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Macronutrient infusion evoked functional magnetic resonance imaging signal changes in the human hypothalamus

Post-ingestive nutrient information is known to strongly modulate feeding behavior based on the current energy status by providing feedback to the central nervous system. Post-ingestive gut-brain communication is mediated by the vagus nerve, which transmits intestinal nutrient signals to the central nervous system and exerts biological and physiological responses to maintain metabolic homeostasis. Animal studies have revealed that information regarding ingested nutrients is sent to the hypothalamus, striatum, and other related brain regions. However, the impact of ingestion of each macronutrient on brain activity in these regions remains to be examined in humans. Therefore, we performed a repeated measures functional magnetic resonance imaging (fMRI) study to assess brain response to glucose, amino acid, and fatty acid consumption. Eighteen healthy adults (mean age, 22.4 years; standard deviation, 4.8; age range, 19 – 39 years; 12 males and seven females) participated in this study. All participants provided written informed consent, and the Ethics Committee at the School of Medicine, The University of Tokyo (approval number 11703) approved this study. Participants underwent four fMRI sessions. On the day of each session, the participants visited our laboratory after at least 5 h of fasting, and during the scan, one of the four solutions (glucose, glutamic acid, oleic acid, or saline) were administered through a nasogastric tube. Compared to saline infusion, glucose and glutamic acid, but not oleic acid infusion evoked a hypothalamic response. These results indicate that postprandial nutrient information would be processed in different brain regions corresponding to the consumed nutrients.

Identification of likely locations of hypothalamic nuclei based on resting-state functional connectivity in standard resolution

Human functional magnetic resonance imaging has not extensively investigated hypothalamic nuclei due to their smallness, despite their critical roles in autonomic functions. Discrimination of individual hypothalamic nuclei using parcellation analyses requires a higher spatial resolution, which necessitates a large amount of data to compensate for the low signal-to-noise ratio. Identification of individual hypothalamic nuclei is still difficult using 3-T MRI scanners. For this purpose, we develop a workflow to identify the likely locations of hypothalamic nuclei in the standard 2-mm isotropic resolution based on our dataset of the higher 1.25-mm isotropic resolution. The spatial patterns of functional connectivity on the cerebral cortex for each medial hypothalamic nucleus were calculated using our high-resolution dataset. The resting-state functional images of the Human Connectome Project (HCP) database were used as standard resolution images. Voxels that most shared connectivity patterns with the same nucleus were identified in the hypothalamus in the standard resolution images. The locations of the identified voxels were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate for the nuclei. These results suggest that the workflow can refine the voxels representing hypothalamic nuclei in the standard resolution and is available for various settings, such as patient studies where lengthy scans are distant.

States and Traits in Human Brain Networks

Humans can easily and flexibly accomplish a wide variety of tasks, with different perceptual and cognitive demands, depending on their goals. This ability appears to depend on the coordinated interactions between brain regions that are organized into large-scale networks. In my research, I am interested in characterizing how human brain networks are organized, how they contribute to the myriad goal-directed behaviors that are essential to our daily lives, and how these processes break down. In my talk, I will present an overview of my research, highlighting recent projects centered on understanding functional brain network organization and how it changes over different timescales. I will introduce the use of a "precision" high-data approach to measuring brain networks reliably at the individual level and discuss some of the insights from this work.