Frank C Meinecke

Publications of Frank C Meinecke

• Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions.

  Authors: Felix Bießmann, Yusuke Murayama, Nikos K Logothetis, Klaus-Robert Müller, Frank C Meinecke

  NeuroImage. 04/2012;

  The goal of most functional Magnetic Resonance Imaging (fMRI) analyses is to investigate neural activity. Many fMRI analysis methods assume that the temporal dynamics of the hemodynamic responseThe goal of most functional Magnetic Resonance Imaging (fMRI) analyses is to investigate neural activity. Many fMRI analysis methods assume that the temporal dynamics of the hemodynamic response function (HRF) to neural activation is separable from its spatial dynamics. Although there is empirical evidence that the HRF is more complex than suggested by space-time separable canonical HRF models, it is difficult to assess how much information about neural activity is lost when assuming space-time separability. In this study we directly test whether spatiotemporal variability in the HRF that is not captured by separable models contains information about neural signals. We predict intracranially measured neural activity from simultaneously recorded fMRI data using separable and non-separable spatiotemporal deconvolutions of voxel time series around the recording electrode. Our results show that abandoning the spatiotemporal separability assumption consistently improves the decoding accuracy of neural signals from fMRI data. We compare our findings with results from optical imaging and fMRI studies and discuss potential implications for classical fMRI analyses without invasive electrophysiological recordings.

• Estimating true brain connectivity from EEG/MEG data invariant to linear and static transformations in sensor space.

  Authors: Arne Ewald, Laura Marzetti, Filippo Zappasodi, Frank C Meinecke, Guido Nolte

  NeuroImage. 12/2011; 60(1):476-88.

  The imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sourcesThe imaginary part of coherency is a measure to investigate the synchronization of brain sources on the EEG/MEG sensor level, robust to artifacts of volume conduction meaning that independent sources cannot generate a significant result. It does not mean, however, that volume conduction is irrelevant when true interactions are present. Here, we analyze in detail the possibilities to construct measures of true brain interactions which are strictly invariant to linear spatial transformations of the sensor data. Specifically, such measures can be constructed from maximization of imaginary coherency in virtual channels, bivariate measures as a corrected variate of imaginary coherence, and global measures indicating the total interaction contained within a space or between two spaces. A complete theoretic framework on this question is provided for second order statistical moments. Relations to existing linear and nonlinear approaches are presented. We applied the methods to resting state EEG data, showing clear interactions at all bands, and to a combined measurement of EEG and MEG during rest condition and a finger tapping task. We found that MEG was capable of observing brain interactions which were not observable in the EEG data.

• Analysis of multimodal neuroimaging data.

  Authors: Felix Biessmann, Sergey Plis, Frank C Meinecke, Tom Eichele, Klaus-Robert Müller

  IEEE reviews in biomedical engineering. 01/2011; 4:26-58.

  Each method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings ofEach method for imaging brain activity has technical or physiological limits. Thus, combinations of neuroimaging modalities that can alleviate these limitations such as simultaneous recordings of neurophysiological and hemodynamic activity have become increasingly popular. Multimodal imaging setups can take advantage of complementary views on neural activity and enhance our understanding about how neural information processing is reflected in each modality. However, dedicated analysis methods are needed to exploit the potential of multimodal methods. Many solutions to this data integration problem have been proposed, which often renders both comparisons of results and the choice of the right method for the data at hand difficult. In this review we will discuss different multimodal neuroimaging setups, the advances achieved in basic research and clinical application and the methods used. We will provide a comprehensive overview of mathematical tools reoccurring in multimodal neuroimaging studies for artifact removal, data-driven and model-driven analyses, enabling the practitioner to try established or new combinations from these algorithmic building blocks.

• An Information Geometrical View of Stationary Subspace Analysis.

  Authors: Motoaki Kawanabe, Wojciech Samek, Paul von Bünau, Frank C. Meinecke

  Artificial Neural Networks and Machine Learning - ICANN 2011 - 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part II; 01/2011

• Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA.

  Authors: Yusuke Murayama, Felix Biessmann, Frank C Meinecke, Klaus-Robert Müller, Mark Augath, Axel Oeltermann, Nikos K Logothetis

  Magnetic resonance imaging. 10/2010; 28(8):1095-103.

  Functional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the largeFunctional magnetic resonance imaging (fMRI) based on the so-called blood oxygen level-dependent (BOLD) contrast is a powerful tool for studying brain function not only locally but also on the large scale. Most studies assume a simple relationship between neural and BOLD activity, in spite of the fact that it is important to elucidate how the "when" and "what" components of neural activity are correlated to the "where" of fMRI data. Here we conducted simultaneous recordings of neural and BOLD signal fluctuations in primary visual (V1) cortex of anesthetized monkeys. We explored the neurovascular relationship during periods of spontaneous activity by using temporal kernel canonical correlation analysis (tkCCA). tkCCA is a multivariate method that can take into account any features in the signals that univariate analysis cannot. The method detects filters in voxel space (for fMRI data) and in frequency-time space (for neural data) that maximize the neurovascular correlation without any assumption of a hemodynamic response function (HRF). Our results showed a positive neurovascular coupling with a lag of 4-5 s and a larger contribution from local field potentials (LFPs) in the γ range than from low-frequency LFPs or spiking activity. The method also detected a higher correlation around the recording site in the concurrent spatial map, even though the pattern covered most of the occipital part of V1. These results are consistent with those of previous studies and represent the first multivariate analysis of intracranial electrophysiology and high-resolution fMRI.

• Finding stationary brain sources in EEG data.

  Authors: Paul von Bunau, Frank C Meinecke, Simon Scholler, Klaus-Robert Muller

  Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference. 01/2010; 2010:2810-3.

  Neurophysiological measurements obtained from e.g. EEG or fMRI are inherently non-stationary because the properties of the underlying brain processes vary over time. For example, inNeurophysiological measurements obtained from e.g. EEG or fMRI are inherently non-stationary because the properties of the underlying brain processes vary over time. For example, in Brain-Computer-Interfacing (BCI), deteriorating performance (bitrate) is a common phenomenon since the parameters determined during the calibration phase can be suboptimal under the application regime, where the brain state is different, e.g. due to increased tiredness or changes in the experimental paradigm. We show that Stationary Subspace Analysis (SSA), a time series analysis method, can be used to identify the underlying stationary and non-stationary brain sources from high-dimensional EEG measurements. Restricting the BCI to the stationary sources found by SSA can significantly increase the performance. Moreover, SSA yields topographic maps corresponding to stationary- and non-stationary brain sources which reveal their spatial characteristics.

• Finding stationary subspaces in multivariate time series.

  Authors: Paul von Bünau, Frank C Meinecke, Franz C Király, Klaus-Robert Müller

  Physical review letters. 11/2009; 103(21):214101.

  Identifying temporally invariant components in complex multivariate time series is key to understanding the underlying dynamical system and predict its future behavior. In this Letter, we propose aIdentifying temporally invariant components in complex multivariate time series is key to understanding the underlying dynamical system and predict its future behavior. In this Letter, we propose a novel technique, stationary subspace analysis (SSA), that decomposes a multivariate time series into its stationary and nonstationary part. The method is based on two assumptions: (a) the observed signals are linear superpositions of stationary and nonstationary sources; and (b) the nonstationarity is measurable in the first two moments. We characterize theoretical and practical properties of SSA and study it in simulations and cortical signals measured by electroencephalography. Here, SSA succeeds in finding stationary components that lead to a significantly improved prediction accuracy and meaningful topographic maps which contribute to a better understanding of the underlying nonstationary brain processes.

• Stationary Subspace Analysis.

  Authors: Paul von Bünau, Frank C. Meinecke, Klaus-Robert Müller

  Independent Component Analysis and Signal Separation, 8th International Conference, ICA 2009, Paraty, Brazil, March 15-18, 2009. Proceedings; 01/2009

• Identifying interactions in mixed and noisy complex systems.

  Authors: Guido Nolte, Frank C Meinecke, Andreas Ziehe, Klaus-Robert Müller

  Physical review. E, Statistical, nonlinear, and soft matter physics. 06/2006; 73(5 Pt 1):051913.

  We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources.We present a technique that identifies truly interacting subsystems of a complex system from multichannel data if the recordings are an unknown linear and instantaneous mixture of the true sources. The method is valid for arbitrary noise structure. For this, a blind source separation technique is proposed that diagonalizes antisymmetrized cross-correlation or cross-spectral matrices. The resulting decomposition finds truly interacting subsystems blindly and suppresses any spurious interaction stemming from the mixture. The usefulness of this interacting source analysis is demonstrated in simulations and for real electroencephalography data.

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• From outliers to prototypes: Ordering data.

  Authors: Stefan Harmeling, Guido Dornhege, David M. J. Tax, Frank C. Meinecke, Klaus-Robert Müller

  Neurocomputing. 01/2006; 69:1608-1618.

• Measuring phase synchronization of superimposed signals.

  Authors: Frank C Meinecke, Andreas Ziehe, Jürgen Kurths, Klaus-Robert Müller

  Physical review letters. 04/2005; 94(8):084102.

  Phase synchronization is an important phenomenon that occurs in a wide variety of complex oscillatory processes. Measuring phase synchronization can therefore help to gain fundamental insight intoPhase synchronization is an important phenomenon that occurs in a wide variety of complex oscillatory processes. Measuring phase synchronization can therefore help to gain fundamental insight into nature. In this Letter we point out that synchronization analysis techniques can detect spurious synchronization, if they are fed with a superposition of signals such as in electroencephalography or magnetoencephalography data. We show how techniques from blind source separation can help to nevertheless measure the true synchronization and avoid such pitfalls.

• Analyzing Coupled Brain Sources: Distinguishing True from Spurious Interaction.

  Authors: Guido Nolte, Andreas Ziehe, Frank C. Meinecke, Klaus-Robert Müller

  Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]; 01/2005

• Robust ICA for Super-Gaussian Sources.

  Authors: Frank C. Meinecke, Stefan Harmeling, Klaus-Robert Müller

  Independent Component Analysis and Blind Signal Separation, Fifth International Conference, ICA 2004, Granada, Spain, September 22-24, 2004, Proceedings; 01/2004

• Blind Source Separation Techniques for Decomposing Event-Related Brain Signals.

  Authors: Klaus-Robert Müller, Ricardo Vigário, Frank C. Meinecke, Andreas Ziehe

  I. J. Bifurcation and Chaos. 01/2004; 14:773-791.

• Injecting noise for analysing the stability of ICA components.

  Authors: Stefan Harmeling, Frank C. Meinecke, Klaus-Robert Müller

  Signal Processing. 01/2004; 84:255-266.

• Estimating the Reliability of ICA Projections.

  Authors: Frank C. Meinecke, Andreas Ziehe, Motoaki Kawanabe, Klaus-Robert Müller

  Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, NIPS 2001, December 3-8, 2001, Vancouver, British Columbia, Canada]; 01/2001