Author name cluster

Nikos Logothetis

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

YNIMG Journal 2015 Journal Article

Simultaneous epidural functional near-infrared spectroscopy and cortical electrophysiology as a tool for studying local neurovascular coupling in primates

Ali Danish Zaidi
Matthias H.J. Munk
Andreas Schmidt
Cristina Risueno-Segovia
Rebekka Bernard
Eberhard Fetz
Nikos Logothetis
Niels Birbaumer

Simultaneous measurements of intra-cortical electrophysiology and hemodynamic signals in primates are essential for relating human neuroimaging studies with intra-cortical electrophysiology in monkeys. Previously, technically challenging and resourcefully demanding techniques such as fMRI and intrinsic-signal optical imaging have been used for such studies. Functional near-infrared spectroscopy is a relatively less cumbersome neuroimaging method that uses near-infrared light to detect small changes in concentrations of oxy-hemoglobin (HbO), deoxy-hemoglobin (HbR) and total hemoglobin (HbT) in a volume of tissue with high specificity and temporal resolution. FNIRS is thus a good candidate for hemodynamic measurements in primates to acquire local hemodynamic signals during electrophysiological recordings. To test the feasibility of using epidural fNIRS with concomitant extracellular electrophysiology, we recorded neuronal and hemodynamic activity from the primary visual cortex of two anesthetized monkeys during visual stimulation. We recorded fNIRS epidurally, using one emitter and two detectors. We performed simultaneous cortical electrophysiology using tetrodes placed between the fNIRS sensors. We observed robust and reliable responses to the visual stimulation in both [HbO] and [HbR] signals, and quantified the signal-to-noise ratio of the epidurally measured signals. We also observed a positive correlation between stimulus-induced modulation of [HbO] and [HbR] signals and strength of neural modulation. Briefly, our results show that epidural fNIRS detects single-trial responses to visual stimuli on a trial-by-trial basis, and when coupled with cortical electrophysiology, is a promising tool for studying local hemodynamic signals and neurovascular coupling.

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NeurIPS Conference 2013 Conference Paper

Statistical analysis of coupled time series with Kernel Cross-Spectral Density operators.

Michel Besserve
Nikos Logothetis
Bernhard Schölkopf

Many applications require the analysis of complex interactions between time series. These interactions can be non-linear and involve vector valued as well as complex data structures such as graphs or strings. Here we provide a general framework for the statistical analysis of these interactions when random variables are sampled from stationary time-series of arbitrary objects. To achieve this goal we analyze the properties of the kernel cross-spectral density operator induced by positive definite kernels on arbitrary input domains. This framework enables us to develop an independence test between time series as well as a similarity measure to compare different types of coupling. The performance of our test is compared to the HSIC test using i. i. d. assumptions, showing improvement in terms of detection errors as well as the suitability of this approach for testing dependency in complex dynamical systems. Finally, we use this approach to characterize complex interactions in electrophysiological neural time series.

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YNIMG Journal 2007 Journal Article

Robust detection of ocular dominance columns in humans using Hahn Spin Echo BOLD functional MRI at 7 Tesla

Essa Yacoub
Amir Shmuel
Nikos Logothetis
Kâmil Uğurbil

Cells in the mammalian brain tend to be grouped together according to their afferent and efferent connectivity, as well as their physiological properties. The columnar structures of neocortex are prominent examples of such modular organization, and have been studied extensively in anatomical and physiological experiments in rats, cats and monkeys. The importance of noninvasive study of such structures, in particular in human subjects, cannot be overemphasized. Not surprisingly, therefore, many attempts were made to map cortical columns using functional magnetic resonance imaging (fMRI). Yet, the robustness, repeatability, and generality of the hitherto used fMRI methodologies have been a subject of intensive debate. Using differential mapping in a high magnetic field magnet (7 T), we demonstrate here the ability of Hahn Spin-Echo (HSE) BOLD to map the ocular dominance columns (ODCs) of the human visual cortex reproducibly over several days with a high degree of accuracy, relative to expected spatial patterns from post-mortem data. On the other hand, the conventional Gradient-Echo (GE) blood oxygen level dependent (BOLD) signal in some cases failed to resolve ODCs uniformly across the selected gray matter region, due to the presence of non-specific signals. HSE signals uniformly resolved the ODC patterns, providing a more generalized mapping methodology (i. e. one that does not require adjusting experimental approaches based on prior knowledge or assumptions about functional organization and vascular structure in order to avoid confounding large vessel effects) to map unknown columnar systems in the human brain, potentially paving the way both for the study of the functional architecture of human sensory cortices, and of brain modules underlying specific cognitive processes.

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NeurIPS Conference 2003 Conference Paper

Prediction on Spike Data Using Kernel Algorithms

Jan Eichhorn
Andreas Tolias
Alexander Zien
Malte Kuss
Jason Weston
Nikos Logothetis
Bernhard Schölkopf
Carl Rasmussen

We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orienta- tion of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i. e. , the spike data) as well as of the output (i. e. , the orienta- tion), and report the results obtained using different kernel algorithms.

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NeurIPS Conference 1996 Conference Paper

3D Object Recognition: A Model of View-Tuned Neurons

Emanuela Bricolo
Tomaso Poggio
Nikos Logothetis

In 1990 Poggio and Edelman proposed a view-based model of ob(cid: 173) ject recognition that accounts for several psychophysical properties of certain recognition tasks. The model predicted the existence of view-tuned and view-invariant units, that were later found by Lo(cid: 173) gothetis et al. (Logothetis et al. , 1995) in IT cortex of monkeys trained with views of specific paperclip objects. The model, how(cid: 173) ever, does not specify the inputs to the view-tuned units and their internal organization. In this paper we propose a model of these view-tuned units that is consistent with physiological data from single cell responses.

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