@article {Inubushi1469, author = {Tomoo Inubushi and Akihiro Kakimoto and Kazuya Hatano and Hiroyuki Okada and Sadahiko Nishizawa and Yasuomi Ouchi and Etsuji Yoshikawa}, title = {Disrupted metabolic network in the cholinergic projection region in cognitive decline}, volume = {60}, number = {supplement 1}, pages = {1469--1469}, year = {2019}, publisher = {Society of Nuclear Medicine}, abstract = {1469Objectives: There is a growing body of evidence suggesting the link between the cognitive decline and the cholinergic dysfunction. The purpose of this study was to reveal the effect of aging and cognitive decline to the metabolic network in the cholinergic projection regions. Methods: We studied 1,304 participants (564 females: 62.8{\textpm}9.1 yrs.: 741 males: 63.0{\textpm}9.4 yrs.) who underwent 18F-FDG PET and T1-MRI scanning with the same PET and MRI systems. The participants also underwent medical screenings for Alzheimer{\textquoteright}s disease (AD) and mild cognitive impairment (MCI) based on the NINDS-ADRDA and DSM-IV for AD, and Peterson{\textquoteright}s criteria for amnestic MCI, respectively. We divided the participants into four groups according to their age (younger or older than 75 yrs.) and cognitive ability (cognitively normal or with cognitive decline). The numbers of participants of young normal, old normal, young with cognitive decline, and old with cognitive decline groups were 1,073, 95, 96, and 40, respectively. The 18F-FDG PET data of each group were anatomically normalized using their T1-MRI, and ROIs in the cholinergic projection regions were anatomically placed in the left and right nucleus accumbens (NAcc), hypothalamus (HypoT), amygdala, hippocampus (HPC), parahippocampus (ParaHPC), Brodmann area (BA) 24, and BA 32. We extracted the SUVs of these ROIs, and adjusted them for those of the whole brain by calculating the residuals from linear regression of SUVs of each ROI versus those of the whole brain. Metabolic network of each participant group was obtained from intra-individual correlations among these values and hierarchical clustering of them with the complete linkage method. The study was approved by the institutional review board of Hamamatsu Medical Photonics Foundation (No. 112), and written informed consent was obtained from each participant after detailed explanation of the study. Results: Hierarchical clustering analyses of intra-subjective correlations showed that metabolic patterns of all participant groups were clustered into the same three clusters; an anterior cingulate cluster which consists of the bilateral BA 24 and BA 32, a frontal basal nucleus cluster which consist of the bilateral NAcc, and a limbic cluster which consists of the bilateral HypoT, amygdala, HPC, and ParaHPC. This suggests the basic structure of the metabolic network in the cholinergic projection regions was common to the participants groups. On the other hand, we found that intra-individual correlations among cholinergic projection regions showed some negative correlations in the groups with cognitive decline, while all correlations were positive in the normal groups. We also found that these negative correlations were stronger in the older participants group with cognitive decline. The negative correlations in cognitively declined groups were especially prominent in the network from the frontal basal nucleus cluster. Conclusions: The results of the present study suggest that metabolic network in the cholinergic projection regions was disrupted in elderly participants with cognitive decline. The results are consistent with the studies which link AD with the cholinergic dysfunction. The prominent effect in the network from the frontal basal nucleus clearly suggests that this region plays a central role in regulation of cognitive abilities.}, issn = {0161-5505}, URL = {https://jnm.snmjournals.org/content/60/supplement_1/1469}, eprint = {https://jnm.snmjournals.org/content}, journal = {Journal of Nuclear Medicine} }