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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

11. Together, our findings highlight individualized SC deviations as potential developmental markers for symptom monitoring and treatment stratification in ADHD.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

10. Treatment response varies across individuals with ADHD. We found that PC1 of ADHD-related SC deviations predicted greater inattention improvement after short-term atomoxetine treatment, whereas PC2 decreased after pharmacological treatment, indicating reduced SC deviations.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

9. ADHD symptoms tended to improve with age during development at the group level. SC deviations in FP.A–FP.B and FP.B–DM.B mediated symptom reductions, and within-subject decreases in these deviations tracked symptom improvement.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

8. An independent cohort replicated the delayed SC development, spatial organization of SC deviations, and age-related decreases in deviations. ✅

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

7. Given that ADHD emerges in childhood and symptoms change across development, we next asked whether SC deviations also evolve with age.🤔 14 connections showed ADHD-specific age-related decreases in deviation, mainly in higher-order association networks.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

6. We averaged SC deviations within the ADHD and TD test groups and found elevated deviations in youths with ADHD, particularly in connections linking association networks. These heterogeneous deviations were organized along the S–A connectional axis.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

5. After depicting developmental trajectories for both TD and ADHD groups, we observed delayed SC development in connections involving association networks in youths with ADHD compared to TD youths within the age range of 8.9–15.5 years.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

4. Using diffusion MRI, we reconstructed white-matter SC matrices based on the Yeo-17 network parcellation. We then used normative models to estimate developmental trajectories and quantify SC deviations for each observation in the ADHD and TD test groups.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

3. ADHD is a common neurodevelopmental disorder. Youths with ADHD show deviations in white-matter SC from typical development, but where these differences occur and how they change with age remain unclear.🤔

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 01/09/2026

Excited to share our latest @natbiomedeng.nature.com paper!🥳How does structural connectivity (SC) in ADHD deviate from typical development? We show delayed maturation of association-network SC, with some deviations declining with age, tracking symptoms, and predicting treatment response.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

Huge thanks to my coauthors @hangyang.bsky.social , @jing-cong.bsky.social , Haoshu Xu, Jason Kai, Shaoling Zhao, Yang Li, Haochang Shou, Kangcheng Wang, @valeriejsydnor.bsky.social , @tingsterx.bsky.social , @fangchengyeh.bsky.social , and special thanks to my supervisor @zaixucui.bsky.social .

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

10. We now provide a concise framework for describing white-matter structural connectivity development across the human connectome. The beautiful trajectory and tractogram of each connection were provided on an interactive platform (connectcharts.cibr.ac.cn). Try to explore it!

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

9. Finally, we found that the developmental heterogeneity also showed spatial consistency with the SC–cognition and SC–p-factor associations. Developmental trajectories stratified by cognitive or p-factor levels further varied by position along the S-A axis.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

8. These developmental findings generalized to two independent datasets and remained robust across multiple sensitivity analyses.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

7. The non-monotonic trajectories led us to ask how this heterogeneity evolves across youth. Strikingly, alignment with the S-A connectional axis shifted from negative to positive, crossing zero at around age 15, indicating a transition from sensorimotor- to association-dominant strengthening.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

6. We then asked whether this heterogeneous developmental pattern aligns with the S-A axis. After extending the S-A axis to the connectome level, we found strong correspondence between the S-A connectional axis and second-derivative matrix, revealing a continuous axis-ordered spectrum.🥳

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

5. Using HCP-D, we found that most connections significantly strengthened from ages 8 to 22. Their developmental curves varied in curvature, from concave-down to concave-up. We quantified this shape using the average second derivative, revealing a continuous trajectory spectrum.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

4. We used diffusion MRI from three independent datasets: HCP-D (N = 590, 8.1–21.9 years), ABCD (baseline N = 3949; 2-year follow-up N = 3155, 8.9–13.8 years), and a Chinese cohort (N = 947, 6.1–23.4 years). We constructed 12 × 12 large-scale structural connectomes.

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

3. White matter connectivity is refined throughout youth, with different tracts maturing at different paces. Yet prior work has mainly focused on major bundles, leaving the spatiotemporal variability of connectome-wide structural connectivity development under-characterized.🤔

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Xiaoyu Xu @xiaoyuxuu.bsky.social · 26/05/2026

White matter tracts form the structural foundation of brain networks, yet how structural connectivity develops across the whole connectome remains unclear. In our recent @natcomms.nature.com article, we report a continuous, robust developmental pattern along the S-A connectional axis.😁

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