The question of how the structure of a neuronal network affects its functionality has gained a lot of attention in neuroscience. However, the vast majority of the studies on structure-dynamics relationships consider few types of network structures and assess limited numbers of structural measures. In this in silico study, we employ a wide diversity of network topologies and search among many possibilities the aspects of structure that have the greatest effect on the network excitability. The network activity is simulated using two point-neuron models, where the neurons are activated by noisy fluctuation of the membrane potential and their connections are described by chemical synapse models, and statistics on the number and quality of the emergent network bursts are collected for each network type. We apply a prediction framework to the obtained data in order to find out the most relevant aspects of network structure. In this framework, predictors that use different sets of graph-theoretic measures are trained to estimate the activity properties, such as burst count or burst length, of the networks. The performances of these predictors are compared with each other. We show that the best performance in prediction of activity properties for networks with sharp in-degree distribution is obtained when the prediction is based on clustering coefficient. By contrast, for networks with broad in-degree distribution, the maximum eigenvalue of the connectivity graph gives the most accurate prediction. The results shown for small () networks hold with few exceptions when different neuron models, different choices of neuron population and different average degrees are applied. We confirm our conclusions using larger () networks as well. Our findings reveal the relevance of different aspects of network structure from the viewpoint of network excitability, and our integrative method could serve as a general framework for structure-dynamics studies in biosciences.
Teleconsultations were performed between a health centre in a small Finnish town and a university hospital 55 km away. Telemedicine consultations were carried out with a total of 42 patients suffering from various eye and skin disorders. We evaluated the costs of the teleconsultations in the health centre and the conventional alternative of the patient travelling to the hospital. The cost of conventional consultations, which was not affected by the patient workload, was EU126 per patient for ophthalmology and EU143 per patient for dermatology. The cost of the teleconsultations per patient decreased as the number of patients increased. There were cost savings in relation to teleconsultations when the annual numbers of patients were more than 110 in ophthalmology and 92 in dermatology. Benefits and savings achieved through teleconsultations mainly consisted of reduced transportation costs and reduced paperwork both at the health centre and at the university hospital, as well as time savings for the patient. Another important benefit was improved medical education. The present study shows that teleconsultations can be performed in a cost-effective way in a relatively small health centre.
ABSTRACT.Objectives: To give an overview of telemedical applications in ophthalmology and to provide background information on new tele-ophthalmological applications. Methods: We carried out a literature review, a database search and an Internet search. Results: According to published research, the cost-efficiency of telemedicine in ophthalmology has not been established. It has been found to have educational benefits and patients have been satisfied with the possibility of obtaining specialist care without having to travel. Conclusions: Most studies have been pilot studies and telemedicine is still seldom the primary mode of operation. Technical problems have not been significant, but many open questions about organizational and operational issues remain. Further studies should be directed towards solving these problems and establishing technical standards.
As hip OA occurred more frequently in the longer leg the authors speculate whether leg-length inequality might predispose to OA in the hip of the longer leg.
It is shown in this paper that the solution of the initial value problem for a system of ordinary differential equations is computable if the following assumptions are satisfied: The time interval considered is computable, the system is continuous and computable, the initial values are computable, the system is effectively bounded, and the solution is unique. It should be mentioned that for a single ODE this follows immediately from the standard proof of Osgood’s existence theorem, but this approach is not available for systems of ODEs. The key assumption here is uniqueness of solution: a result of Pour-El’s and Richards’ shows that nonunique solutions may be noncomputable, even for a single ODE.
Neuronal networks exhibit a wide diversity of structures, which contributes to the diversity of the dynamics therein. The presented work applies an information theoretic framework to simultaneously analyze structure and dynamics in neuronal networks. Information diversity within the structure and dynamics of a neuronal network is studied using the normalized compression distance. To describe the structure, a scheme for generating distance-dependent networks with identical in-degree distribution but variable strength of dependence on distance is presented. The resulting network structure classes possess differing path length and clustering coefficient distributions. In parallel, comparable realistic neuronal networks are generated with NETMORPH simulator and similar analysis is done on them. To describe the dynamics, network spike trains are simulated using different network structures and their bursting behaviors are analyzed. For the simulation of the network activity the Izhikevich model of spiking neurons is used together with the Tsodyks model of dynamical synapses. We show that the structure of the simulated neuronal networks affects the spontaneous bursting activity when measured with bursting frequency and a set of intraburst measures: the more locally connected networks produce more and longer bursts than the more random networks. The information diversity of the structure of a network is greatest in the most locally connected networks, smallest in random networks, and somewhere in between in the networks between order and disorder. As for the dynamics, the most locally connected networks and some of the in-between networks produce the most complex intraburst spike trains. The same result also holds for sparser of the two considered network densities in the case of full spike trains.
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