Zaixu Cui

Complex human cognition and behavior rely on the coordinated activity of large-scale brain networks. Our laboratory aims to uncover the organizational principles of human brain networks, characterize their development throughout childhood and adolescence, and understand how network organization supports higher-order cognitive functions. We also investigate the relationship between brain network abnormalities and psychiatric disorders and develop noninvasive neuromodulation approaches guided by individualized brain network characteristics. To address these questions, we integrate multimodal neuroimaging, electrophysiological recordings, behavioral experiments, machine learning, and computational modeling. Our research focuses on four main areas.

Childhood and adolescence are critical periods for brain network development and cognitive maturation. Using diffusion MRI and functional MRI, we characterize developmental trajectories of brain connectivity, network topology, and structure–function coupling. We aim to uncover the spatiotemporal organization of brain network maturation and its individual variability, and investigate how brain network development supports higher-order cognitive functions, particularly executive functions, and contributes to individual differences in cognitive abilities.

With a particular focus on attention-deficit/hyperactivity disorder (ADHD), we investigate how brain networks deviate from typical developmental trajectories and how these deviations relate to clinical symptoms, developmental changes, and treatment outcomes. By combining normative developmental modeling with individualized brain network mapping, we aim to identify shared and individual-specific network abnormalities, understand the neurobiological basis of clinical heterogeneity, and explore the potential of neuroimaging for personalized diagnosis and treatment.

Transcranial light stimulation (tLS) is a noninvasive neuromodulation technique that uses near-infrared light to modulate human brain activity. Combining functional MRI, EEG, and computational modeling, we investigate the effects of tLS on functional brain networks, their spatiotemporal dynamics, and their relationships with individual differences in brain network organization. Building on these findings, we aim to optimize stimulation targets and parameters based on individual brain network characteristics and develop personalized neuromodulation approaches for ADHD and other neurodevelopmental disorders.

White matter provides the structural pathways connecting brain regions and supporting neural communication, while also exhibiting spatially organized and task-related functional activity. Integrating diffusion MRI, functional MRI, intracranial stereoelectroencephalography (sEEG), and computational modeling, we investigate white matter structural connectivity, functional organization, and structure–function relationships. We further explore the role of white matter in neural communication and higher-order cognition, as well as the development of its structural and functional connectivity and their associations with psychiatric disorders.

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.