Portfolio Decisions and Brain Reactions via the CEAD method

TL;DR

This paper introduces the CEAD method, a new neuroimaging analysis technique that improves detection of brain activity related to investment decisions by focusing on clusters of voxels and using model-free analysis.

Contribution

The paper presents the CEAD method, combining spectral clustering and DSFM for fMRI analysis, offering higher signal-to-noise ratio and a model-free approach for studying neural correlates of decision making.

Findings

Significant correlation between risk and fMRI signals in anterior insula and prefrontal cortex.

Individual decision reactions predicted risk attitudes using DSFM time series.

CEAD method outperforms traditional voxel-based analysis in detecting neural activity.

Abstract

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: Cluster, Estimation, Activation and Decision (CEAD) method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal to noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible Dynamic Semiparametric Factor Model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.