States and sequences of paired subspace ideals and their relationship to patterned brain function

It is found here that the state of a network of coupled ordinary differential equations is partially localizable through a pair of contractive ideal subspaces, chosen from dual complete lattices related to the synchrony and synchronization of cells within the network. The first lattice is comprised of polydiagonal subspaces, corresponding to synchronous activity patterns that arise from functional equivalences of cell receptive fields. This lattice is dual to a transdiagonal subspace lattice ordering subspaces transverse to these network-compatible synchronies. Combinatorial consideration of contracting polydiagonal and transdiagonal subspace pairs yields a rich array of dynamical possibilities for structured networks. After proving that contraction commutes with the lattice ordering, it is shown that subpopulations of cells are left at fixed potentials when pairs of contracting subspaces span the cells' local coordinates - a phenomenon named glyph formation here. Treatment of mappings between paired states then leads to a theory of network-compatible sequence generation. The theory's utility is illustrated with examples ranging from the construction of a minimal circuit for encoding a simple phoneme to a model of the primary visual cortex including high-dimensional environmental inputs, laminar speficicity, spiking discontinuities, and time delays. In this model, glyph formation and dissolution provide one account for an unexplained anomaly in electroencephalographic recordings under periodic flicker, where stimulus frequencies differing by as little as 1 Hz generate responses varying by an order of magnitude in alpha-band spectral power. Further links between coupled-cell systems and neural dynamics are drawn through a review of synchronization in the brain and its relationship to aggregate observables, focusing again on electroencephalography. Given previous theoretical work relating the geometry of visual hallucinations to symmetries in visual cortex, periodic perturbation of the visual system along a putative symmetry axis is hypothesized to lead to a greater concentration of harmonic spectral energy than asymmetric perturbations; preliminary experimental evidence affirms this hypothesis. To conclude, connections drawn between dynamics, sensation, and behavior are distilled to seven hypotheses, and the potential medical uses of the theory are illustrated with a lattice depiction of ketamine xylazine anaesthesia and a reinterpretation of hemifield neglect

A neuroprothesis for tremor management

Tremor is the most common movement disorder, affecting ∼ 15 % of people over 50 years old according to some estimates. It appears due to a number of syndromes, being essential tremor and Parkinson's disease the most prevalent among them. None of these conditions is fully understood. Tremor is currently treated through drugs or neurosurgery, but unfortunately, it is not managed effectively in ∼25 % of the patients. Therefore, it constitutes a major cause of loss of independence and quality of life. Various alternative approaches for tremor management are reported in the literature. Among them, those devices that rely on the application of forces to the tremulous segments show a considerable potential. A number of prototypes that exploit this principle are available, spanning fixed devices and orthoses. However, none of them has fulfilled user's expectation for continuous use during daily living. This thesis presents the development and validation of a neuroprosthesis for tremor management. A neuroprosthesis is a system that restores or compensates for a neurological function that is lost. In this case, the neuroprosthesis aims at compensating the functional disability caused by the tremor. To this end, it applies forces to the tremulous limb through the control of muscle contraction, which is modulated according to the characteristics of the tremor. The concept design envisions the device as a textile that is worn on the affected limb, thus meeting the usability requirements defined by the patients. The development of the neuroprosthesis comprised the following tasks: 1. The development of a concept design of the neuroprosthesis, which incorporates state of the art knowledge on tremor, and user's needs. 2. The design and validation of a cognitive interface that parameterizes the tremor in functional contexts. This interface provides the information that the neuroprosthesis uses for tremor suppression. Two versions are developed: a multimodal interface that integrates the recordings of the whole neuromusculoskeletal system, and an interface incorporating only wearable movement sensors. The latter is intended for the functional validation of the neuroprosthesis, while the former is a proof of concept of an optimal interface for this type of applications. 3. The development of a novel approach for tremor suppression through transcutaneous neurostimulation. The approach relies on the modulation of muscle cocontraction as a means of attenuating the tremor without the need of conventional actuators. The experimental validation here provided demonstrates the feasibility and interest of the approach. In parallel with the validation of the neuroprosthesis, I performed a detailed study on the physiology of motoneurons in tremor, given the lack of a complete description of its behavior. The outcome of this study contributes to the interpretation of the results obtained with the neuroprosthesis, and opens new research lines, both related to alternative interventions and basic neuroscience. In summary, the results here presented demonstrate that tremor may be accurately parameterized while the patient performs functional activities, and that this information may be exploited to drive a neuroprosthesis for tremor management. Furthermore, the novel approach for tremor suppression presented in this dissertation constitutes a potential approach for treating upper limb tremor, either alone, or as a complement to pharmacotherapy. These results encourage the validation of the neuroprosthesis in a large cohort of patients, in order to enable its translation to the market. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------El temblor es el trastorno del movimiento más común, afectando, según algunas estimaciones, al ∼15 % de la población de más de 50 años. Existen diversos "síndromes" que causan temblor, siendo el temblor esencial y la enfermedad de Parkinson los que presentan mayor prevalencia. Además, cabe resaltar que no existe una descripción completa de ninguno de ellos. En la actualidad el temblor se trata mediante una serie de fármacos o neurocirugía. A pesar de ello, el ∼ 25 % de los pacientes sufren problemas funcionales debido a su condición. Por tanto, es evidente que el temblor constituye una de las principales causas de dependencia y pérdida de calidad de vida. Realizando una revisión de las publicaciones científicas sobre el temblor, se observa que se ha propuesto un considerable número de tratamientos alternativos. Entre ellos destacan los dispositivos que se fundamentan en la aplicación de fuerzas sobre los segmentos afectados por el temblor, de los que ya se ha evaluado una serie de prototipos. Estos abarcan desde dispositivos fijados a otras estructuras hasta ortesis. Sin embargo, ninguno de ellos satisface las expectativas de los usuarios para su uso durante el día a día. Esta tesis presenta el diseño y validación de una neruoprótesis para el tratamiento del temblor. Una neuroprótesis es un sistema que reemplaza o compensa una función neurológica perdida. En este caso, la neuroprótesis tiene como objetivo compensar la discapacidad motora causada por el temblor. Para ello aplica fuerzas al miembro afectado a través del control del nivel de contracción muscular, que se modula según las características del temblor. El diseño conceptual contempla al dispositivo como un textil que se viste en el brazo afectado, satisfaciendo los requisitos de usabilidad definidos por los pacientes. El desarrollo de la neuroprótesis abarcó las siguientes tareas: 1. El desarrollo del diseño conceptual de la neuroprótesis, que incorpora el conocimiento actual sobre el temblor, y las necesidades de los usuarios. 2. El diseño y validación de una interfaz cognitiva que parametriza el temblor durante tareas funcionales. La información obtenida con esta interfaz es usada por la neuroprótesis para modular la corriente aplicada mediante técnicas de neuroestimulación. Se desarrollan dos versiones de la interfaz cognitiva: una interfaz multimodal que integra información de todo el sistema neuromusculoesquelético, y una interfaz que implementa únicamente sensores vestibles de movimiento. La segunda interfaz fue la que se usó durante la validación funcional de la neuroprótesis, mientras que la primera es una prueba de concepto de una interfaz óptima para este tipo de aplicaciones. 3. El desarrollo de una nueva aproximación para la supresión del temblor mediante neuroestimulación transcutánea. Dicha aproximación se fundamenta en la modulación del grado de co-contracción de los músculos afectados como forma de atenuar el temblor, sin necesidad de usar actuadores convencionales. La evaluación experimental sirvió para demostrar la viabilidad e interés de la intervención. En paralelo a la validación de la neuroprótesis, llevé a cabo un estudio detallado de la fisiología de las motoneuronas en el caso del temblor, dado que no existe una descripción del funcionamiento de las mismas en el caso de este trastorno. Este estudio sirve para ayudar a la interpretación de los resultados de la neuroprótesis, y para abrir una serie de líneas futuras de investigación, tanto sobre nuevas intervenciones para el temblor, como sobre neurociencia básica. En resumen, los resultados que se presentan en esta tesis demuestran que es posible parametrizar de una forma precisa el temblor durante la realización de tareas funcionales, y que esta información sirve para controlar una neuroprótesis para el tratamiento del temblor. Además, la nueva aproximación para la compensación del temblor que se presenta tiene el potencial de convertirse en un tratamiento alternativo para el temblor de miembro superior, ya sea de forma independiente o como complemento a los fármacos. Estos resultados alientan la validación de la neuroprótesis en una cohorte grande de pacientes, con el objetivo de facilitar su transferencia al mercado

Dynamic models of brain imaging data and their Bayesian inversion

This work is about understanding the dynamics of neuronal systems, in particular with respect to brain connectivity. It addresses complex neuronal systems by looking at neuronal interactions and their causal relations. These systems are characterized using a generic approach to dynamical system analysis of brain signals - dynamic causal modelling (DCM). DCM is a technique for inferring directed connectivity among brain regions, which distinguishes between a neuronal and an observation level. DCM is a natural extension of the convolution models used in the standard analysis of neuroimaging data. This thesis develops biologically constrained and plausible models, informed by anatomic and physiological principles. Within this framework, it uses mathematical formalisms of neural mass, mean-field and ensemble dynamic causal models as generative models for observed neuronal activity. These models allow for the evaluation of intrinsic neuronal connections and high-order statistics of neuronal states, using Bayesian estimation and inference. Critically it employs Bayesian model selection (BMS) to discover the best among several equally plausible models. In the first part of this thesis, a two-state DCM for functional magnetic resonance imaging (fMRI) is described, where each region can model selective changes in both extrinsic and intrinsic connectivity. The second part is concerned with how the sigmoid activation function of neural-mass models (NMM) can be understood in terms of the variance or dispersion of neuronal states. The third part presents a mean-field model (MFM) for neuronal dynamics as observed with magneto- and electroencephalographic data (M/EEG). In the final part, the MFM is used as a generative model in a DCM for M/EEG and compared to the NMM using Bayesian model selection

Homeostatic compensation and neuromodulation maintain synchronized motor neuron activity in the crustacean cardiac ganglion

Dissertation supervisor: Dr. David J. Schulz.Includes vita.Animals rely on the nervous system to produce appropriate behavior throughout their lives. In sending commands to the musculature for rhythmic motor behaviors such as breathing or walking, neural networks must be stable enough to send a reliable level of drive with the proper temporal coordination. Networks must also be flexible enough to meet changing environmental demands. A network's output ultimately arises from the intrinsic excitability of its constituent neurons and the synaptic connections between them. Interestingly, neurons and networks are able to produce highly conserved output from highly variable underlying intrinsic and synaptic properties. To explore the consequences of this variability, we have used the crustacean cardiac ganglion (CG) which consists of 9 neurons: 4 pacemaker cells that give excitatory input to 5 Large Cell motor neurons (LCs) which are responsible for driving the simultaneous contraction of the musculature that makes up the walls of the animal's single-chambered heart (Alexandrowicz, 1934; Hartline, 1967; Anderson and Cooke, 1971). The intact network can be dissected from the animal in physiological saline and it continues to produce robust, reliable, and rhythmic output (Welsh and Maynard, 1951; Cooke, 2002). LCs have virtually identical synchronized activity, but their intrinsic ionic conductances can be highly variable (Ransdell et al., 2013a). In Chapter 1, we exploit this variability by pharmacologically blocking a subset of their conductances to make LCs hyperexcitable and desynchronize their activity. We find that homeostatic compensation restores synchronized activity and excitability within one hour. This happens via two synergistic mechanisms: the membrane properties of each cell are re-tuned to converge on similar voltage activity, and increased conductance of the gap junctions between the cells helps to buffer away differences in their voltage activity. A separate but related study asked whether naturalistic perturbations of network activity would also result in desynchronization. Neuromodulation provides flexibility in the output of neural networks by altering a subset of their conductances. We hypothesized that this could also cause desynchronization. We found that modulation with serotonin and dopamine both increased the excitability of the CG. Interestingly, serotonin desynchronized the CG, but dopamine did not. We found that dopaminergic modulation directly increases gap junctional conductance. By co-applying these modulators, we found dopamine was able to prevent serotonin from desynchronizing the network without occluding its effects. It was also able to prevent the desynchronization caused by ion channel blockers. Finally, to fully understand the output of LCs, we must recognize that their activity arises not only from their intrinsic properties, but also from their synaptic drive from pacemaker cells. To address how variable this can be from one animal to the next, we analyze the activity of 131 animals taken over the course of approximately 5 years. We use this to address the fundamental question of how variable networks underlying a particular behavior can be across animals. We recognize two distinct classes of pacemaker inputs to LCs, and characterize bursting patterns for both types of pacemaker spike and LC output. We conclude that LCs from different animals receive different temporal patterns of pacemaker drive, which may have important functional implications. We also compare animals from winter and summer months, and find that temperature-independent seasonal effects may explain some of the variance in our data.Includes bibliographical references

Final Report to NSF of the Standards for Facial Animation Workshop

The human face is an important and complex communication channel. It is a very familiar and sensitive object of human perception. The facial animation field has increased greatly in the past few years as fast computer graphics workstations have made the modeling and real-time animation of hundreds of thousands of polygons affordable and almost commonplace. Many applications have been developed such as teleconferencing, surgery, information assistance systems, games, and entertainment. To solve these different problems, different approaches for both animation control and modeling have been developed

Design and Development of a Twisted String Exoskeleton Robot for the Upper Limb

High-intensity and task-specific upper-limb treatment of active, highly repetitive movements are the effective approaches for patients with motor disorders. However, with the severe shortage of medical service in the United States and the fact that post-stroke survivors can continue to incur significant financial costs, patients often choose not to return to the hospital or clinic for complete recovery. Therefore, robot-assisted therapy can be considered as an alternative rehabilitation approach because the similar or better results as the patients who receive intensive conventional therapy offered by professional physicians.;The primary objective of this study was to design and fabricate an effective mobile assistive robotic system that can provide stroke patients shoulder and elbow assistance. To reduce the size of actuators and to minimize the weight that needs to be carried by users, two sets of dual twisted-string actuators, each with 7 strands (1 neutral and 6 effective) were used to extend/contract the adopted strings to drive the rotational movements of shoulder and elbow joints through a Bowden cable mechanism. Furthermore, movements of non-disabled people were captured as templates of training trajectories to provide effective rehabilitation.;The specific aims of this study included the development of a two-degree-of-freedom prototype for the elbow and shoulder joints, an adaptive robust control algorithm with cross-coupling dynamics that can compensate for both nonlinear factors of the system and asynchronization between individual actuators as well as an approach for extracting the reference trajectories for the assistive robotic from non-disabled people based on Microsoft Kinect sensor and Dynamic time warping algorithm. Finally, the data acquisition and control system of the robot was implemented by Intel Galileo and XILINX FPGA embedded system

Imaging the spatial-temporal neuronal dynamics using dynamic causal modelling

Oscillatory brain activity is a ubiquitous feature of neuronal dynamics and the synchronous discharge of neurons is believed to facilitate integration both within functionally segregated brain areas and between areas engaged by the same task. There is growing interest in investigating the neural oscillatory networks in vivo. The aims of this thesis are to (1) develop an advanced method, Dynamic Causal Modelling for Induced Responses (DCM for IR), for modelling the brain network functions and (2) apply it to exploit the nonlinear coupling in the motor system during hand grips and the functional asymmetries during face perception. DCM for IR models the time-varying power over a range of frequencies of coupled electromagnetic sources. The model parameters encode coupling strength among areas and allows the differentiations between linear (within frequency) and nonlinear (between-frequency) coupling. I applied DCM for IR to show that, during hand grips, the nonlinear interactions among neuronal sources in motor system are essential while intrinsic coupling (within source) is very likely to be linear. Furthermore, the normal aging process alters both the network architecture and the frequency contents in the motor network. I then use the bilinear form of DCM for IR to model the experimental manipulations as the modulatory effects. I use MEG data to demonstrate functional asymmetries between forward and backward connections during face perception: Specifically, high (gamma) frequencies in higher cortical areas suppressed low (alpha) frequencies in lower areas. This finding provides direct evidence for functional asymmetries that is consistent with anatomical and physiological evidence from animal studies. Lastly, I generalize the bilinear form of DCM for IR to dissociate the induced responses from evoked ones in terms of their functional role. The backward modulatory effect is expressed as induced, but not evoked responses