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Cerebral blood flow increases across early childhood
Journal Pre-proof Cerebral blood flow increases across early childhood Dmitrii Paniukov, R. Marc Lebel, Gerald Giesbrecht, Catherine Lebel PII:
S1053-8119(19)30815-8
DOI:
https://doi.org/10.1016/j.neuroimage.2019.116224
Reference:
YNIMG 116224
To appear in:
NeuroImage
Received Date: 4 April 2019 Revised Date:
27 June 2019
Accepted Date: 23 September 2019
Please cite this article as: Paniukov, D., Lebel, R.M., Giesbrecht, G., Lebel, C., Cerebral blood flow increases across early childhood, NeuroImage (2019), doi: https://doi.org/10.1016/ j.neuroimage.2019.116224. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Cerebral Blood Flow Increases Across Early Childhood
Dmitrii Paniukov 1,2,3,4, R. Marc Lebel 2,5, Gerald Giesbrecht 1, Catherine Lebel 2,3,4 1
Department of Pediatrics, 2 Department of Radiology, 3 Alberta Children's Hospital Research
Institute at Alberta Children's Hospital, Cumming School of Medicine, University of Calgary 4
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary 5
GE Healthcare, Calgary, Canada
Corresponding author: Catherine Lebel Room B4-513 Alberta Children’s Hospital 28 Oki Drive NW, Calgary, Alberta, Canada T3B6A8 [email protected]
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Abstract Adequate cerebral blood flow (CBF) is essential to proper brain development and function. Detailed characterization of CBF developmental trajectories will lead to better understanding of the development of cognitive, motor, and sensory functions, as well as behaviour in children. Previous studies have shown CBF increases during infancy and decreases during adolescence; however, the trajectories during childhood, and in particular the timing of peak CBF, remain unclear. Here, we used arterial spin labeling to map age-related changes of CBF across a large longitudinal sample that included 279 scans on 96 participants (46 girls and 50 boys) aged 2-7 years. CBF maps were analyzed using hierarchical linear regression for every voxel inside the grey matter mask, controlling for multiple comparisons. The results revealed a significant positive linear association between CBF and age in distributed brain regions including prefrontal, temporal, parietal, and occipital cortex, and in the cerebellum. There were no differences in developmental trajectories between males and females. Our findings show that CBF continues to increase until the age of 7 years, likely supporting ongoing improvements in behaviour, cognition, motor, and sensory functions in early childhood.
Keywords: cerebral blood flow, arterial spin labelling, brain perfusion, brain development, early childhood development, magnetic resonance imaging
Conflict of Interest R.M.L. is an employee of GE.
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1. Introduction Early childhood (2-7 years old) is an important period of development that sees large gains in cognitive, motor, and sensory function, as well as improvements in behaviour, all of which lay the foundation for future life. Changes in cognition and behaviour are supported by ongoing development of brain structure and function. During early childhood, the brain manifests shifts in functional connectivity (Long et al., 2017), cortical surface expansion (Remer et al., 2017), and increases in white matter volume (Deoni et al., 2012). Cerebral blood flow (CBF) may be particularly relevant to these changes, as it supplies blood and nutrients to the brain and supports ongoing development (Kandel et al., 2000). A non-invasive magnetic resonance imaging technique, arterial spin labelling (ASL), can measure CBF in the brain. CBF is a measure of perfusion, which in the broader age-range literature has been found to underlie typical neuronal activity (Fox & Raichle, 1986) and cognitive function (Gur, et al., 1982). It also tends to be altered in disorders or diseases like depression (Bench et al., 1992), anxiety (Gur et al., 1987), and head trauma (Wintermark et al., 2004b), and therefore may be a marker of abnormal development in children. Thus, understanding CBF changes in healthy children is critical for understanding both typical and atypical brain development and its potential impact on adulthood. Previous studies show that cortical CBF increases from birth to early childhood (Wintermark et al., 2004a) and decreases during adolescence (Avants et al., 2015; Satterthwaite et al., 2014; Taki et al., 2011a). This suggests that there is a peak during early childhood, but the limited number of previous studies in this age range have produced conflicting results, showing
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ages of peak CBF that vary from 2 to 10 years (Carsin-Vu et al., 2018; Chiron et al., 1992; Liu et al., 2018; Takahashi et al., 1999; Wintermark et al., 2004a; Wu et al., 2016). Differences between studies are likely due, at least in part, to the very small number of participants included between ages 2-7 years (n=7-29) and the lack of longitudinal data, making it difficult to determine the actual trajectories during early childhood. Furthermore, calculating peak ages can be influenced by the inclusion of much older or much younger subjects, erroneously skewing the peak (Fjell, 2010). Therefore, a large longitudinal sample needs to be used to map the developmental trajectories of CBF. Additionally, findings regarding sex differences have been very mixed. Most studies do not explicitly test sex differences. Among those that do, one study reported higher brain perfusion in parietal cortex in females than in males in participants 5-18 years old (Taki et al., 2011b), while another reported CBF decreases in males across adolescence, but a u-shaped trajectory of decreases then increases in female adolescents (Satterthwaite et al., 2014). The goal of the current study was to characterize the developmental changes of CBF in a large, longitudinal sample of young children and evaluate sex differences.
2. Methods 2.1 Participants Children were recruited from the Calgary area to participate in the study. Children had to be English speakers, born full term, be free from genetic disorders associated with significant intellectual and/or motor impairments as well as neurodevelopmental and/or neurological disorders, and free from contraindications to MRI. Parents/guardians of the children were initially contacted and asked to participate in the study. Interested parents/guardians were
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provided with audio/visual instructional materials that included an e-book Pluto and the MRI Rocket Ship Adventure (freely available at http://www.lulu.com/shop/ashleigh-frayne/pluto-andthe-mri-rocket-ship-adventure/ebook/product-22122518.html), which incorporates our scanning procedures into an engaging story. We encouraged the parents/guardians to review the materials with the child. Parents/guardians were also given the option for their child to practice in a mock scanner before the real scan so their children would get accustomed to the size and the sounds of the machine. Both the mock scanner and our MRI scanner have a space rocket façade. Prior to the scan, a research assistant reviewed the instructional materials with the child once again, and explained the procedure to the child in easy-to-understand language (e.g., the MRI machine takes “pictures” of their brain, it is very important to lay very still to get nicely looking “pictures”). In general, we find that these procedures enable us to collect high quality data even in young children (Thieba et al., 2018). Children underwent an MRI scan between the ages of 2-4 years and were invited to return for follow up imaging sessions approximately every 6 months. 105 children were initially recruited, providing 333 datasets total. One participant was excluded because of an incidental finding (this participant had 6 scans). All other datasets underwent a visual quality check (by DP), and datasets with excessive motion that produced sharp ringing artifacts and/or blurred brain structures on T1 (n=6) and/or CBF (n=40), as well as datasets with scanner artifacts were excluded from the analysis (n=2), leaving 279 datasets on 96 children. These 96 children were 46 girls and 50 boys ranging in age from 1.97 to 6.90 years (M=4.46, SD=1.00 years); 80 were right-handed, 10 left-handed, and 6 with undetermined handedness. 32 children completed a single scan, 16 — two scans, 13 — three scans, 13 — four scans, 13 — five scans, 7 — six scans, 1 — seven scans, and 1 — ten scans (see Figure 1). Parents/guardians provided written
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informed consent, and children provided verbal assent. The study was approved by the conjoint health research ethics board at the University of Calgary (REB18-1011).
Figure 1. Age at scans for all subjects (females are shown in red triangles, males shown in blue circles). Each of the 297 scans is represented by a circle or a triangle; each of the 96 subjects is shown in a different row with their scans connected by a straight line.
2.2 Data acquisition Neuroimaging data were collected using a research-dedicated GE 3T MR750w scanner (General Electric, Waukesha, WI) with a 32-channel head coil at Alberta Children’s Hospital.
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During the scan, children watched a movie of their choice. T1-weighted images were acquired using an FSPGR BRAVO sequence with TR = 8.23 ms, TE = 3.76 ms, TI = 540 ms, flip angle=12 degrees, voxel size = 0.9x0.9x0.9 mm, 210 slices, matrix size=512x512, field of view=23.0 cm. ASL images were acquired with the vendor supplied pseudo continuous 3D ASL sequence with TR = 4.56 s, TE = 10.7 ms, in-plane resolution of 3.5x3.5 mm, single post label delay of 1.5 s (in accordance to a recent consensus paper (Alsop et al., 2015)), labeling duration 1.5 s, and 30 4.0 mm thick slices. The scan time was 4.4 minutes. The label plane was positioned 2.2 cm below the base of the imaging volume, which was placed at the base of the cerebellum in all participants. 2.3 Data preprocessing ASL perfusion DICOM images were converted to CBF DICOM images on the scanner computer using the vendor supplied approach (consistent with Alsop et al., 2015). We used the vendor standard M0 image for quantification. This was a 2 second saturation recovery image. We performed quantification using the general kinetic model proposed by Buxton and colleagues (1998). CBF is reported here in mL/100g/min. T1-weighted and CBF images were converted from DICOM images to NifTi files using dcm2nii from the Mricron software package (Rorden & Brett, 2000). To make our results comparable to other studies and map them on existing pediatric brain atlases, we used a popular pediatric template for children of 4.5 - 8.5 years old (Fonov et al., 2009, 2011), while the construction of our own unbiased pediatric template and a brain atlas would be interesting but outside of the scope of the current work. The T1 images were registered to the MNI template using N4-bias correction and nonlinear ANTs registration (Avants et al., 2009). The MNI template brain mask was transformed back to native T1 space for each subject, and T1 images
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were skull-stripped by multiplying the mask with the T1-image using FSL utilities (Jenkinson et al., 2012). Each individual’s CBF image was registered to their T1 image using rigid transformation with 6 degrees of freedom as an estimator for the affine transformation, and with 6 degrees of freedom and the BBR cost function implemented in FSL’s FLIRT software (Greve and Fischl, 2009; Jenkinson & Smith, 2001, Jenkinson et al., 2002). Then, CBF images were transformed to MNI space using the transformation matrices from the steps above and skullstripped using the MNI brain mask. CBF files were resampled to 2x2x2 mm using ANTs’ ResampleImage and concatenated into a single 4D file for further analysis. 2.4 Statistical Analysis Statistical analysis was performed with hierarchical linear regression using Statsmodels (Seabold & Perktold, 2010) on every voxel inside the gray matter whole brain mask, which was a part of the pediatric MNI template (Fonov et al., 2009, 2011). To examine the significance of the estimated effects, we estimated cluster size threshold, which was 1539 3dFWHMx estimated smoothing of 0.20 4.41 22.99. We used 10 000 permutations using AFNI’s 3dClustSim (Forman et al., 1995) with non-Gaussian filtering, voxel-wise p-threshold of 0.01 for alpha of 0.05. The tmaps were thresholded at t = 1.97 and merged to create final outputs with 3dmerge (Cox, 1996). All image preprocessing and analyses were scripted in Python 3.6 (Python Software Foundation, 2017) and bash (Free Software Foundation, 2007) utilizing Nipype software (Gorgolewski et al., 2011). We analyzed CBF as a function of children’s age (including linear, quadratic, and cubic terms), sex, handedness, grey matter volume, and the interaction of age with sex as fixed effects, and intercept as a random effect, using participant as a nesting factor. We used restricted maximum likelihood and left all other parameters as default (see
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http://www.statsmodels.org/stable/mixed_linear.html for details). Grey matter volume was included as a covariate to account for the grey matter changes that occur across childhood (Giedd et al., 1999). In our data, grey matter volume significantly correlated with age (r = 0.27, p < .001). To examine possible multicollinearity between age and the grey matter volume, we computed the variance inflation factor, which equaled 1.08. Therefore, we concluded there was an absence of severe multicollinearity between the age and the grey matter volume. 2.5 Data and code availability The preprocessing scripts along with analysis scripts and results are available online (https://github.com/dpaniukov/CBF_early_childhood). Research data are available on Open Science Framework as a part of the Calgary Preschool MRI Dataset (http://doi.org/10.17605/OSF.IO/AXZ5R).
3. Results 3.1 Age-related changes in CBF The quadratic and cubic terms for the children’s age were not significant (quadratic term beta values ranged from -24.97 to 35.30, with t-values from -4.14 to 3.48 across the grey matter mask, and were non-significant when corrected for multiple comparisons; cubic term beta values ranged from -4.71 to 2.99, with t-values from -3.64 to 4.17 across the grey matter mask, and were non-significant, when corrected for multiple comparisons), and therefore, were removed from further analyses. The final statistical model estimated the relationship between CBF and age, sex, age-by-sex interaction, grey matter volume, and handedness. The results revealed a significant positive association between CBF and age, such that CBF increased as children got older, in prefrontal, temporal, parietal, and occipital cortex, and
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the cerebellum (see Figures 2-4). 14.82% of voxels in the grey matter mask showed significant increase of CBF with age, distributed across areas of the left frontal pole, left medial prefrontal cortex, bilateral dorsolateral prefrontal cortex, bilateral superior frontal gyrus, bilateral inferior frontal gyrus, bilateral premotor and motor regions, left parietal regions (supramarginal gyrus), bilateral temporal regions, bilateral occipital regions (lateral occipital cortex and occipital pole), and bilateral cerebellum. Across voxels with significant increases, the average estimated increase of CBF was 3.21 mL/100g/min per year, or 29% from baseline values across the age range (SE = 1.19, CI [0.88 5.53]). Across the whole gray matter mask, global CBF increased at a rate of 0.70 ml/100g/min per year, or 5.9% across the entire age range (SE = 1.14, CI [-1.53 2.92]). As an additional quality check, we computed the mean between session variance for participants with multiple scans, which was 3.16 mL/100g/min +/- SD=2.61. These results were similar to the results we found across all participants for CBF vs age, which suggests that the between session variance might be driven by the effects of age.
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Figure 2. Relationship of CBF with age. The left column of images shows brain areas
that had significant associations of CBF with age. The right column shows unthresholded t-
11
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statistics for the association of CBF with age. Red-yellow colors are for the increased association, blue-light blue colors are for the decreased association.
Figure 3. Global CBF increases with age. Each dot represents a scan, each thin line is a
fit line for an individual participant, and the solid lines represent overall fits for males (blue) and females (red). Global CBF increased by 5.85% across the age range, 2-7 years.
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Figure 4. Relationship of CBF with age. These scatterplots are examples of areas with
faster (top) and slower (bottom) rates of CBF change with age. The graphs show the increase of CBF with age for males (blue) and females (red) (no significant sex difference was found). Each dot represents a single scan time point; thin lines are the best fit lines for individual participants; thick lines are the best fit lines for all data.
3.2 CBF and sex differences Females showed slightly lower CBF throughout the brain, but these differences were not significant (beta values ranged from -25.31 to 45.64, with t-values from -2.91 to 4.41 across the grey matter mask, and were non-significant when corrected for multiple comparisons). Interactions of age with sex were also non-significant (beta values ranged from -16.38 to 12.49, with t-values from -5.58 to 3.44 across the grey matter mask, and were non-significant when corrected for multiple comparisons).
4. Discussion Here, in a large, longitudinal sample of young children, we show that CBF increases with age across the cortex and cerebellum in early childhood. Linear increases were seen from 2-7 years, with a mean increase of 29% over baseline values, or approximately 3.20 mL/100g/min per year in areas with significant changes. Trajectories of development were similar between males and females, and show that CBF continues to increase during childhood, with peak values obtained sometime after age 7 years. Previous research found that CBF in infancy starts at ~25 ml/100g/min (Wintermark et al., 2004a) and almost doubles by 6 months of age (Kehrer & Schöning, 2009), with less rapid
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increases continuing throughout infancy. CBF is thought to peak between 2 and 10 years, with peak estimates ranging from 40 to 140 ml/100g/min (Carsin-Vu et al., 2018; Chiron et al., 1992; Liu et al., 2018; Takahashi et al., 1999; Wintermark et al., 2004a; Wu et al., 2016). CBF then decreases in adolescence to ~75 ml/100g/min (Avants et al., 2015; Satterthwaite et al., 2014). Peak ages have varied considerably across studies, likely because of small sample sizes and cross-sectional designs. CBF showed age-related increases across many brain areas. These areas are associated with semantic knowledge selection and attentional control (occipital, temporal, and parietal cortices; Thompson-Schill et al., 1997; Aron et al., 2003), language development and processing (left inferior frontal gyrus; Gabrieli et al., 1998) as well as with the evaluation of abstract information (rostrolateral prefrontal cortex; Vendetti & Bunge, 2014; Paniukov & Davis, 2018), and decision making (dorsolateral prefrontal cortex; Heekeren et al., 2006). These increases were distributed across the brain, and though there is some variation in development rates, regions appear to develop relatively similarly. The unthresholded t-map (Figure 2) shows some areas with negative associations with age, though these were all non-significant. That may imply that these regions are slowing down and perhaps nearing their peak CBF, though non-linear models were also nonsignificant for these regions. Thus, the increases observed here appear to be more widespread in nature, rather than concentrated in specific areas, but future research in other samples and/or with wider age ranges will be useful to pinpoint CBF trajectories in the areas not identified as significant here. The current study produced similar results in both hemispheres; however, most results in the right hemisphere did not meet the statistical threshold for significance. To help understand potential causes of this asymmetry, we acquired B1 field maps in both adults and children, which
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did not show substantial asymmetry. We further examined statistical variance between the hemispheres, which was similar. Finally, we tested whether handedness was driving the observed results, but it was not a significant predictor of CBF or asymmetry. Future studies may be able to explore whether other factors drive the observed asymmetry, or whether right hemisphere changes become significant with a larger sample size. These changes in CBF occur in parallel with changes in brain structure and function During early childhood, brain structure also undergoes substantial development, including cortical thinning across cortex, cortical surface expansion (Remer et al., 2017), increases in white matter volume (Deoni et al., 2012), increases in fractional anisotropy, and decreases in mean diffusivity (Reynolds et al., 2019). Early childhood also sees substantial alterations in network connectivity (Long et al., 2017), when the brain reorganizes itself to support ongoing cognitive development. Increasing CBF may provide nutrients for the neurons in order to enable these structural and functional changes, and thus the significant behavioural and cognitive development during early childhood. We did not find any significant sex differences in CBF or its developmental trajectories. One previous study in older children and adolescents (5-18 years) found higher brain perfusion in females than in males in the medial aspect of the parietal cortex (Taki et al., 2011b). Other studies showed more pronounced sex differences in CBF in adolescents with increase CBF in females later in adolescence while CBF in males still decreases (Satterthwaite et al., 2014). Studies of cortical structure suggest that sex differences may become more prominent later in development, with grey matter volume peaking earlier in males than females (Lenroot et al., 2007). Thus, it may be that CBF sex differences become more pronounced later in childhood or in adolescence, but are not apparent in early childhood.
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A key strength of this study is the large, longitudinal sample, which allowed us to measure changes over time, and to detail CBF changes across early childhood. The ASL acquisition and quantification has several limitations that could bias our results. First, hemoglobin is known to have an inverse relationship with CBF (Ibaraki et al., 2010). We did not measure hemoglobin, which may confound our results; however, hemoglobin does not change across this age range (Osgood and Baker, 1935), and thus would not be expected to have a systematic effect with age. Second, gray matter T1 decreases slightly (~40-45 ms) across early childhood (Deoni et al., 2011; Hales et al., 2014; Eminian et al., 2018; Yeatman et al., 2014), which may impact the calculated CBF since the general kinetic model assumes a constant T1 value. Lower gray matter T1 values would lead to underestimation of CBF in the older kids, suggesting that our results may underestimate the extent of CBF increases. Future studies that measure T1 within children may see more extensive areas of CBF increases than we report. Third, we chose our post labels delay according to the value recommended in the ASL whitepaper (Alsop et al., 2015). A longer post labels delay would desensitise transit effects, but would increase sensitivity to tissue T1. It is likely that some individuals had regions with transit delays greater than our post label delay, however, we are unaware of evidence suggesting an average change in transit delay in this age range. Changes in transit delay remains an open question that could be studied with multi-delay ASL.
5. Conclusion For the first time using a large longitudinal sample, we show that CBF continues increasing until 7 years of age in distributed cortical and cerebellar regions. This suggests that peak CBF occurs sometime in later childhood, before the decreases previously observed in
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adolescence begin. Furthermore, we saw no differences in developmental trajectories between males and females, suggesting that such differences may arise later in childhood and adulthood. These increases of CBF likely support the ongoing development of cognitive, sensory, and motor functions.
6. Acknowledgements This research was funded by CIHR (funding reference numbers IHD-134090, MOP136797, MOP- 142387, and a New Investigator Award to C.L.), and an Owerko Postdoctoral Fellowship given to D.P. We would like to thank the APrON team at the University of Calgary for recruitment assistance.
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S1053-8119(19)30815-8
DOI:
https://doi.org/10.1016/j.neuroimage.2019.116224
Reference:
YNIMG 116224
To appear in:
NeuroImage
Received Date: 4 April 2019 Revised Date:
27 June 2019
Accepted Date: 23 September 2019
Please cite this article as: Paniukov, D., Lebel, R.M., Giesbrecht, G., Lebel, C., Cerebral blood flow increases across early childhood, NeuroImage (2019), doi: https://doi.org/10.1016/ j.neuroimage.2019.116224. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.
Cerebral Blood Flow Increases Across Early Childhood
Dmitrii Paniukov 1,2,3,4, R. Marc Lebel 2,5, Gerald Giesbrecht 1, Catherine Lebel 2,3,4 1
Department of Pediatrics, 2 Department of Radiology, 3 Alberta Children's Hospital Research
Institute at Alberta Children's Hospital, Cumming School of Medicine, University of Calgary 4
Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary 5
GE Healthcare, Calgary, Canada
Corresponding author: Catherine Lebel Room B4-513 Alberta Children’s Hospital 28 Oki Drive NW, Calgary, Alberta, Canada T3B6A8 [email protected]
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Abstract Adequate cerebral blood flow (CBF) is essential to proper brain development and function. Detailed characterization of CBF developmental trajectories will lead to better understanding of the development of cognitive, motor, and sensory functions, as well as behaviour in children. Previous studies have shown CBF increases during infancy and decreases during adolescence; however, the trajectories during childhood, and in particular the timing of peak CBF, remain unclear. Here, we used arterial spin labeling to map age-related changes of CBF across a large longitudinal sample that included 279 scans on 96 participants (46 girls and 50 boys) aged 2-7 years. CBF maps were analyzed using hierarchical linear regression for every voxel inside the grey matter mask, controlling for multiple comparisons. The results revealed a significant positive linear association between CBF and age in distributed brain regions including prefrontal, temporal, parietal, and occipital cortex, and in the cerebellum. There were no differences in developmental trajectories between males and females. Our findings show that CBF continues to increase until the age of 7 years, likely supporting ongoing improvements in behaviour, cognition, motor, and sensory functions in early childhood.
Keywords: cerebral blood flow, arterial spin labelling, brain perfusion, brain development, early childhood development, magnetic resonance imaging
Conflict of Interest R.M.L. is an employee of GE.
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1. Introduction Early childhood (2-7 years old) is an important period of development that sees large gains in cognitive, motor, and sensory function, as well as improvements in behaviour, all of which lay the foundation for future life. Changes in cognition and behaviour are supported by ongoing development of brain structure and function. During early childhood, the brain manifests shifts in functional connectivity (Long et al., 2017), cortical surface expansion (Remer et al., 2017), and increases in white matter volume (Deoni et al., 2012). Cerebral blood flow (CBF) may be particularly relevant to these changes, as it supplies blood and nutrients to the brain and supports ongoing development (Kandel et al., 2000). A non-invasive magnetic resonance imaging technique, arterial spin labelling (ASL), can measure CBF in the brain. CBF is a measure of perfusion, which in the broader age-range literature has been found to underlie typical neuronal activity (Fox & Raichle, 1986) and cognitive function (Gur, et al., 1982). It also tends to be altered in disorders or diseases like depression (Bench et al., 1992), anxiety (Gur et al., 1987), and head trauma (Wintermark et al., 2004b), and therefore may be a marker of abnormal development in children. Thus, understanding CBF changes in healthy children is critical for understanding both typical and atypical brain development and its potential impact on adulthood. Previous studies show that cortical CBF increases from birth to early childhood (Wintermark et al., 2004a) and decreases during adolescence (Avants et al., 2015; Satterthwaite et al., 2014; Taki et al., 2011a). This suggests that there is a peak during early childhood, but the limited number of previous studies in this age range have produced conflicting results, showing
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ages of peak CBF that vary from 2 to 10 years (Carsin-Vu et al., 2018; Chiron et al., 1992; Liu et al., 2018; Takahashi et al., 1999; Wintermark et al., 2004a; Wu et al., 2016). Differences between studies are likely due, at least in part, to the very small number of participants included between ages 2-7 years (n=7-29) and the lack of longitudinal data, making it difficult to determine the actual trajectories during early childhood. Furthermore, calculating peak ages can be influenced by the inclusion of much older or much younger subjects, erroneously skewing the peak (Fjell, 2010). Therefore, a large longitudinal sample needs to be used to map the developmental trajectories of CBF. Additionally, findings regarding sex differences have been very mixed. Most studies do not explicitly test sex differences. Among those that do, one study reported higher brain perfusion in parietal cortex in females than in males in participants 5-18 years old (Taki et al., 2011b), while another reported CBF decreases in males across adolescence, but a u-shaped trajectory of decreases then increases in female adolescents (Satterthwaite et al., 2014). The goal of the current study was to characterize the developmental changes of CBF in a large, longitudinal sample of young children and evaluate sex differences.
2. Methods 2.1 Participants Children were recruited from the Calgary area to participate in the study. Children had to be English speakers, born full term, be free from genetic disorders associated with significant intellectual and/or motor impairments as well as neurodevelopmental and/or neurological disorders, and free from contraindications to MRI. Parents/guardians of the children were initially contacted and asked to participate in the study. Interested parents/guardians were
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provided with audio/visual instructional materials that included an e-book Pluto and the MRI Rocket Ship Adventure (freely available at http://www.lulu.com/shop/ashleigh-frayne/pluto-andthe-mri-rocket-ship-adventure/ebook/product-22122518.html), which incorporates our scanning procedures into an engaging story. We encouraged the parents/guardians to review the materials with the child. Parents/guardians were also given the option for their child to practice in a mock scanner before the real scan so their children would get accustomed to the size and the sounds of the machine. Both the mock scanner and our MRI scanner have a space rocket façade. Prior to the scan, a research assistant reviewed the instructional materials with the child once again, and explained the procedure to the child in easy-to-understand language (e.g., the MRI machine takes “pictures” of their brain, it is very important to lay very still to get nicely looking “pictures”). In general, we find that these procedures enable us to collect high quality data even in young children (Thieba et al., 2018). Children underwent an MRI scan between the ages of 2-4 years and were invited to return for follow up imaging sessions approximately every 6 months. 105 children were initially recruited, providing 333 datasets total. One participant was excluded because of an incidental finding (this participant had 6 scans). All other datasets underwent a visual quality check (by DP), and datasets with excessive motion that produced sharp ringing artifacts and/or blurred brain structures on T1 (n=6) and/or CBF (n=40), as well as datasets with scanner artifacts were excluded from the analysis (n=2), leaving 279 datasets on 96 children. These 96 children were 46 girls and 50 boys ranging in age from 1.97 to 6.90 years (M=4.46, SD=1.00 years); 80 were right-handed, 10 left-handed, and 6 with undetermined handedness. 32 children completed a single scan, 16 — two scans, 13 — three scans, 13 — four scans, 13 — five scans, 7 — six scans, 1 — seven scans, and 1 — ten scans (see Figure 1). Parents/guardians provided written
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informed consent, and children provided verbal assent. The study was approved by the conjoint health research ethics board at the University of Calgary (REB18-1011).
Figure 1. Age at scans for all subjects (females are shown in red triangles, males shown in blue circles). Each of the 297 scans is represented by a circle or a triangle; each of the 96 subjects is shown in a different row with their scans connected by a straight line.
2.2 Data acquisition Neuroimaging data were collected using a research-dedicated GE 3T MR750w scanner (General Electric, Waukesha, WI) with a 32-channel head coil at Alberta Children’s Hospital.
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During the scan, children watched a movie of their choice. T1-weighted images were acquired using an FSPGR BRAVO sequence with TR = 8.23 ms, TE = 3.76 ms, TI = 540 ms, flip angle=12 degrees, voxel size = 0.9x0.9x0.9 mm, 210 slices, matrix size=512x512, field of view=23.0 cm. ASL images were acquired with the vendor supplied pseudo continuous 3D ASL sequence with TR = 4.56 s, TE = 10.7 ms, in-plane resolution of 3.5x3.5 mm, single post label delay of 1.5 s (in accordance to a recent consensus paper (Alsop et al., 2015)), labeling duration 1.5 s, and 30 4.0 mm thick slices. The scan time was 4.4 minutes. The label plane was positioned 2.2 cm below the base of the imaging volume, which was placed at the base of the cerebellum in all participants. 2.3 Data preprocessing ASL perfusion DICOM images were converted to CBF DICOM images on the scanner computer using the vendor supplied approach (consistent with Alsop et al., 2015). We used the vendor standard M0 image for quantification. This was a 2 second saturation recovery image. We performed quantification using the general kinetic model proposed by Buxton and colleagues (1998). CBF is reported here in mL/100g/min. T1-weighted and CBF images were converted from DICOM images to NifTi files using dcm2nii from the Mricron software package (Rorden & Brett, 2000). To make our results comparable to other studies and map them on existing pediatric brain atlases, we used a popular pediatric template for children of 4.5 - 8.5 years old (Fonov et al., 2009, 2011), while the construction of our own unbiased pediatric template and a brain atlas would be interesting but outside of the scope of the current work. The T1 images were registered to the MNI template using N4-bias correction and nonlinear ANTs registration (Avants et al., 2009). The MNI template brain mask was transformed back to native T1 space for each subject, and T1 images
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were skull-stripped by multiplying the mask with the T1-image using FSL utilities (Jenkinson et al., 2012). Each individual’s CBF image was registered to their T1 image using rigid transformation with 6 degrees of freedom as an estimator for the affine transformation, and with 6 degrees of freedom and the BBR cost function implemented in FSL’s FLIRT software (Greve and Fischl, 2009; Jenkinson & Smith, 2001, Jenkinson et al., 2002). Then, CBF images were transformed to MNI space using the transformation matrices from the steps above and skullstripped using the MNI brain mask. CBF files were resampled to 2x2x2 mm using ANTs’ ResampleImage and concatenated into a single 4D file for further analysis. 2.4 Statistical Analysis Statistical analysis was performed with hierarchical linear regression using Statsmodels (Seabold & Perktold, 2010) on every voxel inside the gray matter whole brain mask, which was a part of the pediatric MNI template (Fonov et al., 2009, 2011). To examine the significance of the estimated effects, we estimated cluster size threshold, which was 1539 3dFWHMx estimated smoothing of 0.20 4.41 22.99. We used 10 000 permutations using AFNI’s 3dClustSim (Forman et al., 1995) with non-Gaussian filtering, voxel-wise p-threshold of 0.01 for alpha of 0.05. The tmaps were thresholded at t = 1.97 and merged to create final outputs with 3dmerge (Cox, 1996). All image preprocessing and analyses were scripted in Python 3.6 (Python Software Foundation, 2017) and bash (Free Software Foundation, 2007) utilizing Nipype software (Gorgolewski et al., 2011). We analyzed CBF as a function of children’s age (including linear, quadratic, and cubic terms), sex, handedness, grey matter volume, and the interaction of age with sex as fixed effects, and intercept as a random effect, using participant as a nesting factor. We used restricted maximum likelihood and left all other parameters as default (see
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http://www.statsmodels.org/stable/mixed_linear.html for details). Grey matter volume was included as a covariate to account for the grey matter changes that occur across childhood (Giedd et al., 1999). In our data, grey matter volume significantly correlated with age (r = 0.27, p < .001). To examine possible multicollinearity between age and the grey matter volume, we computed the variance inflation factor, which equaled 1.08. Therefore, we concluded there was an absence of severe multicollinearity between the age and the grey matter volume. 2.5 Data and code availability The preprocessing scripts along with analysis scripts and results are available online (https://github.com/dpaniukov/CBF_early_childhood). Research data are available on Open Science Framework as a part of the Calgary Preschool MRI Dataset (http://doi.org/10.17605/OSF.IO/AXZ5R).
3. Results 3.1 Age-related changes in CBF The quadratic and cubic terms for the children’s age were not significant (quadratic term beta values ranged from -24.97 to 35.30, with t-values from -4.14 to 3.48 across the grey matter mask, and were non-significant when corrected for multiple comparisons; cubic term beta values ranged from -4.71 to 2.99, with t-values from -3.64 to 4.17 across the grey matter mask, and were non-significant, when corrected for multiple comparisons), and therefore, were removed from further analyses. The final statistical model estimated the relationship between CBF and age, sex, age-by-sex interaction, grey matter volume, and handedness. The results revealed a significant positive association between CBF and age, such that CBF increased as children got older, in prefrontal, temporal, parietal, and occipital cortex, and
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the cerebellum (see Figures 2-4). 14.82% of voxels in the grey matter mask showed significant increase of CBF with age, distributed across areas of the left frontal pole, left medial prefrontal cortex, bilateral dorsolateral prefrontal cortex, bilateral superior frontal gyrus, bilateral inferior frontal gyrus, bilateral premotor and motor regions, left parietal regions (supramarginal gyrus), bilateral temporal regions, bilateral occipital regions (lateral occipital cortex and occipital pole), and bilateral cerebellum. Across voxels with significant increases, the average estimated increase of CBF was 3.21 mL/100g/min per year, or 29% from baseline values across the age range (SE = 1.19, CI [0.88 5.53]). Across the whole gray matter mask, global CBF increased at a rate of 0.70 ml/100g/min per year, or 5.9% across the entire age range (SE = 1.14, CI [-1.53 2.92]). As an additional quality check, we computed the mean between session variance for participants with multiple scans, which was 3.16 mL/100g/min +/- SD=2.61. These results were similar to the results we found across all participants for CBF vs age, which suggests that the between session variance might be driven by the effects of age.
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Figure 2. Relationship of CBF with age. The left column of images shows brain areas
that had significant associations of CBF with age. The right column shows unthresholded t-
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statistics for the association of CBF with age. Red-yellow colors are for the increased association, blue-light blue colors are for the decreased association.
Figure 3. Global CBF increases with age. Each dot represents a scan, each thin line is a
fit line for an individual participant, and the solid lines represent overall fits for males (blue) and females (red). Global CBF increased by 5.85% across the age range, 2-7 years.
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Figure 4. Relationship of CBF with age. These scatterplots are examples of areas with
faster (top) and slower (bottom) rates of CBF change with age. The graphs show the increase of CBF with age for males (blue) and females (red) (no significant sex difference was found). Each dot represents a single scan time point; thin lines are the best fit lines for individual participants; thick lines are the best fit lines for all data.
3.2 CBF and sex differences Females showed slightly lower CBF throughout the brain, but these differences were not significant (beta values ranged from -25.31 to 45.64, with t-values from -2.91 to 4.41 across the grey matter mask, and were non-significant when corrected for multiple comparisons). Interactions of age with sex were also non-significant (beta values ranged from -16.38 to 12.49, with t-values from -5.58 to 3.44 across the grey matter mask, and were non-significant when corrected for multiple comparisons).
4. Discussion Here, in a large, longitudinal sample of young children, we show that CBF increases with age across the cortex and cerebellum in early childhood. Linear increases were seen from 2-7 years, with a mean increase of 29% over baseline values, or approximately 3.20 mL/100g/min per year in areas with significant changes. Trajectories of development were similar between males and females, and show that CBF continues to increase during childhood, with peak values obtained sometime after age 7 years. Previous research found that CBF in infancy starts at ~25 ml/100g/min (Wintermark et al., 2004a) and almost doubles by 6 months of age (Kehrer & Schöning, 2009), with less rapid
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increases continuing throughout infancy. CBF is thought to peak between 2 and 10 years, with peak estimates ranging from 40 to 140 ml/100g/min (Carsin-Vu et al., 2018; Chiron et al., 1992; Liu et al., 2018; Takahashi et al., 1999; Wintermark et al., 2004a; Wu et al., 2016). CBF then decreases in adolescence to ~75 ml/100g/min (Avants et al., 2015; Satterthwaite et al., 2014). Peak ages have varied considerably across studies, likely because of small sample sizes and cross-sectional designs. CBF showed age-related increases across many brain areas. These areas are associated with semantic knowledge selection and attentional control (occipital, temporal, and parietal cortices; Thompson-Schill et al., 1997; Aron et al., 2003), language development and processing (left inferior frontal gyrus; Gabrieli et al., 1998) as well as with the evaluation of abstract information (rostrolateral prefrontal cortex; Vendetti & Bunge, 2014; Paniukov & Davis, 2018), and decision making (dorsolateral prefrontal cortex; Heekeren et al., 2006). These increases were distributed across the brain, and though there is some variation in development rates, regions appear to develop relatively similarly. The unthresholded t-map (Figure 2) shows some areas with negative associations with age, though these were all non-significant. That may imply that these regions are slowing down and perhaps nearing their peak CBF, though non-linear models were also nonsignificant for these regions. Thus, the increases observed here appear to be more widespread in nature, rather than concentrated in specific areas, but future research in other samples and/or with wider age ranges will be useful to pinpoint CBF trajectories in the areas not identified as significant here. The current study produced similar results in both hemispheres; however, most results in the right hemisphere did not meet the statistical threshold for significance. To help understand potential causes of this asymmetry, we acquired B1 field maps in both adults and children, which
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did not show substantial asymmetry. We further examined statistical variance between the hemispheres, which was similar. Finally, we tested whether handedness was driving the observed results, but it was not a significant predictor of CBF or asymmetry. Future studies may be able to explore whether other factors drive the observed asymmetry, or whether right hemisphere changes become significant with a larger sample size. These changes in CBF occur in parallel with changes in brain structure and function During early childhood, brain structure also undergoes substantial development, including cortical thinning across cortex, cortical surface expansion (Remer et al., 2017), increases in white matter volume (Deoni et al., 2012), increases in fractional anisotropy, and decreases in mean diffusivity (Reynolds et al., 2019). Early childhood also sees substantial alterations in network connectivity (Long et al., 2017), when the brain reorganizes itself to support ongoing cognitive development. Increasing CBF may provide nutrients for the neurons in order to enable these structural and functional changes, and thus the significant behavioural and cognitive development during early childhood. We did not find any significant sex differences in CBF or its developmental trajectories. One previous study in older children and adolescents (5-18 years) found higher brain perfusion in females than in males in the medial aspect of the parietal cortex (Taki et al., 2011b). Other studies showed more pronounced sex differences in CBF in adolescents with increase CBF in females later in adolescence while CBF in males still decreases (Satterthwaite et al., 2014). Studies of cortical structure suggest that sex differences may become more prominent later in development, with grey matter volume peaking earlier in males than females (Lenroot et al., 2007). Thus, it may be that CBF sex differences become more pronounced later in childhood or in adolescence, but are not apparent in early childhood.
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A key strength of this study is the large, longitudinal sample, which allowed us to measure changes over time, and to detail CBF changes across early childhood. The ASL acquisition and quantification has several limitations that could bias our results. First, hemoglobin is known to have an inverse relationship with CBF (Ibaraki et al., 2010). We did not measure hemoglobin, which may confound our results; however, hemoglobin does not change across this age range (Osgood and Baker, 1935), and thus would not be expected to have a systematic effect with age. Second, gray matter T1 decreases slightly (~40-45 ms) across early childhood (Deoni et al., 2011; Hales et al., 2014; Eminian et al., 2018; Yeatman et al., 2014), which may impact the calculated CBF since the general kinetic model assumes a constant T1 value. Lower gray matter T1 values would lead to underestimation of CBF in the older kids, suggesting that our results may underestimate the extent of CBF increases. Future studies that measure T1 within children may see more extensive areas of CBF increases than we report. Third, we chose our post labels delay according to the value recommended in the ASL whitepaper (Alsop et al., 2015). A longer post labels delay would desensitise transit effects, but would increase sensitivity to tissue T1. It is likely that some individuals had regions with transit delays greater than our post label delay, however, we are unaware of evidence suggesting an average change in transit delay in this age range. Changes in transit delay remains an open question that could be studied with multi-delay ASL.
5. Conclusion For the first time using a large longitudinal sample, we show that CBF continues increasing until 7 years of age in distributed cortical and cerebellar regions. This suggests that peak CBF occurs sometime in later childhood, before the decreases previously observed in
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adolescence begin. Furthermore, we saw no differences in developmental trajectories between males and females, suggesting that such differences may arise later in childhood and adulthood. These increases of CBF likely support the ongoing development of cognitive, sensory, and motor functions.
6. Acknowledgements This research was funded by CIHR (funding reference numbers IHD-134090, MOP136797, MOP- 142387, and a New Investigator Award to C.L.), and an Owerko Postdoctoral Fellowship given to D.P. We would like to thank the APrON team at the University of Calgary for recruitment assistance.
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