Cognitive domain-independent aberrant frontoparietal network strength in individuals with excessive smartphone use

In the past years, negative physical and psychosocial effects associated with excessive smartphone use (ESU) have been emphasized by a growing number of studies (Demirci, Akgönül, & Akpinar, 2015; Duke & Montag, 2017; Grant, Lust, & Chamberlain, 2019; Lepp, Barkley, Sanders, Rebold, & Gates, 2013; M. I. B. Lin & Huang, 2017; Toh, Coenen, Howie, & Straker, 2017). ESU shows behavioral similarities to other addictive disorders, such as failure to resist use, withdrawal, continuation of use despite being aware of negative consequences, or deception of others regarding the amount of time spent using the device (Y. H. Lin et al., 2016). Smartphone-related excessive behaviors show close similarity with criteria specified for Internet Gaming Disorder (IGD) in DSM-5 section 3 (Petry, Rehbein, Ko, & O'Brien, 2015). Following these developments, criteria for “smartphone addiction” (SPA) were introduced (Y. H. Lin et al., 2014) to define a condition characterized by excessive smartphone use and its negative impact on academic achievement and interpersonal relationships, as well physical and mental health (Demirci et al., 2015; Duke & Montag, 2017; Grant et al., 2019; Lepp et al., 2013; M. I. B. Lin & Huang, 2017; Toh et al., 2017).

Although the term “smartphone addiction” (SPA) has been anchored in several validated psychometric instruments, such as the Smartphone Addiction Scale (SAS) (Kwon, Kim, Cho, & Yang, 2013; Kwon, Lee, et al., 2013), or the Smartphone Addiction Inventory (SPAI) (Y. H. Lin et al., 2014; Pavia, Cavani, Di Blasi, & Giordano, 2016), it has been criticized for conceptual and taxonomic reasons, e.g. because the device per se may not lie at the core of the addictive behavior. Rather, specific patterns of smartphone use and features of the content the device provides access to, may constitute its addictive potential (Montag, Wegmann, Sariyska, Demetrovics, & Brand, 2019). As such, the smartphone may be considered as vehicle only, whereas content, i.e. specific patterns of usage, would predominantly drive addictive behavior. In the light of this debate - and because of the possibility that ESU represents a mobile branch of gaming disorder, alternative terms (e.g. “smartphone use disorder”) were suggested (Montag et al., 2019). Beyond the ongoing debate concerning its terminology, the negative physical and psychosocial outcomes related of the condition itself have rarely been disputed. In this report, we adopt a psychometric definition of ESU, as introduced by the SPAI (Y. H. Lin et al., 2014) and several other psychometric instruments assessing the extent of excessive smartphone use, as well as different dimensions of this behavior.

Multiple cognitive domains have been shown to be involved in the development and persistence of addictive behavior, particularly cue-reactivity (CR), inhibitory control, and working memory (WM) (G. Dong, Lin, & Potenza, 2015; Starcke, Antons, Trotzke, & Brand, 2018; L. Wang, Tian, Zheng, Li, & Liu, 2020; Zhou, Zhou, & Zhu, 2016). These processes critically apply to both behavioral addictions and substance-used disorders, as shown by a plethora of studies in the last two decades (Grant, Potenza, Weinstein, & Gorelick, 2010; Volkow, Wang, Fowler, & Tomasi, 2012; Volkow et al., 2006; Zilverstand, Huang, Alia-Klein, & Goldstein, 2018). On a neural level, these domains share common frontal, cingulate, and parietal neural substrates (Grant et al., 2010; Schmitgen et al., 2020; Starcke et al., 2018; H. Wang, Sun, Lan, & Liu, 2020), which were implicated in “top-down” control, suggesting domain-independent involvement of cognitive control mechanisms. In accordance with this notion, substantial evidence has converged to implicate disruptions within a neurocircuitry subserving cognitive control as a core feature of mental disorders, as e.g. recently shown by a meta-analysis of functional and structural imaging findings (McTeague et al., 2017).

Despite the growing body of research addressing the underlying pathophysiological features of excessive smartphone use, systems neuroscience data investigating this condition is scarce at present. Recently published magnetic resonance imaging (MRI) studies have shown specific functional changes in individuals with ESU, such as reduced intrinsic neural activity in anterior cingulate cortex (ACC)(Horvath et al., 2020), reduced parietal cortex connectivity at rest (D. J. Kim, Kim, & Pyeon, 2016), and differences in CR-related medial and lateral prefrontal cortex activity (Schmitgen et al., 2020). In this study, keeping the idea in mind that the common expression of dysfunctional cognitive control mechanisms among categorically distinct psychopathologies are related to common neural network dysfunction (Buckholtz & Meyer-Lindenberg, 2012), we were particularly interested in studying superordinate network function related to ESU. Thus, we addressed the question whether multiple cognitive domains (such as inhibition, CR and WM) that are intricately linked to addictive behavior share common neural patterns that will significantly differ between individuals with ESU compared to controls (n-ESU). To this end, we applied a multivariate statistical data fusion technique for multimodal MRI data analysis, i.e. joint Independent Component Analysis (jICA), to investigate joint brain activity patterns of CR, inhibitory control and WM, respectively.

We expected that a domain-independent cognitive control network, particularly circuits involving the lateral PFC (McTeague et al., 2017), would reflect the main source of common modulation across the three tasks, and that prefrontal network strength would significantly differ between participants with vs. those without ESU. In particular, considering models of disrupted prefrontal cortical control in both substance-use disorders and behavioral addictions (S. K. Kim, Jeong, Im, Lee, & Chung, 2021; Le, Potvin, Zhornitsky, & Li, 2021), we expected reduced prefrontal activity in individuals with ESU compared to those not exhibiting such behavior. In addition, we explored relationships between cross-modality shared components and SPAI and SPAI-I (Pavia et al., 2016) factors to provide a detailed dimensional perspective on brain function and ESU. In this regard, we expected that prefrontal network strength would be strongly associated with ESU dimensions related to executive control of behavior, such as action initiation and termination or compulsivity.

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