Abstract
This chapter describes important ways in which brain imaging and brain stimulation technologies are poised to contribute to disease prevention at the level of whole populations, and how neuroergonomics and cognitive engineering have set the conditions for this to occur. The historically limited influence of neuroscience on population health is discussed with reference to logistics, conceptual barriers, and epistemic considerations. With respect to the latter, the brain is typically viewed as an outcome variable, rather than its more nuanced role as a predictor, mediator or moderator. Yet these later roles potentiate a number of important functions for neuroscience research within disease prevention with wide ranging implications. Using examples from multiple laboratories, I highlight several examples of how neuroimaging and neuromodulation technologies can be used to generate new knowledge to shape disease prevention programs and optimize health communication strategies.
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Notes
- 1.
tES and rTMS are the two major categories of non-invasive brain stimulation technology currently in use for both experimental and clinical treatment applications. Both technologies purport to increase and/or decrease excitability of target neuron populations within the cortex. Although the proposed effect may be similar in terms of neuron population excitability, the mechanism of action is very different: while tES uses a constant (“direct”) current to induce changes in excitability using electrodes places on the scalp, rTMS uses magnetic pulses delivered via an external coil placed against the scalp to induce changes in neuroelectric activity within targeted brain regions.
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Acknowledgments
This work was supported by an operating grant to P. Hall (435-2017-0027) from the Social Sciences and Humanities Research Council of Canada.
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Hall, P.A. (2020). Novel Contributions of Neuroergonomics and Cognitive Engineering to Population Health. In: Ayaz, H. (eds) Advances in Neuroergonomics and Cognitive Engineering. AHFE 2019. Advances in Intelligent Systems and Computing, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-030-20473-0_1
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