Analysis of the dynamics of temporal relationships of neural activities using optical imaging data

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology

Analysis of the dynamics of temporal relationships of neural activities using optical imaging data

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse. This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording

Extracting Cancer pagurus stomatogastric ganglion pyloric rhythm frequency via voltage-sensitive dye imaging data using signal processing techniques

Voltage-sensitive dye imaging (VSDI) has been widely used in the past few decades in both vertebrates and invertebrates to study, in vitro and in vivo, the nervous systems. Cancer pagurus is a seawater crab whose nervous system has a ganglion, the stomatogastric ganglion (STG) that contains a relatively small number of neurons and two rhythm forming central pattern generators (CPGs). The pyloric rhythm is one such spontaneous rhythm that can be readily observed in vitro, which makes the STG an ideal ganglion to study using VSDI. However, a major impediment to the effectiveness of VSDI is that the optically recorded data is often noisy with poor signal-to-noise ratios (SNR), rendering it difficult to study and analyse.This thesis describes the first-ever development of computational signal processing procedures that sought to extract the pyloric rhythm directly from the VSDI data, thus facilitating an accurate identification of the individual neurons in the pyloric circuit. Specifically, a multiresolution procedure based on the sequential Singular Spectrum Analysis (s-SSA) was first constructed to separate the pyloric rhythm from the noisy VSDI recording, enabling potential pyloric neurons to be detected by the presence of the pyloric frequency in the computed spectra of the respective cells. To facilitate identifying the pyloric neurons, the duty cycle (DC) was devised as a biometric, and the corresponding ratio of harmonics (RH) was determined in terms of the harmonic content of the spectrum computed for each cell/neuron as described above. Here, the instantaneous phase of the detected pyloric rhythm was also estimated, allowing it to be compared and aligned with the three distinctive pyloric phases (PD-, LP- and PY-timed) readily measured on the lateral ventricular nerve (lvn). As proof of concepts, finally, an automated method to determine the pyloric frequency directly from VSDI data was developed, over a range of SNRs, demonstrating the possibility to identify prospective pyloric neurons based on the estimated DCs and respective phase shifts measured against the analogue lvn recording

Development of a chick embryo spinal cord injury model as a platform to test neural tissue engineering strategies

Spinal cord injury is a devastating condition affecting thousands of people every year. The spinal cord does not have the intrinsic capacity for regeneration due to a complex cascade of physical and chemical barriers that prevent axonal growth and lead to neuronal death and, therefore, current treatments are yet to achieve full functional repair. The implantation of encapsulated neural stem cells within 3D matrices offers the advantages of cellular repopulation, release of neurotrophic factors and healthy extracellular matrix mimicking, leading to improved motor function. However, neural tissue engineering strategies face three main challenges: the exclusive use of experimental grade biomaterials for research, the overlooking of the highly aligned structure of the spinal cord and the reliance on complex and expensive in vivo rodent animal models, leading to time consuming experiments which make reproducibility challenging. As an alternative, we propose the use of chick embryo spinal cord organotypic slices as a novel spinal cord injury model as it offers a cheaper alternative linked to less ethical implications. Here, we established for the first time a transecting spinal cord injury model using the chick embryo as a donor of spinal cord slices. We also tested two biomaterials for their capacity to incorporate a relevant cell transplant population: HemopatchTM, a clinically available scaffold, and CellevateTM, an aligned nanofibre biomaterial. We demonstrated the viability of both matrices for incorporating a healthy cell population, we showed improvement of cell distribution through laminin engineering on HemopatchTM and we described a protocol for measuring cellular alignment with CellevateTM. Finally, we tested the feasibility of biomaterial implantation in the spinal cord injury model described earlier, resulting in the typical cellular responses expected in an adult injury. The model, which presents comparable responses to those based on murine models, represents a simpler alternative to previously established models. The adoption of this model could lead to impactful research while maintaining a cost effective and technically simple methodology. This could have translational potential for other research areas, such as the study of degenerative diseases, and potentially increase the research output on the study of spinal cord injury therapies which, in turn, would lead to a faster translation of functional therapies to higher complexity models and, finally, to the clinic

Analysis of the dynamics of temporal relationships of neural activities using optical imaging data

The temporal relationship between the activities of neurons in biological neural systems is critically important for the correct delivery of the functionality of these systems. Fine measurement of temporal relationships of neural activities using micro-electrodes is possible but this approach is very limited due to spatial constraints in the context of physiologically valid settings of neural systems. Optical imaging with voltage-sensitive dyes or calcium dyes can provide data about the activity patterns of many neurons in physiologically valid settings, but the data is relatively noisy. Here we propose a numerical methodology for the analysis of optical neuro-imaging data that allows robust analysis of the dynamics of temporal relationships of neural activities. We provide a detailed description of the methodology and we also assess its robustness. The proposed methodology is applied to analyse the relationship between the activity patterns of PY neurons in the crab stomatogastric ganglion. We show for the first time in a physiologically valid setting that as expected on the basis of earlier results of single neuron recordings exposure to dopamine de-synchronises the activity of these neurons. We also discuss the wider implications and application of the proposed methodology