Changes in optimal parameter between different white matter regions.

<p>Comparison of <i>R</i>_<i>rmse</i> in voxels along white matter pathways (<b>a.</b> U-fiber; <b>b.</b> ILF; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.g001" target="_blank">Fig 1</a>) across connectome models. Within SPC models, 0.25 mm performs slightly better along U-fiber whereas 4 mm performs slightly better along the ILF. ETC performs better in both pathways. Error bar depicts ±1 s.e.m. across voxels.</p

ETC supports streamlines with a wide range of curvatures.

<p>Distributions of radius of curvature in optimized connectomes in six connectome models are shown. The results in occipital cortex in one hemisphere (left panel) and group average (right panel, <i>N</i> = 10 hemispheres) from STN96 dataset are depicted (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s003" target="_blank">S2 Fig</a> for occipital white matter regions used for analysis in these subjects). Vertical axis is the number of streamlines. Horizontal axis is the mean radius of curvature averaged along individual streamlines. Distributions of the candidate connectomes are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s004" target="_blank">S3 Fig</a>. The distributions obtained using the PICo algorithm in the Camino package are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s005" target="_blank">S4 Fig</a>.</p

Example of Ensemble Tractography architecture.

<p>Using five different curvature thresholds (0.25 to 4 mm), we generated five candidate Single Parameter Connectomes (SPC; green colors). We first combined SPC candidate connectomes to generate a candidate Ensemble Tractography Connectome (ETC). We then generated an optimized ETC using LiFE (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#sec016" target="_blank">Material and Methods</a> section for technical detail). We also describe alternative ET architecture (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s011" target="_blank">S10 Fig</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s001" target="_blank">S1 Text</a>, Section 5).</p

Whole brain ETC performance.

<p><b>a.</b> Optimized connectome size of SPCs and ETC with preselection (ETCpre; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.s001" target="_blank">S1 Text</a>, Section 5) using whole-brain white matter. <b>b.</b> White matter coverage. <b>c.</b> Comparison of <i>R</i>_<i>rmse</i> across connectome models covering whole-brain. Error bar depicts ±1 s.e.m. across five individual brains. Conventions are identical to those in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#pcbi.1004692.g004" target="_blank">Fig 4</a>. <b>d.</b> Maps of measured and predicted diffusion signal in a typical coronal brain slice for a single diffusion direction (subject 1, STN96 dataset). Colors indicate the normalized anisotropic diffusion signal for a single diffusion direction (red: higher signal, blue: lower signal). We plot the measured diffusion signal from two independent sessions as well as the diffusion signal prediction from two connectome models (SPC 0.25 mm and ETCpre).</p

Properties of the Ensemble Tractogrpahy Conectome.

<p><b>a.</b> Number of streamlines supported by each optimized connectome model (optimized connectome size). <b>b.</b> Proportion of white matter voxels covered by each connectome model (white matter coverage; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#sec016" target="_blank">Materials and Methods</a> for seeding methods in tractography). Error bars are ±1 s.e.m. across hemispheres. <b>c.</b> Streamline density (number of streamline per voxel) in two connectome models (SPC 2 mm and ETC). Vertical axis depicts the number of voxels averaged across 10 hemispheres.</p

Short- and long-range fascicles supported by different parameter selections.

<p>The two columns compare short-range fascicles (left, U-fiber) connecting V3A/B and V3d and long-range fascicles (right, the inferior longitudinal fasciculus; ILF) segmented from different connectome models. The images show extremely different estimates using a low minimum radius of curvature threshold (<b>a</b>, 0.25 mm) and high threshold (<b>b</b>, 2 mm). <b>a.</b> The 0.25 mm results show a dense set of short-range fascicles, but a thin set of long-range fascicles. <b>b.</b> Conversely the 2 mm results show sparse short-range fascicles and dense long-range fascicles. <b>c.</b> Ensemble Tractography generates connectomes including both short- and long-range fascicles. Streamline colors in <b>c</b> indicate different parameter settings used to generate the streamlines (blue, 0.25 mm; green, 0.5 mm; red, 1 mm; yellow, 2 mm; light blue, 4 mm). Results are shown from one left hemisphere (subject 1, STN96 data set; see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004692#sec016" target="_blank">Material and Methods</a>).</p

Comparison of SPC and ETC connectome relative error.

<p><b>a.</b> Comparison of <i>R</i>_<i>rmse</i> across two representative connectome models (horizontal axis: ETC, vertical axis: SPC 2 mm) in subject 1, left occipital cortex in STN96 dataset. Color chart depicts the number of voxels. In many voxels, ETC error is lower than the error of the 2 mm SPC model. <b>b.</b> Comparison of <i>R</i>_<i>rmse</i> across all models in occipital cortex of 10 hemispheres in STN96 dataset. Vertical axis indicates a median of <i>R</i>_<i>rmse</i> across occipital cortex voxels. Error bar depicts ±1 s.e.m. across hemispheres.</p

A Two-Stage Cascade Model of BOLD Responses in Human Visual Cortex

<div><p>Visual neuroscientists have discovered fundamental properties of neural representation through careful analysis of responses to controlled stimuli. Typically, different properties are studied and modeled separately. To integrate our knowledge, it is necessary to build general models that begin with an input image and predict responses to a wide range of stimuli. In this study, we develop a model that accepts an arbitrary band-pass grayscale image as input and predicts blood oxygenation level dependent (BOLD) responses in early visual cortex as output. The model has a cascade architecture, consisting of two stages of linear and nonlinear operations. The first stage involves well-established computations—local oriented filters and divisive normalization—whereas the second stage involves novel computations—compressive spatial summation (a form of normalization) and a variance-like nonlinearity that generates selectivity for second-order contrast. The parameters of the model, which are estimated from BOLD data, vary systematically across visual field maps: compared to primary visual cortex, extrastriate maps generally have larger receptive field size, stronger levels of normalization, and increased selectivity for second-order contrast. Our results provide insight into how stimuli are encoded and transformed in successive stages of visual processing.</p></div

Behavioral Tract Profiles show the correlation between reading skills and FA along the left superior longitudinal fasciculus and left arcuate fasciculus.

<p>The correlation between reading skills and FA was computed at each point along the Tract FA Profile for the (a) left superior longitudinal fasciculus and (b) left arcuate fasciculus in the children born preterm. The resulting Behavioral Tract Profile is mapped to the fiber tracts of a single representative subject. Colors correspond to the magnitude of correlation between reading scores and FA at each of 100 equidistant points along the tracts for the children born preterm. The correlations were not uniform along the tracts. Scatter plots show the association between FA (x-axis) and Basic Reading Standard Scores (y-axis) for the point of maximal correlation.</p

Development of Tract FA Profiles between childhood and adolescence.

<p>Standardized Tract FA Profiles for three left and right hemisphere tracts and the posterior and anterior segments of the corpus callosum in younger participants (n = 24, mean age 9.8 years sd = 1.4), represented in blue, and older typically developing children (n = 24, mean age 14.3 years sd = 1.1), represented in red. Renderings of each tract indicate the defining regions of interest. Each plot shows the mean Tract FA Profile +/−1 standard error of the mean confidence interval for each group. Differences in FA across groups occur at specific locations on the Tract FA Profiles. Arrows indicate on the area of the Tract FA Profile showing the greatest group difference (discussed in main text).</p