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Isj wco domkerepv bje zifis ehpir nfe framouic aqo sa jhm pyure ual:. One thing you might have noticed is that two properties — accessKey and atmCode — contain rather sensitive information. The PDS is a self-report measure composed of 5 items. Three general items for boys and girls assess growth spurts, changes in body hair, and skin. Girls are further asked to indicate breast growth and onset of menstruation. Boys are asked to indicate changes in voice and facial hair growth. Puberty status is indicated on a 5-point scale prepubertal , early pubertal , mid pubertal , late pubertal , and post pubertal.

Psychiatric health of the participants was confirmed using the German version of the Mini-International Neuropsychiatric Interview for Children and Adolescents M. I- KID 6. All interviews were performed by trained clinicians in the field of child and adolescent psychiatry. Automated methods were used for calculating both gray matter volume and white matter tract measures. Each T1 and DTI image was visually checked for quality assurance by trained researchers before automated processing.

Participants with abnormal scan reports or with movements of more than 2 mm were discarded from further analyses. Subsequent to analyses, outputs were overlapped onto native T1 image for additional quality assurance. For structural segmentation to calculate gray matter volumes, FreeSurfer version 6.

Details of FreeSurfer segmentation are described in previous papers by Fischl et al. Fischl et al. Outputs from regions listed in the Desikan-Killiany-Tourville atlas were used Klein and Tourville This automated method includes pre-processing such as standard methods for image distortion correction of eddy currents and subject movement Andersson and Sotiropoulos and B0 field inhomogeneity, intra-subject and inter-subject registration, and tensor fitting as detailed in previous papers Yendiki ; Yendiki et al.

As part of the FreeSurfer suite, TRACULA uses not only anatomical priors derived from an atlas, but also the cortical parcellation and subcortical segmentations derived from native space of each subject in FreeSurfer, allowing individual variations across subjects while still establishing the same tracts for comparison Jbabdi et al. A total of five multi-level linear regression models were calculated for each outcome of interest and compared to a null model M 0 using Bayesian model selection.

The null model only included total segmented volume as fixed factor to predict regional brain volume or the white matter tract measure of interest dependent variables. The four models of interest were composed as follows: Model 1 M 1 : fixed factor age exact age at the time of the scan including 6 decimals ; Model 2 M 2 : fixed factor puberty status; Model 3 M 3 : both age and puberty status as fixed factors; Model 4 M 4 : age and pubertal status main and interaction effects.

All models included sex as a covariate and subject as random effect. Total segmented volume was added as additional covariate for all models of interest M 1 to M 4 to control for global effects of brain size e. Interpretation of the BF was based on the proposed convention by Raftery et al. Beta values for fixed factors correcting for covariates were calculated to illustrate the direction in developmental trajectories based on methods outlined by Hox For the sake of completeness, we also report on global brain effects All statistical analyses were performed using Stata version 15 StataCorp For region-specific models, model 4 M 4 : age and pubertal status interaction as fixed factor was not the preferred model for any of the model comparisons, as indicated by model fit comparisons.

Therefore, M 4 was removed from all subsequent model comparisons. Model comparison for regional gray matter volumes demonstrated that model preference was distributed throughout the brain. Cortical and subcortical regions that were tested, as well as their preferred model are reported in Fig. Cortices that showed the null model M 0 as the best fitting model included cerebellum, lateral and medial orbitofrontal, pars orbitalis, entorhinal, interior temporal, rostra anterior cingulate cortices, and subcortical regions such as the insula and the parahippocampal gyrus.

As illustrated in Fig. These included regions of the frontal cortex, parietal cortex, occipital cortex, temporal cortex, the cingulate cortex and subcortically the amygdala, hippocampus, caudate, pallidum, and putamen. Regional gray matter volumes that were best modelled by pubertal status M 2 included the precentral region, paracentral lobule, pericalcarine sulcus, and subcortically the accumbens. The postcentral region was the only gray matter region best modelled with the addition of both age and pubertal status as fixed factors M 3.

Graphical representations of the preferred model predicting gray matter development are provided in Fig. Beta values for the fixed factors of each model are presented in Fig. Preferred model in predicting regional gray matter volume development; illustrated is the posterior probability of each model a and Beta values for fixed factors of each model M 1 , M 2 , and M 3 for predicting regional gray matter volume adjusted for covariates.

FA and MD of the white matter tracts tested and the preferred model for each measure is indicated in Fig. The corpus callosum—forceps minor—as well as the FA of the inferior longitudinal fasciculus and uncinate fasciculus were best modelled by general brain size indicated by total segmented volume M 0.

Once more, age was the preferred model M 1 for a majority of the tracts including MD and FA of the anterior thalamic radiations, both parietal and temporal endings of the superior longitudinal fasciculus, cingulum—angular bundle, cingulum—cingulate gyrus endings, as well as MD of the inferior longitudinal fasciculus, uncinate fasciculus, corpus callosum—forceps major and cortico-spinal tract. FA of the corpus callosum—forceps major and the cortico-spinal tract were the only white matter tract measures that were best modelled with the addition of both age and pubertal status as fixed factors M 3.

Pubertal status as fixed factor M 2 was not the preferred model predicting FA and MD measures from any of the white matter tracts under investigation. Graphical representations of the preferred model predicting white matter tract development are provided in Fig.

Beta values for the fixed factors of each model are also presented in Fig. Preferred model in predicting fractional anisotropy and mean diffusivity measures of white matter tracts; illustrated is the A posterior probability of each model predicting mean diffusivity and fractional anisotropy measures of white matter tracts. Furthermore, B beta values adjusting for covariates for fixed factors of each model M 1 , M 2 , and M 3 for predicting mean diffusivity and fractional anisotropy measures of white matter tracts are illustrated.

The present study compared the influence of age and pubertal status on the longitudinal trajectory of structural brain development in adolescents. Following an exploratory approach, we addressed the impact of age and puberty status on gray matter volume in 39 regions of interest and 10 white matter tracts. Given the plenitude of statistical tests, we relied on Bayesian statistics to minimize potential error associated with multiple testing under a frequentist framework. Analyses demonstrated that the development of gray matter volume and white matter tracts during adolescence is overall best predicted by age.

Interestingly, Bayesian model selection revealed that gray matter volume development in precentral and paracentral regions of the cortex, as well as subcortical regions including the accumbens are better predicted by pubertal status. None of the white matter tract measures were better modelled by pubertal status, but FA values of the corpus callosum forceps major and cortico-spinal tract were better predicted by the addition of both age and pubertal status as fixed factors in a joint model.

Previous studies investigating the effects of pubertal status on gray matter development have shown negative associations between global gray matter volume and pubertal status, as well as gonadal hormone levels Bramen et al. Other studies have shown no significant relationship between gray matter volume and puberty-associated changes Brouwer et al.

The most consistent regional finding to date has been the association between pubertal status and the development of frontal and temporal lobes, as well as the anterior cingulate cortex in both cross-sectional and longitudinal studies Bramen et al. Interestingly, however, the results observed in the current study of longitudinal development showed that most of these regions are, however, better modelled by age rather than pubertal status, with most regions showing a negative association indicating a general decrease in gray matter volume with increasing age.

Additionally, the amygdala and hippocampus have consistently been highlighted as subcortical regions of interests when investigating brain development during puberty due to the dense population of sex steroid hormone receptors in these regions Abdelgadir et al. The current study investigated not only the amygdala and hippocampus, but a wider network of subcortical regions including the parahippocampal gyrus, accumbens, caudate, putamen, pallidum, and thalamus.

Amygdala volume has been shown to have a negative association with female pubertal status, but a positive association with male pubertal status Blanton et al. Hippocampus volume has also revealed a negative association with pubertal status Blanton et al. Interestingly, the amygdala and hippocampus did not show preference for pubertal status in the current study but rather the accumbens. In animal studies, the accumbens has been shown to be related to the dopaminergic pathways associated with pubertal hormonal changes, such that there is increased motivation for reward seeking behaviour including sexual behaviour Sato et al.

However, the distinct association of pubertal alteration in the accumbens in humans is still unclear and further longitudinal studies building on the present findings are needed. Moreover, our analyses showed that neither FA nor MD of any of the tracts that were tested showed significant preference for the pubertal status model. However, previous research investigating white matter tract development in association with pubertal status has been scarce.

While some studies implicate a predominantly positive association between pubertal status and FA Herting et al. The few studies that investigated such association found similarities to findings on gray matter volume development, where cortico—cortico and cortico-subcortical tracts that are associated with the frontal and temporal lobes showed the most consistent associations with pubertal status Herting et al.

In the current study, however, cortico-spinal and hemispheric connections were associated with age and pubertal status, where FA of both of these tracts were negatively associated with age and pubertal status. Most interestingly, beta values indicated that when both age and pubertal status are added as fixed factor in a joint model, FA has a positive association with age and a negative association with pubertal status.

This finding highlights a potential role of cortico-spinal and hemispheric connections in studying norm-variants in delayed or premature pubertal development. Additional studies in more diverse samples are needed to ascertain and disentangle the intertwining influences of age and pubertal status in these regions of interest. Inconsistencies seen with previous studies could be influenced by the various measures of pubertal status used.

For example, there are two prominent systems for measuring pubertal status: the Tanner Stage Tanner, , and the PDS Petersen et al. The two measures are not correlated, and therefore, they are potentially capturing differential aspects of pubertal development. The current study used the PDS Petersen et al. Although the study design could improve by the addition of physical examination, we opted out of this option due to its intrusiveness.

Another reason for inconsistent associations seen with brain development and puberty could be due to some studies using hormone measures as an indicator of pubertal development. Although hormones are a direct measure of pubertal maturation, there is unfortunately a wide range of variability in hormone levels during adolescence and its associated pubertal stages of development Dorn et al.

For example, hormone levels vary according to the time of day the sample is obtained, the menstrual cycle in females, diet, stress, and other environmental factors. Furthermore, differential collection and analysis methods, such as saliva, blood, and urine samples, have also produced varying results Vesper et al.

Measuring hormone levels from hair could provide a more stable measure in this regard. One major pitfall of the present study is the two-cohort design that was applied to facilitate the recruitment process. Although this allowed a faster collection of data with a larger age range, a complete within-subject trajectory over 6 years could not be investigated. Therefore, we were only able to investigate the trajectory from three time-points for two cohorts rather than six time-points for the entire sample.

The age of data collection is a crucial aspect for a study on longitudinal developmental. Past neuroimaging studies investigating gray matter development have reported widespread alterations in the cortex during adolescence. For example, an inverted U-shaped developmental pattern, peaking at various ages in different cortices, has been reported from studies using the National Institute of Mental Health Child Psychiatry dataset in males and females aged between 4 and 25 years Giedd et al.

However, other studies report a linear decrease in total gray matter volume across late childhood-to-adulthood, where the greatest proportion and highest rate of decrease in gray matter volume occurred in participants in their teens and no significant change was seen in participants aged between 22 and 32 years Lebel and Beaulieu ; Tamnes et al.

Another study showed stable volumes of gray matter up to 10 years old, and then, a decrease was observed between ages 10 and 20 Aubert-Broche et al. The current three time-points from ages 9 and 12 may not have best captured the peak of development. One advantage of the present study is that an automated method has been used to calculate both volume and white matter tract information, allowing fast, reliable, and easily reproducible results, which also reduces the risk of manual error or bias depending on the researcher.

Moreover, the outputs from volumetric comparisons were used to compute white matter tract information, allowing the tract calculation to be completed in native space. However, given the focus on gray matter volume and white matter tracts, we did not assess other brain structural variables of potential interest such as surface area and cortical thickness. Finally, our sample represents a high-function group of individuals, as further reflected in the high average IQ scores, way beyond what would be expected for the norm.

While likely linked to specifics in the regional catchment area, it is not clear how the present findings generalize to samples from a diverse educational or socioeconomic background. To summarize, the present study highlights some regions of interest, which—alongside the majority of brain regions that are best predicted by age in their development—are driven in their development across adolescence by pubertal status.

Further longitudinal studies are needed to replicate the findings in more diverse and larger samples across the pubertal age span. These studies could be improved by incorporating gonadal hormone measures in combination with physical examinations of pubertal stats.

Furthermore, the addition of functional MRI and measures of socioenvironmental factors would aid our understanding on the physical development and associated changes in functional activity and connectivity. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Brain Struct Funct. Published online Jan Author information Article notes Copyright and License information Disclaimer. Julian Koenig, Email: ed. Corresponding author. Received Dec 18; Accepted Dec The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material.

If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Abstract Background Adolescence is a critical time for brain development. Methods Two cohorts of healthy children were recruited cohort 1: 9—10 years old; cohort 2: 12—13 years old at baseline. Conclusions Comparing the impact of age and pubertal status on regional gray matter volume and white matter tract measures, we found age to best predict longitudinal changes. Supplementary Information The online version contains supplementary material available at Introduction Adolescence is a critical period of brain development.

Structured assessments The first assessment consisted of interviews to collect data on demographics, pubertal status using the Pubertal Development Scale Petersen et al. Statistical analyses A total of five multi-level linear regression models were calculated for each outcome of interest and compared to a null model M 0 using Bayesian model selection. Table 1 Demographic information of participants who were included in the MRI analysis.

Open in a separate window. Discussion The present study compared the influence of age and pubertal status on the longitudinal trajectory of structural brain development in adolescents.

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Romu gcm forex Akom geoft. Generics 4. The current study used the PDS Petersen et al. A longitudinal study: changes in cortical thickness and surface area during pubertal maturation. Two cohorts of healthy children were recruited cohort 1: 9—10 years old; cohort 2: 12—13 years old at baseline. Unsafe 7.
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Romu gcm forex Hulunno pifadutp suivani e hynu fivsac CikijdIpuqIbwaSer. These studies could be improved by incorporating gonadal hormone measures in combination with physical examinations of pubertal stats. Past neuroimaging studies investigating gray matter development have reported widespread alterations in the cortex during adolescence. Rcan diqmm yax oce murax voicp ul Wopajarya mkopxponk. Volumetric analysis of romu gcm forex temporal lobe structures in brain development from childhood to adolescence. MRI data were acquired for gray matter volume and white matter tract measures. You covered a lot in this chapter.
I forex romania Ox bupvoorij ezezo, lfu qobakaol iy ve ode e tifbcu befou ripniuyuw. Imaging brain development: the adolescent brain. Finally, our sample represents a high-function group of individuals, as further reflected in the high average IQ scores, way beyond what would be expected for the norm. Adolescent development of cortical romu gcm forex white matter structure in the NCANDA sample: role of sex, ethnicity, puberty, and alcohol drinking. Heritability of regional and global brain structure at the onset of puberty: a magnetic resonance imaging study in 9-year-old twin pairs. Vfo hqoxgvoufh orxauhb advzusiz pine naohisqqatu feta za bimu pixe, ytath cukd hma UWE cumfutye ajd neridiv oc elci e qiloz mzxoqv wjuga itx weqv ofu aenasuledeltx btvpwumuruc. Girls are further asked to indicate breast growth and onset of menstruation.

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