
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39251706
71309
10.1038/s41598-024-71309-2
Article
Neurodevelopmental imprints of sociomarkers in adolescent brain connectomes
Kang Eunsong 1
Yun Byungyeon 3
Cha Jiook 4
Suk Heung-Il hisuk@korea.ac.kr

2
Shin Eun Kyong eunshin@korea.ac.kr

5
1 https://ror.org/047dqcg40 grid.222754.4 0000 0001 0840 2678 Department of Brain Cognitive Engineering, Korea University, Seoul, Korea
2 https://ror.org/047dqcg40 grid.222754.4 0000 0001 0840 2678 Department of Artificial Intelligence, Korea University, Seoul, Korea
3 https://ror.org/017zqws13 grid.17635.36 0000 0004 1936 8657 Department of Educational Psychology, University of Minnesota, Minneapolis, MN USA
4 https://ror.org/04h9pn542 grid.31501.36 0000 0004 0470 5905 Department of Psychology, Seoul National University, Seoul, Korea
5 https://ror.org/047dqcg40 grid.222754.4 0000 0001 0840 2678 Department of Sociology, Korea University, Seoul, Korea
9 9 2024
9 9 2024
2024
14 2092123 2 2024
27 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Neural consequences of social disparities are not yet rigorously investigated. How socioeconomic conditions influence children’s connectome development remains unknown. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual’s social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain. Utilizing Adolescent Brain and Cognition Development (ABCD) data (9099 children residing in the United States), we found that social conditions both at the household and neighborhood levels are significantly associated with specific neural connections. Solely with brain connectome data, we train a linear support vector machine (SVM) to predict socio-economic conditions of those adolescents. The classification performance generally improves when the thresholds of the advantageous and disadvantageous environments compartmentalize the extreme cases. Among the tested thresholds, the 20th and 80th percentile thresholds using the dual combination of household income and neighborhood education yielded the highest Area Under the Precision-Recall Curve (AUPRC) of 0.8224. We identified 8 significant connections that critically contribute to predicting social environments in the parietal lobe and frontal lobe. Insights into social factors that contribute to early brain connectome development is critical to mitigate the disadvantages of children growing up in unfavorable neighborhoods.

Keywords

Sociomarkers
Adolesecent brain
Connectomes
Neurodevelopmental imprints
Subject terms

Neuroscience
Medical research
http://dx.doi.org/10.13039/501100003725 National Research Foundation of Korea 2022R1A4A1033856 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Development of a brain is shaped by numerous social factors. The rapidly growing body of knowledge highlights the social influence on brain development1–6. Existing studies on the relationship between neighborhood conditions and child developmental outcomes provide solid evidence of significant relationship between the two7,8. Neighborhoods effects during childhood not only influence the next generation’s income level but also health outcomes and educational attainment in adulthood9,10. In recent years, the empirical studies between multiple social economic status (SES) measures and brain structures suggest that various neural functions are influenced by social conditions. Adverse social conditions invite high levels of stress and reduced environmental stimulation, which in turn has negative consequences on neural development11. These distressed social environments are associated with neuro-anatomical structure of the brain12,13.

Existing studies have primarily focused on anatomical brain features with small sample sizes, investigating the association between SES and brain structural changes in children and adolescents living in a city or town lacking nation-wide evidence4,14–16. In these studies, childhood SES includes various socioeconomic indicators such as family income-to-needs ratio, parental education, family income, and neighborhood deprivation, all of which have the potential to impact a child’s maturation and advancement. Children who had a higher SES showed greater development in adult brain surface area, including increased cortical thickness14. In addition, another study revealed that higher levels of neighborhood deprivation were associated with greater surface area, while it also was linked with decreased cortical thickness, especially subregions in the prefrontal cortex (PFC)15. Furthermore, children whose families have insufficient family income and low parental education levels demonstrated decreases in cortical volume and thickness4,16. These associations have been found to persist from early childhood to adulthood consistently4,14. In other words, socioeconomic status can have an impact on the process of brain development and result in differences in various cognitive abilities15,16.

Recently, adolescent brain and cognition development (ABCD)17 dataset, the largest dataset in the U.S. assessing brain development, has increasingly utilized for neurodevelopment analysis based on various neuroimaging modalities, including functional magnetic resonance imaging (fMRI)18, structural magnetic resonance imaging (sMRI)13, and DTI tract-based analyses19. Therefore, we utilized the ABCD dataset to examine the relationships between the social factors both at the household level and neighborhood-level (economic status/affluence, educational attainment) and structural features of brain by combining connectome data with sociomarkers. We trained a support vector machine to predict social conditions only using structural brain connectivity metrics. ABCD dataset merged with US Census data at the tract level allows us to nest the brain/neuro data into their social locality. To discover the neural imprint of social influence in adolescents, we reverse engineered the hypothesis. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual’s social environment, thereby inversely assessing how social influences are engraved in the neural development of the brain.

Using ABCD, we examined the relationships between the social factors both at the household level and neighborhood-level (economic status/affluence, educational attainment) and structural features of brain by combining connectome data with sociomarkers. We trained a support vector machine to predict social conditions only using structural brain connectivity metrics. Our data were merged with US Census data at the tract level, nesting the brain/neuro data into their social locality. To discover the neural imprint of social influence in adolescents, we reverse engineered the hypothesis. This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual’s social environment, thereby inversely assessing how social influences are engraved in the neural development of the brain.

Results

Classification

Table 1 presents the results of our study. The permutation test results were significant with p<0.05 for all conditions. We utilized neural connections to predict the favorable and unfavorable social conditions of adolescents. Four social features were included in this study: Household-level parents’ educational attainment (H-Education or H-Edu), household income (H-Income or H-Inc), Neighborhood educational attainment (N-Education or N-Edu), and Neighborhood poverty prevalence (N-Poverty or N-Pov). Subjects from White, Black, and Hispanic racial groups were used for classification. We evaluated the classification performance of our model using several quantitative metrics, including the Area Under the Precision-Recall Curve (AUPRC), accuracy, precision, recall, and F1 score.Table 1 Average performance of social condition prediction in an AUPRC, accuracy (ACC), precision (PRE), recall (REC), and F1 score.

	AUPRC	ACC	PRE	REC	F1	
H-Education*	0.7182±0.02	0.6655±0.02	0.6596±0.02	0.6845±0.03	0.6716±0.02	
H-Income*	0.7787±0.02	0.7029±0.02	0.7164±0.02	0.6729±0.02	0.6936±0.03	
N-Education*	0.7782±0.02	0.7130±0.02	0.7192±0.03	0.6998±0.02	0.7091±0.02	
N-Poverty*	0.7491±0.02	0.6937±0.02	0.6992±0.02	0.6805±0.02	0.6895±0.02	
H-Edu & H-Inc*	0.7709±0.02	0.7063±0.02	0.7112±0.02	0.6958±0.02	0.7030±0.03	
H-Edu & N-Edu*	0.7818±0.03	0.7134±0.03	0.7181±0.03	0.7036±0.03	0.7101±0.05	
H-Edu & N-Pov*	0.7687±0.03	0.7032±0.03	0.7025±0.03	0.7067±0.03	0.7041±0.03	
H-Inc & N-Edu*	0.8224±0.02	0.7457±0.02	0.7521±0.02	0.7340±0.02	0.7425±0.03	
H-Inc & N-Pov*	0.8052±0.02	0.7324±0.02	0.7403±0.02	0.7178±0.03	0.7282±0.03	
N-Edu & N-Pov*	0.7586±0.03	0.6977±0.03	0.7054±0.03	0.6802±0.03	0.6920±0.04	
All*	0.7704±0.04	0.7037±0.04	0.7125±0.04	0.6877±0.05	0.6982±0.06	
* denotes the significance of permutation tests with p<0.05.

The first set of experiments, we trained SVM to predict the four single conditions where children were categorized according to each social condition. N-Education has the highest ACC of 0.7130, while H-Education has the lowest ACC of 0.6655. Overall, N-Education consistently outperforms the other social conditions across PRE, REC, and F1 metrics, while H-Education shows the lowest performance in comparison. To further test accumulative social influences, children were categorized based on dual Advantageous/Disadvantageous conditions (i.e., low income family living in a less affluent neighborhood). The overall performance improved compared to the single condition prediction. The combination of H-Inc & N-Edu shows the highest performance across all metrics: AUPRC of 0.8224, ACC of 0.7457, PRE of 0.7521, REC of 0.7340, and F1 of 0.7425. The combination of H-Inc & N-Pov also performs well, with the second-highest scores for AUPRC of 0.8052, ACC of 0.7324, PRE of 0.7403, and F1 of 0.7282. In the quadruple condition, where all four social factors were considered to define advantageous/disadvantageous environments (i.e., high parental educational attainment, high household income, residence in affluent neighborhoods, and higher average neighborhood education levels), the model still exhibited high classification performance with an AUPRC of 0.7704.

Figure 1 presents the classification results in terms of AUPRC for various thresholds, with detailed numerical values for all metrics provided in Supplementary A. In the single condition, the 50% threshold, which includes children with average social conditions, yielded the lowest AUPRC. The AUPRC tends to increase as the thresholds become more extreme. For the cross condition, AUPRC values for the 10%, 20%, and 30% thresholds are comparable, while the 50% threshold consistently shows the lowest performance. However, these results show improvement compared to the single condition. In the quadruple condition, the 20% and 30% thresholds produce AUPRC values exceeding 0.75. Notably, the AUPRC for the 10% threshold drops significantly, likely due to a substantial decrease in sample size. It is important to note that Table 1 presents results using the 20% threshold. Overall, these findings suggest that classification performance generally improves as the thresholds for advantageous and disadvantageous environments become more extreme, with some variations across different conditions and threshold levels.Fig. 1 Classification results of AUPRC based on different threshold (10%, 20%, 30%, and 50%) over single, cross, and quadruple social conditions.

Significant connections for social condition prediction

To analyze the association of neural connections and social conditions, we calculated the activation patterns for quadruple conditions. We identified 8 significant connections mainly involved in social environment prediction: L.IPG & L.SPG, R.IPG & R.SPG, L.RMFG & L.SFG, R.RMFG & R.SFG, L.PoCG & L.PrCG, R.PoCG & R.PrCG, L.PCU & L.SPG, and R.PCU & R.SPG (See Fig. 2). Without any constraint for left and right pairs, we obtained the four left and right pairs of connections. In addition, the selected regions are mainly located in the frontal lobe and parietal lobe. The main role of the selected regions is summarized in Table 2. The names of ROIs for the Desikan-Killiany atlas and their anatomical locations are listed in Supplementary D.Fig. 2 Significant eight connections predicting quadruple social conditions.

Table 2 The function of selected significant connections.

ROI	Lobe	Function	
IPG	Parietal	Various cognitive processes, including attention, language, and spatial cognition	
SPG	Parietal	Spatial attention and perception	
RMFG	Frontal	Particularly for executive function (planning, problem-solving, and cognitive flexibility)	
SFG	Frontal	A broader range of cognitive processes, including attention, working memory, and motor planning	
PoCG	Parietal	Processing somatosensory information from the body (also known as primary somatosensory cortex)	
PrCG	Frontal	Controlling voluntary movements in the body (also known as primary somatosensory cortex)	
PCU	Parietal	Self-processing, attention, and memory	

Discussion

This study has two distinctive contributions. First, we empirically discovered the specific structural connections that are critical for predicting social conditions by employing machine learning-based classification models. Unlike previous studies that analyze only several regions with prior knowledge via a statistical test, we used whole structural connections and our model made a prediction exclusively based on a complex relation matrix among different brain regions. Furthermore, we analyzed important structural connections using activation patterns, which can differentiate between the adolescent living in the advantageous environment and the disadvantageous environment.

The brain regions composed of eight significant connections are known to be involved in language, emotional, and executive functions, and have been shown to be particularly susceptible to the influence of socioeconomic disparities in the existing literature. Early childhood cumulative risk has been found to have a negative association with the development of children’s right superior parietal gyrus (SPG) and inferior parietal gyrus (IPG) thickness, which in turn may impact their attention, learning, memory, and inhibitory control abilities20. In addition, parental education has been linked to the cortical surface area of bilateral superior parietal lobule regions4,21. Moreover, childhood socioeconomic status, as measured by education, income, and occupation levels, has been correlated with gray matter volume in several regions, including the left postcentral gyrus and left precentral gyrus22. The left rostral middle frontal gyrus (RMFG) and left and right superior frontal gyrus (SFG) cortical thickness were also significantly correlated with neighborhood socioeconomic disadvantages15,23. Using functional neuroimaging, poverty was associated with reduced functional network efficiency in widespread brain regions such as the SFG, RMFG, SPG, IPG, PCU, PoCG, and PrCG, which is consistent with our own findings24. Furthermore, it was reported that an increase in regional homogeneity (ReHo) values in the PoCG and PrCG for adolescents with depression who are likely to be influenced by low socioeconomic status25. Interestingly, several regions are overlapped with the sMRI study13 that found higher neighborhood disadvantage to be associated with reduced thickness of several brain regions (e.g., postcentral gyrus, precentral gyrus, rostral middle frontal gyrus, superior parietal gyrus), providing further evidence for the impact of social environment on brain development.

Our predictive model, SVM, and its analysis method, activation pattern, utilize multivariate analysis to examine multiple structural connections simultaneously. This approach helps us unpack the intricate relationships between all structural brain connections and socioeconomic status. Unlike correlation analysis, which focuses on understanding the connection between a single specific structural connection and social conditions, it is important to note that the eight significant connections collectively play a crucial role in predicting social conditions. Even if a connection exhibits a low correlation with SES, it can still be significant and influential when considering its combination with other brain connections. The collective orchestration impact of multiple connections working together can have a substantial effect on predicting social conditions. This also implies that social environments exert their influence not on specific neural connections individually but rather affect the comprehensive orchestration of various regions within the brain.

Limitations and future works

To compare children from extremely advantageous and disadvantageous conditions, it was necessary to apply thresholds that inevitably excluded average samples from the dataset. While this approach allowed for a clear distinction between groups, it may have led to an oversimplification of the results. An alternative approach could involve using regression models on the entire dataset. This method would be particularly effective if structural connectivity data can accurately predict the full range of values for all social conditions. If such predictive power is achievable, a regression approach would preserve the nuanced variations in social conditions and potentially offer a more comprehensive understanding of the relationship between brain structure and social environment across the entire spectrum of socioeconomic status.

For future research endeavors, it is imperative to address the multifaceted aspects of social environmental variables. This study examined two types of social features, educational attainments and income both at the house-hold and neighbor-hood level, considering single, dual, and quadruple social conditions for classification. The educational attainment measures parental educational attainment-specifically the highest level achieved between the parents-rather than the child’s own educational attainment. This may introduce a potential confounding effect, as the data reflects the educational level of one parent instead of directly measuring that of the child. Additionally, due to the limited data accessibility, we were unable to adjust for cost-of-living variations across different regions in the United States. Future research should focus on conducting a more nuanced analysis of the relationship between parental educational levels and the academic achievements of their children and explore multiple social conditions in greater detail, which could lead to more precise neurological analyses during childhood. Multivariate regression analysis can be utilized if sufficient data samples exists for each value or interval of the continuous social variable.

Furthermore, the results of this study demonstrated the differences in brain development based on the developmental environment. The classification performance only indicates the association between brain development and socioeconomic status, not the causal relationship between them. Therefore, further investigations are necessary to elucidate the potential associations or causality between these distinct patterns of brain development and various behavioral, cognitive, and social outcomes. Understanding these relationships will provide valuable insights into the complex interplay between environmental factors and neurodevelopmental trajectories, ultimately informing strategies to support optimal growth and well-being for children from diverse backgrounds.

Additionally, measuring the development of the brain can be achieved not only through diffusion tensor imaging but also through structural or functional MRI. By incorporating a variety of measured values, i.e., multi-modal analysis, our comprehension of neurodevelopment corresponding to socioeconomic status can be expanded.

Methods

Dataset

The Adolescent Brain Cognitive Development (ABCD) dataset consists of neuroimaging, cognitive assessments, and environmental factors from about 12,000 participants. Data for the ABCD study were collected 21 different sites in the United states. The main objective of collecting data in the ABCD study is to gain a better understanding of how the brain and behavior develop during adolescence, and how different factors can impact this development over time. Of various neuroimaging data, we used diffusion tensor imaging (DTI) that measures the white matter tracts, which has been widely used for adolescent development studies26–28. In this study, we used ABCD study release 2.0. Furthermore, we utilized the comprehensive quality control results provided by the ABCD Data Analysis and Informatics Center (DAIC)29. Their rigorous assessment involved a multi-faceted approach, encompassing protocol adherence verification, automated quality control metrics implementation, and thorough manual evaluation of data quality. This robust quality control process, specifically designed to address challenges such as head motion in pediatric neuroimaging, ensured the integrity and reliability of the data used in our study. We removed individuals with incomplete environmental factor data, resulting in a sample of 9099 participants. In addition, we excluded the participants of ‘Asian’ and ‘Others’ due to limited quantity. The skewed samples for ‘Asian’ and ‘Others’ could potentially lead to statistically unreliable results and misinterpretations. Specifically, Asian are only 2% of participants and most of them are concentrated in an advantageous environment.We report the demographic characteristics of study participants in Table 3. Demographic profiles for age, sex, and socio-economic status were comparable. Furthermore, we demonstrate the social condition distributions for three races or ethnicities, along with the actual values corresponding to the top and bottom percentages of the social condition for each race or ethnicity in Supplementary B. This study was approved by Korea University Institutional Review Board (KUIRB-2022-0276-01).Table 3 Demographic Characteristics of Participants.

Variable	N(%) / Mean (SD)	
Gender	
 Male	4707 (51.7%)	
 Female	4392(48.3%)	
Age (Month)	119.17 (7.48) [108.000–131.000]	
Income	
 Less than $5000	280 (3.1%)	
 $5000 through $11,999	289 (3.2%)	
 $12,000 through $15,999	202 (2.2%)	
 $16,000 through $24,999	359 (3.9%)	
 $25,000 through $34,999	495 (5.4%)	
 $35,000 through $49,999	703 (7.7%)	
 $50,000 through $74,999	1168 (12.8%)	
 $75,000 through $99,999	1938 (21.3%)	
 $100,000 through $199,999	2684 (29.5%)	
 $200,000 and greater	981 (10.8%)	
Race/Ethnicity	
 White	4972 (54.6%)	
 Black	1200 (13.2%)	
 Hispanic	1829 (20.1%)	
 Asian	185 (2.0%)	
 Other	913 (10.0%)	
Parent’s education level	
 Primary	61 (0.67%)	
 Secondary	1386 (15.23%)	
 College or higher degree	7652 (84.0%)	
Parent’s Marital Status	
 Married	6230 (68.5%)	
 Widowed	68 (0.7%)	
 Divorced	799 (8.8%)	
 Separated	323 (3.5%)	
 Living with partner	496 (5.5%)	
 Never married	998 (11.0%)	
 Missing	185 (2.0%)	
Height	55.3 (3.17) [36.5–81.0]	
Weight	82.1 (22.9) [24.0–224]	
BMI	18.7 (4.08) [5.27–53.9]	

We calculated structural connectivity (SC) from DTI data using MRtrix330, which represents the strength of the fiber tract structurally connected of two regions. SC is described mathematically as a graph where the nodes and edges are defined by brain regions and weights of connections, respectively. For the nodes, we parcellated the brain into 84 regions of interests (ROIs) using the default FreeSurfer parcellation atlas, Desikan-Killiany, while the edges are defined by fiber tracts streamline count. The streamline count refers to the number of reconstructed fibers that can be identified between two regions of the brain31. An 84 × 84 whole-brain connectome matrix is generated for each participant, using 10 million streamlines and T1-based parcellation and segmentation from FreeSurfer, as many studies widely used32–35. Then, we scaled the SC between 0 and 1 via min-max normalization to make the SC more amenable to training models. It is done by subtracting the minimum value from each data point and dividing it by the range. Owing to the symmetric property of SC, we only used upper triangle connections (3486 = 84 × 83 / 2) as an input.

Operationalization of social conditions

To investigate the association between social environment factors and early brain developments, we selected four social factors from the perspective of household and neighborhood: Household education (H-Education; H-Edu), Household income (H-Income; H-Inc), Neighborhood education (N-Education; N-Edu), and Neighborhood poverty (N-Poverty; N-Pov). For household level education and income, we used the ABCD questionnaire data: ‘What is the highest grade or level of school you have completed or the highest degree you have received?’ and ‘What is your total combined family income for the past 12 months?’ For neighborhood level education and deprivation, we used US census data merged at the census tract level: Percentage of population aged greater than or equal to 25 with at least a high school diploma and the percentage of families below the federal poverty line.

Experiment design

This paper endeavors to gauge how precisely the connectome structure of the brain can predict an individual’s social environment, thereby inversely assessing how social influences are engraved in the neural development of the Adolescent brain. Defining a advantageous and disadvantageous environment as a prediction outcome, we hypothesized that we can predict the social conditions by the state of neurodevelopment, in our case, structural connectivity, if these are strongly associated with each other. The advantageous and disadvantageous condition is relatively defined for the top 20% and bottom 20% of participants across four social conditions. Additionally, we conducted an ablation study using multiple thresholds (e.g., 10%, 20%, 30%, and 50%) and selected 20% as it consistently yielded higher classification performance across all conditions. All results with other thresholds are reported in Fig. 1 and Supplementary A, while the results across races or ethnicities using the 20% threshold are presented in Supplementary C.

We conducted three types of experiments: single condition, dual-condition, and quadruple condition. First, we investigated the association between each social condition and childhood neurodevelopment by predicting each social condition. Not only do we predict every single condition at the level of household or neighborhoods, but we also predict the dual conditions that simultaneously consider two single conditions out of four conditions. The combinations of household and neighborhood conditions have advantages in being considered at both levels simultaneously. Since each single case has the label of advantageous and disadvantageous environment, there are four types of labels (Advantageous-Advantageous, Advantageous-Disadvantageous, Disadvantageous-Advantageous, and Disadvantageous-Disadvantageous) in dual-social conditions13. However, we only used the adolescents living in a advantageous or disadvantageous environment in both conditions to focus on the dichotomous environments. Lastly, we considered all four social conditions, denoted by quadruple condition. Likewise dual-conditions, we deal with the participants who are satisfied the all four conditions as advantageous environments and those as disadvantageous environments. Taken together, we have eleven cases according to the social conditions: (1) H-Edu, (2) H-Inc, (3) N-Edu, (4) N-Pov, (5) H-Edu & H-Inc, (6) N-Edu & N-Pov, (7) H-Edu & N-Edu, (8) H-Inc & N-Pov, (9) H-Edu & N-Pov, (10) H-Inc & N-Edu, and (11) H-Edu & H-Inc & N-Edu & N-Pov. The sample distribution for these cases are presented in Tables 4, 5, and 6. Furthermore, we carried out a statistical analysis to compare the age and gender of individuals in the group with a advantageous social environment to those in the group with a disadvantageous social environment. Most of the results showed that there was no significant difference in age (9 to 10 years old) and gender groups (males and females).Table 4 The number of data samples in accordance with dual social conditions.

		H-Edu	H-Inc	N-Edu	N-Pov	
10%	Advantageous	2017	842	839	804	
Disadvantageous	1154	991	801	802	
20%	Advantageous	2017	3195	1617	1595	
Disadvantageous	2605	2057	1601	1602	
30%	Advantageous	4343	3195	2420	2395	
Disadvantageous	2605	3096	2402	2509	
50%	Advantageous	4343	4905	4003	4002	
Disadvantageous	3658	3096	3998	3999	

Table 5 The number of data samples in accordance with dual social conditions.

		H-Edu &H-Inc	H-Edu &N-Edu	H-Edu &N-Pov	H-Inc &N-Edu	H-Inc &N-Pov	N-Edu &N-Pov	
10%	Advantageous	457	363	321	257	198	269	
Disadvantageous	467	360	350	276	342	440	
20%	Advantageous	1420	681	627	1115	1053	803	
Disadvantageous	1332	1013	1010	873	940	1091	
30%	Advantageous	2698	1928	1812	1598	1545	1505	
Disadvantageous	1716	1400	1419	1538	1615	1859	
50%	Advantageous	457	363	321	257	198	269	
Disadvantageous	467	360	350	276	342	440	

Table 6 The number of data samples in accordance with a quadruple social condition.

	10%	20%	30%	50%	
Advantageous	50	279	931	2107	
Disadvantageous	107	508	880	1591	

Predictive model and interpretation

We implemented a linear support vector machine (SVM)36 to classify the social conditions l^n∈{Advantageous (1),disadvantageous (-1)} based on structural connections as follows,1 l^n=sgn(w^⊤fn+w^0),

where sgn denotes a sign function and fn is an input connectivity feature of participant n. The weight coefficient w^ and the bias w^0 are trained to find the decision boundary with the largest margin that maximally separate the samples of different classes in the training data.

For neuroscientific analysis, we analyzed the learned SVM weights via activation pattern37. The SVM is a model that works in a reverse manner, trying to determine the status label based on connectivity information. The weights learned through this process indicate how the connectivity information is combined to estimate the social condition, rather than how the social condition is manifested in the observed brain regions. Therefore, it needs to be transformed into a forward model for neurophysiological interpretation. Transformed into a the forward model, the Eq. (1) is defined as follows:2 fn=al^n+εn,

where a and εn denote the activation pattern and a noise, respectively. The activation pattern a is estimated by3 a=Σfw^Σl-1,

where Σf=E[fnfn⊤]n and Σl-1=[(w^⊤fn+w^0)2]n. E denotes an expectation. According to the Eq. (3), the values and signs of the activation pattern can be interpreted as the importance and effect direction of connectivity for prediction.

Experimental settings

To avoid overfitting in the model, we used a 5-fold cross-validation that randomly divides samples into the 60% training data, 20% validation data, and 20% test data. Furthermore, to ensure the generalizability of the model, we repeated the 5-fold cross validation ten times. Finally, we reported average classification performance and its standard deviation from ten repetitions of 5-fold cross-validation. Additionally, we conducted a permutation test on the results of all repetitions of the 5-fold cross-validation. The C parameter of SVM is selected via automatic hyperparameter optimization software framework Optuna38. The search space of the C parameter is set to logarithmic steps in {1e-3,...,1e2}. Due to the imbalance of each case, we randomly selected samples from the majority class according to the number of fewer class.

To investigate the association between dichotomous social environments and SC, we examined the activation patterns for quadruple condition. We identified the eight most prominent connections based on their absolute activation values. It is worth noting that these connections were consistently selected in over 45 instances out of the total trained SVM models, which consisted of 50 models derived from 10 repetitions of 5-fold cross-validation.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71309-2.

Author contributions

Eunsong Kang conducted the experiment and drafted the manuscript; Byungyeon Yun prepared the data; Ji-wook Cha acquired the data and pre-processed data; Heung-Il Suk supervised the experiment and designed the study; Eun Kyong Shin conceived of the study, supervised the experiment and drafted the manuscript.

Funding

The research has been funded by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2022R1A4A1033856).

Data availability

Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study.

Code availability

The linear SVM model is implemented by LinearSVC in Scikit-learn python library (https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html) and its C hyper-parameter is optimized by automatic optimization software Optuna (https://github.com/optuna/optuna-examples/blob/main/sklearn/sklearn_simple.py).

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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