A Model of the Intrinsic Image Signal and an Evaluation of the Methodology of Intrinsic Image Signal Analysis

We have built a simulation to model the intrinsic signal sources. It uses estimates of parameter values derived from the published data from the optical imaging, the functional brain neuroimaging literature and textbook physiology.The temporal dynamics of the intrinsic signals are modelled by gamma functions to give delays with the appropriate time constants. The model generates a time series representing the image intensity signal under different wavelength illumination. Perturbations that mimic systemic noise sources such as heartbeat, breathing and vasomotion can be included as desired. The simulation provides a test bed for the evaluation of the effects of different data capture regimes and methods data analysis and a working hypothesis of the neuro-hemo dynamics that can be used in statistical and model driven analysis of intrinsic image data. In this study we describe the simulation of an ocular dominance column mapping experiment using the methods of intrinsic imaging. We find that though accurate functional maps could be obtained using the methods of analysis in common usage in intrinsic imagery, the estimates of the resolution of the signal sources obtained did not accurately reflect the underlying parameterisation of the model. We ascribe this to the presence of the low frequency modulation of regional cerebral blood flow now known to be present in intrinsic image data, which introduces bias when usual data capture and analysis methodology is used

Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

Real-time diffuse optical tomography using reduced-order light propagation models based on a priori anatomical and functional information

This paper proposes a new fast 3D image reconstruction algorithm for Diffuse Optical Tomography using reduced order polynomial mappings from the space of optical tissue parameters into the space of flux measurements at the detector locations. The polynomial mappings are constructed through an iterative estimation process involving structure detection, parameter estimation and cross-validation using data generated by simulating a diffusion approximation of the radiative transfer equation incorporating a priori anatomical and functional information provided by MR scans and prior psychological evidence. Numerical simulation studies demonstrate that reconstructed images are remarkably similar in quality as those obtained using the standard approach, but obtained at a fraction of the time

Nonlinear Identification and Analysis of Quasiperiodic Oscillations in Reflected Light Measurements of Vasomotion

Nonlinear system identification and analysis methods are employed to study the low frequency oscillations present in time series data obtained from reflectance imagery of microvasculature. Using the method of surrogate data testing the analysis reveals the deterministic nature of these oscillations which initially are believed to be chaotic. Further investigations by means of nonlinear system identification techniques indicate however that the underlying dynamics is described very well by a periodically driven nonlinear dynamical model exhibiting quaiperiodic oscillations

A Three Compartmental Model of the Hemodynamic Response and Oxygen Delivery to Brain

We describe a mathematical model linking changes in cerebral blood flow, blood volume and the blood oxygenation state in response to simulation. The model has three compartments to take into account the fact that the cerebral blood flow and volume ad measured concurrently using laser Doppler flowmetry and optical imaging spectroscopy have contributions from the arterial, capillary as well as the venous compartments of the vasculature. It is an extension to previous one-compartment hemodynamic models which assume that the measured blood volume changes are from the venous compartment only. An important assumption of the model is that the tissue oxygen concentration is a time varying state variable of the the system and is driven by the changes in metabolic demand resulting from changes in neural activity. The model recognises the effects of the pre-capillary arteriolar oxygen perfusion ( that is the situation of the arterial compartment being less than unity). Simulations are used to explore the sensitivity of the model and to optimise the parameters for experimental data. We conclude that the three-compartment model was better than the one-compartment model at capturing the hemodynamics of the response to changes in neural activation following simulation

Building Long-range Cognitive Maps using Local Landmarks

Cognitive maps can be built using only information about the relative positions of locally visible landmarks. We describe a system that can compute paths between any two locations, irrespective of whether they share common visible landmarks, without using 'compass' senses or dead reckoning abilities. This is achieved by encoding the position of each landmark in the barycentric co-ordinate frames defined by groups of neighbouring cues. Paths between distant points are computed by calculating, using these frames, the positions relative to the agent of landmarks further and further away from the immediate scene. Once the relative positions of landmarks local to the goal are known a vector giving its position in the agent's egocentric frame can be found. This implicit map allows the agent to compute the overall distance and direction to distant targets; find and follow paths to goal locations; generate explicitly the layout of the whole environment relative to its own position; and discriminate between perceptually similar landmarks. The system is robust to noise, its calculations require only linear mathematics, and its memory requirements are proportional to the total number of landmarks

Obstacle Avoidance through Reinforcement Learning

A method is described for generating plan-like. reflexive. obstacle avoidance behaviour in a mobile robot. The experiments reported here use a simulated vehicle with a primitive range sensor. Avoidance behaviour is encoded as a set of continuous functions of the perceptual input space. These functions are stored using CMACs and trained by a variant of Barto and Sutton's adaptive critic algorithm. As the vehicle explores its surroundings it adapts its responses to sensory stimuli so as to minimise the negative reinforcement arising from collisions. Strategies for local navigation are therefore acquired in an explicitly goal-driven fashion. The resulting trajectories form elegant collisionfree paths through the environment

An Evaluation of the Nonlinear MultiComponent Analysis Technique for Reflection Spectra

Analysis of the absorption spectra in the visible range has made it possible to monitor the oxygen supply and metabolism of organic tissues continuously and non-invasively. The nonlinear multicomponent analysis (NLMCA) is an evaluation method for the reflection spectra of the tissues. In this report, the derivation of the relation between the reflection and absorption spectra based upon Kubelka-Monk theory is reviewed to introduce the foundation of the NLMCA technique. Then, the procedures of the NLMCA algorithm are described and simulation studies are performed. The results indicate that the NLCMA algorithm generally works well after a minor modification but is sensitive to modelling errors due to invalid assumptions on the scattering properties of samples. A direct algorithm based on linear least squares is the suggested to deal with the same problem and simulation studies are performed to make a comparison with the NLMCA algorithm, which demonstrate a better robust properties of the alternative strategy