A realistic framework for investigating decision-making in the brain with high spatio-temporal resolution using simultaneous EEG/fMRI and joint ICA.

Kyathanahally, Sreenath Pruthviraj; Franco-Watkins, Ana; Zhang, Xiaoxia; Calhoun, Vince; Deshpande, Gopikrishna (2017). A realistic framework for investigating decision-making in the brain with high spatio-temporal resolution using simultaneous EEG/fMRI and joint ICA. IEEE journal of biomedical and health informatics, 21(3), pp. 814-825. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2016.2590434

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Human decision-making is a multidimensional construct, driven by a complex interplay between external factors, internal biases and computational capacity constraints. Here we propose a layered approach to experimental design in which multiple tasks - from simple to complex - with additional layers of complexity introduced at each stage, are incorporated for investigating decision-making. This is demonstrated using tasks involving intertemporal choice between immediate and future prospects. Previous functional magnetic resonance imaging (fMRI) and electroencephalographic (EEG) studies have separately investigated the spatial and temporal neural substrates, respectively, of specific factors underlying decision making. In contrast, we performed simultaneous acquisition of EEG/fMRI data and fusion of both modalities using joint independent component analysis (jICA) such that: (i) the native temporal/spatial resolutions of either modality is not compromised and (ii) fast temporal dynamics of decision-making as well as involved deeper striatal structures can be characterized. We show that spatio-temporal neural substrates underlying our proposed complex intertemporal task simultaneously incorporating rewards, costs and uncertainty of future outcomes can be predicted (using a linear model) from neural substrates of each of these factors, which were separately obtained by simpler tasks. This was not the case for spatial and temporal features obtained separately from fMRI and EEG respectively. However, certain prefrontal activations in the complex task could not be predicted from activations in simpler tasks, indicating that the assumption of pure insertion has limited validity. Overall, our approach provides a realistic and novel framework for investigating the neural substrates of decision making with high spatio-temporal resolution.

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