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Addiction is neurobiologically distinguishable from heavy behavioral engagement
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Peer-reviewed studies and comparative research demonstrate that addictive behaviors can be distinguished from high behavioral engagement through specific differences in loss of control, metric scales, and associated characteristics.

Evidence for · 3
2025 · cited by 11
Abstract Background and aims Behavioral addictions (BAs) represent complex and multifaceted disorders often associated with maladaptive neural alteration. To deepen our understanding of the essence of BAs, this study focuses on the neural mechanisms underlying its three stages: reward seeking, self-control, and decision-making. The aim of the current meta-analysis is to investigate the brain regions and neural networks involved in BAs. Methods Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched for relevant articles published before September 1, 2024, in the Web of Science and PubMed databases, and supplemented our search with Google Scholar. We conducted analyses using activation likelihood estimation (ALE) meta-analysis and meta-analytic connectivity modeling (MACM) analyses. Results A total of 50 functional magnetic resonance imaging studies involving 906 participants were included. The findings showed that individuals with BAs exhibited hyperactivation in the right inferior frontal gyrus (IFG), bilateral caudate and left middle frontal gyrus (MFG), and a high degree of connectivity was found between the right caudate, left caudate, and right IFG. These findings indicated that BAs were associated with the fronto-striatal circuits. Individuals with BAs demonstrate specific neural activation patterns in the reward seeking, self-control, and decision-making stages, characterized by differences in activation and functional connectivity of brain regions associated with these stages. Discussion and conclusions This study verifies the pivotal role of the fronto-striatal circuits in BAs and highlights the specific patterns of brain activity in different stages of addictive behavior. These findings expand our understanding of neural mechanisms underlying BAs and supports and provide partial support for the I-PACE model.
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More for · 2
2025 · cited by 5
Social media platforms have evolved from communication tools into hyperstimulating digital environments that directly engage reward and attention networks in the brain. Emerging neuroimaging studies reveal that heavy use, particularly among adolescents, is linked to functional and structural changes in regions governing emotional regulation, impulse control, and social cognition. These neural patterns resemble those seen in addiction, attention-deficit/hyperactivity disorder, and mood disorders. However, clinical medicine has been slow to respond. This editorial argues that we must reframe social media overuse as a neurologically mediated risk factor rather than just a behavioral concern. In addition to addiction-like engagement, a new affective pattern is emerging: “digital anhedonia,” the diminished ability to find pleasure in real-world experiences after prolonged digital saturation. As a neurologist and neuroscientist who develops smartphone-based applications for therapeutic benefit, I have seen both the healing and the harm these technologies can cause. It is time to recognize, monitor, and mitigate the neurobiological consequences of digital overstimulation and reward desensitization.
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Loss of control is a key feature for distinguishing between high engagement and addictive behavior in video gaming | Discover Psychology | Springer Nature Link # Loss of control is a key feature for distinguishing between high engagement and addictive behavior in video gaming - Research - Open access - Published: 14 October 2025 - Volume 5, article number 117 (2025) - Cite this article You have full access to this open access article ## Abstract ### Background Previous studies have suggested that engagement and addiction are distinct factors in video gaming. We aimed to identify features that can distinguish between engagement and addiction among the diagnostic criteria for internet gaming disorder in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). ### Methods The Internet Gaming Disorder Scale-Short-Form (IGDS9-SF) was used as a measure of internet gaming disorder. The measure was further assessed by using independent scales, i.e., mean time spent on gaming as a scale of engagement, and self-evaluation of academic performance as a scale of addiction. The scores for each IGDS9-SF item were evaluated by two-way analysis of variance (A
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