Voxel-wise topographical correlation (r) of the PD, MSA and PSP-related brain networks.

*<p>p<0.05 after Bonferroni correction for multiple comparisons (3 comparisons: p<0.0167).</p><p>The p-value is empirically calculated based on the rank of r²-value in 1,000 simulations.</p

Regional differences of two covariance patterns.

<p>(<b>A</b>) Standard SPM analysis with paired t-test design for ON vs. OFF medication with 15 PD patients. (<b>B</b>) The PDRP derived from USA (off-medication) was subtracted from the PDRP derived from South Korea (on-medication). The resulting difference map is z-scored. Only the voxels that were reliable in permutation test were shown (p<0.05, 1,000 permutation). The topography of within-subject differences in medication status (A) was significantly correlated with between-group network differences (B) (r = 0.4228, p<0.001). Likewise, key regions of hypometabolism (e.g., M1, cingulate, cerebellum, putamen) and hypermetabolism (e.g., precuneus) were similarly shown.</p

Voxel-wise topographical correlation (r) of the PDRPs from 4 different countries.

*<p>p<0.05 after Bonferroni correction for multiple comparisons (6 comparisons: p<0.00833).</p><p>The p-value is empirically calculated based on the rank of r²-value in 1,000 simulations.</p

Schematic diagram of the simulation study.

<p>The stimulation was conducted to determine the Window size of Moran’s I that best reflected the inflated topological correlation of the two simulated networks. (<b>A</b>) 300 pseudo-random volume-pairs were generated, then box filters were applied to each volume with six different kernel sizes (3×3×3, 7×7×7, 11×11×11, 15×15×15, 19×19×19, 23×23×23). Then, the global Moran’s I of 1800 volume-pairs (300 original volume-pairs×6 different box filters) was estimated with varying window (W) size (3×3, 9×9, 15×15, 21×21, 27×27, 33×33, 45×45, 51×51, 57×57). The volume-pairs were then vector-transformed and tested for voxel-by-voxel Pearson’s correlation (topographical correlation). Multiple regression was utilized to test if the global Moran’s I significantly predicted the box-filtering-induced elevation of topographical correlation. The window size of the Moran’s I (W) that gave the best prediction of the topographical correlation from the global Moran’s I was identified using AIC. (<b>B–D</b>) The inflated topographical correlation was observed regardless of the W of Moran’s I while the best prediction resulted when the W of Moran’s I was 51 (lowest AIC).</p

The result of multiple regression: |r| = MI^*b1+Z^*B.

*<p>The lowest AIC value.</p><p>r: topographical correlation (Pearson’s correlation of the voxel weights of the two simulated patterns; MI: global Moran’s I; b1: coefficient of multiple regression of avgMI; Z: random effects dummy variables for 300 volume-pairs; B: coefficient for random effects; se: standard error of b1; AIC: Akaike Information Criteria for the whole model fit.</p

A new isoflavone glycoside from flowers of <i>Pueraria Montana</i> var. <i>lobata</i> (Willd.) Sanjappa & Pradeep

A new isoflavone glycoside, named 3’-hydroxytectorigenin-7-O-β-D-xylosyl-(1→6)-β-D-glucopyranoside (1) was isolated from the flowers of Pueraria montana var. lobata (Willd.) Sanjappa & Pradeep. The structure of compound 1 was characterised by HR-ESI-MS and NMR spectroscopic methods. In radical scavenging activity test using 2, 2-diphenyl-1-picrylhydrazyl (DPPH), compound 1 showed moderate activity with IC50 value of 42 ± 4.2 μg/mL.</p

First-Principles Calculations on Narrow-Band Gap d¹⁰ Metal Oxides for Photocatalytic H₂ Production: Role of Unusual In²⁺ Cations in Band Engineering

The d10 metal oxides with low effective mass and high mobility of photoexcited electrons have received much attention in photocatalytic water splitting. However, there are still challenges in practical application due to insufficient visible light absorption. Here, an unusual phenomenon of the In2+ cation in PtIn6(GeO4)2O and PtIn6(Ga/InO4)2 with a narrow band gap is systematically investigated using density functional theory calculations. According to chemical bond analysis, the final band edge structure results from the interaction between the empty In-5p orbitals and the occupied antibonding state of the In 5s–O 2p orbitals as well as the further hybridization of adjacent In cations in PtIn6 octahedrons. The unique bonding characteristic of In2+ cations endows them with a narrow band gap and visible light response ability. Moreover, the occupied antibonding state could weaken the strength of the In–O covalent bond and strengthen the orbital hybridization of the In–In bond, causing the conduction band minimum to be located in the electroactive In6 cavity. This work reveals the origin of the narrow band gap of PtIn6(GeO4)2O and PtIn6(Ga/InO4)2 in view of bond theory and shows that they are promising semiconductors for the application of photocatalytic H2 generation

Table_3_The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects.DOCX

Aim: To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA).Methods: 50 healthy subjects (mean age 45.8 ± 18.5 years, range 21–85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample t-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA.Results: Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (R2 = 0.588; P Conclusion: A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI.</p

Additional file 1 of The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer’s disease

Additional file 1: Supplementary Fig. 1. ROC curves of both global and relative regional CBF values for discrimination between patients with AD and NCs. The AUC value was 0.72 with a sensitivity of 65.63% and a specificity of 71.88% for global CBF, and was 0.998 (sensitivity 100.00% and specificity 96.88%), 1.00 (sensitivity 100.00% and a specificity 100.00%), 0.975 (sensitivity 96.88% and specificity 87.50%), 0.996 (sensitivity 96.88% and specificity 96.88%), and 0.979 (sensitivity 93.75% and specificity 96.88%) for right precuneus, left PCC, left angular, right inferior parietal lobule, and right inferior temporal gyrus, respectively. ROC: receiver operator characteristic; CBF: cerebral blood flow; AUC: area under the curve; PCC: posterior cingulate cortex. Supplementary Fig. 2. AD-related covariance patterns (grey matter vs perfusion) in the identification cohort at different levels of reliability at each voxel. Structural covariance pattern (ADRP-GM) was identified from a linear combination of the first 3 principal components (PCs: variance accounting for = 11.5%, 6.9% and 3.6% respectively) accounting for 14% of the total voxel × subject variance. Brain regions in the ADRP-perfusion (displayed at a threshold value with high reliability of P = 0.001 as reported in the manuscript) was compared with their counterparts in the ADRP-GM displayed at threshold values corresponding to low, moderate and high levels of reliability (P = 0.05, 0.01 and 0.001) following the bootstrap test with 1000 iterations. Supplementary Fig. 3. The overlap of regional topographies with decreased loading of ADRP-CBF and ADRP-GM identified from SSM/PCA in the identification cohort. Blue color indicates regions with decreased loading in CBF, orange color indicates regions with decreased loading in gray matter volume, and pink color indicates regions with overlap of decreased loading in CBF and gray matter volume. The structural changes were only observed in a few isolated areas of smaller anatomic extent compared with CBF changes, even at threshold values with the lowest reliability of P = 0.05. Supplementary Fig. 4. The overlap of regional changes in decreased CBF and gray matter atrophy without normalization for the differences in global value across all AD and healthy subjects in the identification cohort. Blue color indicates regions with decreased CBF, orange color indicates regions with gray matter atrophy and pink color indicates regions with overlap of decreased CBF and gray matter atrophy in AD patients compared to NCs. To rule out false positives that were more pronounced in the results without global normalization, a stringent threshold of 4.67 or 5.56 (both at P < 0.05, FWE-corrected) for decreased CBF or gray matter atrophy was used to overlay SPM maps onto a standard MRI brain template. Supplementary Fig. 5. The overlap of regional changes in relative decreased CBF and gray matter atrophy after ANCOVA normalization for the differences in global value across all AD and healthy subjects in the identification cohort. Blue color indicates regions with decreased CBF, orange color indicates regions with gray matter atrophy, and pink color indicates regions with overlap of decreased CBF and gray matter atrophy in AD patients compared to NCs. To better appreciate relevant brain regions involved in the results, a liberal threshold of 1.67 (P < 0.05, uncorrected) was used to overlay both SPM maps onto a standard MRI brain template. Supplementary Fig. 6. Brain regions of abnormal perfusion (AD vs NC) in the identification cohort using masks with different threshold. SPM analysis was repeated in the identification cohort using the brain mask at a compromise threshold (pGM ≥ 0.5). The same regions of relative hypo- and hyper-perfusion were identified (despite slightly smaller extent) as those found with the brain mask of pGM ≥ 0.3 used in the article. A threshold of 3.23 (P < 0.001, uncorrected) was used to overlay SPM maps onto a standard MRI brain template

Image_3_The Age-Related Perfusion Pattern Measured With Arterial Spin Labeling MRI in Healthy Subjects.PDF

Aim: To analyze age-related cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI in healthy subjects with multivariate principal component analysis (PCA).Methods: 50 healthy subjects (mean age 45.8 ± 18.5 years, range 21–85) had 3D structural MRI and pseudo-continuous ASL MRI at resting state. The relationship between CBF and age was examined with voxel-based univariate analysis using multiple regression and two-sample t-test (median age 41.8 years as a cut-off). An age-related CBF pattern was identified using multivariate PCA.Results: Age correlated negatively with CBF especially anteriorly and in the cerebellum. After adjusting by global value, CBF was relatively decreased with aging in certain regions and relatively increased in others. The age-related CBF pattern showed relative reductions in frontal and parietal areas and cerebellum, and covarying increases in temporal and occipital areas. Subject scores of this pattern correlated negatively with age (R2 = 0.588; P Conclusion: A distinct age-related CBF pattern can be identified with multivariate PCA using ASL MRI.</p