Music as a Moderator of Socioeconomic Disparities in Preadolescent Cognitive and Academic Performance: Evidence from the Healthy Brain Network Biobank

Music as a Moderator of Socioeconomic Disparities in Preadolescent Cognitive and Academic Performance: Evidence from the Healthy Brain Network Biobank | 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 Music as a Moderator of Socioeconomic Disparities in Preadolescent Cognitive and Academic Performance: Evidence from the Healthy Brain Network Biobank Nicole O’Malley, Michelle Lim, Janis Gaudreau, Christine M. Clarkin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8166352/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 This study aimed to investigate the moderating effects of music participation on socioeconomic status (SES) related disparities on cognitive and academic performance during preadolescence. Neurotypical children (N = 94, ages 9–12; 42% female) from the Healthy Brain Network were analyzed. SES was categorized using a composite index based on Bureau of Justice Statistics guidelines (low, middle, high). After-school music participation was parent-reported. Cognitive and academic performance were measured using the NIH Toolbox Cognition Battery (NIHTB-Cog), Wechsler Intelligence Scale for Children Fifth Edition (WISC-V), and Wechsler Individual Achievement Test Third Edition (WIAT). Benjamini-Hochberg adjusted linear regressions analyzed the moderating effects of music on SES and cognitive/academic performance. Moderation analyses revealed music participation strengthened performance in WISC-V processing speed for the middle SES group (β = 61.80, p = .04) and high SES group (β = 64.26, p = .02), and in WIAT numerical operations for high SES children (β = 51.96, p = .05). After-school music participation may moderate SES-related differences in processing speed and numerical operations among middle- and high SES preadolescents. Benefits were absent for low SES groups, suggesting structural barriers to enrichment access. Integrating structured music programs within school and community settings may help reduce SES-related disparities in cognitive and academic development. Health sciences/Health care Biological sciences/Neuroscience Biological sciences/Psychology Social science/Psychology Preadolescence Socioeconomic Status Music Cognition Academic Performance Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Preadolescence, spanning from ages 9 to 12, marks a critical developmental window during the transition from childhood into adolescence [ 1 ]. This stage is characterized by heightened neuroplasticity combined with rapid growth in executive, language, and academic skills [ 2 ]. This convergence of biological sensitivity and expansion of cognitive demands offers a unique opportunity to introduce enrichment strategies that reinforce and extend developmental gains. After-school programs in particular offer a structured environment that can consistently support cognitively enriching experiences during this stage [ 3 – 5 ]. Within this context, Positive Childhood Experiences (PCEs), which include nurturing, engaging, and developmentally supportive activities, play a critical role in promoting attention, memory, and learning [ 6 ]. Music participation is one example of such an experience offering structured, multisensory engagement that promotes attention, memory, and learning [ 7 – 9 ] while also supporting social and emotional development consistent with PCE goals. As a structured and cognitively engaging activity, music supports attention, memory, and learning, and aligns with the goals of PCEs by fostering emotional regulation, persistence, and social connection, especially during childhood. A growing body of research highlights music as a promising enrichment strategy for cognitive development [ 9 – 12 ]. Longitudinal and experimental studies demonstrate that structured music engagement enhances executive function, working memory, and language skills [ 7 – 8 , 13 ]. Patel’s OPERA hypothesis suggests that music training strengthens auditory and attentional networks through Overlap, Precision, Emotion, Repetition, and Attention, thereby improving processing speed and language-related skills [ 14 ]. Rhythm-based experiences further support neural synchrony, which supports executive functions and reading development [ 15 ]. Neuroimaging studies demonstrate that music training strengthens networks involved in memory, attention, and executive function; systems that support cognitive performance and may also play a role in maintaining neural efficiency as they mature during preadolescence [ 16 – 17 ]. Despite its benefits, access to such enrichment is not equitably distributed across socioeconomic groups [ 18 ]. Because socioeconomic status remains one of the strongest predictors of both cognitive and academic performance, limited access to music programs risks widening existing disparities [ 4 , 19 ]. By kindergarten entry, children from low SES backgrounds score up to one standard deviation lower on measures of executive function and language, with similar disparities persisting into adolescence [ 4 ]. These deficits are particularly concerning given the foundational role of executive function, working memory, and language in cognitive and academic performance [ 20 ]. SES-related disadvantages extend beyond behavioral performance to brain morphology. Specifically, structural MRI studies reveal that children from low-income backgrounds tend to exhibit larger differences in surface area, mostly in structural regions associated with language, reading, and executive function, compared to children from high-income families [ 21 ]. Similarly, behavioral differences in preadolescent language ability related to SES, as well as structural differences, such that lower SES was associated with smaller volumes of grey matter in areas related to memory performance (bilateral hippocampi, middle temporal gyri, left fusiform and right inferior occipito-temporal gyri) [ 22 ]. Furthermore, childhood poverty, measured by family income and adjusted for family size as a percentage of the federal poverty level (FPL), has been associated with structural differences in several areas of the brain related to school readiness skills [ 5 ]. Such disparities highlight the extent to which socioeconomic factors contribute to both brain and behavioral development. The inequity associated with socioeconomic disadvantage and brain development is particularly concerning, given the potential of music to support cognitive and academic development during preadolescence. Notably, over 3 million U.S. public school students lack access to music instruction, with gaps disproportionately affecting low-income communities [ 18 ]. When children from socioeconomically disadvantaged backgrounds are excluded from enrichment opportunities, it limits their developmental potential and may exacerbate existing disparities in learning and achievement [ 23 – 25 ]. These domain-specific vulnerabilities that span attention, memory, verbal fluency, and processing speed highlight the urgency of identifying scalable enrichment strategies that can mitigate the cognitive and academic impact of socioeconomic disadvantage and promote more equitable developmental outcomes. The intersection between early cognitive vulnerability and limited access to enrichment presents a critical opportunity for investigation. Research on such experience highlights their role in strengthening academic and cognitive development during adolescence [ 6 ]. However, despite growing evidence that music participation enhances executive function, language, and memory, most studies have examined music as a mediator or direct predictor of academic and cognitive outcomes [ 7 , 9 – 10 , 13 ]. Few studies have explored whether music engagement can moderate the relationship between SES and developmental performance, despite evidence that SES strongly influences brain structure and cognitive outcomes [ 4 – 5 , 21 ] and that music training enhances cognitive skills [ 9 – 11 ]This gap is especially important given the persistent inequities in access to music education and the potential of music to serve as a scalable strategy that promotes more equitable developmental outcomes [ 9 – 11 ]. Furthermore, prior studies have used a wide range of metrics to isolate the effects of SES, ranging from single to trichotomous indexes that include household income, parents’ education, parents’ occupation, zip code, and free-or-reduced price lunches. Consequently, a standardized metric of SES is needed. Understanding whether music can attenuate SES-related disparities in cognitive and academic domains remains an open and urgent area of inquiry. To address persistent gaps in enrichment access [ 16 , 26 – 27 ] and explore music’s potential to reduce SES-related cognitive disparities [ 4 – 5 , 21 ], the present study examined whether after-school music participation moderates the associations between a standardized SES metric and cognitive and academic performance. To our knowledge, this is the first study to examine this moderating effect in preadolescent children. By leveraging a large, well-characterized dataset and standardized SES measures, this work aims to clarify whether music engagement can mitigate the potential negative effects of socioeconomic disadvantage. We hypothesized that children from lower SES backgrounds would exhibit the lowest performance across cognitive and academic domains, consistent with prior research showing persistent SES-related disparities in executive function, language, and achievement. We also expected that children from lower SES backgrounds have reduced access to after-school music programs, reflecting broader inequities in enrichment opportunities. We proposed that music participation may attenuate the negative association between SES and cognitive and academic performance, such that music participation strengthens outcomes for children with greater access, offering a scalable strategy to promote equity in developmental outcomes. 2. Methods 2.1. Procedures The present study utilized data from the Healthy Brain Network (HBN), an open-access dataset collected by the Child Mind Institute (CMI). Between 2015 and 2021, participants were recruited from New York City communities and neighborhoods [ 28 ]. Recruitment strategies included participation in local events, outreach via digital and print media, engagement with news outlets, and collaboration with educational institutions, healthcare providers, and community-based organizations [ 28 ]. All study procedures were reviewed and approved by the Chesapeake Institutional Review Board [ 28 ]. Data collection followed a structured protocol consisting of four sessions, each lasting approximately three hours [ 28 ]. The first session involved consent and assent procedures. Written informed consent was obtained from participants aged 18 and older, while child assent was collected in addition to consent from a primary caregiver, for children under 18. Participants completed questionnaires, a clinical intake interview, and baseline cognitive assessments [ 28 ]. The second session included magnetic resonance imaging (MRI), the third involved NIH Toolbox Cognition Battery (NIHTB-Cog) assessments and physical fitness evaluations, and the final session consisted of electroencephalography (EEG) [ 28 ]. For the purpose of the current study, only data from sessions one and three were included for analysis. To minimize potential confounding effects on behavioral and cognitive assessments, participants prescribed stimulant medications were asked to temporarily discontinue usage during study visits [ 28 ]. If discontinuation was not possible, medication use was documented on the day of testing [ 28 ]. 2.2. Participants A total of 4867 individuals between the ages of 5 and 21 were enrolled in the CMI HBN study. Eligibility criteria included fluency in English and the ability to complete study procedures. Spanish-speaking caregivers were accommodated when bilingual staff were available to assist with the consent process. Participants were excluded if they had (i) significant cognitive impairment (IQ < 66), (ii) acute encephalopathy, (iii) neurodegenerative conditions, (iv) uncorrected sensory impairments; (v) recent untreated diagnoses of schizophrenia, schizoaffective disorder, or bipolar disorder; (vi) recent onset of suicidality or homicidality without treatment; (vii) substance dependence requiring chemical replacement therapy; and (viii) under the influence of substances during study visits. For the current analysis, a subset of 94 participants was selected based on the absence of any psychiatric, developmental, or learning diagnosis. These individuals were confirmed as having no psychiatric, developmental or learning diagnoses through two sources: (i) the Consensus Diagnosis process, which integrates information from structured clinical interviews using the Kiddie Schedule for Affective Disorders and Schizophrenia; a semi-structured diagnostic tool designed to assess current and parent episodes of psychopathology in children and adolescents based on DSM-IV criteria [ 29 ], and (ii) parent-reported questionnaires indicating no history of psychiatric or learning disorders. This subset (N = 94) was used to examine academic and cognitive outcomes in a neurotypical sample. 2.3. Materials Phenotypic data used in this study were accessed following institutional approval and completion of a Data Usage Agreement by the Principal Investigator. Data retrieval was conducted through the Longitudinal Online Research and Imaging System (LORIS); a secure, web-based platform designed to support the management of behavioral data. All assessments were administered by, or under the supervision of, licensed clinicians, and questionnaires underwent validity checks to ensure data quality. 2.3.1. Demographics Demographic data included parent-reported age (derived at the date of study enrollment), sex (male/female), and race/ethnicity. Race was categorized using a standardized coding system: White/Caucasian, Black/African American, Hispanic, Asian, Indian, Native American Indian, American Indian/Alaskan Native, Native Hawaiian/Other Pacific Islander, multiracial, or other. Ethnicity was reported as either Hispanic/Latino or not Hispanic/Latino. 2.3.2. SES Index SES was standardized and derived using Index 1 of the Bureau of Justice Statistics (BJS) framework, which combined four components: education, household income, employment status, and housing status [ 30 ]. Education information was collected for both parents (or caregivers), which was assessed using the Barratt Simplified Measure of Social Status; a measure based on Hollingshead’s work that indexes educational attainment as a proxy for social status [ 31 ]. Income, housing status, and employment status were obtained from the Financial Support Questionnaire, which collects information on household income, types of public assistance, health insurance coverage, and employment status for both caregivers. Income was converted to a percentage of the FPL adjusted for household size [ 30 ]. Because the data were collected between 2015 and 2021, each participant was matched to the FPL guidelines for the corresponding year they participated in the study, reducing the risk of temporal mismatch and improving accuracy [ 32 ]. For income conversion, the midpoint of the reported income range was used when both boundaries were available. If only a lower boundary or upper boundary was provided, that boundary was used instead of the midpoint. The FPL percentage was calculated as: $$\:FPL\:Percentage\:=\:\left(\frac{Household\:Income}{FPL\:\:Guideline\:for\:Household\:Size}\right)\:\times\:100$$ Employment was coded based on responses from both caregivers: if both, or at least one caregiver was currently employed, the employment status was classified as employed; if neither caregiver was employed, the employment status was classified as not employed. Housing status was categorized by whether the residence was owned or rented. Each component was initially scored as follows: education and income status (scores 0–3), employment and housing status (scores 0–1), resulting in a composite SES score ranging from 0 to 8. Composite scores were then recategorized into eight ordinal levels: (1) 0 to less than 1, (2) 1 to less than 2, (3) 2 to less than 3, (4) 3 to less than 4, (5) 4 to less than 5, (6) 5 to less than 6, (7) 6 to less than 7, and (8) 7 to less than 8 [ 30 ]. The recategorized ordinal levels were consolidated into three SES groups: low SES (scores 1–3), middle SES (scores 4–6), and high SES (scores 7–8) [ 30 ]. See Table 1 for a visual summary of the SES classification process. Table 1 SES Classification Based on the BJS Framework. SES was derived using Index 1 of the BJS framework, which integrates education, federal poverty level percentage, employment status, and housing status (Berzofsky et al., 2015). Composite SES scores were consolidated into three SES groups: low (scores 1–3), middle (scores 4–6), and high (scores 7–8). Criteria Components Education 0: Less than 7th grade, Junior High/Middle School, Partial High School 1: High School Graduate, Partial College (at least one year) 2: College Education 3: Graduate Degree Income 0: ≤ 100% 1: 101% − 200% 2: 201% − 400% 3: ≥ 400% Employment 0: Both caregivers were not currently employed 1: Both, or at least one, caregivers were currently employed Housing 0: Rent 1: Own Total Composite Index 1: Scores 0 to less than 1 2: Scores 1 to less than 2 3: Scores 2 to less than 3 4: Scores 3 to less than 4 5: Scores 4 to less than 5 6: Scores 5 to less than 6 7: Scores 6 to less than 7 8: Scores 7 to less than 8 Group Category 1–3: Low SES 4–6: Middle SES 7–8: High SES 2.3.3. After-school music participation After-school music participation was assessed during the intake interview conducted by a clinician at the initial visit [ 28 ]. Within the education and social history section, parents were asked whether their child engaged in any extracurricular classes or lessons outside of school. Music was one of the listed options, and responses were coded dichotomously as “yes” or “no” for each activity [ 28 ]. For the current analysis, only music participation was examined, as our primary aim was to assess its potential moderating effect on cognitive and academic outcomes. Although some children may have participated in multiple activities, we did not include other extracurriculars in the analysis, and music participation was treated as a standalone variable. 2.3.4. Cognitive and Academic Performance Assessment Cognitive function was assessed using the two standardized instruments: the NIH Toolbox Cognition Battery (NIHTB-Cog) and the Wechsler Intelligence Scale for Children Fifth Edition (WISC-V). The NIHTB-Cog, designated for individuals aged 3 to 85, included the following subtests: Dimensional Change Card Sort (subdomain: cognitive flexibility and attention), Flanker Inhibitory Control and Attention (subdomain: inhibitory control), List Sorting Working Memory (subdomain: working memory), and Pattern Comparison Processing Speed (subdomain: processing speed) [ 33 ]. The WISC-V, administered to participants aged 6 to 17 years, included subtests representing five domains: Visual Spatial, Verbal Comprehension, Fluid Reasoning, Working Memory, and Processing Speed [ 34 ]. All scores were age-adjusted and calculated as national percentiles, with higher values indicating better cognitive performance [ 33 – 34 ]. Academic performance was assessed using the Wechsler Individual Achievement Test Third Edition (WIAT), a standardized measure for individuals aged 4 to 85 years [ 35 ]. The subtests were administered, representing the following subdomains: Numerical Operations, Pseudo-Word, Spelling, Word Reading, Listening Comprehension Receptive Vocabulary, Listening Comprehension Oral Discourse Comprehension, Listening Comprehension, Reading Comprehension, and Math Problem Solving [ 35 ]. Scores were reported as age-adjusted percentile ranks, with higher percentiles reflecting stronger academic performance [ 35 ]. 3. Statistical analysis All statistical analyses were conducted using R (version 4.3.1) with statistical significance set at p ≤ .05. To explore the distribution of after-school music participation across SES groups within the preadolescent sample, a chi-square test was conducted comparing SES classification (low, med, high) with after-school music participation status (Yes, No). This preliminary analysis provided descriptive insight into group-level differences in access to or engagement with music-based enrichment. Group-level differences in cognitive and academic performance across SES and music groups were assessed using the multivariate analyses of variance (MANOVA), with composite scores from the NIHTB-Cog, WIAT, and WISC-V serving as dependent variables. For MANOVA results reaching statistical significance, follow-up univariate ANOVAs were applied for SES group comparisons, and independent samples t-tests were used for music group comparisons to individual outcomes. Pairwise comparisons were performed using Tukey-adjusted procedures via the emmeans R package (version 1.10.0). Estimated marginal means and standard deviations were calculated for each SES group to support the interpretation of observed differences. Pearson correlation analyses were conducted to examine associations among SES groups, after-school music participation status, and standardized measures of cognitive and academic performance (NIHTB-Cog, WIAT, WISC-V). To test the central hypothesis that after-school music participation moderates the relationship between SES and cognitive and academic performances, moderation analyses were conducted using linear regression. In these models, the SES composite index served as the independent variable, standardized scores from the NIHTB-Cog, WISC-V, WIAT were the age-adjusted dependent variables, and after-school music participation was included as the moderating variable. By modeling SES as a categorical factor (low, middle, high), this approach allowed us to examine whether the effect of after-school music participation on cognitive and academic outcomes differed across SES levels. For categorical variables, low SES was selected as the reference category to facilitate meaningful comparisons and reflect the conceptually minimal level within the SES classification. This choice enabled a clearer interpretation of whether after-school music participation mitigates SES-related disparities in cognitive and academic outcomes. Unstandardized betas are reported. To account for multiple comparisons across outcome domains, p-values were adjusted using the Benjamini-Hochberg procedure, which controls the false discovery rate while maintaining statistical power; an appropriate choice given the exploratory nature of the moderation models and the number of cognitive and academic outcomes tested [ 36 ]. A post hoc power analysis was conducted using GPower (version 3.1.9.6) to evaluate the sensitivity of the primary statistical models given the final sample size of N = 94. For the MANOVA models assessing SES group differences across three cognitive domains, assuming a medium effect size (f²=0.0625), α = .05, and three SES groups, the achieved power was calculated to be 0.99. For the moderation analyses, assuming a medium effect size (f²=0.15), α = .05, and three predictors, the achieved power was 0.92. These powers exceed the conventional threshold of 0.80, confirming sufficient sensitivity to detect group-level and interaction effects. 4. Results The final analytic sample included 94 preadolescent participants, aged 9-12.99 years (42.6% female; ages 10.69 ± 1.08 years), distributed across three SES groups based on a composite index: low SES (N = 11; 36.4% female; ages 10.19 ± 1.05 years), middle SES (N = 23; 47.8% female; ages 10.88 ± 0.99 years), and high SES (N = 60; 41.7% female; ages 10.71 ± 1.10 years). See Table 2 for demographic information of each low, middle, and high SES group. Table 2 Demographic Characteristics of Preadolescents by SES. Sample size and demographic characteristics of the preadolescent sample, stratified by SES groups: low, middle, and high. Low SES Middle SES High SES Sample Size 11 23 60 Sex Male 7 12 35 Female 4 11 25 Ethnicity Not Hispanic or Latino 4 16 50 Hispanic or Latino 6 4 7 Unknown 1 3 3 Race White/Caucasian 3 11 38 Black/African American 0 3 3 Hispanic 2 2 3 Asian 0 1 3 Indian 0 1 0 Native American Indian 0 0 0 American Indian/Alaskan Native 0 0 0 Native Hawaiian/Other Pacific Islander 0 0 0 Two or more races 3 3 9 Other race 1 0 1 Unknown 2 2 3 After-school Music Participation Yes 1 7 10 No 10 16 49 Unknown 0 0 1 Education Less than High School 3 0 0 Highschool Graduate 8 9 4 College Education 0 13 35 Graduate Degree 0 1 21 Income ≤ 100% 4 0 0 101% − 200% 2 2 0 201% − 400% 0 8 8 ≥ 400% 0 2 52 Unknown 5 11 0 Employment Not Employed 2 0 0 Employed 8 23 60 Unknown 1 0 0 Housing Rent 10 9 14 Own 0 11 46 Unknown 1 3 0 4.1. Distribution of After-school Music Participation on SES Rates of after-school music participation were examined across SES groups. Specifically, 9.09% of low, 30.43% of middle, and 16.96% of high SES participants reported engaging in music-based enrichment activities outside of school. A chi-square test of independence indicated no significant association between SES and participation in after-school music opportunities program, χ²(2) = 2.77, p = .25. 4.2. Performance Differences by SES Disparities 4.2.1. SES Disparities on Cognitive Function. Significant SES group differences were observed in the NIHTB-Cog outcomes (F(8,172) = 3.10, p = .003, Wilks’ Λ = .76) and WISC-V outcomes ( F (10,174) = 3.37, p < .001, Wilks’ Λ = .70) Tukey-adjusted post hoc comparisons revealed that children from the low SES (31.36 ± 21.76) scored significantly lower than middle (64.41 ± 25.96, t(89)= -3.50, p = .001 and high SES peers (60.07 ± 26.00, t(89)=-3.42, p = .003 on the NIHTB-Cog list sort task. For the pattern comparison task, children from low SES (32.64 ± 22.70) scored significantly lower than those from middle SES (64.50 ± 29.45), t(89)=-2.73, p = .02. For WISC-V Fluid Reasoning, low SES (24.18 ± 26.16) scored significantly lower than middle (59.87 ± 24.26; t(91) =-3.71; p = .001) and high SES (64.17 ± 26.92; t(91)=-4.65; p < .001). For WISC-V Visual Spatial, low SES (30.64 ± 31.59) scored lower than middle (62.83 ± 30.57; t(91) =-2.91, p = .01) and high SES (63.37 ± 29.73; t(91)=-3.31, p = .004). WISC-V Verbal Comprehension also showed significant differences with low SES (43.45 ± 26.19) scoring lower than middle (67.70 ± 21.53; t(91)=-3.14, p = .01) and high SES (71.34 ± 19.91; t(91)=-4.03; p < .001). For WISC-V Working Memory, low SES (22.45 ± 23.23) scored lower than middle (59.91 ± 25.06; t(91)=-3.89, p < .001) and high SES (65.25 ± 27.20; t(91)=-4.97; p < .001). See Fig. 1 for visualizations of significant post-hoc comparisons in cognitive performance. 4.2.2. SES Disparities on Academic Performance. Significant SES group differences were observed in the WIAT ( F (8,162) = 2.22, p = .005, Wilks’ Λ = .64).. For WIAT Listening Comprehension, low SES (45.45 ± 35.02) scored lower than high SES (68.56 ± 24.88; t(89)=-2.67, p = .02). For WIAT Math Problem Solving, low SES (25.27 ± 29.07) scored lower than middle (65.81 ± 25.42; t(89)=-4.59, p < .001) and high SES (60.50 ± 29.64; t(89)=-3.55, p = .002). WIAT Numerical Operations showed similar differences, with low SES (28.55 ± 28.14) scoring lower than middle (65.59 ± 22.82; t(89)=-4.15, p < .001) and high SES (68.07 ± 23.91; t(89)=-4.98, p < .001). WIAT Pseudo-word Decoding also differed significantly, with low SES (39.55 ± 18.87) scoring lower than middle (64.05 ± 19.27; t(89)=-3.37, p = .003) and high SES (68.53 ± 19.95; t(89)=-4.49, p < .001). For WIAT Reading Comprehension, low SES (43.00 ± 21.35) scored lower than middle (66.86 ± 18.94; t(89)=-3.07, p = .01) and high SES (68.27 ± 21.74; t(89)=-3.65, p = .001). WIAT Spelling also differed significantly, with low SES (30.55 ± 21.94) scoring lower than middle (65.50 ± 28.75; t(89)=-3.65, p = .001) and high SES (67.10 ± 25.44; t(89)=-4.30, p < .001). For WIAT Word Reading, low SES (50.27 ± 28.27) scored lower than middle (72.27 ± 23.37; t(89)=-2.75, p = .02) and high SES (76.59 ± 19.60; t(89)=-3.70, p = .001). See Fig. 2 for significant post-hoc comparisons in academic performance across SES groups. 4.3. Cognitive and Academic Performance Differences by After-school Music Participation Despite the non-significant MANOVA results for music and non-music groups (p > .05), we conducted exploratory independent samples t-tests for NIHTB-Cog, WISC-V, and WIAT task performances to examine potential group differences. Significant differences were observed in Pattern Comparison (music: 71.75 ± 28.24; non-music: 50.29 ± 32.28, t(24.15)=-2.69, p = .01) and WIAT Listening Comprehension Oral Discourse Comprehension (music: 71.23 ± 25.46; non-music: 54.47 ± 28.23, t(23.58)=-2.34, p = .03). See Fig. 3 for the distribution of significant between-group comparisons in cognitive and academic performances by after-school music participation. 4.4. After-school Music Participation as a Moderator of SES-related Cognitive and Academic Outcomes Pearson correlations among SES groups, after-school music participation status, and cognitive and academic performance scores are reported in Table 3 . Table 3 Correlation Matrix of Covariates, Cognitive, and Academic Measures in Preadolescents. Correlations (r) among independent variables (after-school music participation, SES group), cognitive (NIHTB-Cog, WISC-V scores), and academic performance (WIAT scores) in preadolescents. Note: p-values are indicated as follows: p < 0.05*, p < 0.01**, p < 0.001*** After-school Music SES NIHTB-Cog Card Sort -0.023 0.025 Flanker -0.031 -0.060 List Sort 0.162 0.248* Pattern Comparison 0.253* 0.125 WIAT Listening Comprehension Oral Discourse Comprehension 0.226* 0.197 Listening Comprehension Receptive Vocabulary 0.093 0.169 Listening Comprehension 0.160 0.239* Math Problem Solving 0.162 0.391*** Numerical Operations 0.203 0.398*** Pseudo-word Decoding 0.125 0.389*** Reading Comprehension 0.084 0.307** Spelling 0.162 0.349*** Word Reading 0.135 0.333*** WISC-V Fluid Reasoning 0.068 0.384*** Processing Speed 0.099 0.185 Verbal Comprehension 0.158 0.347*** Visual Spatial 0.174 0.270** Working Memory 0.000 0.409*** Significant R 2 were observed for list sort (Adj. R²=0.11, p = .04), pattern comparison (Adj. R²=0.09, p = .05), WIAT Math Problem Solving (Adj. R²=0.18, p = .002), WIAT Numerical Operations (Adj. R²=0.27, p < .001), WIAT Pseudoword Decoding (Adj. R²=0.15, p = .004), WIAT Reading Comprehension (Adj. R²=0.12, p = .01), WIAT Spelling (Adj. R²=0.17, p = .002), WIAT Word Reading (Adj. R²=0.10, p = .02), WISC-V Fluid Reasoning (Adj. R²=0.16, p = .002), WISC-V Verbal Comprehension (Adj. R²=0.16, p = .002), WISC-V Visual Spatial Index (Adj. R²=0.10, p = .02), and WISC-V Working Memory (Adj. R²=0.18, p = .002). See Table 4 for a summary of the linear regression results. Table 4 Linear Regression Models Examining SES and Outcomes Moderated by After-school Music Participation. Associations between SES and cognitive and academic outcomes in preadolescents, with moderating effects of after-school music participation included. P-values are corrected for multiple comparisons using the Benjamini-Hochberg (BH) procedure. F df1 df2 p Adj. R 2 NIHTB-Cog Card Sort 0.27 5 85 0.927 -0.04 Flanker 1.14 5 85 0.458 0.01 List Sort 3.22 5 85 0.041 0.11 Pattern Comparison 2.77 5 85 0.046 0.09 WIAT Listening Comprehension Oral Discourse Comprehension 2.04 5 85 0.091 0.05 Listening Comprehension Receptive Vocabulary 0.96 5 85 0.445 -0.002 Listening Comprehension 2.23 5 85 0.075 0.06 Math Problem Solving 4.96 5 85 0.002 0.18 Numerical Operations 7.59 5 85 < 0.001 0.27 Pseudo-word Decoding 4.21 5 85 0.004 0.15 Reading Comprehension 3.43 5 85 0.013 0.12 Spelling 4.67 5 85 0.003 0.17 Word Reading 3.07 5 85 0.020 0.10 WISC-V Fluid Reasoning 4.39 5 87 0.002 0.16 Processing Speed 1.97 5 87 0.092 0.05 Verbal Comprehension 4.42 5 87 0.002 0.16 Visual Spatial 3.11 5 87 0.016 0.10 Working Memory 4.98 5 87 0.002 0.18 Significant interaction effects were observed for the middle SES group (β = 62.01, p = .04) and high SES group (β = 65.03, p = .02) on WISC-V processing speed. Additionally, a significant effect was found for the interaction between after-school music participation and high SES group in WIAT numerical operation (β = 55.17, p = .04). See Fig. 4 for the visualization of moderating effects of after-school music participation in these associations. 5. Discussion This study examined whether after-school music participation moderates SES-related disparities in executive function, language, and academic achievement in preadolescent children. Understanding this interaction is critical for identifying scalable, enrichment-based strategies that may mitigate the effects of socioeconomic disadvantage on cognitive development. These strategies are often conceptualized within the framework of PCEs, which emphasize the developmental importance of enriching, engaging, and relationally supportive environments. Structured extracurricular activities such as music and sports are key forms of PCEs that promote cognitive and academic growth [ 6 , 37 ]. SES remains a robust predictor of cognitive and academic performance in childhood, shaping access to enrichment opportunities, educational resources, and developmental support [ 11 ]. Consistent with prior research, our findings revealed that children from low SES scored significantly lower than middle and high SES peers on working memory, processing, verbal comprehension, and numerical reasoning [ 2 , 4 – 5 , 21 ]. These results underscore the pervasive impact of socioeconomic disadvantage on cognitive development and academic achievement, likely mediated through both behavioral and neurobiological pathways [ 4 , 21 ]. While we hypothesized that SES would also predict rates of after-school music participation, chi-square analyses revealed no significant differences in participation rates across SES groups. This unexpected finding does not necessarily contradict the broader role of SES in shaping access to enrichment; rather, it may reflect limitations in how participation was captured. Without accounting for factors like duration and engagement type, binary measures may miss meaningful variation in how preadolescent children experience music programs, variation that could be especially relevant across socioeconomic strata. Despite similar participation rates, after-school music participation appeared to confer academic benefits primarily for children from middle and high SES backgrounds. Moderation analyses revealed that music participation strengthened academic performances, particularly in processing speed and numerical operations, within these groups. This pattern suggests that music may act as an enrichment tool that supports academic development when paired with sufficient resources and support. These findings align with prior research that music training enhances working memory, attention, and verbal fluency and are consistent with Patel’s OPERA hypothesis, which explains that music training strengthens auditory and attentional networks through overlap, precision, emotion, repetition, and attention that can enhance processing speed and language-related skills [ 7 , 10 , 14 ]. Rhythm-based training has also been shown to improve neural synchrony, supporting executive functions and reading development, with neuroimaging studies demonstrating music-induced structural and functional changes in frontoparietal and auditory networks that support executive control and working memory [ 11 , 15 – 16 ]. These findings aligned with research on PCEs and other enriching extracurricular activities. For instance, participation in after-school sports has been associated with gains in executive function, processing speed, and language comprehension, even after accounting for SES and IQ [ 37 ]. This convergence suggests that the cognitive and academic benefits observed in music may reflect a broader pattern across structured, engaging activities that support development. The absence of similar cognitive benefits for low SES children, despite comparable participation rates, suggests that structural barriers may constrain the potential impact of music enrichment in this group. Children from low SES backgrounds often encounter programs that differ in music participation intensity and quality, including shorter durations, fewer trained instructors, and limited exposure to cognitively demanding activities such as ensemble performance, music theory, or individualized instruction [ 18 , 38 ]. Logistical challenges such as transportation, scheduling conflicts, or lack of parental availability may further impede sustained engagement [ 39 ]. These barriers may also be compounded by competing stressors, including economic instability, food insecurity, or limited academic support at home, which can diminish the cognitive benefits typically associated with enrichment activities [ 5 , 40 ]. Schools serving low SES communities may offer fewer extracurricular resources or rely on underfunded programs perpetuating disparities in enrichment quality [ 18 ]. These contextual factors underscore the importance of not only increasing access to music programs but also ensuring that such programs are high-quality, developmentally appropriate, and equitably resourced. Without addressing these barriers, enrichment strategies like music and other PCEs may inadvertently reinforce existing disparities rather than mitigate them. Overall, this study introduces several methodological strengths. We were among the first to apply the BJS recommendations for a composite SES classification, improving validity over single indicators such as income or parental education. An additional strength in using this construct was that the BJS recommendations were matched to the years in which the children participated in the study. We also cross-checked clinical and parent-reported diagnoses to ensure a neurotypical sample, reducing the confounding effects of neurodevelopmental conditions. Additionally, this work is one of the first to leverage the NIHTB-Cog in the context of music participation and SES, providing standardized cognitive measures across multiple domains. Findings are specific to neurotypical populations and may differ in neurodivergent groups, where music engagement could interact with distinct developmental trajectories. Future research should explore these differences using longitudinal designs, detailed music engagement metrics, and neuroimaging approaches to clarify mechanisms and inform targeted strategies. 6. Limitations This study has several limitations that warrant consideration. First, the SES index calculated in our study may still not fully capture environmental factors, such as neighborhood resources, school quality, and policy changes, that could influence observed associations. Additionally, the sample was disproportionately skewed toward families in the higher income range, which may limit generalizability and complicate interpretations of SES group comparisons. Future work should incorporate contemporaneous SES measures and contextual variables to better capture these dynamics. Second, music participation was measured as a binary variable (Yes/No), which does not account for critical dimensions such as duration, intensity, type of engagement, or cumulative exposure, which likely modulate cognitive benefits. The lack of granularity limits examination of dose-response relationships and may obscure meaningful variation in engagement across SES groups. Moreover, no data were available on program characteristics such as curriculum content, and there were no direct measures of engagement, making it difficult to determine whether children were actively involved or passively enrolled. Third, potential unmeasured confounders such as parental involvement, school-level resources, and neighborhood-level access to enrichment may have influenced music participation and cognitive and academic outcomes. These factors were not captured in the current dataset and may help explain the absence of observed benefits for low SES children. Fourth, the study’s cross-sectional design limits the causal inference. Although moderation effects were observed for cognitive and academic outcomes, we cannot determine whether music participation directly contributes to developmental gains. Even longitudinal studies face challenges disentangling selection effects and confounding variables, making RCTs the rigorous approach for testing whether sustained music engagement leads to measurable improvements in cognitive and academic outcomes. Additionally, analyses were conducted using an available-case approach, which may introduce bias if missing data were not random. Future studies should consider more robust methods for handling missingness, such as multiple imputation. 7. Conclusions This study offers preliminary evidence that after-school music engagement may moderate the relationship between SES and academic performance in preadolescents, particularly among children from middle- and high SES backgrounds. Although after-school music participation rates did not differ significantly by SES, these findings raise the possibility that music participation may operate as a context-dependent enrichment factor, with its benefits potentially shaped by SES-related environmental stimuli. Rather than viewing music solely as an extracurricular activity, it may be valuable to explore its role as a scalable, evidence-based support for cognitive and academic development. Integrating structured music programs within school and community settings - particularly during preadolescence, a sensitive developmental window characterized by heightened neuroplasticity - could offer one potential avenue for promoting more equitable developmental outcomes and optimizing cognitive trajectories. Future research should extend these findings using longitudinal and RCT designs to examine causal pathways and sensitive periods, incorporate detailed measures of music engagement, and leverage neuroimaging and computational models to explore mechanisms underlying SES-related differences in benefits. Evaluating implementation strategies for scaling music programs in under-resourced communities will be critical for translating these findings into practice. Declarations Funding Statement The authors report no financial support for the research. Competing Interest The authors report no competing interests or financial support for this work. Author Contributions Conceptualization: N. O’Malley, M. Lim, & N.E. Logan Methodology: N. O’Malley, M. Lim, Gaudreau, J., & N.E. Logan Formal Analysis: N. O’Malley, M. Lim, & N.E. Logan Investigation: N. O’Malley, M. Lim, & N.E. Logan Writing – Original Draft: N. O’Malley, M. Lim, & N.E. Logan Writing – Review & Editing: N. O’Malley, M. Lim, J. Gaudreau, C. Clarkin, & N.E. Logan Visualization: M. Lim Supervision: N.E. Logan Project Administration: N.E. Logan Acknowledgements This manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank – Healthy Brain Network. This manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute. Data Availability Statement Preprocessing and analysis code used in this study is available at: https://github.com/loganlaburi/CMI_music.git References Wong, M. & Nadeem, E. Responding to the challenges of preadolescence: roles for caregivers. In Caregiving Across the Lifespan: Research• Practice• Policy (47–59). New York, NY: Springer New York. (2012). Ciccia, A. H., Meulenbroek, P. & Turkstra, L. S. Adolescent Brain and Cognitive Developments: Implications for Clinical Assessment in Traumatic Brain Injury. Top. Lang. disorders . 29 (3), 249–265 (2009). Muir, R. A., Howard, S. J. & Kervin, L. 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Exerc. Sport Mov. , 3 (1), e00032. (2025). Xinyi, G. O. N. G. The Impact of Music Education on Children’s Cognitive Development: From Traditional Classrooms to Modern Digital Platforms. J. Cult. Religious Stud. 12 (9), 2328–2177 (2024). Holster, J. D. The influence of socioeconomic status, parents, peers, psychological needs, and task values on middle school student motivation for school music ensemble participation. Psychol. Music . 51 (2), 447–462 (2023). Duncan, G. J. & Magnuson, K. Socioeconomic status and cognitive functioning: moving from correlation to causation. Wiley Interdisciplinary Reviews: Cogn. Sci. 3 (3), 377–386 (2012). Additional Declarations No competing interests reported. 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. 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1","display":"","copyAsset":false,"role":"figure","size":1255195,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of Cognitive Performance by SES. \u003c/strong\u003eViolin plots with embedded box plots illustrating NIHTB-Cog scores and WISC-V scores in preadolescents across SES groups: low (blue), middle (orange), and high (purple). Only variables with significant between-group differences are visualized. \u003cem\u003eNote: p-values are indicated as follows: p\u0026lt;0.05*, p\u0026lt;0.01**, p\u0026lt;0.001***\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8166352/v1/fc9881e2e3c112d4e0c430e7.png"},{"id":97687704,"identity":"876e53f7-0e86-4c18-bdcd-30320764da12","added_by":"auto","created_at":"2025-12-08 10:29:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1465101,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of Academic Performance by SES. \u003c/strong\u003eViolin plots with embedded box plots illustrating WIAT scores in preadolescents across SES groups: low (blue), middle (orange), and high (purple). Only variables with significant between-group differences are visualized. \u003cem\u003eNote: p-values are indicated as follows: p\u0026lt;0.05*, p\u0026lt;0.01**, p\u0026lt;0.001***\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8166352/v1/93a19823c7c6f074eab0ddc3.png"},{"id":97893459,"identity":"7ab92b82-32cd-421d-b1de-6326ba624607","added_by":"auto","created_at":"2025-12-10 15:30:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":933040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistributions of Academic Performance by After-school Music Participation. \u003c/strong\u003eViolin plots with embedded box plots illustrating NIHTB-Cog and WIAT scores in preadolescents across after-school music participation status: No music (yellow) or after-school music (pink). Only variables with significant between-group differences are visualized. \u003cem\u003eNote: p-values are indicated as follows: p\u0026lt;0.05*, p\u0026lt;0.01**, p\u0026lt;0.001***\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8166352/v1/db315059d21e57b2a4f54946.png"},{"id":97687703,"identity":"ba387d72-f729-4c0d-b81b-0fc72f017dd3","added_by":"auto","created_at":"2025-12-08 10:29:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":353510,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModeration of After-school Music Participation on the Associations Between SES and Cognitive and Academic Performance. \u003c/strong\u003eSignificant linear regression models testing the interaction between SES and after-school music participation in WISC-V and WIAT scores among preadolescents. \u003cem\u003eNote: Unstandardized betas (β) are reported for SES x After-school Music interaction terms (Middle SES x After-school Music and High SES x After-school Music\u003c/em\u003e; \u003cem\u003eLow SES served as the reference group).\u003c/em\u003e \u003cem\u003eSignificance is indicated by symbols in the figure: p\u0026lt;0.05*, p\u0026lt;0.01**, p\u0026lt;0.001***\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8166352/v1/8d8e53f2b839cea98d891e6d.png"},{"id":100420840,"identity":"9ef6e650-92d6-4435-9b17-573348d67199","added_by":"auto","created_at":"2026-01-16 13:29:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4209560,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8166352/v1/cca2119c-cfdf-4ccf-a9a5-4417feb79b24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Music as a Moderator of Socioeconomic Disparities in Preadolescent Cognitive and Academic Performance: Evidence from the Healthy Brain Network Biobank","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePreadolescence, spanning from ages 9 to 12, marks a critical developmental window during the transition from childhood into adolescence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This stage is characterized by heightened neuroplasticity combined with rapid growth in executive, language, and academic skills [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This convergence of biological sensitivity and expansion of cognitive demands offers a unique opportunity to introduce enrichment strategies that reinforce and extend developmental gains. After-school programs in particular offer a structured environment that can consistently support cognitively enriching experiences during this stage [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWithin this context, Positive Childhood Experiences (PCEs), which include nurturing, engaging, and developmentally supportive activities, play a critical role in promoting attention, memory, and learning [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Music participation is one example of such an experience offering structured, multisensory engagement that promotes attention, memory, and learning [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] while also supporting social and emotional development consistent with PCE goals. As a structured and cognitively engaging activity, music supports attention, memory, and learning, and aligns with the goals of PCEs by fostering emotional regulation, persistence, and social connection, especially during childhood. A growing body of research highlights music as a promising enrichment strategy for cognitive development [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Longitudinal and experimental studies demonstrate that structured music engagement enhances executive function, working memory, and language skills [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Patel\u0026rsquo;s OPERA hypothesis suggests that music training strengthens auditory and attentional networks through Overlap, Precision, Emotion, Repetition, and Attention, thereby improving processing speed and language-related skills [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Rhythm-based experiences further support neural synchrony, which supports executive functions and reading development [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Neuroimaging studies demonstrate that music training strengthens networks involved in memory, attention, and executive function; systems that support cognitive performance and may also play a role in maintaining neural efficiency as they mature during preadolescence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite its benefits, access to such enrichment is not equitably distributed across socioeconomic groups [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Because socioeconomic status remains one of the strongest predictors of both cognitive and academic performance, limited access to music programs risks widening existing disparities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. By kindergarten entry, children from low SES backgrounds score up to one standard deviation lower on measures of executive function and language, with similar disparities persisting into adolescence [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These deficits are particularly concerning given the foundational role of executive function, working memory, and language in cognitive and academic performance [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. SES-related disadvantages extend beyond behavioral performance to brain morphology. Specifically, structural MRI studies reveal that children from low-income backgrounds tend to exhibit larger differences in surface area, mostly in structural regions associated with language, reading, and executive function, compared to children from high-income families [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, behavioral differences in preadolescent language ability related to SES, as well as structural differences, such that lower SES was associated with smaller volumes of grey matter in areas related to memory performance (bilateral hippocampi, middle temporal gyri, left fusiform and right inferior occipito-temporal gyri) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, childhood poverty, measured by family income and adjusted for family size as a percentage of the federal poverty level (FPL), has been associated with structural differences in several areas of the brain related to school readiness skills [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Such disparities highlight the extent to which socioeconomic factors contribute to both brain and behavioral development.\u003c/p\u003e\u003cp\u003eThe inequity associated with socioeconomic disadvantage and brain development is particularly concerning, given the potential of music to support cognitive and academic development during preadolescence. Notably, over 3\u0026nbsp;million U.S. public school students lack access to music instruction, with gaps disproportionately affecting low-income communities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. When children from socioeconomically disadvantaged backgrounds are excluded from enrichment opportunities, it limits their developmental potential and may exacerbate existing disparities in learning and achievement [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These domain-specific vulnerabilities that span attention, memory, verbal fluency, and processing speed highlight the urgency of identifying scalable enrichment strategies that can mitigate the cognitive and academic impact of socioeconomic disadvantage and promote more equitable developmental outcomes.\u003c/p\u003e\u003cp\u003eThe intersection between early cognitive vulnerability and limited access to enrichment presents a critical opportunity for investigation. Research on such experience highlights their role in strengthening academic and cognitive development during adolescence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, despite growing evidence that music participation enhances executive function, language, and memory, most studies have examined music as a mediator or direct predictor of academic and cognitive outcomes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Few studies have explored whether music engagement can moderate the relationship between SES and developmental performance, despite evidence that SES strongly influences brain structure and cognitive outcomes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and that music training enhances cognitive skills [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]This gap is especially important given the persistent inequities in access to music education and the potential of music to serve as a scalable strategy that promotes more equitable developmental outcomes [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, prior studies have used a wide range of metrics to isolate the effects of SES, ranging from single to trichotomous indexes that include household income, parents\u0026rsquo; education, parents\u0026rsquo; occupation, zip code, and free-or-reduced price lunches. Consequently, a standardized metric of SES is needed. Understanding whether music can attenuate SES-related disparities in cognitive and academic domains remains an open and urgent area of inquiry.\u003c/p\u003e\u003cp\u003eTo address persistent gaps in enrichment access [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and explore music\u0026rsquo;s potential to reduce SES-related cognitive disparities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], the present study examined whether after-school music participation moderates the associations between a standardized SES metric and cognitive and academic performance. To our knowledge, this is the first study to examine this moderating effect in preadolescent children. By leveraging a large, well-characterized dataset and standardized SES measures, this work aims to clarify whether music engagement can mitigate the potential negative effects of socioeconomic disadvantage. We hypothesized that children from lower SES backgrounds would exhibit the lowest performance across cognitive and academic domains, consistent with prior research showing persistent SES-related disparities in executive function, language, and achievement. We also expected that children from lower SES backgrounds have reduced access to after-school music programs, reflecting broader inequities in enrichment opportunities. We proposed that music participation may attenuate the negative association between SES and cognitive and academic performance, such that music participation strengthens outcomes for children with greater access, offering a scalable strategy to promote equity in developmental outcomes.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Procedures\u003c/h2\u003e\u003cp\u003eThe present study utilized data from the Healthy Brain Network (HBN), an open-access dataset collected by the Child Mind Institute (CMI). Between 2015 and 2021, participants were recruited from New York City communities and neighborhoods [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Recruitment strategies included participation in local events, outreach via digital and print media, engagement with news outlets, and collaboration with educational institutions, healthcare providers, and community-based organizations [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAll study procedures were reviewed and approved by the Chesapeake Institutional Review Board [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Data collection followed a structured protocol consisting of four sessions, each lasting approximately three hours [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The first session involved consent and assent procedures. Written informed consent was obtained from participants aged 18 and older, while child assent was collected in addition to consent from a primary caregiver, for children under 18. Participants completed questionnaires, a clinical intake interview, and baseline cognitive assessments [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The second session included magnetic resonance imaging (MRI), the third involved NIH Toolbox Cognition Battery (NIHTB-Cog) assessments and physical fitness evaluations, and the final session consisted of electroencephalography (EEG) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For the purpose of the current study, only data from sessions one and three were included for analysis. To minimize potential confounding effects on behavioral and cognitive assessments, participants prescribed stimulant medications were asked to temporarily discontinue usage during study visits [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. If discontinuation was not possible, medication use was documented on the day of testing [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Participants\u003c/h2\u003e\u003cp\u003eA total of 4867 individuals between the ages of 5 and 21 were enrolled in the CMI HBN study. Eligibility criteria included fluency in English and the ability to complete study procedures. Spanish-speaking caregivers were accommodated when bilingual staff were available to assist with the consent process. Participants were excluded if they had (i) significant cognitive impairment (IQ\u0026thinsp;\u0026lt;\u0026thinsp;66), (ii) acute encephalopathy, (iii) neurodegenerative conditions, (iv) uncorrected sensory impairments; (v) recent untreated diagnoses of schizophrenia, schizoaffective disorder, or bipolar disorder; (vi) recent onset of suicidality or homicidality without treatment; (vii) substance dependence requiring chemical replacement therapy; and (viii) under the influence of substances during study visits.\u003c/p\u003e\u003cp\u003eFor the current analysis, a subset of 94 participants was selected based on the absence of any psychiatric, developmental, or learning diagnosis. These individuals were confirmed as having no psychiatric, developmental or learning diagnoses through two sources: (i) the Consensus Diagnosis process, which integrates information from structured clinical interviews using the Kiddie Schedule for Affective Disorders and Schizophrenia; a semi-structured diagnostic tool designed to assess current and parent episodes of psychopathology in children and adolescents based on DSM-IV criteria [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and (ii) parent-reported questionnaires indicating no history of psychiatric or learning disorders. This subset (N\u0026thinsp;=\u0026thinsp;94) was used to examine academic and cognitive outcomes in a neurotypical sample.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Materials\u003c/h2\u003e\u003cp\u003ePhenotypic data used in this study were accessed following institutional approval and completion of a Data Usage Agreement by the Principal Investigator. Data retrieval was conducted through the Longitudinal Online Research and Imaging System (LORIS); a secure, web-based platform designed to support the management of behavioral data. All assessments were administered by, or under the supervision of, licensed clinicians, and questionnaires underwent validity checks to ensure data quality.\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Demographics\u003c/h2\u003e\u003cp\u003eDemographic data included parent-reported age (derived at the date of study enrollment), sex (male/female), and race/ethnicity. Race was categorized using a standardized coding system: White/Caucasian, Black/African American, Hispanic, Asian, Indian, Native American Indian, American Indian/Alaskan Native, Native Hawaiian/Other Pacific Islander, multiracial, or other. Ethnicity was reported as either Hispanic/Latino or not Hispanic/Latino.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. SES Index\u003c/h2\u003e\u003cp\u003eSES was standardized and derived using Index 1 of the Bureau of Justice Statistics (BJS) framework, which combined four components: education, household income, employment status, and housing status [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Education information was collected for both parents (or caregivers), which was assessed using the Barratt Simplified Measure of Social Status; a measure based on Hollingshead\u0026rsquo;s work that indexes educational attainment as a proxy for social status [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Income, housing status, and employment status were obtained from the Financial Support Questionnaire, which collects information on household income, types of public assistance, health insurance coverage, and employment status for both caregivers. Income was converted to a percentage of the FPL adjusted for household size [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Because the data were collected between 2015 and 2021, each participant was matched to the FPL guidelines for the corresponding year they participated in the study, reducing the risk of temporal mismatch and improving accuracy [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. For income conversion, the midpoint of the reported income range was used when both boundaries were available. If only a lower boundary or upper boundary was provided, that boundary was used instead of the midpoint. The FPL percentage was calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:FPL\\:Percentage\\:=\\:\\left(\\frac{Household\\:Income}{FPL\\:\\:Guideline\\:for\\:Household\\:Size}\\right)\\:\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEmployment was coded based on responses from both caregivers: if both, or at least one caregiver was currently employed, the employment status was classified as employed; if neither caregiver was employed, the employment status was classified as not employed. Housing status was categorized by whether the residence was owned or rented. Each component was initially scored as follows: education and income status (scores 0\u0026ndash;3), employment and housing status (scores 0\u0026ndash;1), resulting in a composite SES score ranging from 0 to 8. Composite scores were then recategorized into eight ordinal levels: (1) 0 to less than 1, (2) 1 to less than 2, (3) 2 to less than 3, (4) 3 to less than 4, (5) 4 to less than 5, (6) 5 to less than 6, (7) 6 to less than 7, and (8) 7 to less than 8 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The recategorized ordinal levels were consolidated into three SES groups: low SES (scores 1\u0026ndash;3), middle SES (scores 4\u0026ndash;6), and high SES (scores 7\u0026ndash;8) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for a visual summary of the SES classification process.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eSES Classification Based on the BJS Framework.\u003c/b\u003e SES was derived using Index 1 of the BJS framework, which integrates education, federal poverty level percentage, employment status, and housing status (Berzofsky et al., 2015). Composite SES scores were consolidated into three SES groups: low (scores 1\u0026ndash;3), middle (scores 4\u0026ndash;6), and high (scores 7\u0026ndash;8).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCriteria\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComponents\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0: Less than 7th grade, Junior High/Middle School, Partial High School\u003c/p\u003e\u003cp\u003e1: High School Graduate, Partial College (at least one year)\u003c/p\u003e\u003cp\u003e2: College Education\u003c/p\u003e\u003cp\u003e3: Graduate Degree\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0: \u0026le; 100%\u003c/p\u003e\u003cp\u003e1: 101% \u0026minus;\u0026thinsp;200%\u003c/p\u003e\u003cp\u003e2: 201% \u0026minus;\u0026thinsp;400%\u003c/p\u003e\u003cp\u003e3: \u0026ge; 400%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEmployment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0: Both caregivers were not currently employed\u003c/p\u003e\u003cp\u003e1: Both, or at least one, caregivers were currently employed\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0: Rent\u003c/p\u003e\u003cp\u003e1: Own\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComposite Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1: Scores 0 to less than 1\u003c/p\u003e\u003cp\u003e2: Scores 1 to less than 2\u003c/p\u003e\u003cp\u003e3: Scores 2 to less than 3\u003c/p\u003e\u003cp\u003e4: Scores 3 to less than 4\u003c/p\u003e\u003cp\u003e5: Scores 4 to less than 5\u003c/p\u003e\u003cp\u003e6: Scores 5 to less than 6\u003c/p\u003e\u003cp\u003e7: Scores 6 to less than 7\u003c/p\u003e\u003cp\u003e8: Scores 7 to less than 8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup Category\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;3: Low SES\u003c/p\u003e\u003cp\u003e4\u0026ndash;6: Middle SES\u003c/p\u003e\u003cp\u003e7\u0026ndash;8: High SES\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. After-school music participation\u003c/h2\u003e\u003cp\u003eAfter-school music participation was assessed during the intake interview conducted by a clinician at the initial visit [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Within the education and social history section, parents were asked whether their child engaged in any extracurricular classes or lessons outside of school. Music was one of the listed options, and responses were coded dichotomously as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no\u0026rdquo; for each activity [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For the current analysis, only music participation was examined, as our primary aim was to assess its potential moderating effect on cognitive and academic outcomes. Although some children may have participated in multiple activities, we did not include other extracurriculars in the analysis, and music participation was treated as a standalone variable.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4. Cognitive and Academic Performance Assessment\u003c/h2\u003e\u003cp\u003eCognitive function was assessed using the two standardized instruments: the NIH Toolbox Cognition Battery (NIHTB-Cog) and the Wechsler Intelligence Scale for Children Fifth Edition (WISC-V). The NIHTB-Cog, designated for individuals aged 3 to 85, included the following subtests: Dimensional Change Card Sort (subdomain: cognitive flexibility and attention), Flanker Inhibitory Control and Attention (subdomain: inhibitory control), List Sorting Working Memory (subdomain: working memory), and Pattern Comparison Processing Speed (subdomain: processing speed) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The WISC-V, administered to participants aged 6 to 17 years, included subtests representing five domains: Visual Spatial, Verbal Comprehension, Fluid Reasoning, Working Memory, and Processing Speed [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. All scores were age-adjusted and calculated as national percentiles, with higher values indicating better cognitive performance [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAcademic performance was assessed using the Wechsler Individual Achievement Test Third Edition (WIAT), a standardized measure for individuals aged 4 to 85 years [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The subtests were administered, representing the following subdomains: Numerical Operations, Pseudo-Word, Spelling, Word Reading, Listening Comprehension Receptive Vocabulary, Listening Comprehension Oral Discourse Comprehension, Listening Comprehension, Reading Comprehension, and Math Problem Solving [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Scores were reported as age-adjusted percentile ranks, with higher percentiles reflecting stronger academic performance [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Statistical analysis","content":"\u003cp\u003eAll statistical analyses were conducted using R (version 4.3.1) with statistical significance set at p\u0026thinsp;\u0026le;\u0026thinsp;.05. To explore the distribution of after-school music participation across SES groups within the preadolescent sample, a chi-square test was conducted comparing SES classification (low, med, high) with after-school music participation status (Yes, No). This preliminary analysis provided descriptive insight into group-level differences in access to or engagement with music-based enrichment. Group-level differences in cognitive and academic performance across SES and music groups were assessed using the multivariate analyses of variance (MANOVA), with composite scores from the NIHTB-Cog, WIAT, and WISC-V serving as dependent variables. For MANOVA results reaching statistical significance, follow-up univariate ANOVAs were applied for SES group comparisons, and independent samples t-tests were used for music group comparisons to individual outcomes. Pairwise comparisons were performed using Tukey-adjusted procedures via the \u003cem\u003eemmeans\u003c/em\u003e R package (version 1.10.0). Estimated marginal means and standard deviations were calculated for each SES group to support the interpretation of observed differences.\u003c/p\u003e\u003cp\u003ePearson correlation analyses were conducted to examine associations among SES groups, after-school music participation status, and standardized measures of cognitive and academic performance (NIHTB-Cog, WIAT, WISC-V).\u003c/p\u003e\u003cp\u003eTo test the central hypothesis that after-school music participation moderates the relationship between SES and cognitive and academic performances, moderation analyses were conducted using linear regression. In these models, the SES composite index served as the independent variable, standardized scores from the NIHTB-Cog, WISC-V, WIAT were the age-adjusted dependent variables, and after-school music participation was included as the moderating variable. By modeling SES as a categorical factor (low, middle, high), this approach allowed us to examine whether the effect of after-school music participation on cognitive and academic outcomes differed across SES levels. For categorical variables, low SES was selected as the reference category to facilitate meaningful comparisons and reflect the conceptually minimal level within the SES classification. This choice enabled a clearer interpretation of whether after-school music participation mitigates SES-related disparities in cognitive and academic outcomes. Unstandardized betas are reported. To account for multiple comparisons across outcome domains, p-values were adjusted using the Benjamini-Hochberg procedure, which controls the false discovery rate while maintaining statistical power; an appropriate choice given the exploratory nature of the moderation models and the number of cognitive and academic outcomes tested [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA post hoc power analysis was conducted using GPower (version 3.1.9.6) to evaluate the sensitivity of the primary statistical models given the final sample size of N\u0026thinsp;=\u0026thinsp;94. For the MANOVA models assessing SES group differences across three cognitive domains, assuming a medium effect size (f\u0026sup2;=0.0625), α\u0026thinsp;=\u0026thinsp;.05, and three SES groups, the achieved power was calculated to be 0.99. For the moderation analyses, assuming a medium effect size (f\u0026sup2;=0.15), α\u0026thinsp;=\u0026thinsp;.05, and three predictors, the achieved power was 0.92. These powers exceed the conventional threshold of 0.80, confirming sufficient sensitivity to detect group-level and interaction effects.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe final analytic sample included 94 preadolescent participants, aged 9-12.99 years (42.6% female; ages 10.69\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08 years), distributed across three SES groups based on a composite index: low SES (N\u0026thinsp;=\u0026thinsp;11; 36.4% female; ages 10.19\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 years), middle SES (N\u0026thinsp;=\u0026thinsp;23; 47.8% female; ages 10.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99 years), and high SES (N\u0026thinsp;=\u0026thinsp;60; 41.7% female; ages 10.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.10 years). See Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for demographic information of each low, middle, and high SES group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eDemographic Characteristics of Preadolescents by SES.\u003c/b\u003e Sample size and demographic characteristics of the preadolescent sample, stratified by SES groups: low, middle, and high.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow SES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMiddle SES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHigh SES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSample Size\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Hispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWhite/Caucasian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBlack/African American\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIndian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNative American Indian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAmerican Indian/Alaskan Native\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNative Hawaiian/Other Pacific Islander\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTwo or more races\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOther race\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAfter-school Music Participation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLess than High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighschool Graduate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollege Education\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGraduate Degree\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;100%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101% \u0026minus;\u0026thinsp;200%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e201% \u0026minus;\u0026thinsp;400%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;400%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEmployment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot Employed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEmployed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousing\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOwn\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Distribution of After-school Music Participation on SES\u003c/h2\u003e\u003cp\u003eRates of after-school music participation were examined across SES groups. Specifically, 9.09% of low, 30.43% of middle, and 16.96% of high SES participants reported engaging in music-based enrichment activities outside of school. A chi-square test of independence indicated no significant association between SES and participation in after-school music opportunities program, χ\u0026sup2;(2)\u0026thinsp;=\u0026thinsp;2.77, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.25.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Performance Differences by SES Disparities\u003c/h2\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e4.2.1. SES Disparities on Cognitive Function.\u003c/h2\u003e\u003cp\u003eSignificant SES group differences were observed in the NIHTB-Cog outcomes (F(8,172)\u0026thinsp;=\u0026thinsp;3.10, p\u0026thinsp;=\u0026thinsp;.003, Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;.76) and WISC-V outcomes (\u003cem\u003eF\u003c/em\u003e(10,174)\u0026thinsp;=\u0026thinsp;3.37, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001, Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;.70) Tukey-adjusted post hoc comparisons revealed that children from the low SES (31.36\u0026thinsp;\u0026plusmn;\u0026thinsp;21.76) scored significantly lower than middle (64.41\u0026thinsp;\u0026plusmn;\u0026thinsp;25.96, t(89)= -3.50, p\u0026thinsp;=\u0026thinsp;.001 and high SES peers (60.07\u0026thinsp;\u0026plusmn;\u0026thinsp;26.00, t(89)=-3.42, p\u0026thinsp;=\u0026thinsp;.003 on the NIHTB-Cog list sort task. For the pattern comparison task, children from low SES (32.64\u0026thinsp;\u0026plusmn;\u0026thinsp;22.70) scored significantly lower than those from middle SES (64.50\u0026thinsp;\u0026plusmn;\u0026thinsp;29.45), t(89)=-2.73, p\u0026thinsp;=\u0026thinsp;.02. For WISC-V Fluid Reasoning, low SES (24.18\u0026thinsp;\u0026plusmn;\u0026thinsp;26.16) scored significantly lower than middle (59.87\u0026thinsp;\u0026plusmn;\u0026thinsp;24.26; t(91) =-3.71; p\u0026thinsp;=\u0026thinsp;.001) and high SES (64.17\u0026thinsp;\u0026plusmn;\u0026thinsp;26.92; t(91)=-4.65; p\u0026thinsp;\u0026lt;\u0026thinsp;.001). For WISC-V Visual Spatial, low SES (30.64\u0026thinsp;\u0026plusmn;\u0026thinsp;31.59) scored lower than middle (62.83\u0026thinsp;\u0026plusmn;\u0026thinsp;30.57; t(91) =-2.91, p\u0026thinsp;=\u0026thinsp;.01) and high SES (63.37\u0026thinsp;\u0026plusmn;\u0026thinsp;29.73; t(91)=-3.31, p\u0026thinsp;=\u0026thinsp;.004). WISC-V Verbal Comprehension also showed significant differences with low SES (43.45\u0026thinsp;\u0026plusmn;\u0026thinsp;26.19) scoring lower than middle (67.70\u0026thinsp;\u0026plusmn;\u0026thinsp;21.53; t(91)=-3.14, p\u0026thinsp;=\u0026thinsp;.01) and high SES (71.34\u0026thinsp;\u0026plusmn;\u0026thinsp;19.91; t(91)=-4.03; p\u0026thinsp;\u0026lt;\u0026thinsp;.001). For WISC-V Working Memory, low SES (22.45\u0026thinsp;\u0026plusmn;\u0026thinsp;23.23) scored lower than middle (59.91\u0026thinsp;\u0026plusmn;\u0026thinsp;25.06; t(91)=-3.89, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and high SES (65.25\u0026thinsp;\u0026plusmn;\u0026thinsp;27.20; t(91)=-4.97; p\u0026thinsp;\u0026lt;\u0026thinsp;.001). See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for visualizations of significant post-hoc comparisons in cognitive performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e4.2.2. SES Disparities on Academic Performance.\u003c/h2\u003e\u003cp\u003eSignificant SES group differences were observed in the WIAT (\u003cem\u003eF\u003c/em\u003e(8,162)\u0026thinsp;=\u0026thinsp;2.22, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.005, Wilks\u0026rsquo; Λ\u0026thinsp;=\u0026thinsp;.64).. For WIAT Listening Comprehension, low SES (45.45\u0026thinsp;\u0026plusmn;\u0026thinsp;35.02) scored lower than high SES (68.56\u0026thinsp;\u0026plusmn;\u0026thinsp;24.88; t(89)=-2.67, p\u0026thinsp;=\u0026thinsp;.02). For WIAT Math Problem Solving, low SES (25.27\u0026thinsp;\u0026plusmn;\u0026thinsp;29.07) scored lower than middle (65.81\u0026thinsp;\u0026plusmn;\u0026thinsp;25.42; t(89)=-4.59, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and high SES (60.50\u0026thinsp;\u0026plusmn;\u0026thinsp;29.64; t(89)=-3.55, p\u0026thinsp;=\u0026thinsp;.002). WIAT Numerical Operations showed similar differences, with low SES (28.55\u0026thinsp;\u0026plusmn;\u0026thinsp;28.14) scoring lower than middle (65.59\u0026thinsp;\u0026plusmn;\u0026thinsp;22.82; t(89)=-4.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and high SES (68.07\u0026thinsp;\u0026plusmn;\u0026thinsp;23.91; t(89)=-4.98, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). WIAT Pseudo-word Decoding also differed significantly, with low SES (39.55\u0026thinsp;\u0026plusmn;\u0026thinsp;18.87) scoring lower than middle (64.05\u0026thinsp;\u0026plusmn;\u0026thinsp;19.27; t(89)=-3.37, p\u0026thinsp;=\u0026thinsp;.003) and high SES (68.53\u0026thinsp;\u0026plusmn;\u0026thinsp;19.95; t(89)=-4.49, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). For WIAT Reading Comprehension, low SES (43.00\u0026thinsp;\u0026plusmn;\u0026thinsp;21.35) scored lower than middle (66.86\u0026thinsp;\u0026plusmn;\u0026thinsp;18.94; t(89)=-3.07, p\u0026thinsp;=\u0026thinsp;.01) and high SES (68.27\u0026thinsp;\u0026plusmn;\u0026thinsp;21.74; t(89)=-3.65, p\u0026thinsp;=\u0026thinsp;.001). WIAT Spelling also differed significantly, with low SES (30.55\u0026thinsp;\u0026plusmn;\u0026thinsp;21.94) scoring lower than middle (65.50\u0026thinsp;\u0026plusmn;\u0026thinsp;28.75; t(89)=-3.65, p\u0026thinsp;=\u0026thinsp;.001) and high SES (67.10\u0026thinsp;\u0026plusmn;\u0026thinsp;25.44; t(89)=-4.30, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). For WIAT Word Reading, low SES (50.27\u0026thinsp;\u0026plusmn;\u0026thinsp;28.27) scored lower than middle (72.27\u0026thinsp;\u0026plusmn;\u0026thinsp;23.37; t(89)=-2.75, p\u0026thinsp;=\u0026thinsp;.02) and high SES (76.59\u0026thinsp;\u0026plusmn;\u0026thinsp;19.60; t(89)=-3.70, p\u0026thinsp;=\u0026thinsp;.001). See Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for significant post-hoc comparisons in academic performance across SES groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Cognitive and Academic Performance Differences by After-school Music Participation\u003c/h2\u003e\u003cp\u003eDespite the non-significant MANOVA results for music and non-music groups (p\u0026thinsp;\u0026gt;\u0026thinsp;.05), we conducted exploratory independent samples t-tests for NIHTB-Cog, WISC-V, and WIAT task performances to examine potential group differences. Significant differences were observed in Pattern Comparison (music: 71.75\u0026thinsp;\u0026plusmn;\u0026thinsp;28.24; non-music: 50.29\u0026thinsp;\u0026plusmn;\u0026thinsp;32.28, t(24.15)=-2.69, p\u0026thinsp;=\u0026thinsp;.01) and WIAT Listening Comprehension Oral Discourse Comprehension (music: 71.23\u0026thinsp;\u0026plusmn;\u0026thinsp;25.46; non-music: 54.47\u0026thinsp;\u0026plusmn;\u0026thinsp;28.23, t(23.58)=-2.34, p\u0026thinsp;=\u0026thinsp;.03). See Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for the distribution of significant between-group comparisons in cognitive and academic performances by after-school music participation.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4. After-school Music Participation as a Moderator of SES-related Cognitive and Academic Outcomes\u003c/h2\u003e\u003cp\u003ePearson correlations among SES groups, after-school music participation status, and cognitive and academic performance scores are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eCorrelation Matrix of Covariates, Cognitive, and Academic Measures in Preadolescents.\u003c/b\u003e Correlations (r) among independent variables (after-school music participation, SES group), cognitive (NIHTB-Cog, WISC-V scores), and academic performance (WIAT scores) in preadolescents. \u003cem\u003eNote: p-values are indicated as follows: p\u0026thinsp;\u0026lt;\u0026thinsp;0.05*, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01**, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001***\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAfter-school Music\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSES\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHTB-Cog\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCard Sort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlanker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.060\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eList Sort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.248*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern Comparison\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.253*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWIAT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension Oral Discourse Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.226*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.197\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension Receptive Vocabulary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.169\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.239*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMath Problem Solving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.391***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumerical Operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.398***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-word Decoding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.389***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReading Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.307**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpelling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.349***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord Reading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.333***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWISC-V\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluid Reasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.384***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcessing Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.185\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVerbal Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.347***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual Spatial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.270**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking Memory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.409***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSignificant R\u003csup\u003e2\u003c/sup\u003e were observed for list sort (Adj. R\u0026sup2;=0.11, p\u0026thinsp;=\u0026thinsp;.04), pattern comparison (Adj. R\u0026sup2;=0.09, p\u0026thinsp;=\u0026thinsp;.05), WIAT Math Problem Solving (Adj. R\u0026sup2;=0.18, p\u0026thinsp;=\u0026thinsp;.002), WIAT Numerical Operations (Adj. R\u0026sup2;=0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), WIAT Pseudoword Decoding (Adj. R\u0026sup2;=0.15, p\u0026thinsp;=\u0026thinsp;.004), WIAT Reading Comprehension (Adj. R\u0026sup2;=0.12, p\u0026thinsp;=\u0026thinsp;.01), WIAT Spelling (Adj. R\u0026sup2;=0.17, p\u0026thinsp;=\u0026thinsp;.002), WIAT Word Reading (Adj. R\u0026sup2;=0.10, p\u0026thinsp;=\u0026thinsp;.02), WISC-V Fluid Reasoning (Adj. R\u0026sup2;=0.16, p\u0026thinsp;=\u0026thinsp;.002), WISC-V Verbal Comprehension (Adj. R\u0026sup2;=0.16, p\u0026thinsp;=\u0026thinsp;.002), WISC-V Visual Spatial Index (Adj. R\u0026sup2;=0.10, p\u0026thinsp;=\u0026thinsp;.02), and WISC-V Working Memory (Adj. R\u0026sup2;=0.18, p\u0026thinsp;=\u0026thinsp;.002). See Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for a summary of the linear regression results.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eLinear Regression Models Examining SES and Outcomes Moderated by After-school Music Participation.\u003c/b\u003e Associations between SES and cognitive and academic outcomes in preadolescents, with moderating effects of after-school music participation included. P-values are corrected for multiple comparisons using the Benjamini-Hochberg (BH) procedure.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003edf1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003edf2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAdj. R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNIHTB-Cog\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCard Sort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFlanker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.458\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eList Sort\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePattern Comparison\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWIAT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension Oral Discourse Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension Receptive Vocabulary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eListening Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMath Problem Solving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumerical Operations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePseudo-word Decoding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReading Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpelling\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWord Reading\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWISC-V\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFluid Reasoning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProcessing Speed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVerbal Comprehension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVisual Spatial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWorking Memory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSignificant interaction effects were observed for the middle SES group (β\u0026thinsp;=\u0026thinsp;62.01, p\u0026thinsp;=\u0026thinsp;.04) and high SES group (β\u0026thinsp;=\u0026thinsp;65.03, p\u0026thinsp;=\u0026thinsp;.02) on WISC-V processing speed. Additionally, a significant effect was found for the interaction between after-school music participation and high SES group in WIAT numerical operation (β\u0026thinsp;=\u0026thinsp;55.17, p\u0026thinsp;=\u0026thinsp;.04). See Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for the visualization of moderating effects of after-school music participation in these associations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study examined whether after-school music participation moderates SES-related disparities in executive function, language, and academic achievement in preadolescent children. Understanding this interaction is critical for identifying scalable, enrichment-based strategies that may mitigate the effects of socioeconomic disadvantage on cognitive development. These strategies are often conceptualized within the framework of PCEs, which emphasize the developmental importance of enriching, engaging, and relationally supportive environments. Structured extracurricular activities such as music and sports are key forms of PCEs that promote cognitive and academic growth [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSES remains a robust predictor of cognitive and academic performance in childhood, shaping access to enrichment opportunities, educational resources, and developmental support [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Consistent with prior research, our findings revealed that children from low SES scored significantly lower than middle and high SES peers on working memory, processing, verbal comprehension, and numerical reasoning [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These results underscore the pervasive impact of socioeconomic disadvantage on cognitive development and academic achievement, likely mediated through both behavioral and neurobiological pathways [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While we hypothesized that SES would also predict rates of after-school music participation, chi-square analyses revealed no significant differences in participation rates across SES groups. This unexpected finding does not necessarily contradict the broader role of SES in shaping access to enrichment; rather, it may reflect limitations in how participation was captured. Without accounting for factors like duration and engagement type, binary measures may miss meaningful variation in how preadolescent children experience music programs, variation that could be especially relevant across socioeconomic strata.\u003c/p\u003e\u003cp\u003eDespite similar participation rates, after-school music participation appeared to confer academic benefits primarily for children from middle and high SES backgrounds. Moderation analyses revealed that music participation strengthened academic performances, particularly in processing speed and numerical operations, within these groups. This pattern suggests that music may act as an enrichment tool that supports academic development when paired with sufficient resources and support. These findings align with prior research that music training enhances working memory, attention, and verbal fluency and are consistent with Patel\u0026rsquo;s OPERA hypothesis, which explains that music training strengthens auditory and attentional networks through overlap, precision, emotion, repetition, and attention that can enhance processing speed and language-related skills [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Rhythm-based training has also been shown to improve neural synchrony, supporting executive functions and reading development, with neuroimaging studies demonstrating music-induced structural and functional changes in frontoparietal and auditory networks that support executive control and working memory [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These findings aligned with research on PCEs and other enriching extracurricular activities. For instance, participation in after-school sports has been associated with gains in executive function, processing speed, and language comprehension, even after accounting for SES and IQ [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This convergence suggests that the cognitive and academic benefits observed in music may reflect a broader pattern across structured, engaging activities that support development.\u003c/p\u003e\u003cp\u003eThe absence of similar cognitive benefits for low SES children, despite comparable participation rates, suggests that structural barriers may constrain the potential impact of music enrichment in this group. Children from low SES backgrounds often encounter programs that differ in music participation intensity and quality, including shorter durations, fewer trained instructors, and limited exposure to cognitively demanding activities such as ensemble performance, music theory, or individualized instruction [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Logistical challenges such as transportation, scheduling conflicts, or lack of parental availability may further impede sustained engagement [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These barriers may also be compounded by competing stressors, including economic instability, food insecurity, or limited academic support at home, which can diminish the cognitive benefits typically associated with enrichment activities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Schools serving low SES communities may offer fewer extracurricular resources or rely on underfunded programs perpetuating disparities in enrichment quality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These contextual factors underscore the importance of not only increasing access to music programs but also ensuring that such programs are high-quality, developmentally appropriate, and equitably resourced. Without addressing these barriers, enrichment strategies like music and other PCEs may inadvertently reinforce existing disparities rather than mitigate them.\u003c/p\u003e\u003cp\u003eOverall, this study introduces several methodological strengths. We were among the first to apply the BJS recommendations for a composite SES classification, improving validity over single indicators such as income or parental education. An additional strength in using this construct was that the BJS recommendations were matched to the years in which the children participated in the study. We also cross-checked clinical and parent-reported diagnoses to ensure a neurotypical sample, reducing the confounding effects of neurodevelopmental conditions. Additionally, this work is one of the first to leverage the NIHTB-Cog in the context of music participation and SES, providing standardized cognitive measures across multiple domains. Findings are specific to neurotypical populations and may differ in neurodivergent groups, where music engagement could interact with distinct developmental trajectories. Future research should explore these differences using longitudinal designs, detailed music engagement metrics, and neuroimaging approaches to clarify mechanisms and inform targeted strategies.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eThis study has several limitations that warrant consideration. First, the SES index calculated in our study may still not fully capture environmental factors, such as neighborhood resources, school quality, and policy changes, that could influence observed associations. Additionally, the sample was disproportionately skewed toward families in the higher income range, which may limit generalizability and complicate interpretations of SES group comparisons. Future work should incorporate contemporaneous SES measures and contextual variables to better capture these dynamics. Second, music participation was measured as a binary variable (Yes/No), which does not account for critical dimensions such as duration, intensity, type of engagement, or cumulative exposure, which likely modulate cognitive benefits. The lack of granularity limits examination of dose-response relationships and may obscure meaningful variation in engagement across SES groups. Moreover, no data were available on program characteristics such as curriculum content, and there were no direct measures of engagement, making it difficult to determine whether children were actively involved or passively enrolled. Third, potential unmeasured confounders such as parental involvement, school-level resources, and neighborhood-level access to enrichment may have influenced music participation and cognitive and academic outcomes. These factors were not captured in the current dataset and may help explain the absence of observed benefits for low SES children. Fourth, the study\u0026rsquo;s cross-sectional design limits the causal inference. Although moderation effects were observed for cognitive and academic outcomes, we cannot determine whether music participation directly contributes to developmental gains. Even longitudinal studies face challenges disentangling selection effects and confounding variables, making RCTs the rigorous approach for testing whether sustained music engagement leads to measurable improvements in cognitive and academic outcomes. Additionally, analyses were conducted using an available-case approach, which may introduce bias if missing data were not random. Future studies should consider more robust methods for handling missingness, such as multiple imputation.\u003c/p\u003e"},{"header":"7. Conclusions","content":"\u003cp\u003eThis study offers preliminary evidence that after-school music engagement may moderate the relationship between SES and academic performance in preadolescents, particularly among children from middle- and high SES backgrounds. Although after-school music participation rates did not differ significantly by SES, these findings raise the possibility that music participation may operate as a context-dependent enrichment factor, with its benefits potentially shaped by SES-related environmental stimuli. Rather than viewing music solely as an extracurricular activity, it may be valuable to explore its role as a scalable, evidence-based support for cognitive and academic development. Integrating structured music programs within school and community settings - particularly during preadolescence, a sensitive developmental window characterized by heightened neuroplasticity - could offer one potential avenue for promoting more equitable developmental outcomes and optimizing cognitive trajectories. Future research should extend these findings using longitudinal and RCT designs to examine causal pathways and sensitive periods, incorporate detailed measures of music engagement, and leverage neuroimaging and computational models to explore mechanisms underlying SES-related differences in benefits. Evaluating implementation strategies for scaling music programs in under-resourced communities will be critical for translating these findings into practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no financial support for the research.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no competing interests or financial support for this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: N. O\u0026rsquo;Malley, M. Lim, \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eMethodology: N. O\u0026rsquo;Malley, M. Lim, Gaudreau, J., \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eFormal Analysis: N. O\u0026rsquo;Malley, M. Lim, \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eInvestigation: N. O\u0026rsquo;Malley, M. Lim, \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Original Draft: N. O\u0026rsquo;Malley, M. Lim, \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; Review \u0026amp; Editing: N. O\u0026rsquo;Malley, M. Lim, J. Gaudreau, C. Clarkin, \u0026amp; N.E. Logan\u003c/p\u003e\n\u003cp\u003eVisualization: M. Lim\u003c/p\u003e\n\u003cp\u003eSupervision: N.E. Logan\u003c/p\u003e\n\u003cp\u003eProject Administration: N.E. Logan\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis manuscript was prepared using a limited access dataset obtained from the Child Mind Institute Biobank \u0026ndash; Healthy Brain Network. This manuscript reflects the views of the authors and does not necessarily reflect the opinions or views of the Child Mind Institute.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreprocessing and analysis code used in this study is available at: https://github.com/loganlaburi/CMI_music.git\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWong, M. \u0026amp; Nadeem, E. Responding to the challenges of preadolescence: roles for caregivers. In Caregiving Across the Lifespan: Research\u0026bull; Practice\u0026bull; Policy (47\u0026ndash;59). New York, NY: Springer New York. (2012).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCiccia, A. H., Meulenbroek, P. \u0026amp; Turkstra, L. S. Adolescent Brain and Cognitive Developments: Implications for Clinical Assessment in Traumatic Brain Injury. \u003cem\u003eTop. 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Socioeconomic status and cognitive functioning: moving from correlation to causation. \u003cem\u003eWiley Interdisciplinary Reviews: Cogn. Sci.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (3), 377\u0026ndash;386 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Preadolescence, Socioeconomic Status, Music, Cognition, Academic Performance","lastPublishedDoi":"10.21203/rs.3.rs-8166352/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8166352/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to investigate the moderating effects of music participation on socioeconomic status (SES) related disparities on cognitive and academic performance during preadolescence.\u003c/p\u003e\u003cp\u003eNeurotypical children (N\u0026thinsp;=\u0026thinsp;94, ages 9\u0026ndash;12; 42% female) from the Healthy Brain Network were analyzed. SES was categorized using a composite index based on Bureau of Justice Statistics guidelines (low, middle, high). After-school music participation was parent-reported. Cognitive and academic performance were measured using the NIH Toolbox Cognition Battery (NIHTB-Cog), Wechsler Intelligence Scale for Children Fifth Edition (WISC-V), and Wechsler Individual Achievement Test Third Edition (WIAT). Benjamini-Hochberg adjusted linear regressions analyzed the moderating effects of music on SES and cognitive/academic performance. Moderation analyses revealed music participation strengthened performance in WISC-V processing speed for the middle SES group (β\u0026thinsp;=\u0026thinsp;61.80, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.04) and high SES group (β\u0026thinsp;=\u0026thinsp;64.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), and in WIAT numerical operations for high SES children (β\u0026thinsp;=\u0026thinsp;51.96, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.05). After-school music participation may moderate SES-related differences in processing speed and numerical operations among middle- and high SES preadolescents. Benefits were absent for low SES groups, suggesting structural barriers to enrichment access. Integrating structured music programs within school and community settings may help reduce SES-related disparities in cognitive and academic development.\u003c/p\u003e","manuscriptTitle":"Music as a Moderator of Socioeconomic Disparities in Preadolescent Cognitive and Academic Performance: Evidence from the Healthy Brain Network Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 10:29:00","doi":"10.21203/rs.3.rs-8166352/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":"73e57fba-845b-47e5-b364-3edc91546299","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59045215,"name":"Health sciences/Health care"},{"id":59045216,"name":"Biological sciences/Neuroscience"},{"id":59045217,"name":"Biological sciences/Psychology"},{"id":59045218,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-01-16T13:26:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 10:29:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8166352","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8166352","identity":"rs-8166352","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}