Towards novel compact laser sources for non-invasive diagnostics and treatment

An important field of application of lasers is biomedical optics. Here, they offer great utility for diagnosis, therapy and surgery. For the development of novel methods of laser-based biomedical diagnostics careful study of light propagation in biological tissues is necessary to enhance our understanding of the optical measurements undertaken, increase research and development capacity and the diagnostic reliability of optical technologies. Ultimately, fulfilling these requirements will increase uptake in clinical applications of laser based diagnostics and therapeutics. To address these challenges informative biomarkers relevant to the biological and physiological function or disease state of the organism must be selected. These indicators are the results of the analysis of tissues and cells, such as blood. For non-invasive diagnostics peripheral blood, cells and tissue can potentially provide comprehensive information on the condition of the human organism. A detailed study of the light scattering and absorption characteristics can quickly detect physiological and morphological changes in the cells due to thermal, chemical, antibiotic treatments, etc [1-5]. The selection of a laser source to study the structure of biological particles also benefits from the fact that gross pathological changes are not induced and diagnostics make effective use of the monochromatic directional coherence properties of laser radiation

Non-invasive biomedical research and diagnostics enabled by innovative compact lasers

For over half a century, laser technology has undergone a technological revolution. These technologies, particularly semiconductor lasers, are employed in a myriad of fields. Optical medical diagnostics, one of the emerging areas of laser application, are on the forefront of application around the world. Optical methods of non- or minimally invasive bio-tissue investigation offer significant advantages over alternative methods, including rapid real-time measurement, non-invasiveness and high resolution (guaranteeing the safety of a patient). These advantages demonstrate the growing success of such techniques. In this review, we will outline the recent status of laser technology applied in the biomedical field, focusing on the various available approaches, particularly utilising compact semiconductor lasers. We will further consider the advancement and integration of several complimentary biophotonic techniques into single multimodal devices, the potential impact of such devices and their future applications. Based on our own studies, we will also cover the simultaneous collection of physiological data with the aid a multifunctional diagnostics system, concentrating on the optimisation of the new technology towards a clinical application. Such data is invaluable for developing algorithms capable of delivering consistent, reliable and meaningful diagnostic information, which can ultimately be employed for the early diagnosis of disease conditions in individuals from around the world

Oscillations in microvascular flow:their relationship to tissue oxygenation, cellular metabolic function and their diagnostic potential for detecting skin melanoma - clinical, experimental and theoretical investigations

Tumour vasculature is known to be inefficient and abnormal due to poorly regulated angiogenesis during tumour growth. This leads to irregular patterns of blood flow which are spatially and temporally heterogeneous. Many investigations into the characteristics of tumours are invasive and performed on animal models. However, continuous technological and theoretical advancement is leading to the use of non-invasive imaging techniques, providing in vivo information on humans. Here, data recorded using laser Doppler flowmetry (LDF) in malignant melanoma and control lesions are analysed using techniques designed for application to non-stationary, time-varying data. Many studies utilising LDF have previously revealed increased blood flow in malignant lesions, but very little attention has been paid to the dynamics of this blood flow, or how it changes over time. As it has been demonstrated previously that the oscillations observed within blood flow data are physiologically significant, failure to extract these characteristics loses information about the underlying dynamical system from which the blood flow data were recorded. Significant differences in blood flow dynamics are revealed and used in the development of a diagnostic test for melanoma. In addition to the characterization of the blood flow dynamics in melanoma, possible causes for the observed changes are investigated and related to two widely observed characteristics of cancer, intermittent hypoxia and altered cellular energy metabolism. The former is explored through the analysis of blood flow and oxygenation data recorded during dry static apnoea, whilst the latter is modelled using coupled phase oscillators

Identification of linear and nonlinear sensory processing circuits from spiking neuron data

Inferring mathematical models of sensory processing systems directly from input-output observations, while making the fewest assumptions about the model equations and the types of measurements available, is still a major issue in computational neuroscience. This letter introduces two new approaches for identifying sensory circuit models consisting of linear and nonlinear filters in series with spiking neuron models, based only on the sampled analog input to the filter and the recorded spike train output of the spiking neuron. For an ideal integrate-and-fire neuron model, the first algorithm can identify the spiking neuron parameters as well as the structure and parameters of an arbitrary nonlinear filter connected to it. The second algorithm can identify the parameters of the more general leaky integrate-and-fire spiking neuron model, as well as the parameters of an arbitrary linear filter connected to it. Numerical studies involving simulated and real experimental recordings are used to demonstrate the applicability and evaluate the performance of the proposed algorithms

Visualizing Real Time Vasomotion in vivo Using Optical Coherence Tomography.

As blood vessel imaging techniques facilitate the fundamental understanding in vascular performance diagnosis and biomedical research improvement, we aimed to visualize and understand the blood vessels dynamics under human skin and their underlying mechanisms in real time. In this study, a noninvasive imaging system was selected to provide an investigation of the real time oscillation of blood vessels in vivo, using Spectral Radar Optical Coherence Tomography (SROCT). This main goal was achieved by evaluating the precision and confidence in recorded data by using a phantom made of Intralipid (IL) to mimic the physical properties of the skin. Then, we successfully managed to visualize for the first time the vasomotion under human skin using MatLab Image Processing Toolbox. After that, we explored mathematically the cyclic variations of the vascular area obtained from the images for a cohort of six participants. The Fourier and wavelet transforms were applied to identify the characteristic frequencies related to the oscillations in vascular cross sectional area. Finally, we investigated dynamical aspects of vasomotion, in response to temperature change, by using a Melcor Thermoelectric Temperature Controller (MTTC) to produce local heating in conjunction with Spectral Radar Optical Coherence Tomography (SROCT)

Analysis of Dynamic Brain Imaging Data

Modern imaging techniques for probing brain function, including functional Magnetic Resonance Imaging, intrinsic and extrinsic contrast optical imaging, and magnetoencephalography, generate large data sets with complex content. In this paper we develop appropriate techniques of analysis and visualization of such imaging data, in order to separate the signal from the noise, as well as to characterize the signal. The techniques developed fall into the general category of multivariate time series analysis, and in particular we extensively use the multitaper framework of spectral analysis. We develop specific protocols for the analysis of fMRI, optical imaging and MEG data, and illustrate the techniques by applications to real data sets generated by these imaging modalities. In general, the analysis protocols involve two distinct stages: `noise' characterization and suppression, and `signal' characterization and visualization. An important general conclusion of our study is the utility of a frequency-based representation, with short, moving analysis windows to account for non-stationarity in the data. Of particular note are (a) the development of a decomposition technique (`space-frequency singular value decomposition') that is shown to be a useful means of characterizing the image data, and (b) the development of an algorithm, based on multitaper methods, for the removal of approximately periodic physiological artifacts arising from cardiac and respiratory sources.Comment: 40 pages; 26 figures with subparts including 3 figures as .gif files. Originally submitted to the neuro-sys archive which was never publicly announced (was 9804003

Neural Basis of Functional Connectivity MRI

The brain is hierarchically organized across a range of scales. While studies based on electrophysiology and anatomy have been fruitful on the micron to millimeter scale, findings based on functional connectivity MRI (fcMRI) suggest that a higher level of brain organization has been largely overlooked. These findings show that the brain is organized into networks, and each network extends across multiple brain areas. This large-scale, across-area brain organization is functionally relevant and stable across subjects, primate species, and levels of consciousness. This dissertation addresses the neural origin of MRI functional connectivity. fcMRI relies on temporal correlation in at-rest blood oxygen level dependent (BOLD) fluctuations. Thus, understanding the neural origin of at-rest BOLD correlation is of critical significance. By shedding light on the origin of the large-scale brain organization captured by fcMRI, it will guide the design and interpretation of fcMRI studies. Prior investigations of the neural basis of BOLD have not addressed the at-rest BOLD correlation, and they have been focusing on task-related BOLD. At-rest BOLD correlation captured by fcMRI likely reflects a distinct physiological process that is different from that of task-related BOLD, since these two kinds of BOLD dynamics are different in their temporal scale, spatial spread, energy consumption, and their dependence on consciousness. To address this issue, we develop a system to simultaneously record oxygen and electrophysiology in at-rest, awake monkeys. We demonstrate that our oxygen measurement, oxygen polarography, captures the same physiological phenomenon as BOLD by showing that task-related polarographic oxygen responses and at-rest polarographic oxygen correlation are similar to those of BOLD. These results validate the use of oxygen polarography as a surrogate for BOLD to address the neural origin of MRI functional connectivity. Next, we show that at-rest oxygen correlation reflects at-rest correlation in electrophysiological signals, especially spiking activity of neurons. Using causality analysis, we show that oxygen is driven by slow changes in raw local field potential levels (slow LFP), and slow LFP itself is driven by spiking activity. These results provide critical support to the idea that oxygen correlation reflects neural activity, and pose significant challenges to the traditional view of neurohemodynamic coupling. In addition, we find that at-rest correlation does not originate from criticality, which has been the dominant hypothesis in the field. Instead, we show that at-rest correlation likely reflects a specific and potentially localized oscillatory process. We suggest that this oscillatory process could be a result of the delayed negative feedback loop between slow LFP and spiking activity. Thus, we conclude that at-rest BOLD correlation captured by fcMRI is driven by at-rest slow LFP correlation, which is itself driven by spiking activity correlation. The at-rest spiking activity correlation, itself, is likely driven by an oscillatory process. Future studies combining recording with interventional approaches, like pharmacological manipulation and microstimulation, will help to elucidate the circuitry underlying the oscillatory process and its potential functional role

Advances in point process filters and their application to sympathetic neural activity

This thesis is concerned with the development of techniques for analyzing the sequences of stereotypical electrical impulses within neurons known as spikes. Sequences of spikes, also called spike trains, transmit neural information; decoding them often provides details about the physiological processes generating the neural activity. Here, the statistical theory of event arrivals, called point processes, is applied to human muscle sympathetic spike trains, a peripheral nerve signal responsible for cardiovascular regulation. A novel technique that uses observed spike trains to dynamically derive information about the physiological processes generating them is also introduced. Despite the emerging usage of individual spikes in the analysis of human muscle sympathetic nerve activity, the majority of studies in this field remain focused on bursts of activity at or below cardiac rhythm frequencies. Point process theory applied to multi-neuron spike trains captured both fast and slow spiking rhythms. First, analysis of high-frequency spiking patterns within cardiac cycles was performed and, surprisingly, revealed fibers with no cardiac rhythmicity. Modeling spikes as a function of average firing rates showed that individual nerves contribute substantially to the differences in the sympathetic stressor response across experimental conditions. Subsequent investigation of low-frequency spiking identified two physiologically relevant frequency bands, and modeling spike trains as a function of hemodynamic variables uncovered complex associations between spiking activity and biophysical covariates at these two frequencies. For example, exercise-induced neural activation enhances the relationship of spikes to respiration but does not affect the extremely precise alignment of spikes to diastolic blood pressure. Additionally, a novel method of utilizing point process observations to estimate an internal state process with partially linear dynamics was introduced. Separation of the linear components of the process model and reduction of the sampled space dimensionality improved the computational efficiency of the estimator. The method was tested on an established biophysical model by concurrently computing the dynamic electrical currents of a simulated neuron and estimating its conductance properties. Computational load reduction, improved accuracy, and applicability outside neuroscience establish the new technique as a valuable tool for decoding large dynamical systems with linear substructure and point process observations

Nonlinear and nonautonomous dynamical properties of the cardiovascular response to a range of ambient temperatures

There is a significant relationship between temperature and human health. The cardiovascular system undergoes a process of coordinated changes when the external temperature or the amount of heat generated changes. To maintain the internal temperature of the body at a constant level, a variety of physiological and behavioural processes must be controlled. These responses in the cardiovascular system have been shown to manifest themselves in significant changes in cardiac output and regionalblood flow. An increase or decrease in blood flow in the skin is the basic response of the circulatory system to changes in skin surface temperature. In this work, we used the optical technique of laser Doppler flowmetry (LDF) to study the dynamics of blood flow at three different ambient temperatures (20◦C, 26◦C, and 32◦C). We investigated the changes that may be caused by ambient temperature in healthy young subjects on blood flow and cardiovascular dynamics, e.g., heart rate, stroke volume, cardiac output, and blood pressure. Optical methods were used along with a variety of other sensors to assess these changes. In addition, the instantaneous frequencies of heartbeat and respiration were extracted from the measured ECG, blood pressure, and respiration time series. Two additional time series were created from blood pressure, instantaneous systolic and instantaneous diastolic blood pressure. The resulting time series were then analysed using algorithms developed for irregular periodic signals. The wavelet power spectrum was applied to evaluate the contribution of the oscillatory components within the frequency range from 0.0027 to 2 Hz. The physiological characteristics of the six oscillatory components in this range and their changes with temperature are evaluated and discussed. Phase coherence analysis was used to study the interaction between the oscillatory components, and the effects of temperature are evaluated and discussed. We show that while average values are highest at lower temperatures, the coherence is highest at higher temperatures