Biology subjects

Boncompte, G.

Publications and source records attributed to Boncompte, G..

3 recordsLinked to original sources

bioRxiv2026

Aperiodic exponent ceases to track changes in propofol dose at deep surgery-level hypnotic depths

BackgroundAccurately tracking hypnotic depth during surgery is key to deliver personalized and precise anaesthesia. The aperiodic exponent of electroencephalographic (EEG) data has shown great promise as a marker of the balance between excitatory and inhibitory cortical activity and of hypnotic depth. In humans, this relation has been mostly evidenced by pairwise comparisons between awake and superficial anaesthesia states. However, it is unclear if this relation is also present across deeper hypnotic depths. Here we examined the relation between aperiodic exponent and hypnotic depth across different surgery-level propofol doses. MethodsThe present was an exploratory prospective observational analytical study where we recorded high-density EEG data from patients before surgery and during continuous target-controlled propofol infusions with different intraoperative target concentrations. We evaluated the relation between steady-state propofol dose and several EEG features including aperiodic exponent and spectral band powers. ResultsAll EEG measures were different when comparing awake versus anaesthesia conditions. At surgery-level hypnotic depths, some spectral band powers continued to track propofol dose displaying either with positive (delta R2 = 0.23, p = 0.001) or negative correlations (alpha R2 = 0.36, p < 0.001; beta R2 = 0.045, p < 0.001). Importantly, the aperiodic exponent was not associated with propofol doses absolute value (R2 = 0.011, p = 0.43) or with its relative change (R2 = 0.011, p = 0.51). ConclusionsOur results suggest that the relation between aperiodic exponent and excitatory/inhibitory balance is circumscribed to more superficial hypnotic depths than those typically employed for surgery.

neuroscience↗

bioRxiv2024

Aperiodic exponent of brain field potentials is dependent on the frequency range it is estimated

The aperiodic component of brain field potentials, like EEG, LFP and intracortical recordings, has shown to be a valuable tool in basic neuroscience and in clinical applications. Aperiodic activity is modeled as a power law of the power spectral density, with the aperiodic exponent as the key parameter. Part of the interest in this parameter lies in its proposed role as a marker of the balance between excitatory and inhibitory cortical activity. In theory, a perfect power law would imply that the same behaviour exists across all frequencies, however recent evidence has suggested that low and high frequency ranges could present different aperiodic exponents. To elucidate this, we systematically evaluated the relation between frequency range and aperiodic parameters using human resting-state intracortical recordings from 62 patients. We employed two distinct estimation methods, Specparam and IRASA. We found that aperiodic parameters were indeed dependent on frequency range. Specifically, we found that low frequency ranges displayed, on average, lower aperiodic exponents (flatter power spectral density) than high frequency ranges. This behaviour was consistent for Specparam and IRASA estimations in all frequency ranges compatible with EEG. Given that there is currently no consensus for a single frequency range to be used in either clinical or basic neuroscience, our results show that care should be taken when comparing aperiodic exponents derived from different frequency ranges. We believe our results also encourage further research into the possible roles that aperiodic exponents estimated from different frequency ranges could have in reflecting distinct aspects of cortical systems.

neuroscience↗

bioRxiv2020

Complexity and 1/f slope jointly reflect cortical states across different E/I balances

Characterization of brain states is essential for understanding its functioning in the absence of external stimuli. Brain states differ on their balance between excitation and inhibition, and on the diversity of their activity patterns. These can be respectively indexed by 1/f slope and Lempel-Ziv Complexity (LZc). However, whether and how these two brain state properties relate remain elusive. Here we analyzed the relation between 1/f slope and LZc with two in-silico approaches and in both rat EEG and monkey ECoG data. We contrasted resting state with propofol anesthesia, which directly modulates the excitation-inhibition balance. We found convergent results among simulated and empirical data, showing a strong, inverse and non trivial monotonic relation between 1/f slope and complexity, consistent at both ECoG and EEG scales. We hypothesize that differentially entropic regimes could underlie the link between the excitation-inhibition balance and the vastness of the repertoire of brain systems.

neuroscience↗