The Neuroscience Community Has a Role in Environmental Conservation

Measuring and reducing the carbon footprint of fMRI preprocessing in fMRIPrep

Computationally expensive data processing in neuroimaging research places demands on energy consumption-and the resulting carbon emissions contribute to the climate crisis. We measured the carbon footprint of the functional magnetic resonance imaging (fMRI) preprocessing tool fMRIPrep, testing the effect of varying parameters on estimated carbon emissions and preprocessing performance. Performance was quantified using (a) statistical individual-level task activation in regions of interest and (b) mean smoothness of preprocessed data. Eight variants of fMRIPrep were run with 257 participants who had completed an fMRI stop signal task (the same data also used in the original validation of fMRIPrep). Some variants led to substantial reductions in carbon emissions without sacrificing data quality: for instance, disabling FreeSurfer surface reconstruction reduced carbon emissions by 48%. We provide six recommendations for minimising emissions without compromising performance. By varying parameters and computational resources, neuroimagers can substantially reduce the carbon footprint of their preprocessing. This is one aspect of our research carbon footprint over which neuroimagers have control and agency to act upon.

Systems Thinking in an era of climate change: Does cognitive neuroscience hold the key to improving environmental decision making? A perspective on Climate-Smart Agriculture

Systems Thinking (ST) can be defined as a mental construct that recognises patterns and connections in a particular complex system to make the "best decision" possible. In the field of sustainable agriculture and climate change, higher degrees of ST are assumed to be associated with more successful adaptation strategies under changing conditions, and "better" environmental decision making in a number of environmental and cultural settings. Future climate change scenarios highlight the negative effects on agricultural productivity worldwide, particularly in low-income countries (LICs) situated in the Global South. Alongside this, current measures of ST are limited by their reliance on recall, and are prone to possible measurement errors. Using Climate-Smart Agriculture (CSA), as an example case study, in this article we explore: (i) ST from a social science perspective; (ii) cognitive neuroscience tools that could be used to explore ST abilities in the context of LICs; (iii) an exploration of the possible correlates of systems thinking: observational learning, prospective thinking/memory and the theory of planned behaviour and (iv) a proposed theory of change highlighting the integration of social science frameworks and a cognitive neuroscience perspective. We find, recent advancements in the field of cognitive neuroscience such as Near-Infrared Spectroscopy (NIRS) provide exciting potential to explore previously hidden forms of cognition, especially in a low-income country/field setting; improving our understanding of environmental decision-making and the ability to more accurately test more complex hypotheses where access to laboratory studies is severely limited. We highlight that ST may correlate with other key aspects involved in environmental decision-making and posit motivating farmers via specific brain networks would: (a) enhance understanding of CSA practices (e.g., via the frontoparietal network extending from the dorsolateral prefrontal cortex (DLPFC) to the parietal cortex (PC) a control hub involved in ST and observational learning) such as tailoring training towards developing improved ST abilities among farmers and involving observational learning more explicitly and (b) motivate farmers to use such practices [e.g., via the network between the DLPFC and nucleus accumbens (NAc)] which mediates reward processing and motivation by focussing on a reward/emotion to engage farmers. Finally, our proposed interdisciplinary theory of change can be used as a starting point to encourage discussion and guide future research in this space.

Protocol for the development of an international Core Outcome Set for treatment trials in adults with epilepsy: the EPilepsy outcome Set for Effectiveness Trials Project (EPSET)

Background

A Core Outcome Set (COS) is a standardised list of outcomes that should be reported as a minimum in all clinical trials. In epilepsy, the choice of outcomes varies widely among existing studies, particularly in clinical trials. This diminishes opportunities for informed decision-making, contributes to research waste and is a barrier to integrating findings in systematic reviews and meta-analyses. Furthermore, the outcomes currently being measured may not reflect what is important to people with epilepsy.

Therefore, we aim to develop a COS specific to clinical effectiveness research for adults with epilepsy using Delphi consensus methodology.

Methods

The EPSET Study will comprise of three phases and follow the core methodological principles as outlined by the Core Outcome Measures in Effectiveness Trials (COMET) Initiative. Phase 1 will include two focused literature reviews to identify candidate outcomes from the qualitative literature and current outcome measurement practice in phase III and phase IV clinical trials. Phase 2 aims to achieve international consensus to define which outcomes should be measured as a minimum in future trials, using a Delphi process including an online consensus meeting involving key stakeholders. Phase 3 will involve dissemination of the ratified COS to facilitate uptake in future trials and the planning of further research to identify the most appropriate measurement instruments to use to capture the COS in research practice.

Discussion

Harmonising outcome measurement across future clinical trials should ensure that the outcomes measured are relevant to patients and health services, and allow for more meaningful results to be obtained.

Core Outcome Set registration

COMET Initiative as study 118.