Biology subjects

Saunavaara, J.

Publications and source records attributed to Saunavaara, J..

5 recordsLinked to original sources

bioRxiv2023

Infant Left Amygdala Volume Is Negatively Associated with Fecal Microbiota Diversity

IntroductionRodent studies have addressed the importance of early life gut microbiota in the development of emotional and social functioning. Studies in human infants are still scarce, but associations with cognition and temperament have been reported. Neuroimaging studies have linked the amygdala with fecal microbiota diversity in infants, but crucially these studies have not covered the neonatal period, and the current study addressed this gap. MethodsThe study population included 65 infants drawn from the ongoing, general population based FinnBrain Birth Cohort Study. Brain MRI was performed around the age of one month (mean age 25 days). Fecal microbiota profiles (mean 68 days) were assessed by 16s rRNA amplicon sequencing at the age of 2.5 months. ResultsWe found a negative association between infant left amygdala volume and alpha diversity (n=52, beta =-0.0043, p=0.034, adjusted for infant sex, breastfeeding, delivery mode, age during fecal sampling, age from conception during scan, and intracranial volume, Fig.1). Amygdala volumes were not associated with beta diversity (p=0.21), nor with the abundances of individual genera when adjusted for the same covariates and multiple testing. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/537273v1_fig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@bf1304org.highwire.dtl.DTLVardef@798edeorg.highwire.dtl.DTLVardef@92c040org.highwire.dtl.DTLVardef@8bc2a9_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure. 1.C_FLOATNO Gut microbiota alpha diversity associates negatively with left (A), but not with right (B) amygdala volume. The grey areas depict 95% confidence intervals. C_FIG ConclusionOur results provide first evidence for associations between the brain and fecal microbiota in human neonates. Although the reported data do not allow investigation of underlying mechanisms, i.e. about the directionality of the hypothesized gut-brain connection, the reported connection encourages for future investigations of manifestations of gut-brain axis in early life.

neuroscience↗

bioRxiv2023

Structural brain correlates of non-verbal cognitive ability in 5-year-old children: findings from the FinnBrain Birth Cohort study

Non-verbal cognitive ability predicts multiple important life outcomes, e.g., school and job performance. It has been associated with parieto-frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non-verbal cognitive ability in 165 5-year-old participants (mean scan age 5.40 years, SD 0.13; 90 males) from the FinnBrain Birth Cohort study. T1-weighted brain magnetic resonance images were processed using FreeSurfer. Non-verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool And Primary Scale Of Intelligence (WPPSI-III). In vertex-wise general linear models, PIQ scores associated positively with volumes in left caudal middle frontal and right pericalcarine regions, as well as surface area in left caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non-verbal cognitive ability in a large sample of typically developing 5-year-olds. The findings are generally in line with prior findings from older age groups.

neuroscience↗

bioRxiv2023

Sex differences, asymmetry and age-related white matter development in infants and 5-year-olds as assessed with Tract-Based Spatial Statistics

The rapid white matter (WM) maturation of first years of life is followed by slower yet long-lasting development, accompanied by learning of more elaborate skills. By the age of five years, behavioural and cognitive differences between females and males, and functions associated with brain lateralization such as language skills are appearing. Diffusion tensor imaging (DTI) can be used to quantify fractional anisotropy (FA) within the WM and increasing values correspond to advancing brain development. To investigate the normal features of WM development during early childhood, we gathered a DTI data set of 166 healthy infants (mean 3.8 wk, range 2-5wk; 89 males; born on gestational week 36 or later) and 144 healthy children (mean 5.4 years, range 5.1-5.8 years; 76 males). The sex differences, lateralization patterns and age-dependent changes were examined using tractbased spatial statistics (TBSS). In 5-year-olds, females showed higher FA in wide-spread regions in the posterior and the temporal WM and more so in the right hemisphere, while sex differences were not detected in infants. Gestational age showed stronger association with FA values compared to age after birth in infants. Additionally, child age at scan associated positively with FA around the age of 5 years in the body of corpus callosum, the connections of which are important especially for sensory and motor functions. Lastly, asymmetry of WM microstructure was detected already in infants, yet significant changes in lateralization pattern seems to occur during early childhood, and in 5-year-olds the pattern already resembles adult-like WM asymmetry. HighlightsO_LIWhite matter tract integrity shows widespread sex differences at the age of 5 years. C_LIO_LIWhite matter structure is highly lateralized during early childhood, and changes in asymmetry occur between the birth and 5 years of age. C_LIO_LIThe white matter lateralization pattern of 5-year-olds, unlike of infants, resembles asymmetry observed in adults. C_LI

neuroscience↗

bioRxiv2021

Subcortical 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 subcortical nuclei, including the hippocampus, 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-FIRSTs Default pipeline were the most accurate, while FreeSurfers 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-FIRSTs 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 and 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.

neuroscience↗

bioRxiv2021

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. One key challenge is the use of many different atlases, automated segmentation tools, manual edits in semiautomated protocols, and quality control protocols, which complicates comparisons between studies. In this article, we present our semiautomated segmentation protocol using FreeSurfer v6.0, ENIGMA consortium software, and the quality control protocol that was used in FinnBrain Birth Cohort Study. We used a dichotomous quality rating scale for inclusion and exclusion of images, and then 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 minor: less than 2% in all regions. Supplementary materials 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. Our semiautomated segmentation protocol provides high quality pediatric neuroimaging data and could help investigators working with similar data sets.

neuroscience↗