University of Tübingen

About: University of Tübingen is a education organization based out in Tübingen, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 40555 authors who have published 84108 publications receiving 3015320 citations. The organization is also known as: Eberhard Karls University & Eberhard-Karls-Universität Tübingen.

Activation of Cortical and Cerebellar Motor Areas during Executed and Imagined Hand Movements: An fMRI Study

Abstract: Brain activation during executed (EM) and imagined movements (IM) of the right and left hand was studied in 10 healthy right-handed subjects using functional magnetic resonance imagining (fMRI). Low electromyographic (EMG) activity of the musculi flexor digitorum superficialis and high vividness of the imagined movements were trained prior to image acquisition. Regional cerebral activation was measured by fMRI during EM and IM and compared to resting conditions. Anatomically selected regions of interest (ROIs) were marked interactively over the entire brain. In each ROI activated pixels above a t value of 2.45 (p < 0.01) were counted and analyzed. In all subjects the supplementary motor area (SMA), the premotor cortex (PMC), and the primary motor cortex (M1) showed significant activation during both EM and IM; the somatosen sory cortex (S1) was significantly activated only during EM. Ipsilateral cerebellar activation was decreased during IM compared to EM. In the cerebellum, IM and EM differed in their foci of maximal activation: Highest ipsilateral activation of the cerebellum was observed in the anterior lobe (Larsell lobule H IV) during EM, whereas a lower maximum was found about 2-cm dorsolateral (Larsell lobule H VII) during IM. The prefrontal and parietal regions revealed no significant changes during both conditions. The results of cortical activity support the hypothesis that motor imagery and motor performance possess similar neural substrates. The differential activation in the cerebellum during EM and IM is in accordance with the assumption that the posterior cerebellum is involved in the inhibition of movement execution during imagination.

A note on the evaluation of generative models

Abstract: Probabilistic generative models can be used for compression, denoising, inpainting, texture synthesis, semi-supervised learning, unsupervised feature learning, and other tasks. Given this wide range of applications, it is not surprising that a lot of heterogeneity exists in the way these models are formulated, trained, and evaluated. As a consequence, direct comparison between models is often difficult. This article reviews mostly known but often underappreciated properties relating to the evaluation and interpretation of generative models with a focus on image models. In particular, we show that three of the currently most commonly used criteria---average log-likelihood, Parzen window estimates, and visual fidelity of samples---are largely independent of each other when the data is high-dimensional. Good performance with respect to one criterion therefore need not imply good performance with respect to the other criteria. Our results show that extrapolation from one criterion to another is not warranted and generative models need to be evaluated directly with respect to the application(s) they were intended for. In addition, we provide examples demonstrating that Parzen window estimates should generally be avoided.