The temporal response of the brain after eating revealed by functional MRI

Abstract

After eating, the human brain senses a biochemical change and then signals satiation, but precisely when this occurs is unknown. Even for well-established physiological systems like glucose-insulin regulation, the timing of interaction between hormonal processes and neural events is inferred mostly from blood sampling. Recently, neuroimaging studies have provided in vivo information about the neuroanatomical correlates of the regulation of energy intake. Temporal orchestration of such systems, however, is crucial to the integration of neuronal and hormonal signals that control eating behaviour. The challenge of this functional magnetic resonance imaging study is to map not only where but also when the brain will respond after food ingestion. Here we use a temporal clustering analysis technique to demonstrate that eating-related neural activity peaks at two different times with distinct localization. Importantly, the differentiated responses are interacting with an internal signal, the plasma insulin. These results support the concept of temporal parcellation of brain activity, which reflects the different natures of stimuli and responses. Moreover, this study provides a neuroimaging basis for detecting dynamic processes without prior knowledge of their timing, such as the acute effects of medication and nutrition in the brain.

Neural representations of hunger and satiety in Prader–Willi syndrome

“…Different experimental approaches have been used to examine similar questions; some designs have involved feeding a liquid meal (a proportion of the individual's total energy requirements) to participants while in the scanner, 24 or giving participants a fixed-energy glucose drink. 9,11 The aim of this study was to measure changes in neural activity and the conscious experience of the participants with PWS after consumption of a normal meal, increasing the ecological validity of the design. It is acknowledged that using fixed-energy meals does not take individual energy requirements into account; however, for the higher-energy meal at least, some participants consumed a significant proportion of their total daily energy requirement -yet a pattern of neural activation representative of satiety was still not found.…”

“…There is some evidence to suggest a hypothalamic basis, 7,8 which was recently supported by a functional neuroimaging study in three individuals with PWS. 9 Shapira et al 9 showed a significant delay in the negative response of the hypothalamus to glucose ingestion in those with PWS, in comparison to obese 10 and normal-weight 11 groups. However, an imaging study in a control group of nonobese individuals, 12 together with research from other groups, [13][14][15] has shown that the motivation to eat is controlled by an extensive system of reciprocally connected neural areas, beyond the hypothalamus, mediating both the intrinsically derived hunger drive to eat and the extrinsically derived incentive to eat.…”

“…However, an imaging study in a control group of nonobese individuals, 12 together with research from other groups, [13][14][15] has shown that the motivation to eat is controlled by an extensive system of reciprocally connected neural areas, beyond the hypothalamus, mediating both the intrinsically derived hunger drive to eat and the extrinsically derived incentive to eat. 12,16 Key brain regions for the former include the hypothalamus 11,[17][18][19] striatum, 20 orbitofrontal cortex (OFC) [21][22][23] insula [24][25][26] and anterior cingulate cortex, 24 and, for the latter, the amygdala and OFC. [27][28][29][30] Indeed, Lucignani et al 31 have found altered cerebral GABA A receptor function in the insula and cingulate, frontal and temporal neocortices in six adults with PWS compared to controls.…”

Development of 2dTCA for the detection of irregular, transient bold activity

“…After preprocessing, the 2dTCA analysis is performed using programs written in IDL software (ITT Visual Information Solutions, Boulder, CO) based on the modifications of the original TCA methods [Gao and Yee, 2003;Liu et al, 2000;Lu et al, 2006;Morgan et al, 2004;Yee and Gao, 2002;Zhao et al, 2004]. Each voxel time course is treated individually as an array, Z(1:N).…”

“…Other types of data-driven techniques such as hierarchical clustering Keogh et al, 2005;Stanberry et al, 2003], fuzzy clustering [Baumgartner et al, 2000], and temporal clustering analysis (TCA) [Gao and Yee, 2003;Liu et al, 2000;Lu et al, 2006;Makiranta et al, 2005;Yee and Gao, 2002;Zhao et al, 2004] use clustering of similar fMRI signal time courses to group and determine voxel time courses of interest, instead of partitioning into components. Previous studies have reported that these clustering algorithms outperform PCA [Baumgartner et al, 2000] and ICA techniques for fMRI analysis [Meyer-Baese et al, 2004].…”