Biomedical subjects

E H Bastiaanssen

Publications and source records attributed to E H Bastiaanssen.

5 recordsLinked to original sources

PubMed1999

The neuronal control of the lower urinary tract: A model of architecture and control mechanisms.

The human micturition cycle is controlled by central and peripheral nervous structures and connections. In literature, no complete or generally accepted model describes the principles of micturition control. In this paper, the integration of (neuro-)anatomy, (neuro-)physiology and control theory is used to describe and model the neuronal control of the lower urinary tract. Neuroanatomy supplies the most basic information necessary for the modellation of the peripheral pathways and central connections involved in the control of the uropoetic system. It is found that not all the nervous structures and connections have been identified as such yet. The linking up between several nervous structures (e.g., the presence of central and peripheral relay stations) is not completely clear. A s a consequence, each model to describe the micturition cycle from the perspective of control theory is yet of limited physiological value as it cannot exceed a rather general level of modellation. Adding functional considerations (neurophysiology and control theory) to the neuroanatomical skeleton completes the model. Some control mechanisms active during the micturition cycle can still not be revealed in detail. Crucial questions on the neuronal innervation of the human uropoetic system and the control mechanisms active during the micturition cycle remain, like how the supraspinal trigger mechanisms for micturition are organised, or how the voluntary cessation of voiding is realised. A simplified version of the model discussed in this paper can already be used for mathematical modelling, e.g., neural network simulations.

Afferent Pathways↗

PubMed1996

State-space analysis of a myocybernetic model of the lower urinary tract.

To study the control of the lower urinary tract, the state space of the myocybernetic model by Bastiaanssen et al. (1996) is analysed. This model is able to respond to input signals from a neural network and includes descriptions of the muscle dynamics of both the detrusor in the bladder wall and the urethral sphincter. The equilibrium states of the model for constant input signals were found by evaluation of the roots of calculated flow curves. Two types of equilibrium states could be distinguished: (i) the inflow and the outflow of the bladder are both equal to zero and (ii) the bladder in- and outflow are both equal to a prescribed small constant flow from the ureters into the bladder. The first type of equilibrium features a very high bladder pressure, which in vivo could result in a reflux of urine into the ureters. The second type shows a constant loss of urine. For different combinations of constant input signals, several stable equilibrium states of both types were found. The neural controller should avoid these states so that the lower urinary tract fulfils either its storage or its voiding function. Therefore, the trajectory through the state space of a simulated normal filling and micturition event was evaluated here. It appeared that equilibrium states were avoided by rapid changes of the input signals. The behaviour of the model outside the normal trajectory is compared with neurologic urinary tract disorders. Several pathological behaviours are in qualitative agreement with the model predictions.

Computer Simulation↗

PubMed1996

A myocybernetic model of the lower urinary tract.

A biomechanical model of the lower urinary tract which is able to respond to input signals from a neural network is presented. The neural input is the starting point in the description of the relationships between the various physical parameters in the mechanical model of the bladder and the urethra. The cybernetics of the lower urinary tract are described on the basis of the muscle dynamics of simple models of both the detrusor in the bladder wall and the urethral sphincter. The urethral sphincter is not described as a variable resistance, like in other biomechanical models of the lower urinary tract, but is described on the basis of striated muscle dynamics. The forces produced by the detrusor and the urethral sphincter give rise to the bladder pressure and the urethral pressure. Using quasi-steady assumptions, the flow rate of urine is calculated as a result of the pressure difference between the bladder and the urethra. Parameters like the bladder volume, the flow rate and the pressure in the bladder can be compared with clinical data of urodynamic measurements. Simulation results show that the model is able to mimic both a filling and an emptying behaviour which resembles the behaviour of the lower urinary tract. By increasing the resistance of the urethra, a behaviour model of the lower urinary tract appears which is comparable with the pathology of urethral obstruction. A sensitivity analysis of various parameters in the model leads to a better understanding of the biomechanics of the lower urinary tract.

Biomechanical Phenomena↗

PubMed1995

Neuronal circuitry of the lower urinary tract; central and peripheral neuronal control of the micturition cycle.

A new presentation technique is introduce to describe the neuronal circuitry involved in the control of the uropoëtic system and its control mechanisms during the micturition cycle. This method is based on the preparation of flow charts and is applied to the discussion of four qualitative models which are derived from the literature. Opinions concerning the reflex arcs and supraspinal connections said to be involved in micturition and continence are different and sometimes contradictory. Little is known about supraspinal (inter)connections and their function in micturition control is still fragmentary. The control mechanisms which terminate voiding are not totally clear. Moreover, the role of the pelvic floor musculature in the control of the lower urinary tract is probably underestimated. The flow charts presented in this paper contribute to the future design of a single complete qualitative model representing the general central and peripheral nervous connections and control mechanisms. Such a model would provide an approach for future research in neuromodulation and neurostimulation of the uropoëtic system and a reduced version could be used for quantitative modelling, e.g. in neural network simulations.

Humans↗

PubMed1993

Learning procedure in a neural control model for the urinary bladder.

A continuous neural network coupled to a dynamical model of the urinary bladder is defined. The neural network is trained to control the bladder model to track a prescribed volume fluctuation, by adjusting weights and time constants. The gradients of the error in the output neurons of the neural network are unknown. Therefore, the learning procedure discussed here minimizes the error functional without using gradient descent.

Animals↗