A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations

Abstract

Background: The Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain-injured patients, PCI depends on elaborate experimental setups and offline processing, and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings. Objective: We aim to address these limitations by introducing PCI ST , a fast method for estimating perturbational complexity of any given brain response signal. Methods: PCI ST is based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain-injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intracranial single-pulse electrical stimulation (SPES) during wakefulness and sleep. Results: When calculated on TMS/hd-EEG potentials, PCI ST performed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler setup. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracranial complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG. Conclusions: PCI ST represents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.

Early discrimination of cognitive motor dissociation from disorders of consciousness: pitfalls and clues

“…In this respect, considering pitfalls potentially able to interfere with command-following approaches is extremely relevant and might influence positively the detection rates of CMD by subsequent EEG or functional MRI protocols. Indeed, approaches based on direct measurements of corticocortical connectivity using transcranial magnetic stimulation (TMS)-EEG have been proven to discriminate MCS from UWS [32,33]. Furthermore, they are theoretically unaffected by pitfalls; however, TMS-EEG has only been assessed in chronic patients and its ability to detect covert residual cognition at an early phase and feasibility in the ICU setting remain unknown.…”

Consciousness-specific dynamic interactions of brain integration and functional diversity

“…The influential integrated information theory identifies consciousness with a quantity Φ, combining integration and diversity of information within a system 2,3 . Studies using a proxy for Φ based on the evoked EEG response to transcranial magnetic stimulation (TMS), known as perturbational complexity index, have been highly successful at discriminating between different states of consciousness, including anaesthesia and disorders of consciousness 18–21 . These studies demonstrate that diversity and integration are both relevant for consciousness, and raise the question of how they relate to each other neurobiologically.…”

Impact of GABAA and GABAB Inhibition on Cortical Dynamics and Perturbational Complexity during Synchronous and Desynchronized States

“…Another approach to quantifying cortical complexity is to induce a perturbation of the system to investigate the causal interactions that follow. The perturbational complexity index (PCI; Casali et al, 2013 ; Comolatti et al, 2019 ), in which neural activity is exogenously perturbed by means of stimulation (transcranial magnetic stimulation or electrical stimulation) has been proposed as one such measure. This method has been validated for different instances such as physiological brain states ( Casali et al, 2013 ), anesthesia levels ( Sarasso et al, 2015 ; Arena et al, 2020 ; Dasilva et al, 2021 ), and disorders of consciousness ( Casarotto et al, 2016 ).…”