A-002

The set of ground truth long-range fiber bundles was designed to cover the whole human brain and features many of the relevant spatial configurations, such as crossing, kissing, twisting and fanning fibers, thus representing the morphology of the major known in vivo fiber bundles. The process to obtain these bundles consisted of three steps. First, a whole-brain global tractography was performed on a high-quality in vivo diffusion-weighted image. Then, 25 major long-range bundles were manually extracted from the resulting tractogram. In the third step, these bundles were refined to obtain smooth and well-defined bundles. Each of these steps is detailed in the following paragraphs.
We chose one of the diffusion-weighted data sets included in the Q3 data release of the HCP^{39 (link)}, subject 100307, to perform whole-brain global fiber tractography^{52 , 68 (link)}. Among other customizations, the HCP scanners are equipped with a set of high-end gradient coils, enabling diffusion encoding gradient strengths of 100 mT m⁻¹. By comparison, most standard magnetic resonance scanners feature gradient strengths of about 30 to 40 mT m⁻¹. This hardware setup allows the acquisition of data sets featuring exceptionally high resolutions (1.25 mm isotropic, 270 gradient directions) while maintaining an excellent SNR. All data sets were corrected for head motion, eddy currents and susceptibility distortions and are, in general, of very high quality^{69 –73 (link)}. Detailed information regarding the employed imaging protocols as well as the data sets themselves may be found on http://humanconnectome.org.
Global fiber tractography was performed using MITK Diffusion^{74 (link)} with the following parameters: 900,000,000 iterations, a particle length of 1 mm, a particle width of 0.1 mm, and a particle weight of 0.002. Furthermore, we repeated the tractography six times and combined the resulting whole-brain tractograms into one large data set consisting of over five million streamlines. The selected parameters provided for a very high sensitivity of the tractography method. The specificity of the resulting tractogram was of lesser concern since the tracts of interest were extracted manually in the second step.
Bundle segmentation was performed by an expert radiologist using manually placed inclusion and exclusion regions of interest (ROI). We followed the concepts introduced in ref. ⁴⁰ for the ROI placement and fiber extraction. Twenty-five bundles were extracted, covering association, projection, and commissural fibers across the whole brain (Fig. 1): CC, left and right cingulum (Cg), Fornix (Fx), anterior commissure (CA), left and right optic radiation (OR), posterior commissure (CP), left and right inferior cerebellar peduncle (ICP), middle cerebellar peduncle (MCP), left and right superior cerebellar peduncle (SCP), left and right parieto-occipital pontine tract (POPT), left and right cortico-spinal tract (CST), left and right frontopontine tracts (FPT), left and right ILF, left and right UF, and left and right SLF. As mentioned in the “Discussion” section, the IFOF, the MdLF, as well as the middle and inferior temporal projections of the AF were not included.
After manual extraction, the individual long-range bundles were further refined to serve as ground truth for the image simulation as also shown in Fig. 1. The original extracted tracts featured a large number of prematurely ending fibers and the individual streamlines were not smooth. To obtain smooth tracts without prematurely ending fibers, we simulated a diffusion-weighted image from each original tract individually using Fiberfox (www.mitk.org33). Since no complex fiber configurations, such as crossings, were present in the individual tract images and no artifacts were simulated, it was possible to obtain very smooth and complete tracts from these images with a simple tensor-based streamline tractography. Supplementary Fig. 7 illustrates the result of this refining procedure on the left CST.

We tested for association between the 528,509 genotyped SNPs and the normalised expression levels of the 17,926 probes using the FASTASSOC component of MERLIN [57] (link), [58] (link). The FASTASSOC option fits a simple linear regression model to estimate an additive effect for each probe and SNP combination, with SNP genotypes coded as the number of copies of the minor allele (0, 1 or 2) carried by each individual. We used the Lander-Green algorithm [58] (link), [59] (link), implemented in Merlin, to estimate expected genotype scores for individuals with missing genotype data. Covariates of sex and generation were included in the model, where generation denotes either the parental or the adolescent generation. Previous analysis has shown (not published) that generation is a useful substitute for age without the burden of additional degrees of freedom. The model applies a variance component approach to account for the correlations between different expression levels within each family. The model fit is evaluated using a score test, which substantially reduces computational time compared to maximum-likelihood methods, at the expense of a slight loss of power [58] (link).
Conditional regression analysis was used to address the potential to miss secondary eQTL in linkage disequilibrium (LD) with other eQTL. For each probe with an identified eQTL we corrected for the main effects of the top eSNP (SNP with the largest R²) by regressing its genotypes against the expression levels. Residuals from this analysis were then used for second round of eQTL mapping, allowing us to detect independent eQTL. If additional eQTL were identified from this second round of analysis, the process was repeated, correcting for the main effects of the top eSNP from the first and second eQTL using multivariate regression.
Associations were evaluated in two categories depending on the location of the SNP relative to the transcription start site (TSS). Cis-eQTL were defined as associations between SNPs within 2MB of either the 3′ or 5′ end of the TSS. We defined trans-associations as associations involving SNPs elsewhere in the genome. To correct for multiple testing, we used a study-wide significance level of 0.05, corrected for the number of SNP by probe associations tested, corresponding to a p-value threshold of 5.25×10⁻¹².
We tested for the effects of population structure and cryptic relatedness between individuals by applying the method ‘genomic control’ [60] (link) to results of the association analysis. We derived a coefficient of 1.002, indicating negligible population stratification.

The patients were divided into four groups: men and women with and without anemia on admission. These groups were compared. The Kolmogorov–Smirnov test was used to assess normal distribution. Differences between groups in baseline clinical, angiographic, and procedural characteristics were compared with the Jonckheere-Terpstra test for continuous variables and the Chi-square test or Fischer’s exact test for categorical variables. We counted end point events that occurred during the follow-up period and compared their rates between non-anemic/anemic men and women. The cumulative incidence rates of the unadjusted long-term mortality were estimated by the Kaplan–Meier method and compared by the log-rank test. Binary logistic regression models were performed using the Enter mode to determine the possible association between anemia and sex and 30-day mortality, and Cox regression analysis was used to determine hazard ratios (HR) as estimates of long-term mortality. The models were adjusted for age, hypertension, diabetes, hyperlipidemia, renal dysfunction on admission, ST-elevation MI, cardiogenic shock on admission, TIMI 0/1 after PCI, bleeding, P2Y12 receptor antagonists, and sex-specific non-anemic/anemic groups (men and women with and without anemia on admission). HRs were calculated using a model stratified by these groups. The variables included and retained in the model were based on previous literature reports and experience that these factors are known to influence all-cause mortality. All included variables had a univariable association with 30-day mortality p < 0.002 and a variance inflation factor (VIF) <1.4. We calculated adjusted HRs for all four groups. The group of non-anemic men (Hb ≥130 g/L) was used as the reference group. Only cardiogenic shock on admission was included as a covariate in the calculations. In addition, we ran regression models for each sex separately, including anemic/non-anemic men or women as a variable in the model in addition to the other variables listed above. Data were analyzed with SPSS 25.0 software for Windows (IBM Corp., Armonk, NY, USA). All p-values were two-sided, and values less than 0.05 were considered statistically significant.

This cross-sectional online study was conducted among the general population in Hong Kong from July to August 2022. The inclusion criteria were (a) residing in Hong Kong, (b) being 18 years or older, and (c) being literate in traditional Chinese. Those who did not agree to participate were excluded. The sample size calculation was based on an assessment of the factor structure in two random halves. The literature suggested 10–20 subjects per item for the respective factor analysis (Hair et al., 2019 ). For the 18-item CFSS, 360 subjects for a factor analysis would be needed. Thus, 720 participants were required. Allowing sizeable incomplete responses in an online survey, we targeted 1,000 participants. To avoid social contact during the pandemic, we sought a double opt-in online panel service to recruit participants who met the eligibility criteria by email and text messages. The respondents were invited to complete an online questionnaire voluntarily on their electronic devices and a total of 1,002 eligible responses were obtained.

For CH mutation analysis, we developed a custom amplicon-based gene panel (targeting 25 kb of genomic DNA sequence), focusing on detection of the most frequent hematopoietic and lymphoid-attributed mutations in the Catalogue of Somatic Mutations in Cancer (COSMIC) database in the four most frequently mutated driver genes in CH: DNMT3A, TET2, ASXL1 and JAK2. Methods were recently described [17 (link)]. In brief, a total of 50 ng DNA was PCR-amplified in four multiplex reactions primed with oligonucleotide sequences of the genes detailed above.
DNA libraries were prepared from 10 ng of the pooled PCR products using the Illumina Nextera XT kit according to the manufacturers recommendations (Illumina, San Diego, CA, USA). Up to 120 libraries were used for sequencing at a total concentration of 2 nM. Sequencing of 2 × 150 bp was performed with an Illumina NextSeq 550 sequencer at the sequencing core facility of the Faculty of Medicine (University of Leipzig). Demultiplexing of raw reads, adapter trimming and quality filtering were carried out according to Stokowy et al. [36 (link)]. After mapping against the human reference genome (hg38) using bwa [37 (link)] (version 0.7.17-r1188), we employed the VarDict algorithm for variant calling [38 (link)] and ANNOVAR for variant annotation [39 (link)]. If a mutation was detected in at least one patient sample by VarDict, all other samples were also screened manually. Detailed information on the screening approach can be found in the supplemental methods, Supplemental Tables S1 and S2 and Supplemental Figures S1 and S2. Only exonic or splice site mutations were included. Variants occurring in ≥ 5% of all studied patients were considered probable technical artefacts and excluded. Variants that were covered by fewer than 200 reads were also filtered out, as were synonymous SNVs and mutations known to have a minor allele frequency of 0.002 or more in the gnomAD database. To exclude rare germline variants, mutations with VAF > 0.35 were excluded unless they were stopgain, frameshift or splice-site mutations.