Human local field potentials in motor and non-motor brain areas encode upcoming movement direction

• Published in: Communications biology 2024-04, Vol.7 (1), p.506-13, Article 506
• Main Authors: Combrisson, Etienne, Di Rienzo, Franck, Saive, Anne-Lise, Perrone-Bertolotti, Marcela, Soto, Juan L. P., Kahane, Philippe, Lachaux, Jean-Philippe, Guillot, Aymeric, Jerbi, Karim
• Format: Article
• Language: English
• Subjects: 631/378/116/2394; 631/378/2632/2634; 9/30; Adult; Alpha Frequencies; Biomedical and Life Sciences; Brain - physiology; Brain Mapping - methods; Brain Oscillations; Brain-Computer Interface; Cognitive science; Cortex (motor); Delayed Motor Tasks; Electrocorticography; Electroencephalography; Epilepsy; Epilepsy - physiopathology; Female; Gamma Frequencies; Hand - physiology; Humans; Intracranial EEG; Life Sciences; Local Field Potential; Machine Learning; Male; Motor Cortex; Motor Cortex - physiology; Motor Execution; Motor Planning; Movement - physiology; Movement Decoding; Multivariate Classification; Neuronal Synchronization; Neuroplasticity; Neuroscience; Oscillations; Phase-Amplitude Coupling; Planning; Young Adult
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-024-06151-3

Limb movement direction can be inferred from local field potentials in motor cortex during movement execution. Yet, it remains unclear to what extent intended hand movements can be predicted from brain activity recorded during movement planning. Here, we set out to probe the directional-tuning of oscillatory features during motor planning and execution, using a machine learning framework on multi-site local field potentials (LFPs) in humans. We recorded intracranial EEG data from implanted epilepsy patients as they performed a four-direction delayed center-out motor task. Fronto-parietal LFP low-frequency power predicted hand-movement direction during planning while execution was largely mediated by higher frequency power and low-frequency phase in motor areas. By contrast, Phase-Amplitude Coupling showed uniform modulations across directions. Finally, multivariate classification led to an increase in overall decoding accuracy (>80%). The novel insights revealed here extend our understanding of the role of neural oscillations in encoding motor plans. A study using intracranial recordings in humans suggests that upcoming hand movements can be predicted by oscillatory brain features of the LFP signal.