Interactions Between Motor Cortex and Dorsolateral Striatum in a Model of Naturalistic Motor Behavior

Natural behaviors, such as exploration, are composed of distinct, highly stereotyped motor syllables—like locomoting, climbing, and rearing—that are seamlessly sequenced and executed by a network of brain regions. Understanding natural behaviors first requires an understanding of how these motor syllables are represented, transformed, sequenced, and executed through cross-region interactions. However, due to technological limitations, previous studies of motor control have largely examined individual brain regions in isolation and relied on highly constrained behavioral paradigms, leaving open the question of how regions like the motor cortex (M1) and dorsolateral striatum (DLS), two key nodes of the cortico-striatal network, interact to generate spontaneous, natural behaviors. By combining newly available simultaneous multi-region recordings and motor-syllable- segmented behavioral data from mice with theoretical neuroscience tools, this project will develop a theory for how M1 and DLS work together to orchestrate movement sequences. In close collaboration with experimentalists, I will use computational modeling and theoretical analysis to: (1) characterize and compare neural representations of motor actions across M1 and DLS, (2) disentangle their inter-regional interactions, and (3) construct a multi-compartment model explaining how these regions work together to generate and sequence natural behaviors. By explicitly modeling the division of labor between M1 and DLS, this framework will allow me to elucidate why modularity is critical for flexible motor control. Additionally, by aligning the model with the brain’s structure, it will provide a powerful tool for guiding future experiments. This project will advance our understanding of the neural computations underlying natural movement, bridging the gap between theoretical models and biological reality. By capturing how brain areas interact when planning and execution unfold simultaneously in a neurally constrained model, this work will generate experimentally testable predictions. In doing so, it will not only refine our models of motor control but also provide insights that could inform new therapeutic strategies for movement disorders such as Parkinson’s and Huntington’s disease. Ultimately, this work will provide a powerful framework for understanding the distributed computations that underlie flexible, spontaneous behavior. Project Number: 1F32NS146149-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of Neurological Disorders and Stroke (NINDS) | Principal Investigator: Klavdia Zemlianova | Institution: COLUMBIA UNIVERSITY HEALTH SCIENCES, NEW YORK, NY | Award Amount: $75,880 | Activity Code: F32 | Study Section: Special Emphasis Panel[ZRG1 F03D-V (20)] View on NIH RePORTER: https://reporter.nih.gov/project-details/11281419