Cognitive domain-independent aberrant frontoparietal network strength in individuals with excessive smartphone use. (March 2023)

• Record Type: Journal Article
• Authors: Henemann, Gudrun M.; Schmitgen, Mike M.; Wolf, Nadine D.; Hirjak, Dusan; Kubera, Katharina M.; Sambataro, Fabio; Bach, Patrick; Koenig, Julian; Wolf, Robert Christian
• Is Part Of: Psychiatry research. Volume 329(2023)
• Publication Date: 2023-03
• Subjects: Addiction -- Functional magnetic resonance imaging -- Independent component analysis -- Prefrontal cortex -- Smartphone; Psychiatry -- Periodicals; Brain -- Imaging -- Periodicals; Psychiatry -- Periodicals; Diagnostic Imaging -- Periodicals; Psychiatrie -- Périodiques; Cerveau -- Imagerie pour le diagnostic -- Périodiques; 616.890754
• DOI: 10.1016/j.pscychresns.2023.111593
• Languages: English
• ISSNs: 0925-4927

Abstract

Highlights: We investigated neural activity in individuals with excessive smartphone use (ESU). Joint ICA was used to analyze fMRI-data from three separate experimental tasks. A domain-independent frontoparietal network significantly differed between ESU and controls. Significant associations were found between neural network strength and specific ESU dimensions. Abstract: Excessive smartphone use (ESU) may fulfill criteria for addictive behavior. In contrast to other related behavioral addictions, particularly Internet Gaming Disorder, little is known about the neural correlates underlying ESU. In this study, we used functional magnetic resonance imaging (fMRI) to acquire task data from three distinct behavioral paradigms, i.e. cue-reactivity, inhibition, and working memory, in individuals with psychometrically defined ESU ( n = 19) compared to controls (n-ESU; n = 20). The Smartphone Addiction Inventory (SPAI) was used to quantify ESU-severity according to a novel five-factor model (SPAI-I). A multivariate data fusion approach, i.e. joint Independent Component Analysis (jICA) was employed to analyze fMRI-data derived from three separate experimental conditions, but analyzed jointly to detect converging and domain-independent neural signatures that differ between persons with vs. those without ESU. Across the three functional tasks, jICA identified a predominantly frontoparietal system that showed lower network strength in individuals with ESU compared to n-ESU ( p < 0.05 FDR-corrected). Furthermore, significant associations between frontoparietal network strength and SPAI-I's dimensions "time spent" and "craving" were found. The data suggest a frontoparietal cognitive control network as cognitive domain-independent neural signature of excessive and potentially addictive smartphone use.