Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.

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Publication Details

Journal
Nature Biomedical Engineering
Published
2025-09-24
DOI
https://doi.org/10.1038/s41551-025-01467-9
Citations
6
Primary Topic
Neurological disorders and treatments
Type
article
Field-Weighted Citation Impact
8.34
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article

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Clemens Neudorfer, Timon Merk, Andrea A. Kühn, Johannes L. Busch et al.
6 citations
Nature Biomedical Engineering
Neurological disorders and treatments
8.34
article

Invasive neurophysiology and whole brain connectomics for neural decoding in patients with brain implants

Clemens Neudorfer, Timon Merk, Andrea A. Kühn, Johannes L. Busch, Ashley Walton, Jeroen Habets, Meera Chikermane, Thomas S. Binns, Roxanne Lofredi, Baotian Zhao, Katharina Faust, Robert Mark Richardson, Victoria Peterson, Wolf‐Julian Neumann, Guanyu Zhu, Nathan Sisterson, Malek Bajbouj, Zixiao Yin, Patricia Krause, Richard M. Köhler, Ningfei Li, Gerd‐Helge Schneider, Jonathan Vanhoecke, Angela Merkl, Alan Bush, Julius Huebl, Laura Freire Lyra, Samed Rouven Vossberg, Jianguo Zhang, Toni M. Brotons
article en
6 citations

Abstract

Brain-computer interface research can inspire closed-loop neuromodulation therapies, promising spatiotemporal precision for the treatment of brain disorders. Decoding dynamic patient states from brain signals with machine learning is required to leverage this precision, but a standardized framework for invasive brain signal decoding from neural implants does not exist. Here we develop a platform that integrates brain signal decoding with magnetic resonance imaging connectomics and demonstrate its use across 123 h of invasively recorded brain data from 73 neurosurgical patients treated with brain implants for movement disorders, depression and epilepsy. We introduce connectomics-informed movement decoders that generalize across cohorts with Parkinson's disease and epilepsy from the United States, Europe and China. We reveal network targets for emotion decoding in left prefrontal and cingulate circuits in deep brain stimulation patients with major depression. Finally, we showcase opportunities to improve seizure detection in responsive neurostimulation for epilepsy. Our study highlights the clinical use of brain signal decoding for deep brain stimulation and provides methods that allow for rapid, high-accuracy decoding for precision medicine approaches that can dynamically adapt neurotherapies in response to the individual needs of patients.

Nature Biomedical Engineering
Harvard University (US), Baylor College of Medicine (US), Capital Medical University (CN), Beijing Tian Tan Hospital (CN), Massachusetts General Hospital (US), MSB Medical School Berlin (DE), Circuit Therapeutics (United States) (US), Beijing Institute of Neurosurgery (CN), University Hospital Cologne (DE), Einstein Center for Neurosciences Berlin (DE), Instituto de Física del Litoral (AR), Rice University (US), Charité - Universitätsmedizin Berlin (DE)
Openalex Percentile: Top 2%
Neurological disorders and treatments
8.34
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