Di Chen

Addiction stands as a critical public health concern, contributing substantially to morbidity, mortality and societal burden worldwide. It can be conceptualized along a developmental continuum, from risky substance use in adolescence to compulsive use and addiction in adulthood, yet the neural changes underlying this continuum remain poorly understood. Here we show age-specific brain structural trajectories along this continuum by harmonizing multimodal data across large population cohorts spanning adolescence to late adulthood. We identified three life stages with distinct patterns of problematic substance use (PSU). Before age 25, PSU was associated with neurodevelopmental differences, alongside greater impulsivity and rule-breaking, suggesting a key window for early prevention. Between ages 25 and 45, PSU was linked to structural differences in brain regions linked to craving and reward processing, supporting early detection and intervention during the reinforcement and maintenance of substance use. After age 45, PSU-related brain differences may reflect cumulative effects of prolonged substance exposure, emphasizing broader health monitoring. These stage-specific patterns also differed by sex, particularly earlier in life. Together, our findings map structural brain changes across the addiction continuum and provide a lifespan framework for stage-specific prevention, intervention and health monitoring.

The biological mechanisms underlying major neuropsychiatric disorders remain largely elusive. Given the frequent association of immune dysregulation with these conditions, we used blood-derived multi-omics data from 1,274 healthy adolescents in the IMAGEN cohort to identify transdiagnostic biomarkers and mechanisms that could inform diagnosis and treatment. We first conducted genome-wide analyses to identify single nucleotide polymorphisms associated with DNA methylation and gene expression, with findings replicated in external datasets. These quantitative trait loci were further explored through Mendelian randomization analyses across 6 neuropsychiatric disorders (attention deficit hyperactivity disorder, autism spectrum disorder, bipolar disorder, major depressive disorder, schizophrenia and insomnia), leading to the identification of 73 putatively causal CpG sites and 62 genes that were either unique to individual disorders or, as in the case of MRPL2 , shared among the disorders. Notably, the identified genes were significantly enriched in pathways linked to both psychiatric and autoimmune diseases, suggesting a shared genomic architecture between autoimmune and neuropsychiatric disorders. Two-step Mendelian randomization and colocalization analyses revealed potential transdiagnostic regulatory pathways, in which the expression of three genes ( MAD1L1 , MRPL2 and HLA-DRB1 ) mediated the effects of CpG methylation on schizophrenia and insomnia. Specifically, DNA methylation at cg06770790 repressed MRPL2 , which was putatively causal for insomnia ( β = −0.38, P = 1.29 × 10 ⁻⁴ ) and schizophrenia ( β = −0.38, P = 1 × 10 ⁻⁴ ). Conversely, increased expression of MAD1L1 and HLA-DRB1 , driven by methylation at several CpG sites, was potentially causal for schizophrenia. Our findings highlight key molecular mechanisms and genes implicated in neuropsychiatric disorders, offering promising new targets for therapeutic intervention.

Epigenetic mechanisms are thought to contribute to neurodevelopmental vulnerability for psychiatric disorders, yet longitudinal evidence linking DNA methylation (DNAm) to brain maturation and psychopathology is limited. Using epigenome-wide DNAm (372,582 CpGs) and whole-brain structural MRI data from the IMAGEN cohort ( n = 506, ages 14–19), we identified 18 co-regulated DNAm clusters, ten enriched for brain-expressed genes involved in neuronal development and signalling. The clusters showed consistent longitudinal change and were reproducible in independent adult samples (PPMI, n = 513; ADNI, n = 606). Multivariate analyses revealed coordinated coupling between DNAm change and cortical-subcortical maturation, such that greater DNAm reductions in brain-related clusters associated with greater cortical thinning and subcortical volume changes in the fronto-limbic-striatal axis. Increases in depressive symptoms, and frequency of cannabis use and binge drinking were linked to DNAm changes, with mediation models supporting DNAm as a mechanistic bridge between behaviour and brain change. Two clusters (C1 and C7) were associated with depressive and negative psychotic symptoms across adolescence. Associations of these clusters with depressive symptoms replicated in the PPMI dataset. Their coupling with amygdala–striatal maturation suggests that these DNAm signatures index environmentally shaped affective–motivational neurodevelopment.

Substance use disorder (SUD) stands as a critical public health concern, contributing to substantial morbidity, mortality and societal costs. The effects of SUD on structural brain changes have been well documented. However, the neural mechanisms underlying SUD and the spatial-temporal volumetric changes associated with SUD remained underexplored. In this investigation, neuroimaging, behavioral and genomic data across four large population cohorts jointly covering the full lifespan were harmonized, and whole-brain volumetric trajectories between substance use disorders (SUDs) and healthy controls (HCs) were compared, revealing the potential neurobiological mechanisms and the genomic basis underlying SUD. Results highlighted three distinct life stages critical for the development of SUD: 1) adolescence to early adulthood (before 25y), where SUD is suspected to be the consequence of prefrontal-subcortical imbalance during neurodevelopment; 2) early-to-mid adulthood (25y – 45y), where SUD was strongly associated with compulsivity-related brain volumetric changes; 3) mid-to-late adulthood (after 45y), where SUD-related brain structural changes could be explained by neurotoxicity. Results were externally validated both via longitudinal analysis of these population cohorts and in independent cross-sectional samples. In summary, our study demonstrated the lifespan whole-brain volumetric changes associated with SUD, revealed potential neurobehavioral mechanisms for the development of SUD, and suggested critical time window for effective prevention and treatment of SUD.

Neuroimaging data have been widely used to understand the neural bases of human behaviors. However, most studies were either based on a few predefined regions of interest or only able to reveal limited vital regions, hence not providing an overarching description of the relationship between neuroimaging and behaviors. Here, we proposed a voxel‐based pattern regression that not only could investigate the overall brain‐associated variance (BAV) for a given behavioral measure but could also evaluate the shared neural bases between different behaviors across multiple neuroimaging data. The proposed method demonstrated consistently high reliability and accuracy through comprehensive simulations. We further implemented this approach on real data of adolescents (IMAGEN project, n = 2089) and adults (HCP project, n = 808) to investigate brain‐based variances of multiple behavioral measures, for instance, cognitive behaviors, substance use, and psychiatric disorders. Notably, intelligence‐related scores showed similar high BAVs with the gray matter volume across both datasets. Further, our approach allows us to reveal the latent brain‐based correlation across multiple behavioral measures, which are challenging to obtain otherwise. For instance, we observed a shared brain architecture underlying depression and externalizing problems in adolescents, while the symptom comorbidity may only emerge later in adults. Overall, our approach will provide an important statistical tool for understanding human behaviors using neuroimaging data.

The JFK Coma Recovery Scale-Revised (JFK CRSR) is a clinical behavioral method for assessing patients with disorders of consciousness (DOC). However, due to severe brain injury, the DOC patients may lack sufficient behavioral responses, leading to a relatively high clinical misdiagnosis rate of the JFK CRS-R. To address this limitation, this study developed a portable and wearable Brain-Computer Interface (BCI) to evaluate the visual tracking abilities of the DOC patients and enhance the diagnostic accuracy of the JFK CRS-R. During the experiment, patients wore an Electroencephalogram headband, and the Graphical User Interface of the BCI presented four human images with a background captured by a camera. A target human image was randomly selected, then the four images alternately flashed until the target moved to its initial position. The system guided patients to concentrate their attention on the moving target. In a case study, the BCI system was applied to a patient diagnosed with a minimally conscious state (MCS). In contrast to the lack of visual tracking behavior in CRS-R, our experiment result showed a significant accuracy of 60% achieved by the MCS patient, emphasizing the visual tracking abilities of the MCS patients and highlighting the effectiveness of the system.