Papers fMRI Methods

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[edit] fMRI Methods Papers

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Delattre et al.
Magnetic Resonance Imaging/ 2010
Geneva University Hospital
    Spiral acquisition schemes offer unique advantages such as flow compensation, efficient k-space sampling and robustness against motion that make this option a viable choice among other non-Cartesian sampling schemes. For this reason, the main applications of spiral imaging lie in dynamic magnetic resonance imaging such as cardiac imaging and functional brain imaging. However, these advantages are counterbalanced by practical difficulties that render spiral imaging quite challenging. Firstly, the design of gradient waveforms and its hardware requires specific attention. Secondly, the reconstruction of such data is no longer straightforward because k-space samples are no longer aligned on a Cartesian grid. Thirdly, to take advantage of parallel imaging techniques, the common generalized autocalibrating partially parallel acquisitions (GRAPPA) or sensitivity encoding (SENSE) algorithms need to be extended. Finally, and most notably, spiral images are prone to particular artifacts such as blurring due to gradient deviations and off-resonance effects caused by B0 inhomogeneity and concomitant gradient fields. In this article, various difficulties that spiral imaging brings along, and the solutions, which have been developed and proposed in literature, will be reviewed in detail.
    Posted by: Brian Wandell, Aug. 2, 2010


Goense JB, Logothetis NK.
Curr Biol. 2008 May
Department of Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Tuebingen, Germany.
    BACKGROUND: Simultaneous intracortical recordings of neural activity and blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) in primary visual cortex of anesthetized monkeys demonstrated varying degrees of correlation between fMRI signals and the different types of neural activity, such as local field potentials (LFPs), multiple-unit activity (MUA), and single-unit activity (SUA). One important question raised by the aforementioned investigation is whether the reported correlations also apply to alert subjects. RESULTS: Monkeys were trained to perform a fixation task while stimuli within the receptive field of each recording site were used to elicit neural responses followed by a BOLD response. We show -- also in alert behaving monkeys -- that although both LFP and MUA make significant contributions to the BOLD response, LFPs are better and more reliable predictors of the BOLD signal. Moreover, when MUA responses adapt but LFP remains unaffected, the BOLD signal remains unaltered. CONCLUSIONS: The persistent coupling of the BOLD signal to the field potential when LFP and MUA have different time evolutions suggests that BOLD is primarily determined by the local processing of inputs in a given cortical area. In the alert animal the largest portion of the BOLD signal's variance is explained by an LFP range (20-60 Hz) that is most likely related to neuromodulation. Finally, the similarity of the results in alert and anesthetized subjects indicates that at least in V1 anesthesia is not a confounding factor. This enables the comparison of human fMRI results with a plethora of electrophysiological results obtained in alert or anesthetized animals.
    Posted by: LMP 3/27/2009


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