Heterogeneous structural brain signatures of methamphetamine and ketamine use disorders revealed by normative modeling.
Yunkai Sun, Yehong Fang, Pu Peng, Qiuxia Wu, Jinsong Tang, Yanhui Liao
Molecular Psychiatry September 2, 2026 DOI: 10.1038/s41380-026-03859-y (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cross-sectional study with normative modeling and machine learning classification Peer reviewed |
|---|---|
| Sample size | 1,707 |
| Population | 1209 healthy individuals aged 18-65 for the normative model; 184 individuals with methamphetamine use disorder, 94 with ketamine use disorder, and 220 healthy controls from two independent sites |
| Topics | Esketamine Ketamine |
| Key findings | Gray matter volume deviations from a normative model were highly heterogeneous between individuals with methamphetamine and ketamine use disorder, but extreme negative deviations concentrated in default mode network regions across both substances. Individual deviation scores distinguished both disorder groups from healthy controls and replicated in an external dataset, and were associated with myelination and neurotransmitter features. |
Abstract
Marked interindividual heterogeneity in substance use disorder (SUD) impedes the identification of consistent neuroanatomical alterations, limiting the development of robust markers for clinical risk stratification. We applied a normative modeling framework to explore individual neuroanatomical deviations in individuals with SUD across two distinct substance classes, and assessed their potential for classification and risk prediction. We established a developmental normative model of gray matter volume (GMV) using T1-weighted images from 1209 healthy individuals aged 18-65 years. Deviations from this normative model were then estimated for 184 individuals with methamphetamine use disorder (MUD), 94 with ketamine use disorder (KUD), and 220 healthy controls (HC) collected from two independent sites. Based on these individual deviation scores, a machine learning model was constructed to identify individuals at risk for SUD. The normative model revealed high interindividual heterogeneity in GMV deviations among individuals with MUD and KUD. Cross-substance extreme negative deviations were primarily located in regions of the default mode network (DMN). Notably, individual-level deviations could successfully distinguish both MUD and KUD from HC, and replicated in an external dataset. We further identified significant associations between abnormal GMV trajectories and neurobiological features, particularly myelination and neurotransmitter systems. These findings enhance our understanding of the heterogeneous neurobiology underlying SUD, providing potential neurobiological biomarkers for clinical diagnosis, risk stratification, and prevention.
Comparable studies
Other cross-sectional and survey studies on ketamine, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| 5-Year Trends in Use of Hallucinogens and Other Adjunct Drugs among UK Dance Drug Users People who use drugs in dance contexts | 2006 | Repeated-measures cross-sectional survey | |
| Preliminary analysis of positive and negative syndrome scale in ketamine-associated psychosis in comparison with schizophrenia Healthy subjects, ketamine abusers, and schizophrenia patients | 2014 | Cross-sectional study | n = 998 |
| Ketamine as a primary predictor of out-of-body experiences associated with multiple substance use Online survey respondents | 2011 | Cross-sectional survey | n = 192 |
| Self-reported prevalence of dependence of MDMA compared to cocaine, mephedrone and ketamine among a sample of recreational poly-drug users Global non-treatment-seeking sample of last year users of MDMA, cocaine, mephedrone,... | 2014 | Cross-sectional survey | |
| Near-Death States Reported in a Sample of 50 Misusers Previous ketamine misusers who reported a ketamine-related near-death experience | 2010 | Cross-sectional survey | n = 50 |