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It also demands further multidisciplinary interaction between those whose knowledge of time-oriented systems will find in the area of medicine a challenging but highly rewarding field of application purchase 75 mg viagra erectile dysfunction 70 year olds. Researchers and practitioners from medical informatics cheap viagra 50mg mastercard impotence sexual dysfunction, TR in AI, temporal databases, active databases, real-time databases, visualization of dynamic systems, and real-time systems should have some knowledge and experience to share in this fertile area. Although we believe all the areas listed above are important we will exemplify how this context will affect key aspects of the traditional interaction between the professional and patient during effective diagnosis. More specifically, we highlight the importance of handling time-related concepts in Internet-based medicine and how its use may affect the accuracy of diagnosis. We have found that existing systems do not handle the richness of time-related concepts during the diagnosis stage and the variety of temporal refer- ences required. For example, some symptoms are described as occurring on a particular day, like “The symptoms started last Monday”. Some of them are identified precisely once the duration is known, for example, “He had fever for three days”. Sometimes the duration is not precisely identifiable and there is some degree of uncertainty the system should be able to handle, for example, “Started yesterday evening and stopped at some point during the night”. It is important to recognize repetitive processes, for example, “He has headaches each time that he goes to music class” and frequencies of occurrence, for example, “He has been taking this medicine three times each day”. Rich calendric references should be handled, like seasons, in order to discover potential causes of disease, for example allergies. Here we consider why, where and when these issues are important in the context of Internet-based medical assistance. Interaction Shifts with Internet-Based Diagnosis The use of the Internet as an intermediate level between health centers and patients brings a shift in the interaction and the usual tasks involved. The interaction is no longer a physical meeting but instead there is a media that may restrict, sometimes significantly, what each person involved in the communication perceives from each other. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. Here the interaction between (i) a rich interface that allows extraction of information from the patient in terms of the symptoms and (ii) suitable algorithms that can relate a, most possibly, incomplete description of symptoms to a meaningful subset of possible scenarios, will be crucial for the effectiveness of such systems. In a routine visit to a clinician, natural language, body language and other usual means of communications between humans are available. With Internet-based consultations we can consider some substitutes like video and sound but some of them may or may not be available. There may be occasions when these media would not be usable, for example, due to privacy issues. Until image, video and sound are widely available at the level of quality required to replace a face to face clinical examination we focus on the more basic and less sophisticated ways of collecting information via dynamically generated web based forms that can be used as the base for interaction either in synchronous or asynchronous communication between patient and health professional. Other areas of computer science become relevant like Natural Language processing and appropriate interfaces that are friendly enough for the patient while gathering as much information for the clinicians as possible. At the diagnosis level different subtleties will help to identify between a possible dangerous situation and a non-dangerous one or between two diseases that may require very different treatment even when they share similar symptoms, for example, flu and hepatitis. Being able to successfully detect the described symptoms with pre-known patterns of disease will require mechanisms like: a) disambiguating relative orders between events and descriptions, b) inferring possible durations for them when they are not given explicitly, c) dealing with degrees of uncertainty in terms of the temporal scope of a given set of events and conditions, d) using the partial list obtained at any time during the interaction to assess which is the most likely scenario which in turn will help to select which questions to ask next or which information to gather in order to maximize efficiency during the diagnosis process. There are quite a few hypotheses that must be taken into account to supply the system with extra information that is available or gathered by other means. One basic point is that patients should have a history, the normal approach for storing time-related information being temporal databases (Tansel, Clifford, Gadia, Jajodia, Segev, & Snodgrass, 1993; Etzioni, Jajodia, & Sripada, 1998). Also, the system should be time sensitive in the sense that each subsequent visit should provide a different context. For example, if the purpose of the later visit is to incorporate further information about a previous description of symptoms, the system should react accordingly and should present information differ- ently and/or different information. Once all the symptoms have been entered and those that are relevant to the hypothetical syndrome are identified, the system may advise on how to monitor for their evolution in time. The rules for diagnosis should be “time-aware” and the interface with the patient should allow some way to clearly indicate key time-based references, for example, the frequency, Copyright © 2005, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. Management and Analysis of Time-Related Data in Internet-Based Healthcare 43 duration and proximity of symptoms. The inference engine should instantiate internally these temporal references with patterns and use the time of occurrence as a reference during the reasoning. A specific device can be used for daily monitoring so as to reduce errors, but more sophisticated users or applications will demand a more sophisticated interaction and description of events and conditions. For example, if a patient is trying to describe symptoms to ascertain if they have a medical condition, the temporal distance between their occurrence and the relative order of their occurrence can make a difference in the final diagnosis.

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Spengler F order viagra 25mg on line erectile dysfunction treatment in bangkok, Roberts TPL buy 25 mg viagra overnight delivery impotence signs, Poeppel D, Byl N, Wang X, Rowley HA, Merzenich MM (1997) Learning transfer and neuronal plasticity in humans trained in tactile discrim- ination. Stevens JC, Foulke E, Patterson MQ (1996) Tactile acuity, aging and Braille reading in long term blindness. Stickgold R, James L, Hobson JA (2000) Visual discrimination learning requires sleep after training. Summers DC, Lederman SJ (1990) Perceptual asymmetries in the somatosensory system: a dichhaptic experiment and critical review of the literature from 1929 to 1986. Wang X, Merzenich MM, Sameshima K, Jenkins WM (1995) Remodelling of hand representation in adult cortex determined by timing of tactile stimulation. Werhahn K, Mortensen J, van Boven RW, Zeuner KE, Cohen LG (2002) Enhanced tactile spatial acuity and cortical processing during acute hand deafferentation. Westheimer G (1977) Spatial frequency and light spread descriptions of visual acuity and hyperacuity. Westheimer G (2001) Is peripheral visual acuity susceptible to perceptual learning in the adult? Zangaladze A, Epstein CM, Grafton ST, Sathian K (1999) Involvement of visual cortex in tactile discrimination of orientation. The Effects of Sensory 6 Deprivation on Sensory Function of SI Barrel Cortex Ford F. Comparison of Partial with Global Sensory Deprivation © 2005 by Taylor & Francis Group. OVERVIEW The somatic sensory system in rats and mice is very immature at the time of birth, and the final maturation of sensory processing mechanisms requires a certain level of sensory experience in the first few weeks after birth. Research has led to significant insights into the effect of low levels of postnatal activity arising from rodent whiskers on the development of cortical function. The data show that both excitatory and inhibitory processes are affected by sensory deprivation (SD), with the severity of effects depending upon the time of onset, the duration of the deprivation, and the length of the recovery period after deprivation ends. However, even after prolonged recovery periods some SD deficits do not recover completely even after the whiskers regrow to normal lengths. A major impact of SD leads to degraded circuit dynamics in intracortical connections: excitatory inputs do not modify cortical cell responses appropriately and inhibition becomes fixed at some level that is not adjusted up or down appropriately by neural activity. Neural transmission from thalamic input layer IV to more superficial layers II/III is a major site of synaptic dysfunction. Global deprivation (trimming all whiskers) produces a more uniform down-regulation of sensory transmission when compared to trimming a subset of whiskers, presumably because restricted deprivation creates competition between active and relatively inactive interconnected cell groups. This activity-based competition leads to more complex changes depending on the pattern of whisker trimming. In rat barrel cortex activity-based, changes in function can be induced by altered tactile experience throughout life. But early postnatal SD degrades neuronal plasticity in the mature brain and interferes with the ability to learn subtle tactile discriminations, presumably throughout life. INTRODUCTION In this chapter we focus on deficiencies that are induced by controlled manipulations of sensory activity in one sensory system; specifically by producing changes in the level of activity arising from the mystacial vibrissae (a. Since the literature on this system has grown very large, with rare exceptions we further restrict the topic to studies of © 2005 by Taylor & Francis Group. The literature on SD is difficult to compare without some interpretation, since few experiments have been carried out in the same way, or over equivalent periods of development, by different investigators. A brief definition of sensory deprivation leads to a discussion of some of the method- ological variables that affect SD results. We will provide a short description of the sensory pathways that convey whisker information to the cortex for those unfamiliar with the model system. The main review will summarize known effects of SD on the whisker-to-cortex sensory system from birth to adulthood. The final section will summarize our current understanding of the molecular mechanisms that are affected by SD. We assume that understanding inadequate activity levels will shed light on the neural functions that require typical levels and patterns of neural activity for normal development. Finally, the sparse evidence will be mentioned that describes the exploration of changes in subcortical structures after SD. Definition of Sensory Deprivation Levels of neural activity in sensory pathways change moment by moment throughout life, and can be disrupted by events as different as amputation of a limb or solitary confinement in a space that is nearly devoid of novel sensory stimuli. Here, we have restricted our definition of SD to “the effects of simple sensory disuse, without injury to the central or peripheral nervous system. Modifications of sensory processing, representational mapping and synaptic efficacy in neocortex are now well established to depend on the balance and intensity of sensory activity patterns at all ages, no longer being restricted to a functional critical period in early postnatal life.

These range from linear — Kalman filter order viagra 100mg fast delivery short term erectile dysfunction causes,31 Least Mean Squares32 — to nonlinear models cheap 50 mg viagra visa impotence of organic nature, such as Recurrent Artificial Neural Networks32 and Echo State Networks,33 among many others. Although it is beyond the scope of this chapter to go into the details of such models, it is important to mention that none of these algorithms significantly outperforms the Wiener filter. Results of the comparison showed no significant differences in performance between the Wiener filter and the rest of the models tested. Another of the reasons for using a linear model is that it permits us to understand the contributions of the individual neurons of the ensemble to the derived control signals, in contrast to the “black box” nature of the nonlinear algorithms. The main one is the overfitting introduced by the explosion in the number of free parameters as the number of sampled neurons increases — e. One way of minimizing this problem is by optimizing the size of the ensemble, selecting the most contributing neurons in a particular moment of time. For this purpose, we have developed methods for ascertaining the importance of neurons using single neuron correlation analysis, sensitivity analysis through a vector linear model, and directional tuning analysis. Another way of minimizing the number of free parameters is to reduce the number of time-lags by selecting them depending on the cortical area to which a neuron belongs; e. Also, since the purpose of a BMI is to work on-line, the hardware implementation of the models will need to be iterative; i. For this purpose, models based on the Wiener filter solution, such as the least-mean squares (LMS) adaptive filter, are ideal candidates. The latter is an important, yet largely unexplored avenue of research within the BMI field. The most common example of an “encoding BMI” is the widely known cochlear implant,38 in which an implanted device converts the frequency of sound waves into electrical impulses that stimulate the auditory nerve. Another example of encoding BMI is the visual neuroprosthesis, both at the retinal39 and cortical40 levels. However, the state of the art in these neuroprostheses is not as advanced in restoring sensory functionality as in the cochlear implant. Stimulating electrodes were implanted in the somatosensory cortex (S1) and the medial forebrain bundle (MFB), and stimulation was delivered by a remote-con- trolled microstimulator mounted on a backpack. Rats were guided through mazes and other environments by a combination of left and right stimulation cues in the Copyright © 2005 CRC Press LLC S1 whisker area of the right and left hemispheres, respectively, and with a reward signal in the MFB that enacted forward movements. However, for a realistic somatosensory neuroprosthesis, a larger set of “encoding commands” will be needed. For example, a motor task will require the encoding of sensory information from the artificial limb, including parameters such as limb position, velocity, and gripping force, among others. In order to be able to encode these parameters directly into the brain, a much deeper understanding about how sensory information is encoded in the brain is needed. In a BMI context we could think of a “library” of spatiotemporal stimulation patterns that would be applied to evoke particular sensory information in the brain. In this direction, Xu and colleagues are working on stimulation patterns in the rat thalamus that, when applied, will evoke selective and “natural” somatic perceptions. This finding suggests using these cortical responses as the target criteria for optimizing the thalamic stimuli. This form of somatosensory feedback allows the encoding of spatiotemporal patterns of vibration in the skin. Sandler and colleagues43 are currently looking at the electrophysiological changes that occur during conditional motor learning in owl monkeys using this kind of feedback. After training, the subject could learn to use this source of feedback as a source of information that is supplementary to visual feedback. Availability of this “soma- tosensory” feedback in a BMI could be very advantageous in real life situations where a clear visual perception of the artificial limb is absent. These include the type of brain signals17–19,44 (single unit, multiple unit, or field potentials) that would provide the optimal input for a such a device, and the number of single units (small [8–30]6,7 or substantially larger [hundreds to thousands]9,10) that may be necessary to operate a BMI efficiently for many years. These and other questions were investigated in our recent study in which we showed how macaque monkeys learned to use a BMI to reach and grasp virtual objects with a robot even in the absence of overt arm movement signals. Monkeys were implanted with multiple arrays (96 in monkey 1, and 320 in monkey 2) in several frontal and parietal cortical areas (PMd, M1, supplementary motor area [SMA], S1, and posterior parietal [PP]). In this study we used multiple linear models, similar to the one described in Section 1. Although all these parameters were extracted in real time in each session, only some of them were used to control the BMI, depending on each of the three tasks the monkeys had to solve in a given day. In each recording session, an initial 30-minute period was used for training of these models. During this period, monkeys used a handheld pole either to move a cursor on the screen or to change the cursor size by application of GF to the pole. As the models converged to an optimal performance, their coefficients were fixed and the control of the cursor position (tasks 1 and 3) and/or size (tasks 2 and 3) was obtained directly from the output of the linear models. During the brain control mode, animals initially produced arm movements, but they soon realized that these were not necessary and ceased to produce them for periods of time. Accurate performance was possible because large populations of neurons from multiple cortical areas were sampled, showing that large ensembles are preferable for efficient operation of a BMI. This conclusion is consistent with the notion that motor programming and execution is represented in a highly distributed fashion across frontal and parietal areas, and that each of these areas contains neurons that represent multiple motor parameters.

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