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While the ecological validity of virtual reality (VR) applications is usually assessed by behavioral data or interrogation, an alternative approach on a neuronal level is offered by brain imaging methods. Because it is yet unclear if 3D space in virtual environments is processed analogically to the real world, we conducted a study investigating virtual spatial processing in the brain using functional magnetic resonance imaging (fMRI). Results show differences in VR spatial brain processing as compared to known brain activations in reality. Identifying differences and commonalities of brain processing in VR reveals limitations and holds important implications for VR therapy and training tools. When VR therapy aims at the rehabilitation of brain function and activity, differences in brain processing have to be taken into account for designing effective VR training tools. Furthermore, for an evaluation of possible restoration effects caused by VR training, it is necessary to integrate information about the brain activation networks elicited by the training. The present study provides an example for demonstrating the benefit of fMRI as an evaluation tool for the mental processes involved in virtual environments.

Antiparkinsonian drugs are dopamine agonists and they have been reported to increase striatal acetylcholine (ACh) concentrations. U-91356A is a D2 selective dopamine agonist. Therefore, it was investigated for its effect on rat striatal ACh concentration, and the results were compared with those obtained with pergolide and quinpirole under similar conditions. U-91356A was as potent as pergolide and less potent than quinpirole in increasing striatal ACh in non-reserpinized rats. The intrinsic activity of U-91356A was significantly (P < 0.03) greater than pergolide in non-reserpinized rats. In reserpinized rats, the potency of investigated dopamine agonists was similar for elevating ACh levels, and intrinsic activity of U-91356A (P < 0.01) was higher as compared to that obtained with quinpirole or pergolide. In the unilateral substantia nigra lesioned animals, the intrinsic activity of U-91356A for increasing striatal ACh was significantly (P < 0.01) higher in the denervated, as compared to innervated, striatum. The supersensitive response of quinpirole and pergolide for increasing striatal ACh was also observed on the lesioned as compared to intact side. Haloperidol significantly (P < 0.01) blocked U-91356A-induced elevation in striatal ACh in non-reserpinized rats. Based on these results, U-91356A appears to be a potent agonist of striatal post-synaptic D2 dopamine receptors and is expected to be an effective anti-parkinsonian drug. © 1996 Wiley-Liss, Inc.

The prevalence of hyperuricemia is low in Uygurs, who have a high prevalence of cardiovascular risk factors such as hypertension, overweight–obesity, dyslipidemia, hyperglycemia, and insulin resistance (IR). This study sought to investigate the relationships between serum uric acid (UA) and these risk factors in this population. A cross-sectional study was conducted in Uygurs (859 males, 1268 females) aged 20 to 70 years. Demographic data, systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), and fasting and postprandial blood were obtained, and biological measurements were determined. The mean of BMI, SBP, DBP, total cholesterol, high-density lipoprotein cholesterol (HDL-c), low-density lipoprotein cholesterol (LDL-c), triglycerides, fasting blood glucose, fasting insulin, and homeostasis model assessment insulin resistance index (HOMA-IR), and the prevalence of hypertension, IR, hyperglycemia, overweight–obesity, hypercholesteremia, hyper-LDL-c, and hypertriglyceridemia increased with UA but the prevalence of hypo-HDL-c decreased (p < 0.05). Logistic regression analysis showed that the odds ratios for IR, overweight–obesity, hypercholesteremia, hyper-LDL-c, and hypertriglyceridemia against the lowest UA group increased but decreased for hypo-HDL-c (p < 0.05). The UA in the hypo-HDL-c group was lower than that of the controls; the prevalence of hypo-HDL-c in hyperuricemia subjects was lower than in those with normal UA (p < 0.05). But the opposite results were observed between overweight–obesity, hyperglycemia, IR, hypercholesteremia, hypertriglyceridemia, and hyper-LDL-c and correspondence controls, respectively (p < 0.05). In Uygur, elevated UA is associated with overweight–obesity, hypercholesteremia, hyper-LDL-c, hypertriglyceridemia, hyperglycemia, and IR. The HDL-c level significantly increases with UA, whereas the prevalence of hypo-HDL-c decreases. Further studies are needed to clarify why UA is positively correlated to HDL-c.

Combining Virtual Reality (VR) and functional magnetic resonance imaging (fMRI) offers great possibilities to researchers. Brain activation in VR can be studied and more realistic stimuli can be used in fMRI studies. Unfortunately, no standard solution exists for the combination of both methods. As part of an interdisciplinary project addressing the diagnostics and treatment of neglect, we created a neuroscientific VR-fMRI experiment. Our experiences are reported in this paper. The description of problems and our solution are intended as substantial facilitation and help for other researchers interested in creating VR-fMRI experiments.

We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constraint set to prove that the corresponding LP relaxation solves the original integer program. We then exploit connections to combinatorial optimization and develop an efficient primal-dual algorithm, scalable to large datasets. In contrast to our bottom-up approach, which benefits from good RF initialization, conventional methods are top-down acquiring features based on their utility value and is generally intractable, requiring heuristics. Empirically, our pruning algorithm outperforms existing state-of-the-art resource-constrained algorithms.