Type-I error probabilities of the between-group connection differences.

<p>Note: The left column, “NC>AD”, shows the probabilities of type-I error in the hypothesis that connections in NC group are greater than in the AD group. The right column, “AD>NC”, displays the opposite situation. The probabilities in bold indicate significantly greater connections (<i>p</i><0.05).</p

Correlations between RT3DE-derived and CMR-derived RV parameters.

<p>Correlations between RT3DE-derived and CMR-derived RV parameters.</p

Correlation between RT3DE-derived and CMR-derived RV parameters.

<p>(A) Correlation of RVEDV values obtained via RT3DE and CMR. (B) Correlation between RVESV values measured via RT3DE and CMR. (C) Correlation between RVEF values measured by RT3DE and CMR.</p

Patients characteristics (n = 23).

<p>Patients characteristics (n = 23).</p

Bland-Altman analysis of parameters measured by RT3DE and CMR.

<p>(A) Bland-Altman analyses of RT3DE and CMR measurements of RVEDV. (B) Bland-Altman analysis of RT3DE and CMR measurements of RVESV. (C) Bland-Altman analysis of RT3DE and CMR measurements of RVEF.</p

Echocardiography and CMR data.

<p>RVMPI: right ventricle myocardial performance index.</p><p>Echocardiography and CMR data.</p

Independent Component Analysis-Based Identification of Covariance Patterns of Microstructural White Matter Damage in Alzheimer’s Disease

<div><p>The existing DTI studies have suggested that white matter damage constitutes an important part of the neurodegenerative changes in Alzheimer’s disease (AD). The present study aimed to identify the regional covariance patterns of microstructural white matter changes associated with AD. In this study, we applied a multivariate analysis approach, independent component analysis (ICA), to identify covariance patterns of microstructural white matter damage based on fractional anisotropy (FA) skeletonised images from DTI data in 39 AD patients and 41 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative database. The multivariate ICA decomposed the subject-dimension concatenated FA data into a mixing coefficient matrix and a source matrix. Twenty-eight independent components (ICs) were extracted, and a two sample <i>t-test</i> on each column of the corresponding mixing coefficient matrix revealed significant AD/HC differences in ICA weights for 7 ICs. The covariant FA changes primarily involved the bilateral corona radiata, the superior longitudinal fasciculus, the cingulum, the hippocampal commissure, and the corpus callosum in AD patients compared to HCs. Our findings identified covariant white matter damage associated with AD based on DTI in combination with multivariate ICA, potentially expanding our understanding of the neuropathological mechanisms of AD.</p></div

IC spatial maps displaying the covariant FA changes in white matter in AD patients compared to HCs for ICs 1–3.

<p>The colour bar represents Z-score. The middle panel displays their corresponding receiver operating characteristic (ROC) curves, and the right panel displays a boxplot of the between-group differences in ICA weights.</p

Regional information of increased GM volume after HDBR.

<p>Regional information of increased GM volume after HDBR.</p

Regional changes of GM volumes after HDBR as revealed by voxel-based morphometry.

<p>Three-dimensional slices depicting regions showing decreased GM volume (red) in the bilateral frontal lobes, parahippocampal gyrus, insula, right temporal pole, right hippocampus and increased GM volume (blue) in vermis, bilateral paracentral lobule, right precuneus gyrus, left precentral gyrus, left postcentral gyrus overlaid on a T1-weighted MRI anatomical image in the stereotactic space of the Talairach template.</p