The Thalamus and Brainstem Act As Key Hubs in Alterations of Human Brain Network Connectivity Induced by Mild Propofol Sedation

Despite their routine use during surgical procedures, no consensus has yet been reached on the precise mechanisms by which hypnotic anesthetic agents produce their effects. Molecular, animal and human studies have suggested disruption of thalamocortical communication as a key component of anesthetic action at the brain systems level. Here, we used the anesthetic agent, propofol, to modulate consciousness and to evaluate differences in the interactions of remote neural networks during altered consciousness. We investigated the effects of propofol, at a dose that produced mild sedation without loss of consciousness, on spontaneous cerebral activity of 15 healthy volunteers using functional magnetic resonance imaging (fMRI), exploiting oscillations (<0.1 Hz) in blood oxygenation level-dependent signal across functionally connected brain regions. We considered the data as a graph, or complex network of nodes and links, and used eigenvector centrality (EC) to characterize brain network properties. The EC mapping of fMRI data in healthy humans under propofol mild sedation demonstrated a decrease of centrality of the thalamus versus an increase of centrality within the pons of the brainstem, highlighting the important role of these two structures in regulating consciousness. Specifically, the decrease of thalamus centrality results from its disconnection from a widespread set of cortical and subcortical regions, while the increase of brainstem centrality may be a consequence of its increased influence, in the mildly sedated state, over a few highly central cortical regions key to the default mode network such as the posterior and anterior cingulate cortices

Effects of caffeine on reaction time are mediated by attentional rather than motor processes

Background Caffeine has a well-established effect on reaction times (RTs) but the neurocognitive mechanisms underlying this are unclear. Methods In the present study, 15 female participants performed an oddball task after ingesting caffeine or a placebo, and electroencephalographic data were obtained. Single-trial P3b latencies locked to the stimulus and to the response were extracted and mediation models were fitted to the data to test whether caffeine’s effect on RTs was mediated by its effect on either type of P3b latencies. Results Stimulus-locked latencies showed clear evidence of mediation, with approximately a third of the effect of caffeine on RTs running through the processes measured by stimulus-locked latencies. Caffeine did not affect response-locked latencies, so could not mediate the effect. Discussion These findings are consistent with caffeine’s effect on RTs being a result of its effect on perceptual-attentional processes, rather than motor processes. The study is the first to apply mediation analysis to single-trial P3b data and this technique holds promise for mental chronometric studies into the effects of psychopharmacological agents. The R code for performing the single trial analysis and mediation analysis are included as supplementary materials

Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis

Conventional analysis of clinical resting electroencephalography (EEG) recordings typically involves assessment of spectral power in pre-defined frequency bands at specific electrodes. EEG is a potentially useful technique in drug development for measuring the pharmacodynamic (PD) effects of a centrally acting compound and hence to assess the likelihood of success of a novel drug based on pharmacokinetic-pharmacodynamic (PK-PD) principles. However, the need to define the electrodes and spectral bands to be analysed a priori is limiting where the nature of the drug-induced EEG effects is initially not known. We describe the extension to human EEG data of a generalised semi-linear canonical correlation analysis (GSLCCA), developed for small animal data. GSLCCA uses data from the whole spectrum, the entire recording duration and multiple electrodes. It provides interpretable information on the mechanism of drug action and a PD measure suitable for use in PK-PD modelling. Data from a study with low (analgesic) doses of the μ-opioid agonist, remifentanil, in 12 healthy subjects were analysed using conventional spectral edge analysis and GSLCCA. At this low dose, the conventional analysis was unsuccessful but plausible results consistent with previous observations were obtained using GSLCCA, confirming that GSLCCA can be successfully applied to clinical EEG data

Extracting drug mechanism and pharmacodynamic information from clinical electroencephalographic data using generalised semi-linear canonical correlation analysis

Conventional analysis of clinical resting electroencephalography (EEG) recordings typically involves assessment of spectral power in pre-defined frequency bands at specific electrodes. EEG is a potentially useful technique in drug development for measuring the pharmacodynamic (PD) effects of a centrally acting compound and hence to assess the likelihood of success of a novel drug based on pharmacokinetic-pharmacodynamic (PK-PD) principles. However, the need to define the electrodes and spectral bands to be analysed a priori is limiting where the nature of the drug-induced EEG effects is initially not known. We describe the extension to human EEG data of a generalised semi-linear canonical correlation analysis (GSLCCA), developed for small animal data. GSLCCA uses data from the whole spectrum, the entire recording duration and multiple electrodes. It provides interpretable information on the mechanism of drug action and a PD measure suitable for use in PK-PD modelling. Data from a study with low (analgesic) doses of the μ-opioid agonist, remifentanil, in 12 healthy subjects were analysed using conventional spectral edge analysis and GSLCCA. At this low dose, the conventional analysis was unsuccessful but plausible results consistent with previous observations were obtained using GSLCCA, confirming that GSLCCA can be successfully applied to clinical EEG data

Enhanced Stimulus-Induced Gamma Activity in Humans during Propofol-Induced Sedation

Stimulus-induced gamma oscillations in the 30–80 Hz range have been implicated in a wide number of functions including visual processing, memory and attention. While occipital gamma-band oscillations can be pharmacologically modified in animal preparations, pharmacological modulation of stimulus-induced visual gamma oscillations has yet to be demonstrated in non-invasive human recordings. Here, in fifteen healthy humans volunteers, we probed the effects of the GABA(A) agonist and sedative propofol on stimulus-related gamma activity recorded with magnetoencephalography, using a simple visual grating stimulus designed to elicit gamma oscillations in the primary visual cortex. During propofol sedation as compared to the normal awake state, a significant 60% increase in stimulus-induced gamma amplitude was seen together with a 94% enhancement of stimulus-induced alpha suppression and a simultaneous reduction in the amplitude of the pattern-onset evoked response. These data demonstrate, that propofol-induced sedation is accompanied by increased stimulus-induced gamma activity providing a potential window into mechanisms of gamma-oscillation generation in humans

Functional movement disorder gender, age and phenotype study: a systematic review and individual patient meta-analysis of 4905 cases

: Functional movement disorder (FMD) is a common manifestation of functional neurological disorder presenting with diverse phenotypes such as tremor, weakness and gait disorder. Our current understanding of the basic epidemiological features of this condition is unclear. We aimed to describe and examine the relationship between age at onset, phenotype and gender in FMD in a large meta-analysis of published and unpublished individual patient cases. An electronic search of PubMed was conducted for studies from 1968 to 2019 according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Individual patient data were collected through a research network. We described the distribution of age of onset and how this varied by gender and motor phenotype. A one-stage meta-analysis was performed using multilevel mixed-effects linear regression, including random intercepts for country and data source. A total of 4905 individual cases were analysed (72.6% woman). The mean age at onset was 39.6 years (SD 16.1). Women had a significantly earlier age of onset than men (39.1 years vs 41.0 years). Mixed FMD (23.1%), tremor (21.6%) and weakness (18.1%) were the most common phenotypes. Compared with tremor (40.7 years), the mean ages at onset of dystonia (34.5 years) and weakness (36.4 years) were significantly younger, while gait disorders (43.2 years) had a significantly later age at onset. The interaction between gender and phenotype was not significant. FMD peaks in midlife with varying effects of gender on age at onset and phenotype. The data gives some support to 'lumping' FMD as a unitary disorder but also highlights the value in 'splitting' into individual phenotypes where relevant