In Sample 1, approach behaviour in women was related to EEG, but not to fMRI hemispheric asymmetries. In Sample 2, approach/avoidance behaviours were related to fMRI hemispheric asymmetries. Finally, hemispheric asymmetries were not related to either BMI or eating behaviour in any of the samples. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI.Much of our behaviour is driven by two motivational dimensions—approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting‐state EEG studies: Approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right‐brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self‐reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour and BMI. We analysed three samples: Sample 1 ( Such tendencies can be considered fundamental motivational dimensions that steer (not only) human behaviour  and eating behaviour  questionnaires. This was done in a large sample of predominantly lean participants . Second, we aimed to show that the relationship of approach/avoidance, eating behaviour and rsEEG asymmetry can be extended to rsfMRI in the same sample. Here, we also aimed to investigate hemispheric asymmetries in subcortical structures, which cannot be easily done using EEG. Third, we aimed to establish the existence of obesity‐related hemispheric asymmetries in rsfMRI by investigating self‐reported eating behaviours (TFEQ), approach/avoidance behaviours (BIS/BAS), and BMI in two samples including lean, overweight, and obese participants . The three samples enabled us to provide a conceptual replication of previous studies, while at the same time expanding existing knowledge to new behavioural measures and methods.We hypothesised that higher self‐reported approach behaviour (BAS) would be related to increased left versus right hemispheric activity, whereas higher self‐reported avoidance (BIS) would be related to increased right versus left‐hemispheric activity in both rsEEG and rsfMRI. Furthermore, increased cognitive control of food intake was expected to be related to higher right versus left‐hemispheric activity, whereas higher disinhibition was expected to be related to increased left versus right hemispheric activity. Finally, we hypothesised higher BMI to be related to increased left versus right hemispheric activity. We further aimed to investigate whether approach/avoidance‐related hemispheric asymmetries can be measured using both EEG and fMRI neuroimaging, as was previously done in a different context, for example, language research . All participants gave their written informed consent prior to participation.2.12.1.12, range: 17.95–37.80 kg/m2; 42 women, Table Sample 1 consisted of 117 healthy, right‐handed, predominantly lean participants aged 20–35 years . From this database, we selected rsfMRI data of 152 healthy, right‐handed lean, overweight and obese participants aged 18–35 years  questionnaire . We analysed T2*‐weighted rsfMRI, MP2RAGE and fieldmap data. RsfMRI data parameters: 657 volumes, TE = 30 ms, FA = 69°, TR = 1,400 ms, 64 slices in an interleaved order, voxel size: 2.3 × 2.3 × 2.3 mm2.3.33, FoV: 192 mm, acquisition time: 11 min. MPRAGE parameters: TE = 2.01 ms, FA = 9°, TR = 2,300 ms, TI = 900 ms, voxel size: 1 × 1 × 1 mm3, FoV: 256 mm.MRI data for both of the subsamples of this sample were collected with a 3 T Siemens Skyra scanner. We analysed T2*‐weighted rsfMRI, MPRAGE and fieldmap data. RsfMRI parameters: 320 volumes, TE = 22 ms, FA = 90°, TR = 2,000 ms, 40 slices in an ascending order, voxel size: 3.0 × 3.0 × 2.5 mm2.3.4http://fcon_1000.projects.nitrc.org/indi/enhanced/mri_protocol.html): 900 volumes in an interleaved order, TE = 30 ms, FA = 60°, TR = 645 ms, 40 slices, voxel size: 3.0 × 3.0 × 2.5 mm3, FoV: 222 mm, multiband acceleration factor: 4, acquisition time: 10 min. MPRAGE parameters: TE = 2.52 ms, FA = 9°, TR = 2,600 ms, TI = 900 ms, voxel size: 1 × 1 × 1 mm3, FoV: 256 mm.For Sample 3, MRI data were collected with a 3 T Siemens Trio scanner. We analysed T2*‐weighted rsfMRI and MPRAGE data. RsfMRI parameters (2.42.4.1N components (N ≥ 30) that explained 95% of the total variance. PCA was used for the following independent component analysis . As rsfMRI was collected with eyes open to prevent subjects from falling asleep, our main analysis focused on EEG data from the eyes open condition in order to compare it with fMRI findings. We additionally conducted EEG analyses with relative alpha power of eyes closed condition to investigate whether potential effects observed in the eyes open condition are specific to this condition or can be extended to the eyes closed condition as well.We focused on alpha power in the broader spectrum (8–12 Hz) and in the narrower spectrum for low alpha (8–10 Hz) for our analysis. While the broader alpha frequency band (8–12 Hz) has been previously linked to cortical inhibition by top‐down control  f/ALFF function. To compare EEG and fMRI results from our original analysis, we defined a set of regions of interest (ROI) for the fMRI analysis. Based on previous literature , and the nucleus accumbens . For each ROI, which was defined separately for the left and for the right hemisphere , analysis of fMRI data in all three samples was identical. To be able to conceptually compare EEG results with fMRI results, the fractional amplitude of low‐frequency fluctuations (fALFF) was used as a measure of resting‐state brain activity  within JupyterNotebook.For each of the variables of interest, outliers were excluded based on an a priori criterion: 2.2*interquartile range below or above the first or third quartile, respectively , for each participant we calculated the differential BAS − BIS score. We then removed outliers from both measures of interest (rsEEG and questionnaire data) and correlated BAS − BIS scores with absolute alpha asymmetry indices. To analyse the data, we performed Pearson's correlation of the obtained EEG asymmetry indices (section 2.5.1) and the BAS − BIS scores. Final sample size for this analysis after outlier exclusion was 113 participants.To investigate the relationship between approach and avoidance behaviours and hemispheric bias as a direct replication of previous studies, we performed eight separate multiple regression analyses with asymmetry indices from relative frontal alpha power (three pairs of electrodes), relative parietal alpha power (one pairs of electrodes), relative frontal low alpha power (three pairs of electrodes) and relative parietal low alpha power (one pair of electrodes) as outcome variables. This was done separately for the EO and EC conditions. Predictors included BAS fun, BAS drive and BAS reward responsivity as well as BIS anxiety, and FFFS fear scores. Note, however, that due to the relatively low BMI range in Sample 1, in the analysis of this sample BMI served as a variable of no interest. To investigate whether gender influences the relationship between questionnaire measures and hemispheric bias, we added an interaction term with gender for each of the questionnaire variables. This was done because previous findings show that approach/avoidance behaviours might be gender‐dependent  and their interactions with gender, and BMI, and age as variables of no interest (Bonferroni corrected α = .0063).2.6.2First, we wanted to directly investigate the relationship of EEG asymmetries  and whole‐brain fALFF asymmetries in Sample 1 to investigate the relationships between EEG and fMRI measures. Whole‐brain fALFF asymmetries were calculated by means of (a) flipping left and right hemispheres in fALFF images (left becomes right and vice versa), (b) subtracting the flipped image from the original image, (c) adding the flipped image to the original image and (d) dividing the image obtained in Step 2 by the image obtained in Step 3. This resulted in an image of voxel‐wise values corresponding to the asymmetry index (L − R)/(L + R) /(R + L) index of the right side of the brain image). A significant correlation between the EEG asymmetry index as calculated in section 2.5.1 and whole‐brain fALFF asymmetries would indicate that those two measures, even though methodologically very distinct, measure similar brain processes. This analysis was performed in SPM12 using a general linear model with voxel‐wise fALFF asymmetries as an outcome variable and the EEG asymmetry index as an explanatory variable. Results were thresholded on a voxel‐level with a 0.001 threshold and corrected for multiple comparisons using the whole‐brain 0.05 FWE‐corrected threshold.To investigate relationships of fMRI hemispheric bias with approach/avoidance and eating behaviours, we first used rotated principal component analysis (PCA) on the ROI imaging data . This was done to reduce the number of comparisons in further analyses .Furthermore, to investigate relationships of fMRI hemispheric bias and approach/avoidance behaviour, we performed a similar analysis to the one using EEG data. Six rotated principal components were defined as outcome measures, and predictors included BAS fun, BAS drive, BAS reward responsivity, as well as BIS anxiety and FFFS fear scores and their interaction with gender. Additionally, we included BMI and age as variables of no interest .A similar analysis was performed to investigate relationships between fMRI hemispheric bias and eating behaviour. It included similar predictors as the EEG investigation of eating behaviour—cognitive control and disinhibition and their interaction with gender. Outcome variables were six rotated principal components. We added BMI and age as variables of no interest .Investigations of approach/avoidance behaviours in Sample 2 were performed similarly to the ones in Sample 1. Six rotated components were defined as outcome variables, and predictors included BIS/BAS questionnaire measures, their interaction with gender, and BMI. Age was added as a regressor of no interest .A similar analysis was performed to investigate associations of self‐reported eating behaviour and hemispheric asymmetries for Samples 2 and 3. Predictor variables included eating questionnaire measures and their interaction with gender, BMI, age (regressor of no interest), while outcome variables were six rotated components  = .121, p = .202). Partial correlation after controlling for BMI, age and gender also did not reveal a significant relationship (r(113) = .094, p = .325).In this analysis, we aimed to directly replicate findings of Sutton and Davidson  of increp = .0009, BAS drive * gender: p = .0020). This is shown by an interaction of BAS drive with gender, and a significant main effect of BAS drive. In this analysis, women were coded as 0 and were the reference category, hence the main effect of BAS drive shows that this relationship is true for women, because in this case all other interaction terms including gender are also equal to zero. A similar relationship was not significant for broad alpha power. For scatter plots of these relationships see Figure Y‐axis, Figure p = .0002). Post hoc analysis of this effect showed that the association between BAS drive and asymmetry index calculated with the relatively low alpha power is significant for women in the eyes open condition (p = .002). This suggests that the EEG asymmetry findings are specific to the eyes open condition only.Next, we attempted to expand previous findings linking EEG and approach/avoidance behaviours to (a) additional frequency ranges to improve specificity and interpretability of findings, (b) additional questionnaire measures to improve specificity of the findings. We therefore investigated relationships between EEG parietal and frontal asymmetry indices as measured by the relative broad alpha power, as used by Sutton & Davis, and relative low alpha power. In addition to the standard broad alpha power spectrum used in previous studies, low alpha power spectrum due to its specific physiological meaning . Here, we did not find any significant associations for eyes open or eyes closed conditions. Detailed results of these analyses can be found in Tables 3.2First, we investigated direct relationships between EEG asymmetries (using the relative asymmetry index (R − L)/(R + L)) and whole‐brain fALFF asymmetry measures in the same sample. This analysis did not produce significant results, suggesting no correspondence between rsEEG and rsfMRI hemispheric bias measures.n = 110).Next, we investigated relationships between fMRI relative asymmetry indices (L − R)/(L + R) and approach/avoidance behaviours in Sample 1. The analysis included six retained components describing asymmetry data and questionnaire variables—BAS fun, BAS drive, BAS reward responsivity, BIS anxiety and FFFS fear and their interactions with gender. Additionally, we included BMI and age as covariates of no interest. We found no significant associations for this analysis Tables , n = 110n = 106).Furthermore, we investigated whether hemispheric asymmetries measured with fMRI are related to self‐reported eating behaviour (TFEQ). This analysis included cognitive control, disinhibition and their interactions with gender as predictor variables, while the outcome variables were the six rotated components from the PCA analysis. Variables of no interest were BMI and age. Here, we did not find any significant relationships. Results of this analysis can be found in Tables 3.3Here, we investigated relationships between fMRI relative asymmetry indices (L − R)/(L + R) and approach/avoidance behaviours in Sample 2, characterised by a wider BMI range including individuals with overweight and obesity. The analysis included the six retained components describing asymmetry data as outcome variables and questionnaire variables—BAS fun, BAS drive, BAS reward responsivity, BIS anxiety, FFFS fear, their interactions with gender and BMI as predictors. Additionally, we included age as a regressor of no interest. Sample size for this analysis was 85.We found a significant interaction effect of BAS Drive and gender on the rotated component 6 /(L + R) are related to self‐reported eating behaviour in Samples 2 and 3 (characterised by a wider BMI range). These analyses included cognitive control, disinhibition, their interactions with gender and BMI as predictor variables, while the outcome variables were six rotated components from the PCA. Age was entered as a regressor of no interest. Our analyses revealed no relationships between hemispheric asymmetries and eating behaviour in both samples. Details of these analyses can be found in Tables 4In this study, we aimed at replicating previous EEG findings concerning relationships of resting‐state hemispheric asymmetries and approach/avoidance behaviours in healthy participants. Second, we aimed to investigate whether EEG asymmetry findings and fMRI asymmetry findings correspond to each other in the approach/avoidance context, as they do in the language (e.g. syntactic and semantic processing), or attention context  is related to higher left‐brain activity at rest. While Sutton and Davidson  found a It is worth noting that we found significant associations of questionnaire measures and hemispheric asymmetries measured with low relative alpha power, but not with broadband relative alpha power. Since low alpha power represents such attentional processes as vigilance (Klimesch et al., The second aim of our study was to investigate whether approach/avoidance‐related asymmetries can be measured with both EEG and fMRI. We were not able to replicate EEG findings in Sample 1 using rsfMRI. Such lack of replication might be related to the fact that alpha power and fALFF measure different processes. This is also reflected in a lack of direct relationship between EEG and whole‐brain fALFF asymmetries. Alpha power is indeed conceptualised to be inversely related to brain activity by enabling active inhibition (Klimesch et al., Interestingly, in Sample 2, which included overweight and obese individuals we replicated EEG findings from Sample 1: We found relationships between rsfMRI and BAS drive questionnaire. Here, women showed a positive relationship between BAS drive and left vs. right hemispheric activity in the rotated components highly related to the BA10 and VTA. Additionally, we found a significant positive association between BIS anxiety and left versus right hemispheric activity in the component related to the VTA. Findings of the VTA in the context of approach/avoidance behaviour and hemispheric asymmetry are novel, because previous studies used predominantly EEG to measure brain activity, which makes it difficult to obtain measures of activity from subcortical brain regions. The association of VTA and BAS drive confirms our hypothesis that the left brain hemisphere is predominantly related to approach behaviour. However, the association between VTA and BIS anxiety points to an opposite pattern (Harmon‐Jones & Gable, We further investigated the relationship between hemispheric bias and BMI, since BMI in the obese range is related to increased approach behaviour (Mehl et al., Finally, we investigated associations between hemispheric asymmetries and self‐reported eating behaviours in all three samples. Here, we did not find any relationships using rsEEG and rsfMRI data. That is, we were not able to replicate previous rsEEG findings showing hemispheric bias relationships with disinhibition, hunger (Ochner et al., Some limitations of our study need to be acknowledged: EEG data were only available for one sample. It would provide additional evidence to investigate differences between rsEEG and rsfMRI asymmetry associations with behavioural measures in other samples, especially concerning BMI and eating behaviour—aspects not investigated as thoroughly as approach/avoidance behaviours. As our study investigated relationships between self‐reported approach/avoidance behaviours and resting‐state neuroimaging measures, future studies could also include task‐based neuroimaging measures, especially in the context of obesity. This might give a more valid proxy for everyday motivational behaviours and therefore have higher ecological validity.In sum, we conceptually replicated findings showing relationships between hemispheric bias and approach/avoidance behaviours in women, but not self‐reported eating behaviour in both rsEEG and rsfMRI. Moreover, we investigated relationships between rsEEG alpha power measures and rsfMRI fALFF. We show that associations of hemispheric asymmetries measured with rsEEG and rsfMRI are similar, however, we do not provide a replication of rsEEG results and rsfMRI results in the same sample. Future studies should answer the question of how those measures relate to each other in a more systematic way. We suggest that future studies should be performed using samples of lean, overweight and obese participants using both EEG and fMRI measures.All authors declare no conflict of interest.Appendix S1: Supporting informationClick here for additional data file.


Pemphigus and pemphigoid diseases are characterized and caused predominantly by IgG autoantibodies targeting structural proteins of the skin. Their current treatment relies on general and prolonged immunosuppression that causes severe adverse events, including death. Hence, novel safe and more effective treatments are urgently needed. Due to its' physiological functions, the neonatal Fc receptor (FcRn) has emerged as a potential therapeutic target for pemphigus and pemphigoid, primarily because IgG is protected from proteolysis after uptake into endothelial cells. Thus, blockade of FcRn would reduce circulating autoantibody concentrations. However, long‐term effects of pharmacological FcRn inhibition in therapeutic settings of autoimmune diseases are unknown.Therapeutic effects of FcRn blockade were investigated in a murine model of the prototypical autoantibody‐mediated pemphigoid disease, epidermolysis bullosa acquisita (EBA). B6.SJL‐H2s C3c/1CyJ mice with clinically active disease were randomized to receive either an anti‐FcRn monoclonal antibody (4470) or an isotype control over 4 weeks.While clinical disease continued to worsen in isotype control‐treated mice, overall disease severity continuously decreased in mice injected with 4470, leading to almost complete remission in over 25% of treated mice. These clinical findings were paralleled by a reduction of autoantibody concentrations. Reduction of autoantibody concentrations, rather than modulating neutrophil activation, was responsible for the observed therapeutic effects.The clinical efficacy of anti‐FcRn treatment in this prototypical autoantibody‐mediated disease encourages further development of anti‐FcRn antibodies for clinical use in pemphigoid diseases and potentially in other autoantibody mediated diseases. AIBDautoimmune bullous dermatosesCOL7type VII collagenEBAepidermolysis bullosa acquisitaFcRnneonatal Fc receptorPDpemphigoid diseasesThe neonatal Fc receptor (FcRn) controls the half‐life of IgG (auto)antibodies.FcRn‐deficient mice are partly protected from induction of certain autoimmune diseases.Anti‐FcRn treatment improves autoantibody‐mediated experimental autoimmune disease in mice.Anti‐inflammatory effects of FcRn inhibition are paralleled by reduced autoantibody titres.Inhibition of FcRn has potential as a therapeutic pathway in autoantibody‐mediated diseases.1https://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=2628 antibody https://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=6780 with systemic corticosteroid leads to remission, off therapy, in almost 90% of the patients after 24 months, but 40% of patients experience grade 3–4 severe adverse events. Furthermore, the time to achieve complete remission is rather long, more specifically ≥6 months after initiation of treatment  comprise a group of diseases characterized and caused by autoantibodies against structural proteins of the skin. AIBD can be classified into pemphigus diseases, where autoimmunity towards desmosomal antigens is the underlying cause, and pemphigoid diseases with autoimmunity against antigens located along the dermal–epidermal junction , the most common pemphigoid disease  from proteolysis after uptake into endothelial cells and hence is crucial for IgG homeostasis. FcRn is also expressed by antigen‐presenting cells (APC), such as monocytes, macrophages, and dendritic cells, as well as on neutrophils. Here, FcRn functions to recycle IgG after its uptake. Furthermore, and independent of IgG recycling, FcRn expressed on APCs is important for phagocytosis of bacteria, and a role in APC processing of immune complexes has been described . BP was diagnosed based on clinical presentation, IgG, and/or C3 deposition along the dermal–epidermal junction in direct immunofluorescent microscopy from a peri‐lesional skin biopsy, detection of anti‐BP180‐NC16A antibodies by ELISA, and binding of patient IgG to the blister roof in indirect immunofluorescent microscopy using human salt split skin as a substrate  mice develop experimental EBA after immunization against COL7  as previously described  of immunized mice achieved this criterion within 7 weeks of immunization. After allocation, isotype or 4470 was administered at a dose of 30 mg·kg−1 bodyweight i.p. twice weekly. Mice were clinically examined once per week to determine the body surface area affected by EBA skin lesions (primary endpoint of the study) by an investigator not aware of the treatment given to individual mice. Blood was obtained by venepuncture at Weeks 0, 2, and 4 of the treatment period. At the same time points at and after randomization, adverse events were documented. For this, body weight, general condition, spontaneous behaviour, and  respiration rate and temperature were recorded and scored as outlined in Table Adult (8–12 weeks) B6.s mice at an equal sex distribution were immunized with recombinant murine COL72.4British Journal of Pharmacology .H&E histology, staining for IgG , and C3  from mouse skin were performed as described in detail elsewhere . After blocking, diluted samples  were added and incubated for 60 min. Standard reference curves were established by using the mouse reference sera  as described by the manufacturer's protocol using 1:50,000 diluted samples. For detection of circulating anti‐mCOL72.6https://www.euroimmun.de/produkte/indikationen/autoantikrper-diagnostik/rheumatologie/sle/aak-gegen-dsdns/iift/weitere-produkte.html, Euroimmun, Lübeck, Germany) as substrate and a FITC‐labelled, polyclonal donkey anti‐mouse F (ab)2 antibody . Sera were incubated on the Biochips at a 1:100 dilution. Sera from NZM2410/J mice (https://scicrunch.org/resources/Any/search?q=IMSR_JAX%3A002676&l=IMSR_JAX%3A002676) with or without proteinuria (n = 3 per group) from a previous study were included as positive or negative controls .ANA were measured using Biochips coated with HEp‐2 cells .For flow cytometry (FACS) analysis, single cell suspensions of inguinal lymph node, splenic cells, or erythrocyte free blood (containing EDTA) from mice after immunization‐induced EBA were blocked with anti‐FcRII/III blocking antibody (Miltenyi). Next, cells were incubated in 3% BSA/PBS containing various combinations of the following antibodies: FITC‐, phycoerythrin (PE)‐, or allophycocyanin (APC)‐, VioGreen‐conjugated anti‐mouse CD45 (clone 30F11), CD62L (clone MEL‐14), Gr‐1 (clone RB6‐8C5), CD11b (clone REA592), Ly6G (clone REA526), or appropriate isotype control antibodies (obtained from Miltenyi or BD). Dead cells were excluded by staining with PI (Miltenyi). Cells were first gated for singlets (FSC‐H compared with FSC‐A) and leukocytes (SSC‐A compared with FSC‐A). The leukocyte gates were further analysed for their uptake of PI to differentiate live from dead cells and their expression of CD45, taking only the live, healthy leukocyte population. Activation status of neutrophils was assessed using gating for scatter, single cells (FCS‐A vs. FSC‐W) and CD452.8−1) or with 100 μg·ml−1 isotype control antibody. Neutrophils were stimulated with immobilized immune complexes consisting of BP patient immunoadsorption material and recombinant NC16A ‐amplified chemiluminescence assay was used to measure the total of intracellular and extracellular ROS as described previously  or with 100 μg·ml−1 isotype control  for 30 minutes on ice. Subsequently, the different leukocyte fractions were incubated with skin sections for 3 hr at 37°C. After washing with PBS, slides were fixed in formalin and stained with haematoxylin and eosin. Skin sections were photographed, and split formation was determined by measuring section length and split length using ImageJ.Blister‐inducing capacity of patients' autoantibodies was evaluated using the cryosection assay, an ex vivo model of autoantibody‐induced dermal–epidermal separation originally described by Gammon et al.  and SigmaPlot  were used. For Jonckheere Terpstra patterned rank test, Gnu R open source software using the SAGx package was used (http://www.r-project.org). Used tests are indicated at the table and figure legends. According to the guidance for publication in the BJP  using SigmaPlot with the following assumptions: minimum detectable difference of 50%, SD 25%, power 90%, α of 5%, and ANOVA with eight groups . This requires a sample size of 11 per group.The data and statistical analysis comply with the recommendations of the 2.11The antibodies used were as follows. Rozanolixizumab (UCB7665) is an anti‐human FcRn IgG4P mAb under investigation in clinical trials . A pharmacologically equivalent murinised anti‐mouse FcRn IgG1 antibody (4470) was used in a preclinical mouse model of an autoantibody‐driven autoimmune disease. Generation of the anti‐mouse FcRn antibody 4470 and an isotype matched control antibody has recently been described .2.12http://www.guidetopharmacology.org, the common portal for data from the IUPHAR/BPS Guide to PHARMACOLOGY  isotype antibody alone, (b) 4470, (c) TiterMax® and isotype antibody, and (d) TiterMax® and 4470, at the start of treatment and 8 weeks thereafter. All data were normalized to the IgG serum concentrations at the start of treatment. In all groups treated with isotype antibody, a significant increase from pretreatment IgG serum concentration was noted, while this was not observed for mice treated with anti‐FcRn. In addition, in untreated, as well as in TiterMax®‐injected mice, anti‐FcRn treatment led to a significant decrease of IgG at Week 8. Hence, as expected, the 4470 anti‐FcRn antibody reduced total IgG concentrations in B6.s mice, in both untreated and TiterMax® injected mice.3.2To evaluate the effects of anti‐FcRn treatment in already manifest AIBD, we induced experimental EBA in mice by immunization. In order to duplicate the experimental set‐up of clinical trials, only mice that met pre‐defined disease severity were treated with anti‐FcRn or the isotype antibody. To prevent observer bias, the investigator scoring the mice for the primary endpoint  was unaware of the applied treatment Figure a. At ranThere are some reports in the mouse linking genetic deficiency of either the FcRn α chain or β2 microglobulin to the induction of autoantibodies and, indeed, to the spontaneous development of arthritis. To address this possible issue, we recorded the well‐being of the animals during the experiment and recorded any adverse events during the treatment with either isotype or anti‐FcRn antibody. The overall burden of the mice in the treatment group was mild in most cases , while at higher concentrations, anti‐FcRn led to an enhanced ROS release from the neutrophils Figure . In turn4Taking our results together, we have here demonstrated the efficacy of anti‐FcRn treatment in experimental murine EBA and concluded that the therapeutic effects of anti‐FcRn treatment are associated with a reduced concentration of circulating and tissue‐bound autoantibodies targeting COL7.Previous data had indicated that FcRn is involved in the pathogenesis of autoantibody‐mediated diseases: First, genetic alterations of the Fc‐molecule that alter its binding to FcRn, have been shown to be associated with autoantibody‐mediated disease—specifically in pemphigus, where the p.Arg435his variation of IgG3 that has a high affinity to the FcRn is associated with disease susceptibility  and other autoantibody‐mediated disorders . The rationale for FcRn inhibition in diseases with a clearly different pathogenesis  is based on the finding that the (different) autoantibodies reach their target antigen through the bloodstream, where the half‐life is controlled by the FcRn.https://www.guidetopharmacology.org/GRAC/ObjectDisplayForward?objectId=1948) inhibitors may be used. BTK inhibitors are currently in phase II clinical trials for pemphigus (NCT02704429).FcRn‐targeting antibodies alone may be insufficient for many of these diseases, because, with a few exceptions, immunoadsorption/plasmapheresis is insufficient to induce long‐term remission in most autoantibody‐mediated diseases (Meyersburg, Schmidt, Kasperkiewicz, & Zillikens, Based on these insights into FcRn biology and FcRn‐targeting therapies in a prototypical autoantibody‐mediated disease, clinical trials evaluating FcRn‐targeting therapies are merited in pemphigoid diseases and may enable an additional treatment option for the still unmet (Lamberts et al., A.K., M.H., K.B., B.S., and P.W. performed experiments, and B.S., P.W., A.S., R.J.L., and E.S. conceived the study. All authors interpreted the data and wrote and revised the manuscript.In the last 3 years, R.J.L. has received research funding from Almirall, True North Therapeutics, UCB Pharma, ArgenX, TxCell, Topadur, Incyte, and Admirx and fees for consulting or speaking from ArgenX, Immunogenetics, Novartis, and Lilly. E.S. has received research funding from Almirall, UCB Pharma, ArgenX, TxCell, Incyte, Novartis, Euroimmun, and Admirx and fees for consulting or speaking from ArgenX, UCB, TxCell, Novartis, Fresenius, and Almirall. A.S. is an employee of UCB, and P.W. and B.S. are former employees of UCB. All other authors declare no conflict of interest.BJP guidelines for https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/bph.14207, https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/bph.14208, and https://bpspubs.onlinelibrary.wiley.com/doi/full/10.1111/bph.14206, and as recommended by funding agencies, publishers and other organisations engaged with supporting research.This Declaration acknowledges that this paper adheres to the principles for transparent reporting and scientific rigour of preclinical research as stated in the Table S1 The sum of all adverse effects was added to calculate the burden of disease. A score of <10 corresponds to mild burden, scores between 10 and < 20 are considered moderate, while scores ≥20 are considered severe. Mice are killed if the cumulative score is 20 or more, of if the body surface area affected by inflammatory skin lesions is 20% or more, at any time during the experiment.Click here for additional data file.


Abnormal invasive hemodynamics after transcatheter aortic valve replacement (TAVR) is associated with poor survival; however, the mechanism is unknown.Diastolic dysfunction will modify the association between invasive hemodynamics postTAVR and mortality.Patients with echocardiographic assessment of diastolic function and postTAVR invasive hemodynamic assessment were eligible for the present analysis. Diastology was classified as normal or abnormal (Stages 1 to 3). The aorto‐ventricular index (AVi) was calculated as the difference between the aortic diastolic and the left ventricular end‐diastolic pressure divided by the heart rate. AVi was categorized as abnormal (AVi < 0.5 mmHg/beats per minute) or normal (≥ 0.5 mmHg/beats per minute).P < .001). All‐cause mortality was 46% in the abnormal vs 31% in the normal AVi group (P < .001). Adjusted hazard ratio (HR) for AVi < 0.5 mmHg/beats per minute vs AVi ≥0.5 mmHg/beats per minute for intermediate‐term mortality was . This association was the same among those with normal diastolic function and those with diastolic dysfunction (P for interaction = .35).From 1339 TAVR patients, 390 were included in the final analysis. The mean follow‐up was 3.3 ± 1.7 years. Diastolic dysfunction was present in 70.9% of the abnormal vs 55.1% of the normal AVi group (Diastolic dysfunction is prevalent among TAVR patients. Low AVi is an independent predictor for poor intermediate‐term survival, irrespective of co‐morbid diastolic dysfunction. AViaorto‐ventricular indexCIconfidence intervalHRhazard ratioTAVRtranscatheter aortic valve replacement1Hemodynamic assessment was initially developed as a supplemental tool to assess for paravalvular aortic regurgitation after transcatheter aortic valve replacement (TAVR).22.1clinicaltrials.gov, NCT01368250).All patients undergoing TAVR at the University of Bern were consecutively enrolled in an institutional database that is part of the Swiss TAVI Registry  peak tricuspid regurgitation velocity > 2.8 m/s.All patients underwent a transthoracic echocardiogram within 3 months before TAVR. During the echocardiogram, at least three consecutive heart beats were recorded and averaged for each parameter. Echocardiogram loop and still frames were analyzed at a workstation for offline analysis . According to current American Society of Echocardiography and European Association of Cardiovascular Imaging guidelines, the following variables were required for assessment of diastolic dysfunction (see flow diagram from previous publication): (a) annular e' velocity ; (b) average E/e' ratio > 14; (c) left atrial maximum volume index >34 mL/m2.3A multidisciplinary heart team of cardiac surgeons, interventional cardiologists, imaging, and heart failure specialists decided on treatment strategy and suitability for TAVR, which was performed according to current guidelines and standard approaches.After the intervention, all patients were monitored for at least 48 hours. Laboratory examination and a 12‐lead electrocardiogram were routinely performed immediately after the procedure and daily thereafter. In all patients, echocardiogram was performed before discharge.2.4Standardized clinical follow‐up was performed at 30 days, 1 year, and 5 years after TAVR. Telephone interviews, documentation from referring physicians, and hospital discharge summaries were used to ascertain clinical endpoints. All suspected adverse events were independently adjudicated according to the criteria by the valve academic research consortium‐2.2.5P values were derived from student's t tests for continuous and chi‐square tests for categorical variables comparing the two groups. The cumulative incidence of the primary and secondary endpoints was estimated by the Kaplan‐Meier method. Patients were censored at the event of interest, time of last contact, or the maximal follow‐up time. Cox regression was used to compare time‐to‐event data between groups. Crude hazard ratios (HR) were generated with  with P values from Wald chi‐square tests. Adjusted HR (95% CI) included all variables with a P value <.10 from the univariable analysis into the multivariable model.Continuous data are reported as mean ± SD and categorical variables as the number of patients and percentage. P values <.05 were considered statistically significant.All analyses were performed with Stata version 14.2 . Two‐sided 3Between August 2007 and December 2015, 1339 patients were enrolled in the TAVI registry. Seven hundred and seventy underwent detailed echocardiographic assessment and after excluding an additional 387 patients due to inability to grade diastolic dysfunction, lack of postTAVR invasive assessment, or moderate to severe paravalvular aortic regurgitation, 390 were included in the final cohort Figure . The meaP < .001, Table P < .001). Mean total arterial compliance was the same in both groups. Intra‐procedural hemodynamic variables are provided in Table One hundred and eighty‐nine patients (48.5%) were categorized as abnormal (AVi < 0.5) and 201 patients (51.5%) as normal (AVi > 0.5). There were no differences in baseline characteristics, except that the abnormal hemodynamics group had a higher prevalence of peripheral vascular disease and higher estimated surgical risk (Table P < .001) (Table P = .018).Diastolic dysfunction was present in 70.9% of the abnormal AVi group vs 55.1% of the normal AVi group (1) Table . DiastolP = .001) . The multi‐variable association between low vs normal AVi was . Among those with diastolic dysfunction, all‐cause mortality was 59% in the AVi < 0.5 vs 44% in the AVi ≥0.5 group (P = 0.006). The multi‐variable association between low vs normal AVi was   and among those with elevated baseline filling pressures the association was   Baseline diastolic dysfunction was prevalent among TAVR patients. (b) Postimplantation AVi < 0.5 mmHg/beats per minute was an independent predictor for poor intermediate‐term survival. (c) Abnormal invasive hemodynamics was associated with poor prognosis irrespective of baseline diastolic dysfunction. We previously theorized that each component of the AVi would have clinical significance. For example, a low aortic diastolic pressure could represent unrecognized paravalvular aortic regurgitation, poor systemic flow, and/or poor arterial compliance. An elevated left ventricular end‐diastolic pressure could represent left ventricular systolic dysfunction and/or diastolic dysfunction. An elevated heart rate could represent poor systemic flow (since heart rate is inversely related to stroke volume) and/or background medical therapy. Therefore, the AVi is more than just an assessment of diastolic dysfunction, which was born out in the study findings.These findings confirm our previous observation of an association between abnormal hemodynamics and poor survival, but this time in a larger independent cohort. The current analysis leveraged the Bern TAVI registry, which represents the largest published work on diastology and TAVR. Diastolic dysfunction was prevalent among all patients but especially among those with abnormal hemodynamics; however, pre‐operative diastolic dysfunction did not modify the association between abnormal AVi and intermediate‐term survival. We found no evidence for a difference in total arterial compliance between groups, which was another potential mechanism for the hazardous association.Moderate to severe paravalvular aortic regurgitation has consistently been shown to be associated with poor survivalWe previously reported that diastolic dysfunction Stage 3 was the strongest predictor for 1 year mortality. Optimal management of left ventricular diastolic dysfunction among TAVR patients is not well known.Limitations of the current analysis include: (a) Abnormal hemodynamics was present in approximately one‐fifth of our derivation cohort vs approximately one‐half of patients in the current cohort. The reason for this difference is unknown. Aortic diastolic blood pressure was the same between the two cohorts with abnormal hemodynamics; however, the Bern TAVI registry was associated with a higher left ventricular end‐diastolic pressure and heart rate. This may reflect differences in background/procedural medical therapy; however, this information was not available in either registry. (b) Patients with mitral annular calcification, atrial fibrillation, previous pacemaker or surgical mitral valve were excluded from the current study. This could have resulted in unintended selection bias; however, these characteristics were included in our development cohort.5In summary, invasive hemodynamic assessment after TAVR can be used to risk stratify patients. There was a high prevalence of diastolic dysfunction among TAVR patients. AVi < 0.5 mmHg/beats per minute was associated with poor intermediate‐term survival, and baseline diastolic dysfunction did not modify this association.Dr Anthony Bavry has received honoraria from the American College of Cardiology and Edwards Lifesciences (significant); Dr Taishi Okuna has receive lecture fees from Abbott (modest); Dr Stephan Windecker received research and educational grants to the institution from Abbott, Amgen, Bayer, BMS, Biotronik, Boston Scientific, CSL Behring, Edwards, Medtronic, Polares and Sinomed; Dr Thomas Pilgram received research grants to the institution from Biotronik, Boston Scientific, Edwards Lifesciences; speaker fees from Biotronik and Boston Scientific; consultancy for HighLifeSAS.Figure S1 Flow diagram of study cohortClick here for additional data file.Figure S2 Survival analysis among subjects with low vs normal Aorto‐Ventricular index (AVi)Click here for additional data file.Table S1 Clinical outcomesClick here for additional data file.


The non‐invasive prenatal screening (NIPS) has been introduced into clinical practice with a high sensitivity and specificity. Although the false‐negative results are inevitable and important, limited false‐negative NIPS results have been reported and studied previously. In this study, we aim to report and analyze false‐negative results detected in the NIPS cases with a low‐risk result.NIPS was performed using whole‐genome massively parallel shotgun sequencing for screening common trisomies, rare autosomal aneuploidies, and subchromosome copy number variants. All the NIPS cases with a low‐risk result performed in our center in 2017 were followed‐up using medical records and telephone interview at 3 months after delivery. Fetal ultrasound results and available genetic diagnostic testing results were collected for pregnancies with adverse outcomes. The genetic diagnostic testing referred to chromosomal microarray analysis or fluorescent in situ hybridization on amniotic fluid cells, fetal skin tissue, neonatal peripheral blood, or available placental biopsies.By following‐up 10,975 low‐risk results, we found 166 NIPS cases with adverse pregnancy outcomes, in which eight cases had diagnostic testing. Among them, four false‐negative cases were confirmed, including one trisomy 18 caused by placental mosaicism, one mosaic tetrasomy 12p, and 2 microdeletion/microduplication cases.Our results revealed that mosaicism contributes to a major cause of false negative in NIPS, and highlighted the importance of ultrasound in identifying these false‐negative results. In this study, we performed a comprehensive follow‐up on a cohort consisting 10975 NIPS cases with a low‐risk result from a single center, which revealed four false‐negative cases. Our study provides information regarding the etiologies of false‐negative results, which will contribute to improve both the NIPS technique and the related perinatal care in clinic. After library construction, all libraries were sequenced on the BGI‐500 platform . Sequencing reads of 35 bases were trimmed and aligned to a universal unique read set, incised from the human reference genome . Quality control criteria included minimum unique read number of 3.5 Mb, GC content range of 38%–42%, and minimum fetal fraction of 3.5%. Analysis was performed for all samples on aneuploidies of chromosomes 13, 18, 21, X, and Y, as well as other genome‐wide RAT and subchromosome CNV . In our center, tissue samples, such as placental tissues and fetal tissues, were tested using the platform of human cyto12 SNP array, whereas other samples, such as peripheral blood samples and amniotic fluid cells, were tested using the platform of CytoScan 750K array. For Illumina array platform, Human cyto12 SNP array (Illumina) comprising approximately 300,000 SNP probes was applied for the whole‐genome scan. Molecular karyotype analysis was performed by KaryoStudio V 1.4.3.0 (Illumina). For Affymetrix array platform, CytoScan 750K array (Affymetrix) comprising approximately 550,000 copy number CNVs probes and 200,000 SNP probes were applied for the whole‐genome scan. Molecular karyotype analysis was performed by Affymetrix Chromosome Analysis Suite (ChAS). CNVs of two array platforms were all called at an effective minimal resolution of 100kb involving at least 10 contiguous probes.2.5FISH analysis was performed according to the manufacturer's protocols (VYSIS Inc.) using commercially available subtelomeric specific probes.3+6 weeks (ranging from 12+0 to 26+5 weeks). In all, 200 (1.79%) high‐risk results and 10,975 (98.21%) low‐risk results were reported. Among the low‐risk results, 10,476 (95.45%) cases were successfully followed‐up, which revealed 10,310 cases with benign pregnancy outcomes, 166 cases with adverse pregnancy outcomes, including neonatal demise, pregnancy loss, termination of pregnancy (TOP), and live birth with dysmorphic facial features, associated with or without fetal ultrasound findings , and the average gestation was 17Eight of the 166 cases with adverse pregnancy outcomes had diagnostic testing for the fetuses, in which four false‐negative results were confirmed, including one aneuploidy (trisomy 18), one partial aneuploidy (tetrasomy 12p), and 2 subchromosomal microdeletion/microduplication cases. The clinical information of the four false‐negative cases, including NIPS results, ultrasound findings, and pregnancy outcomes, are summarized in Table 4Although NIPS has been applied into clinic with a critical contribution to prenatal care, false negatives are inevitable due to the nature of this cfDNA‐based screening method. Previous studies raised the importance of studying the false‐negative results. However, Hartwig et al. systematically searched and reviewed 22 papers related with discordant NIPS results between 2013 and 2016, and found only 24 false‐negative results for trisomies 21, 18, and 13 were reported, with 45.83% (11/24) had unexplained causes  cases with adverse pregnancy outcomes. As it was reported that 50%–60% of pregnancy losses and about 20% abnormal ultrasound findings were associated with chromosomal abnormalities (Rai & Regan, In summary, we reported four false‐negative results from following‐up 10,975 NIPS cases with a low‐risk result. Our results revealed that mosaicism contributes to a major cause of false negative in NIPS, and highlighted the importance of ultrasound in identifying these false‐negative results. In addition, our results emphasized the importance of following‐up in NIPS, especially in cases with a low‐risk results, which would cost a large number of efforts but was beneficial to realize the limitation for NIPS, and to improve prenatal management in clinic.The authors have no conflict of interest.


Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with a high incidence and poor prognosis. Exploration of the underlying mechanisms and effective prognostic indicators is conducive to clinical management and optimization of treatment. The RNA‐seq and clinical phenotype data of HCC were retrieved from The Cancer Genome Atlas (TCGA), and differential expression analysis was performed. Then, a differential lncRNA‐miRNA‐mRNA regulatory network was constructed, and the key genes were further identified and validated. By integrating this network with the online tool‐based ceRNA network, an HCC‐specific ceRNA network was obtained, and lncRNA‐miRNA‐mRNA regulatory axes were extracted. RNAs associated with prognosis were further obtained, and multivariate Cox regression models were established to identify the prognostic signature and nomogram. As a result, 198 DElncRNAs, 120 DEmiRNAs, and 2827 DEmRNAs were identified, and 30 key genes identified from the differential network were enriched in four cancer‐related pathways. Four HCC‐specific lncRNA‐miRNA‐mRNA regulatory axes were extracted, and SNHG11, CRNDE, MYLK‐AS1, E2F3, and CHEK1 were found to be related with HCC prognosis. Multivariate Cox regression analysis identified a prognostic signature, comprised of CRNDE, MYLK‐AS1, and CHEK1, for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. The AUC of the prognostic signature and nomogram for 1‐year, 3‐year, and 5‐year survival was 0.777 (0.657‐0.865), 0.722 (0.640‐0.848), and 0.630 (0.528‐0.823), and 0.751 (0.664‐0.870), 0.773 (0.707‐0.849), and 0.734 (0.638‐0.845), respectively. These results provided clues for the study of potential biomarkers and therapeutic targets for HCC. In addition, the obtained 30 key genes and 4 regulatory axes might also help elucidate the underlying mechanism of HCC. This article constructed an HCC‐specific ceRNA network by integrating a differential lncRNA‐miRNA‐mRNA regulatory network and an online tool‐based ceRNA network. As a result, four lncRNA–miRNA–mRNA regulatory axes were extracted, and a prognostic signature  was identified for overall survival (OS) of HCC. A nomogram comprising the prognostic signature and pathological stage was established and showed some net clinical benefits. By using the new strategy of integrating differential network with online tool‐based ceRNA network, a robust and reliable HCC‐specific lncRNA‐miRNA‐mRNA network was obtained. Then, the HCC‐specific regulatory axes were extracted, from which, the prognostic signature was further identified. The new strategy in this study could provide powerful guidance in elucidating lncRNA‐mediated underlying regulatory mechanisms and identifying potential biomarkers or therapeutic targets for HCC. Additionally, the prognostic signature identified from the regulatory axes was of more biological significance.2The workflow of the present study is displayed in Figure 1.2.1https://xenabrowser.net) on April 7, 2020. The gene expression profiles of 374 HCC and 50 adjacent normal liver tissues and the miRNA data of 371 HCC and 49 adjacent normal liver tissues were included in the present study. LncRNAs and mRNAs in the expression matrix were annotated and distinguished according to the GTF file (gencode.v33.annotation.gtf) from GENCODE (https://www.gencodegenes.org/). The raw log2 (FPKM +1) RNA expression data were transformed into transcripts per million (TPM) using the following formula:The gene expression RNA‐seq data of the GDC TCGA‐LIHC cohort, miRNA mature strand expression RNA‐seq data of the TCGA‐LIHC cohort, and the corresponding phenotype data were retrieved from UCSC Xena Browser | >1 and adjusted 2.3The expression matrixes of DElncRNAs, DEmiRNAs, and DEmRNAs were merged by sample ID, and samples that failed to match were excluded. Then, 49 normal tissues and the corresponding 48 tumor tissues were selected to construct a lncRNA‐miRNA‐mRNA triple network for the normal and tumor groups, respectively. The edges that did not overlap in the two networks were selected to construct the differential network, which was named as data‐driven ceRNA network. The remaining samples in the merged RNA data set  were matched with the corresponding phenotype data and served as a validation set for further analysis; duplicate samples and those with incomplete survival information were deleted.http://www.bioconductor.org). Qpgraph was a comprehensive network analysis method by means of q‐order correlation graph. By performing conditional independence tests of order q, many indirect or spurious relationships could be removed. Another advantage of qpgraph was that we can choose to calculate and output only the edges of interest, thus, saving a lot of time and computing space.q was assumed as 1, 10, 15, and 20, and the average NRR was calculated, whose threshold was defined as 0.05. In addition, only lncRNA‐miRNA interactions and miRNA‐mRNA interactions were calculated and output.LncRNA‐miRNA‐mRNA interactive networks were constructed with R/Bioconductor package qpgraph  terms, cellular component (CC) terms, molecular function (MF) terms, and all the KEGG pathways were filtered to perform PPI network analysis with an online STRING database (https://string‐db.org/). Only experiments or co‐expression gene pairs with a combined score ≥0.9 (the highest confidence) were included in the PPI network. Then, the MCODE algorithm was applied to identify the densely connected module using Cytoscape plugin MCODE with the threshold parameters degree cutoff =4, node score cutoff =0.6, K‐core =4, and maximum depth =100. The key genes in the highly correlated subnetworks were identified using a Cytoscape plugin CytoNCA with centrality analyses methods. The expression and survival analyses of the key genes were further performed in the GEPIA database (http://gepia.cancer‐pku.cn/).https://www.cbioportal.org/). To explore the functions and potential molecular mechanisms underlying the identified key genes, gene set enrichment analysis (GSEA) was performed using the clusterProfiler package. After performing 1000 permutations, gene sets with a P value <0.05 and a q value <0.05 were considered significantly enriched.GO and KEGG pathway enrichment analyses were performed for DEmRNAs in the differential network with R/Bioconductor package clusterProfiler. GO terms and KEGG pathways with a 2.5http://www.mircode.org). The target mRNAs of miRNAs were predicted using miRWalk . Only mRNAs predicted by three databases miRDB, Targetscan, and miRTarBase together were considered as miRNA targets. Then, lncRNA‐miRNA interactions and miRNA‐mRNA interactions sharing the same miRNAs were selected to construct a ceRNA network, which was named as a prior‐driven ceRNA network.The identified DElncRNAs, DEmiRNAs, and DEmRNAs were used to construct a ceRNA network based on the hypothesis that lncRNAs affected the regulation of miRNAs on mRNAs by playing a sponge‐like role. The lncRNA‐miRNA interactions were predicted using miRcode (2.6GSE45436 using Kaplan‐Meier (KM) estimate, log‐rank (LR) test, Spearman correlation analysis, and Wilcoxon rank sum test.The intersection of prior‐driven and data‐driven ceRNA networks was taken as the HCC‐specific ceRNA network, which was defined as “hub network” and visualized with R package igraph. The expressions, prognostic values and associations of interested RNAs in the hub network were evaluated in the validation set, GEPIA database and GEO dataset 2.7Multivariate Cox regression analysis was performed on DERNAs with prognostic values to obtain the prognostic signature for HCC using R package survival. Then, the risk score of every patient was calculated using regression coefficients, and the samples were divided into high‐ and low‐risk groups with the median risk score as a cutoff. The Kaplan‐Meier estimate and LR test were further performed to compare the survival time between the two groups. The C‐index and time‐dependent ROC curve were used to evaluate the predictive value of the prognostic signature. Subsequently, univariate and multivariate Cox regression analyses were performed on the prognostic signature along with the clinical variables. A nomogram was constructed with significant clinical characteristics and risk scores for better prediction and more convenient clinical application. The clinical and prognostic values of the nomogram were evaluated with C‐index, time‐dependent ROC curve, calibration curve, and decision curve analysis using R package survival, survivalROC, timeROC, rms, and stdca.R function.33.1According to the differential expression analysis, 198 DElncRNAs (171 upregulated and 27 downregulated), 120 DEmiRNAs (27 upregulated and 93 downregulated), and 2827 DEmRNAs (2375 upregulated and 452 downregulated) were identified between the tumor and adjacent normal tissues. The distributions and expression patterns of the DERNAs were displayed using volcano plots and heatmaps in Figure 2A‐Figure 2C.3.2q  and average NRR cutoff of 0.05, 11,024, and 4616 edges were identified in the tumor and normal groups, respectively. Then, a differential network containing 2654 vertices  was constructed with the 15,018 non‐overlapping edges between the two groups  and 14 KEGG pathways were significantly enriched. The most significant BP, CC, MF, and KEGG pathway were carboxylic acid biosynthetic process , cytoplasmic vesicle lumen , cofactor binding , and biosynthesis of amino acids , respectively. Figure 3 displays the top 20 GO terms and KEGG pathways, among which 864 DEmRNAs were used to perform PPI network analysis and 1104 edges were output with a PPI enrichment P value <1.0e ‐ 16, under the threshold of a combined score ≥0.9. Then, MCODE module analysis identified four significantly correlated modules, and the top two most significant modules were used for the subsequent analysis, in which module 1 (Score =35.611) contained 37 nodes and 641 edges , progesterone‐mediated oocyte maturation , cellular senescence , and cell cycle  (Table A1).GO and KEGG enrichment analyses were performed on DEmRNAs involved in the network to explore the biological functions of the constructed differential network. With the threshold ges Fig. , and modges Fig. . Next, 1ges Fig. . GSEA wa3.3For DElncRNAs, 729 lncRNA‐miRNA interactions were predicted by miRcode; for DEmiRNAs and DEmRNAs, 273 miRNA‐mRNA interactions were predicted by miRWalk. In total, 255 lncRNA‐miRNA pairs and 247 miRNA‐mRNA pairs sharing the common miRNAs were selected to construct a prior‐driven ceRNA network, which included 27 DElncRNAs, 30 DEmiRNAs, and 182 DEmRNAs Fig. .3.4In order to elucidate the lncRNA‐miRNA‐mRNA regulatory mechanism in HCC, we take the intersection of the prior‐driven ceRNA network and the differential ceRNA regulatory network, and an HCC‐specific ceRNA network was obtained . This network consisted of 16 edges including nine lncRNA‐miRNA pairs and seven miRNA‐mRNA pairs. These comprised four lncRNA‐miRNA‐mRNA regulatory axes, namely SNHG11/hsa‐miR‐199a‐5p/E2F3, CRNDE/hsa‐miR‐199a‐5p/E2F3, MYLK‐AS1/hsa‐miR‐195‐5p/CHEK1, and MYLK‐AS1/hsa‐miR‐195‐5p/RASGEF1B. The eight DERNAs in the four regulatory axes were selected for further analysis. Of these, lncRNA SNHG11, CRNDE, and MYLK‐AS1 were upregulated, and miRNA miR‐199a‐5p and hsa‐miR‐195‐5p were downregulated. Further, mRNA E2F3 and CHEK1 were upregulated in the tumor group, which conformed to the ceRNA theory. However, mRNA RASGEF1B was downregulated in the tumor group, which deserves further investigation  =1.762, 95% CI: 1.161‐2.675], CRNDE , MYLK‐AS1 , E2F3 , and CHEK1  were risk factors for overall survival time of HCC. Also, the survival time was significantly shorter in the high‐expression group than in the low‐expression group . The survival analysis in GEPIA had roughly the same conclusions except SNHG11 , p = 1e‐07]. The risk score of every patient was calculated using the following formula: risk score =0.02083 × Exp(CRNDE) +0.02074 × Exp(MYLK‐AS1) +0.10721 × Exp(CHEK1), where “Exp” denoted the expression level of lncRNA normalized by mean. Then, patients were further divided into high‐ and low‐risk groups according to the median risk score. The KM survival curves demonstrated that patients in the high‐risk group had significantly poorer outcomes compared with those in the low‐risk group  . The time‐dependent ROC curve analysis showed that the AUC and 95% CI  of 1‐year, 3‐year, and 5‐year survival predicted by the prognostic signature (risk score) was 0.777 (95%CI: 0.657‐0.865), 0.722 (95%CI: 0.640‐0.848), and 0.630 (95%CI: 0.528‐0.823), respectively, indicating its effectiveness as a potential prognostic biomarker . The risk heatmap, risk curve and survival state diagram for high‐ and low‐ risk groups were displayed in Figure 7B, Figure 7C and Figure 7D, respectively.In order to construct the prognostic model of HCC, lncRNA SNHG11, CRNDE, and MYLK‐AS1, and mRNA E2F3 and CHEK1, which were significantly associated with the prognosis of HCC, were selected to perform multivariate Cox regression with the optimal subset strategy. The final model consisting of CRNDE, MYLK‐AS1, and CHEK1 was established and served as a prognostic predictive model of HCC [C‐index =0.705 .3.6Univariate and multivariate Cox regression analyses were performed for the clinical characteristics, including age, sex, body mass index (BMI), family history, pathological stage, and histological grade, along with the prognostic signature. The pathological stage and prognostic signature were found to be independent prognostic factors for the overall survival of HCC Table . To gain4HCC is a highly heterogeneous malignancy with a poor prognosis and remains a major public health hazard. An in‐depth exploration of the underlying mechanisms and identification of effective prognostic indicators for HCC are conducive to clinical treatment decisions and patient management. Recently, an increasing number of studies have illustrated that lncRNA plays critical regulatory roles in HCC through lncRNA‐miRNA‐mRNA axes. CACNA1G‐AS1 promoted the progression of HCC via competitively binding miR‐2392 and alleviating its inhibition on C1orf61.The differential ceRNA network was constructed using qpgraph, a network reconstruction method that makes full use of the disease‐specific data to analyze complex interactions among genes. In qpgraph, many indirect and spurious associations could be eliminated through conditional independent tests. Moreover, it allowed to calculate only interested edges so as to obtain a relatively concise and reliable network.b;b to inhibit tumors;CIP1 and p16INK4a/Rb tumor suppressor pathways.A total of 30 key genes were obtained from PPI network analysis for mRNAs in the top 20 GO terms and KEGG pathways of the data‐driven differential network. Among them, 18 ribosomal protein genes were enriched in the ribosomal pathway. Besides being important components of ribosomes, ribosomal proteins also performed some extra‐ribosomal functions, including oncogenic, tumor‐suppressive, and immune responses.Likewise, an HCC‐specific ceRNA network was constructed by integrating the prior‐driven and data‐driven ceRNA network, and four HCC‐related lncRNA‐miRNA‐mRNA regulatory axes SNHG11/hsa‐miR‐199a‐5p/E2F3, CRNDE/hsa‐miR‐199a‐5p/E2F3, MYLK‐AS1/hsa‐miR‐195‐5p/CHEK1, and MYLK‐AS1/hsa‐miR‐195‐5p/RASGEF1B were identified. Considering the potential advantages of molecular marker as a biomarker or therapeutic target, DERNAs with prognostic values in the four regulatory axes were further selected to construct a multivariate Cox regression model. Finally, CRNDE, MYLK‐AS1, and CHEK1 were identified as the prognostic signature, and patients were divided into high‐ and low‐risk groups with significantly different prognostic characteristics. The RNA expression levels in the four regulatory axes between high‐ and low‐risk groups were further analyzed. SNHG11, CRNDE, MYLK‐AS1, E2F3, and CHEK1 were found to be significantly highly expressed in the high‐risk group, while miR‐195‐5p, miR‐199a‐5p, and RASGEF1B were lowly expressed in the high‐risk group, demonstrating the vital roles of the four regulatory axes in the prognosis of HCC and the prognostic value of the prognostic signature.SNHG11 was a member of a small nucleolar RNA host gene family involved in various tumor progressions, such as promoting proliferation and metastasis by targeting the Wnt/β‐catenin signaling pathway and the Hippo pathway in lung cancer and colorectal cancer, respectively.MYLK antisense RNA 1 (MYLK‐AS1) was found to be downregulated in colon adenocarcinoma and upregulated in HCC.The underlying functions and regulatory mechanisms of the aforementioned four regulatory axes indicated the potential value of CRNDE, MYLK‐AS1, and CHEK1 as prognostic markers. A combination of prognostic signature with conventional clinical characteristics might provide better predictive efficacy compared with a single biomarker. Hence, a nomogram was established in this study using the prognostic signature along with the tumor pathological stage, which also acted as an impact factor on the overall survival of HCC. The nomogram showed excellent performance in predicting the 1‐year, 3‐year, and 5‐year survival rates, and had a good clinical application value.In summary, the identified 30 key genes functioned in HCC through four pathways: ribosomes, progesterone‐mediated oocyte maturation, cell senescence, and cell cycle. The four lncRNA‐miRNA‐mRNA regulatory axes might play vital roles in HCC by regulating cell proliferation, cell migration, cell cycle, and immune response. Importantly, CRNDE, MYLK‐AS1, and CHEK1 were identified as potential prognostic markers for HCC, and the corresponding nomogram showed some clinical net benefits. However, this study still had some limitations. First, the performance of the prognostic signature and nomogram could not be validated in external independent datasets due to either lack of MYLK‐AS1 expression data or lack of clinical and survival data. Second, the functions and regulatory mechanisms of the identified lncRNA‐miRNA‐mRNA axes in HCC need to be further verified by experimental studies.5The authors declare that they have no conflicts of interest.No approval from the ethics committee was needed because all the information was required from the publicly available database.6Fig S1Click here for additional data file.Fig S2Click here for additional data file.Fig S3Click here for additional data file.Fig S4Click here for additional data file.Fig S5Click here for additional data file.Fig S6Click here for additional data file.Fig S7Click here for additional data file.Fig S8Click here for additional data file.


Maintained structural integrity of hippocampal and cortical gray matter may explain why some older adults show rather preserved episodic memory. However, viable measurement models for estimating individual differences in gray matter structural integrity are lacking; instead, findings rely on fallible single indicators of integrity. Here, we introduce multitrait–multimethod methodology to capture individual differences in gray matter integrity, based on multimodal structural imaging in a large sample of 1522 healthy adults aged 60–88 years from the Berlin Aging Study II, including 333 participants who underwent magnetic resonance imaging. Structural integrity factors expressed the common variance of voxel-based morphometry, mean diffusivity, and magnetization transfer ratio for each of four regions of interest: hippocampus, parahippocampal gyrus, prefrontal cortex, and precuneus. Except for precuneus, the integrity factors correlated with episodic memory. Associations with hippocampal and parahippocampal integrity persisted after controlling for age, sex, and education. Our results support the proposition that episodic memory ability in old age benefits from maintained structural integrity of hippocampus and parahippocampal gyrus. Exploratory follow-up analyses on sex differences showed that this effect is restricted to men. Multimodal factors of structural brain integrity might help to improve our biological understanding of human memory aging. Performance in episodic memory tasks typically declines after the age of 60 years , but theHere, we combine multimodal imaging with multitrait–multimethod (MTMM) modeling  to repreRegarding the role of hippocampal microstructural integrity in age-related cognitive decline, there is some indication that higher mean diffusivity (MD) in hippocampus, indicating a less dense tissue structure, is related to poorer episodic memory performance in older adults . In studHippocampus does not operate in isolation . The parFurther cortical regions are known to interact with hippocampus in support of episodic memory, including prefrontal areas, retrosplenial/posterior cingulate cortex, and lateral parietal cortices  method , signal Microstructural properties of gray matter regions, and age-related differences therein, can be probed by MT imaging  psychological construct by measuring a range of indicators and interpreting their common variance as representative of the target construct. By using different indicators, we triangulate our target construct, integrity, which is sensible even if our indicators should only have limited overlap in variance .In a second set of analyses, we combined the latent brain integrity model with a latent episodic memory factor to investigate the associations of brain integrity and episodic memory performance. We hypothesize that gray matter integrity in regions of the episodic memory network is related to episodic memory performance.P < 0.0001 of robust Mahalanobis distances; detected using R-package faoutlier, version 0.7.2, n = 1523; one outlier found) and for the 12 MR variables in the MR sample . Hence, the effective sample with cognitive data consisted of 1522 older adults  = 2500 ms; echo time (TE) = 4.77 ms; TI = 1100 ms, acquisition matrix = 256 × 256 × 176, flip angle = 7°; 1 × 1 × 1 mm3 voxel size). Diffusion-weighted images were obtained with a single-shot diffusion-weighted spin-echo-refocused echo-planar imaging sequence . MT ratio images consisting of two volumes were acquired with identical settings . The first image (MT image) was acquired with a magnetic saturation pulse  and the second (noMT image) without a magnetic saturation pulse resulting in a proton-density-like image.Images were acquired on a Siemens Tim Trio 3T scanner  using a 32-channel head coil. The T1 images were obtained using a 3D T1-weighted magnetization prepared gradient-echo (MPRAGE) sequence based on the ADNI protocol , a toolbox that is implemented in Statistical Parametric Mapping  for VBM analysis of imaging data. We applied the CAT12 default cross-sectional preprocessing stream, which implements correction of the T1-weighted images for bias-field inhomogeneities, segmentation into gray matter, white matter and CSF, and spatial normalized using the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) algorithm. Modulation with Jacobian determinants was applied in order to preserve the volume of a particular tissue within a voxel leading to a measure of volume of gray matter. Gray matter images were used for the current set of analyses and smoothed with a Gaussian kernel of 8 mm .The MT ratio (MTR) maps for each subject were calculated on a voxel-by-voxel basis according to the formula MTR = (noMT − MT)/noMT. The data were then normalized into MNI space.dtifit to fit a diffusion tensor model at each voxel and obtain the MD values. The MNI based maps were produced using the standard TBSS pipeline , where b is the slope of regression of the appropriate ROI volume on ICV.We used intracranial volume (ICV) to adjust the VBM values for each ROI via the analysis of covariance formula : adjusteAll BASE-II participants were invited to 2 cognitive test sessions with an exact interval of 7 days and at the same time of day to avoid circadian confounding effects on performance. Each session lasted about 3.5 h. Participants were tested in groups of 4–6 individuals. Each group was instructed via a standardized session manual. Each task started with a practice trial to ensure that every participant understood the task. Depending on the task, responses were given via button boxes, the computer mouse, or a keyboard.The cognitive battery of BASE-II covers key cognitive abilities measured by 21 tasks, 4 of which assess aspects of episodic memory and were thus selected for the present study: 1) “Verbal Learning and Memory,” assessing free recall of auditorily presented words after each of five learning trials each consisting of the same 15 words . Participants typed the words they recalled on the keyboard, one by one visible on the screen; (1) “Face–Profession,” testing associative recognition memory 5 min after incidental encoding of 45 face-profession pairs. Participants were instructed to judge whether the face matches the profession. During recall, they were presented with 27 old, 9 new, and 18 rearranged pairs and were asked to provide old–new judgments ; (3) “Scene Encoding,” measuring recognition memory of 88 incidentally encoded scenes (task: indoor–outdoor judgment) after a delay of 2.5 h ; (4) “Object Location,” assessing free recall of 12 deliberately encoded object locations in a 6 by 6 locations grid . The tasks are described in detail in We used structural equation modeling to investigate the relationship between episodic memory performance and structural gray matter integrity in a multivariate approach, for two reasons. First, it enables us to capture variance shared across three different structural brain-imaging modalities in a latent factor of structural integrity for any given brain region. This makes sense from a theoretical perspective, as we aim to define a statistically plausible index of region-specific gray matter structural integrity. While the three imaging modalities are designed to assess different characteristics of gray matter structure, their shared variance can be interpreted as indicating a common cause for relatively good or relatively poor gray matter structural integrity. By separating the shared variance  from the unique, modality-specific variance , we hope to acquire a more reliable and valid estimate of integrity. In addition, we defined latent method factors for each modality  that capture common variance within the modality across all regions (capturing what is common to the measurement instrument only but not to the common factor). As a consequence, the residual variance estimates in our model represent variance that is neither shared by all ROIs in a given modality nor shared by all modalities in a given ROI .Second, we used structural equation modeling to examine whether the region-specific factors of gray matter structural integrity were associated with episodic memory performance. In this context, a particular virtue of structural equation modeling is that we can model gray matter integrity for each of the ROIs as well as episodic memory performance as latent factors.2 difference between the models indicated a significant difference in fit  from the lavaan output.We specified and estimated structural equation models in Onyx , versione in fit . For loar > 1), hence we decided to specify a single latent prefrontal cortex factor with six indicators, two per modality, from medio-orbitofrontal cortex and dorsolateral prefrontal cortex. Next, we added modality-specific latent factors (methods factors), which were defined to be orthogonal to the ROI factors, so that they represent the modality-specific share of the variance in the measures after the ROI-specific variance is accounted for. The methods factors were allowed to correlate with one another, and to be measured by all indicators that were derived from the respective modality, with loadings freely estimated  defined by the indicators representing the three imaging modalities. In a next set of analyses, we combined the individual, fully saturated models to one structural equation model, in which all latent integrity factors were allowed to covary with one another. The intercorrelation of the medio-orbitofrontal cortex factor and the dorsolateral prefrontal cortex factor was too high for the two factors to be meaningfully modeled as separate latent variables  as well as sex into the correlational model to statistically control for the extent to which potential associations between episodic memory and gray matter integrity are being caused by these covariates. With respect to age, we expected that older participants tended to show lower gray matter integrity and lower episodic memory ability, so that not controlling for age in this age-heterogeneous sample would most likely yield a strong association between gray matter and episodic memory that is at least partly driven by those age differences. Education was expected to be related to episodic memory performance such that persons with more years of education tend to score better in episodic memory tests . EducatiAs an additional ad-hoc exploratory analysis, we investigated differences between men and women in the factor models and in the associations between gray matter integrity and episodic memory .n = 333), and models that entailed episodic memory data were fitted to data from the total sample, under the assumption that the MR data were missing at random (Models that entailed only brain data or brain data and covariates were fitted to data from the MR sample (t random . This asr > 1) that we rather estimated only latent factor  with all six indicators from the two regions, which is a more parsimonious representation of the common variance of these regions. The model included method-specific factors  that were orthogonal to the ROI factors and captured the shared variance of measures within imaging modalities and across ROIs (2(df = 64) = 152.876; CFI = 0.965; RMSEA = 0.065; SRMR = 0.046. As the residual variance of the indicator variable MD medio-orbitofrontal cortex was estimated at a low negative value, we constrained that parameter to zero in all following models. This constraint did not result in worse model fit (Δχ2(df = 1) = 0.01, P = 0.91). All observed variables loaded reliably on the postulated latent ROI factors except for MD of medio-orbitofrontal cortex on the prefrontal cortex factor . All indicators loaded reliably on the postulated method factors . We estimated covariances among the ROI integrity factors and among method factors, while method factors were defined as being orthogonal to ROI factors. For covariances among the latent factors see We succeeded in establishing a factor model with four ROI factors capturing shared variance across VBM, MT, and MD within each ROI . We extracted ROI-wise data from five ROIs, but the two frontal ROIs medio-orbitofrontal cortex and dorsolateral–prefrontal cortex shared such a large amount of their variance  = 171.707; CFI = 0.962; RMSEA = 0.064; SRMR = 0.045. Age was negatively associated with all ROI factors except precuneus, rage, PFC = −0.21, Δχ2(df = 1) = 5.66, P = 0.017; rage, HC = −0.22, Δχ2(df = 1) = 13.2, P = 0.0003; rage, PHG = −0.22, Δχ2(df = 1) = 9.22, P = 0.0024; rage, PRE = 0.01, Δχ2(df = 1) = 0.026; P = 0.87. In addition, age was associated with the method factors  = 4.35, P = 0.037; rage, MT = −0.29, Δχ2(df = 1) = 11.3, P = 0.0008; rage, MD = 0.40, Δχ2(df = 1) = 35.4, P < 0.0001). We had also tested for measurement invariance across age groups after a median split, which suggested no age differences in the loadings on the ROI factors , assuming missingness of MR data to be at random. The fit was acceptable, χ2(df = 120) = 203.27; CFI = 0.97; RMSEA = 0.02; SRMR = 0.05 , indicating that they contributed similarly to the latent factor. Episodic memory was significantly associated with all ROIs except precuneus, rEM, PFC = 0.24, Δχ2(df = 1) = 7.02; P = 0.008; rEM, HC = 0.33, Δχ2(df = 1) = 16; P < 0.0001; rEM, PHG = 0.27, Δχ2(df = 1) = 9.28; P = 0.0023; rEM, PRE = 0.16, Δχ2(df = 1) = 3.29; P = 0.07. In contrast, episodic memory was not reliably related to the method factors, rEM, V = 0.07, Δχ2(df = 1) = 0.44, P = 0.51; rEM, MT =  −0.02, Δχ2(df = 1) = 0.03; P = 0.86; rEM, MD = −0.12, Δχ2(df = 1) = 2.19; P = 0.41  = 3.5, P = 0.32, and was used in the following extensions of the model (χ2(df = 123) = 206.77; CFI = 0.973; RMSEA = 0.021, SRMR = 0.05).We added an episodic memory latent factor to the model, which was indexed by the four episodic memory task scores. We then estimated correlations between the episodic memory factor and the ROI integrity factors  as well as the method factors. We fit this model to the data from all included participants with episodic memory data available (R2 = 0.125). None of the ROIs showed a significant unique effect. However, the unique effect of hippocampus had the largest effect size; it was greater than those of parahippocampal gyrus, precuneus, and prefrontal cortex by a factor of 2.5, 3.2, and 4.8, respectively  = 3.12; P = 0.08; std. βEM, PHG = −0.15, Δχ2(df = 1) = 0.28; std. βEM, PRE = 0.12, Δχ2(df = 1) = 1.05; P = 0.31; P = 0.59; std. βEM, PFC = 0.08, Δχ2(df = 1) = 0.33; P = 0.57).To assess how much variance in episodic memory is uniquely and jointly predicted by the latent ROI factors, we refit the previous model with directed paths from each ROI to latent episodic memory (regression model). This is a latent multiple regression model regressing episodic memory on each ROI integrity factor. Note that the directionality of effects  is merely hypothesized and cannot be tested with the data at hand. Importantly, instead of interpreting first-order correlations, we now examined the total effect and the unique effects of each ROI integrity factor on episodic memory. All ROIs together explained 12.5% of the variance in episodic memory  = 2.98; P = 0.08; rEM, HC = 0.27, Δχ2(df = 1) = 10.22; P = 0.0014; rEM, PHG = 0.21, Δχ2(df = 1) = 5.2; P = 0.023; rEM, PRE = 0.13, Δχ2(df = 1) = 1.95; P = 0.16. Thus, the gray matter integrity factors of hippocampus and parahippocampal gyrus shared a significant amount of variance with episodic memory that was not collinear with age, further suggesting that the structural integrity of these two regions might be critical for episodic memory.In follow-up analyses, we entered age as a covariate into the correlational model. As before, in the brain-only model from the first set of analyses, age was negatively associated with all ROI factors except precuneus and with all method factors. Here, we regressed the ROI integrity factors, the method factors, and the episodic memory factor on age and examined the residual covariances between episodic memory and the ROI integrity factors. Age was negatively associated with episodic memory . MoreoverEM, PFC = 0.16, Δχ2(df = 1) = 3.04; P = 0.08; rEM, HC = 0.27, Δχ2(df = 1) = 10.43; P = 0.0012; rEM, PHG = 0.23, Δχ2(df = 1) = 5.92; P = 0.015; rEM, PRE = 0.15, Δχ2(df = 1) = 2.6; P = 0.11. For a test of measurement invariance across education, see rEM, PFC = 0.05, Δχ2(df = 1) = 0.29; P = 0.59; rEM, HC = 0.19, Δχ2(df = 1) = 4.66; P = 0.031; rEM, PHG = 0.13, Δχ2(df = 1) = 4.08; P = 0.043; rEM, PRE = 0.07, Δχ2(df = 1) = 1.76; P = 0.19  variance shared within each ROI across imaging modalities ; 2) variance shared within each imaging modality across ROIs ; 3) variance unique to each ROI in each modality .The psychometric viability of the MTMM representation of regional gray matter integrity demonstrates that macro- and micro-structural indicators of gray matter can indeed be combined to yield latent factors of gray matter integrity. In addition, the latent integrity factors formed a positive manifold, indicating that individual differences in gray matter integrity are correlated across regions. By moving away from specific aspects of integrity indicators to the expression of their common variance at the latent level, we pave the way for a deeper understanding of relations between brain structure and cognitive performance.Older participants tended to show lower values on all ROI-wise latent integrity factors except precuneus. This result is largely in line with previous findings based on single indicators focusing on volume , and MT To examine whether latent gray matter integrity factors are related to episodic memory, we tested their associations with episodic memory ability at the latent level. Episodic memory showed first-order associations with the structural integrity factors of all ROIs except precuneus, but not with the modality-specific method factors. When adjusted for age differences, hippocampus and parahippocampal gyrus continued to be associated with episodic memory. That is, while prefrontal cortex’s first-order association with episodic memory could be accounted for by age differences in both gray matter structure and performance, the integrity of hippocampal and parahippocampal gray matter not only reflected individual differences collinear with chronological age, but also associations with episodic memory performance over and above age. Adjusting for interindividual differences in years of education did not substantially affect the associations, with hippocampus and parahippocampal gyrus still showing the strongest associations with episodic memory. When adjusting for sex differences in episodic memory and ROI integrity, hippocampus and parahippocampal gyrus were still significantly associated with episodic memory. This is corroborated in the regression model, with hippocampus showing the numerically largest unique effect. Overall, this result strongly supports the hypothesis that maintained structural integrity of the hippocampus is germane to preserved episodic memory ability in old age .To note, we conceptualize episodic memory on a relatively broad level. Our current focus on the latent factor, which captures the shared between-person variance across these four different tasks, implies that we abstract from the details of the tasks and focus on the commonalities when interpreting associations with gray matter integrity in the ROIs. The chosen tasks are heterogeneous in terms of stimulus material , sensory modality of presentation , type of memory , or retrieval type . Performance may be differentially influenced by component processes such as familiarity and recognition . HoweverThe aims of this study were to establish a gray matter integrity factor model and validate it by associating its latent factors with episodic memory performance. We did not previously plan to investigate sex differences in measurement models or associations. Only after observing sex differences both in estimates of average gray matter integrity and episodic memory, we ran additional post hoc exploratory analyses to compare the models across men and women. We found the associations between hippocampal and parahippocampal gray matter integrity to be restricted to men. We provide details on these additional analyses and a short discussion in the Our results also suggest that the combination of multimodal data yields information about general properties of gray matter tissue that differ between younger and older individuals above 60 years of age, and are relevant for older adults’ episodic memory performance. This raises the important question of which physiological aspects of gray matter are captured by the common variance of regional brain integrity as estimated by VBM, MT, and MD. Given that MD and MT ratio load on the same factor as VBM, it seems worthwhile to consider physiological factors that affect the physical properties of the tissue and its overall size. Normal aging is marked by the loss of dendritic spines, dendritic arbors, synaptic density, and myelinated axons ; in addiThe MTMM model of regional gray matter integrity introduced in this article reflects correlated traits and correlated methods, and properly accounts for the nonindependent structure of measurement errors in our data. Similar to other confirmatory factor analysis variants of structural equation modeling, it links the measurement model  to the structural model (associations between latent factors). The structural part of the model allows researchers to explore relations to behavior, and their modulation by covariates.The proposed models are certainly not the only ways to model associations between brain structure and cognition . Hence, In this study, we have used a statistical approach to model the common variance across multiple indicators of gray matter integrity in latent factors for each ROI. At this point, we can only speculate about the physiological basis of individual differences in gray matter integrity captured with the MTMM approach. To overcome these ambiguities, the field needs a stronger coalition between animal models and human research, with structural MRI serving as a critical link .One may wonder whether age-related artifacts present in each of the imaging modalities might account for age differences in the latent ROI and method factors. To reiterate, we modeled the variability in the ROI-wise data from each modality as a combination of a ROI-specific, modality-general part; a modality-specific, ROI-general part; and a residual part that is both ROI- and modality-specific. As ROI factors represent common variance across methods in a specific ROI, older individuals tended to show lower volumes, lower MT and higher MD in these three ROIs. This suggests that older individuals tend to possess lower gray matter integrity in the ROIs, and/or that age-associated artifacts of the methods play out to a similar degree in the different methods in these ROIs. We are not able to tease apart these two possible causes of the age-ROI associations with the current modeling approach. However, here, the differences between what is measured by the three imaging modalities renders it unlikely that correlated age-associated measurement error dominates the common variance to such an extent that it would explain the emergence of a factor structure. In addition, it is actually a strength of the present approach that it allows to estimate age differences in the methods factors, and statistically adjust for them if deemed meaningful.Furthermore, the present findings are based on cross-sectional data. The observed associations with age represent the joint outcome of individual differences in normal aging and more stable individual differences that were present in early adulthood . It remaAlso note that this paper focuses on the association structure of individual differences in a healthy older population. Our results might not hold for all subgroups. Plausibly, there are hidden heterogeneities in the association structures that should be elucidated by follow-up studies. For instance, associations between dopamine availability and cognition have been found to differ between subgroups in a latent class analysis . AnotherBy applying MTMM modeling to data from a large sample of BASE-II participants, we established latent factors of gray matter integrity in hippocampus, parahippocampal gyrus, prefrontal cortex, and precuneus, which represented the shared variance of VBM, MT, and MD for each of these regions. Further, we found that older adults with greater structural integrity in hippocampus and parahippocampal gyrus also showed higher levels of episodic memory performance, with hippocampus showing the largest unique association. Our results are consistent with the hypothesis that maintained structural integrity of the hippocampus helps to preserve episodic memory in old age. Future research needs to corroborate the content validity of the latent brain factors, and extend the present approach to longitudinal observations.Conflict of interest: None declared.We are grateful for the assistance of the MRI team at the Max Planck Berlin Institute for Human Development consisting of Sonali Beckmann, Nils Bodammer, Thomas Feg, Sebastian Schröder, and Nadine Taube, for the team leading the cognitive tests, and for all participants of BASE-II. European Commission as part of the Lifebrain Consortium (grant number 732592) within the Horizon 2020 programme; German Federal Ministry of Education and Research (grant number 01GQ1421B); the MINERVA program of the Max Planck Society (to M.C.S.).https://www.base2.mpg.de/en.Data can be requested from the steering committee of the Berlin Aging Study II. Further information regarding the application can be found under Koehncke_et_al_Grey_Matter_Integrity_Supplement_R1_200907_bhaa287Click here for additional data file.