Pencitraan Otak Dengan Diffusion Tensor Imaging Dalam Riset Psikologi Menggunakan Tract Based Spatial Statistics

Satriyo Priyo Adi, Sri Kusrohmaniah
(Submitted 18 November 2024)
(Published 25 June 2025)

Abstract


White matter otak memainkan peran sentral dalam menjembatani komunikasi antar bagian otak, yang secara langsung berkontribusi pada berbagai fungsi kognitif dan perilaku. Dalam konteks penelitian psikologi, perhatian terhadap struktur dan integritas white matter meningkat seiring dengan ditemukannya hubungan antara konektivitas saraf dan performa kognitif. Diffusion Tensor Imaging (DTI), sebagai salah satu teknik pencitraan berbasis Magnetic Resonance Imaging (MRI) yang bersifat non-invasif, memungkinkan peneliti mengeksplorasi struktur white matter secara lebih rinci melalui parameter seperti fraksi anisotropi (FA). Salah satu metode analisis data DTI yang banyak digunakan adalah Tract-Based Spatial Statistics (TBSS), yang memungkinkan perbandingan spasial antar individu secara sistematis dan objektif. Meskipun DTI dan TBSS telah banyak diaplikasikan dalam studi psikologi di tingkat global, literatur berbahasa Indonesia yang membahas penerapan teknik ini dalam riset psikologi masih sangat terbatas. Artikel ini bertujuan mengisi kekosongan tersebut dengan memberikan tinjauan konseptual mengenai prinsip dasar MRI dan DTI, perbedaan DTI dari MRI konvensional, serta peran TBSS dalam menganalisis data DTI untuk memahami fenomena psikologis yang berkaitan dengan integritas white matter.


Keywords


magnetic resonance imaging; diffusion tensor imaging; pencitraan otak; tract based spatial statistics; anatomi otak

Full Text: PDF

DOI: 10.22146/buletinpsikologi.101634

References


Abe, O., Takao, H., Gonoi, W., Sasaki, H., Murakami, M., Kabasawa, H., Kawaguchi, H., Goto, M., Yamada, H., Yamasue, H., Kasai, K., Aoki, S., & Ohtomo, K. (2010). Voxel-based analysis of the diffusion tensor. Neuroradiology, 52(8), 699–710. https://doi.org/10.1007/s00234-010-0716-3

Acosta-Cabronero, J., Alley, S., Williams, G. B., Pengas, G., & Nestor, P. J. (2012). Diffusion tensor metrics as biomarkers in Alzheimer’s disease. PLOS ONE, 7(11), e49072. https://doi.org/10.1371/journal.pone.0049072

Alves, G. S., Oertel Knöchel, V., Knöchel, C., Carvalho, A. F., Pantel, J., Engelhardt, E., & Laks, J. (2015). Integrating retrogenesis theory to Alzheimer’s disease pathology: Insight from DTI-TBSS investigation of the white matter microstructural integrity. BioMed Research International, 2015, 291658. https://doi.org/10.1155/2015/291658

Anderson, J. A. E., Grundy, J. G., De Frutos, J., Barker, R. M., Grady, C., & Bialystok, E. (2018). Effects of bilingualism on white matter integrity in older adults. NeuroImage, 167, 143–150. https://doi.org/10.1016/j.neuroimage.2017.11.038

Ashburner, J., & Friston, K. J. (2000). Voxel-based morphometry—The methods. NeuroImage, 11(6), 805–821. https://doi.org/10.1006/nimg.2000.0582

Bach, M., Laun, F. B., Leemans, A., Tax, C. M. W., Biessels, G. J., Stieltjes, B., & Maier-Hein, K. H. (2014). Methodological considerations on tract-based spatial statistics (TBSS). NeuroImage, 100, 358–369. https://doi.org/10.1016/j.neuroimage.2014.06.021

Balachandar, R., John, J. P., Saini, J., Kumar, K. J., Joshi, H., Sadanand, S., Aiyappan, S., Sivakumar, P. T., Loganathan, S., Varghese, M., & Bharath, S. (2015). A study of structural and functional connectivity in early Alzheimer’s disease using rest fMRI and diffusion tensor imaging. International Journal of Geriatric Psychiatry, 30(5), 497–504. https://doi.org/10.1002/gps.4168

Banich, M. T., & Compton, R. J. (2018). Cognitive neuroscience. Cambridge University Press. https://doi.org/10.1017/9781316664018

Basser, P. J., & Pierpaoli, C. (1996). Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. Journal of Magnetic Resonance, Series B, 111(3), 209–219. https://doi.org/10.1006/jmrb.1996.0086

Basu, K., Appukuttan, S., Manchanda, R., & Sik, A. (2022). Difference in axon diameter and myelin thickness between excitatory and inhibitory callosally projecting axons in mice. Cerebral Cortex, 33(7), 4101–4114. https://doi.org/10.1093/cercor/bhac329

Berger, A. (2002). How does it work?: Magnetic resonance imaging. BMJ, 324(7328), 35. https://doi.org/10.1136/bmj.324.7328.35

Boulant, N., Quettier, L., Aubert, G., Amadon, A., Belorgey, J., Berriaud, C., Bonnelye, C., Bredy, P., Chazel, E., Dilasser, G., Dubois, O., Giacomini, E., Gilgrass, G., Gras, V., Guihard, Q., Jannot, V., Juster, F. P., Lannou, H., Leprêtre, F., … Vignaud, A. (2023). Commissioning of the Iseult CEA 11.7 T whole-body MRI: Current status, gradient–magnet interaction tests and first imaging experience. MAGMA, 36(2), 175–193. https://doi.org/10.1007/s10334-023-01063-5

Braak, E., Griffing, K., Arai, K., Bohl, J., Bratzke, H., & Braak, H. (1999). Neuropathology of Alzheimer’s disease: What is new since A. Alzheimer? European Archives of Psychiatry and Clinical Neuroscience, 249(Suppl. 3), S14–S22. https://doi.org/10.1007/pl00014168

Broadhouse, K. M. (2019). The physics of MRI and how we use it to reveal the mysteries of the mind. Frontiers for Young Minds, 7. https://doi.org/10.3389/frym.2019.00023

Burzynska, A. Z., Preuschhof, C., Bäckman, L., Nyberg, L., Li, S. C., Lindenberger, U., & Heekeren, H. R. (2010). Age-related differences in white matter microstructure: Region-specific patterns of diffusivity. NeuroImage, 49(3), 2104–2112. https://doi.org/10.1016/j.neuroimage.2009.09.041

Caron, B., Stuck, R., McPherson, B., Bullock, D., Kitchell, L., Faskowitz, J., Kellar, D., Cheng, H., Newman, S., Port, N., & Pestilli, F. (2021). Collegiate athlete brain data for white matter mapping and network neuroscience. Scientific Data, 8(1), 1–17. https://doi.org/10.1038/s41597-021-00823-z

Cercignani, M., Inglese, M., Pagani, E., Comi, G., & Filippi, M. (2001). Mean diffusivity and fractional anisotropy histograms of patients with multiple sclerosis. American Journal of Neuroradiology, 22(5), 952–958.

Chee, M. W. L., Zheng, H., Goh, J. O. S., Park, D., & Sutton, B. P. (2011). Brain structure in young and old East Asians and Westerners: Comparisons of structural volume and cortical thickness. Journal of Cognitive Neuroscience, 23(5), 1065–1079. https://doi.org/10.1162/jocn.2010.21513

Chen, H., Wang, K., Yao, J., Dai, J., Ma, J., Li, S., Ai, L., Chen, Q., Chen, X., Zhang, Y., & Chen, H. Y. (2015). White matter changes in Alzheimer’s disease revealed by diffusion tensor imaging with TBSS. World Journal of Neuroscience, 5(1), 58–65. https://doi.org/10.4236/wjns.2015.51007

Chen, X., Qu, L., Xie, Y., Ahmad, S., & Yap, P. T. (2023). A paired dataset of T1- and T2-weighted MRI at 3 Tesla and 7 Tesla. Scientific Data, 10(1), 1–5. https://doi.org/10.1038/s41597-023-02400-y

Chung, S., Fieremans, E., Kucukboyaci, N. E., Wang, X., Morton, C. J., Novikov, D. S., Rath, J. F., & Lui, Y. W. (2018). Working memory and brain tissue microstructure: White matter tract integrity based on multi-shell diffusion MRI. Scientific Reports, 8(1), 1–7. https://doi.org/10.1038/s41598-018-21428-4

De Wilde, J. P., Grainger, D., Price, D. L., & Renaud, C. (2007). Magnetic resonance imaging safety issues including an analysis of recorded incidents within the UK. Progress in Nuclear Magnetic Resonance Spectroscopy, 51(1), 37–48. https://doi.org/10.1016/j.pnmrs.2007.01.003

Douglas, D. B., Iv, M., Douglas, P. K., Anderson, A., Vos, S. B., Bammer, R., Zeineh, M., & Wintermark, M. (2015). Diffusion tensor imaging of TBI: Potentials and challenges. Topics in Magnetic Resonance Imaging, 24(5), 241–251. https://doi.org/10.1097/RMR.0000000000000062

Ellison-Wright, I., Nathan, P. J., Bullmore, E. T., Zaman, R., Dudas, R. B., Agius, M., Fernandez-Egea, E., Müller, U., Dodds, C. M., Forde, N. J., Scanlon, C., Leemans, A., McDonald, C., & Cannon, D. M. (2014). Distribution of tract deficits in schizophrenia. BMC Psychiatry, 14(1), 99. https://doi.org/10.1186/1471-244X-14-99

Fields, R. D. (2008). White matter in learning, cognition and psychiatric disorders. Trends in Neurosciences, 31(7), 361–370. https://doi.org/10.1016/j.tins.2008.04.001

Filley, C. M., & Fields, R. D. (2016). White matter and cognition: Making the connection. Journal of Neurophysiology, 116(5), 2093–2104. https://doi.org/10.1152/jn.00221.2016

Fushimi, Y., Miki, Y., Okada, T., Yamamoto, A., Mori, N., Hanakawa, T., Urayama, S. I., Aso, T., Fukuyama, H., Kikuta, K. I., & Togashi, K. (2007). Fractional anisotropy and mean diffusivity: Comparison between 3.0-T and 1.5-T diffusion tensor imaging with parallel imaging using histogram and region of interest analysis. NMR in Biomedicine, 20(8), 743–748. https://doi.org/10.1002/nbm.1139

García-Pentón, L., Pérez Fernández, A., Iturria-Medina, Y., Gillon-Dowens, M., & Carreiras, M. (2014). Anatomical connectivity changes in the bilingual brain. NeuroImage, 84, 495–504. https://doi.org/10.1016/j.neuroimage.2013.08.064

Hämäläinen, S., Sairanen, V., Leminen, A., & Lehtonen, M. (2017). Bilingualism modulates the white matter structure of language-related pathways. NeuroImage, 152, 249–257. https://doi.org/10.1016/j.neuroimage.2017.02.081

Hulkower, M. B., Poliak, D. B., Rosenbaum, S. B., Zimmerman, M. E., & Lipton, M. L. (2013). A decade of DTI in traumatic brain injury: 10 years and 100 articles later. American Journal of Neuroradiology, 34(11), 2064–2074. https://doi.org/10.3174/ajnr.a3395

Inano, S., Takao, H., Hayashi, N., Abe, O., & Ohtomo, K. (2011). Effects of age and gender on white matter integrity. American Journal of Neuroradiology, 32(11), 2103–2109. https://doi.org/10.3174/ajnr.a2785

Jo, Y. T., Joo, S. W., Choi, W., Joe, S., & Lee, J. (2024). White matter tract alterations in schizophrenia identified by DTI-based probabilistic tractography: A multisite harmonisation study. Acta Neuropsychiatrica, 1–10. https://doi.org/10.1017/neu.2024.2

Jones, D. K., Knösche, T. R., & Turner, R. (2013). White matter integrity, fiber count, and other fallacies: The do’s and don’ts of diffusion MRI. NeuroImage, 73, 239–254. https://doi.org/10.1016/j.neuroimage.2012.06.081

Kalat, J. W. (2020). Biological psychology (with APA card). Cengage Learning.

Kitayama, S., & Park, J. (2010). Cultural neuroscience of the self: Understanding the social grounding of the brain. Social Cognitive and Affective Neuroscience, 5(2–3), 111–129. https://doi.org/10.1093/scan/nsq052

Kolb, B., & Whishaw, I. Q. (2021). Fundamentals of human neuropsychology. https://search.worldcat.org/title/1196196585

Kubicki, M., McCarley, R., Westin, C. F., Park, H. J., Maier, S., Kikinis, R., Jolesz, F. A., & Shenton, M. E. (2007). A review of diffusion tensor imaging studies in schizophrenia. Journal of Psychiatric Research, 41(1–2), 15–30. https://doi.org/10.1016/j.jpsychires.2005.05.005

Kubicki, M., Westin, C. F., McCarley, R. W., & Shenton, M. E. (2005). The application of DTI to investigate white matter abnormalities in schizophrenia. Annals of the New York Academy of Sciences, 1064(1), 134–148. https://doi.org/10.1196/annals.1340.024

Kumar, R., Gupta, R. K., Husain, M., Chaudhry, C., Srivastava, A., Saksena, S., & Rathore, R. K. S. (2009). Comparative evaluation of corpus callosum DTI metrics in acute mild and moderate traumatic brain injury: Its correlation with neuropsychometric tests. Brain Injury, 23(7–8), 675–685. https://doi.org/10.1080/02699050903014915

Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging, 13(4), 534–546. https://doi.org/10.1002/jmri.1076

Li, Q., Zhao, Y., Huang, Z., Guo, Y., Long, J., Luo, L., You, W., Sweeney, J. A., Li, F., & Gong, Q. (2021). Microstructural white matter abnormalities in pediatric and adult obsessive-compulsive disorder: A systematic review and meta-analysis. Brain and Behavior, 11(2), e01975. https://doi.org/10.1002/brb3.1975

Liu, X., Lai, Y., Wang, X., Hao, C., Chen, L., Zhou, Z., Yu, X., & Hong, N. (2013). Reduced white matter integrity and cognitive deficit in never-medicated chronic schizophrenia: A diffusion tensor study using TBSS. Behavioural Brain Research, 252, 157–163. https://doi.org/10.1016/j.bbr.2013.05.061

Liu, Y., Spulber, G., Lehtimäki, K. K., Könönen, M., Hallikainen, I., Gröhn, H., Kivipelto, M., Hallikainen, M., Vanninen, R., & Soininen, H. (2011). Diffusion tensor imaging and tract-based spatial statistics in Alzheimer’s disease and mild cognitive impairment. Neurobiology of Aging, 32(9), 1558–1571. https://doi.org/10.1016/j.neurobiolaging.2009.10.006

Lu, H., Ayers, E., Patel, P., & Mattoo, T. K. (2023). Body water percentage from childhood to old age. Kidney Research and Clinical Practice, 42(3), 340. https://doi.org/10.23876/j.krcp.22.062

Male, A. G., Goudzwaard, E., Nakahara, S., Turner, J. A., Calhoun, V. D., Mueller, B. A., Lim, K. O., Bustillo, J. R., Belger, A., Voyvodic, J., O’Leary, D., Mathalon, D. H., Ford, J. M., Potkin, S. G., Preda, A., & van Erp, T. G. M. (2024). Structural white matter abnormalities in schizophrenia and associations with neurocognitive performance and symptom severity. Psychiatry Research: Neuroimaging, 342, 111843. https://doi.org/10.1016/j.pscychresns.2024.111843

Mori, S., & Tournier, J. D. (2013). Introduction to diffusion tensor imaging: And higher order models: Second edition. Introduction to diffusion tensor imaging: And higher order models: Second edition, 1–126. https://doi.org/10.1016/c2011-0-07607-x

Murman, D. L. (2015). The impact of age on cognition. Seminars in Hearing, 36(3), 111. https://doi.org/10.1055/s-0035-1555115

Nazeri, A., Chakravarty, M. M., Rajji, T. K., Felsky, D., Rotenberg, D. J., Mason, M., Xu, L. N., Lobaugh, N. J., Mulsant, B. H., & Voineskos, A. N. (2015). Superficial white matter as a novel substrate of age-related cognitive decline. Neurobiology of Aging, 36(6), 2094–2106. https://doi.org/10.1016/j.neurobiolaging.2015.02.022

Neil, J. J. (2008). Diffusion imaging concepts for clinicians. Journal of Magnetic Resonance Imaging, 27(1), 1–7. https://doi.org/10.1002/jmri.21087

Ohtani, T., Bouix, S., Hosokawa, T., Saito, Y., Eckbo, R., Ballinger, T., Rausch, A., Melonakos, E., & Kubicki, M. (2014). Abnormalities in white matter connections between orbitofrontal cortex and anterior cingulate cortex and their associations with negative symptoms in schizophrenia: A DTI study. Schizophrenia Research, 157(1–3), 190–197. https://doi.org/10.1016/j.schres.2014.05.016

Oishi, K., Faria, A. V., & van Zijl, P. C. M. (2012). MRI atlas of human white matter (2nd ed.). 266.

Oishi, K., Mielke, M. M., Albert, M., Lyketsos, C. G., & Mori, S. (2011). DTI analyses and clinical applications in Alzheimer’s disease. Journal of Alzheimer’s Disease, 26(Suppl 3), 287. https://doi.org/10.3233/jad-2011-0007

Pardini, M., Elia, M., Garaci, F. G., Guida, S., Coniglione, F., Krueger, F., Benassi, F., & Emberti Gialloreti, L. (2012). Long-term cognitive and behavioral therapies, combined with augmentative communication, are related to uncinate fasciculus integrity in autism. Journal of Autism and Developmental Disorders, 42(4), 585–592. https://doi.org/10.1007/s10803-011-1281-2/figures/3

Park, Y. W., Han, K., Ahn, S. S., Choi, Y. S., Chang, J. H., Kim, S. H., Kang, S. G., Kim, E. H., & Lee, S. K. (2018). Whole-tumor histogram and texture analyses of DTI for evaluation of IDH1-mutation and 1p/19q-codeletion status in World Health Organization grade II gliomas. American Journal of Neuroradiology, 39(4), 693–698. https://doi.org/10.3174/ajnr.a5569

Pecheva, D., Kelly, C., Kimpton, J., Bonthrone, A., Batalle, D., Zhang, H., & Counsell, S. J. (2018). Recent advances in diffusion neuroimaging: Applications in the developing preterm brain. F1000Research, 7, 1326. https://doi.org/10.12688/f1000research.15073.1

Pierpaoli, C., & Basser, P. J. (1996). Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine, 36(6), 893–906. https://doi.org/10.1002/mrm.1910360612

Ribeiro, M., Yordanova, Y. N., Noblet, V., Herbet, G., & Ricard, D. (2024). White matter tracts and executive functions: A review of causal and correlation evidence. Brain, 147(2), 352–371. https://doi.org/10.1093/brain/awad308

Roberts, A. (1990). How does a nervous system produce behaviour? A case study in neurobiology. Science Progress, 74(1), 230. https://www.jstor.org/stable/43423875?seq=1

Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507. https://doi.org/10.1016/j.neurobiolaging.2008.09.023

Samson, A. C., Dougherty, R. F., Lee, I. A., Phillips, J. M., Gross, J. J., & Hardan, A. Y. (2016). White matter structure in the uncinate fasciculus: Implications for socio-affective deficits in autism spectrum disorder. Psychiatry Research: Neuroimaging, 255, 66–74. https://doi.org/10.1016/j.pscychresns.2016.08.004

Sanders, F. K., & Whitteridge, D. (1946). Conduction velocity and myelin thickness in regenerating nerve fibres. The Journal of Physiology, 105(2), 152. https://pmc.ncbi.nlm.nih.gov/articles/PMC1393620/

Siddiqui, F., Höllt, T., & Vilanova, A. (2021). A progressive approach for uncertainty visualization in diffusion tensor imaging. Computer Graphics Forum, 40(3), 411–422. https://doi.org/10.1111/cgf.14317

Smith, S. M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., & Behrens, T. E. J. (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage, 31(4), 1487–1505. https://doi.org/10.1016/j.neuroimage.2006.02.024

Smith, S. M., Johansen-Berg, H., Jenkinson, M., Rueckert, D., Nichols, T. E., Klein, J. C., Robson, M. D., Jones, D. K., & Behrens, T. E. J. (2007). Acquisition and voxelwise analysis of multi-subject diffusion data with tract-based spatial statistics. Nature Protocols, 2(3), 499–503. https://doi.org/10.1038/nprot.2007.45

Smith, S. M., & Nichols, T. E. (2009). Threshold-free cluster enhancement: Addressing problems of smoothing, threshold dependence and localisation in cluster inference. NeuroImage, 44(1), 83–98. https://doi.org/10.1016/j.neuroimage.2008.03.061

Stigler, K. A., & McDougle, C. J. (2013). Structural and functional MRI studies of autism spectrum disorders. In The neuroscience of autism spectrum disorders (pp. 251–266). https://doi.org/10.1016/b978-0-12-391924-3.00017-x

Tandon, R., Gaebel, W., Barch, D. M., Bustillo, J., Gur, R. E., Heckers, S., Malaspina, D., Owen, M. J., Schultz, S., Tsuang, M., Van Os, J., & Carpenter, W. (2013). Definition and description of schizophrenia in the DSM-5. Schizophrenia Research, 150(1), 3–10. https://doi.org/10.1016/j.schres.2013.05.028

Thiebaut de Schotten, M., Tomaiuolo, F., Aiello, M., Merola, S., Silvetti, M., Lecce, F., Bartolomeo, P., & Doricchi, F. (2014). Damage to white matter pathways in subacute and chronic spatial neglect: A group study and 2 single-case studies with complete virtual “in vivo” tractography dissection. Cerebral Cortex, 24(3), 691–706. https://doi.org/10.1093/cercor/bhs351

Tost, H., Champagne, F. A., & Meyer-Lindenberg, A. (2015). Environmental influence in the brain, human welfare and mental health. Nature Neuroscience, 18(10), 1421–1431. https://doi.org/10.1038/nn.4108

Vestergaard, M., Skakmadsen, K., Baaré, W. F. C., Skimminge, A., Ejersbo, L. R., Ramsøy, T. Z., Gerlach, C., Åkeson, P., Paulson, O. B., & Jernigan, T. L. (2011). White matter microstructure in superior longitudinal fasciculus associated with spatial working memory performance in children. Journal of Cognitive Neuroscience, 23(9), 2135–2146. https://doi.org/10.1162/jocn.2010.21592

Wang, Y., & Olson, I. R. (2018). The original social network: White matter and social cognition. Trends in Cognitive Sciences, 22(6), 504. https://doi.org/10.1016/j.tics.2018.03.005

Wright, I. C., McGuire, P. K., Poline, J. B., Travere, J. M., Murray, R. M., Frith, C. D., Frackowiak, R. S. J., & Friston, K. J. (1995). A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. NeuroImage, 2(4), 244–252. https://doi.org/10.1006/nimg.1995.1032


Refbacks

  • There are currently no refbacks.




Copyright (c) 2025 Buletin Psikologi

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.