The effect of perturbations of the glycocalyx on microvascular perfusion in the obese trauma population: an in vitro study

Objectives

Patients with morbid obesity have impaired responses to resuscitation following severe injury, which may contribute to adverse outcomes. Obesity is associated with microvascular dysfunction and metabolic changes associated with altered hemorheological profiles. These include decreased red blood cell (RBC) deformity associated with increased aggregation and adhesion. These RBC changes may be impacted by the glycocalyx layer of the endothelial cell (EC) and RBC. Degradation of either or both glycocalyx layers may impair microvascular perfusion. This was studied from blood obtained from patients with obesity and in an in vitro microfluidic device to mimic the microvascular environment.

Methods

RBCs were obtained from fresh whole blood from normal controls and patients with obesity (body mass index 37.6–60.0). RBC glycocalyx was indexed by fluorescent intensity and shedding of EC glycocalyx components into the serum was determined by measurement of syndecan-1 and hyaluronic acid. In a second set of experiments, human umbilical vein endothelial cell monolayers (HUVEC) were perfused with RBC suspensions from control and patients with obesity using a microfluidic device and RBC adherence under normoxic or shock conditions (hypoxia+epinephrine) was determined using confocal microscopy. HUVEC glycocalyx thickness and shedding were also measured.

Results

Microfluidic studies demonstrated that RBC obtained from subjects with obesity had increased adhesion to the endothelial layer, which was more profound under shock conditions versus normal subjects. This appeared to be related to increased shedding of the endothelial glycocalyx following shock as well as a diminished RBC glycocalyx layer in the obese population.

Conclusion

Blood from patients with obesity have decreased RBC glycocalyx thickness accompanied by evidence of increased EC glycocalyx shedding. In vitro adhesion to the endothelium was more pronounced with RBC from patients with obesity and was significantly greater under ‘shock conditions’. Hemorheological properties of RBC from patients with obesity may account for failure of standard resuscitation procedures in the trauma patient.

Level of evidence

The first 142 amino acids of glutamate decarboxylase do not contribute to epitopes recognized by autoantibodies associated with Type 1 diabetes

AIMS

Glutamate decarboxylase (GAD) antibodies are the most widely used predictive marker for Type 1 diabetes, but many individuals currently found to be GAD antibody-positive are unlikely to develop diabetes. We have shown previously that radioimmunoassays using N-terminally truncated ³⁵ S-GAD₆₅ (96-585) offer better disease specificity with similar sensitivity to full-length ³⁵ S-GAD₆₅ (1-585). To determine whether assay performance could be improved further, we evaluated a more radically truncated ³⁵ S-GAD₆₅ (143-585) radiolabel.

METHODS

Samples from people with recent-onset Type 1 diabetes (n = 157) and their first-degree relatives (n = 745) from the Bart's-Oxford family study of childhood diabetes were measured for GAD antibodies using ³⁵ S-labelled GAD₆₅ (143-585). These were screened previously using a local radioimmunoassay with ³⁵ S-GAD₆₅ (1-585). A subset was also tested by enzyme-linked immunosorbent assay (ELISA), which performs well in international workshops, but requires 10 times more serum. Results were compared with GAD antibody measurements using ³⁵ S-GAD₆₅ (1-585) and ³⁵ S-GAD₆₅ (96-585).

RESULTS

Sensitivity of GAD antibody measurement was maintained using ³⁵ S-GAD₆₅ (143-585) compared with ³⁵ S-GAD₆₅ (1-585) and ³⁵ S-GAD₆₅ (96-585). Specificity for Type 1 diabetes was improved compared with ³⁵ S-GAD₆₅ (1-585), but was similar to ³⁵ S-GAD₆₅ (96-585). Relatives found to be GAD antibody-positive using these truncated labels were at increased risk of diabetes progression within 15 years, compared with those positive for GAD(1-585) antibody only, and at similar risk to those found GAD antibody-positive by ELISA.

CONCLUSIONS

The first 142 amino acids of GAD₆₅ do not contribute to epitopes recognized by Type 1 diabetes-associated GAD antibodies. Low-volume radioimmunoassays using N-terminally truncated ³⁵ S-GAD₆₅ are more specific than those using full-length GAD₆₅ and offer practical alternatives to the GAD antibody ELISA for identifying children at increased risk of Type 1 diabetes.

ARX Filtering of Single-Sweep Movement-Related Brain Macropotentials in Mono- and Multi-Channel Recordings

A technique of stochastic parametric identification and filtering is applied to the analysis of single-sweep event-related potentials. This procedure, called AutoRegressive with n exogenous inputs (ARXn), models the recorded signal as the sum of n+1 signals: the background EEG activity, modeled as an autoregressive process driven by white noise, and n signals, one of which represents a filtered version of a reference signal carrying the average information contained in each sweep. The other (n-1) signals could represent various sources of noise (i.e., artifacts, EOG, etc.). An evaluation of the effects of both artifact suppression and accurate selection of the average signal on mono- or multi-channel scalp recordings is presented.

Modeling the Iatrogenic Pancreatic Cancer Risk After Islet Autotransplantation in Mouse

Iatrogenic pancreatic cancer metastasis after islet infusion is a potential risk of islet autotransplantation performed after pancreatectomy. To model this risk, islets and/or pancreatic exocrine clusters obtained from a genetically engineered mouse model for pancreatic ductal adenocarcinoma (the LSL-Kras^G12D/+ ;LSL-Trp53^R172H/+ ;Pdx-1-Cre, termed KPC mouse) were transplanted via the portal vein in syngeneic wild type (WT) severely diabetic recipients in the following treatment groups: group A (n = 11) received KPC exocrine clusters in volume equal to 250 islet equivalents (IEQs); group B (n = 12) received 250 WT IEQs mixed with KPC exocrine clusters (1:1 volume ratio); group C (n = 5) received 250 KPC IEQs, and group D (n = 7) received 250 WT IEQs. The incidence of hepatic metastasis was assessed by magnetic resonance imaging and histology over the 13 months of follow-up. Overall survival was not different in the four groups. No mice developed liver metastases during the follow-up. Two mice developed spontaneous tumors: a liver hepatocellular tumor in group A and a malignant lymphoma in group D. Islets and/or exocrine clusters obtained by KPC mouse, a model that develops pancreatic cancer with 100% penetrance, do not retain the same risk of tumor development when transplanted via the portal vein in a syngeneic diabetic recipient.

Forecasting the growth of multicell tumour spheroids: implications for the dynamic growth of solid tumours

The growth dynamics of multicell tumour spheroids (MTS) were analysed by means of mathematical techniques derived from signal processing theory. Volume vs. time trajectories of individual spheroids were fitted with the Gompertz growth equation and the residuals (i.e. experimental volume determinations minus calculated values by fitting) were analysed by fast fourier transform and power spectrum. Residuals were not randomly distributed around calculated growth trajectories demonstrating that the Gompertz model partially approximates the growth kinetics of three-dimensional tumour cell aggregates. Power spectra decreased with increasing frequency following a 1/f(delta) power-law. Our findings suggest the existence of a source of 'internal' variability driving the time-evolution of MTS growth. Based on these observations, a new stochastic Gompertzian-like mathematical model was developed which allowed us to forecast the growth of MTS. In this model, white noise is additively superimposed to the trend described by the Gompertz growth equation and integrated to mimic the observed intrinsic variability of MTS growth. A correlation was found between the intensity of the added noise and the particular upper limit of volume size reached by each spheroid within two MTS populations obtained with two different cell lines. The dynamic forces generating the growth variability of three-dimensional tumour cell aggregates also determine the fate of spheroid growth with a strong predictive significance. These findings suggest a new approach to measure tumour growth potential.

Stimulated secretion of pituitary hormones in normal humans: a novel direct assessment from blood concentrations

Gland responsiveness is usually assessed by administering suitable secretagogues and measuring the resulting hormone concentration in blood after the specific stimulus. Such response-to-stimulus tests are routinely conducted for the clinical diagnosis of pathologies involving the pituitary hormones growth hormone, prolactin, luteinizing hormone, follicle stimulating hormone, adrenocorticotropic hormone, and thyrotropin hormone. However, the current evaluation approaches, based on the maximum peak value or the (normalized) area under the curve, are inadequate under several respects. A more physiologically based index of responsiveness is the amount of released hormone. This is not directly accessible but is typically estimated by (computationally expensive) deconvolution analysis. The present work derives a simple formula yielding the amount of released hormone as a linear combination of blood concentrations through proper weights depending on hormone kinetics and sampling protocol. The weights are derived and reported for all six pituitary hormones and the more common sampling protocols. A validation study involving 174 test experiments has been carried out. The use of the formula shows excellent agreement with the cumulative secretion estimates obtained through deconvolution analysis.

Hamming clustering techniques for the identification of prognostic indices in patients with advanced head and neck cancer treated with radiation therapy

The aim of the study is to demonstrate the usefulness of a new, non-linear classifier method, called Hamming clustering (HC), in selecting prognostic variables affecting overall survival in patients with head and neck cancer. In particular, the aim is to identify whether tumour proliferation parameters can be predictive factors of response in a set of 115 patients that receive either alternating chemo-radiotherapy or accelerated or conventional radiotherapy. HC is able to generate a set of understandable rules underlying the study objective; it can also select a subset of input variables that represent good prognostic factors. HC has been compared with other standard classifiers, providing better results in terms of classification accuracy. In particular, HC obtains the best accuracy of 74.8% (sensitivity of 51.1% and specificity of 91.2%) about survival. The rules found show that, besides the classical, well-known variables concerning the tumour dimension and the involved lymphonodes, some biological parameters, such as DNA ploidy, are also useful as predictive factors.

Abnormal LH pulsatility in women with hyperprolactinaemic amenorrhoea normalizes after bromocriptine treatment: deconvolution-based assessment

OBJECTIVE

The present study examines the LH secretory process in hyperprolactinaemic women before, during and after bromocriptine therapy, using restrictive clinical selection criteria as well as improved methodological tools.

PATIENTS AND DESIGN

Six women (aged 20-40 years) with microprolactinomas (mean +/- SE prolactin, PRL: 2478 +/- 427 mU/l, range: 1370-3800 mU/l) and four age- and sex-matched healthy controls were admitted to the study. After an overnight fast, all patients and controls had blood samples withdrawn at 10 minute intervals for 6 h (during saline infusion) from 0800 h to 1400 h to determine serum LH and PRL concentrations. After baseline evaluation, patients were treated with bromocriptine, which was started at a daily dose of 1.25 mg for 7 days; the dose was then increased to 2.5 mg daily for the next 7 days and subsequently to 2.5 mg twice daily. PRL levels were evaluated at weekly intervals after the beginning of bromocriptine therapy for the duration of the study. The 6 h pulsatility study was repeated on four patients during treatment at a time when PRL levels were decreased, although not normalized (PRL range: 450-1350 mU/l) and, on four patients, with the attainment of normal serum PRL levels (PRL < 450 mU/l) in the early follicular phase of the menstrual cycle (days 2-5). The LH instantaneous secretion rate was reconstructed by a nonparametric deconvolution method. In addition to pulse analysis made using the program DETECT, the evaluation of the secretion rate yielded the pulse frequency as well as the pulse amplitude distribution.

RESULTS

Each time series was submitted to deconvolution analysis using a nonparametric method in order to estimate the instantaneous secretion rate (ISR). Hyperprolactinaemic patients had very few high-amplitude LH pulses above 0.2 IU/(l minutes) before treatment (average frequency: 0.83 +/- 0.40 pulses/6 h) and at the intermediate evaluation (0.25 +/- 0.25 pulses/6 h). In both cases, the pulse frequency was significantly lower than in controls (P < 0.05 and P < 0.01, respectively). When PRL was normalized, the number of high-amplitude LH pulses (4.25 +/- 1.03 pulses/6 h), became statistically different from the pulse number before (P < 0.01) and during (P < 0.01) therapy; in particular the pulse frequency after therapy rose to a level not statistically different from that in controls.

CONCLUSION

The present study shows the presence of reduced LH pulsatility in hyperprolactinaemic women that recovers completely to within the physiological distribution when PRL levels are normalized by bromocriptine therapy.

Comparison between human and artificial neural network detection of Laplacian-derived electroencephalographic activity related to unilateral voluntary movements

A back-propagation artificial neural network (ANN) was tested to verify its capacity to select different classes of single trials (STs) based on the spatial information content of electroencephalographic activity related to voluntary unilateral finger movements. The rationale was that ipsilateral and contralateral primary sensorimotor cortex can be involved in a nonstationary way in the control of unilateral voluntary movements. The movement-related potentials were surface Laplacian-transformed (SL) to reduce head volume conductor effects and to model the response of the primary sensorimotor cortex. The ANN sampled the SL from four or two central channels overlying the primary motor area of both sides in the period of 80 ms preceding the electromyographic response onset in the active muscle. The performance of the ANN was evaluated statistically by calculating the percentage value of agreement between the STs classified by the ANN and those of two investigators (used as a reference). The results showed that both investigator and ANN were capable of selecting STs with the SL maximum in the central area contralateral to the movement (contralateral STs, about 25%), STs with considerable SL values also in the ipsilateral central area (bilateral STs, about 50%), and STs with neither the contralateral nor bilateral pattern ("spatially incoherent" single trials; about 25%). The maximum agreement (64-84%) between the ANN and the investigator was obtained when the ANN used four spatial inputs (P < 0.0000001). Importantly, the common means of all single trials showed a weak or absent ipsilateral response. These results may suggest that a back-propagation ANN could select EEG single trials showing stationary and nonstationary responses of the primary sensorimotor cortex, based on the same spatial criteria as the experimenter.

Estimating germinability of Plasmopara viticola oospores by means of neural networks

Neural networks are trained to estimate the germination percentages of Plasmopara viticola oospores, overwintered in natural conditions in two viticultural areas in northern Italy, by using climatic (temperature and rainfall) data, as well as the previous germination measurement, as input variables. The 288 available patterns consist of a set of selected independent variables associated with the corresponding germination percentage. All 12 networks investigated converge to a non-linear relationship between the selected independent variables and oospore germination. The highest correlation coefficient (equal to 0.83) between the real and estimated germination percentages is obtained by considering, as input to the network, the climatic data (both temperature and rainfall) recorded during the 40 days before sampling and the germination percentage assessed in the germination assay carried out immediately before the present sampling.

LH and FSH secretory responses to GnRH in normal individuals: a non-parametric deconvolution approach

OBJECTIVE

To reconstruct the instantaneous secretion rate (ISR) of LH and FSH after GnRH administration in normal volunteers using non-parametric deconvolution, and to derive a direct integration formula to evaluate the amount of LH and FSH secreted during the first 60 min after the stimulus.

DESIGN AND METHODS

First, the deconvolution method was validated in vivo by reconstructing doses ranging from 7.5 IU to 75 IU injected in three healthy adult volunteers whose endogenous LH had previously been downregulated by pretreating them, 3-4 weeks earlier, with 3.75 mg GnRH agonist i.m. Then, 40 healthy adult male volunteers were tested with a single 100 microg GnRH bolus, administered at 0 min. LH and FSH concentrations were determined at -30, 0, 15, 30, 45, 60, 90, and 120 min.

RESULTS AND CONCLUSIONS

The validation study, conducted over a 10-fold range of doses, demonstrated that non-parametric deconvolution provided a reasonably accurate estimate of the amount of hormone entering the circulation. Applying deconvolution to the LH and FSH responses to GnRH, the ISRs of both hormones were shown to have a similar pattern, with a clearly delimited pulse after the GnRH bolus. In conjunction with earlier analyses of estimates of GHRH-stimulated GH secretion, we conclude that secretagogues evoke discrete LH, FSH, and GH secretory bursts of about 60 min total duration, despite markedly unequal (glyco-)protein hormone half-lives (18-500 min). With respect to the assessment of total hormone release during the first 60 min after the stimulus, the integration formula provided a reliable approximation of the result obtained by deconvolution, and had a negligible dependence on the samples at times 90 and 120 min.

A non-parametric method for the analysis of experimental tumour growth data

Analysis of tumour growth is required to investigate the biology of tumours and to determine the effects of new anti-tumour therapies. A non-parametric mathematical method for the analysis of a set of experimental tumour growth data is described. The method is based on the similarity between time series of tumour size measurements (e.g. tumour volume), similarity being defined as the Euclidean distance between data measured for each tumour at the same time. Subsets of similar time series are found for a given population of tumours. A biologically meaningful parameter H has been derived which is a measure of the scattering of experimental volume samples. The method has been applied to the analysis of the growth of (i) untreated multicellular tumour spheroids obtained with different cell lines and (ii) spheroids treated with cytotoxic drugs (immunotoxins). Results are compared with those previously obtained by applying the classical Gompertz growth model to the analysis of treated and untreated spheroids.

Oscillating growth patterns of multicellular tumour spheroids

The growth kinetics of 9L (rat glioblastoma cell line) and U118 (human glioblastoma cell line) multicellular tumour spheroids (MTS) have been investigated by non-linear least square fitting of individual growth curves with the Gompertz growth equation and power spectrum analysis of residuals. Residuals were not randomly distributed around calculated growth trajectories. At least one main frequency was found for all analysed MTS growth curves, demonstrating the existence of time-dependent periodic fluctuations of MTS volume dimensions. Similar periodic oscillations of MTS volume dimensions were also observed for MTS generated using cloned 9L cells. However, we found significant differences in the growth kinetics of MTS obtained with cloned cells if compared to the growth kinetics of MTS obtained with polyclonal cells. Our findings demonstrate that the growth patterns of three-dimensional tumour cell cultures are more complex than has been previously predicted using traditional continuous growth models.

Linear spectral deconvolution of catabolic plasma concentration decay in dialysis

Deconvolution can be a useful step in the process of modelling biological data, as it produces an overview of the information content of the data, as well as directions about the structure of the mathematical model able to describe the generating system. This paper concerns the application of a deconvolution technique, spectral analysis, to the modelling process of the concentrations of metabolites sampled in plasma during dialysis: the spectral analysis consists in linearly identifying the whole spectrum of multi-exponential decays, describing the compartmental nature of the process. The application to urea and creatinin time series provides a careful determination of the spectra of the exponential decays, thus giving interesting insight into the system kinetics: a sharp, slow decay (about 0.23 h-1 for urea and 0.17 h-1 for creatinin) affects all the subjects, whereas a variable set of smaller and faster components accounts for interpatient variability as well as for the multicompartmental nature of the process. The power ratio of the components is an index of the relative amount of volume in the related compartments. The identified spectra provide a description of the data that, although computed in a very simple way, is consistent with the results of the classical identification techniques previously applied in building compartmental models of dialysis.

EEG coherence in Alzheimer's disease

EEG coherence can be used to evaluate the functionality of cortical connections and to get information about the synchronization of the regional cortical activity. We studied EEG coherence in patients affected by clinically probable Alzheimer's disease (AD) in order to quantify the modifications in the cortico-cortical or cortico-subcortical connections. The EEGs were recorded in 10 AD patients (with mild or moderate degrees of dementia) and in 10 normal age-matched subjects, at rest and eye-closed, from 16 electrodes with linked-ears reference. Spectral parameters and coherence were calculated by a multichannel autoregressive model using 50 artifact-free epochs, 1 s duration each. Alpha coherence was significantly decreased in 6 patients, the decrease being more accentuated in the area near the electrode taken into account; a significant delta coherence increase was found in a few patients between frontal and posterior regions. The AD group showed a significant decrease of alpha band coherence, in particular in temporo-parieto-occipital areas, more evident in patients with a more severe cognitive impairment. These abnormalities could reflect two different pathophysiological changes: the alpha coherence decrease could be related to alterations in cortico-cortical connections, whereas the delta coherence increase could be related to the lack of influence of subcortical cholinergic structures on cortical electrical activity.

Measuring regularity by means of a corrected conditional entropy in sympathetic outflow

A new method for measuring the regularity of a process over short data sequences is reported. This method is based on the definition of a new function (the corrected conditional entropy) and on the extraction of its minimum. This value is taken as an index in the information domain quantifying the regularity of the process. The corrected conditional entropy is designed to decrease in relation to the regularity of the process (like other estimates of the entropy rate), but it is able to increase when no robust statistic can be performed as a result of a limited amount of available samples. As a consequence of the minimisation procedure, the proposed index is obtained without an a-priori definition of the pattern length (i.e. of the embedding dimension of the reconstructed phase space). The method is validated on simulations and applied to beat-to-beat sequences of the sympathetic discharge obtained from decerebrate artificially ventilated cats. At control, regular, both quasiperiodic and periodic (locked to ventilation) dynamics are observed. During the sympathetic activation induced by inferior vena cava occlusion, the presence of phase-locked patterns and the increase in regularity of the sympathetic discharge evidence an augmented coupling between the sympathetic discharge and ventilation. The reduction of complexity of the neural control obtained by spinalization decreases the regularity in the sympathetic outflow, thus pointing to a weaker coupling between the sympathetic discharge and ventilation.

Non-parametric deconvolution provides an objective assessment of GH responsiveness to GH-releasing stimuli in normal subjects

OBJECTIVE

Deconvolution analysis has been proposed as an effective method for analysing the physiology of GH secretion. In the literature, it has been applied to spontaneous secretion data characterized by long and uniform sampling paradigms. In the present study we investigated the applicability of non-parametric deconvolution to the analysis of response-to-stimuli (RTS) data characterized by infrequent and non-uniform sampling.

PATIENTS

Thirty-six healthy adult male volunteers (age range 24-37 years) were randomly subdivided into two groups (group I, n = 30; group II, n = 6).

DESIGN

Subjects of group I were tested with a single 1 microgram/kg body weight GH-releasing hormone (GHRH) bolus, administered at 0 minutes. Subjects of group II were tested, in random order, with a 4- or 5-day interval, with (1) two consecutive 1 microgram/kg body weight GHRH boluses at 0 and 120 minutes and (2) two consecutive 1 microgram/kg body weight hexarelin boluses, administered at 0 and 120 minutes.

MEASUREMENTS

GH levels were determined at 0, 15, 30, 45, 60, 90 and 120 minutes (group I) and -30, 0, 15, 30, 45, 60, 120, 135, 150, 165, 180 and 240 minutes (group II). A numerically efficient regularization-based non-parametric deconvolution algorithm incorporating non-negativity constraints was used to estimate the time profile of the instantaneous secretion rate (ISR). Confidence limits allowing for both measurement error and kinetic model uncertainty were computed using a Monte-Carlo procedure. In order to validate the deconvolution method, a simulated benchmark problem was set up.

RESULTS

The analysis of the benchmark problem showed that the proposed method is capable of providing an accurate reconstruction of the ISR (as measured by the root mean square (RMS) error). Moreover, it appeared that reliable confidence limits cannot be obtained unless the kinetic model uncertainty is taken into account. The analysis of the data showed a clear rise in the ISR subsequent to the first bolus (either GHRH or hexarelin), with most of the response occurring within 60 minutes of the stimulus. In group I, it was also seen that discarding the samples collected at times 90 and 120 minutes only marginally affected the estimate of the cumulated ISR over 0-60 minutes (the variation was always less than 3%). The analysis of GH responsiveness to repeated stimuli (group II) showed that the amount of hormone secreted after the second bolus was clearly reduced in comparison with the elicited by the first stimulus, most of the response occurring within 60 minutes of the injection. The amount of GH secreted after the second stimulus ranged from 13 to 36% (GHRH 17-36%; hexarelin 13-36%) of the overall amount of hormone secreted after time 0 minutes.

CONCLUSIONS

Even with relatively few samples, non-parametric deconvolution of response-to-stimulus data is capable of providing a reliable, smooth and non-negative estimate of the GH instantaneous secretion rate that offers a realistic representation of the GH secretory dynamics. The non-parametric approach compares favourably with respect to discrete deconvolution methods, that yield discontinuous instantaneous secretion rates profiles, and parametric methods that would require more stringent assumptions on the shape of the instantaneous secretion rate. When assessing confidence limits it is essential to take into account both measurement error and kinetic model uncertainty. Using deconvolution in normal subjects, the estimated instantaneous secretion rate between 0 and 60 minutes is scarcely affected by samples taken after time 60 minutes. Since most of the secretory response takes place during this time interval, there is motivation for investigating the use of shorter sampling protocols in conjunction with deconvolution analysis. Although pulse detection and the assessment of the shape of spontaneous pulses have not been investigated, it could be interesting to apply non-parametric deconvolution to spontaneous sec

Total and partial coherence analysis of spontaneous and evoked EEG by means of multi-variable autoregressive processing

The joint use of total and partial coherence between pairs of EEGs simultaneously recorded in a standard set, is shown to enhance what is caused by direct correlation between cortical subsystems and what is instead related to the spread of the electromagnetic field. A multi-variable autoregressive approach is employed in the computation, giving results even for a very short time window, thus allowing coherence to be investigated at the main cortical latencies of evoked potentials. In particular, when a combined visual and somatosensory stimulation is applied, cortical interactions are captured in the frequency domain.

Surface mechanomyogram reflects muscle fibres twitches summation

The aim of this work is to define the pattern of summation of the muscle fibre twitches in the surface mechanomyogram (MMG) generation process. For this purpose, two groups of muscle fibres of the extensor digitorum communis (EDC) were stimulated using needle electrodes. To these two artificial (because made by different muscle fibre types) motor units (MU1 and MU2), we administered: (a) separate stimulations: 3 and 9 Hz (MU1), 8 and 20 Hz (MU2) for 5 s; (b) simultaneous stimulation: 3 Hz (MU1) + 8 Hz (MU2); 9 Hz (MU1) + 20 Hz (MU2) for 5 s. The mechanomyograms, recorded during separate stimulation of MU1 and MU2, were linearly summated for the generation of a mechanomyographic signal to be compared with the one detected during (b) stimulation procedure. The bispectrum and the bicoherence of the generated MMG (MMGg) and of the MMG recorded during simultaneous (MMGs) stimulation were calculated for the detection of the quadratic non-linearity in the system responses. It was found that the MMGg and MMGs presented difference in the bispectrum and bicoherence index only when the 9-20 Hz stimulation pair was considered In conclusion, our data indicate that the MMG derives from the summation of the active muscle fibres twitches and that the latter is linear only for very low firing rates. This is to be carefully considered when studies on MMG modelling will be undertaken.

Deconvolution of infrequently sampled data for the estimation of growth hormone secretion

In this paper, the deconvolution of infrequently and nonuniformly sampled data is addressed. A nonparametric technique is worked out that provides a smooth estimate of the unknown input signal and takes into account nonnegativity constraints. In spite of the size of the problem, efficient algorithms for solving the constrained optimization problem and computing confidence intervals are proposed. The new technique is used to estimate growth hormone (GH) secretion after repeated GH-releasing hormone (GHRH) administration from samples of blood concentration.

Analysis of single trial movement-related brain macropotential

A parametric method of identification of movement-related brain macropotentials on a single trial basis through an ARX (autoregressive with exogenous inputs) algorithm is presented. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model to drastically improve the signal/noise ratio in each single trial and to satisfactorily identify the contributions of signal and noise in the overall recording. This way, using the same algorithm, a particularly efficient reduction of ocular artifacts is also achieved. The movement-related brain macropotentials recorded in three subjects show a high degree of variability from trial and this effect seems to be related to programming processes and evaluation of errors.

ARX filtering of single-sweep movement-related brain macropotentials in mono- and multi-channel recordings

A technique of stochastic parametric identification and filtering is applied to the analysis of single-sweep event-related potentials. This procedure, called AutoRegressive with n eXogenous inputs (ARXn), models the recorded signal as the sum of n+1 signals: the background EEG activity, modeled as an autoregressive process driven by white noise, and n signals, one of which represents a filtered version of a reference signal carrying the average information contained in each sweep. The other (n-1) signals could represent various sources of noise (i.e., artifacts, EOG, etc.). An evaluation of the effects of both artifact suppression and accurate selection of the average signal on mono- or multi-channel scalp recordings is presented.

Linear and nonlinear techniques for the deconvolution of hormone time-series

Pulsatile hormone secretion is usually investigated by measuring hormone concentration in samples of peripheral plasma. In this paper, the deconvolution of hormone time-series to reconstruct the instantaneous secretion rate of glands is considered. Various techniques are discussed and compared in order to overcome the ill-conditioning of the problem and reduce the computational burden. In particular, linear techniques based on least squares, maximum a posteriori (MAP) estimation, and Wiener filtering are compared. A new nonlinear MAP estimator that keeps into account the non-Gaussian distribution of the unknown signal is worked out and shown to yield the best results. The performances of the algorithms are tested on simulated time-series as well as on series of Luteinizing Hormone (LH).

New compartmental model approach to dialysis

Compartmental models are used for solving the problem of the control of dialysis therapy. The inadequacy of the existing monocompartmental model is faced, first with a careful analysis of the physiology of the system, then with a method focused on the construction of a new multicompartmental model. Moreover, impedance techniques allow us to solve the problem of measuring the total body water for each patient.

Topographic mapping of single sweep evoked potentials in the brain

Single trial analysis of brain-evoked potentials via stochastic parametric identification and filtering is here extended to multichannel recordings, leading to the topographic mapping of the brain activity elicited by a single stimulus, instead of the usual averaged mapping. The temporal dynamics of the subsequent sweeps in the protocol of a neurophysiologic experiment can thus be recovered and quantified also on its spatial characteristic.

The dynamic behaviour of the evoked magnetoencephalogram detected through parametric identification

A single trial technique has been used for the analysis of neuromagnetic signals. This method, applied to evoked magnetoencephalograms, permitted us to evaluate the dynamics of brain responses under repetitive somatosensory stimulation. Two different phenomena have been investigated: synchronized spontaneous activity (SSA), elicited by bursts of stimuli at a given frequency; and somatosensory evoked fields (SEF), the transient response to a single stimulus. A transient period of adaptation of the SSA response to the particular stimulation paradigm is reported at specific stimulation frequencies; no significant decrease of the response amplitude even after 300 repetitions is observed. The amplitudes of single SEFs also appear to be constant. Possible physiological implications of the SSA response parameters studied (amplitude, frequency and decay time), and a preliminary discussion of the relationship between SEF and SSA, are presented.

A model for the cortico-cortical neural interaction in multisensory-evoked potentials

This paper addresses the methodological problem of enhancing selective responses from the central nervous system when two (or more) different sensorial stimuli are simultaneously presented to the subject. In particular, contemporaneous visual and somato-sensory stimulation is considered and a model of signal and noise interaction is developed for the processing of the evoked responses. An ARXX parametric model (AutoRegressive with two eXogenous inputs) is introduced and a least squares algorithm is used to determine the selective response of the two neural systems from the overall evoked response. Such an analysis may be also carried out on a sweep-by-sweep basis. Applications of this method are the following ones: i) modeling of multisensory potentials; ii) description of facilitation or defacilitation phenomena in multitasking experiments; iii) analysis of cortico-cortical neural interactions.

Assessment of the neural control of the circulation during psychological stress

In this study, we used spectral analysis of short-term R-R and systolic arterial pressure (SAP) variabilities to estimate the changes in neural control of the circulation produced by psychological stress. The 0.1 Hz low-frequency (LF) component of R-R and SAP variabilities provided a quantitative index of the sympathetic activity controlling heart rate and vasomotion. Conversely the high-frequency (HF) respiratory component of R-R variability provided an index of vagal tone. In conscious dogs we used the seemingly stressful situation of being accompanied for the first time to the experimental laboratory as a stimulus. In human subjects we used mental arithmetic. In both cases LF of R-R and SAP variabilities increased significantly suggesting enhanced sympathetic activity both to the SA node and the vasculature. In man, the index alpha, a measure of the overall gain of baroreceptor mechanisms, was found to be reduced during mental arithmetic. Spectral analysis of cardiovascular variabilities thus suggests that in man and in conscious dogs psychological challenges induce a profound re-arrangement of neural control of the circulation, which appears to be characterised by sympathetic predominance and which can be monitored by this technique.

Sympathovagal interaction during mental stress. A study using spectral analysis of heart rate variability in healthy control subjects and patients with a prior myocardial infarction

We tested the hypothesis that psychological stress testing in the clinical laboratory provokes changes in the sympathetic and vagal activities regulating heart rate that can be assessed noninvasively using spectral analysis of RR variability. To account for the effects on respiration produced by talking, this study was performed with two different procedures: the I.K.T. (i.e., a computer-controlled mental task that is performed in silence and does not entail human confrontation) and a stressful interview. Finally, we assessed whether ischemic heart disease modifies the spectral changes induced by psychological stress by comparing a group of healthy subjects (age, 38 +/- 2 years) with a group of patients (age, 52 +/- 3 years) recovering from 1-month-old myocardial infarctions. The findings indicate that psychological stress induced marked changes in the sympathovagal balance, which moved toward sympathetic predominance. The low-frequency component of RR variability, a marker of sympathetic activity, increased from 58 +/- 5 normalized units (NU) to 68 +/- 3 NU with the I.K.T. and to 76 +/- 3 NU with the interview. This increase was absent in the group of post-myocardial infarction patients. However, arterial pressure increased significantly in both groups of subjects. The possibility of age playing an important role in determining the differences observed was disproved by the findings of a marked increase in low frequency with mental stimuli in an additional group of borderline hypertensive subjects with ages (55 +/- 2 years) comparable to those of post-myocardial infarction patients.

Parametric method for the detection of inter- and intrasweep variability in VEP processing

The paper introduces a Kalman filter procedure for the processing of single-sweep visual evoked potentials (VEPs). The identification of the filter coefficients is based on a model of signal and noise interaction which considers the generating process as the superposition of the true evoked response to an AR process (the background EEG) and a broader spectrum noise. Intersweep variability is thus evident on the filtered response and a functional parameter of the filter (VP(t), namely variability path) is proposed for the automatic determination of the latencies associated with the main peaks of the response. Finally, the time-variant algorithm allows the quantification of the intrasweep variability for possible interpretation of the physiological mechanism involved.

Analysis of the neuromagnetic data on the frequency responsivity of the human brain: a progress report

The study of the responsivity of the human brain at different rates of sensory stimulation has provided several pieces of information on brain functionality. The new analysis technique used in this study permits us to investigate the dynamics of these responses and to obtain a larger amount of statistical data to allow for fine frequency discrimination. This allows us to suggest, for example, phase locked systems as models for synchronisable spontaneous activity. Absence of subject fatigue during the repetition of the stimulation paradigm, as well as the presence of a 'learning period' characterised by transient dynamics of the responses, are also discussed.

The implementation of an autoregressive model with exogenous input in a single sweep visual evoked potential analysis

Based on a model of signal-noise interaction, we present a method for single-sweep analysis of Visual Evoked Potentials. The EEG is represented as an autoregressive process and the single-sweep VEP as a filtered version of a reference signal taken as the running average of 20 consecutive sweeps. The algorithm for model identification and filtering is an ARX (AutoRegressive with eXogenous input) which provides a fast and efficient solution by means of a least squares approach. The choice of reference signal, as well as the complexity of the model, is also discussed. A further advantage of this approach is parameter reduction: all the single-sweep information is contained in 18 model coefficients and the reference signal.

Analysis of visual evoked potentials through Wiener filtering applied to a small number of sweeps

We introduce a method for processing visual evoked potentials, on the basis of a Wiener filter algorithm applied to a small number of consecutive responses. The transfer function of the filter is obtained by taking into account both the average of 99 sweeps (as an estimate of the true signal) and the EEG signal just before the stimulus onset (as an estimate of the noise superimposed on each individual response). The process acts as a sweep-by-sweep filter (in the sense of the mean square error) which considers the possible non-stationarities of the EEG signal during a complete clinical procedure. The average of a small number of consecutive filtered sweeps reveals variations in the morphology of the evoked responses which produce a change in the principal latencies. Applications are foreseen in neurophysiological studies of visual evoked potential responses, and in the clinic, where it is important to evaluate adaptive mechanisms, dynamic changes in single groups of visual evoked potentials and cognitive responses.

Spectral and cross-spectral analysis of heart rate and arterial blood pressure variability signals

A parametric method for autoregressive (AR) auto- and cross-spectral analysis is presented for the contemporaneous processing of heart rate and arterial blood pressure variability signals. In particular, the introduced bivariate spectral analysis (phase and coherence spectra) provides quantitative and objective means which are useful to measure the role played by the neural controlling systems (sympathetic and parasympathetic systems) on the cardiovascular signals under different pathophysiological conditions. Algorithmic aspects, connected to the way of processing discrete numerical series synchronized to single cardiac beats, are particularly stressed. Important applications are foreseen both in physiological studies and in clinical practice as an aid to the detection of various relevant cardiovascular pathologies such as hypertension and diabetes.

Classification of the EEG during neurosurgery. Parametric identification and Kalman filtering compared

A procedure is described which aims to classify an EEG recorded during neurosurgery, for example intracerebral aneurysm clipping. A parametric approach is used; it employs auto-regressive (AR) modelling and Kalman filtering to quantify directly the dynamics of the EEG generating mechanism, supposing it to be a linear, time-invariant system driven by white noise. The results of this EEG processing are analysed together with simultaneous values of arterial blood pressure (ABP) as surgery of this kind is carried out under conditions of controlled hypotension. The object is to compare the sensitivity of ABP data with that obtained from the EEG and so provide an early warning of a potentially dangerous non-physiological state induced by the hypotensive drug (in this case sodium nitroprusside). Some methodological comments on the correct implementation of these algorithms are given and the procedure is compared with similar approaches which have appeared in the literature during the last few years. Particular emphasis is placed on the power spectral analysis of the signal by pointing out a method for spectral decomposition, related to AR power density estimation, which permits the separation of single spectral components in terms of central frequencies and their associated power. Other potential applications of this method are in long term EEG monitoring for the detection of changes due for example to drug infusion, to fast transient events, or to changes in the stationary condition.