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Subcortical brain regions play essential roles in the pathology of social anxiety disorder (SAD). While adolescence is the peak period of SAD, the relationships between altered development of the subcortical regions during this period and SAD are still unclear. This study investigated the age-dependent alterations in structural co-variance among subcortical regions and between subcortical and cortical regions, aiming to reflect aberrant coordination during development in the adolescent with SAD. High-resolution T1-weighted images were obtained from 76 adolescents with SAD and 67 healthy controls (HC), ranging from 11 to 17.9 years. Symptom severity was evaluated with the Social Anxiety Scale for Children (SASC) and the Depression Self Rating Scale for Children (DSRS-C). Structural co-variance and sliding age-window analyses were used to detect age-dependent group differences in inter-regional coordination patterns among subcortical regions and between subcortical and cortical regions. The volume of the striatum significantly correlated with SAD symptom severity. The SAD group exhibited significantly enhanced structural co-variance among key regions of the striatum (putamen and caudate). While the co-variance decreased with age in healthy adolescents, the co-variance in SAD adolescents stayed high, leading to more apparent group differences in middle adolescence. Moreover, the striatum's mean structural co-variance with cortical regions decreased with age in HC but increased with age in SAD. Adolescents with SAD suffer aberrant developmental coordination among the key regions of the striatum and between the striatum and cortical regions. The degree of incoordination is age-dependent, which may represent a neurodevelopmental trait of SAD. [ABSTRACT FROM AUTHOR]

Social anxiety disorder (SAD) is one of the most common psychiatric disorders in adolescents. The heterogeneity of both symptoms and etiology is an essential source of difficulties in the treatment and prevention of SAD. The study aimed to identify subtypes of adolescent SAD based on etiology-related phenotype dimensions and examine symptom and brain associations of the subtypes. We used a deeply phenotyped sample (47 phenotype subscales from 13 measures) of adolescents with SAD (n = 196) and healthy controls (n = 109) to extract etiology-relevant risk factors, based on which we identified subtypes of SAD. We compared the subtypes on clinical characteristics and brain morphometrics and functional connectivity, and examined subtype-specific links between risk factors, brain aberrance, and clinical characteristics. We identified six etiology-relevant risk factors and two subtypes of adolescent SAD. One subtype showed mainly elevated negative emotionality trait and coping style and diminished positive emotionality trait and coping style, while the other additionally had significantly high environmental risk factors, more severe impairments in social functioning, and significant abnormalities in brain structure and function. There were subtype-specific links between the risk factor profiles, brain aberrance, and clinical characteristics. The finding suggests two etiology-based subtypes of adolescent SAD, providing novel insights to the diversity of pathological pathways and precise intervention strategies. [ABSTRACT FROM AUTHOR]

• We used a machine learning approach to compare the effectiveness of structural imaging, resting-state fMRI, and naturalistic fMRI. • The metrics in naturalistic fMRI showed superior validity in identifying SAD than the other modalities. • The pooling of the features from the three modalities did not help improve the classification performance. • The global mean functional connectivity measures of SAD were higher than HC in naturalistic fMRI. Social anxiety disorder (SAD) is a common anxiety disorder in childhood and adolescence. Studies on SAD in adults have reported both structural and functional aberrancies of the brain at the group level. However, evidence has shown differences in anxiety-related brain abnormalities between adolescents and adults. Since children and adolescents can afford limited scan time, optimizing the scan tasks is essential for SAD research in children and adolescents. Thus, we need to address whether brain structure, resting-state fMRI, and naturalistic imaging enable individualized identification of SAD in children and adolescents, which measurement is more effective, and whether pooling multi-modal features can improve the identification of SAD. We comprehensively addressed these questions by building machine learning models based on parcel-wise brain features. We found that naturalistic fMRI yielded higher classification accuracy (69.17%) than the other modalities and the classification performance showed dependence on the contents of the movie. The classification models also identified contributing brain regions, some of which exhibited correlations with the symptoms scores of SAD. However, pooling brain features from the three modalities did not help enhance the classification accuracy. These results support the application of carefully designed naturalistic imaging in recognizing children and adolescents at risk of SAD. [ABSTRACT FROM AUTHOR]