The neurobiology of smartphone addiction in emerging adults evaluated using brain morphometry and resting-state functional MRI

Abstract

The characteristics of smartphone addiction (SPA) can be evaluated by neuroimaging studies. Information on the brain structural alterations, and effects on psychosocial wellbeing, however, have not been concurrently evaluated. The aim of this study was to identify abnormalities in gray matter volume using voxel-based morphometry (VBM) and neuronal functional alterations using resting-state functional MRI (rs-fMRI) in emerging adults with SPA. We correlated the neuroimaging parameters with indices for psychosocial wellbeing such as depression, anxiety, stress, and impulsivity. Forty participants (20 SPA and 20 age-matched healthy controls) were assessed using VBM and rs-fMRI. The smartphone addiction scale – Malay version (SAS-M) questionnaire scores were used to categorize the SPA and healthy control groups. DASS-21 and BIS-11 questionnaires were used to assess for psychosocial wellbeing and impulsivity, respectively. VBM identified the SPA group to have reduced gray matter volume in the insula and precentral gyrus; and increased grey matter volume in the precuneus relative to controls. Moderate correlation was observed between the precuneus volume and the SAS-M scores. Individuals with SPA showed significant rs-fMRI activations in the precuneus, and posterior cingulate cortex (FWE uncorrected, p<0.001). The severity of SPA was correlated with depression. Anxiety score was moderately correlated with reduced GMV at the precentral gyrus. Collectively, these results can be used to postulate that the structural and neuronal functional changes in the insula are linked to the neurobiology of SPA that shares similarities with other behavioural addictions.

Neuroimaging the effects of smartphone (over-)use on brain function and structure—a review on the current state of MRI-based findings and a roadmap for future research

“…In this context, recent studies have shown that the specific brain structural variations that are identified may strongly depend on the choice of processing pipeline (see Zhou et al ., 2022 for a methodological evaluation of the effects of choice of processing pipeline on brain structural analyses). Moreover, some of the identified studies focused in their analysis on specific hypothesis-driven brain regions such as the brain stem (Cho et al ., 2021 ) or specifically on striatal morphology (Yoo et al ., 2021 ), whereas other studies used whole-brain analytic approaches (Horvath et al ., 2020 ; Rashid et al ., 2021 ). Further complicating matters is the use of different rigor in the brain analysis and in the level of description of the analyses used, such that for several studies the exact multiple comparisons approach used remains unclear.…”

“…Table 2 shows studies investigating the neural correlates of SmUD tendencies using fMRI. Most previous studies employed a resting state fMRI approach examining associations between SmUD tendencies and the intrinsic functional organization of the brain (Ahn et al ., 2021 ; Chun et al ., 2018 ; Horvath et al ., 2020 ; Kwon et al ., 2022 ; D. Liu et al ., 2022 ; Lou et al ., 2019 ; Paik et al ., 2019 ; Pyeon et al ., 2021 ; Rashid et al ., 2021 ; Schmitgen et al ., 2022 ; Zou et al ., 2022 ). A direct comparison between the studies is hindered by differences in the methodological approaches, ranging from different preprocessing methods to different network analytic strategies.…”

“…The initial yet rapidly growing literature has used different MRI-based approaches to examine variations in the structural organization of the brain using e.g. voxel-based morphometry (VBM) or diffusion tensor imaging (DTI), the functional organization of the brain using resting state functional MRI (fMRI) techniques, and functional alterations during cognitive and affective processes, using task-based fMRI (Ahn et al ., 2021 ; Arató et al ., 2023 ; Cho et al ., 2021 ; Choi et al ., 2021 ; Chun et al ., 2017 , 2018 ; Han & Kim, 2022 ; Hirjak et al ., 2022 ; Horvath et al ., 2020 ; Hu et al ., 2017 ; Kwon et al ., 2022 ; Lee et al ., 2019 ; D. Liu et al ., 2022 ; Lou et al ., 2019 ; Paik et al ., 2019 ; Pyeon et al ., 2021 ; Rashid et al ., 2021 ; Schmitgen et al ., 2020 , 2022 ; Tymofiyeva et al ., 2020 ; Wang et al ., 2016 ; Yoo et al ., 2021 ; Zou et al ., 2021 , 2022 ). Please note that the papers cited here will be reviewed in the next section of the present work (for a summary, see Tables 1 and 2 ; see also an overview in Fig.…”

Default mode network perturbations in Alzheimer's disease: an fMRI study in Klang Valley, Malaysia

“…The DMN is a constellation of nodes in the brain that are functionally connected to the posterior cingulate cortex node in the brain and appear to be activated when a person is at rest and not performing any tasks. Historically, the FC of the nodes of the DMN have been implicated with altered brain morphometry in various neurological and psychological disorders (Rashid et al, 2021). A previous study has revealed that decreased FC is widespread in the brain of AD subjects compared to HC subjects, particularly involving the nodes of the DMN (Soman et al, 2020).…”

Seed-based morphometry of nodes in the default mode network among patients with Alzheimer’s disease in Klang Valley, Malaysia

“…SBA is useful for the detailed analysis of a particular Region of Interest (ROI), to measure functional changes between participants and to reveal the FC among the nodes of the DMN (19, 33). Historically, DMN FC has been implicated with altered brain morphometry in various neurological and psychological disorders (34). Previous study has revealed that there is decreased FC that is widespread in the brain of AD compared to HC participants, particularly involving the nodes of the DMN (35).…”