Biology subjects

Rosberg, A.

Publications and source records attributed to Rosberg, A..

5 recordsLinked to original sources

bioRxiv2025

Exposure to Maternal Pre- and Postnatal Psychological Distress: Associations with Brain Structure in 5-year-old Children

BackgroundMaternal mental health is an important contributor to child neurodevelopment. While there are multiple studies on prenatal exposure, early postnatal exposure has received little attention in neuroimaging research. Methods5-year-old children (n = 173) were recruited from the FinnBrain Birth Cohort study. Maternal distress was assessed using questionnaires on depressive and anxiety symptoms at 14, 24 and 34 gestational weeks and postnatally at 3, 6 and 24 months. T1-weighted structural images were processed using a voxel-based morphometry pipeline to map associations between maternal distress exposure and regional gray matter (GM) volumes, while accounting for potential confounders. ResultsWe found widespread associations between maternal distress symptoms and offspring brain morphology. Higher prenatal distress at 14 gestational weeks was positively associated with regional GM volume in the right superior parietal lobe and precuneus. In contrast, postnatal distress at 3 months was negatively associated with GM volumes in multiple motor regions, the left anterior insula, right superior frontal areas and supramarginal gyrus. Postnatal distress at 6 months demonstrated a positive relationship with GM volumes in the right calcarine and lingual gyri, while distress at 24 months was negatively associated with GM volumes in the left supramarginal and right superior frontal gyri. ConclusionsThis study provides support for hypotheses proposing that fetal and early life exposure to maternal distress can influence the structural development of the brain. Furthermore, it highlights the role of early postnatal period and calls for further research into this so far overlooked period and pathways that explain the associations.

neuroscience↗

bioRxiv2025

skiftiTools: An R package for reading, writing, analysing, and visualising, tract-based spatial statistics (TBSS) derived diffusion MR images

skiftiTools processes three- and four-dimensional neuroimaging data, facilitating advanced statistical modelling with voxelwise data in any software of choice. Tract-Based Spatial Statistics (TBSS) is a conventionally used tool to make statistical calculations in voxel space for brain imaging data. While pre-existing software packages provide support for general linear model based statistics, there is a clear need for more sophisticated modeling. skiftiTools writes subject-per-volume NIfTI files as tab-separated value ASCII files, which are easily readable by most commonly used statistical tools such as R language (RStudio), SPSS, SAS, and GraphPad Prism. This facilitates a wide range of voxel-level statistical analyses from TBSS data, including estimation of standardised effect sizes, clustering, dimensionality reduction, non-linear and machine learning predictive modelling, which we showcase in this article using FinnBrain and developing Human Connectome Project diffusion MRI data. After statistical processing, the resulting ASCII data can then be read again for visualization. The package supports NIfTI image format, tab-separated ASCII format, and its own stand-alone format for efficient disk usage. It is open source (https://github.com/haanme/skiftiTools), built on R-language and has easy installation from Rs CRAN package repository. In addition, we provide basic functions available in Docker containers for further platform independence. HighlightsO_LIThe skiftiTools R package is an open-source, user-friendly interface for analysing voxelwise diffusion tensor imaging (DTI) data following tract-based spatial statistics (TBSS) processing C_LIO_LIIt supports reading, writing, visualization, mathematical operations, and data manipulation and thus allows comprehensive conventional and advanced statistics, including machine learning C_LIO_LIskiftiTools bridges a critical gap between statistical tools in R and voxelwise neuroimaging data - including comparable means to perform multiple comparison corrections and much needed possibility to use non-linear statistics C_LI

neuroscience↗

bioRxiv2025

Neonatal White Matter Microstructure Predicts Infant Attention Disengagement from Fearful Faces

Infants develop an attentional bias towards faces already at birth, with further specification towards fearful faces emerging at 6 months and diminishing around 11 months of age. However, the neurobiological origins of attentional bias to fear are still poorly understood. To understand the neural structures underlying perception of facial expressions, the current study utilized newborn diffusion magnetic resonance images (N = 86; 41 females; = 27.15 days) and eye tracking from the same infants at 8-months ( = 8.75 months) as a behavioural measure. An overlap paradigm was used to measure attention disengagement from fearful, happy, and neutral faces. Tract-based spatial statistics revealed that higher white matter (WM) mean diffusivity in widespread regions across the brain was associated with lower attention disengagement from fearful faces. The same association was found with happy faces but was limited to only the splenium of the corpus callosum and sensorimotor pathways. Variance in neonatal WM microstructure may reflect individual differences in growth that is related to attentional bias development later in infancy.

neuroscience↗

bioRxiv2024

The FinnBrain Multimodal Neonatal Template and Atlas Collection: T1, T2, and DTI brain templates, and accompanying cortical and subcortical atlases

The accurate processing of neonatal and infant brain MRI data is crucially important for developmental neuroscience, but presents challenges that child and adult data do not. Tissue segmentation and image coregistration accuracy can be improved by optimizing template images and / or related segmentation procedures. Here, we describe the construction of the FinnBrain Neonate (FBN-125) template; a multi-contrast template with T1- and T2-weighted as well as diffusion tensor imaging derived fractional anisotropy and mean diffusivity images. The template is symmetric and aligned to the Talairach-like MNI 152 template and has high spatial resolution (0.5 mm3). In addition, we provide atlas labels, constructed from manual segmentations, for cortical grey matter, white matter, cerebrospinal fluid, brainstem, and cerebellum as well as the bilateral hippocampi, amygdalae, caudate nuclei, putamina, globi pallidi, and thalami. We provide this multi-contrast template along with the labelled atlases for the use of the neuroscience community in the hope that it will prove useful in advancing developmental neuroscience, for example, by helping to achieve reliable means for spatial normalization and measures of neonate brain structure via automated computational methods. Additionally, we provide standard co-registration files that will enable investigators to reliably transform their statistical maps to the adult MNI space, which has the potential to improve the consistency and comparability of neonatal studies or the use of adult MNI space atlases in neonatal neuroimaging.

neuroscience↗

bioRxiv2023

Associations between maternal pre-pregnancy BMI and infant striatal mean diffusivity

Background/ObjectivesIt is well-established that parental obesity is a strong risk factor for associates with offspring obesity. Further, a converging body of evidence now suggests that maternal weight profiles may affect the developing offspring brain in a manner that confers future obesity risk. Here, we investigated how pre-pregnancy maternal weight status influences the reward-related striatal areas of the offspring brain during in utero development. MethodsWe used diffusion tensor imaging to quantify the microstructure of the striatal brain regions of interest in neonates (N = 116 mean gestational weeks at birth 39.88, SD = 1.14; and at scan 43.56, SD = 1.05). Linear regression was used to test the associations between maternal pre-pregnancy body mass index and infant striatal mean diffusivity. ResultsA strong positive association was found between the maternal pre-pregnancy body mass index and newborn left caudate nucleus mean diffusivity. Results remained unchanged after the adjustment for covariates. ConclusionsIn utero exposure to maternal adiposity might have a growth impairing impact on the mean diffusivity of infant left caudate nucleus. Considering the involvement of caudate nucleus in regulating eating behaviour and food-related reward processing later in life, this finding calls for further investigations to define the prognostic relevance of early life caudate development and weight trajectories of the offspring.

neuroscience↗