Skip to content

Author

Xiaoqian Yan

2 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

Postnatal maturation of putamen microstructure accompanies topographic white matter connectivity and altered circuits in autism

The putamen is a major hub of the basal ganglia that emerges early in gestation. However, whether its mature organization is established before birth or emerges postnatally remains unknown. Using cross-sectional and longitudinal quantitative MRI (R1 and R2*, related to tissue density and iron, respectively), and diffusion MRI, we characterized the development of putamen’s microstructure and its white matter connectivity with cortex from birth to 12 months and compared their trajectories with those in adults. Despite its prenatal emergence, the putamen undergoes substantial postnatal development. R1 increases from birth to 12 months, producing a prominent anterior–posterior gradient, whereas R2* increases primarily between age one and adulthood, producing a medial– lateral gradient. Cortico-putamen white matter connectivity is diffuse in infants but becomes topographic in adults, with anterior putamen linked to frontal cortex and posterior putamen to sensorimotor cortex. In autism spectrum disorder, this organization is largely preserved and accompanied by increased anterior putamen–prefrontal connectivity. Our findings reveal distinct spatial developmental trajectories of putamen microstructure and cortical connectivity providing a developmental framework for understanding the organization of the putamen in infancy, which has implications for assessing neurodevelopmental disorders of the basal ganglia. Teaser From birth to one year, the putamen develops distinct microstructural gradients and increasingly topographic cortical connections.

Vaidehi S. Natu, Christina Tyagi, Xiao-Qian Yan et al. · 0 citations
Open access Aug 2026

Deep learning reveals a neurocomputational mechanism predicting depression risk in adolescents

Early detection and prevention of psychiatric disorders, particularly depression, remain as major global health challenges, yet reliable tools for identifying individuals before symptom onset are lacking. Here, we combine functional neuroimaging with computational modeling to identify a mechanistic biomarker of depression risk. In a population-based adolescent cohort (IMAGEN, N = 1332), we found that weakened neural representations of emotional signals were linked to depressive symptoms. Perturbation experiments in a brain-aligned deep learning model showed that this deficit reflects overregularized emotion perception, producing a negative perceptual bias. A neurocomputational signature of this mechanism predicted depression symptom onset up to 4 years later at the IMAGEN follow-up (N = 725), was associated with both a genetic-risk variant and polygenic risk for depression, and improved depression classification in a patient cohort (STRATIFY, N = 411). These findings suggest a possible mechanism linking genetic vulnerability to altered emotion perception and future depression, and propose a predictive computational marker with potential for early detection and prevention.

Han Lu, Xiaoqian Yan, Benjamin Becker et al. · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.