Abstract
Human neuroscience is becoming more common as noninvasive imaging modalities are being developed and used by researchers. A challenge that comes with humans as they execute structured tasks is that their behaviors often are shaped not only by external stimuli presented by the experimentalist but also by internal states that are not easily measurable. This presents a problem if one is trying to identify neural correlates of specific stimuli as brain activity is also modulated by these dynamic internal states. An example of this is when humans perform gambling tasks. Their betting strategies may fluctuate based on how “lucky” they feel, which is not directly measurable but clearly shapes behavior and neural activity. In this chapter, we propose a systematic framework for (1) characterizing variability within and across individual behavior performing a task by estimating internal latent states that affect behavior, and (2) identifying neural correlates of stimuli, responses, and states. The framework consists of first constructing state-space models from behavioral data using maximum likelihood methods, and then identifying neural correlates of external stimuli, behavioral responses, and internal states using nonparametric statistical tests and point process models. The framework is general in that all types of behavioral data are possible to model (e.g., binary, categorical, continuous) and all types of neural activity are possible to analyze (spike trains, local field potentials, electroencephalogram, functional magnetic resonance imaging, etc.). The methods for modeling and statistical tests presented in this chapter are not new. Rather, we propose a systematic approach to analyzing complex data sets generated by neuroscience experiments with human subjects, highlight challenges that may arise, and propose solutions to address these challenges.
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Acknowledgements
This MIST work was supported by the National Institutes of Health (NIH R01 MH106700, NIH K12 NS080223, NIH S10 OD018211, NIH R01 NS084142) and the Dana Foundation. This gambling work was supported by the National Science Foundation (NSF EFRI-MC3: #1137237). P.S. was supported by the Kavli Neuroscience Discovery Institute at the Johns Hopkins University.
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Sarma, S.V., Sacré, P. (2018). Characterizing Complex Human Behaviors and Neural Responses Using Dynamic Models. In: Chen, Z., Sarma, S.V. (eds) Dynamic Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-71976-4_7
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DOI: https://doi.org/10.1007/978-3-319-71976-4_7
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