Monitoring effects of disease or therapeutic intervention on brain function is increasingly important for clinical trials, albeit hampered by inter-individual variability and subtle effects. Here, we apply complementary biomarker algorithms to electroencephalography (EEG) recordings to capture the brain’s multi-faceted signature of disease or pharmacological intervention and use machine learning to improve classification performance. Using data from healthy subjects receiving scopolamine we developed an index of the muscarinic acetylcholine receptor antagonist (mAChR) consisting of 14 EEG biomarkers. This mAChR index yielded higher classification performance than any single EEG biomarker with cross-validated accuracy, sensitivity, specificity and precision ranging from 88–92%. The mAChR index also discriminated healthy elderly from patients with Alzheimer’s disease (AD); however, an index optimized for AD pathophysiology provided a better classification. We conclude that integrating multiple EEG biomarkers can enhance the accuracy of identifying disease or drug interventions, which is essential for clinical trials.

This study demonstrated that mecamylamine causes nicotinic receptor specific temporary decline in cognitive functioning. Compared with the scopolamine model, pharmacodynamic effects were less pronounced at the dose levels tested; however, mecamylamine caused less sedation. The cognitive effects of scopolamine might at least partly be caused by sedation. Whether the mecamylamine model can be used for proof-of-pharmacology of nicotinic acetylcholine receptor agonists remains to be established.

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AIMSubjects with increasing age are more sensitive to the effects of the anti-muscarinic agent scopolamine, which is used (among other indications) to induce temporary cognitive dysfunction in early phase drug studies with cognition enhancing compounds. The enhanced sensitivity has always been attributed to incipient cholinergic neuronal dysfunction, as a part of the normal aging process. The aim of the study was to correlate age-dependent pharmacodynamic neuro-physiologic effects of scopolamine after correcting for differences in individual exposure. METHODSWe applied a pharmacokinetic and pharmacodynamic modelling approach to describe individual exposure and neurocognitive effects of intravenous scopolamine administration in healthy subjects. RESULTSA two-compartment linear kinetics model best described the plasma concentrations of scopolamine. The estimated scopolamine population mean apparent central and peripheral volume of distribution was 2.66 ± 1.050 l and 62.10 ± 10.100 l, respectively and the clearance was 1.09 ± 0.096 l min À1 . Age was not related to a decrease of performance in the tests following scopolamine administration in older subjects. Only the saccadic peak velocity showed a positive correlation between age and sensitivity to scopolamine. Age was, however, correlated at baseline with an estimated slower reaction time while performing the cognitive tests and to higher global δ and frontal θ frequency bands measured with the surface EEG. CONCLUSIONSMost of the differences in response to scopolamine administration between young and older subjects could be explained by pharmacokinetic differences (lower clearance) and not to an enhanced sensitivity when corrected for exposure levels.

The respiratory system reacts instantaneously to intrinsic and extrinsic inputs. This adaptability results in significant fluctuations in breathing parameters, such as respiratory rate, tidal volume, and inspiratory flow profiles. Breathing variability is influenced by several conditions, including sleep, various pulmonary diseases, hypoxia, and anxiety disorders. Recent studies have suggested that weaning failure during mechanical ventilation may be predicted by low respiratory variability. This review describes methods for quantifying breathing variability, summarises the conditions and comorbidities that affect breathing variability, and discusses the potential implications of breathing variability for anaesthesia and intensive care.