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Transcranial brain atlas for school-aged children and adolescents

  • Zong Zhang
    Affiliations
    State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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  • Zheng Li
    Affiliations
    Center for Cognition and Neuroergonomics, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University at Zhuhai, Zhuhai, China

    State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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  • Xiang Xiao
    Affiliations
    Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
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  • Yang Zhao
    Affiliations
    State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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  • Xi-Nian Zuo
    Affiliations
    State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    Developmental Population Neuroscience Research Center, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China

    IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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  • Chaozhe Zhu
    Correspondence
    Corresponding author. State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
    Affiliations
    State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China

    IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China

    Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
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Open AccessPublished:May 21, 2021DOI:https://doi.org/10.1016/j.brs.2021.05.004

      Highlights

      • Age-specific TBAs with 2-years interval for school-aged children/adolescents were built.
      • These atlases provide age-specific, high-resolution cranio-cortical correspondence.
      • Differences in cranio-cortical correspondence between children/adolescents with adults were observed.
      • Age-matched TBA provides higher transcranial locating and targeting accuracy.

      Abstract

      Background

      Both fNIRS optodes and TMS coils are placed on the scalp, while the targeted brain activities are inside the brain. An accurate cranio-cortical correspondence is crucial to the precise localization of the cortical area under imaging or stimulation (i.e. transcranial locating), as well as guiding the placement of optodes/coils (i.e. transcranial targeting). However, the existing normative cranio-cortical correspondence data used as transcranial references are predominantly derived from the adult population, and whether and how correspondence changes during childhood and adolescence is currently unclear.

      Objective

      This study aimed to build the age-specific cranio-cortical correspondences for school-aged children and adolescents and investigate its differences to adults.

      Methods

      Age-specific transcranial brain atlases (TBAs) were built with age groups: 6–8, 8–10, 10–12, 12–14, 14–16, and 16–18 years. We compared the performance in both transcranial locating and targeting when using the age-appropriate TBA versus the adult TBA (derived from adult population) for children.

      Results

      These atlases provide age-specific probabilistic cranio-cortical correspondence at a high resolution (average scalp spacing of 2.8 mm). Significant differences in cranio-cortical correspondence between children/adolescents and adults were found: the younger the child, the greater the differences. For children (aged 6–12 years), locating and targeting errors when using the adult TBA reached 10 mm or more in the bilateral temporal lobe and frontal lobe. In contrast, the age-matched TBA reduced these errors to 4–5 mm, an approximately 50% reduction in error.

      Conclusion

      Our work provides an accurate and effective anatomical reference for studies in children and adolescents.

      Keywords

      Introduction

      Non-invasive transcranial brain mapping techniques, such as functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS), have been recognized as promising methods to record or stimulate cortical activity for children and adolescents. FNIRS is well suited for functional neuroimaging of children due to its quietness, portability, relative insensitivity to body movement, and fewer restrictions on speech [
      • Ferrari M.
      • Quaresima V.
      A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application.
      ,
      • Lloyd-Fox S.
      • Blasi A.
      • Elwell C.E.
      Illuminating the developing brain: the past, present and future of functional near infrared spectroscopy.
      ]. TMS has demonstrated both safety and efficacy in children according to the accumulated evidence over the past decade [
      • Krishnan C.
      • Santos L.
      • Peterson M.D.
      • Ehinger M.
      Safety of noninvasive brain stimulation in children and adolescents.
      ,
      • Allen C.H.
      • Kluger B.M.
      • Buard I.
      Safety of transcranial magnetic stimulation in children: a systematic review of the literature.
      ]. It has emerged as an important research, diagnostic, and therapeutic tool for neurodevelopmental abnormalities, such as attention deficit hyperactivity disorder [
      • Hameed M.Q.
      • Dhamne S.C.
      • Gersner R.
      • Kaye H.L.
      • Oberman L.M.
      • Pascual-Leone A.
      • et al.
      Transcranial magnetic and direct current stimulation in children.
      ], adolescent depression [
      • Croarkin P.E.
      • Wall C.A.
      • Lee J.
      Applications of transcranial magnetic stimulation (TMS) in child and adolescent psychiatry.
      ,
      • Croarkin P.E.
      • Nakonezny P.A.
      • Wall C.A.
      • Murphy L.L.
      • Sampson S.M.
      • Frye M.A.
      • et al.
      Transcranial magnetic stimulation potentiates glutamatergic neurotransmission in depressed adolescents.
      ] as well as reading disabilities [
      • van den Noort M.
      • Struys E.
      • Bosch P.
      Transcranial magnetic stimulation research on reading and dyslexia: a new clinical intervention technique for treating dyslexia?.
      ].
      In transcranial brain studies, there are two fundamental technical problems: transcranial locating and transcranial targeting. Transcranial locating is identifying the imaged or stimulated cortical location given a scalp placement of fNIRS optode or TMS coil. Transcranial targeting is finding the scalp location to place an optode or coil to image or stimulate the desired brain target. The optode/coil is placed on the scalp surface while the targeted brain activity is located in the not-directly-visible brain, an accurate cranio-cortical correspondence is therefore of crucial importance to bridge this gap. An ideal solution to build cranio-cortical correspondence is the co-registration with participant's structural magnetic resonance imaging (sMRI) data via a neuronavigation system, which ensures high accuracy by considering the individual anatomy. However, extra sMRI is costly, time-consuming, and susceptible to motion artifacts, limiting the use of neuronavigation in clinical practice.
      Our previous work addressed this challenge by building transcranial brain atlases (TBA) [
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ]. Specifically, a continuous proportional coordinate (CPC) system was devised for the scalp surface, which provides a standardized specification of scalp positions and a well-established interindividual scalp anatomical correspondence. Based on the CPC system, a probabilistic transcranial mapping (PTM) from scalp space to brain space was established. We then combined this PTM with a traditional brain atlas to build a TBA, bringing atlas labels to the scalp surface. This PTM and TBA provided normative cranio-cortical correspondence as references for transcranial locating and targeting without a participant's sMRI data. Promising results based on adult TBA-assisted probe arrangement have been demonstrated in multiple tasks (‘finger tapping’, ‘action execution’, ‘N-back’, and ‘working memory’) [
      • Jiang Y.
      • Li Z.
      • Zhao Y.
      • Xiao X.
      • Zhang W.
      • Sun P.
      • et al.
      Targeting brain functions from the scalp: transcranial brain atlas based on large-scale fMRI data synthesis.
      ]. Furthermore, we have proposed a theoretical basis for TBA-based optimization and an automatic probe arrangement algorithm. Taking finger-tapping and working memory tasks as examples, our method outperformed the traditional 10-20-based method, providing more accurate, consistent, and efficient fNIRS optode arrangements for group imaging [
      • Zhao Y.
      • Xiao X.
      • Jiang Y.H.
      • Sun P.P.
      • Zhang Z.
      • Gong Y.L.
      • et al.
      Transcranial brain atlas-based optimization for functional near-infrared spectroscopy optode arrangement: theory, algorithm, and application.
      ].
      Of note, the established PTM and TBA were constructed based upon the sMRI data from adults, and their usability remains unknown for developmental transcranial studies due to rapid morphological changes of the human brain and cranium in both size and shape during the early years of life [
      • Reardon P.K.
      • Seidlitz J.
      • Vandekar S.
      • Liu S.
      • Patel R.
      • Park M.T.M.
      • et al.
      Normative brain size variation and brain shape diversity in humans.
      ,
      • Libby J.
      • Marghoub A.
      • Johnson D.
      • Khonsari R.H.
      • Fagan M.J.
      • Moazen M.
      Modelling human skull growth: a validated computational model.
      ]. However, to date, little is known about how cranio-cortical correspondence changes with age. Practically, it is important to researchers working on transcranial studies on children/adolescents to answer the following questions: 1) how much difference in cranio-cortical correspondence is there between children/adolescents and adults? 2) How much is the bias when applying the adult PTM/TBA to transcranial locating or targeting in developing samples? 3) Are age-specific PTM/TBAs necessary? To answer these questions, using a large-scale (N = 281) sMRI dataset from school-aged children and adolescents, we constructed age-specific PTM/TBAs (ages 6–8, 8–10, 10–12, 12–14, 14–16, and 16–18) and compared them with the adult PTM/TBAs. Further, we assessed and compared the transcranial locating and targeting performance of two types of PTMs/TBAs for child/adolescent individuals. The analysis results support the necessity of age-specific PTM/TBAs.

      Materials and methods

      Participants and imaging data

      T1-weighted MRI images (n = 281) were collected from typically developing Chinese children (161 females and 120 males, mean age 12.07 ± 2.9 years). 190 participants came from the SWU413 dataset [
      • Yang N.
      • He Y.
      • Zhang Z.
      • Dong H.
      • Zhang L.
      • Zhu X.
      • et al.
      Chinese color nest project: growing up in China.
      ] and 91 participants came from the SMU130 dataset [
      • Dong H.M.
      • Castellanos F.X.
      • Yang N.
      • Zhang Z.
      • Zhou Q.
      • He Y.
      • et al.
      Charting brain growth in tandem with brain templates for schoolchildren.
      ]. Detailed acquisition parameters for the T1-weighted MRI images are listed in Table S1 of Supplementary Material 2. All participants were healthy children without neurologic, endocrinologic, or bone disease. We divided the children/adolescents into six age groups with a two-year increment (Table 1). The two-year interval was chosen to minimize variability associated with age while ensuring sufficient numbers of participants in each age group.
      Table 1Demographic Information for all age groups.
      Age groupNumGenderAge (Mean ± SD)
      6–8 years2613 M/13 F7.37 ± 0.41
      8–10 years4522 M/23 F8.96 ± 0.57
      10–12 years7641 M/35 F11.02 ± 0.49
      12–14 years5820 M/38 F13.02 ± 0.56
      14–16 years4115 M/26 F14.94 ± 0.56
      16–18 years359 M/26 F16.82 ± 0.54

      Construction of age-specific PTM and TBA

      For age group g, the age-specific probabilistic transcranial mapping (PTM) was constructed as depicted in Fig. 1. First, for each participant in group g, the CPC100 (100 x 100 grid) was identified based on his/her sMRI (Fig. 1a, left panel), resulting in a high density (2.8 mm average) sampling of scalp surface [
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ]. Second, for each CPC100 point (e.g. si, pink dots), its unique cortical projection (yellow dots) in the native MRI space was determined using the balloon-inflation algorithm [
      • Okamoto M.
      • Dan I.
      Automated cortical projection of head-surface locations for transcranial functional brain mapping.
      ]. For each CPC100 point, its distance from the cortical projection was measured as scalp-brain distance. Third, the cortical projections were normalized to standard MNI space to build Pg(B|si), the age-specific PTM at si (Fig. 1a, right panel). Repeating this process for all CPC100 points, resulting in Pg(B|S), the age-specific PTM for the whole scalp.
      Fig. 1
      Fig. 1Flowchart of age-specific PTM and TBA construction. For an age group g, (a) given one CPC position si (pink dot), find its transcranial projection (yellow dot) on each cortical surface in the individual MRI space. Normalize all participants' cortical projections to MNI space to build the age-specific probabilistic transcranial mapping (PTM) at si, Pg(B|si). Project the (b) LPBA40 brain atlas to the scalp by the age-specific PTM for standard scalp space Pg(B|S), resulting in (c) TBA_LPBA, the age-specific transcranial brain atlas built from LPBA40 brain atlas. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
      As children's brains were different in size and shape from adult brains, aligning children's brains into adult brain templates directly is likely to result in systematic distortions [
      • Phan T.V.
      • Smeets D.
      • Talcott J.B.
      • Vandermosten M.
      Processing of structural neuroimaging data in young children: bridging the gap between current practice and state-of-the-art methods.
      ]. Therefore, we adopted a two-step spatial normalization implemented by the Dartel Toolbox of SPM12 [
      • Ashburner J.
      A fast diffeomorphic image registration algorithm.
      ]. Step 1 was warping individual brains to unbiased age-specific brain templates to register cortical projections to a common stereotactic space. An unbiased age-matched brain template was created based on the participants of group g. Step 2 was normalizing the cortical projections to MNI space via the transformation obtained by registering the unbiased age-specific template to the MNI152 template.
      Finally, the brain atlas labels were projected to the scalp by combining the age-specific PTM and a traditional brain atlas P(L|B) with a two-step Markov chain [
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ], resulting in the age-specific TBA Pg(L|S) (Fig. 1c). We built three kinds of age-specific TBAs from three well-known brain atlases: (a) LONI Probabilistic Brain Atlas (LPBA40) (as an example below); (b)Brodmann Atlas, and (c) Automated Anatomical Labelling Atlas (AAL2) are included in Supplementary Material 1.
      The resulting PTM and TBA describe the age-specific probabilistic cranio-cortical correspondence in both fine (i.e. coordinate) and coarse (i.e. brain region) spatial scales. Specifically, for children/adolescents whose age matches group g, the averaged brain location bg (si) is the estimate of the most likely brain coordinate corresponding to scalp point si, and the variability of this transcranial mapping is estimated by the standard deviation sdg(si).
      bg(si)=(x¯,y¯,z¯)=(xNg,yNg,zNg)


      sdg(si)=(xx¯)2+(yy¯)2+(zz¯)2Ng1


      Here, Ng is the number of participants in group g; x, y, and z are the MNI coordinates of cortical projections. For a given scalp point si, the maximum likelihood labeling for age group g is given by
      lg(si)=argmaxlj{Pg(lj|si)}


      The corresponding maximum probability pg(si) represents the consistency of lg(si) among individuals:where i is the index of the scalp point, and j is the index of a brain atlas label.

      PTM/TBA differences between children/adolescents and adults

      To answer the question of how much difference exists in cranio-cortical correspondence between children/adolescents and adults, we compared children/adolescents’ PTM and TBA with those of adults. For each scalp position si, the difference in PTM between each group g and adults was quantified as follows:
      ΔPTMg(si)=bg(si)badult(si)


      badult(si) is the most likely brain coordinate at si estimated based on adult data, and denotes the Euclidean norm. The discrepancy of TBA between age group g and adult was quantified as follows:
      ΔTBAg(si)=1δlg(si),ladult(si),whereδij={1,ifi=j0,ifij


      ladult(si) is the most likely atlas label corresponding to si in the adult population.

      Individual-level transcranial locating: age-matched versus adult PTM/TBA

      One motivation for establishing the multiple age-specific PTM/TBAs is to support more accurate transcranial locating in an age-matched way for developmental studies when participants’ MRI data are unavailable. Specifically, for a child/adolescent participant, the age-matched PTM/TBA can provide a prediction of the most likely brain coordinate and atlas label corresponding to a given scalp position si where an optode/coil is located. To investigate which PTM/TBA is more accurate, we conducted a validation experiment based on the sMRI dataset used for the construction of the age-specific PTM.
      Specifically, for a child/adolescent j and a scalp point si, the ground-truth corresponding MNI brain coordinate b(si,j) and atlas label l(si,j) can be obtained with the participant's own sMRI data. When the adult PTM/TBA is used, it predicts a brain location badult(si) and atlas label ladult(si). The locating error of using the adult PTM/TBA is defined as follows:
      LocatingErroradultcoordinate(si,j)=badult(si)b(si,j)


      LocatingErroradultlabel(si,j)=1δladult(si,j),l(si,j)


      When validating the performance of the age-matched PTM/TBA, we adopted a leave-one-out cross-validation approach to ensure the independence between the testing child/adolescent and the PTM/TBA used. That is, for the testing child/adolescent j in group g, we rebuilt an age-matched PTM/TBA based on the remaining participants predict the underlying brain location and atlas label for j, resulting in bmatch(si) and lmatch(si). The locating error of using age-matched PTM/TBA is defined as follows:
      LocatingErrormatchcoordinate(si,j)=bmatch(si)b(si,j)


      LocatingErrormatchlabel(si,j)=1δlmatch(si),l(si,j)


      We repeated the above process for all CPC100 points (for all si) and all 281 children/adolescents (for all j) in our dataset. A one-tailed paired t-test was performed on the averaged individual-level locating error across all CPC100 points to determine whether the age-matched PTM/TBA outperforms the adult ones.

      Individual-level transcranial targeting: age-matched versus adult PTM

      Another important motivation for establishing the age-specific PTM/TBAs is to facilitate transcranial targeting for TMS studies in children when the participant's sMRI data are not available. Specifically, for a child/adolescent participant, the age-matched PTM outputs the prediction of the “optimal TMS coil position on the scalp” for a given stimulation target btarget :
      smatch=argminsiCPCbmatch(si)btarget


      Given a stimulation target, the age-matched PTM/TBA and the adult PTM/TBA, however, may give different predictions for the “optimal TMS coil position on the scalp”. With different coil positions, TMS may stimulate quite different cortical positions, and in turn, result in divergent clinical outcomes. To investigate which PTM/TBA would achieve more accurate stimulation, we conducted a validation experiment based on the sMRI dataset used in the construction of the age-specific PTM, taking the depression treatment target (MNI coordinate: 44, 40, 29) as an example btarget [
      • Fox M.D.
      • Buckner R.L.
      • White M.P.
      • Greicius M.D.
      • Pascual-Leone A.
      Efficacy of transcranial magnetic stimulation targets for depression is related to intrinsic functional connectivity with the subgenual cingulate.
      ].
      Specifically, for child/adolescent participant j, the adult PTM outputs the “optimal TMS coil position on the scalp”:
      sadult=argminsiCPCbadult(si)btarget


      The stimulated brain sites corresponding to sadult are b(sadult,j), found using the sMRI data of child/adolescent j. Its deviation from the given btarget, the targeting error, is defined as follows:
      TargetingErroradult(j)=b(sadult,j)btarget


      When validating the performance of age-matched PTM, we again adopted a leave-one-out cross-validation approach to ensure independence between the testing child/adolescent and the PTM used. That is, for testing child/adolescent j in group g, we rebuilt an age-matched PTM based on the other participants in g. Then, the independent age-matched PTM was used to predict the “optimal TMS coil position on the scalp”:
      smatch,j=argminsiCPCbmatch(si,j)btarget


      Similarly, the stimulated brain sites corresponding to smatch,j are b(smatch,j). The corresponding targeting error is:
      TargetingErrormatch(j)=b(smatch,j)btarget


      We repeated the above process for all 281 children/adolescents in our dataset. Then, for each age group, a one-tailed paired t-test on targeting error was performed to evaluate whether the age-matched PTM achieves more accurate targeting. Finally, we extended the whole process from the example depression target to each possible cortical target, to investigate the spatial distribution of transcranial targeting error of the age-matched and the adult PTM.

      Results

      Age-specific PTM and TBA

      We constructed six age-specific PTM/TBAs for school-aged children/adolescents: 6–8, 8–10, 10–12, 12–14, 14–16, and 16–18 years. Take the age group 6–8 and the International 10–20 system (a subset of points in CPC100) as an example, the transcranial correspondence is presented in Table 2. Specifically, for each 10–20 landmark, the most likely MNI coordinate, LPBA40 label, and their variability, estimated from the sMRI dataset sample, are shown. Results for other age groups are provided in the Supplementary Material 1(Tables S1–S5).
      Table 2Cranio-cortical correspondence of 10-20 system points for age group 6-8
      10-20CPC(si)MNI coordinatesAtlas label of LPBA40
      bg(si)sdg(si)lg(si)Pg(si)
      Fp10.900.41-20.764.8-11.73.8L.middle_frontal_gyrus0.79
      Fp20.900.5921.464.7-11.54.6R.middle_frontal_gyrus0.83
      Fz0.300.501.247.540.87.5R.superior_frontal_gyrus0.55
      F30.270.34-40.346.823.24.6L.middle_frontal_gyrus0.95
      F40.270.6640.34724.45.3R.middle_frontal_gyrus0.96
      F70.140.22-48.838.1-15.15.9L.lateral_orbitofrontal_gyrus0.73
      F80.130.7849.239.3-15.34.4R.lateral_orbitofrontal_gyrus0.79
      Cz0.500.50-0.4-9.269.79.4L.superior_frontal_gyrus0.46
      C30.500.30-57.1-11.248.35.1L.postcentral_gyrus

      0.58
      C40.500.7058.2-11.349.84.8R.postcentral_gyrus0.80
      T30.500.10-68.2-14.2-20.64L.middle_temporal_gyrus0.96
      T40.500.9068.7-14.8-18.83.7R.middle_temporal_gyrus0.97
      Pz0.700.50-2.4-64.161.810.3L.superior_parietal_gyrus0.49
      P30.730.34-48-67.545.36.4L.angular_gyrus0.92
      P40.730.6646.7-66.946.36.4R.angular_gyrus0.9
      T50.870.22-59.6-63.7-8.86L.middle_temporal_gyrus0.44
      T60.860.7858.4-63.2-6.95.4R.inferior_temporal_gyrus0.47
      O10.910.41-25.4-102.96.87.8L.middle_occipital_gyrus0.82
      O20.910.6024.6-100.86.17.4R.middle_occipital_gyrus0.84
      For each CPC100 position, the standard deviation (SD) of its cortical projections is calculated to quantify the transcranial variability (Fig. 2a). The average SDs among CPC100 are 5.57 mm (6–8 years), 5.79 mm (8–10 years), 5.74 mm (10–12 years), 5.29 mm (12–14 years), 5.62 mm (14–16 years), and 5.03 mm (16–18 years). Spatially, the larger variation mainly occurs at the boundaries of brain lobes, i.e. the central sulcus, lateral sulcus, and medial longitudinal fissure. Fig. 2b shows age-specific TBA_LPBAs, wherein the most likely label of the LPBA40 atlas at each CPC100 position is shown. The age-specific TBA_BAs and TBA_AALs are given in the Supplementary Material 1 (Figs. S1–S2). Fig. 2c shows ΔPTMg maps, which give the coordinate-scale difference of cranio-cortical correspondence between children/adolescents and adults. The smaller the age of children/adolescents, the greater the differences between adults. The age group 6–8 exhibited considerable differences in the bilateral temporal and lateral frontal lobes, reaching about 10 mm. As age increases, those differences gradually go down. Fig. 2d shows ΔTBAg_LPBA maps, depicting points where each age group and the adult atlas had different labels. Most CPC100 positions had the same LPBA label, while differences are only observed near the boundaries of atlas areas. Fig. 2e shows a medio-lateral gradient in the average scalp-brain distance. Relative distance decreases from the parietal to the temporal sites.
      Fig. 2
      Fig. 2Age-specific TBA_LPBAs and cranio-cortical correspondences difference between children/adolescents and adults (a) Variability of PTM, assessed by the SD of cortical projections among individuals in MNI space (in units of mm). (b) Maximum likelihood labeling map (MLLM) of TBA_LPBA. (c) ΔPTMg, the Euclidean distance (mm) between the most likely MNI coordinate given by PTM of children/adolescents versus adults. (d) ΔTBAg, the locations which differ in label between child/adolescent LPBA40 TBA versus adult LPBA40 TBA are marked in red. (e) Average scalp-brain distance (mm). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

      Individual-level transcranial locating: age-matched versus adult PTM/TBA

      Fig. 3 compares the transcranial locating performance of age-matched versus adult PTMs/TBAs for children/adolescents of different ages. When applying the adult PTM, the transcranial locating error (mm in MNI coordinates) shows a decreasing trend with age (Fig. 3c left): the older the children, the lower the transcranial locating error in bilateral temporal and frontal lobes (Fig. 3a). The greatest error occurs for children aged 6–8 years, reaching up to 8–10 mm. In contrast, the locating errors in the bilateral temporal and frontal lobes decrease about 50% (4–5 mm) when using the age-matched PTM. Significant accuracy improvement when using age-matched PTM/TBA was found in all age groups (Fig. 3c left, all p-values < 0.001, one-tailed paired t-test). In terms of atlas labels, for CPC100 positions whose transcranial projections are far from the boundaries of brain regions, age-matched and adult TBAs provide the same label (Fig. 3b). However, for CPC positions whose transcranial projections are near the boundaries of brain regions, the label error of age-matched TBA is significantly reduced compared to the adult TBA. On average, the age-matched TBA provides significantly more accurate locating (Fig. 3c right, p-values for 6–16 years < 0.001, p-values for 16–18 years < 0.01, one-tailed paired t-test).
      Fig. 3
      Fig. 3Individual transcranial locating performance, comparing age-matched versus adult PTM/TBA. (a) Averaged transcranial locating error maps in MNI coordinates (in units of mm); (b) LPBA labeling error maps (the percentage of participants); (c) Mean locating error across CPC100 space and individuals. Error bars indicate the SD of averaged locating errors among individuals. (one-tailed paired t-test, ∗∗p < 0.01; ∗∗∗p < 0.001). Detail statistical results are given in of Supplementary Material 2.

      Individual-level transcranial targeting: age-matched versus adult PTM

      Fig. 4a shows the depression target with MNI coordinate (−44, 40, 29). Fig. 4b shows “optimal TMS coil positions on the scalp” predicted by age-matched PTM (blue dots) and the adult PTM (red dots) for children/adolescents of different ages. For the identical depression target, the “optimal TMS coil positions on the scalp” predicted by age-matched and adult PTMs are distinct. For children/adolescents aged 6–8, 8–10, and 10–12 years, the difference is two CPC grid points apart, about a distance of 6 mm on the scalp. As age increases, the difference reduces to one CPC grid point. For children/adolescents aged 6–8, 8–10, 10–12, and 12–14 years, the transcranial targeting errors for this depression target when using age-matched PTM are significantly smaller than errors when using the adult PTM (Fig. 4c). Fig. 5 shows the overall spatial pattern of targeting error for all possible cortical targets. We can see that targeting error when using the adult PTM is age-dependent: the younger the child/adolescent, the larger the targeting error. For children (6–12 years), compared with using the age-matched PTM, using the adult one introduced considerably larger targeting error in frontal and temporal lobes. In contrast, for the ventral parts of the parietal lobe, the adult PTM performed close to the age-matched one.
      Fig. 4
      Fig. 4Individual transcranial targeting performance for depression target. (a) The depression target btarget with MNI coordinate (44,40,29). (b) For children/adolescents of different ages, the “optimal TMS coil positions on the scalp” for the depression target are shown in the CPC system, (Pnz, Pal) ε [0 1] × [0 1]. Detailed description of the CPC system is given in Supplementary Material 2; (c) Comparison of transcranial targeting error for the depression target between age-matched and adult PTMs (one-tailed paired-t-test, ∗∗p < 0.01; ∗∗∗p < 0.001). Error bars indicate the SD of targeting errors among individuals. Detail statistical results are given in of Supplementary Material 2.
      Fig. 5
      Fig. 5Individual transcranial targeting performance for all possible cortical targets. For children/adolescents of different ages, the average transcranial targeting error spatial pattern when using age-matched (upper) or adult (lower) PTMs.

      Discussion

      The current study constructed a set of novel PTM/TBAs. To our knowledge, this is the first time the age-specific probabilistic cranio-cortical correspondence for school-aged children and adolescents has been established. These age-specific PTM/TBAs can serve as anatomical references when implementing transcranial locating and targeting in developmental research.
      To date, many efforts have been made to resolve sMRI dependency in building cranio-cortical correspondence. One approach is to use standardized head templates (such as ICBM152 and Colin27) or an MRI database (including 17 adults) as substitutes for anatomical localization and optode holder placement [
      • Cutini S.
      • Scatturin P.
      • Zorzi M.
      A new method based on ICBM152 head surface for probe placement in multichannel fNIRS.
      ,
      • Zimeo Morais G.A.
      • Balardin J.B.
      • Sato J.R.
      FNIRS Optodes' Location Decider (fOLD): a toolbox for probe arrangement guided by brain regions-of-interest.
      ]. Another practical approach is to use the International 10–20 system. The relationship between the 10–20 points and their underlying cerebral structures has been established with a good consistency across individuals [
      • Okamoto M.
      • Dan H.
      • Sakamoto K.
      • Takeo K.
      • Shimizu K.
      • Kohno S.
      • et al.
      Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping.
      ,
      • Tsuzuki D.
      • Watanabe H.
      • Dan I.
      • Taga G.
      MinR 10/20 system: quantitative and reproducible cranial landmark setting method for MRI based on minimum initial reference points.
      ]. Accordingly, once the 10–20 landmarks near the optode/coil are identified, the imaged/stimulated cortical area can be estimated. In Table 3, we summarize the current status of cranio-cortical correspondence research. Only one study focused on children and adolescents, and its age coverage is limited (5–11 years). Consequently, many transcranial studies with children and adolescents had to rely on cranio-cortical correspondence derived from adults, even though this age mismatch may bring systematic error.
      Table 3List of cranio-cortical correspondence studies.
      Age-range (Number of participants)Scalp landmarksSpatial scaleReference
      3.4–16.3 week (16)10-20 systemSulci and gyri[
      • Kabdebon C.
      • Leroy F.
      • Simmonet H.
      • Perrot M.
      • Dubois J.
      • Dehaene-Lambertz G.
      Anatomical correlations of the international 10-20 sensor placement system in infants.
      ]
      3–22 months (16)10-10 systemSulci and gyri[
      • Tsuzuki D.
      • Homae F.
      • Taga G.
      • Watanabe H.
      • Matsui M.
      • Dan I.
      Macroanatomical landmarks featuring junctions of major sulci and fissures and scalp landmarks based on the international 10-10 system for analyzing lateral cortical development of infants.
      ]
      5–11 years (90)10-5 systemBrodmann atlas[
      • Whiteman A.C.
      • Santosa H.
      • Chen D.F.
      • Perlman S.
      • Huppert T.
      Investigation of the sensitivity of functional near-infrared spectroscopy brain imaging to anatomical variations in 5- to 11-year-old children.
      ]
      18–26 years (114)CPC100Brodmann atlas, AAL atlas, LPBA40 atlas[
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ]
      20–42 years (16)10-10 systemTalairach coordinates, sulci and gyri, Brodmann atlas[
      • Koessler L.
      • Maillard L.
      • Benhadid A.
      • Vignal J.P.
      • Felblinger J.
      • Vespignani H.
      • et al.
      Automated cortical projection of EEG sensors: anatomical correlation via the international 10-10 system.
      ]
      22–51 years (17)10-20 systemTalairach coordinates, MNI coordinates[
      • Okamoto M.
      • Dan H.
      • Sakamoto K.
      • Takeo K.
      • Shimizu K.
      • Kohno S.
      • et al.
      Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping.
      ]
      The transcranial mapping remains highly consistent across individuals in the same age group, with an average(across six age groups) inter-individual variability of 5.51 mm (Fig. 2a), which is similar to our previous observations for the adult population (5.31 mm in average) [
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ]. Beyond this consistency, a clear age effect on the cranio-cortical correspondence was found. For children of 6–18 years, the deviations of transcranial mapping from adults reached to about 10 mm in the bilateral temporal lobes (Fig. 2c). When using the adult PTM as anatomical reference for children/adolescents, the locating errors varied as a function of age and were quite considerable (≥ 10 mm) for younger children (aged 6–12 years). These age-related differences in the locating performance showed the highest values in bilateral areas of temporal and lateral frontal lobes. These high-order associative areas are rapidly developed in terms of not only their morphological characteristics such as gray matter volume [
      • Dong H.M.
      • Castellanos F.X.
      • Yang N.
      • Zhang Z.
      • Zhou Q.
      • He Y.
      • et al.
      Charting brain growth in tandem with brain templates for schoolchildren.
      ,
      • Gogtay N.
      • Giedd J.N.
      • Lusk L.
      • Hayashi K.M.
      • Greenstein D.
      • Vaituzis A.C.
      • et al.
      Dynamic mapping of human cortical development during childhood through early adulthood.
      ,
      • Giedd J.N.
      • Blumenthal J.
      • Jeffries N.O.
      • Castellanos F.X.
      • Liu H.
      • Zijdenbos A.
      • et al.
      Brain development during childhood and adolescence: a longitudinal MRI study.
      ] but also their functional correlates in terms of default mode network connectivity [
      • Fair D.A.
      • Cohen A.L.
      • Dosenbach N.U.F.
      • Church J.A.
      • Miezin F.M.
      • Barch D.M.
      • et al.
      The maturing architecture of the brain's default network.
      ] and language task activation [
      • Schlaggar B.L.
      Functional neuroanatomical differences between adults and school-age children in the processing of single words.
      ]. Understanding these developmental changes is thus highly important for transcranial developmental studies, especially for language and cognitive control [
      • Quaresima V.
      • Bisconti S.
      • Ferrari M.
      A brief review on the use of functional near-infrared spectroscopy (fNIRS) for language imaging studies in human newborns and adults.
      ,
      • Moriguchi Y.
      • Hiraki K.
      Prefrontal cortex and executive function in young children: a review of NIRS studies.
      ]. Similarly, for children (6–12 years), in both frontal and temporal lobes, using the age-matched PTM predicts a more accurate scalp position with lower targeting error (Fig. 5). However, for age-matched PTM, large targeting errors still occurred at the ventral parts of the parietal lobe. This may be because the larger scalp-cortex distance in this region (Fig. 2e), which makes the scalp-brain projection more sensitive to the morphological changes of the cortex in development. Additionally, we found a lower targeting accuracy in sulcal regions. This may be because the projection-based model is dependent on a geometrical relationship (i.e. scalp-cortex distance) and the sulcal parts are more difficult to target. Age-specific atlases can also be used to guide montage placement for transcranial direct current (tDCS) and alternating current (tACS) stimulation, as their montages are commonly placed close to targets of interest. However, tDCS and tACS tend to affect a large area of the brain and additional targeting benefits from using age-specific atlases may be relatively small. Moreover, the averaged scalp-to-brain distance per age group can be useful for the determination of stimulation dosage [
      • Stokes M.G.
      • Chambers C.D.
      • Gould I.C.
      • Henderson T.R.
      • Janko N.E.
      • Allen N.B.
      • et al.
      Simple metric for scaling motor threshold based on scalp-cortex distance: application to studies using transcranial magnetic stimulation.
      ,
      • Stokes M.G.
      • Chambers C.D.
      • Gould I.C.
      • English T.
      • McNaught E.
      • McDonald O.
      • et al.
      Distance-adjusted motor threshold for transcranial magnetic stimulation.
      ].
      To facilitate the description and interpretation of transcranial neurodevelopmental imaging data, the age-specific cranio-cortical correspondence was built on the stereotaxic MNI space. Considering the discrepancies in brain size and morphometry between adults and children, we created age-group specific templates and adopted the DARTEL normalization procedure to reduce potential registration errors [
      • Phan T.V.
      • Smeets D.
      • Talcott J.B.
      • Vandermosten M.
      Processing of structural neuroimaging data in young children: bridging the gap between current practice and state-of-the-art methods.
      ]. We adopted a projection-based method, the balloon-inflation model, to determine the cortical location corresponding to a scalp location. Due to its simplicity and practical effectiveness, projection-based methods have been widely used for building cranio-cortical correspondence for different populations [
      • Okamoto M.
      • Dan H.
      • Sakamoto K.
      • Takeo K.
      • Shimizu K.
      • Kohno S.
      • et al.
      Three-dimensional probabilistic anatomical cranio-cerebral correlation via the international 10-20 system oriented for transcranial functional brain mapping.
      ,
      • Tsuzuki D.
      • Watanabe H.
      • Dan I.
      • Taga G.
      MinR 10/20 system: quantitative and reproducible cranial landmark setting method for MRI based on minimum initial reference points.
      ,
      • Kabdebon C.
      • Leroy F.
      • Simmonet H.
      • Perrot M.
      • Dubois J.
      • Dehaene-Lambertz G.
      Anatomical correlations of the international 10-20 sensor placement system in infants.
      ,
      • Koessler L.
      • Maillard L.
      • Benhadid A.
      • Vignal J.P.
      • Felblinger J.
      • Vespignani H.
      • et al.
      Automated cortical projection of EEG sensors: anatomical correlation via the international 10-10 system.
      ,
      • Lloyd-Fox S.
      • Richards J.E.
      • Blasi A.
      • Murphy D.G.M.
      • Elwell C.E.
      • Johnson M.H.
      Coregistering functional near-infrared spectroscopy with underlying cortical areas in infants.
      ,
      • Giacometti P.
      • Perdue K.L.
      • Diamond S.G.
      Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain.
      ], including both transcranial locating with fNIRS [
      • Cutini S.
      • Scatturin P.
      • Zorzi M.
      A new method based on ICBM152 head surface for probe placement in multichannel fNIRS.
      ,
      • Fekete T.
      • Rubin D.
      • Carlson J.M.
      • Mujica-Parodi L.R.
      A stand-alone method for anatomical localization of NIRS measurements.
      ,
      • Tsuzuki D.
      • Cai D.S.
      • Dan H.
      • Kyutoku Y.
      • Fujita A.
      • Watanabe E.
      • et al.
      Stable and convenient spatial registration of stand-alone NIRS data through anchor-based probabilistic registration.
      ,
      • Tsuzuki D.
      • Jurcak V.
      • Singh A.K.
      • Okamoto M.
      • Watanabe E.
      • Dan I.
      Virtual spatial registration of stand-alone fNIRS data to MNI space.
      ] and TMS navigation [
      • Liu S.
      • Shi L.
      • Wang D.
      • Chen J.
      • Jiang Z.
      • Wang W.
      • et al.
      MRI-guided navigation and positioning solution for repetitive transcranial magnetic stimulation.
      ]. Meanwhile, there are also physical models (specifically, an electric-field model for TMS and a photo-transport model for fNIRS), taking the physical mechanisms of transcranial techniques and individual brain anatomy into account. Theoretically, when the individual's sMRI data is available, using the physical model can achieve more accurate targeting of coil and probe. Our TBA, however, was developed to deal with the localization and targeting problems in the case where no participant's structural MRI data are available. In a previous study, we compared the photo-transport model with the projection-based model in an fNIRS probe placement experiment. For two ROIs (DLPFC and TPJ), we separately designed the fNIRS probe based on TBA (by projection-based model) and photo transport simulation with ‘Colin27’ sMRI data. Then, we transferred the two probe arrangements to a test group (15 participants). No significant differences in sensitivity between the two methods were found in both DLPFC (t14 = 1.41, p = 0.18) and TPJ (t14 = 0.04, p = 0.97) [
      • Jiang Y.
      • Li Z.
      • Zhao Y.
      • Xiao X.
      • Zhang W.
      • Sun P.
      • et al.
      Targeting brain functions from the scalp: transcranial brain atlas based on large-scale fMRI data synthesis.
      ]. This may be because Colin27's gyrus geometry was different from participants' gyrus geometry, thus the optimal design based on Colin27 may not be optimal when transferred to other people. Moreover, we added a simulation experiment, taking the 6–8 age group as an example, to explore the difference between the electric-field model and the projection-based model in coil targeting. For the DLPFC target, the optimal coil locations predicted by the two models are close to each other, only one CPC grid unit apart, or about 3 mm. For the motor target, the optimal coil locations predicted by the two models are the same (Fig. S2 in Supplementary Material 2). This suggests that, when participants' sMRI data are not available, the advantage of sophisticated approaches may decrease due to individual differences in gyri geometry and the projection-based method can achieve comparable performance. To provide transcranial locating and targeting with real-time, interactive visualization for developmental studies, we have integrated these age-specific PTM/TBAs into a navigation system [
      • Xiao X.
      • Yu X.
      • Zhang Z.
      • Zhao Y.
      • Jiang Y.
      • Li Z.
      • et al.
      Transcranial brain atlas.
      ,
      • Jiang Y.
      • Li Z.
      • Zhao Y.
      • Xiao X.
      • Zhang W.
      • Sun P.
      • et al.
      Targeting brain functions from the scalp: transcranial brain atlas based on large-scale fMRI data synthesis.
      ].
      Several limitations should be noted for our study. The coil orientation has a significant impact on TMS effects, which cannot be captured by the projection-based method. A simulation experiment was performed to explore whether the coil orientation modifies the best location on the scalp (Fig. S3 in Supplementary Material 2). We found that the optimal coil location varied between coil orientations, with an average pairwise distance of 3.65 ± 0.23 mm and a maximum pairwise distance of 8.11 ± 0.69 mm for the DLPFC target among the age group 6–8 years. Similarly, for the motor target, the average and maximum pairwise distances are 3.89 ± 0.16 mm and 6.97 ± 0.36 mm. Additionally, the electrical conductivity of head tissues changes with age, which may result in large differences in the strength and distribution of the electrical fields among populations [
      • McCann H.
      • Pisano G.
      • Beltrachini L.
      Variation in reported human head tissue electrical conductivity values.
      ,
      • Antonakakis M.
      • Schrader S.
      • Aydin Ü.
      • Khan A.
      • Gross J.
      • Zervakis M.
      • et al.
      Inter-subject variability of skull conductivity and thickness in calibrated realistic head models.
      ]. We are making efforts to integrate the electric-field model into our framework to capture the effects of both coil orientation and age-related conductivity. The numbers of participants are not perfectly balanced across age groups and are relatively small for 6–8 years (N = 26) and 16–18 years (N = 35). The smallest size (26) just meets the lower limit for DARTEL registration (25), and this may reduce the high registration accuracy that DARTEL achieves and introduce a systematic bias in cranial-cortical mapping [
      • Shen S.
      • Sterr A.
      Is DARTEL-based voxel-based morphometry affected by width of smoothing kernel and group size? A study using simulated atrophy.
      ]. Our future work will increase sample sizes to improve reliability. The cranial-cortical mapping is a consequence of both external scalp and internal cortical factors, which are affected by head malformation due to bone disease and cortical morphological changes due to neurologic or endocrinologic diseases.
      Also, we only included sMRI data from Chinese school-aged children/adolescents and thus the influence of ethnicity on the derived PTMs and TBAs as well as their generalizability to children/adolescents of other ethnicities deserves further investigation. Previous studies have found racial differences in head size and shape [
      • Xie W.
      • Richards J.E.
      • Lei D.
      • Zhu H.
      • Lee K.
      • Gong Q.
      The construction of MRI brain/head templates for Chinese children from 7 to 16 years of age.
      ,
      • Zhao T.
      • Liao X.
      • Fonov V.S.
      • Wang Q.
      • Men W.
      • Wang Y.
      • et al.
      Unbiased age-specific structural brain atlases for Chinese pediatric population.
      ] as well as their morphological growth patterns [
      • Dong H.M.
      • Castellanos F.X.
      • Yang N.
      • Zhang Z.
      • Zhou Q.
      • He Y.
      • et al.
      Charting brain growth in tandem with brain templates for schoolchildren.
      ]. We plan to investigate whether these differences can impact the transcranial correspondence on both Negroid and Caucasoid samples. We will also expand PTMs and TBAs to infants and younger children, due to the fact that the human head and brain develop rapidly before 6 years of age [
      • Phan T.V.
      • Smeets D.
      • Talcott J.B.
      • Vandermosten M.
      Processing of structural neuroimaging data in young children: bridging the gap between current practice and state-of-the-art methods.
      ].

      Conclusion

      This study built the age-specific PTM/TBAs for school-aged children/adolescents in age groups 6–8, 8–10, 10–12, 12–14, 14–16, and 16–18 years. These atlases, for the first time, provide age-specific probabilistic cranio-cortical correspondence, covering the whole scalp surface at high resolution (discretized into 100x100 grid, average spacing 2.8 mm). For each scalp landmark, the corresponding cortical projection was probabilistically expressed in both MNI space and brain atlas labels (LPBA40, AAL, BA). For young children (aged 6–12 years), whose cranio-cortical correspondences are significantly different from adults, using the adult PTM/TBA made considerable transcranial locating and targeting errors (≥ 10 mm), while the age-matched PTM/TBA can decrease these errors to 4–5 mm. One caveat to these findings is that these comparisons were dependent on the isotropic projection-based model used and did not consider the coil orientation. Our work can serve as an accurate and effective anatomical reference for transcranial studies in children and adolescents, and facilitate transcranial locating and targeting with high precision, without needing individual sMRI.

      Declarations of interest

      None.

      CRediT authorship contribution statement

      Zong Zhang: Conceptualization, Methodology, Investigation, Visualization, Writing – original draft, Writing – review & editing. Zheng Li: Writing – original draft, Writing – review & editing. Xiang Xiao: Methodology, Software. Yang Zhao: Methodology. Xi-Nian Zuo: Resources, Writing – original draft. Chaozhe Zhu: Conceptualization, Methodology, Investigation, Writing – review & editing.

      Acknowledgements

      This work was supported by the National Natural Science Foundation of China (Grant Nos. 61431002 , 82071999 , 31521063 , and 61273287 ), and the National 973 Program (Grant No. 2014CB846100 ). Dr. Xi-Nian Zuo received support from the Natural Science Foundation of China ( 81220108014 ), the Beijing Municipal Science and Tech Commission ( Z161100002616023 ), the Key Realm R&D Program of Guangdong Province ( 2019B030335001 ), and the Startup Funds for Leading Talents at Beijing Normal University .

      Appendix A. Supplementary data

      The following are the Supplementary data to this article:

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