Journal: Journal of Neuroscience Methods

Abbreviation

J. neurosci. methods

Publisher

Elsevier

Journal Volumes

ISSN

0165-0270
1872-678X

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Publications1 - 10 of 32
  • A systematic random sampling scheme optimized to detect the proportion of rare synapses in the neuropil
    Item type: Journal Article
    Costa, Nuno Macarico da; Hepp, Klaus; Martin, Kevan A.C. (2009)
    Journal of Neuroscience Methods
  • Recording from defined populations of retinal ganglion cells using a high-density CMOS-integrated microelectrode array with real-time switchable electrode selection
    Item type: Journal Article
    Fiscella, Michele; Farrow, Karl; Jones, Ian L.; et al. (2012)
    Journal of Neuroscience Methods
  • A simple and fast method for tissue cryohomogenization enabling multifarious molecular extraction
    Item type: Journal Article
    von Ziegler, Lukas; Saab, Bechara J.; Mansuy, Isabelle (2013)
    Journal of Neuroscience Methods
  • TrackFly
    Item type: Journal Article
    Fry, Steven N.; Rohrseitz, Nicola; Straw, Andrew D.; et al. (2008)
    Journal of Neuroscience Methods
  • A hierarchical model for integrating unsupervised generative embedding and empirical Bayes
    Item type: Journal Article
    Raman, Sudhir; Deserno, Lorenz; Schlagenhauf, Florian; et al. (2016)
    Journal of Neuroscience Methods
  • The PhysIO Toolbox for Modeling Physiological Noise in fMRI Data
    Item type: Journal Article
    Kasper, Lars; Bollmann, Steffen; Diaconescu, Andreea O.; et al. (2017)
    Journal of Neuroscience Methods
    Background Physiological noise is one of the major confounds for fMRI. A common class of correction methods model noise from peripheral measures, such as ECGs or pneumatic belts. However, physiological noise correction has not emerged as a standard preprocessing step for fMRI data yet due to: (1) the varying data quality of physiological recordings, (2) non-standardized peripheral data formats and (3) the lack of full automatization of processing and modeling physiology, required for large-cohort studies. New methods We introduce the PhysIO Toolbox for preprocessing of physiological recordings and model-based noise correction. It implements a variety of noise models, such as RETROICOR, respiratory volume per time and heart rate variability responses (RVT/HRV). The toolbox covers all intermediate steps − from flexible read-in of data formats to GLM regressor/contrast creation − without any manual intervention. Results We demonstrate the workflow of the toolbox and its functionality for datasets from different vendors, recording devices, field strengths and subject populations. Automatization of physiological noise correction and performance evaluation are reported in a group study (N = 35). Comparison with existing methods The PhysIO Toolbox reproduces physiological noise patterns and correction efficacy of previously implemented noise models. It increases modeling robustness by outperforming vendor-provided peak detection methods for physiological cycles. Finally, the toolbox offers an integrated framework with full automatization, including performance monitoring, and flexibility with respect to the input data. Conclusions Through its platform-independent Matlab implementation, open-source distribution, and modular structure, the PhysIO Toolbox renders physiological noise correction an accessible preprocessing step for fMRI data.
  • Hardware-in-the-loop simulation and analysis of magnetic recording of nerve activity
    Item type: Journal Article
    Jezernik, Sašo (2005)
    Journal of Neuroscience Methods
  • An extended drawing test for the assessment of arm and hand function with a performance invariant for healthy subjects
    Item type: Journal Article
    Vuillermot, Stéphanie; Pescatore, Aniña; Holper, Lisa; et al. (2009)
    Journal of Neuroscience Methods
  • Measuring spike pattern reliability with the Lempel–Ziv-distance
    Item type: Journal Article
    Christen, Markus; Kohn, Adam; Ott, Thomas; et al. (2006)
    Journal of Neuroscience Methods
    Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel–Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calculating the deviation of the mean distance of spike trains obtained from multiple presentations of an identical stimulus from a Poisson reference. Using both the Lempel–Ziv-distance (LZ-distance) and a distance focussing on coincident firing, the pattern and timing reliability of neuronal firing is determined for spike data obtained along the visual information processing pathway of macaque monkey (LGN, simple and complex cells of V1, and area MT). In combination with the sequential superparamagnetic clustering algorithm, we show that the LZ-distance groups together spike trains with similar but not necessarily synchronized firing patterns. For both applications, we show how the LZ-distance gives additional insights, as it adds a new perspective on the problem of firing reliability determination and allows neuron classifications in cases, where other distance measures fail.
  • Global field synchronization in gamma range of the sleep EEG tracks sleep depth: Artifact introduced by a rectangular analysis window
    Item type: Journal Article
    Rusterholz, Thomas; Achermann, Peter; Dürr, Roland; et al. (2017)
    Journal of Neuroscience Methods
Publications1 - 10 of 32