Biology subjects

Wiersch, L.

Publications and source records attributed to Wiersch, L..

6 recordsLinked to original sources

bioRxiv2026

Investigating the impact of stressful life events on neuroanatomy across adolescence

Early-life stress elevates the risk of developing neuropsychiatric disorders. However, the mechanism underlying this vulnerability, and how they contribute to sex differences in these disorders, remain to be understood. Here, we use multivariate brain-based predictive models to examine how the number, positive or negative appraisal, and impact of adolescent stressful life events reported either by the youth or their caregivers are reflected in neuroanatomy (cortical thickness, surface area, cortical and subcortical gray matter volume, and T1 intensity measures). We used data from the Adolescent Brain Cognitive Development (ABCD) study at 2-year (N = 6,301, age 11-12), 4-year (N = 5,000, age 13-14) and 6-year (N = 3,226, age 15-16) follow-up time points to examine the sex-independent and sex-specific neural correlates of stressful life events. Our analyses showed mostly non-significant associations between stressful life events and neuroanatomy. However, we did find that the number of positively appraised stressful life events reported by the caregivers at the 4-year follow-up was significantly associated with cortical thickness, independent of sex, and with surface area in females only. Across three developmental timepoints, seven neuroanatomical measures, two reporting perspectives, and both sex-independent and sex-specific analyses, we show that the number, appraisal, and impact of stressful life events are largely not reflected in adolescent neuroanatomy.

neuroscience↗

bioRxiv2025

Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in Youth

Early life adversity is a known risk factor for psychopathology, yet the neurodevelopmental impacts of distinct types of adversity remain unclear. Using machine learning, we examined how adversity (physical and sexual abuse, neighborhood threat, scarcity, household dysfunction, prenatal substance exposure, parental psychopathology) and socioeconomic status are associated with cortical thickness in youth. We used data from the Adolescent Brain Cognitive Development Study at three time points: baseline (N=6,908, ages 9-10), two-year follow-up (N=5,808), and four-year follow-up (N=2,245). Cortical thickness was linked to socioeconomic status and neighborhood threat at all time points, and scarcity at baseline. The strongest negative associations were in medial temporal and occipital regions. Our analyses revealed neural effects of socioeconomic status, neighborhood threat, and scarcity, converging on regions involved in memory, visual processing, and higher-order cognition. These findings suggest consistent neural signatures linked to socioeconomic disadvantage, highlighting the importance of addressing inequality to promote neurodevelopmental health.

neuroscience↗

bioRxiv2025

Neuroanatomy Reflects Individual Variability In Impulsivity in Youth

Individual differences in neural circuits underlying emotional regulation, motivation, and decision-making are implicated in many psychiatric illnesses. Interindividual variability in these circuits may manifest, at least in part, as individual differences in impulsivity at both normative and clinically significant levels. Impulsivity reflects a tendency towards rapid, unplanned reactions to internal or external stimuli without considering potential negative consequences coupled with difficulty inhibiting responses. Here, we use multivariate brain-based predictive models to explore the neural bases of impulsivity across multiple behavioral scales, neuroanatomical features (cortical thickness, surface area, and gray matter volume), and sexes (females and males) in a large sample of youth from the Adolescent Brain Cognitive Development (ABCD) Study at baseline (n=9,099) and two-year follow-up (n=6,432). Impulsivity is significantly associated with neuroanatomical variability, and these associations vary across behavioral scales and neuroanatomical features. Impulsivity broadly maps onto cortical thickness in dispersed regions (e.g., inferior frontal, lateral occipital, superior frontal, entorhinal), as well as surface area and gray matter volume in specific medial (e.g., parahippocampal, cingulate) and polar (e.g., frontal and temporal) territories. Importantly, while many relationships are stable across sexes and time points, others are sex-specific and dynamic. These results highlight the complexity of the relationships between neuroanatomy and impulsivity across scales, features, sexes, and time points in youth. These findings suggest that neuroanatomy, in combination with other biological and environmental factors, reflects a key driver of individual differences in impulsivity in youth. As such, neuroanatomical markers may help identify youth at increased risk for developing impulsivity-related illnesses. Furthermore, this work emphasizes the importance of adopting a multidimensional and sex-specific approach in neuroimaging and behavioral research.

neuroscience↗

bioRxiv2023

Sex differences in intrinsic functional cortical organization reflect differences in network topology rather than cortical morphometry

Brain size robustly differs between sexes. However, the consequences of this anatomical dimorphism on sex differences in intrinsic brain function remain unclear. We investigated the extent to which sex differences in intrinsic cortical functional organization may be explained by differences in cortical morphometry, namely brain size, microstructure, and the geodesic distances of connectivity profiles. For this, we computed a low dimensional representation of functional cortical organization, the sensory-association axis, and identified widespread sex differences. Contrary to our expectations, observed sex differences in functional organization were not fundamentally associated with differences in brain size, microstructural organization, or geodesic distances, despite these morphometric properties being per se associated with functional organization and differing between sexes. Instead, functional sex differences in the sensory-association axis were associated with differences in functional connectivity profiles and network topology. Collectively, our findings suggest that sex differences in functional cortical organization extend beyond sex differences in cortical morphometry. TeaserInvestigating sex differences in functional cortical organization and their association to differences in cortical morphometry.

neuroscience↗

bioRxiv2023

Sex classification from functional brain connectivity: Generalization to multiple datasets

Machine learning (ML) approaches are increasingly being applied to neuroimaging data. Studies in neuroscience typically have to rely on a limited set of training data which may impair the generalizability of ML models. However, it is still unclear which kind of training sample is best suited to optimize generalization performance. In the present study, we systematically investigated the generalization performance of sex classification models trained on the parcelwise connectivity profile of either single samples or a compound sample containing data from four different datasets. Generalization performance was quantified in terms of mean across-sample classification accuracy and spatial consistency of accurately classifying parcels. Our results indicate that generalization performance of pwCs trained on single dataset samples is dependent on the specific test samples. Certain datasets seem to "match" in the sense that classifiers trained on a sample from one dataset achieved a high accuracy when tested on the respected other one and vice versa. The pwC trained on the compound sample demonstrated overall highest generalization performance for all test samples, including one derived from a dataset not included in building the training samples. Thus, our results indicate that a big and heterogenous training sample comprising data of multiple datasets is best suited to achieve generalizable results.

neuroscience↗

bioRxiv2022

Accurate sex prediction of cisgender and transgender individuals without brain size bias

Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual qualitative sex differences in brain organization but rather learn to classify an individuals sex based on brain size differences, thus leading to spurious and misleading conclusions, for example when comparing brain morphology between cisgender- and transgender individuals. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making. TeaserAccurate non-biased structural sex classification in cis- and transgender individuals by matching training samples for TIV

neuroscience↗