Towards a taxonomy of attention shifting: Individual Towards a taxonomy of attention shifting: Individual differences in fMRI during multiple shift types

Poster submitted to OHBM 2005

Data_Sheet_1_Sight restoration reverses blindness-induced cross-modal functional connectivity changes between the visual and somatosensory cortex at rest.docx

Resting-state functional connectivity (rsFC) has been used to assess the effect of vision loss on brain plasticity. With the emergence of vision restoration therapies, rsFC analysis provides a means to assess the functional changes following sight restoration. Our study demonstrates a partial reversal of blindness-induced rsFC changes in Argus II retinal prosthesis patients compared to those with severe retinitis pigmentosa (RP). For 10 healthy control (HC), 10 RP, and 7 Argus II subjects, four runs of resting-state functional magnetic resonance imaging (fMRI) per subject were included in our study. rsFC maps were created with the primary visual cortex (V1) as the seed. The rsFC group contrast maps for RP > HC, Argus II > RP, and Argus II > HC revealed regions in the post-central gyrus (PostCG) with significant reduction, significant enhancement, and no significant changes in rsFC to V1 for the three contrasts, respectively. These findings were also confirmed by the respective V1-PostCG ROI-ROI analyses between test groups. Finally, the extent of significant rsFC to V1 in the PostCG region was 5,961 in HC, 0 in RP, and 842 mm3 in Argus II groups. Our results showed a reduction of visual-somatosensory rsFC following blindness, consistent with previous findings. This connectivity was enhanced following sight recovery with Argus II, representing a reversal of changes in cross-modal functional plasticity as manifested during rest, despite the rudimentary vision obtained by Argus II patients. Future investigation with a larger number of test subjects into this rare condition can further unveil the profound ability of our brain to reorganize in response to vision restoration.</p

Regression of Dimension 1 on features from the First set experiment.

<p>Note. Adjusted R² = 0.40, F (8, 61) = 6.66.</p><p>*P<0.05, CI  =  Confidence interval for α = 0.05, SE = Standard error.</p><p>Regression of Dimension 1 on features from the First set experiment.</p

Scree plots for the <i>First set</i> (left) and the <i>Second set</i> (right) of images.

<p>Stress values are plotted as a function of the dimensionality in which the MDS data were scaled.</p

Plotted results of MDS dimensions 1 (X-axis) and 2 (Y-axis) for the <i>first</i> set, with pictures superimposed.

<p>The pictures are placed in the image based on their weights on dimension 1 and 2. A subset of the 70 images is plotted here because there are too many images to make this plot readable.</p

The Perception of Naturalness Correlates with Low-Level Visual Features of Environmental Scenes

<div><p>Previous research has shown that interacting with natural environments vs. more urban or built environments can have salubrious psychological effects, such as improvements in attention and memory. Even viewing pictures of nature vs. pictures of built environments can produce similar effects. A major question is: What is it about natural environments that produces these benefits? Problematically, there are many differing qualities between natural and urban environments, making it difficult to narrow down the dimensions of nature that may lead to these benefits. In this study, we set out to uncover visual features that related to individuals' perceptions of naturalness in images. We quantified naturalness in two ways: first, implicitly using a multidimensional scaling analysis and second, explicitly with direct naturalness ratings. Features that seemed most related to perceptions of naturalness were related to the density of contrast changes in the scene, the density of straight lines in the scene, the average color saturation in the scene and the average hue diversity in the scene. We then trained a machine-learning algorithm to predict whether a scene was perceived as being natural or not based on these low-level visual features and we could do so with 81% accuracy. As such we were able to reliably predict subjective perceptions of naturalness with objective low-level visual features. Our results can be used in future studies to determine if these features, which are related to naturalness, may also lead to the benefits attained from interacting with nature.</p></div

Feature weights for the LD classification algorithm in predicting high- vs. low- perceived naturalness of the images.

<p>A high absolute value of the weight indicates that that feature is important for classification. A positive weight indicates that that increasing this feature would lead to increased perceived naturalness; a negative weight indicates that increasing this feature would lead to a decrease in perceived naturalness. Error bars reflect 2 standard deviations from the mean.</p

Correlation Matrix of low-level visual features with perceived naturalness.

<p>The color bar indicates the strength of the correlation from -.8 to 1.</p

Comparison of two images in their color diversity properties.

<p>a) SDHue  = 0.11, SDSat = 0.22, SDbright = 0.21 b) SDHue = 0.19, SDSat = 0.26, SDbright = 0.26.</p

Within-person and between-person zero-order correlations.

<p><i>Note</i>. Correlations above the dashed diagonal line represent within-person correlations obtained from multi-level analyses. Correlations below the dashed diagonal line represent between-person correlations.</p>*<p><i>p</i><.05.</p>**<p><i>p</i><.01.</p>***<p><i>p</i><.001.</p