Correlation between resting state fMRI total neuronal activity and PET metabolism in healthy controls and patients with disorders of consciousness.

INTRODUCTION: The mildly invasive 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established imaging technique to measure \u27resting state\u27 cerebral metabolism. This technique made it possible to assess changes in metabolic activity in clinical applications, such as the study of severe brain injury and disorders of consciousness. OBJECTIVE: We assessed the possibility of creating functional MRI activity maps, which could estimate the relative levels of activity in FDG-PET cerebral metabolic maps. If no metabolic absolute measures can be extracted, our approach may still be of clinical use in centers without access to FDG-PET. It also overcomes the problem of recognizing individual networks of independent component selection in functional magnetic resonance imaging (fMRI) resting state analysis. METHODS: We extracted resting state fMRI functional connectivity maps using independent component analysis and combined only components of neuronal origin. To assess neuronality of components a classification based on support vector machine (SVM) was used. We compared the generated maps with the FDG-PET maps in 16 healthy controls, 11 vegetative state/unresponsive wakefulness syndrome patients and four locked-in patients. RESULTS: The results show a significant similarity with ρ = 0.75 ± 0.05 for healthy controls and ρ = 0.58 ± 0.09 for vegetative state/unresponsive wakefulness syndrome patients between the FDG-PET and the fMRI based maps. FDG-PET, fMRI neuronal maps, and the conjunction analysis show decreases in frontoparietal and medial regions in vegetative patients with respect to controls. Subsequent analysis in locked-in syndrome patients produced also consistent maps with healthy controls. CONCLUSIONS: The constructed resting state fMRI functional connectivity map points toward the possibility for fMRI resting state to estimate relative levels of activity in a metabolic map

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Intrinsic fMRI functional architecture differentiates single patients after severe brain injury

Background: Resting state acquisitions are by definition appropriate to assess non-communicating subjects. We here assessed the clinical relevance of systems-level resting state fMRI in patients with disorders of consciousness with the aim to promote single-patient diagnostics. Methods: Seventy three patients in minimally conscious state (MCS), vegetative state/unresponsive wakefulness syndrome (VS/UWS) and coma were scanned in 3 different centers. The main analysis was performed on the dataset coming from one centre (Liège, 51 patients; 26 MCS, 19 VS/UWS, 6 coma; 16 traumatic, 32 non-traumatic, 3 mixed; 35 patients assessed >1 month post-insult) for whom the clinical diagnosis with the Coma Recovery Scale-Revised (CRS-R) was congruent with positron emission tomography scanning. Using a multiple-seed correlation approach, group-level functional connectivity was investigated for the default mode, frontoparietal, salience, auditory, sensorimotor and visual networks. Between-group inferential statistics and machine learning were used to identify each network’s capacity to discriminate between patients in MCS and VS/UWS. Data from 22 patients independently scanned in two other centres (Salzburg: 10 MCS, 5 VS/UWS; New York: 5 MCS, 1 VS/UWS, 1 emerged from MCS) were used to validate the classification with the indetified features. Results: CRS-R total scores correlated with key regions of each network reflecting their involvement in consciousness-related processes. Although all networks had a high discriminative capacity (>80%) for separating patients in MCS and VS/UWS, the auditory network was ranked the most highly. Specifically, bilateral auditory and visual cortices of the auditory network were more functionally connected in patients in MCS compared to VS/UWS. Connectivity values in these three regions discriminated congruently 20 out of 22 independently assessed patients. Conclusions: These findings highlight the significance of preserved multisensory integration and top-down processing in minimal consciousness which are seemingly supported by auditory-visual crossmodal connectivity, and promote the clinical utility of the resting paradigm for single-patient diagnostics