Clinical herniation – how to improve differentiation between WFNS grade IV and V

Objective: Current data show a favorable outcome after poor grade subarachnoid hemorrhage (SAH) in up to 50% of patients. This limits the use of the WFNS scale for drawing treatment decisions. We therefore analyzed how clinical signs of herniation might improve the existing WFNS grading. Therefore we compared the current WFNS grading and a modified WFNS grading with respect to outcome. Method: We performed a retrospective study including 182 poor grade SAH patients. Patients were graded according to the original WFNS scale and additionally into a modified classification the “WFNS herniation” (WFNSh grade IV: no herniation; grade V clinical signs of herniation). Outcome was compared between these two grading systems with respect to the dichotomized modified Rankin scale after 6 months. Results: The WFNS and WFNSh showed a positive predictive value (PPV) for poor outcome of 74.3% (OR 3.79, 95% confidence interval [CI]=1.94, 7.54) and 85.7% (OR 8.27, 95% CI=3.78, 19.47), respectively. With respect to mortality the PPV was 68.3% (OR 3.9, 95% CI=2.01, 7.69) for the WFNS grade V and 77.9% (OR 6.22, 95% CI=3.07, 13.14) for the WFNSh grade V. Conclusions: Using positive clinical signs of herniation instead of “no response to pain stimuli” (motor Glasgow Coma Scale Score) can improve WFNS V grading. Using this modification, prediction of poor outcome or death improves.

What do brain network interactions tell about cognitive processes? How to seek causality?

Optimal adjustment of brain networks allows the biased processing of information in response to the demand of environments and is therefore prerequisite for adaptive behaviour. It is widely shown that a biased state of networks is associated with a particular cognitive process. However, those associations were identified by backward categorization of trials and cannot provide a causal association with cognitive processes. This problem still remains a big obstacle to advance the state of our field in particular human cognitive neuroscience. In my talk, I will present two approaches to address the causal relationships between brain network interactions and behaviour. Firstly, we combined connectivity analysis of fMRI data and a machine leaning method to predict inter-individual differences of behaviour and responsiveness to environmental demands. The connectivity-based classification approach outperforms local activation-based classification analysis, suggesting that interactions in brain networks carry information of instantaneous cognitive processes. Secondly, we have recently established a brand new method combining transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and EEG. We use the method to measure signal transmission between brain areas while introducing extrinsic oscillatory brain activity and to study causal association between oscillatory activity and behaviour. We show that phase-matched oscillatory activity creates the phase-dependent modulation of signal transmission between brain areas, while phase-shifted oscillatory activity blunts the phase-dependent modulation. The results suggest that phase coherence between brain areas plays a cardinal role in signal transmission in the brain networks. In sum, I argue that causal approaches will provide more concreate backbones to cognitive neuroscience.

Soil and land information: How to support decision-making?

Soils are fundamental to ensuring water, energy and food security. Within the context of sus- tainable food production, it is important to share knowledge on existing and emerging tech- nologies that support land and soil monitoring. Technologies, such as remote sensing, mobile soil testing, and digital soil mapping, have the potential to identify degraded and non- /little-responsive soils, and may also provide a basis for programmes targeting the protection and rehabilitation of soils. In the absence of such information, crop production assessments are often not based on the spatio-temporal variability in soil characteristics. In addition, uncertain- ties in soil information systems are notable and build up when predictions are used for monitor- ing soil properties or biophysical modelling. Consequently, interpretations of model-based results have to be done cautiously. As such they provide a scientific, but not always manage- able, basis for farmers and/or policymakers. In general, the key incentives for stakeholders to aim for sustainable management of soils and more resilient food systems are complex at farm as well as higher levels. The same is true of drivers of soil degradation. The decision- making process aimed at sustainable soil management, be that at farm or higher level, also in- volves other goals and objectives valued by stakeholders, e.g. land governance, improved envi- ronmental quality, climate change adaptation and mitigation etc. In this dialogue session we will share ideas on recent developments in the discourse on soils, their functions and the role of soil and land information in enhancing food system resilience.

How to prevent overdiagnosis.

Overdiagnosis is the diagnosis of an abnormality that is not associated with a substantial health hazard and that patients have no benefit to be aware of. It is neither a misdiagnosis (diagnostic error), nor a false positive result (positive test in the absence of a real abnormality). It mainly results from screening, use of increasingly sensitive diagnostic tests, incidental findings on routine examinations, and widening diagnostic criteria to define a condition requiring an intervention. The blurring boundaries between risk and disease, physicians' fear of missing a diagnosis and patients' need for reassurance are further causes of overdiagnosis. Overdiagnosis often implies procedures to confirm or exclude the presence of the condition and is by definition associated with useless treatments and interventions, generating harm and costs without any benefit. Overdiagnosis also diverts healthcare professionals from caring about other health issues. Preventing overdiagnosis requires increasing awareness of healthcare professionals and patients about its occurrence, the avoidance of unnecessary and untargeted diagnostic tests, and the avoidance of screening without demonstrated benefits. Furthermore, accounting systematically for the harms and benefits of screening and diagnostic tests and determining risk factor thresholds based on the expected absolute risk reduction would also help prevent overdiagnosis.