Biology subjects

Moxon, K. A.

Publications and source records attributed to Moxon, K. A..

6 recordsLinked to original sources

bioRxiv2025

Neural dynamics encoding risky choices during deliberation reveal separate choice subspaces

Human decision-making involves the coordinated activity of multiple brain areas, acting in concert, to enable humans to make choices. Most decisions are carried out under conditions of uncertainty, where the desired outcome may not be achieved if the wrong decision is made. In these cases, humans deliberate before making a choice. The neural dynamics underlying deliberation are unknown and intracranial recordings in clinical settings present a unique opportunity to record high temporal resolution electrophysiological data from many (hundreds) brain locations during behavior. Combined with dynamic systems modeling, these allow identification of latent brain states that describe the neural dynamics during decision-making, providing insight into these neural dynamics and computations. Results show that the neural dynamics underlying risky decision, but not decisions without risk, converge to separate subspaces depending on the subjects preferred choice and that the degree of overlap between these subspaces declines as choice approaches, suggesting a network level representation of evidence accumulation. These results bridge the gap between regression analyses and data driven models of latent states and suggest that during risky decisions, deliberation and evidence accumulation toward a final decision are represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. HighlightsO_LIHighly accurate decoding can be accomplished with dynamical systems modeling that revealed distinct attractor-like subspaces, one for each of the two options (gamble or safe bet). C_LIO_LIEarly during deliberation, single trial neural trajectories show rapid transitioning between these subspaces, but as the time of choice selection approached, the single trial trajectories converged towards one of the subspaces, whose identity was consistent with the subjects choice. C_LIO_LIThese dynamics are specific to risky decisions as we found classification accuracy dropped to chance for trials where the gamble option was guaranteed to be successful (100% win probability) or unsuccessful (0% win probability). C_LIO_LIThese results suggest that deliberation and evidence accumulation toward a final decision in the presence of any risk can be represented by the same neural dynamics, providing novel insights into the neural computations underlying human choice. C_LI

neuroscience↗

bioRxiv2022

Multi-areal neural dynamics encode human decision making

Value-based decision-making involves multiple cortical and subcortical brain areas, but the distributed nature of neurophysiological activity underlying economic choices in the human brain remains largely unexplored. Here, we use intracranial recordings from neurosurgical patients to show that risky choices are reflected in high frequency activity distributed across multiple prefrontal and subcortical brain regions, whereas reward-related computations are less widely represented and more modular. State space modeling reveals sub-second neural dynamics underlying choices during deliberation and allows high-accuracy trial-by-trial decoding of subjects choices robustly across patients despite differences in anatomical coverage. These results shed light into the neural basis of choice across brain areas and open the door to new intracranial approaches for brain state decoding.

neuroscience↗

bioRxiv2020

Trunk sensory and motor cortex ispreferentially integrated with hindlimb sensoryinformation that supports trunk stabilization

Sensorimotor integration in the trunk system has been poorly studied despite its importance for examining functional recovery after neurological injury or disease. Here, we mapped the relationship between thoracic dorsal root ganglia and trunk sensory cortex (S1) to create a detailed map of the extent and internal organization of trunk primary sensory cortex, and trunk primary motor cortex (M1) and showed that both cortices are somatotopically complex structures that are larger than previously described. Surprisingly, projections from trunk S1 to trunk M1 were not anatomically organized. We found relatively weak sensorimotor integration between trunk M1 and S1 and between trunk M1 and forelimb S1 compared to extensive integration between trunk M1 and hindlimb S1 and M1. This strong trunk/hindlimb connection was identified for high intensity stimuli that activated proprioceptive pathways. To assess the implication of this integration, the responses in sensorimotor cortex were examined during a postural control task and supported sensorimotor integration between hindlimb sensory and lower trunk motor cortex. Together, these data suggest that trunk M1 is guided predominately by hindlimb proprioceptive information that reached the cortex directly via the thalamus. This unique sensorimotor integration suggests an essential role for the trunk system in postural control, and its consideration could be important for understanding studies regarding recovery of function after spinal cord injury. SignificanceThis work identifies extensive sensorimotor integration between trunk and hindlimb cortices, demonstrating that sensorimotor integration is an operational mode of the trunk cortex in intact animals. The functional role of this integration was demonstrated for postural control when the animal was subjected to lateral tilts. Furthermore, these results provide insight into cortical reorganization after spinal cord injury making clear that sensorimotor integration after SCI is an attempt to restore sensorimotor integration that existed in the intact system. These results could be used to tailor rehabilitative strategies to optimize sensorimotor integration for functional recovery.

neuroscience↗

bioRxiv2020

At-level allodynia after mid-thoracic contusion in the rat

The rat mid-thoracic contusion model has been used to study at-level tactile allodynia after spinal cord injury (SCI), one of the more common types of allodynia. An important advantage of this model is that not all animals develop allodynia and, therefore, it could be used to more clearly examine mechanisms that are strictly related to pain development separately from mechanisms related to the injury itself. However, how to separate those that develop allodynia from those that do not is unclear. The aims of the current study were to identify where allodynia and spasticity develop and use this information to identify metrics that separate animals that develop allodynia from those that do not in order study difference in their behavior. To accomplish these aims, a standardized grid was used to localize pain on the dorsal trunk and map it to thoracic dermatomes, providing for the development of a pain score that relied on supraspinal responses and separated subgroups of animals. Similar to human studies, the development of allodynia often occurred with the development of spasticity or hyperreflexia. Moreover, the time course and prevalence of pain phenotypes (at-, above-, or below level) produced by this model were similar to that observed in humans who have sustained an SCI. However, the amount of spared spinal matter in the injured cord did not explain the development of allodynia, as was previously reported. This approach can be used to study the mechanism underlying the development of allodynia separately from mechanisms related to the injury alone.

physiology↗

bioRxiv2020

Head-mounted microendoscopic calcium imaging in dorsal premotor cortex of behaving rhesus macaque

A major effort is now underway across the brain sciences to identify, characterize and manipulate mesoscale neural circuits in order to elucidate the mechanisms underlying sensory perception, cognition and behavior. Optical imaging technologies, in conjunction with genetically encoded sensors and actuators, serve as important tools toward these goals, allowing access to large-scale genetically defined neuronal populations. In particular, one-photon miniature microscopes, coupled with genetically encoded calcium indicators and microendoscopic gradient-refractive index (GRIN) lenses, enable unprecedented readout of neural circuit dynamics in cortical and deep subcortical brain regions during active behavior in rodents. This has already led to breakthrough discoveries across a wide array of rodent brain regions and behaviors. However, in order to study the neural circuit mechanisms underlying more complex and clinically relevant human behaviors and cognitive functions, it is crucial to translate this technology to non-human primates. Here, we describe the first successful application of this technology in the rhesus macaque. We identified a viral strategy for robust expression of GCaMP, optimized a surgical protocol for microendoscope GRIN lens insertion, and created a chronic cranial chamber and lens mounting system for imaging in gyral cortex. Using these methods, we demonstrate the ability to perform plug-and-play, head-mounted recordings of cellular-resolution calcium dynamics from over 100 genetically-targeted neurons simultaneously in dorsal premotor cortex while the macaque performs a naturalistic motor reach task with the head unrestrained and freely moving. The recorded population of neurons exhibited calcium dynamics selective to the direction of reach, which we show can be used to decode the animals trial-by-trial motor behavior. Recordings were stable over several months, allowing us to longitudinally track large populations of individual neurons and monitor their relationship to motor behavior over time. Finally, we demonstrate the ability to conduct simultaneous, multi-site imaging in bilateral dorsal premotor cortices, offering an opportunity to study distributed networks underlying complex behavior and cognition. Together, this work establishes head-mounted microendoscopic calcium imaging in macaque as a powerful new approach for studying the neural circuit mechanisms underlying complex and clinically relevant behaviors, and promises to greatly advance our understanding of human brain function, as well as its dysfunction in neurological disease. HighlightsFirst demonstration of head-mounted microendoscopic calcium imaging in behaving macaque. Surgical protocols developed for preparing the animal for calcium imaging, including virus injections to express GCaMP and chronic implantation of a GRIN lens to enable optical access to gyral cortex. Proof of concept plug-and-play calcium imaging in behaving macaques with months long stable recording capability allowing populations of individual neurons to be tracked longitudinally. Bilateral calcium imaging from dorsal premotor cortex exhibited dynamics selective to the animals direction of reach and allowed decoding of the animals motor behavior

neuroscience↗

bioRxiv2020

Transitioning from global to local computational strategies during brain-machine interface learning

When learning to use a brain-machine interface (BMI), the brain modulates neuronal activity patterns, exploring and exploiting the state space defined by their neural manifold. Neurons directly involved in BMI control can display marked changes in their firing patterns during BMI learning. However, whether these changes extend to neurons not directly involved in BMI control remains unclear. To clarify this issue, we studied BMI learning in animals that were required to control the position of a platform with their neural signals. Animals that learned to control the platform and improved their performance in the task shifted from a global strategy, where both direct and indirect neurons modified their firing patterns, to a local strategy, where only direct neurons modified their firing rate, as animals became expert in the task. These results provide important insights into what differentiates successful and unsuccessful BMI learning and the computational mechanisms adopted by the neurons.

neuroscience↗