Hub regions of the human brain anatomical networks derived from both the conventional DTI (C-DTI) and FLAIR-DTI (F-DTI) datasets.

<p>1. Achard et al 2006.</p><p>2. He et al 2007.</p><p>3. Hagmann et al 2008.</p><p>4. Iturria-Medina et al 2008.</p><p>5. Gong et al 2009.</p><p>6. He et al 2009.</p><p>7. Li et al 2009.</p><p>8. Shu et al 2009.</p><p>9. Yan et al 2010.</p><p>10. Tian et al 2011.</p><p>11. Chen et al 2008.</p><p>Note: <b>^a</b>The hub regions shared by the networks derived from both types of DTI datasets. <b>^b</b>The hub regions detected only from the conventional DTI datasets. The remaining six regions are the hubs detected only from the FLAIR-DTI datasets. The symbol “–” stands for “not reported” in these previous studies. “Y” indicates that the region has been identified as a “hub”, and “N” indicates that it has not been identified as a hub.</p

Locations of the cortical regions showing statistically significant differences in two nodal parameters, and , of the anatomical networks.

<p>The lower panel shows the differences in the nodal global parameter (), and the upper panel shows the differences in degree () of the anatomical networks. The dotted white line shows the critical FDR threshold (<i>q</i> = 0.05; see Materials and Methods). Most of the significantly different regions were in the brain medial plane. In these regions, the values of these two nodal parameters were higher in the anatomical networks derived from the FLAIR-DTI datasets than those derived from the conventional DTI datasets.</p

Increased Global and Local Efficiency of Human Brain Anatomical Networks Detected with FLAIR-DTI Compared to Non-FLAIR-DTI

<div><p>Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures <i>in vivo</i>, has been widely used in both fundamental studies and clinical applications. Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.</p></div

Variability in the connectivity patterns of the anatomical networks corresponding to the conventional DTI datasets and the FLAIR-DTI datasets.

<p>(a) The weighted connectivity matrix constructed from the conventional DTI data for a single subject. The weighted connectivity matrix is displayed using a logarithmic color map. (b) Same as (a) but showing the FLAIR-DTI data from a single subject. (c) Histograms of the edges derived from the backbone anatomical networks corresponding to the two types of DTI datasets. (d) Difference between the two types of DTI-based networks in the number of edges vs. connectivity density. Bars in blue (red) indicate the number of edges in the anatomical networks of the FLAIR-DTI datasets that were higher (lower) than those of the conventional DTI datasets.</p

Statistically significant differences in the nodal parameters of the anatomical networks between the conventional DTI (C-DTI) and FLAIR-DTI (F-DTI) datasets.

<p>Note: and represent nodal global efficiency and degree, respectively. Bold, italic text indicates that these common brain regions showed statistically significant differences in the anatomical networks corresponding to the two types of DTI datasets with respect to both the parameters, and . A negative <i>t</i>-value indicates that the value of the nodal parameter corresponding to the FLAIR-DTI datasets is higher than that of the conventional DTI dataset. The symbol “–” shows that these regions were not statistically significantly different with respect to or .</p>*<p><i>p</i><0.01 (uncorrected),</p>**<p><i>p</i><0.05 (FDR corrected).</p

Flowchart for constructing human brain anatomical networks using DTI datasets and tractography.

<p>(1) Individual anatomical images were first coregistered into b0 images to obtain rT1 images in diffusion space. The rT1 images were then mapped to a T1-weighted template of ICBM152 in MNI space. (2) The obtained inverse matrix was used to transform the AAL template from the MNI space into individual diffusion space. (3) Fibers in the whole brain were reconstructed using the deterministic tractographic method (DtiStudio software). For display purposes only, fibers shown here were calculated using TrackVis software. (4) Construction of the weighted connectivity matrices and the human brain anatomical networks.</p

The positive (+) and negative (-) correlation brain regions with r values between local topological characteristics (i. e., <i>L</i>_p, <i>C</i>_p, <i>E</i>_loc) and calligraphy skills.

The positive (+) and negative (-) correlation brain regions with r values between local topological characteristics (i. e., Lp, Cp, Eloc) and calligraphy skills.</p

Bilateral middle occipital region showed higher <i>E</i>_loc in the CCH group than in the control group (red) in Module I, with 1000 permutations (p <.05).

Note: 1. Calcarine_L; 2. Calcarine_R; 3. Cuneus_L; 4. Cuneus_R; 5. Lingual_L; 6. Lingual_R; 7. Occipital_Sup_L; 8. Occipital_Sup_R; 9. Occipital_Mid_L; 10. Occipital_Mid_R; 11. Occipital_Inf_L; 12. Occipital_Inf_R; 13. Fusiform_L; 14. Fusiform_R.</p

Rendering plot of the hub regions detected in the human brain anatomical networks for the conventional DTI (upper) and for the FLAIR-DTI (lower).

<p>Sixteen hub regions were identified in the anatomical network derived from the FLAIR-DTI datasets, whereas thirteen hub regions were derived in the network from the conventional DTI datasets. The size of the node represents the magnitude of the normalized betweenness centrality (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0071229#pone-0071229-t003" target="_blank">Table 3</a> for more details). Nodes in red represent hub regions shared by the networks derived from both types of DTI datasets. Nodes in green (blue) represent the hub regions specific to the network derived from the conventional DTI (FLAIR-DTI) datasets.</p

Brain regions with significant group differences in local clustering coefficient between the CCH and control groups (corrected p <.05).

Brain regions with significant group differences in local clustering coefficient between the CCH and control groups (corrected p <.05).</p