EEG-based effective connectivity distinguishes between unresponsive states with and without report of conscious experience and correlates with brain complexity

Objective methods for distinguishing conscious from unconscious states in humans are of key importance for clinical evaluation of general anesthesia and patients with disorders or consciousness. Here, we test the generalizability of a DTF-based algorithm - a measure of effective connectivity - as an objective measure of conscious experience during anesthesia and correlate it with a well-tested index of consciousness: the Perturbational Complexity Index (PCI). We reanalyzed EEG data from an experimental study in which 18 healthy volunteers were randomly assigned to one of three types of general anesthesia: propofol, xenon, and ketamine. EEG was recorded before and during anesthesia, and DTF was calculated from every 1-second segment of the EEG data to quantify the effective connectivity between channel pairs. This was used to classify the state of each participant as either conscious or unconscious, and the classifications were compared with the participant’s delayed report of experience, and the PCI. The algorithm was more likely to classify participants as conscious in the awake state than during propofol and xenon anesthesia (p0.05). Furthermore, the DTF-based confidence of being classified as conscious was highly correlated with PCI (r2=0.48, p<0.05). These results provide further support for the notion that effective connectivity measured between EEG electrodes can be used to distinguish between conscious and unconscious states in humans

Neuroimaging Studies on Disorders of Consciousness : a Meta-Analytic Evaluation

Neuroimaging tools could open a window on residual neurofunctional activity in the absence of detectable behavioural responses in patients with disorders of consciousness (DOC). Nevertheless, the literature on this topic is characterised by a large heterogeneity of paradigms and methodological approaches that can undermine the reproducibility of the results. To explicitly test whether task-related functional magnetic resonance imaging (fMRI) can be used to systematically detect neurofunctional differences between different classes of DOC, and whether these differences are related with a specific category of cognitive tasks (either active or passive), we meta-analyzed 22 neuroimaging studies published between 2005 and 2017 using the Activation Likelihood Estimate method. The results showed that: (1) active and passive tasks rely on well-segregated patterns of activations; (2) both unresponsive wakeful syndrome and patients in minimally conscious state activated a large portion of the dorsal-attentional network; (3) shared activations between patients fell mainly in the passive activation map (7492 voxels), while only 48 voxels fell in a subcortical region of the active-map. Our results suggest that DOCs can be described along a continuum-rather than as separated clinical categories-and characterised by a widespread dysfunction of brain networks rather than by the impairment of a well functionally anatomically defined one

Autonomic responses to emotional linguistic stimuli and amplitude of low-frequency fluctuations predict outcome after severe brain injury

An accurate prognosis on the outcome of brain-injured patients with disorders of consciousness (DOC) remains a significant challenge, especially in the acute stage. In this study, we applied a multiple-technique approach to provide accurate predictions on functional outcome after 6 months in 15 acute DOC patients. Electrophysiological correlates of implicit cognitive processing of verbal stimuli and data-driven voxel-wise resting-state fMRI signals, such as the fractional amplitude of low-frequency fluctuations (fALFF), were employed. Event-related electrodermal activity, an index of autonomic activation, was recorded in response to emotional words and pseudo-words at baseline (T0). On the same day, patients also underwent a resting-state fMRI scan. Six months later (T1), patients were classified as outcome-negative and outcome-positive using a standard functional outcome scale. We then revisited the baseline measures to test their predictive power for the functional outcome measured at T1. We found that only outcome-positive patients had an earlier, higher autonomic response for words compared to pseudo-words, a pattern similar to that of healthy awake controls. Furthermore, DOC patients showed reduced fALFF in the posterior cingulate cortex (PCC), a brain region that contributes to autonomic regulation and awareness. The event-related electrodermal marker of residual cognitive functioning was found to have a significant correlation with residual local neuronal activity in the PCC. We propose that a residual autonomic response to cognitively salient stimuli, together with a preserved resting-state activity in the PCC, can provide a useful prognostic index in acute DOC

New steps on an old path: Novel estrogen receptor inhibitors in breast cancer

Estrogen receptor (ER) signaling represents the main driver of tumor growth and survival in hormone receptor positive (HR+) breast cancer (BC). Thus, endocrine therapy (ET) alone or in combination with targeted agents constitutes the mainstay of the treatment for this BC subtype. Despite its efficacy, intrinsic or acquired resistance to ET occurs in a large proportion of cases, mainly due to aberrant activation of ER signaling (i.e. through ligand-independent ER activation, in the presence of estrogen receptor 1 (ESR1) gene aberration or ER protein phosphorylation) and/or the upregulation of escape pathways, such as the PI3K/AKT/mTOR pathway. Therefore, the development of new ER pathway targeting agents remains essential to delay and overcome ET resistance, enhance treatment efficacy and tolerability, and ultimately prolong patient survival and improve their quality of life. Several novel ER targeting agents are currently under investigation. Among these, the oral selective ER degraders (SERDs) represent the pharmacological class at the most advanced stage of development and promise to enrich the therapeutic armamentarium of HR+ BC in the next few years, as they showed promising results in several clinical trials, either as single ET agents or in combination with targeted therapies. In this manuscript, we aim to provide a comprehensive overview on the clinical development of novel ER targeting agents, reporting the most up-to-date evidence on oral SERDs and other compounds, including new selective ER modulators (SERMs), ER proteolysis targeting chimera (PROTACs), selective ER covalent antagonists (SERCAs), complete ER antagonists (CERANs), selective human ER partial agonists (ShERPAs). Furthermore, we discuss the potential implications of introducing these novel treatment strategies in the evolving and complex therapeutic scenario of HR+ BC

Stratification of unresponsive patients by an independently validated index of brain complexity.

OBJECTIVE: Validating objective, brain-based indices of consciousness in behaviorally unresponsive patients represents a challenge due to the impossibility of obtaining independent evidence through subjective reports. Here we address this problem by first validating a promising metric of consciousness-the Perturbational Complexity Index (PCI)-in a benchmark population who could confirm the presence or absence of consciousness through subjective reports, and then applying the same index to patients with disorders of consciousness (DOCs). METHODS: The benchmark population encompassed 150 healthy controls and communicative brain-injured subjects in various states of conscious wakefulness, disconnected consciousness, and unconsciousness. Receiver operating characteristic curve analysis was performed to define an optimal cutoff for discriminating between the conscious and unconscious conditions. This cutoff was then applied to a cohort of noncommunicative DOC patients (38 in a minimally conscious state [MCS] and 43 in a vegetative state [VS]). RESULTS: We found an empirical cutoff that discriminated with 100% sensitivity and specificity between the conscious and the unconscious conditions in the benchmark population. This cutoff resulted in a sensitivity of 94.7% in detecting MCS and allowed the identification of a number of unresponsive VS patients (9 of 43) with high values of PCI, overlapping with the distribution of the benchmark conscious condition. INTERPRETATION: Given its high sensitivity and specificity in the benchmark and MCS population, PCI offers a reliable, independently validated stratification of unresponsive patients that has important physiopathological and therapeutic implications. In particular, the high-PCI subgroup of VS patients may retain a capacity for consciousness that is not expressed in behavior

Complexity of multi-dimensional spontaneous EEG decreases during propofol induced general anaesthesia

Emerging neural theories of consciousness suggest a correlation between a specific type of neural dynamical complexity and the level of consciousness: When awake and aware, causal interactions between brain regions are both integrated (all regions are to a certain extent connected) and differentiated (there is inhomogeneity and variety in the interactions). In support of this, recent work by Casali et al (2013) has shown that Lempel-Ziv complexity correlates strongly with conscious level, when computed on the EEG response to transcranial magnetic stimulation. Here we investigated complexity of spontaneous high-density EEG data during propofol-induced general anaesthesia. We consider three distinct measures: (i) Lempel-Ziv complexity, which is derived from how compressible the data are; (ii) amplitude coalition entropy, which measures the variability in the constitution of the set of active channels; and (iii) the novel synchrony coalition entropy (SCE), which measures the variability in the constitution of the set of synchronous channels. After some simulations on Kuramoto oscillator models which demonstrate that these measures capture distinct ‘flavours’ of complexity, we show that there is a robustly measurable decrease in the complexity of spontaneous EEG during general anaesthesia

Methods for analysis of brain connectivity : An IFCN-sponsored review

The goal of this paper is to examine existing methods to study the "Human Brain Connectome" with a specific focus on the neurophysiological ones. In recent years, a new approach has been developed to evaluate the anatomical and functional organization of the human brain: the aim of this promising multimodality effort is to identify and classify neuronal networks with a number of neurobiologically meaningful and easily computable measures to create its connectome. By defining anatomical and functional connections of brain regions on the same map through an integrated approach, comprising both modern neurophysiological and neuroimaging (i.e. flow/metabolic) brain-mapping techniques, network analysis becomes a powerful tool for exploring structural-functional connectivity mechanisms and for revealing etiological relationships that link connectivity abnormalities to neuropsychiatric disorders. Following a recent IFCN-endorsed meeting, a panel of international experts was selected to produce this current state-of-art document, which covers the available knowledge on anatomical and functional connectivity, including the most commonly used structural and functional MRI, EEG, MEG and non-invasive brain stimulation techniques and measures of local and global brain connectivity. (C) 2019 Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.Peer reviewe

Excitability of the supplementary motor area in Parkinson&apos;s disease depends on subcortical damage

Background: Cortical dysfunctioning significantly contributes to the pathogenesis of motor symptoms in Parkinson's disease (PD). Objective: We aimed at testing whether an acute levodopa administration has measurable and specific cortical effects possibly related to striatal dopaminergic deficit. Methods: In thirteen PD patients, we measured the electroencephalographic responses to transcranial magnetic stimulation (TMS/EEG) of the supplementary motor area and superior parietal lobule (n = 8) before and after an acute intake of levodopa. We also performed a single-photon emission computed tomography and [123I]N-\u3c9-fluoropropyl-2\u3b2-carbomethoxy-3\u3b2-(4-iodophenyl)nortropane to identify the more affected and the less affected brain side in each patient, according to the dopaminergic innervation loss of the putamen. Cortical excitability changes before and after an acute intake of levodopa were computed and compared between the more and the less affected brain side at the single-patient as well as at the group level. Results: We found that levodopa intake induces a significant increase (P < 0.01) of cortical excitability nearby the supplementary motor area in the more affected brain side, greater (P < 0.025) than in the less affected brain side. Notably, cortical excitability changes nearby the superior parietal lobule were not statistically significant. Conclusions: These results strengthen the idea that dysfunction of specific cortico-subcortical circuits may contribute to pathophysiology of PD symptoms. Most important, they support the use of navigated TMS/EEG as a non-invasive tool to better understand the pathophysiology of PD