MPI for Biological Cybernetics


MPI for Biological Cybernetics

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Reichenbach A.,Magnetic Resonance Center | Whittingstall K.,MPI for Biological Cybernetics | Thielscher A.,Magnetic Resonance Center
NeuroImage | Year: 2011

Transcranial magnetic stimulation (TMS) can non-invasively modify cortical neural activity by means of a time-varying magnetic field. For example, in cognitive neuroscience, it is applied to create reversible "virtual lesions" in healthy humans (usually assessed as diminished performance in a behavioral task), thereby helping to establish causal structure-function relationships. Despite its widespread use, it is still rather unclear how TMS acts on existing, task-related neural activity, potentially resulting in a measurable effect on the behavioral level. Here, we deliver TMS to early visual areas while recording EEG in order to directly characterize the interaction between TMS-evoked (TEPs) and visual-evoked potentials (VEPs). Simultaneously, the subjects' performance is assessed in a visual forced-choice task. This allows us to compare the TMS effects on the VEPs across different levels of behavioral impairment. By systematically varying the stimulation intensity, we demonstrate that TMS strongly enhances the overall visual stimulus-related activity (rather than disrupting it). This enhancement effect saturates when behavior is impaired. This might indicate that the neural coding of the visual stimulus is robust to noise within a certain dynamic range (as indexed by the enhancement). Strong disturbances might saturate this range, causing behavioral impairment. Variation of the timing between the visual stimulus and the magnetic pulse reveals a "constructive interference" between the TEPs and VEPs: The better the overlap between both evoked potentials, the stronger the interaction effect when TMS and visual stimulation are combined. Importantly, however, this effect is uncorrelated with the strength of behavioral impairment. © 2010 Elsevier Inc.

Celicanin Z.,University of Basel | Auboiroux V.,University of Geneva | Bieri O.,University of Basel | Petrusca L.,University of Geneva | And 5 more authors.
Magnetic Resonance in Medicine | Year: 2014

Purpose: Magnetic resonance-guided high-intensity focused ultrasound is considered to be a promising treatment for localized cancer in abdominal organs such as liver, pancreas, or kidney. Abdominal motion, anatomical arrangement, and required sustained sonication are the main challenges. Methods: MR acquisition consisted of thermometry performed with segmented gradient-recalled echo echo-planar imaging, and a segment-based one-dimensional MR navigator parallel to the main axis of motion to track the organ motion. This tracking information was used in real-time for: (i) prospective motion correction of MR thermometry and (ii) HIFU focal point position lock-on target. Ex vivo experiments were performed on a sheep liver and a turkey pectoral muscle using a motion demonstrator, while in vivo experiments were conducted on two sheep liver. Results: Prospective motion correction of MR thermometry yielded good signal-to-noise ratio (range, 25 to 35) and low geometric distortion due to the use of segmented EPI. HIFU focal point lock-on target yielded isotropic in-plane thermal build-up. The feasibility of in vivo intercostal liver treatment was demonstrated in sheep. Conclusion: The presented method demonstrated in moving phantoms and breathing sheep accurate motioncompensated MR thermometry and precise HIFU focal point lock-on target using only real-time pencil-beam navigator tracking information, making it applicable without any pretreatment data acquisition or organ motion modeling. © 2013 Wiley Periodicals, Inc.

Gretton A.,MPI for Biological Cybernetics | Gretton A.,Carnegie Mellon University | Gyorfi L.,Budapest University of Technology and Economics
Journal of Machine Learning Research | Year: 2010

Three simple and explicit procedures for testing the independence of two multi-dimensional random variables are described. Two of the associated test statistics (L1, log-likelihood) are defined when the empirical distribution of the variables is restricted to finite partitions. A third test statistic is defined as a kernel-based independence measure. Two kinds of tests are provided. Distributionfree strong consistent tests are derived on the basis of large deviation bounds on the test statistics: these tests make almost surely no Type 1 or Type II error after a random sample size. Asymptotically α-level tests are obtained from the limiting distribution of the test statistics. For the latter tests, the Type I error converges to a fixed non-zero value a, and the Type II error drops to zero, for increasing sample size. All tests reject the null hypothesis of independence if the test statistics become large. The performance of the tests is evaluated experimentally on benchmark data. © 2010 Arthur Gretton and László Györfi.

Cherian A.,University of Minnesota | Sra S.,MPI for Biological Cybernetics | Papanikolopoulos N.,University of Minnesota
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings | Year: 2011

The idea of learning overcomplete dictionaries based on the paradigm of compressive sensing has found numerous applications, among which image denoising is considered one of the most successful. But many state-of-the-art denoising techniques inherently assume that the signal noise is Gaussian. We instead propose to learn overcomplete dictionaries where the signal is allowed to have both Gaussian and (sparse) Laplacian noise. Dictionary learning in this setting leads to a difficult non-convex optimization problem, which is further exacerbated by large input datasets. We tackle these difficulties by developing an efficient online algorithm that scales to data size. To assess the efficacy of our model, we apply it to dictionary learning for data that naturally satisfy our noise model, namely, Scale Invariant Feature Transform (SIFT) descriptors. For these data, we measure performance of the learned dictionary on the task of nearest-neighbor retrieval: compared to methods that do not explicitly model sparse noise our method exhibits superior performance. © 2011 IEEE.

Browatzki B.,MPI for Biological Cybernetics | Fischer J.,Fraunhofer Institute for Manufacturing Engineering and Automation | Graf B.,Fraunhofer Institute for Manufacturing Engineering and Automation | Bulthoff H.H.,MPI for Biological Cybernetics | Wallraven C.,Korea University
Proceedings of the IEEE International Conference on Computer Vision | Year: 2011

Categorization of objects solely based on shape and appearance is still a largely unresolved issue. With the advent of new sensor technologies*such as consumer-level range sensors*new possibilities for shape processing have become available for a range of new application domains. In the first part of this paper*we introduce a novel*large dataset containing 18 categories of objects found in typical household and office environmentswe envision this dataset to be useful in many applications ranging from robotics to computer vision. The second part of the paper presents computational experiments on object categorization with classifiers exploiting both two-dimensional and three-dimensional information. We evaluate categorization performance for both modalities in separate and combined representations and demonstrate the advantages of using range data for object and shape processing skills. © 2011 IEEE.

Sriperumbudur B.K.,University of California at San Diego | Gretton A.,MPI for Biological Cybernetics | Gretton A.,Carnegie Mellon University | Fukumizu K.,The Institute of Statistical Mathematics of Tokyo | And 2 more authors.
Journal of Machine Learning Research | Year: 2010

A Hilbert space embedding for probability measures has recently been proposed, with applications including dimensionality reduction, homogeneity testing, and independence testing. This embedding represents any probability measure as a mean element in a reproducing kernel Hilbert space (RKHS). A pseudometrie on the space of probability measures can be defined as the distance between distribution embeddings: we denote this as γk, indexed by the kernel function k that defines the inner product in the RKHS. We present three theoretical properties of γk. First, we consider the question of determining the conditions on the kernel k for which γk is a metric: such k are denoted characteristic kernels. Unlike pseudometrics, a metric is zero only when two distributions coincide, thus ensuring the RKHS embedding maps all distributions uniquely (i.e., the embedding is injective). While previously published conditions may apply only in restricted circumstances (e.g., on compact domains), and are difficult to check, our conditions are straightforward and intuitive: integrally strictly positive definite kernels are characteristic. Alternatively, if a bounded continuous kernel is translation-invariant on ℝd, then it is characteristic if and only if the support of its Fourier transform is the entire ℝd. Second, we show that the distance between distributions under γk results from an interplay between the properties of the kernel and the distributions, by demonstrating that distributions are close in the embedding space when their differences occur at higher frequencies. Third, to understand the nature of the topology induced by γk, we relate γk to other popular metrics on probability measures, and present conditions on the kernel k under which γk metrizes the weak topology. ©2010 Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard Schölkopf and Gert R. G. Lanckriet.

Gomez-Rodriguez M.,MPI for Biological Cybernetics | Gomez-Rodriguez M.,Stanford University | Peters J.,MPI for Biological Cybernetics | Hill J.,MPI for Biological Cybernetics | And 3 more authors.
Journal of Neural Engineering | Year: 2011

The combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs. © 2011 IOP Publishing Ltd.

Celicanin Z.,University of Basel | Bieri O.,University of Basel | Preiswerk F.,University of Basel | Cattin P.,University of Basel | And 3 more authors.
Magnetic Resonance in Medicine | Year: 2015

Purpose: Respiratory organ motion is still the major challenge of various image-guided treatments in the abdomen. Dynamic organ motion tracking, necessary for the treatment control, can be performed with volumetric time-resolved MRI that sequentially acquires one image and one navigator slice. Here, a novel imaging method is proposed for truly simultaneous high temporal resolution acquisition. Methods: A standard balanced steady state free precession sequence was modified to simultaneously acquire two superimposed slices with different phase cycles, namely an image and a navigator slice. Instead of multiband RF pulses, two separate RF pulses were used for the excitation. Images were reconstructed using offline CAIPIRINHA reconstruction. Phantom and in vivo measurements of healthy volunteers were performed and evaluated. Results: Phantom and in vivo measurements showed good image quality with high signal-to-noise ratio (SNR) and no reconstruction issues. Conclusion: We present a novel imaging method for truly simultaneous acquisition of image and navigator slices for four-dimensional (4D) MRI of organ motion. In this method, the time lag between the sequential acquisitions is eliminated, leading to an improved accuracy of organ motion models, while CAIPIRINHA reconstruction results in an improved SNR compared with an existing 4D MRI approach. © 2014 Wiley Periodicals, Inc.

Hirsch M.,MPI for Biological Cybernetics | Sra S.,MPI for Biological Cybernetics | Scholkopf B.,MPI for Biological Cybernetics | Harmeling S.,MPI for Biological Cybernetics
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2010

Ultimately being motivated by facilitating space-variant blind deconvolution, we present a class of linear transformations, that are expressive enough for space-variant filters, but at the same time especially designed for efficient matrix-vector-multiplications. Successful results on astronomical imaging through atmospheric turbulences and on noisy magnetic resonance images of constantly moving objects demonstrate the practical significance of our approach. ©2010 IEEE.

Harmeling S.,MPI for Biological Cybernetics | Sra S.,MPI for Biological Cybernetics | Hirsch M.,MPI for Biological Cybernetics | Scholkopf B.,MPI for Biological Cybernetics
Proceedings - International Conference on Image Processing, ICIP | Year: 2010

We formulate the multiframe blind deconvolution problem in an incremental expectation maximization (EM) framework. Beyond deconvolution, we show how to use the same framework to address: (i) super-resolution despite noise and unknown blurring; (ii) saturation-correction of overexposed pixels that confound image restoration. The abundance of data allows us to address both of these without using explicit image or blur priors. The end result is a simple but effective algorithm with no hyperparameters. We apply this algorithm to real-world images from astronomy and to super resolution tasks: for both, our algorithm yields increased resolution and deconvolved images simultaneously. © 2010 IEEE.

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