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Research Article | Open Access

Graph-Theoretical Signature from Neural and Vascular Signals Reveals Spinal Cord Stimulation Frequency-Specific Brain Network in Disorders of Consciousness Patients

Nan Wang1,2,Xiaoke Chai1,3,Yifang He4Jiuxiang Song5Tianqing Cao6Qiheng He1Sipeng Zhu1Yitong Jia1Juanning Si4Yi Yang1,3,7( )Jizong Zhao1( )
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
School of Instrumentation Science and Opto-Electronics Engineering, Beijing Information Science and Technology University, Beijing 102206, China
School of Advanced Manufacturing, Nanchang University, Nanchang, Jiangxi 330031, China
Department of Neurosurgery, Aviation General Hospital, Beijing 100012, China
Brain Computer Interface Transitional Research Center, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China

†These author contributed equally to this work.

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Abstract

Introdution

Spinal cord stimulation (SCS) has emerged as a promising neuromodulatory intervention for patients with disorders of consciousness (DoC). However, the identification of optimal stimulation frequencies remains a subject of ongoing debate. Although previous electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) studies have suggested the therapeutic efficacy of 5- and 70-Hz, respectively, the integrative neurovascular mechanisms and frequency-specific network dynamics underlying these effects remain to be elucidated.

Objective and Impact Statement

This study aims to characterize frequency-dependent network reconfiguration in DoC using simultaneous EEG-fNIRS recordings and graph theoretical analysis. By delineating distinct neurophysiological and hemodynamic signatures, our findings establish a mechanistic framework for the optimization of SCS parameters, thereby advancing personalized neuromodulation strategies for the promotion of consciousness recovery.

Methods

This prospective trial used simultaneous EEG–fNIRS and graph theory in 16 patients with DoC undergoing multifrequency SCS at 5, 20, 70, and 100 Hz to decode frequency-specific network dynamics. Our integrated EEG–fNIRS analysis revealed 3 principal advances. First, multimodal cortical mapping via a unified anatomical atlas quantified frequency-dependent network reconfiguration, generating graph-theoretical metrics (global and nodal efficiency, characteristic path length, and clustering coefficients) from source-localized EEG (delta–gamma bands) and fNIRS (oxyhemoglobin and deoxygenated) data. Second, we identified frequency-dependent neurophysiological profiles.

Results

Five-hertz stimulation produced acute enhancement of theta-band global network efficiency coupled with elevated gamma-band nodal efficiency in the right cingulate motor area, indicating immediate frontolimbic engagement. Conversely, 70-Hz stimulation selectively evoked delayed hemodynamic responses in the visual cortices and increased occipital hemoglobin oxygenation without concomitant EEG alterations, suggesting preferential retinotopic pathway recruitment.

Conclusion

Multimodal EEG–fNIRS analysis elucidates frequency-specific SCS mechanisms, where 5-Hz stimulation optimizes local information integration through theta and gamma modulation, while 70-Hz enhances long-range connectivity, exposing frequency-specific neural plasticity mechanisms.

References

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Cyborg and Bionic Systems
Article number: 0539

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Cite this article:
Wang N, Chai X, He Y, et al. Graph-Theoretical Signature from Neural and Vascular Signals Reveals Spinal Cord Stimulation Frequency-Specific Brain Network in Disorders of Consciousness Patients. Cyborg and Bionic Systems, 2026, 7: 0539. https://doi.org/10.34133/cbsystems.0539

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Received: 09 September 2025
Revised: 24 January 2026
Accepted: 02 February 2026
Published: 23 April 2026
© 2026 Nan Wang et al. Exclusive licensee Beijing Institute of Technology Press. No claim to original U.S. Government Works.