靜息態

靜息態 （英語：resting state），是指相對於人在執行特定任務的狀態的一種清醒、放鬆的狀態。靜息態通常在人類的神經科學研究中使用。

靜息態與任務態

相比於實驗者人為操縱實驗刺激造成神經系統的激活，研究者還發現人在處於休息、放鬆狀態下的自發的、不間斷的腦活動同樣可以用來研究神經系統。1995年，Biswal等最先報告了第一個靜息態功能磁共振成像 (fMRI)研究^[1]。Biswal等人的研究發現人類受試者左右兩側感覺運動皮層在靜息態時的fMRI血氧水平信號 (BOLD)存在很高的相關性。人腦在靜息態時的腦活動，被認為是一種不間斷的、自發的腦活動。因此，通常認為靜息態腦活動與動物電生理研究中的不間斷的腦活動 (ongoing activity)有着密切聯繫^[2]。

默認網絡

早期使用正電子發射計算機斷層掃描 (PET)進行腦功能研究的實驗中，通常使用靜息態作為一個基線條件。研究記錄不同實驗任務的腦活動，與靜息態腦活動相減，以定位活動變化的腦區^[3]。但研究者們逐漸注意到一個有趣的現象，就是有一系列腦區在進行各種不同任務時的腦活動相比於靜息態時會降低 ^[4]。這一發現導致Marcus Raichle等人在2001年提出了大腦默認模式的概念^[5]。Marcus Raichle等人提出有一系列腦區在大腦的默認模式——靜息態時會保持很高的活動水平，而在大腦需要和外界交互完成各種任務時激活水平會下降。這個大腦的默認網絡主要包含後扣帶回、內側前額葉以及左右頂葉下回。

靜息態大腦網絡

從Biswal等人1995年的研究開始，一系列的研究發現大腦中功能相關的區域在靜息態的腦活動也會有很高的相關 ^[6]^[7]。這使得研究者可以使用靜息態功能成像研究大腦大範圍的功能組織。相關的最常用的數據分析方法是數據驅動的獨立成分分析 (ICA) ^[8]。把應用獨立成分分析到靜息態功能磁共振數據上，研究者可以同時得到大腦中很多個相對獨立的腦網絡，比如默認網絡、左/右偏側的額頂網絡、警覺網絡 (salience network)、注意網絡、視皮層網絡、運動網絡等等。

應用

因為靜息態功能磁共振操作簡單，使得它有非常廣闊的臨床應用前景。很多精神疾病病人大腦的靜息態腦活動相比於正常人會有改變。目前，靜息態功能成像幾乎應用到所有精神疾病的臨床研究中，比如精神分裂症 ^[9]、抑鬱症 ^[10]、孤獨症 ^[11]等等。因為靜息態功能磁共振實驗設計簡單，還使得實驗室間共享數據成為可能。2010年，Biswal等人整合了全球35個實驗室的一共1414名被試的靜息態功能磁共振數據，並顯示了靜息態功能網絡的穩定性 ^[12]。基於靜息態功能磁共振數據的數據分享成為功能成像數據分享的最主要組成部分。其中比較重要的精神疾病數據分享包括孤獨症 ^[11]和多動症 ^[13]。在美國國家衛生研究所 (NIH)資助的人腦連接組項目中 (The Human Connectome Project （頁面存檔備份，存於網際網路檔案館）)，靜息態磁共振數據是其中一個重要的組成部分。

參考資料

[1]
Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541.
[2]
Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A., 1996. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871.
[3]
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M., & Raichle, M. E. (1988). Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature, 331(6157), 585-589.
[4]
Shulman, G. L., Fiez, J. A., Corbetta, M., Buckner, R. L., Miezin, F. M., Raichle, M. E., & Petersen, S. E. (1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of cognitive neuroscience, 9(5), 648-663.
[5]
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676-682.
[6]
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., ... & Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21(9), 1636-1644.
[7]
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253-258.
[8]
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1457), 1001-1013.
[9]
Jafri, M. J., Pearlson, G. D., Stevens, M., & Calhoun, V. D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39(4), 1666-1681.
[10]
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., ... & Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological psychiatry, 62(5), 429-437.
[11]
Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Deen, B. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.
[12]
Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., ... & Dogonowski, A. M. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107(10), 4734-4739.
[13]
ADHD-200 Consortium. (2012). The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in systems neuroscience, 6.

[1] [1]
Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34, 537–541.

[2] [2]
Arieli, A., Sterkin, A., Grinvald, A., Aertsen, A., 1996. Dynamics of ongoing activity: explanation of the large variability in evoked cortical responses. Science 273, 1868–1871.

[3] [3]
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M., & Raichle, M. E. (1988). Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature, 331(6157), 585-589.

[4] [4]
Shulman, G. L., Fiez, J. A., Corbetta, M., Buckner, R. L., Miezin, F. M., Raichle, M. E., & Petersen, S. E. (1997). Common blood flow changes across visual tasks: II. Decreases in cerebral cortex. Journal of cognitive neuroscience, 9(5), 648-663.

[5] [5]
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences, 98(2), 676-682.

[6] [6]
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., ... & Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. American Journal of Neuroradiology, 21(9), 1636-1644.

[7] [7]
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253-258.

[8] [8]
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society of London B: Biological Sciences, 360(1457), 1001-1013.

[9] [9]
Jafri, M. J., Pearlson, G. D., Stevens, M., & Calhoun, V. D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39(4), 1666-1681.

[10] [10]
Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., ... & Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological psychiatry, 62(5), 429-437.

[#1-11] [11]
Di Martino, A., Yan, C. G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., ... & Deen, B. (2014). The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19(6), 659-667.

[12] [12]
Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., ... & Dogonowski, A. M. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107(10), 4734-4739.

[13] [13]
ADHD-200 Consortium. (2012). The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in systems neuroscience, 6.

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