Connectome-based predictive modeling of Internet addiction symptomatology

Area of Science:

• Neuroscience
• Psychiatry
• Computational Neuroscience

Background:

• Internet addiction symptomatology (IAS) involves compulsive internet use causing significant impairments.
• Understanding the neurobiological underpinnings of IAS is crucial for developing effective interventions.

Purpose of the Study:

• To decode IAS using whole-brain resting-state functional connectivity in a healthy population.
• To identify predictive brain network features associated with Internet addiction disorder (IAD) susceptibility.

Main Methods:

• Applied connectome-based predictive modeling to resting-state functional connectivity data.
• Utilized whole-brain analysis in a healthy population to identify predictive network features.
• Validated the generalizability of identified connections in an independent sample.

Main Results:

• IAS was predicted by functional connectivity between the prefrontal cortex, cerebellum, and limbic lobe.
• Connections between the occipital lobe, limbic lobe, and insula were also associated with IAS.
• A unique network involving the occipital lobe distinguished IAS prediction from alcohol use disorder prediction.

Conclusions:

• This study provides the first data-driven evidence of predictive brain features for IAS based on intrinsic brain network organization.
• The findings advance the understanding of the neurobiological basis of IAD susceptibility.
• These insights may inform timely interventions for individuals at risk of IAD.