崔再续

人类复杂的认知与行为依赖于大尺度脑网络的协调活动。我们实验室致力于揭示人脑网络的组织原则、儿童青少年时期的发育规律及其支持高级认知功能的网络机制，探索脑网络异常与精神疾病的关系，并发展基于个体脑网络特征的非侵入神经调控方法。我们综合运用多模态神经影像、电生理记录、行为学实验、机器学习及计算建模等方法，重点开展以下四个方向的研究。

1. 脑网络的典型发育

儿童青少年期是脑网络快速发育和高级认知功能逐渐成熟的关键阶段。我们结合弥散磁共振与功能磁共振成像，系统刻画脑连接、网络拓扑及结构–功能关系的发育轨迹，揭示脑网络成熟的时空规律及其个体差异。在此基础上，进一步探索脑网络发育如何支持执行功能等高级认知能力的成熟，以及脑网络发育的个体差异与认知功能的关系。

2. 神经发育障碍中的脑网络异常发育

我们重点关注注意缺陷多动障碍（ADHD），研究脑网络如何偏离典型发育轨迹，以及这些偏离与临床症状、发育变化和治疗反应的关系。通过建立脑网络规范化发育模型，结合个体化脑网络分析，我们致力于揭示神经发育障碍中共有及个体特异性的脑网络异常，解析疾病的神经生物学异质性，并探索脑影像特征在个体化诊疗中的应用价值。

3. 脑网络引导的个体化经颅光刺激

经颅光刺激（tLS）是一种利用近红外光非侵入性调节人脑活动的神经调控技术。我们结合功能磁共振、脑电及计算建模，研究光刺激对脑功能网络的调控效应，揭示刺激效应的时空演变规律及其与个体脑网络特征的关系。在此基础上，我们探索基于个体脑网络的刺激靶点选择与参数优化，发展面向ADHD等神经发育障碍的个体化神经调控方法。

4. 白质的结构与功能

白质是连接不同脑区、支持神经信息传递的重要结构基础，其自身也表现出具有空间组织和认知任务相关性的功能活动。我们结合弥散磁共振、功能磁共振、颅内立体定向脑电（sEEG）及计算建模，系统研究白质的结构连接、功能组织及结构–功能关系，探索白质在神经信息传递和高级认知中的作用，并进一步揭示白质结构与功能连接的发育规律及其与精神疾病的关系。

1. Xu X.#, Fu Z.#, Xu H., Zhang K., Cong J., Yang H., Li Y., Zhao Y., Zheng X., Keller A. S., Xu T., Chen R., Cai W., Cao Q.*, Yang L.*, & Cui Z.* (2026). Developmental deviations of association-network structural connectivity in youths with ADHD predict symptom and treatment outcomes. Nat. Biomed. Eng. DOI: 10.1038/s41551-026-01779-4.

2. Li Y.#, Tan L.#, Duan Y.#, Xu X.#, Xu H., Yu M., Jia L., Li Z., Zhao C., Chen Q., Larsen B., Pines A., Wang T.*, Chen R.*, & Cui Z.* (2026). Developmental benchmarks of executive function reveal sensitive periods for adolescent mental health. Nat. Ment. Health, 4(10): 1560-1571. DOI: 10.1038/s44220-026-00711-8.

3. Cong J.#, Zhao S.#, Xu H.#, Xu X., Li C., Li H., Xu T., Li Y., Yang H., & Cui Z.* (2026). White-matter functional connectivity uniquely predicts brain age, cognition, and psychopathology beyond gray-matter connectivity. Proc. Natl. Acad. Sci. U.S.A., 123(34): e2604191123. DOI: 10.1073/pnas.2604191123.

4. Zhang S.#, Wei P.-H.#, Xu L.#, Zhang C.#, Ao N., Xu H., Yang Y., Tan L., Li Z., Huang Y., Li J., Liu J., Shan Y.*, Zhao G.*, & Cui Z.* (2026). Intracranial EEG reveals working memory-related dynamics and connectivity in human white matter. Sci. Bull., 71(16): 4218-4228. DOI: 10.1016/j.scib.2026.07.051.

5. Xu X.#, Yang H., Cong J., Xu H., Kai J., Zhao S., Li Y., Shou H., Wang K., Sydnor V. J., Xu T., Yeh F.-C., & Cui Z.* (2026). Mapping the spatiotemporal continuum of structural connectivity development across the human connectome in youth. Nat. Commun., 17: 6550. DOI: 10.1038/s41467-026-73072-6.

6. Xu L.#, Zhang S., Wei P.-H., Zhang C., Yang Y., Shan Y., Zhao G.*, & Cui Z.* (2026). Long-range white-matter pathways enable efficient spontaneous neural activity propagation in the human brain. J. Neurosci., 46(23): e0039262026. DOI: 10.1523/JNEUROSCI.0039-26.2026.

7. Yang H.#, Wu G.#, Li Yaoxin., Xu X., Cong J., Xu H., Ma Y., Li Yang., Chen R., Pines A., Xu T., Sydnor V. J., Satterthwaite T. D., & Cui Z.* (2025). Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition. Proc. Natl. Acad. Sci. U.S.A., 122(12): e2420228122. DOI: 10.1073/pnas.2420228122.

8. Li Z.#, Zhao Y.#, Hu Y., Li Y., Zhang K., Gao Z., Tan L., Jia H., Cong J., Liu H., Li X., Cao A.*, Cui Z.*, & Zhao C.* (2024). Transcranial low-level laser stimulation in the near-infrared-II region (1064 nm) for brain safety in healthy humans. Brain Stimul., 17(6): 1307-1316. DOI: 10.1016/j.brs.2024.11.010.

9. Zhao S.#, Su H.#, Cong J., Wen X., Yang H., Chen P., Wu G., Fan Q., Ma Y., Xu X., Hu C., Li H., Keller A., Pines A., Chen R.*, & Cui Z.* (2024). Hierarchical individual variation and socioeconomic impact on personalized functional network topography in children. BMC Med., 22: 556. DOI: 10.1186/s12916-024-03784-3.

10. Chen P.#, Yang H.#, Zheng X., Jia H., Hao J., Xu X., Li C., He X., Chen R., Okubo T. S.*, & Cui Z.* (2024). Group-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks. Imaging Neurosci., 2: 1-21. DOI: 10.1162/imag_a_00378.

11. Huang Y.#, Wei P.-H.#, Xu L.#, Chen D., Yang Y., Song W., Yi Y., Jia X., Wu G., Fan Q., Cui Z.*, & Zhao G.* (2023). Intracranial electrophysiological and structural basis of BOLD functional connectivity in human brain white matter. Nat. Commun., 14: 3414. DOI: 10.1038/s41467-023-39067-3.

12. Cui Z.*, Pines A. R., Larsen B., Sydnor V. J., Li H., Adebimpe A., Alexander-Bloch A. F., Bassett D. S., Bertolero M., Calkins M. E., Davatzikos C., Fair D. A., Gur R. C., Gur R. E., Moore T. M., Shanmugan S., Shinohara R. T., Vogel J. W., Xia C. H., Fan Y., & Satterthwaite T. D.* (2022). Linking individual differences in personalized functional network topography to psychopathology in youth. Biol. Psychiatry, 92(12): 973-983. DOI: 10.1016/j.biopsych.2022.05.014.

13. Chen R.*, Cui Z.*, Capit￣o L., Wang G., Satterthwaite T. D., & Harmer C. J. (2020). Precision biomarkers for mood disorders based on brain imaging. BMJ, 371: m3618. DOI: 10.1136/bmj.m3618.

14. Cui Z., Li H., Xia C. H., Larsen B., Adebimpe A., Baum G. L., Cieslak M., Gur R. E., Gur R. C., Moore T. M., Oathes D. J., Alexander-Bloch A. F., Raznahan A., Roalf D. R., Shinohara R. T., Wolf D. H., Davatzikos C., Bassett D. S., Fair D. A., Fan Y., & Satterthwaite T. D. (2020). Individual variation in functional topography of association networks in youth. Neuron, 106(2): 340-353.e8. DOI: 10.1016/j.neuron.2020.01.029.

15. Cui Z., Stiso J., Baum G. L., Kim J. Z., Roalf D. R., Betzel R. F., Gu S., Lu Z., Xia C. H., He X., Ciric R., Oathes D. J., Moore T. M., Shinohara R. T., Ruparel K., Davatzikos C., Pasqualetti F., Gur R. E., Gur R. C., Bassett D. S., & Satterthwaite T. D. (2020). Optimization of energy state transition trajectory supports the development of executive function during youth. eLife, 9: e53060. DOI: 10.7554/eLife.53060.

16. Cui Z., & Gong G. (2018). The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features. NeuroImage, 178: 622-637. DOI: 10.1016/j.neuroimage.2018.06.001.

17. Cui Z.*, Su M.*, Li L., Shu H., & Gong G. (2018). Individualized prediction of reading comprehension ability using gray matter volume. Cereb. Cortex, 28(5): 1656-1672. DOI: 10.1093/cercor/bhx061.

18. Cui Z.*, Xia Z.*, Su M., Shu H., & Gong G. (2016). Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach. Hum. Brain Mapp., 37(4): 1443-1458. DOI: 10.1002/hbm.23112.

19. Cui Z., Zhong S., Xu P., He Y., & Gong G. (2013). PANDA: A pipeline toolbox for analyzing brain diffusion images. Front. Hum. Neurosci., 7: 42. DOI: 10.3389/fnhum.2013.00042.