這些年來，靜息態功能性磁振造影(rs-fMRI)逐漸成熟，大家對大腦的運作漸漸感到好奇，所以開始了一些研究，而比起在清醒的狀態下，人們發現大腦在休息時某些區域也會進行運作，這些大腦休息時活躍的區域定義為靜息態網路(resting state networks, RSN)。預設模式網路(default mode network, DMN)被證實在大部分人腦中皆可以找到的一種靜息態網路，而且也比其他的靜息態網路更能顯示出大腦靜息時的狀態。先前也有一些論文發現了腦中的代謝物濃度跟預設模式網路有很大的關聯性，可以在預設模式網路和其他腦區進行調節。此實驗使用EPSI序列是用來收取MRSI資料，再使用LCmodel對代謝物濃度做定量分析並做部分容積校正(partial volume correction, PVC)，並對代謝物設定門檻值，最後得到代謝物濃度圖。而預設模式網路腦區的腦強度圖則是使用獨立成像分析(independent components analysis, ICA)來分析受試者的DMN腦區強度，最後在使用體像素線性回歸來探討每個代謝物濃度跟DMN腦區之間的關聯性。

In recent years, resting functional magnetic resonance imaging (rs-fMRI) has gradually matured. People have Interested in the functioning of the brain, So some research began ,Compared with the waking state, people find that certain areas of the brain also operate when resting. These regions of the brain that are active when resting are defined as Resting State Networks (RSN). The Default Mode Network (DMN) has been confirmed to be a resting state network (RSN) that can be found in most people's brains, and it can show the brain's resting state better than other RSNs. Some thesis have found that metabolites in the brain are closely related to DMN, and can be regulated in DMN and other brain regions. This experiment uses EPSI to collect MRSI data, and then uses LCmodel to quantitative analyze the metabolite concentration and Partial Volume Correction (PVC), and set the threshold value for the metabolite, finally obtain the metabolite concentration map. The brain intensity map of the DMN brain region uses independent components analysis (ICA) to analyze the intensity of the DMN brain area of the subject, and finally uses volume pixel linear regression to explore the relationship between each metabolite and the DMN brain regions.

1. Di Martino, A., et al., Functional connectivity of human striatum: a resting state FMRI study. Cerebral cortex, 2008. 18(12): p. 2735-2747.
2. Biswal, B., et al., Functional connectivity in the motor cortex of resting human brain using echo‐planar MRI. Magnetic resonance in medicine, 1995. 34(4): p. 537-541.
3. Greicius, M.D., et al., Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 2003. 100(1): p. 253-258.
4. Fox, M.D., et al., The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences, 2005. 102(27): p. 9673-9678.
5. Greicius, M.D., et al., Default-mode network activity distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences, 2004. 101(13): p. 4637-4642.
6. Liu, Y., et al., Disrupted small-world networks in schizophrenia. Brain, 2008. 131(4): p. 945-961.
7. Buckner, R.L., J.R. Andrews-Hanna, and D.L. Schacter, The brain's default network: anatomy, function, and relevance to disease. 2008.
8. Tsai, S.Y., W.C. Wang, and Y.R. Lin, Comparison of sagittal and transverse echo planar spectroscopic imaging on the quantification of brain metabolites. Journal of neuroimaging, 2015. 25(2): p. 167-174.
9. Enzi, B., et al., Glutamate modulates resting state activity in the perigenual anterior cingulate cortex–A combined fMRI–MRS study. Neuroscience, 2012. 227: p. 102-109.
10. Stagg, C.J., et al., Local GABA concentration is related to network-level resting functional connectivity. Elife, 2014. 3: p. e01465.
11. Comon, P., Independent component analysis, a new concept? Signal processing, 1994. 36(3): p. 287-314.
12. Bell, A.J. and T.J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution. Neural computation, 1995. 7(6): p. 1129-1159.
13. Hyvärinen, A., Fast ICA by a fixed-point algorithm that maximizes non-Gaussianity. Independent component analysis: principles and practice, 2001. 1.
14. Beckmann, C.F., et al., Group comparison of resting-state FMRI data using multi-subject ICA and dual regression. Neuroimage, 2009. 47(Suppl 1): p. S148.
15. Marchitelli, R., et al., Test‐retest reliability of the default mode network in a multi‐centric f MRI study of healthy elderly: Effects of data‐driven physiological noise correction techniques. Human brain mapping, 2016. 37(6): p. 2114-2132.
16. Postema, M.C., et al., A study of within-subject reliability of the brain’s default-mode network. Magnetic Resonance Materials in Physics, Biology and Medicine, 2019. 32(3): p. 391-405.
17. Cadena, E.J., et al., A longitudinal multimodal neuroimaging study to examine relationships between resting state glutamate and task related BOLD response in schizophrenia. Frontiers in psychiatry, 2018. 9: p. 632.
18. Duncan, N.W., et al., Glutamate concentration in the medial prefrontal cortex predicts resting-state cortical-subcortical functional connectivity in humans. PloS one, 2013. 8(4): p. e60312.
19. Modulation between Metabolite Concentrations and Resting State Connectivity in Default Mode Network. Yu-Jen Chen