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Experiments with stable isotope tracers such as 13C and 15N are increasingly used to gain insights into metabolism. However, mass spectrometric measurements of stable isotope labeling experiments should be corrected for the presence of naturally occurring stable isotopes and for impurities of the tracer substrate. Here, we analyzed the effect that such correction has on the data: omitting correction or performing invalid correction can result in largely distorted data, potentially leading to misinterpretation. IsoCorrectoR is the first R-based tool to offer said correction capabilities. It is easy-to-use and comprises all correction features that comparable tools can offer in a single solution: correction of MS and MS/MS data for natural stable isotope abundance and tracer impurity, applicability to any tracer isotope and correction of multiple-tracer data from high-resolution measurements. IsoCorrectoR's correction performance agreed well with manual calculations and other available tools including Python-based IsoCor and Perl-based ICT. IsoCorrectoR can be downloaded as an R-package from: .
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Encountering environmental adversities may increase the risk of developing psychiatric disorders in adulthood1,2. Through adaptations in the regulation of emotional, cognitive and behavioral processes, individuals strive to cope with challenging environmental conditions. However, this can be maladaptive and thereby increase the risk for psychopathology (for example, ref. 3). Despite the clear adversity-induced vulnerability to developing psychiatric disorders, the neurobiological mechanisms underlying this association have remained elusive for several reasons. First, the focus on regions of interest to replicate previous findings of neurobiological correlates of this association and gain a better understanding of them has led to increased attention on the limbic system and its regulatory control regions (for example, refs. 4,5,6). However, concentrating only on localized effects may neglect interindividual differences in structural or functional organization (for example, individual variation in the spatial distribution of different regions), and may overlook substantial whole-brain changes due to widespread restructuring during development7,8,9,10. Indeed, meta-analyses have synthesized evidence on a whole-brain level and confirmed a convergence of developmental risk factors in key regions of affective and cognitive regulatory processing, both within and beyond the limbic system11,12,13,14,15. Although these results shed light on the interplay between adversities and neural plasticity, they may, however, be influenced by between-study heterogeneity including differences in assessments, participants and statistical analyses.
Second, the abundance of inconsistent findings, even when testing associations between the same adversity and the same brain outcome4, has impeded the discovery of the exact neurobiological mechanisms. One reason for such inconsistent findings pertains to the dominance of studies designed to test differences in terms of group means (that is, averages). In such studies, individual-level variability is obscured by averaging across groups. As such, high subject variability can result in null findings because opposing effects observed at the individual subject level may cancel each other out. Likewise, contradictory findings can arise across studies, for instance, an increase or a decrease in brain volume associated with adversities (as reviewed by, for example, refs. 4,16).
Third, the impact of adversity during development on brain structure and function has predominantly been investigated without taking into account the typical patterns of brain growth and development. This poses a challenge in identifying neurobiological alterations amidst individual age and sex-specific trajectories and hinders the discovery of the precise mechanisms underlying adaptation. For instance, if the volume of a region follows an inverted u-shaped trajectory that reaches its peak in youth, then a larger volume in childhood associated with adversity would suggest accelerated maturation. In contrast, the same pattern observed after adolescence would suggest delayed maturation (for example, ref. 4). This issue could be addressed by referencing adversity-related effects to normative brain growth charts17,18,19,20, which, akin to pediatric growth charts, enable the quantification of individual variation with respect to population percentiles.
Fourth, adversities are by nature correlated; an individual growing up in a poor environment is more likely to encounter family adversity and stressful life events over their lifetime21. Therefore, particularly in adulthood, studies assessing the effect of single adversity are difficult to interpret, as any brain differences may reflect multiple stressors with potentially distinct neural effects4.
Fifth, longitudinal studies investigating the enduring effects of adversity on brain structure are limited, with a few exceptions22,23. Furthermore, the scarcity of studies examining the effect of adversity on brain development has impeded the possibility to probe whether neurobiological correlates of environmental adversity are stable. Although initial studies in children and adolescents indicate that these correlates may be stable over development24,25,26, it is premature to draw strong conclusions, considering the different methodologies applied in these and the lack of replication in independent cohorts.
Thus, there is a need for predictive mechanistic models that can account for the long-lasting effects of lifespan adversity on a whole-brain level while simultaneously accommodating the interindividual neurobiological heterogeneity.
We also quantified the overlap between the effects of different adversities directly. The highest whole-brain overlap was observed for the structure coefficient maps of the psychosocial risks, that is, family adversity, trauma and stressful life events, with dice coefficients up to 0.54 (Supplementary Table 21). In general, the dice coefficients indicated a low to moderate overlap between the brain patterns associated with different adversities. These findings suggest the emergence of adversity- and region-specific volumetric expansions or contractions with increasing adversity. The average dice coefficients of 0.23 for prenatal smoke exposure and 0.24 for obstetric adversity showed that these had the least overlap with the structure coefficient maps of other adversities. For obstetric adversity, we observed volume expansions in the ventromedial orbitofrontal cortex (vmOFC) and volume contractions in the ACC. For prenatal smoke exposure, a different pattern emerged, with expansions in the hippocampus and contractions in the postcentral and occipital gyrus. By contrast, the highest mean dice coefficient was found for psychosocial family adversity (0.36), with expansions in subcortical limbic areas and contractions in the vmOFC.
To better understand the effects of multiple adversities in combination, we conducted a principal component (PC) analysis to summarize the primary sources of variance within the set of adversities. This is important because adversities are known to be correlated, as was also the case in our sample (Extended Data Fig. 1b). We, therefore, re-estimated the model using three components that account for two-thirds of the cumulative variance in the adversity scores (Extended Data Fig. 3a,b depict the strong correspondence to the original normative model). Next, we mapped morphological patterns associated with variation across these adversity factors (that is, PCs) by plotting the predictions from these models across the range of adversities spanned by these components. The PC loadings are shown in Supplementary Table 22, and their associated morphometric patterns are shown in Fig. 3. The first PC (PC1) reflected lifespan psychosocial family adversities and prenatal smoking and was mostly related to cortical and subcortical volume contractions, particularly in the vmOFC and the medial frontal gyrus, precentral and postcentral gyrus, frontal pole/middle frontal gyrus, superior frontal gyrus, the inferior temporal gyrus, the caudate, as well as in the occipital lobe. Slight volume expansions were only observed in small clusters in the vmPFC, paracingulate gyrus, superior frontal gyrus and the lateral frontal pole. By contrast, a more balanced picture of region-specific expansions and contractions emerged with regard to PC2, representing early exposures to obstetric risk and prenatal maternal stress. Contractions were most pronounced in the lateral frontal pole, frontal orbital cortex, inferior and superior frontal gyrus, middle temporal gyrus, fusiform gyrus, hippocampus, supramarginal gyrus, parietal and occipital gyrus, and expansions were most pronounced in the medial frontal pole, including the vmOFC, the caudate and the superior frontal gyrus. Similarly for PC3, representing early maternal sensitivity, contractions, particularly in the paracingulate gyrus, supramarginal gyrus, insula, precuneus, lateral occipital gyrus, precentral gyrus, supplementary motor cortex, and expansions in the middle/superior frontal gyrus, the vmPFC/perigenual anterior cingulate, vmOFC, precentral gyrus, the angular gyrus and the thalamus, are revealed. Interestingly, we observed opposing trajectories in, for example, the vmOFC, with volume contractions as a function of higher scores on PC1 and volume expansions as a function of higher scores on PC2 and PC3 (Extended Data Fig. 4). 152ee80cbc
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