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<b>Background</b> <p>Addiction is a brain disorder marked by profound inter-individual heterogeneity, a factor that limits the clinical utility of group-level neuroimaging findings.</p> <b>Methods</b> <p>To address this, we constructed a precision neuroimaging framework to capture individual variability. We quantified gray matter volume (GMV) deviations in 464 individuals with five substance-related and addictive disorders using a normative model derived from over 1000 healthy controls. Subsequently, we applied a dimensionality reduction technique to decompose these individual deviations into distinct spatial patterns.</p> <b>Results</b> <p>We observe a shared pattern involving the insula and prefrontal cortex that reflects common underlying biology across different addictions. In contrast, a pattern centered on the basal ganglia captures the differences between specific addiction types. Importantly, only transdiagnostic factors correlated with clinical measures, whereas the heterogeneity factor did not.</p> <b>Conclusions</b> <p>By resolving neurobiological heterogeneity into distinct, structural atrophy subtypes, this framework offers insights into understanding structural heterogeneity as a local effect of common networks.</p>

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Individual atrophy patterns clarify latent neural subtypes in addiction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月21日

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  • Min Wang,
  • Lei Guo,
  • Ningning Zeng,
  • Zhoukang Wu,
  • Liangjiecheng Huang,
  • Yijun Chen,
  • Tianzhen Chen,
  • Ziliang Wang,
  • Qiang Hu,
  • Guangheng Dong,
  • Jun Yin,
  • Hang Su,
  • Hui Zheng

摘要

Background

Addiction is a brain disorder marked by profound inter-individual heterogeneity, a factor that limits the clinical utility of group-level neuroimaging findings.

Methods

To address this, we constructed a precision neuroimaging framework to capture individual variability. We quantified gray matter volume (GMV) deviations in 464 individuals with five substance-related and addictive disorders using a normative model derived from over 1000 healthy controls. Subsequently, we applied a dimensionality reduction technique to decompose these individual deviations into distinct spatial patterns.

Results

We observe a shared pattern involving the insula and prefrontal cortex that reflects common underlying biology across different addictions. In contrast, a pattern centered on the basal ganglia captures the differences between specific addiction types. Importantly, only transdiagnostic factors correlated with clinical measures, whereas the heterogeneity factor did not.

Conclusions

By resolving neurobiological heterogeneity into distinct, structural atrophy subtypes, this framework offers insights into understanding structural heterogeneity as a local effect of common networks.