Reading Your Own Mind: Dynamic Visualization of Real-Time Neural Signals

Brain Computer Interfaces: BCI) systems which allow humans to control external devices directly from brain activity, are becoming increasingly popular due to dramatic advances in the ability to both capture and interpret brain signals. Further advancing BCI systems is a compelling goal both because of the neurophysiology insights gained from deriving a control signal from brain activity and because of the potential for direct brain control of external devices in applications such as brain injury recovery, human prosthetics, and robotics. The dynamic and adaptive nature of the brain makes it difficult to create classifiers or control systems that will remain effective over time. However it is precisely these qualities that offer the potential to use feedback to build on simple features and create complex control features that are robust over time. This dissertation presents work that addresses these opportunities for the specific case of Electrocorticography: ECoG) recordings from clinical epilepsy patients. First, queued patient tasks were used to explore the predictive nature of both local and global features of the ECoG signal. Second, an algorithm was developed and tested for estimating the most informative features from naive observations of ECoG signal. Third, a software system was built and tested that facilitates real-time visualizations of ECoG signal patients and allows ECoG epilepsy patients to engage in an interactive BCI control feature screening process

DIMENSIONALITY REDUCTION INCONTROL AND COORDINATION OF HUMAN HAND

The human hand is an excellent example of versatile architecture which can easily accomplish numerous tasks with very least effort possible. Researchers have been trying to analyze the complex architecture of the human hand. It is an unsolved mystery even today how Central Nervous System (CNS) controls the high degree of freedom (DoF) of the human hand. Investigators have put forth numerous theories which support movement planning both at higher and lower levels of the neural system as well as the bio mechanical system. This planning is hypothesized to happen in a reduced dimensionality space of tiny modules of movement called movement primitives often referred to as synergies. These synergies are physiologically significant in planning and control of movement.This dissertation presents time-varying kinematic synergies which linearly combine to generate the entire movement. The decomposition of these synergies becomes an exciting optimization problem and even more fascinating as it addresses two most important problems of motor control—coordination and dimensionality reduction. In this dissertation, a new model of convolutive mixtures for generation of joint movements is proposed. According to this model, an impulse originated in the higher-level neural system evokes the activation of some circuits in the lower-level neural system, then stimulates certain biomechanical structures, and eventually creates a stereotyped angular change at each finger-joint of the hand. Current model enabled greater access to existing blind source separation algorithms which reduce the computational complexity. First, kinematic synergies were extracted from a well known matrix factorization method, namely principal component analysis. By using the above kinematic synergies, a method to obtain temporal postural synergies is established. These temporal postural synergies were further used in the model of convolutive mixtures. An optimal selection of these temporal synergies which can reconstruct movements is then achieved by l1-minimization. The realization of the model by l1-minimization out performed the previous models which use steepest descent gradient methods. Synergies have received increased attention in the fields of robotics, human computer interface, telesurgery and rehabilitation. Improved performance and new computational model to decompose synergies presented here might enable them to be appropriate for real time applications

The Neural Circuit for Behavioral Responses to Pheromone and Social Behavior in Caenorhabditis Elegans

Social behavior is a widespread phenomenon across species from microorganisms to humans. Chemical communication using pheromones is particularly interesting for its versatile use in many organisms, and has been studied at both molecular and circuit levels. However, the central issue of how genes and molecular components contribute to neural circuits that eventually lead to complex social behaviors remains unclear. Using the nematode model organism Caenorhabditis elegans, I have examined two behavioral responses to pheromones, pheromone avoidance and pheromoneregulated social aggregation behavior, at genetic and circuit levels. The known C. elegans pheromones are a set of related compounds called ascarosides. Specific ascarosides are known to regulate development and male attraction to potential mates. To characterize hermaphrodite responses to ascarosides, I used a behavioral assay for acute avoidance called the drop test, which measures reversals of a forward-moving worm upon an encounter with a diluted pheromone. I found that wildtype hermaphrodites from the laboratory N2 strain avoid one of the ascarosides, ascr#3/C9. Through in vivo functional imaging using a genetically encoded calcium sensor and behavioral analysis of sensory mutants, I found that the nociceptive ADL neurons sense ascr#3/C9. Behavioral analysis of synaptically manipulated worms indicated that ADL chemical synapses promotes avoidance behavior. Animals with null mutation in the neuropeptide receptor npr-1 have reduced avoidance of ascr#3/C9. I found that this effect is mediated by ADL participation in a gap junction circuit with the “social†hub interneuron RMG. Thus ADL chemical synapses and ADL gap junctions appear to have antagonistic effects on behavior. Avoidance is further suppressed by another modulatory ascaroside cue, ascr#5/C3, sensed by the ASK sensory neurons that are also connected to RMG by gap junctions. Males do not avoid ascr#3/C9, and they have reduced ADL sensory responses to this ascaroside. Genetic masculinization of ADL does not reduce its sensory responses, suggesting that the sensory change has a non-autonomous component. npr-1 males appear to be attracted to ascr#3/C9, and in these animals ASK strongly responds to ascr#3/C9 and contributes to the switch in behavioral responses. Together, these results indicate that behavioral avoidance of the pheromone ascr#3/C9 is modulated by neuropeptide signaling and sexual dimorphism that change neuronal properties at the circuit level. Invertebrate gap junctions are comprised of innexin subunits. I conducted a systematic genetic study to identify innexin genes that affect npr-1-dependent aggregation. Several weak suppressors of npr-1 aggregation behavior were identified, suggesting functional redundancy between innexins. In addition, I found that an allele of glb-5 present in the wild strain CB4856 suppresses aggregation, probably by modulating sensitivity to oxygen in oxygen-sensing neurons that promote aggregation. My thesis work should help expand our understanding of neural circuits and molecules used for chemical communication, simple animal social behaviors, and perhaps complex human behaviors

The cognitive neuroscience of visual working memory

Visual working memory allows us to temporarily maintain and manipulate visual information in order to solve a task. The study of the brain mechanisms underlying this function began more than half a century ago, with Scoville and Milner’s (1957) seminal discoveries with amnesic patients. This timely collection of papers brings together diverse perspectives on the cognitive neuroscience of visual working memory from multiple fields that have traditionally been fairly disjointed: human neuroimaging, electrophysiological, behavioural and animal lesion studies, investigating both the developing and the adult brain