BME PhD Dissertation Defense: Nicole Carr

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Title: "From Networks To Microcircuits: Understanding Neural Computations Underlying Perception And Grasping" Advisory Committee:Chandramouli Chandrasekaran, PhD – BU BME, A&N (Research Advisor)Tara Moore, PhD – CAMED A&N (Research Advisor)Xue Han, PhD – BU BME (Chair)Kamal Sen, PhD – BU BMELaura Lewis, PhD – MIT IMES, EECSJonathon Kao, PhD – UCLA EE, CS, Neurobiology Abstract:Primates can easily reach to and grasp objects of various sizes, shapes, and weights to achieve goals. This remarkable capability is thought to emerge from coordinated activity across premotor, motor, and somatosensory cortical areas. However, despite decades of research, our understanding of how the primate motor system orchestrates reach-to-grasp movements remains incomplete for two reasons. First, unlike reaching or other behaviors, it has been difficult to develop behavioral setups and tasks that engage rich, naturalistic grasping behavior. Second, much of our knowledge of the motor system has emerged from studies of single-neuron tuning within individual brain areas, with comparatively less emphasis on neural population activity and how information is coordinated across areas during reach-to-grasp movements. Here, we combine engineering and neuroscience approaches to address these questions and advance our understanding of how the primate motor system orchestrates a reach-to-grasp. To study natural reach-to-grasp behavior in macaque monkeys, I adapted and expanded a turntable-based setup and combined it with multi-camera video acquisition and markerless hand tracking to quantify grasp kinematics. This setup enabled us to present objects of various sizes, shapes, and weights and thereby investigate how premotor and motor cortices support natural reach-to-grasp behavior. We then combined this behavioral approach with high-density electrophysiological recordings to examine how neural populations across motor cortical areas represent task-relevant information. We show that PMd exhibits functional organization along the rostrocaudal axis, rather than acting as a uniform area devoted to motor planning. Specifically, reach direction and object weight are represented differently in rostral and caudal PMd. We next examined the temporal organization of task-relevant information across the primary motor cortex (M1), ventral premotor cortex (PMv), and PMd using simultaneous Utah array recordings during reach-to-grasp movements. We found that location and object size information were represented across all three areas, with temporal profiles that varied across cortical areas and movement epochs. In contrast, object weight did not become reliably decodable until object contact, despite behavioral evidence that the animals had learned associations between visual object cues and weight. These findings suggest that the temporal availability of information across motor cortical areas depends on both the type of information being represented and the behavioral demands of the movement. Finally, to investigate how neuronal diversity contributes to cortical computation, we applied high-density electrophysiology and WaveMAP, a nonlinear waveform-clustering approach, to recordings from macaque primary visual cortex (V1). We identified nine candidate cell classes with distinct laminar distributions and visual response properties, revealing functional heterogeneity beyond cortical layer alone. Together, these studies establish complementary behavioral and analytical approaches for investigating neural computation, linking the organization of individual neurons and cortical layers to the distributed representation of task-relevant information across brain areas. This multiscale perspective provides a foundation for understanding how specialized cortical circuits coordinate complex, goal-directed behavior.