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  • 한국과학기술정보연구원(KISTI) 서울분원 대회의실(별관 3층)
  • 2024년 07월 03일(수) 13:30
 

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행동 접근 체계(BAS) 민감성과 휴지기 두뇌의 기능적 네트워크: 그래프-이론 분석

Behavioral approach system (BAS) sensitivity and functional brain networks during rest: graph-theory analysis

한국심리학회지: 인지 및 생물 / The Korean Journal of Cognitive and Biological Psychology, (P)1226-9654; (E)2733-466X
2017, v.29 no.2, pp.105-125
https://doi.org/10.22172/cogbio.2017.29.2.001
정호진 (국립과학수사연구원)
김진희 (강원대학교)
강은주 (강원대학교)
  • 다운로드 수
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초록

휴지기 동안 두뇌의 기능적 네트워크 특성은 정신 병리나 인지 처리의 개인차와 관련이 있다고 알려져 있다. 본 연구는 성격 특성 중 하나로 잘 알려진 행동 접근 체계(behavioral approach system, BAS) 민감성 정도의 개인차이가 휴지기 두뇌 네트워크 특성과 같은 생물학적 특성의 차이에 근거하고 있는지를 조사하기 위해 수행되었다. 이를 위하여 정상 성인(N=30)으로부터 아무런 과제를 수행하지 않는 휴지기 동안 fMRI 영상이 획득되었고, 두뇌를 영역(node)간 서로 연결(edge)된 네트워크로 간주하고, 이를 분석하기 위해 그래프 이론 기반 네트워크 분석 방법을 적용하였다. 그 결과, 보상 처리에 관여한다고 알려진 측핵에서 네트워크를 구성하는 다른 영역들에 미치는 영향력에 대한 지표(betweenness)가 BAS 민감성이 높은 개인일수록 더 높은 경향이 있는 것으로 나타났다. 그리고 우반구 시각 피질에서 군집 계수 감소, 국소적 효율성 감소, 그리고 매개중심성의 증가와 같은 특징들이 관찰되어, 국소적 정보처리 보다 광범위한 범위의 네트워크 정보처리가 높은 경향을 보여주었다. 그 외에도 BAS 민감성이 높은 개인에서 우반구 상전두회의 연결성(degree)과 정보처리 효율성(global efficiency)이 감소된 경향이 발견되었다. 본 연구의 결과들은 측핵, 시각 피질, 전두 피질에서 두뇌 네트워크 특성의 개인차가 BAS가 높은 개인들이 보이는 보상 민감성, 자극 추구 성향, 그리고 충동성과 관련이 있음을 시사한다.

keywords
functional brain network, graph-theory analysis, behavioral approach system, resting-state fMRI, 기능적 네트워크, 그래프 이론 접근법, 행동 접근 체계(BAS), 휴지 상태 fMRI

Abstract

The properties of functional brain networks during rest are known to be related to individual differences in cognitive processing or psychopathology. Here, we ask if a personality trait, the behavioral approach system (BAS), is based on individual differences in neurobiological substrates, namely those of the resting-state functional brain network. Resting-state fMRI data were acquired for 30 healthy, normal participants during rest, and brain networks were analyzed using a graph-theoretical approach in which the brain is viewed as a network composed of connections (edges) between brain regions (nodes). The influence of the left nucleus accumbens on other brain regions (quantified by the metric ‘betweenness’), was found to greater tendency in those individuals with higher BAS sensitivity. High BAS sensitivity was also related to a higher tendency for global information processing in the network, rather than local information processing in the right visual cortex, as indicated by increased betweenness and decreased clustering and local efficiency. Finally, Higher BAS-sensitivity individuals also showed tendency of decreased connectivity (‘degree’) and information processing efficiency (‘global efficiency’) in the right superior frontal gyrus. These findings suggest that the differences in brain network properties in the nucleus accumbens, visual cortex, and frontal cortex are related to the greater reward sensitivity, high novelty seeking, and greater impulsivity in high-BAS individuals.

keywords
functional brain network, graph-theory analysis, behavioral approach system, resting-state fMRI, 기능적 네트워크, 그래프 이론 접근법, 행동 접근 체계(BAS), 휴지 상태 fMRI

참고문헌

1.

Achard, S., & Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Computational Biology, 3(2), e17.

2.

Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X. J., & Krystal, J. H. (2012). The role of default network deactivation in cognition and disease. Trends in Cognitive Science, 16, 584-592.

3.

Barros-Loscertales, A., Meseguer, V., Sanjuan, A., Belloch, V., Parcet, M. A., Torrubia, R., & Avila, C. (2006). Striatum gray matter reduction in males with an overactive behavioral activation system. European Journal of Neuroscience, 24, 2071-2074.

4.

Barros-Loscertales, A., Meseguer, V., Sanjuan, A., Belloch, V., Parcet, M. A., Torrubia, R., & Avila, C. (2006). Behavioral Inhibition System activity is associated with increased amygdala and hippocampal gray matter volume: A voxel-based morphometry study. NeuroImage, 33, 1011-1015.

5.

Barros-Loscertales, A., Ventura-Campos, N., Sanjuan-Tomas, A., Belloch, V., Parcet, M. A., & Avila, C. (2010). Behavioral activation system modulation on brain activation during appetitive and aversive stimulus processing. Social Cognitive and Affective Neuroscience, 5, 18-28.

6.

Beaver, J. D., Lawrence, A. D., van Ditzhuijzen, J., Davis, M. H., Woods, A., & Calder, A. J. (2006). Individual differences in reward drive predict neural responses to images of food. Journal of Neuroscience, 26, 5160-5166.

7.

Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the royal statistical society. Series B (Methodological), 57, 289-300.

8.

Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10, 186-198.

9.

Carver, C. S. (2004). Negative affects deriving from the behavioral approach system. Emotion, 4, 3-22.

10.

Carver, C. S., & White, T. L. (1994). Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS scales. Journal of Personality and Social Psychology, 67, 319-333.

11.

Chang, M. S., Kim, H. M., & Kim, S. Y. (2013). The effect of behavioral activation system/behavioral inhibition system (BAS/BIS) on Decision-making in Internet Game Addict. The Korean Journal of Health Psychology, 18, 69-85.

12.

Chao-Gan, Y., & Yu-Feng, Z. (2010). DPARSF: A MATLAB Toolbox for “Pipeline” data analysis of resting-state fMRI. Frontiers in Systems Neuroscience, 4, 13.

13.

Corr, P. J. (2004). Reinforcement sensitivity theory and personality. Neuroscience and Biobehavioral Reviews, 28, 317-332.

14.

DeYoung, C. G., Hirsh, J. B., Shane, M. S., Papademetris, X., Rajeevan, N., & Gray, J. R. (2010). Testing predictions from personality neuroscience. Brain structure and the big five. Psychological Science, 21, 820-828.

15.

Fox, M. D., Corbetta, M., Snyder, A. Z., Vincent, J. L., & Raichle, M. E. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences of the United States of America, 103, 10046-10051.

16.

Franken, I. H. (2002). Behavioral approach system (BAS) sensitivity predicts alcohol craving. Personality and Individual Differences, 32, 349-355.

17.

Gray, J. A., & McNaughton, N. (2000). The neuropsychology of anxiety: An enquiry into the function of the septo-hippocampal system. New York, NY: Oxford University Press.

18.

Gray, J. R., & Braver, T. S. (2002). Personality predicts working-memory—related activation in the caudal anterior cingulate cortex. Cognitive, Affective, & Behavioral Neuroscience, 2, 64-75.

19.

Hahn, T., Dresler, T., Ehlis, A. C., Plichta, M. M., Heinzel, S., Polak, T., Lesch, K. P. Breuer, F., Jakob, P. M., & Fallgatter, A. J. (2009). Neural response to reward anticipation is modulated by Gray's impulsivity. NeuroImage, 46, 1148-1153.

20.

Hahn, T., Dresler, T., Ehlis, A. C., Pyka, M., Dieler, A. C., Saathoff, C., Jakob, P. M., Lesch, K. P., & Fallgatter, A. J. (2012). Randomness of resting-state brain oscillations encodes Gray's personality trait. NeuroImage, 59, 1842-1845.

21.

Harmon-Jones, E., & Allen, J. J. (1997). Behavioral activation sensitivity and resting frontal EEG asymmetry: covariation of putative indicators related to risk for mood disorders. Journal of Abnormal Psychology, 106, 159.

22.

Heubeck, B. G., Wilkinson, R. B., & Cologon, J. (1998). A second look at Carver and White's (1994) BIS/BAS scales. Personality and Individual Differences, 25, 785-800.

23.

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, 1666-1681.

24.

Jeong, H. J. (2016). Brain's resting-state intrinsic activity relates to individual difference in sensitivity of Gray's behavioral system (Master's thesis). Kangwon National University, Kangwon.

25.

Kennedy, D. N., Lange, N., Makris, N., Bates, J., Meyer, J., & Caviness, V. S. (1998). Gyri of the human neocortex: an MRI-based analysis of volume and variance. Cerebral Cortex, 8, 372-384.

26.

Kim, H. Y., & Choi, J. Y. (2016). Aging and Efficiency of Brain Functional Networks:Preliminary Study in Korean Women. The Korean Journal of Cognitive and Biological Psychology, 28, 675-682.

27.

Kim, K. H., & Kim, W. S. (2001). Korean-BAS/BIS Scale. The Korean Journal of Health Psychology, 6, 19-37.

28.

Kim, S. H., Kim, J. H., & Kang, E. (2015). Dynamic changes in feedback processing as learning progresses. The Korean Journal of Cognitive and Biological Psychology, 27, 419-450.

29.

Kunisato, Y., Okamoto, Y., Okada, G., Aoyama, S., Nishiyama, Y., Onoda, K., & Yamawaki, S. (2011). Personality traits and the amplitude of spontaneous low-frequency oscillations during resting state. Neuroscience Letter, 492, 109-113.

30.

Lawson, A. L., Liu, X., Joseph, J., Vagnini, V. L., Kelly, T. H., & Jiang, Y. (2012). Sensation seeking predicts brain responses in the old-new task: converging multimodal neuroimaging evidence. International Journal of Psychophysiology, 84, 260-269.

31.

Lynall, M. E., Bassett, D. S., Kerwin, R., McKenna, P. J., Kitzbichler, M., Muller, U., & Bullmore, E. (2010). Functional connectivity and brain networks in schizophrenia. Journal of Neuroscience, 30, 9477-9487.

32.

Makris, N., Meyer, J. W., Bates, J. F., Yeterian, E. H., Kennedy, D. N., & Caviness, V. S. (1999). MRI-based topographic parcellation of human cerebral white matter and nuclei: II. Rationale and applications with systematics of cerebral connectivity. NeuroImage, 9, 18-45.

33.

Mathews, A., Yiend, J., & Lawrence, A. D. (2004). Individual differences in the modulation of fear-related brain activation by attentional control. Journal of Cognitive Neuroscience, 16, 1683-1694.

34.

McClure, S. M., York, M. K., & Montague, P. R. (2004). The neural substrates of reward processing in humans: the modern role of FMRI. The Neuroscientist, 10, 260-268.

35.

Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., & Carter, C. S. (2012). Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cognitive, Affective, &Behavioral Neuroscience, 12, 241-268.

36.

O'Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: insights from neuroimaging. Current Opinion in Neurobiology, 14, 769-776.

37.

Owen, A. M. (1997). The Functional Organization of Working Memory Processes Within Human Lateral Frontal Cortex: The Contribution of Functional Neuroimaging. European Journal of Neuroscience, 9, 1329-1339.

38.

Petrides, M. (2000). The role of the middorsolateral prefrontal cortex in working memory. Experimental Brain Research, 133, 44-54.

39.

Pickering, A. D., & Gray, J. A. (1999). The neuroscience of personality. Handbook of Personality: Theory and Research, 2, 277-299.

40.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52, 1059-1069.

41.

Schweinhardt, P., Seminowicz, D. A., Jaeger, E., Duncan, G. H., & Bushnell, M. C. (2009). The anatomy of the mesolimbic reward system: a link between personality and the placebo analgesic response. Journal of Neuroscience, 29, 4882-4887.

42.

Simon, J. J., Walther, S., Fiebach, C. J., Friederich, H. C., Stippich, C., Weisbrod, M., & Kaiser, S. (2010). Neural reward processing is modulated by approach- and avoidancerelated personality traits. NeuroImage, 49, 1868-1874.

43.

Sombers, L. A., Beyene, M., Carelli, R. M., & Wightman, R. M. (2009). Synaptic overflow of dopamine in the nucleus accumbens arises from neuronal activity in the ventral tegmental area. Journal of Neuroscience, 29, 1735-1742.

44.

Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17, 652-660.

45.

Sporns, O., & Betzel, R. F. (2016). Modular brain networks. Annual Review of Psychology, 67, 613-640.

46.

Sun, Y., Lim, J., Dai, Z., Wong, K., Taya, F., Chen, Y., Li, J., Thankor, N., & Bezerianos, A. (2017). The effects of a mid-task break on the brain connectome in healthy participants:A resting-state functional MRI study. NeuroImage, 152, 19-30.

47.

Tschernegg, M., Crone, J. S., Eigenberger, T., Schwartenbeck, P., Fauth-Buhler, M., Lemenager, T., Mann, K., Thon, N., Wurst, F. M., & Kronbichler, M. (2013). Abnormalities of functional brain networks in pathological gambling: a graph-theoretical approach. Frontiers in Human Neuroscience, 7, 625.

48.

van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Efficiency of functional brain networks and intellectual performance. Journal of Neuroscience, 29, 7619- 7624.

49.

Vossel, S., Geng, J. J., & Fink, G. R. (2014). Dorsal and ventral attention systems distinct neural circuits but collaborative roles. The Neuroscientist, 20, 150-159.

50.

Wang, J., Wang, X., Xia, M., Liao, X., Evans, A., & He, Y. (2015). GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Frontiers in Human Neuroscience, 9, 386.

51.

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’networks. Nature, 393, 440-442.

52.

Wee, C. Y., Zhao, Z., Yap, P. T., Wu, G., Shi, F., Price, T., Du, Y., Xu, J., Zhou, Y., & Shen, D. (2014). Disrupted brain functional network in internet addiction disorder: a resting-state functional magnetic resonance imaging study. PLoS ONE, 9, e107306.

53.

Yi, I. H., & Hwang, H. K. (2015). Personality-Personality Disorder Symptoms Associations based on Revised Reinforcement Sensitivity Theory. Journal of Social Science, 54, 231-261.

54.

Zhang, J., Wang, J., Wu, Q., Kuang, W., Huang, X., He, Y., & Gong, Q. (2011). Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biological Psychiatry, 70, 334-342.

55.

Zinbarg, R. E., & Mohlman, J. (1998). Individual differences in the acquisition of affectively valenced associations. Journal of Personality and Social Psychology, 74, 1024-1040.

한국심리학회지: 인지 및 생물