxiv2023

Adaptive Bayesian Optimization for State-Dependent Brain Stimulation

Brain stimulation has become an important treatment option for a variety of neurological and psychiatric diseases. A key challenge in improving brain stimulation is selecting the optimal set of stimulation parameters for each patient, as parameter spaces are too large for brute-force search and their induced effects can exhibit complex subject-specific behavior. To achieve greatest effectiveness, stimulation parameters may additionally need to be adjusted based on an underlying neural state, which may be unknown, unmeasurable, or challenging to quantify a priori. In this study, we first develop a simulation of a state-dependent brain stimulation experiment using rodent optogenetic stimulation data. We then use this simulation to demonstrate and evaluate two implementations of an adaptive Bayesian optimization algorithm that can model a dynamically changing response to stimulation parameters without requiring knowledge of the underlying neural state. We show that, while standard Bayesian optimization converges and overfits to a single optimal set of stimulation parameters, adaptive Bayesian optimization can continue to update and explore as the neural state is changing and can provide more accurate optimal parameter estimation when the optimal stimulation parameters shift. These results suggest that learning algorithms such as adaptive Bayesian optimization can successfully find optimal state-dependent stimulation parameters, even when brain sensing and decoding technologies are insufficient to track the relevant neural state.

neuroscience↗