Prediction of Behavioral Improvement Through Resting-State Electroencephalography and Clinical Severity in a Randomized Controlled Trial Testing Bumetanide in Autism Spectrum Disorder

Background: Mechanism-based treatments such as bumetanide are being repurposed for autism spectrum disorder. We recently reported beneficial effects on repetitive behavioral symptoms that might be related to regulating excitation-inhibition (E/I) balance in the brain. Here, we tested the neurophysiological effects of bumetanide and the relationship to clinical outcome variability and investigated the potential for machine learning–based predictions of meaningful clinical improvement. Methods: Using modified linear mixed models applied to intention-to-treat population, we analyzed E/I-sensitive electroencephalography (EEG) measures before and after 91 days of treatment in the double-blind, randomized, placebo-controlled Bumetanide in Autism Medication and Biomarker study. Resting-state EEG of 82 subjects out of 92 participants (7–15 years) were available. Alpha frequency band absolute and relative power, central frequency, long-range temporal correlations, and functional E/I ratio treatment effects were related to the Repetitive Behavior Scale-Revised (RBS-R) and the Social Responsiveness Scale 2 as clinical outcomes. Results: We observed superior bumetanide effects on EEG, reflected in increased absolute and relative alpha power and functional E/I ratio and in decreased central frequency. Associations between EEG and clinical outcome change were restricted to subgroups with medium to high RBS-R improvement. Using machine learning, medium and high RBS-R improvement could be predicted by baseline RBS-R score and EEG measures with 80% and 92% accuracy, respectively. Conclusions: Bumetanide exerts neurophysiological effects related to clinical changes in more responsive subsets, in whom prediction of improvement was feasible through EEG and clinical measures

predictive precision medicine towards the computational challenge

The emerging fields of predictive and precision medicine are changing the traditional medical approach to disease and patient. Current discoveries in medicine enable to deepen the comprehension of diseases, whereas the adoption of high-quality methods such as novel imaging techniques (e.g. MRI, PET) and computational approaches (i.e. machine learning) to analyse data allows researchers to have meaningful clinical and statistical information. Indeed, applications of radiology techniques and machine learning algorithms rose in the last years to study neurology, cardiology and oncology conditions. In this chapter, we will provide an overview on predictive precision medicine that uses artificial intelligence to analyse medical images to enhance diagnosis, prognosis and treatment of diseases. In particular, the chapter will focus on neurodegenerative disorders that are one of the main fields of application. Despite some critical issues of this new approach, adopting a patient-centred approach could bring remarkable improvement on individual, social and business level

History effect of light and temperature on monoterpenoid emissions from Fagus sylvatica L.

Monoterpenoid emissions from Fagus sylvatica L trees have been measured at light- and temperature-controlled conditions in a growth chamber, using Proton Transfer Reaction Mass Spectrometry (PTR-MS) and the dynamic branch enclosure technique. De novo synthesized monoterpenoid Standard Emission Factors, obtained by applying the G97 algorithm (Guenther, 1997), varied between 2 and 32 mu g g(-1)DW h(-1) and showed a strong decline in late August and September, probably due to senescence. The response of monoterpenoid emissions to temperature variations at a constant daily light pattern could be well reproduced with a modified version of the MEGAN algorithm (Guenther et al., 2006), with a typical dependence on the average temperature over the past five days. The diurnal emissions at constant temperature showed a typical hysteretic behaviour, which could also be adequately described with the modified MEGAN algorithm by taking into account a dependence on the average light levels experienced by the trees during the past 10-13 h. The impact of the past light and temperature conditions on the monoterpenoid emissions from E sylvatica L was found to be much stronger than assumed in previous algorithms. Since our experiments were conducted under low light intensity, future studies should aim at confirming and completing the proposed algorithm updates in sunny conditions and natural environments. (C) 2010 Elsevier Ltd. All rights reserved

EEG machine learning for accurate detection of cholinergic intervention and Alzheimer's disease

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

Effect of seasonality and short-term light and temperature history on monoterpene emissions from European beech (Fagus sylvatica L.)

Branch enclosure measurements of monoterpene emision rates have been performed at different positions in the canopy of a European beech tree in natural environmental conditions. Strong and position-dependent standard emission rate variations were observed in the course of the growth season. By using the obtained dataset and a modified vesrion of the MEGAN algorithm, the response of the emissions to short-term light and temperature history was investigate

Fruit quality and volatile compound composition of processing tomato as affected by fertilisation practices and arbuscular mycorrhizal fungi application

The effects of different fertilisation treatments with arbuscular mycorrhizal fungi (AMF) inoculation on AMF root colonisation, fruit yield, nutrient and total phenol contents, volatile compound composition, and sensory attributes of tomato (Solanum lycopersicum L.) were investigated. Mineral, organic, and mineral + organic fertiliser application positively affected tomato yield (35%–50%) and phosphorus concentration (24%–29%) compared with controls. AMF application had a significant impact on the total nitrogen (+9%), manganese (+12%), and hydrophilic phenol (+8%) contents in the fruit. Volatile compounds were affected by the interactive effects of fertilisation and AMF application. The response of tomato fruit sensory quality indicators was relatively modest, with only a few sensory characteristics affected to a lesser extent. Although tomato showed susceptibility to field-native AMF, particular combinations of fertilisation and AMF inoculation were more effective at improving the quality parameters of tomatoes under field conditions applied in this study