Figure 1.

Methodological Overview and Conceptual Model. (a) Experiment Design: Ninety images were sampled from the International Affective Picture System (IAPS) to form a subset that maximally spanned the affective properties of valence (v) and arousal (a). (b) Signal Acquisition: Image stimuli were presented for 2 s interleaved with random inter-trial intervals [2–6 s]; fMRI measurements of the blood oxygen level dependent (BOLD) response were recorded concurrently with the skin conductance response (SCR). (‡) Conceptual Model: We hypothesize that brain states, s, simultaneously encode the dimensional affective properties of their image stimuli as well as the attendant psychophysiological responses. (c) Brain and Physiological State Estimation: fMRI signals were preprocessed to remove noise and motion artifacts and segmented to remove all voxels except gray matter (GM); SCR signals were preprocessed to remove noise and tonic signal components; neural activation patterns were extracted for each stimulus according to the beta-series method; and, dimensionally reduced. (d) Prediction of Affective Signals: Intra-subject cross-validated linear support vector machine (SVM) regression was conducted on the beta-series (labeled according to the stimulus from which they were extracted). The figure depicts the regression model labeling the affective property of a novel point. (e) Effect Size Estimation: Group-level predictions of affective properties and measurements were conducted via General Linear Mixed-Effects Models (GLMMs) in three tests: (1) the measurements of interest were the normative affective properties of the stimuli (v, a) and the fixed effects were the SCR measurements of affect state (β^SCR); (2) the measurements of interest were the affective properties (v, a) and the fixed effects were the SVM-predicted properties (~v, ~a); and, (3) the measurements of interest were β^SCR and the fixed effects were the SVM-predicted affective responses (~β^SCR). (*) The individual SVM models of GM-based features were transformed into encoding representations of affect state²⁴ and anatomically analyzed group-wise (not pictured). (**) GLMM random effects for slope and intercept were modeled subject-wise. Note, details of the experiment design, preprocessing pipeline, and brain state estimation methodology have been reported previously⁷.