The interaction between depression diagnosis and BMI is related to altered activation pattern in the right inferior frontal gyrus and anterior cingulate cortex during food anticipation.

BACKGROUND: Depression and overweight/obesity often cooccur but the underlying neural mechanisms for this bidirectional link are not well understood. METHODS: In this functional magnetic resonance imaging study, we scanned 54 individuals diagnosed with depressive disorders (DD) and 48 healthy controls (HC) to examine how diagnostic status moderates the relationship between body mass index (BMI) and brain activation during anticipation and pleasantness rating of food versus nonfood stimuli. RESULTS: We found a significant BMI-by-diagnosis interaction effect on activation in the right inferior frontal gyrus (RIFG) and anterior cingulate cortex (ACC) during food versus nonfood anticipation (p < .0125). Brain activation in these regions was greater in HC with higher BMI than in HC with lower BMI. Individuals with DD showed an opposite pattern of activation. Structural equation modeling revealed that the relationship between BMI, activation in the RIFG and ACC, and participants' desire to eat food items shown in the experiment depended on the diagnostic status. CONCLUSIONS: Considering that food anticipation is an important component of appetitive behavior and that the RIFG and ACC are involved in emotion regulation, response inhibition and conflict monitoring necessary to control this behavior, we propose that future clinical trials targeting weight loss in DD should investigate whether adequate mental preparation positively affects subsequent food consumption behaviors in these individuals.

Six problems for causal inference from fMRI.

Neuroimaging (e.g. fMRI) data are increasingly used to attempt to identify not only brain regions of interest (ROIs) that are especially active during perception, cognition, and action, but also the qualitative causal relations among activity in these regions (known as effective connectivity; Friston, 1994). Previous investigations and anatomical and physiological knowledge may somewhat constrain the possible hypotheses, but there often remains a vast space of possible causal structures. To find actual effective connectivity relations, search methods must accommodate indirect measurements of nonlinear time series dependencies, feedback, multiple subjects possibly varying in identified regions of interest, and unknown possible location-dependent variations in BOLD response delays. We describe combinations of procedures that under these conditions find feed-forward sub-structure characteristic of a group of subjects. The method is illustrated with an empirical data set and confirmed with simulations of time series of non-linear, randomly generated, effective connectivities, with feedback, subject to random differences of BOLD delays, with regions of interest missing at random for some subjects, measured with noise approximating the signal to noise ratio of the empirical data.

Bottom-up and top-down brain functional connectivity underlying comprehension of everyday visual action.

How can the components of visual comprehension be characterized as brain activity? Making sense of a dynamic visual world involves perceiving streams of activity as discrete units such as eating breakfast or walking the dog. In order to parse activity into distinct events, the brain relies on both the perceptual (bottom-up) data available in the stimulus as well as on expectations about the course of the activity based on previous experience with, or knowledge about, similar types of activity (top-down data). Using fMRI, we examined the contribution of bottom-up and top-down processing to the comprehension of action streams by contrasting familiar action sequences with those having exactly the same perceptual detection and motor responses (yoked control), but no visual action familiarity. New methods incorporating structural equation modeling of the data yielded distinct patterns of interactivity among brain areas as a function of the degree to which bottom-up and top-down data were available.