Keyword(s): Attention/physiology,
Brain/blood supply/*physiology,
*Brain Mapping,
Comprehension/*physiology,
Humans,
Image Processing,
Computer-Assisted/methods,
Magnetic Resonance Imaging,
Models,
Neurological,
Oxygen/blood,
Pattern Recognition,
Visual/*physiology,
Photic Stimulation/methods,
Psychomotor Performance/physiology,
Time Factors,
*Vision.
| How can the components of visual comprehension be characterized as brain activity? Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog. In order to parse activity into distinct events, the brain relies on both the perceptual (bottom-up) data available in the stimulus as well as on expectations about the course of the activity based on previous experience with, or knowledge about, similar types of activity (top-down data). Using fMRI, we examined the contribution of bottom-up and top-down processing to the comprehension of action streams by contrasting familiar action sequences with those having exactly the same perceptual detection and motor responses (yoked control), but no visual action familiarity. New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available. |
@Article{HHH+07,
Author = {Hanson, S. J. and Hanson, C. and Halchenko, Y. O. and Matsuka, T. and Zaimi, A.},
Title = {Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action.},
Journal = {Brain Struct Funct},
Volume = {212},
Number = {3-4},
Pages = {231-44},
abstract = {How can the components of visual comprehension be characterized as brain activity? Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog. In order to parse activity into distinct events, the brain relies on both the perceptual (bottom-up) data available in the stimulus as well as on expectations about the course of the activity based on previous experience with, or knowledge about, similar types of activity (top-down data). Using fMRI, we examined the contribution of bottom-up and top-down processing to the comprehension of action streams by contrasting familiar action sequences with those having exactly the same perceptual detection and motor responses (yoked control), but no visual action familiarity. New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available.},
keywords = {Attention/physiology ; Brain/blood supply/*physiology ; *Brain Mapping ; Comprehension/*physiology ; Humans ; Image Processing, Computer-Assisted/methods ; Magnetic Resonance Imaging ; Models, Neurological ; Oxygen/blood ; Pattern Recognition, Visual/*physiology ; Photic Stimulation/methods ; Psychomotor Performance/physiology ; Time Factors ; *Vision},
url = {http://dx.doi.org/10.1007/s00429-007-0160-2},
year = 2007
} | A mode-based clustering method is developed for identifying spatially dense clusters in brain maps. This type of clustering focuses on identifying clusters in brain maps independent of their shape or overall variance. This can be useful for both localization in terms of interpretation and for subsequent graphical analysis that might require more coherent or dense regions of interest as starting points. The method automatically does signal/noise sharpening through density mode seeking. We also discuss the problem of parameter selection with this method and propose a new method involving 2-parameter control surface, in which we show that the same cluster solution results from tradeoff of these 2 parameters (the local density k and the radius r of the spherical kernel). We benchmark the new dense mode clustering by using several artificially created data sets and brain imaging data sets from an event perception task by perturbing the data set with noise and measuring three kinds of deviation from the original cluster solution. We present benchmark results that demonstrate that the mode clustering method consistently outperforms the commonly used single-linkage clustering, k means method (centroid method) and Ward's method (variance method). |
@Article{HRH+07,
Author = {Hanson, S. J. and Rebbechi, D. and Hanson, C. and Halchenko, Y. O.},
Title = {Dense mode clustering in brain maps.},
Journal = {Magn Reson Imaging},
Volume = 25,
Number = 9,
Pages = {1249-62},
abstract = {A mode-based clustering method is developed for identifying spatially dense clusters in brain maps. This type of clustering focuses on identifying clusters in brain maps independent of their shape or overall variance. This can be useful for both localization in terms of interpretation and for subsequent graphical analysis that might require more coherent or dense regions of interest as starting points. The method automatically does signal/noise sharpening through density mode seeking. We also discuss the problem of parameter selection with this method and propose a new method involving 2-parameter control surface, in which we show that the same cluster solution results from tradeoff of these 2 parameters (the local density k and the radius r of the spherical kernel). We benchmark the new dense mode clustering by using several artificially created data sets and brain imaging data sets from an event perception task by perturbing the data set with noise and measuring three kinds of deviation from the original cluster solution. We present benchmark results that demonstrate that the mode clustering method consistently outperforms the commonly used single-linkage clustering, k means method (centroid method) and Ward's method (variance method).},
url = {http://dx.doi.org/10.1016/j.mri.2007.03.013},
year = 2007
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