Prenatal maternal health and child brain structure: Implications for non-verbal ability and optimizing subcortical segmentation

Brain development starts in utero, and the fetal brain can already be affected by the environment, including chemical exposures and maternal health characteristics. These factors range from exposures to large quantities of teratogens (such as alcohol) to variations in the behaviors and characteristics of healthy individuals (such as age, diet, and subclinical levels of depressive and anxiety symptoms), which can nonetheless have long-lasting adverse effects. In this thesis, we reviewed the literature on the effects of prenatal exposures on human neurodevelopment, as well as cognitive, behavioral, and health outcomes. In Study I we found that prenatal exposures are often reported poorly in infant neuroimaging studies and gave recommendations for reporting in future studies. In Study II, we examined which early life factors predicted cortical structure in 5-year-olds. The results from Study II were utilized to make an informed decision regarding confounders in future studies in the 5-year-old neuroimaging sample of the FinnBrain Birth Cohort study. In Study III, we explored the cortical structural correlates of non-verbal ability in 5-year-olds. The findings were generally in line with prior results from adult and adolescent studies, with the important addition of a positive association between gray matter volume and surface area in the right medial occipital region and non-verbal ability as well as visual abstract reasoning ability. Finally, in Study IV, we compared the results from two common segmentation tools, FSL-FIRST and FreeSurfer, against manual segmentation in the hippocampus and subcortical structures. Overall, the agreement with manual segmentation was good, although results were suboptimal for the hippocampus, amygdala, and nucleus accumbens, and careful visual quality control is still recommended. This thesis summarized different perinatal factors affecting the developing brain, and ensured the high quality of our neuroimaging data. This foundational work, together with the multidisciplinary, longitudinal data collection in the FinnBrain Birth Cohort study, can be used to discover how environmental factors affect brain development.Äidin raskausajan terveys ja lapsen aivojen rakenne: yhteydet ei-kielellisiin taitoihin ja subkortikaalisen segmentaation optimointi Aivojen kehitys alkaa kohdussa ja jatkuu läpi elämän. Jo sikiöaikana aivot ovat alttiina ympäristön vaikutuksille, ml. kemialliset altisteet sekä äidin terveyteen liittyvät tekijät. Nämä altisteet vaihtelevat suurista annoksista teratogeeneille (esim. alkoholille) eroihin terveiden yksilöiden ominaisuuksissa ja toiminnassa (esim. ikä, ruokavalio sekä vähäiset masennus- ja ahdistusoireet ilman mielenterveyshäiriön diagnoosia), joilla voi kuitenkin olla kauaskantoisia seuraamuksia. Tässä väitöskirjassa teemme katsauksen raskaudenaikaisten altisteiden vaikutuksista yksilön kehitykseen sekä siihen liittyviin muutoksiin aivoissa. Tutkimuksessa I toteamme, että raskaudenaikaiset altisteet kuvataan usein puutteellisesti vauvojen aivokuvantamista koskevissa tutkimuksissa ja annamme suosituksia raportoinnista. Tutkimuksessa II tutkimme varhaisten altisteiden yhteyksiä 5-vuotiaiden aivojen rakenteeseen. Tämän tutkimuksen tulokset ohjasivat kontrolloitavien muuttujien valintaa. Tutkimuksessa III tutkimme aivokuoren rakenteen yhteyksiä ei-kielelliseen kognitiiviseen kyvykkyyteen 5-vuotiailla. Tulokset olivat pitkälti linjassa aiempien, vanhemmilla osallistujilla tehtyjen tutkimusten kanssa. Uutena tuloksena löysimme yhteyden oikean takaraivolohkon mediaalisen osan tilavuuden ja pinta-alan olevan yhteydessä ei-kielelliseen kyvykkyyteen sekä erityisesti näönvaraiseen päättelyyn. Tutkimuksessa IV vertailimme kahta yleistä segmentointityökalua (FreeSurfer ja FSL-FIRST) käsin tehtyyn segmentaatioon hippokampuksessa ja aivokuoren alaisissa tumakkeissa. Tulokset vaihtelivat paljon rakenteesta riippuen. Huolellista laadunvarmistusta aivoalueiden koon määrityksen yhteydessä suositellaan vahvasti. Tämä väitöskirja antaa kokonaisvaltaisen ymmärryksen aivoihin vaikuttavista varhaisen elämän altisteista. Yhdessä korkealaatuisen aivokuvantamisdatamme sekä muun FinnBrain-syntymäkohortissa kerättävän aineiston kanssa tätä tietoa voidaan hyödyntää useissa tulevissa aivojen kehitystä selvittävissä tutkimuksissa

Effect of number of diffusion-encoding directions in diffusion metrics of 5-year-olds using tract-based spatial statistical analysis

Methodological aspects and effects of different imaging parameters on DTI (diffusion tensor imaging) results and their reproducibility have been recently studied comprehensively in adult populations. Although MR imaging of children's brains has become common, less interest has been focussed on researching whether adult-based optimised parameters and pre-processing protocols can be reliably applied to paediatric populations. Furthermore, DTI scalar values of preschool aged children are rarely reported. We gathered a DTI dataset from 5-year-old children (N = 49) to study the effect of the number of diffusion-encoding directions on the reliability of resultant scalar values with TBSS (tract-based spatial statistics) method. Additionally, the potential effect of within-scan head motion on DTI scalars was evaluated. Reducing the number of diffusion-encoding directions deteriorated both the accuracy and the precision of all DTI scalar values. To obtain reliable scalar values, a minimum of 18 directions for TBSS was required. For TBSS fractional anisotropy values, the intraclass correlation coefficient with two-way random-effects model (ICC[2,1]) for the subsets of 6 to 66 directions ranged between 0.136 [95%CI 0.0767;0.227] and 0.639 [0.542;0.740], whereas the corresponding values for subsets of 18 to 66 directions were 0.868 [0.815;0.913] and 0.995 [0.993;0.997]. Following the exclusion of motion-corrupted volumes, minor residual motion did not associate with the scalar values. A minimum of 18 diffusion directions is recommended to result in reliable DTI scalar results with TBSS. We suggest gathering extra directions in paediatric DTI to enable exclusion of volumes with motion artefacts and simultaneously preserve the overall data quality

Test-retest reliability of diffusion tensor imaging scalars in 5-year-olds

Diffusion tensor imaging (DTI) has provided great insights into the microstructural features of the developing brain. However, DTI images are prone to several artifacts and the reliability of DTI scalars is of paramount importance for interpreting and generalizing the findings of DTI studies, especially in the younger population. In this study, we investigated the intrascan test-retest repeatability of four DTI scalars: fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) in 5-year-old children (N = 67) with two different data preprocessing approaches: a volume censoring pipeline and an outlier replacement pipeline. We applied a region of interest (ROI) and a voxelwise analysis after careful quality control, tensor fitting and tract-based spatial statistics. The data had three subsets and each subset included 31, 32, or 33 directions thus a total of 96 unique uniformly distributed diffusion encoding directions per subject. The repeatability of DTI scalars was evaluated with intraclass correlation coefficient (ICC(3,1)) and the variability between test and retest subsets. The results of both pipelines yielded good to excellent (ICC(3,1) > 0.75) reliability for most of the ROIs and an overall low variability (<10%). In the voxelwise analysis, FA and RD had higher ICC(3,1) values compared to AD and MD and the variability remained low (<12%) across all scalars. Our results suggest high intrascan repeatability in pediatric DTI and lend confidence to the use of the data in future cross-sectional and longitudinal studies

Prenatal and early-life environmental factors, family demographics and cortical brain anatomy in 5-year-olds: an MRI study from FinnBrain Birth Cohort

The human brain develops dynamically during early childhood, when the child is sensitive to both genetic programming and extrinsic exposures. Recent studies have found links between prenatal and early life environmental factors, family demographics and the cortical brain morphology in newborns measured by surface area, volume and thickness. Here in this magnetic resonance imaging study, we evaluated whether a similar set of variables associates with cortical surface area and volumes measured in a sample of 170 healthy 5-year-olds from the FinnBrain Birth Cohort Study. We found that child sex, maternal pre-pregnancy body mass index, 5 min Apgar score, neonatal intensive care unit admission and maternal smoking during pregnancy associated with surface areas. Furthermore, child sex, maternal age and maternal level of education associated with brain volumes. Expectedly, many variables deemed important for neonatal brain anatomy (such as birth weight and gestational age at birth) in earlier studies did not associate with brain metrics in our study group of 5-year-olds, which implies that their effects on brain anatomy are age-specific. Future research may benefit from including pre- and perinatal covariates in the analyses when such data are available. Finally, we provide evidence for right lateralization for surface area and volumes, except for the temporal lobes which were left lateralized. These subtle differences between hemispheres are variable across individuals and may be interesting brain metrics in future studies

Feasibility of FreeSurfer Processing for T1-Weighted Brain Images of 5-Year-Olds: Semiautomated Protocol of FinnBrain Neuroimaging Lab

Pediatric neuroimaging is a quickly developing field that still faces important methodological challenges. Pediatric images usually have more motion artifact than adult images. The artifact can cause visible errors in brain segmentation, and one way to address it is to manually edit the segmented images. Variability in editing and quality control protocols may complicate comparisons between studies. In this article, we describe in detail the semiautomated segmentation and quality control protocol of structural brain images that was used in FinnBrain Birth Cohort Study and relies on the well-established FreeSurfer v6.0 and ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) consortium tools. The participants were typically developing 5-year-olds [n = 134, 5.34 (SD 0.06) years, 62 girls]. Following a dichotomous quality rating scale for inclusion and exclusion of images, we explored the quality on a region of interest level to exclude all regions with major segmentation errors. The effects of manual edits on cortical thickness values were relatively minor: less than 2% in all regions. Supplementary Material cover registration and additional edit options in FreeSurfer and comparison to the computational anatomy toolbox (CAT12). Overall, we conclude that despite minor imperfections FreeSurfer can be reliably used to segment cortical metrics from T1-weighted images of 5-year-old children with appropriate quality assessment in place. However, custom templates may be needed to optimize the results for the subcortical areas. Through visual assessment on a level of individual regions of interest, our semiautomated segmentation protocol is hopefully helpful for investigators working with similar data sets, and for ensuring high quality pediatric neuroimaging data.</p

Subcortical and hippocampal brain segmentation in 5-year-old children: Validation of FSL-FIRST and FreeSurfer against manual segmentation

Developing accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time-consuming manual segmentation. In this study, the accuracy of two automated segmentation tools, FSL-FIRST (with three different boundary correction settings) and FreeSurfer, were compared against manual segmentation of the hippocampus and subcortical nuclei, including the amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5-year-olds.Both FSL-FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL-FIRST's default pipeline were the most accurate, whereas FreeSurfer's results were weaker across the structures.In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL-FIRST's agreement could be considered satisfactory (Pearson correlation > 0.74, intraclass correlation coefficient (ICC) > 0.68 and Dice score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus, caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.</p

Prenatal exposures and infant brain: Review of magnetic resonance imaging studies and a population description analysis

Brain development is most rapid during the fetal period and the first years of life. This process can be affected by many in utero factors, such as chemical exposures and maternal health characteristics. The goal of this review is twofold: to review the most recent findings on the effects of these prenatal factors on the developing brain and to qualitatively assess how those factors were generally reported in studies on infants up to 2 years of age. To capture the latest findings in the field, we searched articles from PubMed 2012 onward with search terms referring to magnetic resonance imaging (MRI), brain development, and infancy. We identified 19 MRI studies focusing on the effects of prenatal environment and summarized them to highlight the recent advances in the field. We assessed population descriptions in a representative sample of 67 studies and conclude that prenatal factors that have been shown to affect brain metrics are not generally reported comprehensively. Based on our findings, we propose some improvements for population descriptions to account for plausible confounders and in time enable reliable meta‐analyses to be performed. This could help the pediatric neuroimaging field move toward more reliable identification of biomarkers for developmental outcomes and to better decipher the nuances of normal and abnormal brain development