AI Insight
This study challenges the assumption that local field potentials (LFPs) reliably reflect population neural activity in all brain regions. Using computational modeling validated against patient data, researchers found that in the subthalamic nucleus (STN), unlike in the cortex, LFPs cannot reliably predict synaptic activity or population dynamics due to the STN's symmetric neuron shape and lack of internal connections, which cause signal cancellation. However, pathological synchronization in Parkinson's disease restores the relationship between synaptic activity and LFPs, and certain signal features can still track neural properties like firing rate and neuron morphology.
Why it matters
These findings have direct implications for deep brain stimulation therapy in Parkinson's disease, where STN LFPs are used as biomarkers to adjust stimulation parameters. The research provides a mechanistic understanding of when and why these signals are interpretable, potentially improving adaptive stimulation strategies and challenging assumptions about using LFPs as universal measures of brain activity across different structures.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Local field potentials (LFPs) are widely interpreted as readouts of population synaptic activity, an assumption derived almost entirely from cortical recordings. Whether these principles extend to subcortical structures remains unclear. We address this question in the subthalamic nucleus (STN), where LFPs are routinely recorded and used to guide adaptive deep brain stimulation for Parkinson’s disease, using a biophysically detailed population model benchmarked against patient microelectrode recordings. As in the cortex, STN extracellular potentials were dominated by synaptic currents. Differently from the cortex, however, LFPs could not be reliably predicted from these currents or other average population quantities. This dissociation arises from the STN’s symmetric neuronal morphology and lack of recurrent connectivity, which promote destructive interference among single-neuron contributions, decoupling the LFP from population-level dynamics. This decoupling was not absolute: pathological beta synchrony restored a robust synapse-LFP relationship by consistent underlying dynamics, while the aperiodic slope of the power spectral density tracked STN neuronal morphology, firing rate, and excitatory-inhibitory balance. Together, these findings challenge the prevailing view of LFPs as universal readouts of population activity. Our results show that the interpretability of extracellular signals depends critically on neuronal morphology and synchronization state, and provide a mechanistic framework for the use of STN LFPs as biomarkers in adaptive deep brain stimulation for Parkinson’s disease.