Global cortical morphometry as a mediator of longitudinal associations between music participation and language outcomes in a population-based cohort

Global cortical morphometry as a mediator of longitudinal associations between music participation and language outcomes in a population-based cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global cortical morphometry as a mediator of longitudinal associations between music participation and language outcomes in a population-based cohort Avery Wang, Mekibib Altaye, Zhixiu Lu, Junqi Wang, Jiyo Athertya, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8664008/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Associations between music participation and language outcomes have been widely reported, yet the extent to which brain structure statistically accounts for these relationships remains unclear, particularly at the population level. Using longitudinal data from the population-based Adolescent Brain Cognitive Development (ABCD) Study, we examined prospective associations between music participation at ages 9–10 and language outcomes two years later and tested whether global cortical morphometry accounted for these associations. The analytic sample included 5,993 children (baseline mean age 10.0 years; 47.1% female), including a twin subsample of 936 participants. Music participation was assessed via parent report of sustained participation, frequency, and intensity. Language outcomes were measured using NIH Toolbox assessments of picture vocabulary, oral reading recognition, and crystallized cognition. Cortical morphometry was quantified across multiple metrics, including global surface area, volume, thickness, and sulcal depth. Longitudinal associations were estimated using linear mixed-effects models, with mediation analyses conducted to quantify the indirect effect of cortical morphometry after adjusting for baseline language performance, sociodemographic factors, and relevant covariates. Sustained music participation was associated with higher crystallized cognition, picture vocabulary, and oral reading recognition scores two years later. Mediation analyses indicated that global cortical surface area and volume, but not cortical thickness or sulcal depth, statistically accounted for a modest proportion of these longitudinal associations (approximately 5–9%). Analyses of practice intensity showed weaker total associations with language outcomes but proportionally greater mediation by surface area and volume, whereas practice frequency exhibited minimal associations and no evidence of mediation. Sensitivity analyses in the twin subsample yielded qualitatively similar patterns, with larger mediated proportions observed for crystallized cognition and picture vocabulary. These findings suggest that global cortical morphometry explains a limited but reproducible component of the longitudinal association between music participation and language outcomes in population-based developmental samples. Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Music participation Language development Cortical structure Mediation analysis Adolescent Brain Cognitive Development (ABCD) study Longitudinal study Figures Figure 1 Figure 2 Introduction Music participation has been consistently associated with enhanced language and literacy skills in children. Musically trained youth often show higher performance in vocabulary, reading fluency, and verbal reasoning ( 1 , 2 ). Early work demonstrated that children receiving one year of music lessons showed larger gains in IQ and academic achievement, including reading comprehension, than those in drama or no-lesson control groups ( 3 ). Longitudinal research has provided additional support for these associations. For example, classroom-based group music instruction has been linked to maintained or improved reading performance over time in school-aged children ( 4 ), and controlled intervention studies have shown that sustained music training is associated with gains in language-related skills such as speech segmentation, phonological processing, and reading-related outcomes ( 5 , 6 ). Reviews of this body of work have summarized consistent connections between musical experience and literacy development ( 1 , 2 , 7 ). Experimental interventions further indicate that even relatively short periods of rhythmic or melodic training can enhance verbal memory and auditory discrimination ( 6 , 8 ). These findings suggest that musical experience engages cognitive functions shared with language, such as auditory discrimination, phonological awareness, and working memory, through overlapping cognitive processes. However, it remains unclear whether these associations reflect training-related effects or are driven by confounding factors, such as motivation or the family environment. A meta-analysis of over 50 intervention studies found that, after accounting for publication bias, the cognitive benefits of music training were modest and inconsistent ( 9 ). Other studies showed that associations between music training and literacy outcomes were substantially attenuated after controlling for socioeconomic status, and correlations between music participation and verbal ability were largely explained by shared genetic and environmental factors rather than direct training effects ( 10 , 11 ). Related work similarly suggests that higher baseline intelligence, motivation, and family resources may account for much of the observed relationship between music participation and language performance ( 7 , 10 , 11 ). Recent large-scale longitudinal evidence from the Adolescent Brain Cognitive Development (ABCD) Study reported modest improvements across several cognitive domains, including language-related measures, associated with continuous music training over two years, with substantial moderation by socioeconomic context ( 12 ). These prior studies indicate that music participation is associated with language and literacy skills, while these associations are modest in magnitude and substantially influenced by pre-existing individual, familial, and socioeconomic factors, leaving the underlying neurodevelopmental correlates unresolved. Neuroimaging findings have therefore been used to probe potential neural substrates of these associations and broadly align with observed behavioral patterns. Musical training has been associated with structural and functional brain differences, particularly within auditory, motor, and frontotemporal systems. For example, increased gray-matter volume and cortical thickness have been observed in auditory and motor regions following instrumental training ( 13 , 14 ). Longitudinal and intervention studies suggest that music training can influence auditory processing and language-relevant skills, with evidence of functional changes in speech-auditory networks ( 15 , 16 ). These observations suggest that music engagement may be associated with neural systems supporting speech and language. Several studies have examined brain structure as candidate neural correlates or intermediate phenotypes linking music experience to language-related outcomes ( 17 – 19 ), but to date, none have formally tested whether brain structure statistically accounts for these associations in large, population-based longitudinal samples. Central methodological and analytic challenges include the need for large longitudinal samples with repeated neuroimaging and language assessments, rigorous control for baseline ability and socioeconomic context, appropriate modeling of family structure and shared familial factors, and careful selection of brain phenotypes that capture distributed neural variation. To address these gaps, we analyzed data from the large, population-based, longitudinal ABCD Study ( 20 ), which provides harmonized MRI and cognitive assessments across diverse sociodemographic backgrounds. We examined associations linking baseline music participation to baseline cortical structural measures (cortical surface area, volume, thickness, and sulcal depth) and to language outcomes assessed two years later. All models were explicitly conditioned on baseline language performance and adjusted for demographic characteristics, socioeconomic factors, prenatal and perinatal factors, behavioral functioning, extracurricular activities, and study site. To our knowledge, this is the first population-based longitudinal study to formally test whether global cortical structural measures statistically account for part of the prospective association between baseline music participation and language outcomes at 2-year follow-up. We first evaluated the longitudinal association between baseline music participation and subsequent language skills in the full analytic sample and then evaluated whether baseline cortical structure statistically mediated these associations. Robustness to shared familial influences, including genetic relatedness, was assessed in a twin subsample using mixed-effects models. Methods 2.1 Study Population This study used ABCD data release 3.0, which enrolled participants aged 9–10 years at baseline (n = 11,878) from 21 U.S. sites ( 21 ). All data are de-identified and available to qualified investigators through a data use agreement ( https://nda.nih.gov/abcd ). Follow-up assessments were conducted 2-year later at ages 11–12. Because of expected attrition and incomplete data, we analyzed an analytic subset (n = 6,571) with complete 2-year follow-up data. A χ² automatic interaction detection (CHAID) analysis of baseline characteristics was used as an exploratory assessment of differential follow-up, and we found no evidence of selection bias between participants with and without 2-year follow-up data ( 22 , 23 ) ( eMethod 1 in Supplementary Material). The final analytic sample included 5,993 participants with complete data on baseline demographics, music participation, structural MRI measures, and language assessments at the 2-year follow-up (Fig. 1 ). A genetically informative twin subsample (n = 936) was also identified for sensitivity analyses ( eFigure in Supplementary Material). The study was approved by the institutional review boards at all participating sites, with central IRB oversight by the University of California, San Diego. Parents or legal guardians provided written informed consent. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. All methods were performed in accordance with applicable guidelines and regulations. 2.2. Measures Exposures: music participation (baseline) Music participation measures were obtained from the parent-reported Sports and Activities Involvement Questionnaire (SAIQ) ( eTable 1 in Supplementary Material). The primary exposure was sustained music participation, a binary variable defined as participation in organized music activities (e.g., singing, choir, guitar, piano, drums, violin, flute, band, rock band, orchestra) for at least 4 consecutive months (yes/no). This threshold (4 consecutive months) reflects regular, ongoing engagement, as opposed to brief or sporadic involvement, and was selected a priori based on the structure of the ABCD data collection rather than post hoc optimization. Secondary exposure variables include practice frequency (days per week of participation) and practice intensity (minutes per week, calculated as days per week multiplied by hours per day). These variables capture both the regularity and depth of musical engagement, allowing quantitative evaluation of potential dose-response relationships between the extent of music participation and developmental outcomes. Outcomes: language assessments (2-year follow-up) Language outcomes were assessed using the NIH Toolbox Cognition Battery ( 24 ) ( eTable 2 in Supplementary Material), which provides psychometrically validated measures of key language and reading abilities. The assessments included three primary outcomes: ( 1 ) the Picture Vocabulary Test, measuring receptive vocabulary and verbal comprehension; ( 2 ) the Oral Reading Recognition Test, assessing reading decoding and word recognition accuracy, and ( 3 ) the Crystallized Composite ( 25 ), a comprehensive index of crystallized intelligence that integrates the Picture Vocabulary and Oral Reading Recognition scores to reflect accumulated knowledge and language-based skills. All scores were age-adjusted and standardized to the NIH Toolbox normative sample (mean = 100, standard deviation [SD] = 15), with higher scores indicating better performance. These language measures have demonstrated strong reliability and validity in school-aged populations and are sensitive to developmental and experiential influences such as educational enrichment and cognitive stimulation ( 26 ). The same NIH Toolbox language measures were administered at baseline and were included as conditioning variables in all longitudinal analyses to account for pre-existing differences in language ability. Mediators: global cortical measures (baseline) Prior work has demonstrated that cortical structure is related to both music engagement and language-related abilities ( 13 , 27 , 28 ). While much of the prior literature has focused on specific regions of interest, evidence that music and language processing rely on distributed cortical networks ( 29 , 30 ), and work demonstrating that global cortical morphometric measures capture meaningful variation related to cognitive function ( 31 , 32 ), motivates the examination of global cortical measures rather than region-specific effects. The global cortical measures we included capture complementary aspects of cortical architecture, cortical size (cortical surface area and volume) and morphology (cortical thickness and sulcal depth) ( eTable 3 in the Supplementary Material) ( 33 , 34 ). Methodological work has additionally demonstrated that proportional intracranial volume correction can attenuate behaviorally relevant signal in global surface area and cortical volume measures, including in ABCD and other large datasets ( 35 ). Accordingly, analyzing absolute global cortical metrics allows evaluation of whether overall cortical structural variation statistically accounts for associations between music participation and language outcomes, rather than isolating proportional differences. Details regarding MRI acquisition, preprocessing, and quality control are provided in eMethod 2 in the Supplementary Material. Covariates Analyses were adjusted for prespecified covariates selected based on prior evidence and biological plausibility ( eTables 4 in Supplementary Material). These covariates were grouped into major domains reflecting demographic, socioeconomic, behavioral, and other extracurricular activity factors. Demographic factors included child age, sex, and parent-reported race and ethnicity ( 36 , 37 ). Socioeconomic indicators included parental income, education, and marital status ( 38 , 39 ). Prenatal and perinatal factors included preterm birth, maternal alcohol and tobacco use during pregnancy ( 40 , 41 ). Child behavioral and emotional functioning was measured using Child Behavior Checklist (CBCL/6–18) T-scores for Attention-Deficit/Hyperactivity Disorder (ADHD) Problems based on DSM-5 criteria, Social Problems and broadband Internalizing and Externalizing domains ( 42 ). Additional covariates included participation in other structured extracurricular activities, measured as separate count variables representing the number of team sports, individual sports, and non-music creative arts activities in which each child participated. This approach captures the breadth of extracurricular involvement across domains and is consistent with conceptual frameworks used in prior population-based studies ( 43 ), as well as study site ( 44 ). 2.3 Statistical Analysis Baseline sample characteristics were summarized using means (SD) and proportions. Standardized mean differences (SMDs) were calculated to compare characteristics between ( 1 ) the full analysis sample and the twin subsample and ( 2 ) participants with and without sustained music participation. SMDs were computed using Cohen’s d for continuous variables and Cohen’s h for categorical variables ( 45 ). Pearson correlation coefficients were calculated between each baseline characteristic and continuous music exposures (practice intensity and frequency) ( 46 ). For binary variables, correlations were computed using 0/1 coding (equivalent to a point-biserial correlation); and for categorical variables with more than two levels, one-versus-rest coding was applied. We conducted a mediation analysis to evaluate whether global cortical structural measures were associated with both music participation and subsequent language outcomes, and statistically accounted for a portion of the observed prospective association between music participation and language outcomes assessed two years later (Fig. 2 ). We defined three baseline exposures (sustained music participation, practice frequency, and intensity), four baseline mediators (global cortical surface area, volume, thickness, and sulcal depth), and three outcomes at 2-year follow-up (picture vocabulary, oral reading recognition, and crystallized intelligence). This design yields 36 exposure-mediator-outcome combinations (3 × 4 × 3). Exposures, mediators, and outcomes were modeled separately to reduce collinearity and to reflect conceptual distinctions among constructs. For each combination, we fit three linear mixed-effects models (LMMs) ( 47 ): ( 1 ) an outcome-exposure model estimating the total association between the exposure and the outcome, without including the mediator; ( 2 ) a mediator-exposure model estimating the association between exposure and mediator; and ( 3 ) an outcome-mediator-exposure model estimating the association between the mediator and the outcome while adjusting for the exposure, yielding the direct association. The indirect association was quantified as the product of the exposure–mediator and mediator–outcome coefficients, and the proportion mediated was calculated as the ratio of the indirect association to the total association. All models additionally adjusted for baseline language performance to account for preexisting differences in language ability and to strengthen longitudinal interpretation. Prespecified covariates include demographic, socioeconomic, and prenatal and perinatal factors; child behavioral characteristics; and broader extracurricular activities. Study site was modeled as a fixed effect and random intercepts for family ID were accounted for within-family clustering. Each exposure-mediator-outcome combination was analyzed separately to minimize collinearity and reflect construct differences. Multiple comparisons were addressed using the Benjamini–Hochberg false discovery rate (FDR) procedure ( 48 ), with a two-sided FDR-adjusted q < .05 considered statistically significant. Sensitivity analyses repeated primary and mediation models within the twin subsample using the same LMM specification. Family ID was used to account for shared familial factors, and zygosity (monozygotic vs. dizygotic) was included as a fixed effect to account for differences in genetic relatedness between twin types ( 49 ). All analyses were conducted in Python 3.11 using the statsmodels library ( 50 ). Results 3.1. Study Population Among 5,993 participants (baseline mean age, 10.0 [SD, 0.6] years; 47.1% female), the sample included 123 (2.1%) Asian, 650 (10.8%) Black, 1,098 (18.3%) Hispanic, and 3,530 (58.9%) White individuals, as well as 592 (9.9%) individuals identifying as multiracial and/or other races. The analytic twin subsample included 936 twins. Table 1 summarizes baseline characteristics of the full analytic sample and the twin subsample. Compared with the overall cohort, the twin sample had a markedly higher prevalence of preterm birth (57.0% vs 19.9%; SMD, 0.79), higher household income (SMD, 0.56), and a lower proportion of Hispanic participants (SMD, 0.30). Other sociodemographic and child behavior measures were generally balanced (SMDs < 0.20). At baseline, 42.7% of children engaged in sustained music participation, with a mean practice frequency of 2.1 days/week and mean intensity of 1.3 hours/week. Table 1 Baseline Characteristics of the Full analytic Sample and Twin Subsample Characteristic Full Sample (n = 5,993) Twin Sample (n = 936) SMD a Demographics Age, y 10.0 (0.6) 10.2 (0.6) -0.36 Sex Female 2,823 (47.1) 454 (48.5) -0.03 Male 3,170 (52.9) 482 (51.5) 0.03 Race and ethnicity Asian 123 (2.1) 1 (0.1) 0.23 Black 650 (10.8) 122 (13.0) -0.07 Hispanic 1,098 (18.3) 79 (8.4) 0.30 White 3,530 (58.9) 666 (71.2) -0.26 Multiracial and/or others b 592 (9.9) 68 (7.3) 0.09 Socioeconomic Factors Annual household income, $ <50 000 1,594 (26.6) 63 (6.7) 0.56 ≥50 000 4,399 (73.4) 873 (93.3) -0.56 Parental marital status Married 4,296 (71.7) 716 (76.5) -0.11 Living with partner 283 (4.7) 18 (1.9) 0.16 Single 1,414 (23.6) 202 (21.6) 0.05 Parental education < Some college 757 (12.6) 63 (6.7) 0.20 ≥ Some college 5,236 (87.4) 873 (93.3) -0.20 Prenatal and Perinatal Factors Parent-reported preterm-birth status Yes 1,191 (19.9) 534 (57.0) -0.79 No 4,736 (79.0) 394 (42.1) 0.77 Don’t know 66 (1.1) 8 (0.9) 0.02 Maternal alcohol use in pregnancy Yes 178 (3.0) 17 (1.8) 0.08 No 5,677 (94.7) 913 (97.5) -0.15 Don’t Know 138 (2.3) 6 (0.6) 0.15 Maternal tobacco use in pregnancy Yes 251 (4.2) 39 (4.2) 0.00 No 5,605 (93.5) 891 (95.2) -0.07 Don’t know 137 (2.3) 6 (0.6%) 0.15 Child Behavior and Emotional Functioning c ADHD DSM-5 Scale 53.1 (5.6) 52.4 (4.9) 0.13 Social problems 52.7 (4.5) 52.0 (3.6) 0.17 Internalizing symptoms 48.6 (10.5) 46.7 (10.2) 0.18 Externalizing symptoms 45.5 (10.2) 44.1 (9.9) 0.14 Broader Enrichment Activities d Team sports, count 1.2 (1.2) 1.4 (1.3) -0.16 Individual sports, count 0.7 (0.9) 0.8 (0.9) -0.11 Creative arts, count 0.9 (1.0) 0.7 (0.9) 0.21 Values are presented as No. (%) or mean (standard deviation [SD]). a Standardized mean difference (SMD) was calculated using Cohen’s d for continuous variables and Cohen’s h for categorical variables. b Primary caregivers could select multiple racial or ethnic subgroups for their children; the “other” category indicates that no specific race or ethnicity was identified. c Child Behavior and Emotional Functioning were assessed using Child Behavior Checklist (CBCL) T scores, standardized to a mean of 50 (SD, 10). d Broader enrichment activities reflect the total number of extracurricular activities in which the child participated for ≥4 months continuously in the past year, as reported in the Sports and Activities Involvement Questionnaire. Team sports include activities such as baseball or softball, basketball, football, and soccer. Individual sports include gymnastics, martial arts, and swimming. Creative arts activities include ballet or dance, as well as visual arts such as drawing, painting, graphic art, photography, pottery, and sculpting. 3.2 Baseline Characteristics by Music Exposure Variables Among all participants, 2,558 (42.7%) engaged in sustained music participation (Table 2). Children in this group were more likely to come from higher-income households (≥$50,000: 35.2% vs. 15.0%; SMD, 0.48) and have parents with higher educational attainment (≥ some colleges: 94.0% vs 82.4%; SMD, 0.37) and married status (81.5% vs 64.4%; SMD, 0.39). They also participated more frequently in other enrichment activities, including non–team sports (mean [SD], 0.99 [0.93] vs 0.53 [0.73]; SMD, 0.55) and creative arts (1.22 [1.08] vs 0.64 [0.83]; SMD, 0.60). Prenatal and perinatal factors and child behavior measures were largely comparable between groups (SMDs < 0.20). Patterns were consistent when examining music practice frequency and intensity as continuous variables (Table 3). Practice frequency and intensity were positively associated with older age, higher household income, parental education, and married parental status, and inversely associated with preterm birth. Intensity demonstrated slightly stronger correlations with other enrichment activities, particularly individual sports and creative arts, while correlations with prenatal and perinatal factors and child behavior measures were weak or negligible. Table 3 Correlations of Baseline Characteristics with Music Practice Intensity and Frequency in Full Sample (N = 5,993) Characteristics Mean (SD) or No. (%) Correlation with Practice Intensity (95% CI) a P value Correlation with Practice Frequency (95% CI) P value Demographics Age, y 10.0 (0.6) 0.08 (0.06, 0.11) < .001 0.03 (-0.00, 0.05) 0.06 Sex, female 2,823 (47.1) 0.04 (0.01, 0.06) .006 -0.01 (-0.04, 0.01) 0.32 Race and ethnicity, white 3,530 (58.9) 0.01 (-0.02, 0.04) .42 0.03 (0.00, 0.05) 0.025 Socioeconomic factor Household income ≥$50,000 4,399 (73.4) 0.05 (0.02, 0.07) < .001 0.02 (-0.01, 0.04) 0.2 Parental marital status, married 4,296 (71.7) 0.04 (0.01, 0.06) .006 0.03 (-0.00, 0.05) 0.054 Parental education, ≥ some college 5,236 (87.4) 0.05 (0.02, 0.07) < .001 0.02 (-0.01, 0.05) 0.1 Prenatal and perinatal factors Child preterm-birth status 1,191 (19.9) -0.05 (-0.07, -0.02) < .001 -0.05 (-0.07, -0.02) < 0.001 Maternal alcohol use in pregnancy 178 (3.0) 0.02 (-0.01, 0.05) .11 0.01 (-0.02, 0.03) 0.71 Maternal tobacco use in pregnancy 251 (4.2) -0.02 (-0.04, 0.01) .19 0.00 (-0.02, 0.03) 0.88 Child behavior and emotional functioning b ADHD DSM-5 Scale 53.1 (5.6) -0.03 (-0.06, -0.01) .02 -0.02 (-0.05, 0.00) 0.09 Social problems 52.7 (4.6) -0.01 (-0.04, 0.02) .46 -0.01 (-0.04, 0.02) 0.45 Internalizing symptoms 48.6 (10.5) -0.01 (-0.03, 0.02) .67 0.01 (-0.02, 0.03) 0.64 Externalizing symptoms 45.5 (10.2) -0.02 (-0.05, 0.00) .08 -0.01 (-0.03, 0.02) 0.67 Broader enrichment activities c Team sports, count 1.2 (1.2) 0.02 (-0.01, 0.04) .26 -0.01 (-0.04, 0.02) 0.45 Non-team sports, count 0.7 (0.9) 0.09 (0.06, 0.11) < 0.001 -0.01 (-0.03, 0.02) 0.68 Creative arts, count 0.9 (1.0) 0.11 (0.09, 0.14) < 0.001 0.00 (-0.03, 0.02) 0.78 Values are mean (SD) for continuous variables or number (percentage) for categorical variables. a Pearson correlation coefficients were calculated separately between each baseline characteristic and music practice intensity or frequency (both continuous). For binary variables, r was computed with 0/1 coding (equivalent to a point-biserial correlation); and for categorical variables with more than 2 levels, one-vs-rest coding was applied. 95% Confidence Intervals (Cis) computed using ordinary standard errors. Music practice intensity was defined as total minutes of practice per week (days/week × minutes/session, assuming one session per day). Music practice frequency was defined as reported days per week of practice. b Child Behavior and Emotional Functioning were assessed using Child Behavior Checklist (CBCL) T scores, standardized to a mean of 50 (SD, 10). c Broader enrichment activities reflect the extracurricular activities in which the child participated for ≥4 months continuously in the past year, as reported in the Sports and Activities Involvement Questionnaire. Team sports include baseball or softball, basketball, football, and soccer. Individual sports include gymnastics, martial arts, and swimming. Creative arts activities include ballet or dance, as well as visual arts such as drawing, painting, graphic art, photography, pottery, and sculpting. Counts represent the total number of activities endorsed in each category. 3.3 Global Cortical Mediation of Longitudinal Associations Between Music Participation and Language Outcomes Primary exposure: sustained music participation In models adjusting for baseline language performance and covariates, sustained music participation at baseline remained positively associated with language outcomes assessed two years later (Table 4). In the full sample, music participation was associated with higher crystallized cognition scores (β = 1.093; 95% CI, 0.461–1.725), picture vocabulary scores (β = 1.748; 95% CI, 1.076–2.419), and oral reading recognition scores (β = 1.082; 95% CI, 0.381–1.783). These estimates reflect residual longitudinal associations after accounting for baseline performance and indicate modest but statistically significant differences in standardized language scores. Mediation analyses demonstrated that global cortical surface area and volume accounted for a small but significant proportion of these associations, whereas cortical thickness and sulcal depth did not show evidence of mediation. For crystallized cognition, surface area mediated 7.3% of the total association (indirect β = 0.080; 95% CI, 0.021–0.140; q = 0.008), and cortical volume mediated 8.1% (indirect β = 0.088; 95% CI, 0.024–0.153; q = 0.008). For picture vocabulary, surface area and volume each mediated 4.7% of the association (indirect β = 0.083; 95% CI, 0.021–0.144; q = 0.008 for both). For oral reading recognition, surface area mediated 7.5% (indirect β = 0.081; 95% CI, 0.020–0.142; q = 0.009), and volume mediated 9.0% (indirect β = 0.097; 95% CI, 0.027–0.168; q = 0.009) of the total effect. Secondary exposure: practice intensity Analyses using practice intensity as the exposure showed smaller total associations with language outcomes than those observed for sustained music participation (Table 4). In the full sample, total effects for practice intensity were modest across outcomes, with β estimates of 0.321 for crystallized cognition, 0.569 for picture vocabulary, and 0.253 for oral reading recognition with the latter not reaching statistical significance. For crystallized cognition, surface area and volume mediated 8.7% and 10.0% of the total association, respectively (indirect q = 0.034 for both). For picture vocabulary, surface area and volume mediated 5.1% and 5.4% of the association (indirect q = 0.035). For oral reading recognition, proportions mediated were larger (11.1% for surface area and 14.2% for volume; indirect q = 0.036), although the total association was not statistically significant. In contrast, no significant indirect effects were observed for cortical thickness or sulcal depth in any practice intensity models (all q ≥ .962). Secondary exposure: practice frequency Analyses using practice frequency as the exposure yielded similarly modest total associations with language outcomes (β = 0.313 for crystallized cognition, 0.473 for picture vocabulary, and 0.218 for oral reading recognition; Table 4). Indirect effects through global cortical surface area or volume did not reach statistical significance (all indirect q ≈ 0.087–0.089). No evidence of mediation was observed for cortical thickness or sulcal depth in frequency-based models (all q ≥ 0.837). Sensitivity Analyses Sensitivity analyses conducted in the twin subsample showed patterns broadly consistent with those observed in the full cohort ( eTable 5 ). Sustained music participation remained positively associated with crystallized cognition, picture vocabulary, and oral reading recognition, with larger point estimates than in the full sample. Mediation analyses indicated that global cortical surface area and volume statistically accounted for a significant proportion of the associations for crystallized cognition and picture vocabulary. For crystallized cognition, the proportion mediated was 16.0% for surface area and 15.9% for volume; for picture vocabulary, 10.1% and 10.2%, respectively. For oral reading recognition, indirect effects via surface area and volume were also observed (indirect q = 0.047), corresponding to proportions mediated of approximately 13%; however, these effects were supported by weaker statistical evidence than those observed for crystallized cognition and picture vocabulary. For sustained music participation, cortical thickness and sulcal depth showed no evidence of mediation across outcomes (all q ≥ 0.655). In the twin subsample, analyses using practice intensity and practice frequency as exposures did not demonstrate statistically significant associations with language outcomes, nor evidence of mediation by any cortical structural measure. Discussion Approximately 43% of children in the analytic sample engaged in sustained music participation at baseline. Consistent with prior population-based studies, children who participated in music activities were more likely to come from families with greater socioeconomic resources and to engage in other extracurricular activities, including sports and creative arts, while behavioral and emotional characteristics were largely similar between music participants and nonparticipants. These patterns provide important context for interpreting observed associations (Table 2 ) ( 12 , 51 ). Sustained music participation in early adolescence was associated with modestly higher language performance two years later across crystallized cognition, picture vocabulary, and oral reading recognition. These associations were estimated using models that adjusted for baseline language performance as well as a comprehensive set of demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular covariates, indicating that the observed differences reflect residual longitudinal associations beyond prior performance and measured confounders rather than cross-sectional differences. While modest in magnitude, the observed effect sizes are consistent with those commonly reported for educational and extracurricular exposures in population-based studies, suggesting that such differences, though small, may still have meaningful implications at the population level during adolescence, a period of rapid cognitive and educational development ( 52 ). Mediation analyses indicated that global cortical surface area and cortical volume accounted for a small but statistically significant proportion of the associations between sustained music participation and language outcomes, whereas cortical thickness and sulcal depth did not show evidence of mediation. These findings are consistent with prior work suggesting that measures related to cortical size (surface area and volume) and measures related to cortical shape or refinement (thickness and sulcal depth) reflect distinct dimensions of cortical organization, rather than overlapping morphometric properties ( 33 , 34 , 53 ). The observed mediation for cortical volume likely reflects variance shared with surface area-related features, with limited contribution from thickness-related processes ( 53 ). The modest magnitude of mediation indicates partial accounting rather than a dominant explanatory pathway, suggesting that additional neural or experiential factors not indexed by global cortical morphometry may contribute to the observed associations. Analyses examining practice intensity and practice frequency as secondary exposure measures yielded smaller total associations with language outcomes than those observed for sustained music participation. In the full sample, mediation by global cortical surface area and volume was observed for practice intensity but not for practice frequency, while no evidence of mediation was detected for cortical thickness or sulcal depth. These findings suggest that different dimensions of music engagement may relate differently to language outcomes and underlying brain structure, with sustained participation showing the most consistent associations. Sensitivity analyses conducted in a twin subsample showed patterns broadly consistent with those observed in the full cohort. Associations between sustained music participation and crystallized cognition and picture vocabulary remained positive, with larger mediated proportions for surface area and volume than in the full sample. In contrast, mediation effects for oral reading recognition were weaker and less robust, although total associations remained positive. These differences may reflect reduced residual confounding due to shared familial factors, limited statistical power in the smaller twin subsample, or outcome-specific neural correlates of reading-related skills ( 54 , 55 ). The observed associations support the theoretical framework of a mediation model where music participation influences brain structure, specifically cortical surface area and volume, which then contribute to language outcomes. This model aligns with the shared sound-processing hypothesis, which suggests overlapping neural mechanisms between music and language processing ( 56 , 57 ). However, it is important to interpret these findings as statistical associations rather than direct evidence of causal pathways, as the observed effects may be influenced by additional factors not captured in the current study. This study extends prior research linking music engagement to language development by providing, to our knowledge, the first large-scale longitudinal investigation of multiple dimensions of music participation and global cortical structural mediators within a population-based cohort. Key strengths include the prospective design with temporally ordered assessment of music participation, brain structure, and language outcomes; explicit adjustment for baseline language performance; comprehensive control for demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular factors; the use of a twin subsample for sensitivity analyses to evaluate robustness to shared familial influences; and mixed-effects modeling with correction for multiple testing. These design features strengthen confidence in the observed associations while appropriately avoiding assumptions of causality. Several limitations should be considered. First, music participation was assessed via parent report, which may be subject to measurement error. Second, analyses focused intentionally on global cortical structural measures, which are well suited to capturing distributed neural variation at the population level. As a result, we did not examine regional cortical features, white matter microstructure, or functional connectivity, which may capture more localized or network-level associations relevant to language development and should be examined in future work. Third, while this study utilizes the ABCD Release 3.0 dataset, chosen for its comprehensive baseline coverage and high data density, newer releases (e.g., Release 6.0) now offer extended longitudinal follow-up. It is important to note, however, that later waves in longitudinal cohorts are inherently subject to greater participant attrition and potential missingness. By utilizing Release 3.0, we prioritized sample stability during the early neurodevelopmental window. Future research should leverage subsequent time points to evaluate the long-term developmental trajectory of these brain-behavior associations while carefully accounting for the increased complexity of missing data in later years. Finally, despite extensive covariate adjustment, residual confounding and selection bias remain possible, and mediation analyses in observational data do not establish causal mechanisms. Future studies should investigate the durability of these associations across later developmental stages and explore whether specific forms or contexts of music engagement are differentially related to neurodevelopmental and educational outcomes. Conclusions In this large, population-based longitudinal cohort, sustained music participation in early adolescence was associated with modestly higher language performance two years later, even after conditioning on baseline language ability and adjusting for a comprehensive set of demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular covariates. By explicitly accounting for baseline performance, these findings support interpretation of the observed associations as residual prospective differences in language outcomes rather than simple cross-sectional correlations. Mediation analyses indicated that global cortical surface area and volume statistically accounted for a small proportion of these associations, whereas cortical thickness and sulcal depth did not, indicating partial accounting and placing quantitative limits on the extent to which global cortical morphometry contributes to music-language associations. Sensitivity analyses in a twin subsample yielded broadly similar patterns, supporting the robustness of these findings under more stringent control for shared familial factors without implying causal or genetic inference. These results align with prior large-scale longitudinal, meta-analytic, and genetically informed studies showing that associations between music participation and language outcomes are modest in magnitude and partially attributable to pre-existing individual and familial differences. At the same time, this study extends the literature by providing a rigorous, population-level test of prospective associations conditioned on baseline ability, and by directly evaluating the contribution of global cortical structure within a brain-behavior mediation framework. Future randomized or quasi-experimental studies incorporating multimodal neuroimaging will be important for clarifying more specific neural pathways through which music engagement may relate to language development. Declarations Author Contributions Conceptualization: A.W., J.D. Data curation: A.W., Z.L., J.W. Formal analysis: A.W., M.A., Z.L.,J.W., J.D. Methodology: A.W., M.A., J.W., J.D. Project administration: Z.L., J.A., J.D. Resources: Z.L., J.D. Supervision: J.D. Validation: A.W., M.A., Z.L., J.W., J.D. Visualization: A.W., Z.L., J.W., J.D. Writing – original draft: A.W. Writing – review & editing: A.W., M.A., Z.L., J.W., J.A., C.B.C., J.D. Funding Declaration This study was not supported by any funding. Data Availability The data used in this study are from the Adolescent Brain Cognitive Development (ABCD) Study and are available through the National Institute of Mental Health Data Archive (NDA; https://nda.nih.gov) under controlled access. Researchers may obtain access to the ABCD data by submitting a data use request to the NDA and complying with the ABCD data use agreements. References Neves, L., Correia, A. I., Castro, S. L., Martins, D. & Lima, C. F. Does music training enhance auditory and linguistic processing? A systematic review and meta-analysis of behavioral and brain evidence. Neurosci. Biobehav Rev. 140 , 104777 (2022). Pino, M. C., Giancola, M. & D'Amico, S. The Association between Music and Language in Children: A State-of-the-Art Review. Child. (Basel) . 10 (5), 801 (2023). Schellenberg, E. G. Music lessons enhance IQ. Psychol. Sci. 15 (8), 511–514 (2004). Slater, J. et al. Longitudinal effects of group music instruction on literacy skills in low-income children. PLoS One . 9 (11), e113383 (2014). François, C., Chobert, J., Besson, M. & Schön, D. Music training for the development of speech segmentation. Cereb. Cortex . 23 (9), 2038–2043 (2013). Moreno, S. et al. Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. Cereb. Cortex . 19 (3), 712–723 (2009). Schellenberg, E. G. & Lima, C. F. Music Training and Nonmusical Abilities. Annu. Rev. Psychol. 75 , 87–128 (2024). Moreno, S. et al. Short-term music training enhances verbal intelligence and executive function. Psychol. Sci. 22 (11), 1425–1433 (2011). Sala, G. & Gobet, F. Cognitive and academic benefits of music training with children: A multilevel meta-analysis. Mem. Cognit . 48 (8), 1429–1441 (2020). Hille, K., Gust, K., Bitz, U. & Kammer, T. Associations between music education, intelligence, and spelling ability in elementary school. Adv. Cogn. Psychol. 7 , 1–6 (2011). Wesseldijk, L. W., Gordon, R. L., Mosing, M. A. & Ullén, F. Music and verbal ability - a twin study of genetic and environmental associations. Psychol. Aesthet. Creat Arts . 17 (6), 675–681 (2023). Habibi, A., Hsu, E., Villanueva, J. & Luo, S. Longitudinal Effects of Continuous Music Training on Cognitive Development: Evidence From the Adolescent Brain Cognitive Development (ABCD) Study. Ann. N Y Acad. Sci. 1553 (1), 283–299 (2025). Hyde, K. L. et al. Musical training shapes structural brain development. J. Neurosci. 29 (10), 3019–3025 (2009). Herholz, S. C. & Zatorre, R. J. Musical training as a framework for brain plasticity: behavior, function, and structure. Neuron 76 (3), 486–502 (2012). Kraus, N. & Chandrasekaran, B. Music training for the development of auditory skills. Nat. Rev. Neurosci. 11 (8), 599–605 (2010). Habibi, A., Cahn, B. R., Damasio, A. & Damasio, H. Neural correlates of accelerated auditory processing in children engaged in music training. Dev. Cogn. Neurosci. 21 , 1–14 (2016). Habibi, A., Damasio, A., Ilari, B., Elliott Sachs, M. & Damasio, H. Music training and child development: a review of recent findings from a longitudinal study. Ann. N Y Acad. Sci. (2018). Schlaug, G., Norton, A., Overy, K. & Winner, E. Effects of music training on the child's brain and cognitive development. Ann. N Y Acad. Sci. 1060 , 219–230 (2005). Papatzikis, E., Agapaki, M., Selvan, R. N., Pandey, V. & Zeba, F. Quality standards and recommendations for research in music and neuroplasticity. Ann. N Y Acad. Sci. 1520 (1), 20–33 (2023). Volkow, N. D. et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. Dev. Cogn. Neurosci. 32 , 4–7 (2018). Garavan, H. et al. Recruiting the ABCD sample: Design considerations and procedures. Dev. Cogn. Neurosci. 32 , 16–22 (2018). Nohr, E. A. & Liew, Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstet. Gynecol. Scand. 97 (4), 407–416 (2018). Little, R. J. A. & Rubin, D. B. Statistical Analysis with Missing Data 3rd edn (Wiley, 2019). Gershon, R. C. et al. Language measures of the NIH Toolbox Cognition Battery. J. Int. Neuropsychol. Soc. 20 (6), 642–651 (2014). Akshoomoff, N. et al. VIII. NIH Toolbox Cognition Battery (CB): composite scores of crystallized, fluid, and overall cognition. Monogr. Soc. Res. Child. Dev. 78 (4), 119–132 (2013). Weintraub, S. et al. Cognition assessment using the NIH Toolbox. Neurology 80 (11 Suppl 3), S54–64 (2013). Gaser, C. & Schlaug, G. Brain structures differ between musicians and non-musicians. J. Neurosci. 23 (27), 9240–9245 (2003). Bahar, N. et al. Differences in Cortical Surface Area in Developmental Language Disorder. Neurobiol. Lang. (Camb) . 5 (2), 288–314 (2024). Peretz, I., Vuvan, D., Lagrois, M. & Armony, J. L. Neural overlap in processing music and speech. Philos. Trans. R Soc. Lond. B Biol. Sci. 370 (1664), 20140090 (2015). Te Rietmolen, N., Mercier, M. R., Trébuchon, A., Morillon, B. & Schön, D. Speech and music recruit frequency-specific distributed and overlapping cortical networks. Elife 13 , RP94509 (2024). Schnack, H. G. et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cereb. Cortex . 25 (6), 1608–1617 (2015). Cox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. & Deary, I. J. Structural brain imaging correlates of general intelligence in UK Biobank. Intelligence 76 , 101376 (2019). Raznahan, A. et al. How does your cortex grow? J. Neurosci. 31 (19), 7174–7177 (2011). Wierenga, L. M., Langen, M., Oranje, B. & Durston, S. Unique developmental trajectories of cortical thickness and surface area. Neuroimage 87 , 120–126 (2014). Dhamala, E. et al. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. Neuroimage 260 , 119485 (2022). Gennatas, E. D. et al. Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood. J. Neurosci. 37 (20), 5065–5073 (2017). Meier, A., Hartmann, B. S. & Larson, R. A Quarter Century of Participation in School-Based Extracurricular Activities: Inequalities by Race, Class, Gender and Age? J. Youth Adolesc. 47 (6), 1299–1316 (2018). Rakesh, D., Zalesky, A. & Whittle, S. Assessment of Parent Income and Education, Neighborhood Disadvantage, and Child Brain Structure. JAMA Netw. Open. 5 (8), e2226208 (2022). Miksza, P. Music Participation and Socioeconomic Status as Correlates of Change: A Longitudinal Analysis of Academic Achievement. Bull. Council Res. Music Educ. (172):41–58. (2007). Kelly, C. E. et al. Cortical growth from infancy to adolescence in preterm and term-born children. Brain 147 (4), 1526–1538 (2024). El Marroun, H. et al. Prenatal tobacco exposure and brain morphology: a prospective study in young children. Neuropsychopharmacology 39 (4), 792–800 (2014). Achenbach, T. M. & Rescorla, L. A. Manual for the ASEBA School-Age Forms & Profiles (University of Vermont, Research Center for Children, Youth, & Families, 2001). Crosnoe, R., Smith, C. & Leventhal, T. Family Background, School-Age Trajectories of Activity Participation, and Academic Achievement at the Start of High School. Appl. Dev. Sci. 19 (3), 139–152 (2015). Casey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Dev. Cogn. Neurosci. 32 , 43–54 (2018). Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn (Lawrence Erlbaum Associates, 1988). Rodgers, J. L. & Nicewander, W. A. Thirteen Ways to Look at the Correlation Coefficient. Am. Stat. 42 (1), 59–66 (1988). Pinheiro, J. & Bates, D. Mixed-Effects Models in S and S-PLUS (Springer, 2000). Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J. Royal Stat. Soc. Ser. B (Methodological) . 57 (1), 289–300 (1995). Peper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S. & Hulshoff Pol, H. E. Genetic influences on human brain structure: a review of brain imaging studies in twins. Hum. Brain Mapp. 28 (6), 464–473 (2007). Seabold, S., Perktold, J. & Statsmodels Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference. Austin, TX: SciPy; pp. 92 – 6. (2010). Corrigall, K. A. & Schellenberg, E. G. Predicting who takes music lessons: parent and child characteristics. Front. Psychol. 6 , 282 (2015). Kraft, M. Interpreting effect sizes of education interventions. Educational Researcher . 49 (4), 241–253 (2020). Winkler, A. M. et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53 (3), 1135–1146 (2010). Marek, S. et al. Reproducible brain-wide association studies require thousands of individuals. Nature 603 (7902), 654–660 (2022). Yeatman, J. D., Dougherty, R. F., Ben-Shachar, M. & Wandell, B. A. Development of white matter and reading skills. Proc. Natl. Acad. Sci. U S A . 109 (44), E3045–E3053 (2012). Patel, A. D. The OPERA hypothesis: assumptions and clarifications. Ann. N Y Acad. Sci. 1252 , 124–128 (2012). Patel, A. D. Music, Language, and the Brain (Oxford University Press, 2008). Table 2 and 4 Table 2 and 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialSR123final.docx Table24.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8664008","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":583628126,"identity":"bc4bc84c-a2d5-49eb-afd3-ebb1eba6248a","order_by":0,"name":"Avery Wang","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Avery","middleName":"","lastName":"Wang","suffix":""},{"id":583628127,"identity":"3a16de5d-fd20-480f-88c4-95f482a902bb","order_by":1,"name":"Mekibib Altaye","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Mekibib","middleName":"","lastName":"Altaye","suffix":""},{"id":583628131,"identity":"8a1fe062-d94d-4d12-978a-a399c6eb7d36","order_by":2,"name":"Zhixiu Lu","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Zhixiu","middleName":"","lastName":"Lu","suffix":""},{"id":583628134,"identity":"6dcc7718-c517-4365-9740-0254f6491181","order_by":3,"name":"Junqi Wang","email":"","orcid":"","institution":"Cincinnati Children's Hospital Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Junqi","middleName":"","lastName":"Wang","suffix":""},{"id":583628136,"identity":"531fea02-e626-4d5a-a0e5-9ce143bca83f","order_by":4,"name":"Jiyo Athertya","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Jiyo","middleName":"","lastName":"Athertya","suffix":""},{"id":583628137,"identity":"f0ee530d-114c-4d38-9dce-64b421941b7e","order_by":5,"name":"Christine Chung","email":"","orcid":"","institution":"University of California, San Diego","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Chung","suffix":""},{"id":583628138,"identity":"f18951e4-23be-4479-a891-6e67edc29391","order_by":6,"name":"Jiang Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYBACxmYQycYgZwBkH2BsAAsagEQIajEGKmM4cJAYLRDAxpC4gWgtzO3Mzx5+KTucvp29+cHhjzsYEvv7D29g+FB2GI/D2MyNZc4dzt3Zc8zgwMEzDIkzbqQVMM44h08Lg5m0ZNvh3A03EoBa2oAulOAxYOZtw6eF/RtIS7rB/ecfIFr4zxgw/8WrhcdM8mPb4QSDGzxQWxhyDJgZ8Wspk2Y4l2644UxOwYGzbRLGIL8c7DmXjlOLYf/xbZI/yqzlDY4f3/igss1GFhhiGx8ARXBraQAGNA+CLwEmD+BUDwTyIMf9wKdiFIyCUTAKRgEAt89gRVJKFPgAAAAASUVORK5CYII=","orcid":"","institution":"University of California, San Diego","correspondingAuthor":true,"prefix":"","firstName":"Jiang","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2026-01-22 00:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8664008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8664008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101790161,"identity":"156850df-6c1d-4158-8b69-78e21267e6af","added_by":"auto","created_at":"2026-02-03 16:04:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273504,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Full Analytic Sample Inclusion\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8664008/v1/b1b22429c4e328cefa0384d5.png"},{"id":101880983,"identity":"1d94e191-ff90-4955-97b8-5761cf05e3e9","added_by":"auto","created_at":"2026-02-04 15:08:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":726884,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model illustrating hypothesized associations between music participation, global cortical structure, and 2-year language outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArrows denote hypothesized statistical associations used to guide model specification and covariate adjustment and do not imply causal effects. Solid blue arrows indicate direct associations; dashed arrows indicate indirect (mediated) associations; solid gray arrows indicate covariates included for statistical adjustment in the corresponding models. All models were adjusted for prespecified covariates, including demographic (age, sex, race/ethnicity, study site), socioeconomic (household income, parental education, marital status), prenatal and perinatal (preterm birth status, maternal alcohol and tobacco use during pregnancy), behavioral and emotional functioning (DSM-5-oriented ADHD Problems, Internalizing, Externalizing, and Social Problems), and broader enrichment activities (counts of team sports, individual sports, and creative arts). Baseline language performance was included as a covariate in mediator-outcome models.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8664008/v1/880b02b845197a060899176c.png"},{"id":105034921,"identity":"09df0891-c4ec-49ae-9b80-ea176484f15a","added_by":"auto","created_at":"2026-03-20 07:24:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2307883,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8664008/v1/fbfbb993-69a8-4cbd-8571-af69991d3632.pdf"},{"id":101790164,"identity":"be80032d-ad5d-44d7-8b31-591ff1f17fa2","added_by":"auto","created_at":"2026-02-03 16:04:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":98114,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterialSR123final.docx","url":"https://assets-eu.researchsquare.com/files/rs-8664008/v1/8935333cbe4172fe792770bf.docx"},{"id":101942800,"identity":"d6b364d0-9ea0-461d-84f9-d6462d089493","added_by":"auto","created_at":"2026-02-05 09:38:19","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":32687,"visible":true,"origin":"","legend":"","description":"","filename":"Table24.docx","url":"https://assets-eu.researchsquare.com/files/rs-8664008/v1/c83e3762dab6c90be1af06a3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global cortical morphometry as a mediator of longitudinal associations between music participation and language outcomes in a population-based cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMusic participation has been consistently associated with enhanced language and literacy skills in children. Musically trained youth often show higher performance in vocabulary, reading fluency, and verbal reasoning (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Early work demonstrated that children receiving one year of music lessons showed larger gains in IQ and academic achievement, including reading comprehension, than those in drama or no-lesson control groups (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Longitudinal research has provided additional support for these associations. For example, classroom-based group music instruction has been linked to maintained or improved reading performance over time in school-aged children (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), and controlled intervention studies have shown that sustained music training is associated with gains in language-related skills such as speech segmentation, phonological processing, and reading-related outcomes (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Reviews of this body of work have summarized consistent connections between musical experience and literacy development (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Experimental interventions further indicate that even relatively short periods of rhythmic or melodic training can enhance verbal memory and auditory discrimination (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These findings suggest that musical experience engages cognitive functions shared with language, such as auditory discrimination, phonological awareness, and working memory, through overlapping cognitive processes.\u003c/p\u003e \u003cp\u003eHowever, it remains unclear whether these associations reflect training-related effects or are driven by confounding factors, such as motivation or the family environment. A meta-analysis of over 50 intervention studies found that, after accounting for publication bias, the cognitive benefits of music training were modest and inconsistent (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Other studies showed that associations between music training and literacy outcomes were substantially attenuated after controlling for socioeconomic status, and correlations between music participation and verbal ability were largely explained by shared genetic and environmental factors rather than direct training effects (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Related work similarly suggests that higher baseline intelligence, motivation, and family resources may account for much of the observed relationship between music participation and language performance (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recent large-scale longitudinal evidence from the Adolescent Brain Cognitive Development (ABCD) Study reported modest improvements across several cognitive domains, including language-related measures, associated with continuous music training over two years, with substantial moderation by socioeconomic context (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese prior studies indicate that music participation is associated with language and literacy skills, while these associations are modest in magnitude and substantially influenced by pre-existing individual, familial, and socioeconomic factors, leaving the underlying neurodevelopmental correlates unresolved. Neuroimaging findings have therefore been used to probe potential neural substrates of these associations and broadly align with observed behavioral patterns. Musical training has been associated with structural and functional brain differences, particularly within auditory, motor, and frontotemporal systems. For example, increased gray-matter volume and cortical thickness have been observed in auditory and motor regions following instrumental training (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Longitudinal and intervention studies suggest that music training can influence auditory processing and language-relevant skills, with evidence of functional changes in speech-auditory networks (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). These observations suggest that music engagement may be associated with neural systems supporting speech and language. Several studies have examined brain structure as candidate neural correlates or intermediate phenotypes linking music experience to language-related outcomes (\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), but to date, none have formally tested whether brain structure statistically accounts for these associations in large, population-based longitudinal samples. Central methodological and analytic challenges include the need for large longitudinal samples with repeated neuroimaging and language assessments, rigorous control for baseline ability and socioeconomic context, appropriate modeling of family structure and shared familial factors, and careful selection of brain phenotypes that capture distributed neural variation.\u003c/p\u003e \u003cp\u003eTo address these gaps, we analyzed data from the large, population-based, longitudinal ABCD Study (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), which provides harmonized MRI and cognitive assessments across diverse sociodemographic backgrounds. We examined associations linking baseline music participation to baseline cortical structural measures (cortical surface area, volume, thickness, and sulcal depth) and to language outcomes assessed two years later. All models were explicitly conditioned on baseline language performance and adjusted for demographic characteristics, socioeconomic factors, prenatal and perinatal factors, behavioral functioning, extracurricular activities, and study site.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first population-based longitudinal study to formally test whether global cortical structural measures statistically account for part of the prospective association between baseline music participation and language outcomes at 2-year follow-up. We first evaluated the longitudinal association between baseline music participation and subsequent language skills in the full analytic sample and then evaluated whether baseline cortical structure statistically mediated these associations. Robustness to shared familial influences, including genetic relatedness, was assessed in a twin subsample using mixed-effects models.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThis study used ABCD data release 3.0, which enrolled participants aged 9\u0026ndash;10 years at baseline (n\u0026thinsp;=\u0026thinsp;11,878) from 21 U.S. sites (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). All data are de-identified and available to qualified investigators through a data use agreement (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://nda.nih.gov/abcd\u003c/span\u003e\u003cspan address=\"https://nda.nih.gov/abcd\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Follow-up assessments were conducted 2-year later at ages 11\u0026ndash;12. Because of expected attrition and incomplete data, we analyzed an analytic subset (n\u0026thinsp;=\u0026thinsp;6,571) with complete 2-year follow-up data. A χ\u0026sup2; automatic interaction detection (CHAID) analysis of baseline characteristics was used as an exploratory assessment of differential follow-up, and we found no evidence of selection bias between participants with and without 2-year follow-up data (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (\u003cb\u003eeMethod 1\u003c/b\u003e in Supplementary Material).\u003c/p\u003e \u003cp\u003eThe final analytic sample included 5,993 participants with complete data on baseline demographics, music participation, structural MRI measures, and language assessments at the 2-year follow-up (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A genetically informative twin subsample (n\u0026thinsp;=\u0026thinsp;936) was also identified for sensitivity analyses (\u003cb\u003eeFigure\u003c/b\u003e in Supplementary Material). The study was approved by the institutional review boards at all participating sites, with central IRB oversight by the University of California, San Diego. Parents or legal guardians provided written informed consent. The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. All methods were performed in accordance with applicable guidelines and regulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Measures\u003c/h2\u003e \u003cp\u003e \u003cem\u003eExposures: music participation (baseline)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eMusic participation measures were obtained from the parent-reported Sports and Activities Involvement Questionnaire (SAIQ) (\u003cb\u003eeTable 1\u003c/b\u003e in Supplementary Material). The primary exposure was sustained music participation, a binary variable defined as participation in organized music activities (e.g., singing, choir, guitar, piano, drums, violin, flute, band, rock band, orchestra) for at least 4 consecutive months (yes/no). This threshold (4 consecutive months) reflects regular, ongoing engagement, as opposed to brief or sporadic involvement, and was selected a priori based on the structure of the ABCD data collection rather than post hoc optimization. Secondary exposure variables include practice frequency (days per week of participation) and practice intensity (minutes per week, calculated as days per week multiplied by hours per day). These variables capture both the regularity and depth of musical engagement, allowing quantitative evaluation of potential dose-response relationships between the extent of music participation and developmental outcomes.\u003c/p\u003e \u003cp\u003e \u003cem\u003eOutcomes: language assessments (2-year follow-up)\u003c/em\u003e \u003c/p\u003e \u003cp\u003eLanguage outcomes were assessed using the NIH Toolbox Cognition Battery (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) (\u003cb\u003eeTable 2\u003c/b\u003e in Supplementary Material), which provides psychometrically validated measures of key language and reading abilities. The assessments included three primary outcomes: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the Picture Vocabulary Test, measuring receptive vocabulary and verbal comprehension; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the Oral Reading Recognition Test, assessing reading decoding and word recognition accuracy, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the Crystallized Composite (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), a comprehensive index of crystallized intelligence that integrates the Picture Vocabulary and Oral Reading Recognition scores to reflect accumulated knowledge and language-based skills. All scores were age-adjusted and standardized to the NIH Toolbox normative sample (mean\u0026thinsp;=\u0026thinsp;100, standard deviation [SD]\u0026thinsp;=\u0026thinsp;15), with higher scores indicating better performance. These language measures have demonstrated strong reliability and validity in school-aged populations and are sensitive to developmental and experiential influences such as educational enrichment and cognitive stimulation (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The same NIH Toolbox language measures were administered at baseline and were included as conditioning variables in all longitudinal analyses to account for pre-existing differences in language ability.\u003c/p\u003e \u003cp\u003e \u003cem\u003eMediators: global cortical measures (baseline)\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePrior work has demonstrated that cortical structure is related to both music engagement and language-related abilities (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). While much of the prior literature has focused on specific regions of interest, evidence that music and language processing rely on distributed cortical networks (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), and work demonstrating that global cortical morphometric measures capture meaningful variation related to cognitive function (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), motivates the examination of global cortical measures rather than region-specific effects. The global cortical measures we included capture complementary aspects of cortical architecture, cortical size (cortical surface area and volume) and morphology (cortical thickness and sulcal depth) (\u003cb\u003eeTable 3\u003c/b\u003e in the Supplementary Material) (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Methodological work has additionally demonstrated that proportional intracranial volume correction can attenuate behaviorally relevant signal in global surface area and cortical volume measures, including in ABCD and other large datasets (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Accordingly, analyzing absolute global cortical metrics allows evaluation of whether overall cortical structural variation statistically accounts for associations between music participation and language outcomes, rather than isolating proportional differences. Details regarding MRI acquisition, preprocessing, and quality control are provided in \u003cb\u003eeMethod 2\u003c/b\u003e in the Supplementary Material.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCovariates\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAnalyses were adjusted for prespecified covariates selected based on prior evidence and biological plausibility (\u003cb\u003eeTables 4\u003c/b\u003e in Supplementary Material). These covariates were grouped into major domains reflecting demographic, socioeconomic, behavioral, and other extracurricular activity factors. Demographic factors included child age, sex, and parent-reported race and ethnicity (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Socioeconomic indicators included parental income, education, and marital status (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Prenatal and perinatal factors included preterm birth, maternal alcohol and tobacco use during pregnancy (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Child behavioral and emotional functioning was measured using Child Behavior Checklist (CBCL/6\u0026ndash;18) T-scores for Attention-Deficit/Hyperactivity Disorder (ADHD) Problems based on DSM-5 criteria, Social Problems and broadband Internalizing and Externalizing domains (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Additional covariates included participation in other structured extracurricular activities, measured as separate count variables representing the number of team sports, individual sports, and non-music creative arts activities in which each child participated. This approach captures the breadth of extracurricular involvement across domains and is consistent with conceptual frameworks used in prior population-based studies (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), as well as study site (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eBaseline sample characteristics were summarized using means (SD) and proportions. Standardized mean differences (SMDs) were calculated to compare characteristics between (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the full analysis sample and the twin subsample and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) participants with and without sustained music participation. SMDs were computed using Cohen\u0026rsquo;s d for continuous variables and Cohen\u0026rsquo;s h for categorical variables (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Pearson correlation coefficients were calculated between each baseline characteristic and continuous music exposures (practice intensity and frequency) (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). For binary variables, correlations were computed using 0/1 coding (equivalent to a point-biserial correlation); and for categorical variables with more than two levels, one-versus-rest coding was applied.\u003c/p\u003e \u003cp\u003eWe conducted a mediation analysis to evaluate whether global cortical structural measures were associated with both music participation and subsequent language outcomes, and statistically accounted for a portion of the observed prospective association between music participation and language outcomes assessed two years later (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We defined three baseline exposures (sustained music participation, practice frequency, and intensity), four baseline mediators (global cortical surface area, volume, thickness, and sulcal depth), and three outcomes at 2-year follow-up (picture vocabulary, oral reading recognition, and crystallized intelligence). This design yields 36 exposure-mediator-outcome combinations (3 \u0026times; 4 \u0026times; 3). Exposures, mediators, and outcomes were modeled separately to reduce collinearity and to reflect conceptual distinctions among constructs. For each combination, we fit three linear mixed-effects models (LMMs) (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e): (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) an outcome-exposure model estimating the total association between the exposure and the outcome, without including the mediator; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a mediator-exposure model estimating the association between exposure and mediator; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) an outcome-mediator-exposure model estimating the association between the mediator and the outcome while adjusting for the exposure, yielding the direct association. The indirect association was quantified as the product of the exposure\u0026ndash;mediator and mediator\u0026ndash;outcome coefficients, and the proportion mediated was calculated as the ratio of the indirect association to the total association.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll models additionally adjusted for baseline language performance to account for preexisting differences in language ability and to strengthen longitudinal interpretation. Prespecified covariates include demographic, socioeconomic, and prenatal and perinatal factors; child behavioral characteristics; and broader extracurricular activities. Study site was modeled as a fixed effect and random intercepts for family ID were accounted for within-family clustering. Each exposure-mediator-outcome combination was analyzed separately to minimize collinearity and reflect construct differences. Multiple comparisons were addressed using the Benjamini\u0026ndash;Hochberg false discovery rate (FDR) procedure (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e), with a two-sided FDR-adjusted q \u0026lt; .05 considered statistically significant.\u003c/p\u003e \u003cp\u003eSensitivity analyses repeated primary and mediation models within the twin subsample using the same LMM specification. Family ID was used to account for shared familial factors, and zygosity (monozygotic vs. dizygotic) was included as a fixed effect to account for differences in genetic relatedness between twin types (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). All analyses were conducted in Python 3.11 using the \u003cem\u003estatsmodels\u003c/em\u003e library (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e3.1. Study Population\u003c/h2\u003e\n \u003cp\u003eAmong 5,993 participants (baseline mean age, 10.0 [SD, 0.6] years; 47.1% female), the sample included 123 (2.1%) Asian, 650 (10.8%) Black, 1,098 (18.3%) Hispanic, and 3,530 (58.9%) White individuals, as well as 592 (9.9%) individuals identifying as multiracial and/or other races. The analytic twin subsample included 936 twins. Table 1 summarizes baseline characteristics of the full analytic sample and the twin subsample. Compared with the overall cohort, the twin sample had a markedly higher prevalence of preterm birth (57.0% vs 19.9%; SMD, 0.79), higher household income (SMD, 0.56), and a lower proportion of Hispanic participants (SMD, 0.30). Other sociodemographic and child behavior measures were generally balanced (SMDs\u0026thinsp;\u0026lt;\u0026thinsp;0.20). At baseline, 42.7% of children engaged in sustained music participation, with a mean practice frequency of 2.1 days/week and mean intensity of 1.3 hours/week.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eBaseline Characteristics of the Full analytic Sample and Twin Subsample\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristic\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFull Sample\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;5,993)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTwin Sample\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;936)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSMD\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.2 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,823 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e454 (48.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,170 (52.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e482 (51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace and ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e123 (2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (0.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e650 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,098 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79 (8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,530 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e666 (71.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMultiracial and/or others\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e592 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual household income, $\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;50 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,594 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;50 000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,399 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e873 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParental marital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,296 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e716 (76.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiving with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e283 (4.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,414 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202 (21.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParental education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; Some college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e757 (12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge; Some college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,236 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e873 (93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrenatal and Perinatal Factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eParent-reported preterm-birth status\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,191 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e534 (57.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,736 (79.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394 (42.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66 (1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMaternal alcohol use in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,677 (94.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e913 (97.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDon\u0026rsquo;t Know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eMaternal tobacco use in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,605 (93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e891 (95.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDon\u0026rsquo;t know\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e137 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild Behavior and Emotional Functioning\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADHD DSM-5 Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.4 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.7 (4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.0 (3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternalizing symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.7 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExternalizing symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.1 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eBroader Enrichment Activities\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeam sports, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4 (1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual sports, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreative arts, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eValues are presented as No. (%) or mean (standard deviation [SD]).\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eStandardized mean difference (SMD) was calculated using Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e for continuous variables and Cohen\u0026rsquo;s \u003cem\u003eh\u003c/em\u003e for categorical variables.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Primary caregivers could select multiple racial or ethnic subgroups for their children; the \u0026ldquo;other\u0026rdquo; category indicates that no specific race or ethnicity was identified.\u003c/p\u003e\n \u003cp\u003e\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e Child Behavior and Emotional Functioning\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ewere assessed using Child Behavior Checklist (CBCL) T scores, standardized to a mean of 50 (SD, 10).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Broader enrichment activities reflect the total number of extracurricular activities in which the child participated for \u0026ge;4 months continuously in the past year, as reported in the Sports and Activities Involvement Questionnaire. Team sports include activities such as baseball or softball, basketball, football, and soccer. Individual sports include gymnastics, martial arts, and swimming. Creative arts activities include ballet or dance, as well as visual arts such as drawing, painting, graphic art, photography, pottery, and sculpting.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e3.2 Baseline Characteristics by Music Exposure Variables\u003c/h2\u003e\n \u003cp\u003eAmong all participants, 2,558 (42.7%) engaged in sustained music participation (Table 2). Children in this group were more likely to come from higher-income households (\u0026ge;$50,000: 35.2% vs. 15.0%; SMD, 0.48) and have parents with higher educational attainment (\u0026ge;\u0026thinsp;some colleges: 94.0% vs 82.4%; SMD, 0.37) and married status (81.5% vs 64.4%; SMD, 0.39). They also participated more frequently in other enrichment activities, including non\u0026ndash;team sports (mean [SD], 0.99 [0.93] vs 0.53 [0.73]; SMD, 0.55) and creative arts (1.22 [1.08] vs 0.64 [0.83]; SMD, 0.60). Prenatal and perinatal factors and child behavior measures were largely comparable between groups (SMDs\u0026thinsp;\u0026lt;\u0026thinsp;0.20).\u003c/p\u003e\n \u003cp\u003ePatterns were consistent when examining music practice frequency and intensity as continuous variables (Table 3). Practice frequency and intensity were positively associated with older age, higher household income, parental education, and married parental status, and inversely associated with preterm birth. Intensity demonstrated slightly stronger correlations with other enrichment activities, particularly individual sports and creative arts, while correlations with prenatal and perinatal factors and child behavior measures were weak or negligible.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCorrelations of Baseline Characteristics with Music Practice Intensity and Frequency in Full Sample (N\u0026thinsp;=\u0026thinsp;5,993)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean (SD)\u003c/p\u003e\n \u003cp\u003eor No. (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation with Practice Intensity\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCorrelation with Practice Frequency\u003c/p\u003e\n \u003cp\u003e(95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0 (0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08 (0.06, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (-0.00, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,823 (47.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.01, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.04, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRace and ethnicity, white\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3,530 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (0.00, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocioeconomic factor\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHousehold income \u0026ge;$50,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,399 (73.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 (0.02, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.01, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParental marital status, married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,296 (71.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04 (0.01, 0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.03 (-0.00, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParental education, \u0026ge; some college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,236 (87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05 (0.02, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.01, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrenatal and perinatal factors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChild preterm-birth status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,191 (19.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05 (-0.07, -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.05 (-0.07, -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal alcohol use in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e178 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.01, 0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaternal tobacco use in pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e251 (4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02 (-0.04, 0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00 (-0.02, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild behavior and emotional functioning\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eADHD DSM-5 Scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.1 (5.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.03 (-0.06, -0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02 (-0.05, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSocial problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.7 (4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.04, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.04, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternalizing symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.6 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.03, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (-0.02, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExternalizing symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45.5 (10.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.02 (-0.05, 0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.03, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cstrong\u003eBroader enrichment activities\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTeam sports, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.2 (1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02 (-0.01, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.04, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-team sports, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7 (0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.09 (0.06, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.01 (-0.03, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCreative arts, count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11 (0.09, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00 (-0.03, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003cp\u003eValues are mean (SD) for continuous variables or number (percentage) for categorical variables.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003ePearson correlation coefficients were calculated separately between each baseline characteristic and music practice intensity or frequency (both continuous). For binary variables, \u003cem\u003er\u003c/em\u003e was computed with 0/1 coding (equivalent to a point-biserial correlation); and for categorical variables with more than 2 levels, one-vs-rest coding was applied. 95% Confidence Intervals (Cis) computed using ordinary standard errors. Music practice intensity was defined as total minutes of practice per week (days/week \u0026times; minutes/session, assuming one session per day). Music practice frequency was defined as reported days per week of practice.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eChild Behavior and Emotional Functioning\u003cstrong\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003c/strong\u003ewere assessed using Child Behavior Checklist (CBCL) T scores, standardized to a mean of 50 (SD, 10).\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eBroader enrichment activities reflect the extracurricular activities in which the child participated for \u0026ge;4 months continuously in the past year, as reported in the Sports and Activities Involvement Questionnaire. Team sports include baseball or softball, basketball, football, and soccer. Individual sports include gymnastics, martial arts, and swimming. Creative arts activities include ballet or dance, as well as visual arts such as drawing, painting, graphic art, photography, pottery, and sculpting. Counts represent the total number of activities endorsed in each category.\u003c/p\u003e\n \u003ch2\u003e3.3 Global Cortical Mediation of Longitudinal Associations Between Music Participation and Language Outcomes\u003c/h2\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary exposure: sustained music participation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eIn models adjusting for baseline language performance and covariates, sustained music participation at baseline remained positively associated with language outcomes assessed two years later (Table 4). In the full sample, music participation was associated with higher crystallized cognition scores (\u0026beta;\u0026thinsp;=\u0026thinsp;1.093; 95% CI, 0.461\u0026ndash;1.725), picture vocabulary scores (\u0026beta;\u0026thinsp;=\u0026thinsp;1.748; 95% CI, 1.076\u0026ndash;2.419), and oral reading recognition scores (\u0026beta;\u0026thinsp;=\u0026thinsp;1.082; 95% CI, 0.381\u0026ndash;1.783). These estimates reflect residual longitudinal associations after accounting for baseline performance and indicate modest but statistically significant differences in standardized language scores.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eMediation analyses demonstrated that global cortical surface area and volume accounted for a small but significant proportion of these associations, whereas cortical thickness and sulcal depth did not show evidence of mediation. For crystallized cognition, surface area mediated 7.3% of the total association (indirect \u0026beta;\u0026thinsp;=\u0026thinsp;0.080; 95% CI, 0.021\u0026ndash;0.140; q\u0026thinsp;=\u0026thinsp;0.008), and cortical volume mediated 8.1% (indirect \u0026beta;\u0026thinsp;=\u0026thinsp;0.088; 95% CI, 0.024\u0026ndash;0.153; q\u0026thinsp;=\u0026thinsp;0.008). For picture vocabulary, surface area and volume each mediated 4.7% of the association (indirect \u0026beta;\u0026thinsp;=\u0026thinsp;0.083; 95% CI, 0.021\u0026ndash;0.144; q\u0026thinsp;=\u0026thinsp;0.008 for both). For oral reading recognition, surface area mediated 7.5% (indirect \u0026beta;\u0026thinsp;=\u0026thinsp;0.081; 95% CI, 0.020\u0026ndash;0.142; q\u0026thinsp;=\u0026thinsp;0.009), and volume mediated 9.0% (indirect \u0026beta;\u0026thinsp;=\u0026thinsp;0.097; 95% CI, 0.027\u0026ndash;0.168; q\u0026thinsp;=\u0026thinsp;0.009) of the total effect.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary exposure: practice intensity\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAnalyses using practice intensity as the exposure showed smaller total associations with language outcomes than those observed for sustained music participation (Table 4). In the full sample, total effects for practice intensity were modest across outcomes, with \u0026beta; estimates of 0.321 for crystallized cognition, 0.569 for picture vocabulary, and 0.253 for oral reading recognition with the latter not reaching statistical significance. For crystallized cognition, surface area and volume mediated 8.7% and 10.0% of the total association, respectively (indirect q\u0026thinsp;=\u0026thinsp;0.034 for both). For picture vocabulary, surface area and volume mediated 5.1% and 5.4% of the association (indirect q\u0026thinsp;=\u0026thinsp;0.035). For oral reading recognition, proportions mediated were larger (11.1% for surface area and 14.2% for volume; indirect q\u0026thinsp;=\u0026thinsp;0.036), although the total association was not statistically significant. In contrast, no significant indirect effects were observed for cortical thickness or sulcal depth in any practice intensity models (all q \u0026ge; .962).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary exposure: practice frequency\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAnalyses using practice frequency as the exposure yielded similarly modest total associations with language outcomes (\u0026beta;\u0026thinsp;=\u0026thinsp;0.313 for crystallized cognition, 0.473 for picture vocabulary, and 0.218 for oral reading recognition; Table 4). Indirect effects through global cortical surface area or volume did not reach statistical significance (all indirect q\u0026thinsp;\u0026asymp;\u0026thinsp;0.087\u0026ndash;0.089). No evidence of mediation was observed for cortical thickness or sulcal depth in frequency-based models (all q\u0026thinsp;\u0026ge;\u0026thinsp;0.837).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity Analyses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSensitivity analyses conducted in the twin subsample showed patterns broadly consistent with those observed in the full cohort (\u003cstrong\u003eeTable 5\u003c/strong\u003e). Sustained music participation remained positively associated with crystallized cognition, picture vocabulary, and oral reading recognition, with larger point estimates than in the full sample. Mediation analyses indicated that global cortical surface area and volume statistically accounted for a significant proportion of the associations for crystallized cognition and picture vocabulary. For crystallized cognition, the proportion mediated was 16.0% for surface area and 15.9% for volume; for picture vocabulary, 10.1% and 10.2%, respectively. For oral reading recognition, indirect effects via surface area and volume were also observed (indirect q\u0026thinsp;=\u0026thinsp;0.047), corresponding to proportions mediated of approximately 13%; however, these effects were supported by weaker statistical evidence than those observed for crystallized cognition and picture vocabulary. For sustained music participation, cortical thickness and sulcal depth showed no evidence of mediation across outcomes (all q\u0026thinsp;\u0026ge;\u0026thinsp;0.655). In the twin subsample, analyses using practice intensity and practice frequency as exposures did not demonstrate statistically significant associations with language outcomes, nor evidence of mediation by any cortical structural measure.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eApproximately 43% of children in the analytic sample engaged in sustained music participation at baseline. Consistent with prior population-based studies, children who participated in music activities were more likely to come from families with greater socioeconomic resources and to engage in other extracurricular activities, including sports and creative arts, while behavioral and emotional characteristics were largely similar between music participants and nonparticipants. These patterns provide important context for interpreting observed associations (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSustained music participation in early adolescence was associated with modestly higher language performance two years later across crystallized cognition, picture vocabulary, and oral reading recognition. These associations were estimated using models that adjusted for baseline language performance as well as a comprehensive set of demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular covariates, indicating that the observed differences reflect residual longitudinal associations beyond prior performance and measured confounders rather than cross-sectional differences. While modest in magnitude, the observed effect sizes are consistent with those commonly reported for educational and extracurricular exposures in population-based studies, suggesting that such differences, though small, may still have meaningful implications at the population level during adolescence, a period of rapid cognitive and educational development (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e Mediation analyses indicated that global cortical surface area and cortical volume accounted for a small but statistically significant proportion of the associations between sustained music participation and language outcomes, whereas cortical thickness and sulcal depth did not show evidence of mediation. These findings are consistent with prior work suggesting that measures related to cortical size (surface area and volume) and measures related to cortical shape or refinement (thickness and sulcal depth) reflect distinct dimensions of cortical organization, rather than overlapping morphometric properties (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The observed mediation for cortical volume likely reflects variance shared with surface area-related features, with limited contribution from thickness-related processes (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). The modest magnitude of mediation indicates partial accounting rather than a dominant explanatory pathway, suggesting that additional neural or experiential factors not indexed by global cortical morphometry may contribute to the observed associations.\u003c/p\u003e \u003cp\u003e Analyses examining practice intensity and practice frequency as secondary exposure measures yielded smaller total associations with language outcomes than those observed for sustained music participation. In the full sample, mediation by global cortical surface area and volume was observed for practice intensity but not for practice frequency, while no evidence of mediation was detected for cortical thickness or sulcal depth. These findings suggest that different dimensions of music engagement may relate differently to language outcomes and underlying brain structure, with sustained participation showing the most consistent associations.\u003c/p\u003e \u003cp\u003eSensitivity analyses conducted in a twin subsample showed patterns broadly consistent with those observed in the full cohort. Associations between sustained music participation and crystallized cognition and picture vocabulary remained positive, with larger mediated proportions for surface area and volume than in the full sample. In contrast, mediation effects for oral reading recognition were weaker and less robust, although total associations remained positive. These differences may reflect reduced residual confounding due to shared familial factors, limited statistical power in the smaller twin subsample, or outcome-specific neural correlates of reading-related skills (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed associations support the theoretical framework of a mediation model where music participation influences brain structure, specifically cortical surface area and volume, which then contribute to language outcomes. This model aligns with the shared sound-processing hypothesis, which suggests overlapping neural mechanisms between music and language processing (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). However, it is important to interpret these findings as statistical associations rather than direct evidence of causal pathways, as the observed effects may be influenced by additional factors not captured in the current study.\u003c/p\u003e \u003cp\u003eThis study extends prior research linking music engagement to language development by providing, to our knowledge, the first large-scale longitudinal investigation of multiple dimensions of music participation and global cortical structural mediators within a population-based cohort. Key strengths include the prospective design with temporally ordered assessment of music participation, brain structure, and language outcomes; explicit adjustment for baseline language performance; comprehensive control for demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular factors; the use of a twin subsample for sensitivity analyses to evaluate robustness to shared familial influences; and mixed-effects modeling with correction for multiple testing. These design features strengthen confidence in the observed associations while appropriately avoiding assumptions of causality.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, music participation was assessed via parent report, which may be subject to measurement error. Second, analyses focused intentionally on global cortical structural measures, which are well suited to capturing distributed neural variation at the population level. As a result, we did not examine regional cortical features, white matter microstructure, or functional connectivity, which may capture more localized or network-level associations relevant to language development and should be examined in future work. Third, while this study utilizes the ABCD Release 3.0 dataset, chosen for its comprehensive baseline coverage and high data density, newer releases (e.g., Release 6.0) now offer extended longitudinal follow-up. It is important to note, however, that later waves in longitudinal cohorts are inherently subject to greater participant attrition and potential missingness. By utilizing Release 3.0, we prioritized sample stability during the early neurodevelopmental window. Future research should leverage subsequent time points to evaluate the long-term developmental trajectory of these brain-behavior associations while carefully accounting for the increased complexity of missing data in later years. Finally, despite extensive covariate adjustment, residual confounding and selection bias remain possible, and mediation analyses in observational data do not establish causal mechanisms. Future studies should investigate the durability of these associations across later developmental stages and explore whether specific forms or contexts of music engagement are differentially related to neurodevelopmental and educational outcomes.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this large, population-based longitudinal cohort, sustained music participation in early adolescence was associated with modestly higher language performance two years later, even after conditioning on baseline language ability and adjusting for a comprehensive set of demographic, socioeconomic, prenatal and perinatal, behavioral, and extracurricular covariates. By explicitly accounting for baseline performance, these findings support interpretation of the observed associations as residual prospective differences in language outcomes rather than simple cross-sectional correlations. Mediation analyses indicated that global cortical surface area and volume statistically accounted for a small proportion of these associations, whereas cortical thickness and sulcal depth did not, indicating partial accounting and placing quantitative limits on the extent to which global cortical morphometry contributes to music-language associations. Sensitivity analyses in a twin subsample yielded broadly similar patterns, supporting the robustness of these findings under more stringent control for shared familial factors without implying causal or genetic inference. These results align with prior large-scale longitudinal, meta-analytic, and genetically informed studies showing that associations between music participation and language outcomes are modest in magnitude and partially attributable to pre-existing individual and familial differences. At the same time, this study extends the literature by providing a rigorous, population-level test of prospective associations conditioned on baseline ability, and by directly evaluating the contribution of global cortical structure within a brain-behavior mediation framework. Future randomized or quasi-experimental studies incorporating multimodal neuroimaging will be important for clarifying more specific neural pathways through which music engagement may relate to language development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConceptualization: A.W., J.D.\u003cbr\u003e\u0026nbsp;Data curation: A.W., Z.L., J.W.\u003cbr\u003e\u0026nbsp;Formal analysis: A.W., M.A., Z.L.,J.W., J.D.\u003cbr\u003e\u0026nbsp;Methodology: A.W., M.A., J.W., J.D.\u003cbr\u003e\u0026nbsp;Project administration: Z.L., J.A., J.D.\u003cbr\u003e\u0026nbsp;Resources: Z.L., J.D.\u003cbr\u003e\u0026nbsp;Supervision: J.D.\u003cbr\u003e\u0026nbsp;Validation: A.W., M.A., Z.L., J.W., \u0026nbsp;J.D.\u003cbr\u003e\u0026nbsp;Visualization: A.W., Z.L., J.W., J.D.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; original draft: A.W.\u003cbr\u003e\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing: A.W., M.A., Z.L., J.W., J.A., C.B.C., J.D.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding Declaration \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any funding.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are from the Adolescent Brain Cognitive Development (ABCD) Study and are available through the National Institute of Mental Health Data Archive (NDA; https://nda.nih.gov) under controlled access. Researchers may obtain access to the ABCD data by submitting a data use request to the NDA and complying with the ABCD data use agreements.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNeves, L., Correia, A. I., Castro, S. L., Martins, D. \u0026amp; Lima, C. F. Does music training enhance auditory and linguistic processing? A systematic review and meta-analysis of behavioral and brain evidence. \u003cem\u003eNeurosci. Biobehav Rev.\u003c/em\u003e \u003cb\u003e140\u003c/b\u003e, 104777 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePino, M. C., Giancola, M. \u0026amp; D'Amico, S. The Association between Music and Language in Children: A State-of-the-Art Review. \u003cem\u003eChild. (Basel)\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e (5), 801 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchellenberg, E. G. Music lessons enhance IQ. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (8), 511\u0026ndash;514 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSlater, J. et al. Longitudinal effects of group music instruction on literacy skills in low-income children. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (11), e113383 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFran\u0026ccedil;ois, C., Chobert, J., Besson, M. \u0026amp; Sch\u0026ouml;n, D. Music training for the development of speech segmentation. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e23\u003c/b\u003e (9), 2038\u0026ndash;2043 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno, S. et al. Musical training influences linguistic abilities in 8-year-old children: more evidence for brain plasticity. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e19\u003c/b\u003e (3), 712\u0026ndash;723 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchellenberg, E. G. \u0026amp; Lima, C. F. Music Training and Nonmusical Abilities. \u003cem\u003eAnnu. Rev. Psychol.\u003c/em\u003e \u003cb\u003e75\u003c/b\u003e, 87\u0026ndash;128 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoreno, S. et al. Short-term music training enhances verbal intelligence and executive function. \u003cem\u003ePsychol. Sci.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e (11), 1425\u0026ndash;1433 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSala, G. \u0026amp; Gobet, F. Cognitive and academic benefits of music training with children: A multilevel meta-analysis. \u003cem\u003eMem. Cognit\u003c/em\u003e. \u003cb\u003e48\u003c/b\u003e (8), 1429\u0026ndash;1441 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHille, K., Gust, K., Bitz, U. \u0026amp; Kammer, T. Associations between music education, intelligence, and spelling ability in elementary school. \u003cem\u003eAdv. Cogn. Psychol.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 1\u0026ndash;6 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWesseldijk, L. W., Gordon, R. L., Mosing, M. A. \u0026amp; Ull\u0026eacute;n, F. Music and verbal ability - a twin study of genetic and environmental associations. \u003cem\u003ePsychol. Aesthet. Creat Arts\u003c/em\u003e. \u003cb\u003e17\u003c/b\u003e (6), 675\u0026ndash;681 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabibi, A., Hsu, E., Villanueva, J. \u0026amp; Luo, S. Longitudinal Effects of Continuous Music Training on Cognitive Development: Evidence From the Adolescent Brain Cognitive Development (ABCD) Study. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1553\u003c/b\u003e (1), 283\u0026ndash;299 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHyde, K. L. et al. Musical training shapes structural brain development. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e29\u003c/b\u003e (10), 3019\u0026ndash;3025 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerholz, S. C. \u0026amp; Zatorre, R. J. Musical training as a framework for brain plasticity: behavior, function, and structure. \u003cem\u003eNeuron\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e (3), 486\u0026ndash;502 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraus, N. \u0026amp; Chandrasekaran, B. Music training for the development of auditory skills. \u003cem\u003eNat. Rev. Neurosci.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (8), 599\u0026ndash;605 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabibi, A., Cahn, B. R., Damasio, A. \u0026amp; Damasio, H. Neural correlates of accelerated auditory processing in children engaged in music training. \u003cem\u003eDev. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 1\u0026ndash;14 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabibi, A., Damasio, A., Ilari, B., Elliott Sachs, M. \u0026amp; Damasio, H. Music training and child development: a review of recent findings from a longitudinal study. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlaug, G., Norton, A., Overy, K. \u0026amp; Winner, E. Effects of music training on the child's brain and cognitive development. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1060\u003c/b\u003e, 219\u0026ndash;230 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePapatzikis, E., Agapaki, M., Selvan, R. N., Pandey, V. \u0026amp; Zeba, F. Quality standards and recommendations for research in music and neuroplasticity. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1520\u003c/b\u003e (1), 20\u0026ndash;33 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolkow, N. D. et al. The conception of the ABCD study: From substance use to a broad NIH collaboration. \u003cem\u003eDev. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 4\u0026ndash;7 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaravan, H. et al. Recruiting the ABCD sample: Design considerations and procedures. \u003cem\u003eDev. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 16\u0026ndash;22 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNohr, E. A. \u0026amp; Liew, Z. How to investigate and adjust for selection bias in cohort studies. \u003cem\u003eActa Obstet. Gynecol. Scand.\u003c/em\u003e \u003cb\u003e97\u003c/b\u003e (4), 407\u0026ndash;416 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittle, R. J. A. \u0026amp; Rubin, D. B. \u003cem\u003eStatistical Analysis with Missing Data\u003c/em\u003e 3rd edn (Wiley, 2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGershon, R. C. et al. Language measures of the NIH Toolbox Cognition Battery. \u003cem\u003eJ. Int. Neuropsychol. Soc.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (6), 642\u0026ndash;651 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkshoomoff, N. et al. VIII. NIH Toolbox Cognition Battery (CB): composite scores of crystallized, fluid, and overall cognition. \u003cem\u003eMonogr. Soc. Res. Child. Dev.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e (4), 119\u0026ndash;132 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeintraub, S. et al. Cognition assessment using the NIH Toolbox. \u003cem\u003eNeurology\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e (11 Suppl 3), S54\u0026ndash;64 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaser, C. \u0026amp; Schlaug, G. Brain structures differ between musicians and non-musicians. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (27), 9240\u0026ndash;9245 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBahar, N. et al. Differences in Cortical Surface Area in Developmental Language Disorder. \u003cem\u003eNeurobiol. Lang. (Camb)\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e (2), 288\u0026ndash;314 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeretz, I., Vuvan, D., Lagrois, M. \u0026amp; Armony, J. L. Neural overlap in processing music and speech. \u003cem\u003ePhilos. Trans. R Soc. Lond. B Biol. Sci.\u003c/em\u003e \u003cb\u003e370\u003c/b\u003e (1664), 20140090 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTe Rietmolen, N., Mercier, M. R., Tr\u0026eacute;buchon, A., Morillon, B. \u0026amp; Sch\u0026ouml;n, D. Speech and music recruit frequency-specific distributed and overlapping cortical networks. \u003cem\u003eElife\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, RP94509 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchnack, H. G. et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. \u003cem\u003eCereb. Cortex\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e (6), 1608\u0026ndash;1617 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCox, S. R., Ritchie, S. J., Fawns-Ritchie, C., Tucker-Drob, E. M. \u0026amp; Deary, I. J. Structural brain imaging correlates of general intelligence in UK Biobank. \u003cem\u003eIntelligence\u003c/em\u003e \u003cb\u003e76\u003c/b\u003e, 101376 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaznahan, A. et al. How does your cortex grow? \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e (19), 7174\u0026ndash;7177 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWierenga, L. M., Langen, M., Oranje, B. \u0026amp; Durston, S. Unique developmental trajectories of cortical thickness and surface area. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cb\u003e87\u003c/b\u003e, 120\u0026ndash;126 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhamala, E. et al. Proportional intracranial volume correction differentially biases behavioral predictions across neuroanatomical features, sexes, and development. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cb\u003e260\u003c/b\u003e, 119485 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGennatas, E. D. et al. Age-Related Effects and Sex Differences in Gray Matter Density, Volume, Mass, and Cortical Thickness from Childhood to Young Adulthood. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (20), 5065\u0026ndash;5073 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeier, A., Hartmann, B. S. \u0026amp; Larson, R. A Quarter Century of Participation in School-Based Extracurricular Activities: Inequalities by Race, Class, Gender and Age? \u003cem\u003eJ. Youth Adolesc.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e (6), 1299\u0026ndash;1316 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRakesh, D., Zalesky, A. \u0026amp; Whittle, S. Assessment of Parent Income and Education, Neighborhood Disadvantage, and Child Brain Structure. \u003cem\u003eJAMA Netw. Open.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (8), e2226208 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiksza, P. Music Participation and Socioeconomic Status as Correlates of Change: A Longitudinal Analysis of Academic Achievement. \u003cem\u003eBull. Council Res. Music Educ.\u003c/em\u003e (172):41\u0026ndash;58. (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKelly, C. E. et al. Cortical growth from infancy to adolescence in preterm and term-born children. \u003cem\u003eBrain\u003c/em\u003e \u003cb\u003e147\u003c/b\u003e (4), 1526\u0026ndash;1538 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Marroun, H. et al. Prenatal tobacco exposure and brain morphology: a prospective study in young children. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e (4), 792\u0026ndash;800 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchenbach, T. M. \u0026amp; Rescorla, L. A. \u003cem\u003eManual for the ASEBA School-Age Forms \u0026amp; Profiles\u003c/em\u003e (University of Vermont, Research Center for Children, Youth, \u0026amp; Families, 2001).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrosnoe, R., Smith, C. \u0026amp; Leventhal, T. Family Background, School-Age Trajectories of Activity Participation, and Academic Achievement at the Start of High School. \u003cem\u003eAppl. Dev. Sci.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (3), 139\u0026ndash;152 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasey, B. J. et al. The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. \u003cem\u003eDev. Cogn. Neurosci.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 43\u0026ndash;54 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohen, J. \u003cem\u003eStatistical Power Analysis for the Behavioral Sciences\u003c/em\u003e 2nd edn (Lawrence Erlbaum Associates, 1988).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodgers, J. L. \u0026amp; Nicewander, W. A. Thirteen Ways to Look at the Correlation Coefficient. \u003cem\u003eAm. Stat.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (1), 59\u0026ndash;66 (1988).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinheiro, J. \u0026amp; Bates, D. \u003cem\u003eMixed-Effects Models in S and S-PLUS\u003c/em\u003e (Springer, 2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenjamini, Y. \u0026amp; Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. \u003cem\u003eJ. Royal Stat. Soc. Ser. B (Methodological)\u003c/em\u003e. \u003cb\u003e57\u003c/b\u003e (1), 289\u0026ndash;300 (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeper, J. S., Brouwer, R. M., Boomsma, D. I., Kahn, R. S. \u0026amp; Hulshoff Pol, H. E. Genetic influences on human brain structure: a review of brain imaging studies in twins. \u003cem\u003eHum. Brain Mapp.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (6), 464\u0026ndash;473 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeabold, S., Perktold, J. \u0026amp; Statsmodels Econometric and statistical modeling with Python. Proceedings of the 9th Python in Science Conference. Austin, TX: SciPy; pp. 92\u0026thinsp;\u0026ndash;\u0026thinsp;6. (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorrigall, K. A. \u0026amp; Schellenberg, E. G. Predicting who takes music lessons: parent and child characteristics. \u003cem\u003eFront. Psychol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 282 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKraft, M. Interpreting effect sizes of education interventions. \u003cem\u003eEducational Researcher\u003c/em\u003e. \u003cb\u003e49\u003c/b\u003e (4), 241\u0026ndash;253 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWinkler, A. M. et al. Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. \u003cem\u003eNeuroimage\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e (3), 1135\u0026ndash;1146 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarek, S. et al. Reproducible brain-wide association studies require thousands of individuals. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e603\u003c/b\u003e (7902), 654\u0026ndash;660 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYeatman, J. D., Dougherty, R. F., Ben-Shachar, M. \u0026amp; Wandell, B. A. Development of white matter and reading skills. \u003cem\u003eProc. Natl. Acad. Sci. U S A\u003c/em\u003e. \u003cb\u003e109\u003c/b\u003e (44), E3045\u0026ndash;E3053 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel, A. D. The OPERA hypothesis: assumptions and clarifications. \u003cem\u003eAnn. N Y Acad. Sci.\u003c/em\u003e \u003cb\u003e1252\u003c/b\u003e, 124\u0026ndash;128 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel, A. D. \u003cem\u003eMusic, Language, and the Brain\u003c/em\u003e (Oxford University Press, 2008).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2 and 4","content":"\u003cp\u003eTable 2 and 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Music participation, Language development, Cortical structure, Mediation analysis, Adolescent Brain Cognitive Development (ABCD) study, Longitudinal study","lastPublishedDoi":"10.21203/rs.3.rs-8664008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8664008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAssociations between music participation and language outcomes have been widely reported, yet the extent to which brain structure statistically accounts for these relationships remains unclear, particularly at the population level. Using longitudinal data from the population-based Adolescent Brain Cognitive Development (ABCD) Study, we examined prospective associations between music participation at ages 9\u0026ndash;10 and language outcomes two years later and tested whether global cortical morphometry accounted for these associations. The analytic sample included 5,993 children (baseline mean age 10.0 years; 47.1% female), including a twin subsample of 936 participants. Music participation was assessed via parent report of sustained participation, frequency, and intensity. Language outcomes were measured using NIH Toolbox assessments of picture vocabulary, oral reading recognition, and crystallized cognition. Cortical morphometry was quantified across multiple metrics, including global surface area, volume, thickness, and sulcal depth. Longitudinal associations were estimated using linear mixed-effects models, with mediation analyses conducted to quantify the indirect effect of cortical morphometry after adjusting for baseline language performance, sociodemographic factors, and relevant covariates. Sustained music participation was associated with higher crystallized cognition, picture vocabulary, and oral reading recognition scores two years later. Mediation analyses indicated that global cortical surface area and volume, but not cortical thickness or sulcal depth, statistically accounted for a modest proportion of these longitudinal associations (approximately 5\u0026ndash;9%). Analyses of practice intensity showed weaker total associations with language outcomes but proportionally greater mediation by surface area and volume, whereas practice frequency exhibited minimal associations and no evidence of mediation. Sensitivity analyses in the twin subsample yielded qualitatively similar patterns, with larger mediated proportions observed for crystallized cognition and picture vocabulary. These findings suggest that global cortical morphometry explains a limited but reproducible component of the longitudinal association between music participation and language outcomes in population-based developmental samples.\u003c/p\u003e","manuscriptTitle":"Global cortical morphometry as a mediator of longitudinal associations between music participation and language outcomes in a population-based cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 16:03:56","doi":"10.21203/rs.3.rs-8664008/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b5471e74-d459-4add-bbed-b5ab15427c5b","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62095125,"name":"Biological sciences/Neuroscience"},{"id":62095126,"name":"Biological sciences/Psychology"},{"id":62095127,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-03-19T04:25:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 16:03:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8664008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8664008","identity":"rs-8664008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}