317 research outputs found

    Affine Registration of label maps in Label Space

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    Two key aspects of coupled multi-object shape\ud analysis and atlas generation are the choice of representation\ud and subsequent registration methods used to align the sample\ud set. For example, a typical brain image can be labeled into\ud three structures: grey matter, white matter and cerebrospinal\ud fluid. Many manipulations such as interpolation, transformation,\ud smoothing, or registration need to be performed on these images\ud before they can be used in further analysis. Current techniques\ud for such analysis tend to trade off performance between the two\ud tasks, performing well for one task but developing problems when\ud used for the other.\ud This article proposes to use a representation that is both\ud flexible and well suited for both tasks. We propose to map object\ud labels to vertices of a regular simplex, e.g. the unit interval for\ud two labels, a triangle for three labels, a tetrahedron for four\ud labels, etc. This representation, which is routinely used in fuzzy\ud classification, is ideally suited for representing and registering\ud multiple shapes. On closer examination, this representation\ud reveals several desirable properties: algebraic operations may\ud be done directly, label uncertainty is expressed as a weighted\ud mixture of labels (probabilistic interpretation), interpolation is\ud unbiased toward any label or the background, and registration\ud may be performed directly.\ud We demonstrate these properties by using label space in a gradient\ud descent based registration scheme to obtain a probabilistic\ud atlas. While straightforward, this iterative method is very slow,\ud could get stuck in local minima, and depends heavily on the initial\ud conditions. To address these issues, two fast methods are proposed\ud which serve as coarse registration schemes following which the\ud iterative descent method can be used to refine the results. Further,\ud we derive an analytical formulation for direct computation of the\ud "group mean" from the parameters of pairwise registration of all\ud the images in the sample set. We show results on richly labeled\ud 2D and 3D data sets

    Button-pressing affects P300 amplitude and scalp topography

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    Abstract Background: Scant and equivocal research exists examining the effects of button-pressing on P300. Button-pressing may decrease P300 latency and amplitude. The melding of motor potentials and P300 may also confound studies of P300 topography, such as studies of temporal scalp-area asymmetries in schizophrenia. Method: P300 was measured on button-press and silent-count tasks in control subjects. An estimate of motor activity was constructed from a simple reaction time task, with reaction times matched to the button-press task. The motor estimate was subtracted from the buttonpress P300 to assess Results: P300 was smaller and its topography different in the button-pressing task relative to silent-counting. The motor-correction procedure generated a P300 with normal topography. Comparison of the button-press P300 in controls to the silent-count P300 in schizophrenia patients reduced a signi®cant lateral asymmetry to trend level. This asymmetry was signi®cant after the correction procedure. Conclusions: Button-pressing generates smaller P300 than silent-counting. Also, P300 topography in button-pressing tasks is confounded by motor potentials. The distortion can be corrected with a motor potential estimate. Motor potentials can occlude differences in P300 topography between groups.
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