Determinants of psychological distress among Latina young adults during initial years in the United States.

Screen media activity (SMA) among developing youth is a significant public health concern (). Currently youth aged 13–17 years spend considerable time engaging in SMA, especially social media. The widespread use of social media has reshaped adolescent experiences, offering highly stimulating and rewarding platforms on which boundaries between healthy and problematic engagement are blurred. Though not formally recognized as a clinical diagnosis, problematic use of social media (PUSM) has been reported by 35 % of adolescents (). PUSM shares features with other behavioral addictions (; ; ), such as gaming disorder and gambling disorder, which are characterized in the ICD-11 by impaired control, increased prioritization, and continuation of gaming/gambling despite negative consequences (, ). Other components, such as tolerance and mood modification, are relevant to PUSM as with other addictive behaviors (; ). Research indicates that the severity of PUSM may be greater in adolescents with attention-deficit/hyperactivity disorder and internalizing psychopathology, such as depression and anxiety (; ; ; ; ; Wang et al., 2017). Despite growing recognition of the need to address PUSM in adolescents, including acknowledgement in the US Surgeon General’s Advisory on Social Media and Youth Mental Health (), there is limited research identifying neural networks predictive of PUSM within developing youth. Identifying at early stages of human development potential risk factors for PUSM could provide insight into PUSM development and inform intervention approaches. Previous resting-state neuroimaging studies of internet-enabled addictive behaviors have provided preliminary evidence of neural correlates that may underlie PUSM. These studies have reported altered functional connectivity spanning several networks in individuals with problematic usage of the internet (PUI) and problematic smartphone use (PSU), including the default mode, salience, frontoparietal, visual attention, and cognitive control networks (; ; ; ; ; Wang et al., 2017; ). However, PUSM has typically been considered within the broader frameworks of PUI and PSU, which are umbrella terms encompassing various behaviors such as online gaming, shopping, and pornography use. Thus, much of the current neuroimaging literature misses distinct features of PUSM and dilutes the specificity of the unique characteristics and experiences associated with social media (e.g., images that trigger social comparison), which may interact differently with underlying youth vulnerabilities to influence PUSM-specific outcomes, such as issues around body image in girls (; ). Although numerous brain networks have been implicated in internet-enabled addictive behaviors, significant challenges remain in establishing clinically useful neuromarkers. The existing literature typically relies on seed-based functional connectivity analyses, which focus on predefined regions of interest and limit insights into whole-brain connectivity patterns (; ). Moreover, traditional neuroimaging methods that use simple correlation or regression techniques often risk overfitting, reducing the generalizability of behavioral predictions. Connectome-based predictive modelling (CPM) is one method of addressing these issues by applying machine-learning techniques to whole-brain functional connectivity data to develop predictive brain-behavior models (; ). By incorporating cross-validation procedures with model-testing in held-out samples, CPM protects against overfitting and enhances model rigor and generalizability. Moreover, CPM is a fully data-driven approach that does not require selection of regions/networks and facilitates the identification of “neural fingerprints;” i.e., networks subserving specific behaviors, such as PUSM (; ).