Deep brain microelectrode signal: $q$-statistical approach
Ana Luiza Souza Tavares, Henrique Santos Lima, Artur Pedro Martins Neto, Bruno Duarte Gomes, and Constantino Tsallis

TL;DR
This study analyzes intraoperative microelectrode recordings during deep brain stimulation in Parkinson's patients, revealing a universal $q$-Gaussian statistical pattern indicative of near-critical brain dynamics.
Contribution
It introduces a $q$-statistical framework to characterize MER amplitude distributions, uncovering a universal $q$-$eta$ relationship linked to near-criticality in Parkinson's disease.
Findings
MER signals follow a $q$-Gaussian distribution with $q > 1$
The $q$ and $eta$ parameters collapse onto a single constraint across recordings
The $q$-$eta$ coupling indicates near-critical brain dynamics in Parkinson's disease
Abstract
We characterize the amplitude statistics of intraoperative microelectrode recordings (MERs) obtained during deep brain stimulation (DBS) surgery in 46 patients with Parkinson's disease, using 184 recordings equally balanced between inside and outside the subthalamic nucleus (STN). The probability density of every recording is quantitatively well described by a -Gaussian (grounded on a nonadditive entropic functional), , with in all cases, reflecting persistent long-range temporal correlations inconsistent with Gaussian dynamics. Within the superstatistics framework, the slowly fluctuating local variance visible in the raw MER signals is a physical mechanism that directly generates the form. Beyond individual fits, and collapse across all 184 recordings onto the single functional constraint $q = 3 -…
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