Cluster-Based Statistics for Brain Connectivity in Correlation with Behavioral Measures

<div><p>Graph theoretical approaches have successfully revealed abnormality in brain connectivity, in particular, for contrasting patients from healthy controls. Besides the group comparison analysis, a correlational study is also challenging. In studies with patients, for example, finding brain connections that indeed deepen specific symptoms is interesting. The correlational study is also beneficial since it does not require controls, which are often difficult to find, especially for old-age patients with cognitive impairment where controls could also have cognitive deficits due to normal ageing. However, one of the major difficulties in such correlational studies is too conservative multiple comparison correction. In this paper, we propose a novel method for identifying brain connections that are correlated with a specific cognitive behavior by employing cluster-based statistics, which is less conservative than other methods, such as Bonferroni correction, false discovery rate procedure, and extreme statistics. Our method is based on the insight that multiple brain connections, rather than a single connection, are responsible for abnormal behaviors. Given brain connectivity data, we first compute a partial correlation coefficient between every edge and the behavioral measure. Then we group together neighboring connections with strong correlation into clusters and calculate their maximum sizes. This procedure is repeated for randomly permuted assignments of behavioral measures. Significance levels of the identified sub-networks are estimated from the null distribution of the cluster sizes. This method is independent of network construction methods: either structural or functional network can be used in association with any behavioral measures. We further demonstrated the efficacy of our method using patients with subcortical vascular cognitive impairment. We identified sub-networks that are correlated with the disease severity by exploiting diffusion tensor imaging techniques. The identified sub-networks were consistent with the previous clinical findings having valid significance level, while other methods did not assert any significant findings.</p></div

The identified sub-network correlated with the disease severity: subcortical vascular dementia.

<p>The figure shows in the lateral view of the left hemisphere (A), the transverse view of both hemispheres (B), and the lateral view of the right hemisphere (C). The identified connection was shown as an orange line, whose thickness represents the magnitude of its correlation coefficient between its edge weight and CDR-SOB. The identified node was shown with a colored sphere, whose color represents the lobe to which it belongs: frontal (cyan), limbic (blue), central (magenta), temporal (green), parietal and occipital (red).</p

Demography of participants.

*<p>: significant, We used a 2-sample <i>t</i>-test for group difference in age, Chi-Square test for gender ratio difference, and Wilcoxon’s ranksum test for the others.</p

Comparison to the other multiple comparison correction methods.

<p>To compare with Bonferroni correction and FDR procedure, we drew histogram of <i>p</i>-values in log-scale whose correlation coefficients are negative, showing the thresholding <i>p</i>-value of Bonferroni correction with α = 0.10, (thin solid vertical line), and the maximum of uncorrected <i>p</i>-values of network connections in the proposed cluster-based correction, <i>p</i>_max (thick solid vertical line), for patients with svMCI (A) and SVaD (B). We note that the thresholding <i>p</i>-values of the FDR procedure with q = 0.05 and 0.1 cannot be shown in log-scale, because they both are exactly zero, leading no significant findings. To compare with extreme statistics, we drew the histogram of raw correlation coefficients (Spearman, partial correlation adjusting age and gender), showing 10% threshold of the extreme statistics, (thin solid vertical line), along with the initial threshold, (thick solid vertical line), in patients with svMCI (C) and SVaD (D), where dotted vertical line indicates the zero correlation coefficient.</p

The identified sub-network correlated with the disease severity: subcortical vascular mild cognitive impairment.

<p>The figure shows in the lateral view of the left hemisphere (A), the transverse view of both hemispheres (B), and the lateral view of the right hemisphere (C). The identified connection was shown as an orange line, whose thickness represents the magnitude of its correlation coefficient between its edge weight and CDR-SOB. The identified node was shown with a colored sphere, whose color represents the lobe to which it belongs: frontal (cyan), limbic (blue), central (magenta), temporal (green), parietal and occipital (red).</p

The identified sub-network correlated with the disease severity in patients with SVaD ( = –0.32, <i>p</i> = 0.0443).

†<p>: uncorrected, ‡: the anterior-to-posterior connectivity.</p

Skeleton of nested cross-validation.

<p>The skeleton of the nested cross-validation for measuring the performance of the proposed method.</p

Bile acid production in primary cultured human cumulus granulosa cells (CGC).

<p>Cholesterol entering granulosa cells may be used to form sex steroids or bile acids (Panel a). With increasing cholesterol concentration in media, CGC demonstrate dose-dependent bile acid content (Panel b).</p

An Example-Based Multi-Atlas Approach to Automatic Labeling of White Matter Tracts

<div><p>We present an example-based multi-atlas approach for classifying white matter (WM) tracts into anatomic bundles. Our approach exploits expert-provided example data to automatically classify the WM tracts of a subject. Multiple atlases are constructed to model the example data from multiple subjects in order to reflect the individual variability of bundle shapes and trajectories over subjects. For each example subject, an atlas is maintained to allow the example data of a subject to be added or deleted flexibly. A voting scheme is proposed to facilitate the multi-atlas exploitation of example data. For conceptual simplicity, we adopt the same metrics in both example data construction and WM tract labeling. Due to the huge number of WM tracts in a subject, it is time-consuming to label each WM tract individually. Thus, the WM tracts are grouped according to their shape similarity, and WM tracts within each group are labeled simultaneously. To further enhance the computational efficiency, we implemented our approach on the graphics processing unit (GPU). Through nested cross-validation we demonstrated that our approach yielded high classification performance. The average sensitivities for bundles in the left and right hemispheres were 89.5% and 91.0%, respectively, and their average false discovery rates were 14.9% and 14.2%, respectively.</p></div

Presence of receptors known to regulate the bile acids synthesis pathways.

<p>Protein presence was analyzed with receptor specific antibodies and either immunofluorescence (IF) or western blot (WB). (Present) – the receptor is present at a protein level; (absent) – the receptor is not present at a protein level.</p