Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris

Strube-Bloss M, Brown A, Spaethe J, Schmitt T, Rössler W. Extracting the Behaviorally Relevant Stimulus: Unique Neural Representation of Farnesol, a Component of the Recruitment Pheromone of Bombus terrestris. PLOS ONE. 2015;10(9): e0137413

Sensory to motor transformation during innate and adaptive behavior in the cockroach

Animal behavior is the result of processing and integrating various internal and external information. It can be highly flexible and vary between individuals. In insects, the mushroom body output region is an essential higher-order brain area in this process. Integration of various sensory and internal information takes place here as well as memory formation. To investigate adaptive behavior, we established classical and operant conditioning paradigms with a focus on inter-individual differences: American cockroaches were trained harnessed as well as freely moving. To gain insight into the transformation from sensory input to motor output behind innate and adaptive behavior, we established an extracellular recording setup including different sensory stimulators: 1) We simultaneously recorded mushroom body output neurons (MBONs) and initial feeding behavior in single animals during odor stimulation and 2) we recorded MBON responses to different sensory modalities. On the behavioral level, cockroaches were successful in memory formation across different paradigms and sensory modalities. Inter-individual differences regarding their cognitive abilities were discovered. Simultaneous neuronal and behavioral recordings revealed a correlation between MBON and feeding responses to food odors, which allowed for prediction of the behavior. Furthermore, neuronal recordings demonstrated that MBONs encode stimulus on- and off-responses, show adaptation during rapid successive stimulation and differ in response latencies to different sensory modalities. Our results strengthen the idea that the mushroom body output region is not only important for memory formation. In addition, it is crucial for the integration as well as categorization of different sensory modalities. Moreover, it is involved in the sensory to motor transformation. Combining the successfully established behavioral and electrophysiological setups builds a solid base to investigate the role of MBONs in memory formation with high temporal resolution and with regard to inter-individual differences

A framework for the comparative assessment of neuronal spike sorting algorithms towards more accurate off-line and on-line microelectrode arrays data analysis

Neuronal spike sorting algorithms are designed to retrieve neuronal network activity on a single-cell level from extracellular multiunit recordings with Microelectrode Arrays (MEAs). In typical analysis of MEA data, one spike sorting algorithm is applied indiscriminately to all electrode signals. However, this approach neglects the dependency of algorithms' performances on the neuronal signals properties at each channel, which require data-centric methods. Moreover, sorting is commonly performed off-line, which is time and memory consuming and prevents researchers from having an immediate glance at ongoing experiments. The aim of this work is to provide a versatile framework to support the evaluation and comparison of different spike classification algorithms suitable for both off-line and on-line analysis. We incorporated different spike sorting "building blocks" into a Matlab-based software, including 4 feature extraction methods, 3 feature clustering methods, and 1 template matching classifier. The framework was validated by applying different algorithms on simulated and real signals from neuronal cultures coupled to MEAs. Moreover, the system has been proven effective in running on-line analysis on a standard desktop computer, after the selection of the most suitable sorting methods. This work provides a useful and versatile instrument for a supported comparison of different options for spike sorting towards more accurate off-line and on-line MEA data analysis

The Impact of Mild Traumatic Brain injury on Neuronal Networks and Neurobehavior

Despite its enormous incidence, mild traumatic brain injury is not well understood. One aspect that needs more definition is how the mechanical energy during injury affects neural circuit function. Recent developments in cellular imaging probes provide an opportunity to assess the dynamic state of neural networks with single-cell resolution. In this dissertation, we developed imaging methods to assess the state of dissociated cortical networks exposed to mild injury. We probed the microarchitecture of an injured cortical circuit subject to two different injury levels, mild stretch (10% peak) and mild/moderate (35%). We found that mild injury produced a transient increase in calcium activity that dissipated within 1 h after injury. Alternatively, mild/moderate mechanical injury produced immediate disruption in network synchrony, loss in excitatory tone, and increased modular topology, suggesting a threshold for repair and degradation. The more significant changes in network behavior at moderate stretch are influenced by NMDA receptor activation and subsequent proteolytic changes in the neuronal populations. With the ability to analyze individual neurons in a circuit before and after injury, we identified several biomarkers that confer increased risk or protection from mechanical injury. We found that pre-injury connectivity and NMDA receptor subtype composition (NR2A and NR2B content) are important predictors of node loss and remodeling. Mechanistically, stretch injury caused a reduction in voltage-dependent Mg2+ block of the NR2B-cotaning NMDA receptors, resulting in increased uncorrelated activity both at the single channel and network level. The reduced coincidence detection of the NMDA receptor and overactivation of these receptors further impaired network function and plasticity. Given the demonstrated link between NR2B-NMDARs and mitochondrial dysfunction, we discovered that neuronal de-integration from the network is mediated through mitochondrial signaling. Finally, we bridged these network level studies with an investigation of changes in neurobehavior following blast-induced traumatic brain injury (bTBI), a form of mild TBI. We first developed and validated an open-source toolbox for automating the scoring of several common behavior tasks to study the deficits that occur following bTBI. We then specifically evaluated the role of neuronal transcription factor Elk-1 in mediating deficits following blast by exposing Elk-1 knockout mouse to equivalent blast pressure loading. Our systems-level behavior analysis showed that bTBI creates a complex change in behavior, with an increase in anxiety and loss of habituation in object recognition. Moreover, we found these behavioral deficits were eliminated in Elk-1 knockout animals exposed to blast loading. Together, we merged information from different perspectives (in silico, in vitro, and in vivo) and length scales (single channels, single-cells, networks, and animals) to study the impact of mild traumatic brain injury on neuronal networks and neurobehavior

Multi-electrode array recording and data analysis methods for molluscan central nervous systems

In this work the use of the central nervous system (CNS) of the aquatic snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was developed and analysis methods for the data generated were created. A variety of different combinations of configurations of tissue from the Lymnaea CNS were explored to determine the signal characteristics that could be recorded by sixty channel MEAs. In particular, the suitability of the semi-intact system consisting of the lips, oesophagus, CNS, and associated nerve connectives was developed for use on the planar MEA. The recording target area of the dorsal surface of the buccal ganglia was selected as being the most promising for study and recordings of its component cells during fictive feeding behaviour stimulated by sucrose were made. The data produced by this type of experimentation is very high volume and so its analysis required the development of a custom set of software tools. The goal of this tool set is to find the signal from individual neurons in the data streams of the electrodes of a planar MEA, to estimate their position, and then to predict their causal connectivity. To produce such an analysis techniques for noise filtration, neural spike detection, and group detection of bursts of spikes were created to pre-process electrode data streams. The Kohonen self-organising map (SOM) algorithm was adapted for the purpose of separating detected spikes into data streams representing the spike output of individual cells found in the target system. A significant addition to SOM algorithm was developed by the concurrent use of triangulation methods based on current source density analysis to predict the position of individual cells based on their spike output on more than one electrode. The likely functional connectivity of individual neurons identified by the SOM technique were analysed through the use of a statistical causality method known as Granger causality/causal connectivity. This technique was used to produce a map of the likely connectivity between neural sources

Developing a new generation of neuro-prosthetic interfaces: structure-function correlates of viable retina-CNT biohybrids

PhD ThesisOne of the many challenges in the development of neural prosthetic devices is the choice of electrode material. Electrodes must be biocompatible, and at the same time, they must be able to sustain repetitive current injections in a highly corrosive physiological environment. We investigated the suitability of carbon nanotube (CNT) electrodes for retinal prosthetics by studying prolonged exposure to retinal tissue and repetitive electrical stimulation of retinal ganglion cells (RGCs). Experiments were performed on retinal wholemounts isolated from the Cone rod homeobox (CRX) knockout mouse, a model of Leber congenital amaurosis. Retinas were interfaced at the vitreo-retinal juncture with CNT assemblies and maintained in physiological conditions for up to three days to investigate any anatomical (immunohistochemistry and electron microscopy) and electrophysiological changes (multielectrode array stimulation and recordings; electrodes were made of CNTs or commercial titanium nitride). Anatomical characterisation of the inner retina, including RGCs, astrocytes and Müller cells as well as cellular matrix and inner retinal vasculature, provide strong evidence of a gradual remodelling of the retina to incorporate CNT assemblies, with very little indication of an immune response. Prolonged electrophysiological recordings, performed over the course of three days, demonstrate a gradual increase in signal amplitudes, lowering of stimulation thresholds and an increase in cellular recruitment for RGCs interfaced with CNT electrodes, but not with titanium nitride electrodes. These results provide for the first time electrophysiological, ultrastructural and cellular evidence of the time-dependent formation of strong and viable bio-hybrids between the RGC layer and CNT arrays in intact retinas. We conclude that CNTs are a promising material for inclusion in retinal prosthetic devices

Methods toward improved lower extremity rehabilitation

Thesis (Ph. D. in Electrical and Medical Engineering)--Harvard-MIT Program in Health Sciences and Technology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.Ambulation is a very important part of everyday life and its absence has a detrimental effect on an individual's quality of life. While much is understood about the neurobiological systems involved in locomotion through detailed anatomical connectivity and lesion studies, it is not well understood how neurons across different regions of the nervous system share information and coordinate their firing activity to achieve ambulation. Moreover, while it is clear that understanding the processes involved in healthy ambulation are essential to understanding how diseases affect an individual's ability to walk, diseases such as stroke tend to "take out" large portions of the underlying system. Until technologies are developed to allow restoration of damaged neural tissue back to its original state, physical therapy (which aims to restore function by establishing new motor-cortical connections among the remaining neurons) remains the most viable option for patients. The aim of this thesis is to elucidate some of the underlying neurobiological mechanisms of walking and to develop tools for rehabilitation robotics that allow finer quantification of patient improvement. To elucidate the neural mechanisms of locomotion, we studied how task relevant information (e.g. positions, velocities, and forces) modulate single unit neural activity from hindlimb/trunk region of the rat motor cortex during adaptations to robot-applied elastic loads and closed-loop brain-machine-interface (BMI) control during treadmill locomotion. Using the Point Process-Generalized Linear Model (PP-GLM) statistical framework we systematically tested parametric and non-parametric point process models of increased complexity for 573 individual neurons recorded over multiple days in six animals. The developed statistical model captures within gait-cycle modulation, load-specific modulation, and intrinsic neural dynamics. Our proposed model accurately describes the firing statistics of 98.5% (563/573) of all the recorded units and allows characterization of the neural receptive fields associated with gait phase and loading force. Understanding how these receptive fields change during training and with experience will be central to developing rehabilitation strategies that optimize motor adaptations and motor learning. The methods utilized for this analysis were developed into an open source neural Spike Train Analysis Toolbox (nSTAT) for Matlab (Mathworks, Natick MA). Systematic analyses have demonstrated the effectiveness of physical therapy, but have been unable to determine which approaches tend to be most effective in restoring function. This is likely due to the multitude of approaches, diseases, and assessment scales used. To address this issue, we develop an extension of the Force Field Adaptation Paradigm, originally developed to quantitatively assess upper extremity motor adaptation, to the lower extremity. The algorithm is implemented on the Lokomat (Hocoma HG) lower extremity gait orthosis and is currently being utilized to assess short-term motor adaptation in 40 healthy adult subjects (ClinicalTrials.gov NCT01361867). Establishing an understanding of how healthy adults' motor systems adapt to external perturbations will be important to understanding how the adaptive mechanisms involved in gait integrate information and how this process is altered by disease.by Iahn Cajigas González.Ph.D.in Electrical and Medical Engineerin