@Article{ BiessmannMLMM2012,
title = {Improved decoding of neural activity from fMRI signals using non-separable spatiotemporal deconvolutions},
journal = {NeuroImage},
year = {2012},
month = {7},
volume = {61},
number = {4},
pages = {1031–1042},
abstract = {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 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.},
web_url = {http://www.sciencedirect.com/science/article/pii/S1053811912003965},
state = {published},
DOI = {10.1016/j.neuroimage.2012.04.015},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Murayama Y{yusuke}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes}, M\"uller KR{klaus} and Meinecke FC}
}

@Article{ 6272,
title = {Relationship between neural and hemodynamic signals during spontaneous activity studied with temporal kernel CCA},
journal = {Magnetic Resonance Imaging},
year = {2010},
month = {10},
volume = {28},
number = {8},
pages = {1095-1103},
abstract = {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 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.},
web_url = {http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6T9D-4Y6T7WF-2-C&_cdi=5112&_user=29041&_pii=S0730725X09003087&_orig=search&_coverDate=01%2F21%2F2010&_sk=999999999&view=c&wchp},
state = {published},
DOI = {10.1016/j.mri.2009.12.016},
author = {Murayama Y{yusuke}{Department Physiology of Cognitive Processes}, Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Meinecke FC, M\"uller K-R{klaus}, Augath M{mark}{Department Physiology of Cognitive Processes}, Oeltermann A{axel} and Logothetis NK{nikos}{Department Physiology of Cognitive Processes}}
}

@Article{ 6134,
title = {Temporal Kernel CCA and its Application in Multimodal Neuronal Data Analysis},
journal = {Machine Learning},
year = {2010},
month = {5},
volume = {79},
number = {1-2},
pages = {5-27},
abstract = {Data recorded from multiple sources sometimes exhibit non-instantaneous couplings. For simple data sets, cross-correlograms may reveal the coupling dynamics. But when dealing with high-dimensional multivariate data there is no such measure as the cross-correlogram. We propose a simple algorithm based on Kernel Canonical Correlation Analysis (kCCA) that computes a multivariate temporal filter which links one data modality to another one. The filters can be used to compute a multivariate extension of the cross-correlogram, the canonical correlogram, between data sources that have different dimensionalities and temporal resolutions. The canonical correlogram reflects the coupling dynamics between the two sources. The temporal filter reveals which features in the data give rise to these couplings and when they do so. We present results from simulations and neuroscientific experiments showing that tkCCA yields easily interpretable temporal filters and correlograms. In the experiments, we simultaneously performed electrode recordings and functional magnetic resonance imaging (fMRI) in primary visual cortex of the non-human primate. While electrode recordings reflect brain activity directly, fMRI provides only an indirect view of neural activity via the Blood Oxygen Level Dependent (BOLD) response. Thus it is crucial for our understanding and the interpretation of fMRI signals in general to relate them to direct measures of neural activity acquired with electrodes. The results computed by tkCCA confirm recent models of the hemodynamic response to neural activity and allow for a more detailed analysis of neurovascular coupling dynamics.},
file_url = {/fileadmin/user_upload/files/publications/Machine-Learning-2009-Biessmann_[0].pdf},
web_url = {http://www.springerlink.com/content/e1425487365v2227/fulltext.pdf},
state = {published},
DOI = {10.1007/s10994-009-5153-3},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Meinecke FC, Gretton A{arthur}{Department Empirical Inference}, Rauch A{arauch}{Department Physiology of Cognitive Processes}, Rainer G{gregor}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and M\"uller K-R{klaus}{Department Empirical Inference}}
}

@Inproceedings{ 5433,
title = {Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance},
journal = {Advances in neural information processing systems 21 : 22nd Annual Conference on Neural Information Processing Systems 2008},
year = {2009},
month = {6},
pages = {665-672},
abstract = {From an information-theoretic perspective, a noisy transmission system such as a visual Brain-Computer Interface (BCI) speller could benefit from the use of errorcorrecting codes. However, optimizing the code solely according to the maximal minimum-Hamming-distance criterion tends to lead to an overall increase in target frequency of target stimuli, and hence a significantly reduced average
target-to-target interval (TTI), leading to difficulties in classifying the individual event-related potentials (ERPs) due to overlap and refractory effects. Clearly any change to the stimulus setup must also respect the possible psychophysiological consequences. Here we report new EEG data from experiments in which we explore stimulus types and codebooks in a within-subject design, finding an interaction between the two factors. Our data demonstrate that the traditional, rowcolumn code has particular spatial properties that lead to better performance than one would expect from its TTIs and Hamming-distances alone, but nonetheless error-correcting codes can improve performance provided the right stimulus type is used.},
file_url = {/fileadmin/user_upload/files/publications/ecspeller_5433[0].pdf},
file_url2 = {/fileadmin/user_upload/files/publications/hill-etal-nips2008-poster_5433[1].pdf},
web_url = {http://books.nips.cc/nips21.html},
editor = {Koller, D. , D. Schuurmans, Y. Bengio, L. Bottou},
publisher = {Curran},
address = {Red Hook, NY, USA},
booktitle = {Advances in neural information processing systems 21},
event_name = {Twenty-Second Annual Conference on Neural Information Processing Systems (NIPS 2008)},
event_place = {Vancouver, BC, Canada},
state = {published},
ISBN = {978-1-605-60949-2},
author = {Hill J{jez}{Department Empirical Inference}, Farquhar J{jdrf}{Department Empirical Inference}, Martens SMM{smartens}{Department Empirical Inference}, Biessmann F{fbiessma} and Sch\"olkopf B{bs}{Department Empirical Inference}}
}

@Techreport{ 4477,
title = {New Methods for the P300 Visual Speller},
year = {2006},
month = {11},
number = {1},
file_url = {/fileadmin/user_upload/files/publications/lab_rotation_[0].pdf},
editor = {Hill, J.},
state = {published},
author = {Biessmann F{fbiessma}}
}

@Poster{ BiessmannGMZRLMR2013,
title = {Investigating neurovascular coupling using canonical correlation analysis between pharmacological MRI and electrophysiology},
journal = {BMC Neuroscience},
year = {2013},
volume = {10},
number = {Suppl 1},
pages = {P86},
abstract = {Despite its young age, functional Magnetic Resonance Imaging (fMRI) has become one of the most popular brain imaging techniques. However, the relationship between brain activity and the blood oxygen level dependent (BOLD) contrast as measured with fMRI, the so called neurovascular coupling, is not yet fully understood. One possibility of experimentally manipulating the neurovascular coupling mechanisms is administration of vaso-active and neuro-active substances, such as Acetylcholine (ACh). Combining those pharmacological interventions with simultaneous measurements of electrophysiological and BOLD response allows for deeper insights in the dependencies between neural and hemodynamic response to sensory stimulation.

We developed a method based on kernel canonical correlation analysis that is able to deal with the high dimensionality of the fMRI signal while exploiting the high temporal resolution of the electrophysiology. The algorithm finds filters for fMRI and electrophysiological data that maximize the crosscorrelation between the two data sources. Projecting the data onto those filters allows to compute a crosscorrelation function between fMRI and electrophysiological data that reflects the coupling between neural and hemodynamic response. We present data recorded in primary visual cortex of the non-human primate during a visual stimulation paradigm and local application of ACh. Comparing the neurovascular crosscorrellograms after local injections of ACh and with those from control conditions, we find that the coupling is dramatically affected by ACh. In particular, the extent to which the stimulus is reflected in the crosscorrelation function is decreased under influence of ACh. Inspection of the spatial filters of the BOLD response shows that this change is primarily accounted for by cholinergic effects on voxels around the injection site. The filters computed for the neurophysiological data suggest that it is mainly neural activity in the alpha and gamma band that contributes to the change in coupling.
In summary, the results provide preliminary evidence for a change in neurovascular coupling induced by high levels of ACh. The voxel patterns (for fMRI data) and patterns in the time-frequency domain (for electrophysiological data) that give rise to this change can be revealed using a novel analysis method.},
web_url = {http://www.biomedcentral.com/1471-2202/10/S1/P86},
event_name = {Eighteenth Annual Computational Neuroscience Meeting (CNS*2009)},
event_place = {Berlin, Germany},
state = {published},
DOI = {10.1186/1471-2202-10-S1-P86},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Gretton A{arthur}{Department Empirical Inference}, Meinecke FC, Zhang X{xiaozhe}{Department Physiology of Cognitive Processes}, Rainer G{gregor}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes}, M\"uller K-R{klaus}{Department Empirical Inference} and Rauch A{arauch}{Department Physiology of Cognitive Processes}}
}

@Poster{ 6813,
title = {Spatio-Temporal Coupling between Neural Activity and Bold Response in Primary Visual Cortex},
year = {2010},
month = {6},
volume = {2010},
pages = {51},
abstract = {Neural activity in the brain is correlated with the blood-oxygen level dependent (BOLD) contrast
which can be measured non-invasively by functional magnetic resonance imaging (fMRI).
Up to date, many fMRI analysis methods are based on simplifying assumptions about the BOLD
signal. Two popular assumptions are spatial independence and homogeneity of the haemodynamic
response function (HRF) across voxels. As single voxels usually are not independent
and moreover also exhibit different haemodynamic response characteristics, these assumptions
might lead astray interpretations of fMRI data.
In this study we present an analysis framework that reveals the spatio-temporal correlation
structure between simultaneously measured intracortical neurophysiological activity in primary
visual cortex of the non-human primate and BOLD response. Given the spectrograms of
neurophysiological activity and the simultaneously recorded BOLD data we compute a spatiotemporal
convolution that links the activity measured at the electrode to the multivariate
BOLD response. The convolution can be interpreted as the pattern in time-voxel space that
reflects best the neural activity as it maximises the canonical correlation [1] between neural
and haemodynamic activity.
Inspection of the estimated time-voxel patterns yields new insights in the spatio-temporal dependency
structure of neurovascular coupling mechanisms. This study thereby extends previous
results reported in [2,3], where the convolution was a time-frequency convolution estimated
for the neurophysiological activity.
We show results from data collected during spontaneous activity and during visual stimulation.
The analysis resulted in robust spatio-temporal coupling patterns across different experimental
conditions. We compared the multivariate patterns with univariate coupling measures and
spatial principal component analysis (SPCA), a popular method for connectivity analysis on
fMRI data. Our findings suggest that neither univariate methods nor unimodal methods such
as SPCA, which are based on autocorrelations of fMRI data only, can recover the multivariate
spatio-temporal coupling structure in primary visual cortex.},
web_url = {http://www.areadne.org/2010/home.html},
editor = {Hatsopoulos, N. G., S. Pezaris},
event_name = {AREADNE 2010: Research in Encoding And Decoding of Neural Ensembles},
event_place = {Santorini, Greece},
state = {published},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Murayama Y{yusuke}{Department Physiology of Cognitive Processes}, Meinecke FC, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and M\"uller K-R{klaus}}
}

@Poster{ BiessmannMBVKMR2010,
title = {Comparison of V1 receptive fields mapped with spikes and local field potentials},
journal = {Frontiers in Neuroscience},
year = {2010},
month = {2},
volume = {Conference Abstract: Computational and Systems Neuroscience 2010},
abstract = {Extracellular neurophysiological recordings are typically separated in two frequency bands. Low frequency content, also called local field potentials (LFPs), reflect subthreshold integrative processes of a population of neurons. High frequency content, or multi-unit activity (MUA), contains the information conveyed by action potentials, or spikes. Spikes reflect neuronal output and are generally considered as the main currency of information in the brain. For a long time receptive field mapping methods have focused exclusively on spiking information, although some recent studies have begun to address spatial characterstics of LFP responses (Xing/Yeh/Shapley, 2009, J Neurosci). In order to compare the information about visual stimuli carried by the LFP signal and spiking activity we mapped receptive fields in primary visual cortex of the tree shrew using spike count and LFP timeseries recorded at different cortical depths. We presented white noise checkerboard patterns and sparse noise patterns and computed the standard spike triggered average (STA) receptive fields. Moreover we extracted the LFP timeseries, in different frequency bands, and the spike histograms following each stimulus and computed receptive fields for each signal employing standard canonical correlation analysis (CCA) between stimulus and LFP and spike response, respectively. Receptive fields as estimated from LFP data have two main advantages over traditional STA estimates. For one, LFP receptive fields do not suffer from binning artefacts, in contrast to STA receptive fields. Besides, CCA allows for computing a temporal filter for the respective neural signal. Receptive fields estimated using spikes were very similar to those computed from LFP signals, also for LFP bands below 20Hz. In particular the spatial extent of receptive fields computed from LFPs was comparable to that of spikes, in line with previous studies reporting a small spatial focus of LFP selectivity (Katzner et al. 2009, Neuron, Xing/Yeh/Shapley, 2009, J Neurosci). The receptive field size of both LFP and spikes varied with cortical depth. In summary our results confirm that in early stages of the visual processing hierarchy LFP signals contain to a large extent the same information about the visual stimulus as the spiking activity. In line with the above mentioned studies on non-human primates our findings suggest that the spatial selectivity of LFP signals with respect to the visual stimulus is comparable to that of spikes.},
web_url = {http://www.frontiersin.org/10.3389/conf.fnins.2010.03.00101/event_abstract},
event_name = {Computational and Systems Neuroscience Meeting (COSYNE 2010)},
event_place = {Salt Lake City, UT, USA},
state = {published},
DOI = {10.3389/conf.fnins.2010.03.00101},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Meinecke F, Bhattacharyya A{anwesha}{Department Physiology of Cognitive Processes}, Veit J{jveit}{Department Physiology of Cognitive Processes}, Kretz R, M\"uller K-R{klaus}{Department Empirical Inference} and Rainer G{gregor}}
}

@Poster{ 5336,
title = {Investigating the relationship between pharmacological MRI and electrophysiology using Canonical Correlation Analysis},
year = {2008},
month = {7},
volume = {6},
number = {123.3},
abstract = {Pharmacological MRI (phMRI) is a rapidly advancing field whose goal it is to map the modulatory effects of pharmacological agents on the large-scale brain networks that underlie cognition. However, the relation between these effects on functional imaging signals and the underlying neural activity is unclear. We have combined phMRI with electrophysiological recordings of neural activity to link effects at the level of imaging signals to those observed in electrical recordings from neuronal populations. During fMRI acquisition, we recorded the broad-band comprehensive neuronal signal, and extract from it time courses of four relevant frequency bands: low (1-12Hz), medium (12-24Hz) gamma (24-90) and multi-unit-activity (400-3000Hz). At the same time we registered BOLD activity in a region of interest around the electrode tip, placed in the primary visual cortex. Scans were about 40 minutes long, during which we delivered a visual stimulus for periods of 30 seconds followed by blank periods of equal length. During visual stimulation we then locally applied either an inhibitory neurotransmitter (GABA), an excitatory neuromodulator (ACh) or just saline solution. We used a recently proposed algorithm for performing Canonical Correlation Analysis (CCA) between fMRI data and electrophysiological activity. Preliminary results show, that CCA robustly finds dependencies between groups of voxels in the fMRI data and frequency bands in the electrophysiological data. For example, a component dominated by the MUA signal was associated with voxels that tended to cluster near the injector and showed inhibitory effects for GABA injection and excitatory effects for ACh injection. These findings suggest that CCA is a promising candidate for revealing relations between neural activity and the fMRI signal during pharmacological manipulations.},
web_url = {http://fens2008.neurosciences.asso.fr/},
event_name = {6th Forum of European Neuroscience (FENS 2008)},
event_place = {Geneva, Switzerland},
state = {published},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Rauch A{arauch}{Department Physiology of Cognitive Processes}, Meinecke F, Zhang X{xiaozhe}{Department Physiology of Cognitive Processes}, Rainer G{gregor}, M\"uller K-R{klaus} and Logothetis NK{nikos}{Department Physiology of Cognitive Processes}}
}

@Thesis{ 5132,
title = {Error Correcting Codes for the P300 Visual Speller},
year = {2007},
month = {7},
abstract = {The aim of brain-computer interface (BCI) research is to establish
a communication system based on intentional modulation of brain
activity. This is accomplished by classifying patterns of brain ac-
tivity, volitionally induced by the user. The BCI presented in this
study is based on a classical paradigm as proposed by (Farwell and
Donchin, 1988), the P300 visual speller. Recording electroencephalo-
grams (EEG) from the scalp while presenting letters successively to
the user, the speller can infer from the brain signal which letter the
user was focussing on. Since EEG recordings are noisy, usually many
repetitions are needed to detect the correct letter. The focus of this
study was to improve the accuracy of the visual speller applying some
basic principles from information theory: Stimulus sequences of the
speller have been modified into error-correcting codes. Additionally
a language model was incorporated into the probabilistic letter de-
coder. Classification of single EEG epochs was less accurate using
error correcting codes. However, the novel code could compensate for
that such that overall, letter accuracies were as high as or even higher
than for classical stimulus codes. In particular at high noise levels,
error-correcting decoding achieved higher letter accuracies.},
file_url = {/fileadmin/user_upload/files/publications/thesis_[0].pdf},
state = {published},
type = {Diplom},
author = {Biessmann F{fbiessma}}
}

@Conference{ BiessmannMMLM2010,
title = {Comparison of Mass-Univariate, Unimodal and Multivariate Multimodal Analysis Methods for Neurovascular Coupling Analysis},
journal = {Frontiers in Computational Neuroscience},
year = {2010},
month = {10},
volume = {Conference Abstract: Bernstein Conference on Computational Neuroscience},
abstract = {In the past years multimodal brain imaging methods have yielded valuable insights into how functional
magnetic resonance imaging (fMRI) signals are related to the underlying neural activity. However,
the rapid advances in multimodal imaging technology were not accompanied by the development of
appropriate analysis methods for multimodal data. We present a multimodal analysis framework, temporal kernel Canonical Correlation Analysis (tkCCA) [1,2], and show how it can be used to analyse the spatio-temporal and time-frequency correlation structure between simultaneously measured intracortical neurophysiological recordings and high dimensional fMRI signals. Given the spectrograms of neurophysiological activity and the simultaneously recorded fMRI data we estimate a convolution linking di_erent bands of neural bandpower to an activity pattern of fMRI signals. The convolution can be interpreted as the pattern in time-frequency and time-voxel space that maximises the canonical correlation [3] between neural and haemodynamic activity.
We show results from data recorded in primary visual cortex during spontaneous activity and during visual stimulation. The analysis resulted in robust neurovascular coupling patterns across different experimental conditions. We compared the multivariate patterns with univariate coupling measures and spatial principal component analysis (SPCA) by measuring the accuracy when predicting neural activity from BOLD signals. Our _ndings suggest that the _lters estimated by tkCCA predict neural activity better than univariate methods and unimodal methods such as SPCA.},
web_url = {http://www.frontiersin.org/10.3389/conf.fncom.2010.51.00075/event_abstract},
event_name = {Bernstein Conference on Computational Neuroscience 2010},
event_place = {Berlin, Germany},
state = {published},
DOI = {10.3389/conf.fncom.2010.51.00075},
author = {Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, Meinecke FC, Murayama Y{yusuke}{Department Physiology of Cognitive Processes}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and M\"uller KR{klaus}{Department Empirical Inference}}
}

@Conference{ RauchMBMLR2008,
title = {The effect of a serotonine agonist on neural activity and BOLD activity in monkey primary visual cortex, a pharmacological fMRI (PhMRI) study},
journal = {Frontiers in Computational Neuroscience},
year = {2008},
month = {10},
volume = {Conference Abstract: Bernstein Symposium 2008},
abstract = {Functional magnetic resonance imaging (fMRI) offers great diagnostic potential for monitoring brain activity due to its non-invasiveness. However the neurophysiological basis of BOLD contrast mechanisms in fMRI is not fully understood. Pharmacological functional magnetic resonance imaging (PhMRI) is a promising new direction in biomedical imaging, which allows for monitoring drug related effects on brain processes. When using drugs with known pharmacodynamics (drug effects on the brain), PhMRI offers great possibilities to get a better understanding of the neuronal basis of the BOLD signal. It can provide the link between drug induced biomolecular changes and their corresponding BOLD response. To take full advantage of PhMRI we are developing an integrated software and hardware platform to record in real-time mode simultaneously neurophysiological and BOLD signals to follow drug induced changes in both signals. Real-time mode allows for controlling drug induced effects tightly and offers the possibility to online modify application parameters of the drug. We started to test pharmacological agents and investigated the effect of the neuromodulator BP554 in the primary visual cortex (V1), of anesthetized monkeys. BP554 is a 5-HT1A agonist acting primarily on the membrane of efferent neurons by potassium-induced hyperpolarization. Combined electrophysiology and (fMRI) experiments suggested that local field activity (LFP) is a better predictor of the BOLD signal than multi-unit activity (MUA). This is particularly true because BOLD responses remain undiminished in cases where spiking might be entirely absent despite clear, strong stimulus-induced modulation of the field potentials. To further test this hypothesis we induced the dissociation of MUA from LFP activity with injections of BP554 into primary visual cortex. Neuroimaging was performed in a 4.7 Tesla Scanner (Bruker, Germany). Recorded were spiking activity and local field potentials. V1 was stimulated by rotating polar checkerboard stimulus (blocks by 30 sec stimulus, 30 sec blank, 37 repetitions). 300 microm to the recording electrode we injected BP554 (100 microM solution). The infusion of BP554 in visual cortex reliably reduced MUA without affecting LFP and BOLD activity. This finding suggests that the efferents of a neuronal network pose little metabolic burden compared to the overall pre- and postsynaptic processing of incoming afferents. These results show how powerful PhMRI can be in approaching the still open issue of the coupling between neuronal activity and the BOLD signal, when appropriate hardware and software achievements are incorporated.},
web_url = {http://www.frontiersin.org/10.3389/conf.neuro.10.2008.01.013/event_abstract},
event_name = {Bernstein Symposium 2008},
event_place = {München, Germany},
state = {published},
DOI = {10.3389/conf.neuro.10.2008.01.013},
author = {Rauch A{arauch}{Department Physiology of Cognitive Processes}, Meinec FC, Biessmann F{fbiessma}{Department Physiology of Cognitive Processes}, M\"uller K-R{klaus}{Department Empirical Inference}, Logothetis NK{nikos}{Department Physiology of Cognitive Processes} and Rainer G{gregor}}
}