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Multiscale functional connectivity reveals imbalanced interplay between higher- and lower-order brain networks in ADHD

Nature Mental Health (2025)Cite this article

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Abstract

Attention deficit hyperactivity disorder (ADHD) is characterized by dysconnectivity among large-scale brain functional networks. How these changes manifest across different spatial scales remains unclear. We investigated ADHD-related functional connectivity alterations at global, region-to-region and network scales using resting-state functional MRI data from 454 children and adolescents with ADHD and typically developing controls from three cohorts. At the global level, individuals with ADHD exhibited hypoconnectivity in default-mode network (DMN) hubs and visual areas. Region‑to‑region analysis revealed hypoconnectivity within the DMN, between DMN hubs and visual regions, and between nodes of salience (SAN) and frontoparietal networks and auditory/sensorimotor areas, alongside hyperconnectivity linking DMN with SAN, frontoparietal and auditory regions. At the network level, SAN-auditory/sensorimotor hypoconnectivity persisted whereas DMN alterations were no longer significant. These results were independent of age and sex but influenced by medication status and comorbidity. Collectively, our findings indicate a scale-dependent imbalanced interplay between higher-order cognitive and lower-order sensory networks in ADHD.

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Fig. 1: Study design and workflow of data processing.
Fig. 2: Group differences in global-brain, region-to-region, and network-level FC between patients with ADHD and TDC.
Fig. 3: Performance of classification models distinguishing patients with ADHD from TDC using different FC features.
Fig. 4: Summary of brain network dysfunction patterns in ADHD.

Data availability

The ADHD-200 datasets are publicly available and can be accessed via https://fcon_1000.projects.nitrc.org/indi/adhd200. The SCU data originate from previously conducted and published studies and are not collected as part of this study. De-identified SCU data may be shared upon reasonable request, subject to approval by the relevant principal investigator, compliance with participants’ consent, and the institution’s data-sharing policies and procedures. Requests may be sent to the corresponding author.

Code availability

Publicly available software, packages and codes were used for all imaging preprocessing and analyses conducted in this study. Preprocessing of rs-fMRI data and voxel-wise GBC calculation were conducted in DPABI v4.2_190919 (accessible at http://rfmri.org/dpabi) implemented in MATLAB R2018b. Region-to-region connectivity and network-level connectivity were calculated using Nibabel v4.0.2 (https://github.com/nipy/nibabel) and Nilearn v0.9.2 (https://github.com/nilearn/nilearn) packages implemented in Python. Combat harmonization packages included neuroComBat v0.2.12 (https://github.com/Jfortin1/neuroCombat) and FCHarmony (https://github.com/andy1764/FCHarmony). Statistical analyses for GBC, region-to-region FC and network-level FC were conducted in the SPM12 (https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), NBS v1.2 (https://www.nitrc.org/projects/nbs) and SPSS v19 (https://www.ibm.com/spss), respectively. Machine-learning classification was performed using PyCaret v3.3.2 (https://pycaret.org/) and Scikit-learn v1.4.2 (https://scikit-learn.org/stable/index.html) packages implemented in Python. Codes for hyperparameters optimization using nested cross-validation were available at https://colab.research.google.com/github/MLMH-Lab/How-To-Build-A-Machine-Learning-Model/blob/master/chapter_19_script.ipynb. BrainNet Viewer v1.7 (https://www.nitrc.org/projects/bnv/) was used for visualization.

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (grant number 82372080 to X. Huang), Natural Science Foundation of Sichuan Province (grant number 2024NSFSC1771 to Y.G.) and Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (grant number GZC20231804 to Y.G.). Data for the SCU cohort were collected at the Department of Psychiatry and Department of Radiology, West China Hospital of Sichuan University. Data collection for the ADHD-200-PKU cohort was conducted at the Institute of Mental Health, Peking University, and supported by the Commonwealth Sciences Foundation, Ministry of Health, China (200802073), The National Foundation, Ministry of Science and Technology, China (2007BAI17B03), The National Natural Sciences Foundation, China (30970802), the Funds for International Cooperation of the National Natural Science Foundation of China (81020108022), the National Natural Science Foundation of China (8100059) and Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning. Data collection for the ADHD-200-NYU cohort was conducted at Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the Child Study Center, New York University Langone Medical Center, and funded by the National Institute of Mental Health (R01MH083246), Autism Speaks, The Stavros Niarchos Foundation, The Leon Levy Foundation and an endowment provided by Phyllis Green and Randolph Cōwen. The authors thank all the participants for their involvement in this study.

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Authors and Affiliations

  1. Department of Radiology, Huaxi MR Research Center (HMRRC), Institute of Radiology and Medical Imaging. West China Hospital of Sichuan University, Chengdu, PR China

    Yingxue Gao, Zilin Zhou, Weijie Bao, Ying Chen, Xinyue Hu, Hailong Li, Lianqing Zhang, Qiyong Gong & Xiaoqi Huang

  2. Psychoradiology Key Laboratory of Sichuan Province, West China Hospital of Sichuan University, Chengdu, PR China

    Yingxue Gao, Zilin Zhou, Weijie Bao, Ying Chen, Xinyue Hu, Hailong Li, Lianqing Zhang, Qiyong Gong & Xiaoqi Huang

  3. Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, PR China

    Qiyong Gong & Xiaoqi Huang

  4. Xiamen Key Lab of Psychoradiology and Neuromodulation, Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, PR China

    Qiyong Gong & Xiaoqi Huang

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  1. Yingxue Gao
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  2. Zilin Zhou
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Contributions

X. Huang and Q.G. conceptualized the study. Y.G. designed the analytic approach. Y.C. contributed to the participant recruitment and data collection. Y.G., Z.Z. and W.B. contributed to the data analysis. Y.G. drafted the paper. L.Z., H.L. and X. Hu helped with visualization and the interpretation of results. X. Huang and H.L. reviewed and revised the paper.

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Correspondence to Qiyong Gong or Xiaoqi Huang.

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Gao, Y., Zhou, Z., Bao, W. et al. Multiscale functional connectivity reveals imbalanced interplay between higher- and lower-order brain networks in ADHD. Nat. Mental Health (2025). https://doi.org/10.1038/s44220-025-00512-5

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