Data Management Plan

We aim to generate insights that will drive new cell- and circuit-based therapeutics for autism spectrum disorder (ASD). We will study 4 genetic mouse models of ASD (Fmr1, Cntnap2, Neurexin1⍺ and Shank3B). These were selected because the underlying genes are known to cause disruptions likely to be relevant to decision-making, a cognitive behavior that can be rigorously and reproducibly studied in the laboratory. We will leverage a standardized decision-making paradigm developed by the International Brain Laboratory (IBL). The IBL task encourages animals to use prior beliefs about stimulus location to guide choice. To understand how decision circuits are altered in ASD, and can be targeted therapeutically, we will combine behavior with powerful tools for neural measurement: cell-type specific widefield imaging and ultra-large-scale Neuropixels electrophysiology. We will leverage tools (from IBL and a recent NIH U19) for processing neural data and connecting it to decision-making behavior and spontaneous movements. Our central hypothesis is that the multiple ASD genetic risk factors drive (1) maladaptive reorganization of multi-area decision circuits and (2) detrimental noise and that the resulting decision-making impairments can be rescued with targeted manipulation. To test this hypothesis, we will first determine how neural activity patterns during decision-making differ in wild-type (WT) vs. ASD model mice. By using multiple behavioral/neural metrics for each mouse to create a “neurobehavioral barcode,” we can compare genetic backgrounds. Next, we will establish causal links between neural activity and behavior via optogenetic manipulation and administration of methylphenidate (MPH). MPH acts on dopaminergic circuits, ameliorates some ASD symptoms, and in monkeys, reduces “noise correlations”: detrimental neural activity that is shared across neurons in local microcircuits and multi-area macrocircuits. Our manipulations will test the causal role of these correlations on neural activity and decision-making, affording mechanistic insight into behavioral signatures of ASD. Finally, we will evaluate the translational potential of candidate mechanisms by testing whether the disruption barcodes and intervention outcomes generalize to new behavioral contexts: dynamic foraging and social interaction. Taken together, our approach will (1) define large-scale, cell-type specific neural activity patterns in ASD, (2) causally test candidate mechanisms for behavioral disruption and (3) validate translational potential by assessing generalizability to new behaviors. Our ability to generate these insights will be bolstered by our experience deploying large-scale behavior and neural measurement pipelines, and our demonstrated commitment to reproducibility. By identifying specific cell types, long-range cortical pathways, and population-level coordination patterns that causally impair decision-making, this work will define concrete, circuit-level targets for therapeutic intervention. These targets, such as maladaptive noise correlations within and between defined cortical networks, provide actionable mechanisms that can be modulated pharmacologically or through circuit-based neuromodulation strategies across genetically diverse forms of ASD.