Multiple-input nonlinear modelling of cerebral haemodynamics using spontaneous arterial blood pressure, end-tidal CO2 and heart rate measurements.

In order to examine the effect of changes in heart rate (HR) upon cerebral perfusion and autoregulation, we include the HR signal recorded from 18 control subjects as a third input in a two-input model of cerebral haemodynamics that has been used previously to quantify the dynamic effects of changes in arterial blood pressure and end-tidal CO2upon cerebral blood flow velocity (CBFV) measured at the middle cerebral arteries via transcranial Doppler ultrasound. It is shown that the inclusion of HR as a third input reduces the output prediction error in a statistically significant manner, which implies that there is a functional connection between HR changes and CBFV. The inclusion of nonlinearities in the model causes further statistically significant reduction of the output prediction error. To achieve this task, we employ the concept of principal dynamic modes (PDMs) that yields dynamic nonlinear models of multi-input systems using relatively short data records. The obtained PDMs suggest model-driven quantitative hypotheses for the role of sympathetic and parasympathetic activity (corresponding to distinct PDMs) in the underlying physiological mechanisms by virtue of their relative contributions to the model output. These relative PDM contributions are subject-specific and, therefore, may be used to assess personalized characteristics for diagnostic purposes.

Compartmental and Data-Based Modeling of Cerebral Hemodynamics: Linear Analysis.

Compartmental and data-based modeling of cerebral hemodynamics are alternative approaches that utilize distinct model forms and have been employed in the quantitative study of cerebral hemodynamics. This paper examines the relation between a compartmental equivalent-circuit and a data-based input-output model of dynamic cerebral autoregulation (DCA) and CO2-vasomotor reactivity (DVR). The compartmental model is constructed as an equivalent-circuit utilizing putative first principles and previously proposed hypothesis-based models. The linear input-output dynamics of this compartmental model are compared with data-based estimates of the DCA-DVR process. This comparative study indicates that there are some qualitative similarities between the two-input compartmental model and experimental results.

Differentiation of BIRADS-4 small breast lesions via Multimodal Ultrasound Tomography.

PURPOSE: To demonstrate the use of a new 3D diagnostic imaging technology, termed Multimodal Ultrasonic Tomography (MUT), for the detection of solid breast lesions < 15 mm in maximum dimension. METHODS AND MATERIALS: 3D MUT imaging was performed on 71 volunteers presenting BIRADS-4 nodules, asymmetrical densities, and architectural distortions in X-ray mammograms, who subsequently underwent biopsy. MUT involved D tomographic imaging of the pendulant breast in a water bath using transmission ultrasound and constructed multimodal images corresponding to refractivity and frequency-dependent attenuation (calibrated relative to water). The multimodal images were fused into composite images and a composite index (CI) was calculated and used for diagnostic purposes. The composite images were evaluated against results of histopathology on biopsy specimens. RESULTS: Histopathology revealed 22 malignant and 49 benign lesions. The pixels of 22 malignant lesions exhibited high values in both refractivity and attenuation, resulting in CI values > 1. In contrast, 99.9% of benign lesions and normal tissue pixels exhibited lower values of at least one of the attributes measured, corresponding to CI values < 1. CONCLUSIONS: MUT imaging appears to differentiate small malignant solid breast lesions as exhibiting CI values >1, while benign lesions or normal breast tissues exhibit CI values <1. KEY POINTS: • MUT was able to detect all 22 biopsy-confirmed malignant lesions. • MUT was able to differentiate the malignant from the benign lesions. • Additional MUT detections outside the biopsy area must be evaluated prospectively.

Model-based physiomarkers of cerebral hemodynamics in patients with mild cognitive impairment.

In our previous studies, we have introduced model-based "functional biomarkers" or "physiomarkers" of cerebral hemodynamics that hold promise for improved diagnosis of early-stage Alzheimer's disease (AD). The advocated methodology utilizes subject-specific data-based dynamic nonlinear models of cerebral hemodynamics to compute indices (serving as possible diagnostic physiomarkers) that quantify the state of cerebral blood flow autoregulation to pressure-changes (CFAP) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. The model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. In a previous study, it was found that a CVMR index quantifying the impairment in CO2 vasomotor reactivity correlates with clinical indications of early AD, offering the prospect of a potentially useful diagnostic tool. In this paper, we explore the use of the same model-based indices for patients with amnestic Mild Cognitive Impairment (MCI), a preclinical stage of AD, relative to a control subjects and clinical cognitive assessments. It was found that the model-based CVMR values were lower for MCI patients relative to the control subjects.

On parsing the neural code in the prefrontal cortex of primates using principal dynamic modes.

Nonlinear modeling of multi-input multi-output (MIMO) neuronal systems using Principal Dynamic Modes (PDMs) provides a novel method for analyzing the functional connectivity between neuronal groups. This paper presents the PDM-based modeling methodology and initial results from actual multi-unit recordings in the prefrontal cortex of non-human primates. We used the PDMs to analyze the dynamic transformations of spike train activity from Layer 2 (input) to Layer 5 (output) of the prefrontal cortex in primates performing a Delayed-Match-to-Sample task. The PDM-based models reduce the complexity of representing large-scale neural MIMO systems that involve large numbers of neurons, and also offer the prospect of improved biological/physiological interpretation of the obtained models. PDM analysis of neuronal connectivity in this system revealed "input-output channels of communication" corresponding to specific bands of neural rhythms that quantify the relative importance of these frequency-specific PDMs across a variety of different tasks. We found that behavioral performance during the Delayed-Match-to-Sample task (correct vs. incorrect outcome) was associated with differential activation of frequency-specific PDMs in the prefrontal cortex.

Model-based quantification of cerebral hemodynamics as a physiomarker for Alzheimer's disease?

Previous studies have found that Alzheimer's disease (AD) impairs cerebral vascular function, even at early stages of the disease. This offers the prospect of a useful diagnostic method for AD, if cerebral vascular dysfunction can be quantified reliably within practical clinical constraints. We present a recently developed methodology that utilizes a data-based dynamic nonlinear closed-loop model of cerebral hemodynamics to compute "physiomarkers" quantifying the state of cerebral flow autoregulation to pressure-changes (CA) and cerebral CO2 vasomotor reactivity (CVMR) in each subject. This model is estimated from beat-to-beat measurements of mean arterial blood pressure, mean cerebral blood flow velocity and end-tidal CO2, which can be made reliably and non-invasively under resting conditions. This model may also take an open-loop form and comparisons are made with the closed-loop counterpart. The proposed model-based physiomarkers take the form of two indices that quantify the gain of the CA and CVMR processes in each subject. It was found in an initial set of clinical data that the CVMR index delineates AD patients from control subjects and, therefore, may prove useful in the improved diagnosis of early-stage AD.

Closed-loop dynamic modeling of cerebral hemodynamics.

The dynamics of cerebral hemodynamics have been studied extensively because of their fundamental physiological and clinical importance. In particular, the dynamic processes of cerebral flow autoregulation (CFA) and CO2 vasomotor reactivity have attracted broad attention because of their involvement in a host of pathologies and clinical conditions (e.g., hypertension, syncope, stroke, traumatic brain injury, vascular dementia, Alzheimer's disease, mild cognitive impairment etc.). This raises the prospect of useful diagnostic methods being developed on the basis of quantitative models of cerebral hemodynamics, if cerebral vascular dysfunction can be quantified reliably from data collected within practical clinical constraints. This paper presents a modeling method that utilizes beat-to-beat measurements of mean arterial blood pressure, cerebral blood flow velocity and end-tidal CO2 (collected non-invasively under resting conditions) to quantify the dynamics of CFA and cerebral vasomotor reactivity (CVMR). The unique and novel aspect of this dynamic model is that it is nonlinear and operates in a closed-loop configuration.

Novel technology of multimodal ultrasound tomography detects breast lesions.

OBJECTIVES: To introduce a new three-dimensional (3D) diagnostic imaging technology, termed "multimodal ultrasonic tomography" (MUT), for the detection of breast cancer without ionising radiation or compression. METHODS: MUT performs 3D tomography of the pendulant breast in a water-bath using transmission ultrasound in a fixed-coordinate system. Specialised electronic hardware and signal processing algorithms are used to construct multimodal images for each coronal slice, corresponding to measurements of refractivity and frequency-dependent attenuation and dispersion. In-plane pixel size is 0.25 mm × 0.25 mm and the inter-slice interval can vary from 1 to 4 mm, depending on clinical requirements. MUT imaging was performed on 25 patients ("off-label" use for research purposes only), presenting lesions with sizes >10 mm. Histopathology of biopsy samples, obtained from all patients, were used to evaluate the MUT outcomes. RESULTS: All lesions (21 malignant and four benign) were clearly identified on the MUT images and correctly classified into benign and malignant based on their respective multimodal information. Malignant lesions generally exhibited higher values of refractivity and frequency-dependent attenuation and dispersion. CONCLUSION: Initial clinical results confirmed the ability of MUT to detect and differentiate all suspicious lesions with sizes >10 mm discernible in mammograms of 25 female patients.

Design of optimal stimulation patterns for neuronal ensembles based on Volterra-type hierarchical modeling.

This paper presents a general methodology for the optimal design of stimulation patterns applied to neuronal ensembles in order to elicit a desired effect. The methodology follows a variant of the hierarchical Volterra modeling approach that utilizes input-output data to construct predictive models that describe the effects of interactions among multiple input events in an ascending order of interaction complexity. The illustrative example presented in this paper concerns the multi-unit activity of CA1 neurons in the hippocampus of a rodent performing a learned delayed-nonmatch-to-sample (DNMS) task. The multi-unit activity of the hippocampal CA1 neurons is recorded via chronically implanted multi-electrode arrays during this task. The obtained model quantifies the likelihood of having correct performance of the specific task for a given multi-unit (spatiotemporal) activity pattern of a CA1 neuronal ensemble during the 'sample presentation' phase of the DNMS task. The model can be used to determine computationally (off-line) the 'optimal' multi-unit stimulation pattern that maximizes the likelihood of inducing the correct performance of the DNMS task. Our working hypothesis is that application of this optimal stimulation pattern will enhance performance of the DNMS task due to enhancement of memory formation and storage during the 'sample presentation' phase of the task.

Diagnostic biomarkers for Alzheimer's disease using dynamic nonlinear models based on Principal Dynamic Modes.

Sensitive and robust diagnostic biomarkers for Alzheimer's disease (AD) were sought using dynamic nonlinear models of the causal interrelationships among time-series (beat-to-beat) data of arterial blood pressure, end-tidal CO(2) and cerebral blood flow velocity collected in human subjects (4 AD patients and 4 control subjects). These models were based on Principal Dynamic Modes (PDM) and yielded a reliable biomarker for AD diagnosis in the form of the "Effective CO(2) Reactivity Index" (ECRI). The results from this initial set of subjects corroborated the efficacy of the ECRI biomarker for accurate AD diagnosis.