Multivariate neural signatures for health neuroscience: assessing spontaneous regulation during food choice

Abstract

Establishing links between neural systems and health can be challenging since there is not a one-to-one mapping between brain regions and psychological states. Building sensitive and specific predictive models of health-relevant constructs using multivariate activation patterns of brain activation is a promising new direction. We illustrate the potential of this approach by building two 'neural signatures' of food craving regulation (CR) using multivariate machine learning and, for comparison, a univariate contrast. We applied the signatures to two large validation samples of overweight adults who completed tasks measuring CR ability and valuation during food choice. Across these samples, the machine learning signature was more reliable. This signature decoded CR from food viewing and higher signature expression was associated with less craving. During food choice, expression of the regulation signature was stronger for unhealthy foods and inversely related to subjective value, indicating that participants engaged in CR despite never being instructed to control their cravings. Neural signatures thus have the potential to measure spontaneous engagement of mental processes in the absence of explicit instruction, affording greater ecological validity. We close by discussing the opportunities and challenges of this approach, emphasizing what machine learning tools bring to the field of health neuroscience.

Keywords: craving regulation; food valuation; health neuroscience; multivariate fMRI; neural signature.

© The Author(s) 2020. Published by Oxford University Press.

Figures

Fig. 1

CR task design for the neural signature development and partial validation samples. Each trial consisted of a 2 s instruction period, a 5 s image presentation and a 4 s craving rating period (1 = no desire to eat the food, 5 = strong desire to eat the food). Participants in the complete validation sample had 2.5 s to rate foods and used a 4-point scale with the same anchors. Between trials, participants viewed a jittered fixation cross (M = 1 s for neural signature development and partial validation samples, M = 4.1 s for the complete validation sample).

Fig. 2

FV task design for the partial validation sample. Each trial consisted of a 4 s snack food presentation, followed by a 4 s bid period. Snack foods were either healthy (e.g. carrot sticks, yogurt) or unhealthy (e.g. candy, chips). In the complete validation sample, the bid period lasted 2.5 s and they made bids ranging from $0 to $1.50. All trials ended with a jittered fixation cross (M = 4.38 s).

Fig. 3

Samples and analytic overview. (A) The neural signature development sample included participants from four different neuroimaging studies who completed the CR task. One of these studies included participants (n = 50; dotted rectangle) from the partial validation sample. Participants in the partial validation sample and the complete validation sample completed the CR task as well as the FV task. (B) Average instruction effects (i.e. mean look > baseline and regulate > baseline) from participants in the neural signature development sample were used to create two neural signatures. The multivariate signature was developed using a machine learning classifier and 5-fold cross-validation to decode instruction; the univariate signature was created using the group-level regulate > look contrast. (C) The resulting neural signatures were applied to trial-level data from the partial validation and complete validation samples for the CR and FV tasks by taking the dot product of each signature and each trial beta image. This process yielded scalar pattern expression values for each trial for each participant and task.

Fig. 4

Receiver operating characteristic curves as a function of signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample).

Fig. 5

(A) Mean difference in standardized regulation pattern expression values as a function of instruction (look or regulate), signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). (B) Group means are overlaid on individual participant means; each thin line represents a single participant. Higher positive values represent relatively higher evidence for regulation, whereas lower negative values represent relatively higher evidence for viewing; zero is the decision boundary between conditions. Error bars are 95% confidence intervals across trials. Pattern expression values are standardized within participant and signature type. PEV = pattern expression value.

Fig. 6

The relationship between craving ratings and mean standardized regulation pattern expression values as a function of signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). Top panel: continuous craving ratings (A) collapsed across instruction (look or regulate), scaled by the number of observations in each rating category, represented by the size of the point, and (B) as a function of instruction. Bottom panel: mean dichotomized craving ratings (low = lower than scale midpoint, high = higher than scale midpoint) (C) collapsed across instruction and (D) as a function of instruction. In (D), group means are overlaid on individual participant means; each thin line represents a participant. Error bars are 95% confidence intervals across all trials. The partial validation sample used a 1–5 craving rating scale, whereas the complete validation sample used a 1–4 craving rating scale. PEV = pattern expression value.

Fig. 7

Specification curves of 13 unique models regressing trial-level craving ratings on predictors ordered based on model fit (AIC) for (A) the partial validation sample and (B) the complete validation sample. Each column corresponds to a single model specification. The AIC value for each model specification is plotted in top panels, and the variables included in each model are visualized in the bottom panels. Because each panel is ordered based on AIC, specification numbers do not necessarily correspond to the same model specifications in each panel. The base model, which included instruction (look or regulate) as the only predictor, is highlighted in blue and the dotted blue line represents the AIC for this model. Models with AIC values lower than the base model are highlighted in red. Potential variables in each model included: intercept, instruction (look or regulate), standardized pattern expression values for the multivariate and univariate signatures, and the interaction between instruction and each signature type.

Fig. 8

Mean standardized regulation pattern expression values as a function of food type (healthy or unhealthy), signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). Top panel A shows group-level means, whereas panel B shows group-level means overlaid on individual participant means; each thin line represents a participant. Panels C and D visualize this relationship within the complete validation sample only as a function of pre-session palatability ratings (relatively disliked = ratings 1–2, liked = ratings 3–4). Higher positive values represent relatively higher evidence for regulation, whereas lower negative values represent relatively higher evidence for viewing; zero is the decision boundary between conditions. Error bars are 95% confidence intervals across trials. Pattern expression values are standardized within participant and signature type. PEV = pattern expression value.

Fig. 9

The relationship between bid values and mean standardized regulation pattern expression values as a function of signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). Top panel: continuous bid values (A) collapsed across food type (healthy or unhealthy), scaled by the number of observations in each rating category, represented by the size of the point, and (B) as a function of food type. Bottom panel: mean dichotomized bid (low = lower than scale midpoint, high = higher than scale midpoint) (C) collapsed across food type and (D) as a function of food type. The complete validation sample in A includes both liked and relatively disliked foods, whereas in panels B–D, it includes liked foods only (i.e. pre-session palatability ratings >2 on a 1–4 scale). In (D), group means are overlaid on individual participant means; each thin line represents a single participant. Error bars are 95% confidence intervals across all trials. The partial validation sample used a $0–$2 bid value scale, whereas the complete validation sample used a $0–$1.5 bid value scale. Pattern expression values are standardized within participant and signature type. PEV = pattern expression value.

Fig. 10

Specification curves of 13 unique models regressing trial-level bid values on predictors ordered based on model fit (AIC) for (A) the partial validation sample and (B) the complete validation sample. Each column corresponds to a single model specification. The AIC value for each model specification is plotted in top panels, and the variables included in each model are visualized in the bottom panels. Because each panel is ordered based on AIC, specification numbers do not necessarily correspond to the same model specifications in each panel. The base model, which included food type (healthy or unhealthy) as the only predictor, is highlighted in blue and the dotted blue line represents the AIC for this model. Models with AIC values lower than the base model are highlighted in red. Potential variables in each model included: intercept, food type (healthy or unhealthy), standardized pattern expression values for the univariate contrast and the multivariate classifier, and the interaction between food type and each signature type.

Fig. 11

The relationship between participant mean standardized regulation pattern expression values and mean craving ratings during the CR task as a function of instruction (look or regulate), signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). Condition outliers are visualized as grey dots, but were excluded when computing linear effects. PEV = pattern expression value.

Fig. 12

The relationship between participant mean standardized regulation pattern expression values and mean bids values during the FV task as a function of food type (healthy or unhealthy), signature type (multivariate classifier or univariate contrast) and sample (partial validation or complete validation sample). The bottom panel shows correlations within the complete validation sample for liked snack foods items only (i.e. they were rated as 3 or 4 during the pre-session palatability rating task on a 1–4 scale). No liking ratings were collected for the partial validation sample. PEV = pattern expression value.