Resting-state functional magnetic resonance imaging: features of statistical processing of ROI-analysis data
Shamil’ K. Abdulaev , Dmitriy A. Tarumov , Kirill V. Markin , Aleksandra А. Ustyuzhina
Russian Military Medical Academy Reports ›› 2024, Vol. 43 ›› Issue (1) : 5 -12.
Resting-state functional magnetic resonance imaging: features of statistical processing of ROI-analysis data
BACKGROUND: In many works, to study intra- and inter-network connections, a method for constructing networks is used — ROI-analysis (region of interest analysis). The conflicting results obtained when assessing brain connectivity using ROI-analysis can be explained by methodological differences associated with the statistical processing of fMRI data. In this regard, it is relevant to conduct a study with a comparative assessment of various statistical methods of ROI-analysis in processing resting state fMRI data.
AIM: to assess the functional connectivity of the main resting state networks of the brain using ROI-analysis using various statistical approaches.
MATERIALS AND METHODS: We analyzed data from 15 resting-state fMRI studies of the brain of patients without neurological and mental pathology. fMRI scanning was performed on a Phillips Ingenia 1.5 T scanner using a gradient echo-planar imaging (EPI-BOLD) sequence. ROI-analysis was used to build networks. Statistical data processing was performed using methods: functional network connectivity, randomization/permutation spatial pairwise clustering statistics, and threshold-free cluster enhancement.
RESULTS: The number of connections between the structures of brain networks recorded using the method of functional network connectivity is 280, spatial pairwise clustering — 186, threshold-free cluster enhancement — 182. An interesting fact is that negative connections were identified only when using parametric statistics.
CONCLUSION: A comparative assessment of methods for statistical processing of fMRI data during ROI-analysis was carried out. The functional network connectivity method based on multivariate parametric statistics turned out to be more informative than randomization/permutation spatial pairwise clustering statistics and the method based on threshold-free cluster enhancement. Despite the growing popularity in recent years of resting-state fMRI in the study of functional activity and connectivity of the brain, there are no standardized algorithms for constructing networks of the brain.
resting-state networks / resting-state fMRI / ROI-analysis / statistics
| [1] |
Kremneva EI, Sinitsyn DO, Dobrynina LA, et al. Resting state functional MRI in neurology and psychiatry. S.S. Korsakov Journal of Neurology and Psychiatry. 2022;122(2):5–14. (In Russ.) EDN: FWPFIM doi: 10.17116/jnevro20221220215 |
| [2] |
Кремнева Е.И., Синицын Д.О., Добрынина Л.А., и др. Функциональная МРТ покоя в неврологии и психиатрии // Журнал неврологии и психиатрии им. С.С. Корсакова. 2022. Т. 122, № 2. С. 5–14. EDN: FWPFIM doi: 10.17116/jnevro20221220215 |
| [3] |
Abdulaev ShK, Tarumov DA, Shamrey VK, et al. Functional impairments in large-scale brain projects in opioid addiction. S.S. Korsakov Journal of Neurology and Psychiatry. 2023;123(5):165–170. (In Russ.) EDN: SWMZBG doi: 10.17116/jnevro2023123051165 |
| [4] |
Абдулаев Ш.К., Тарумов Д.А., Шамрей В.К., и др. Функциональные нарушения в крупномасштабных сетях покоя головного мозга при опиоидной наркомании // Журнал неврологии и психиатрии им. С.С. Корсакова. 2023. Т. 123, № 5. С. 165–170. EDN: SWMZBG doi: 10.17116/jnevro2023123051165 |
| [5] |
Ublinskiy MV, Semenova NA, Manzhurtsev AV, et al. Dysfunction of cerebellum functional connectivity between default mode network and cerebellar structures in patients with mild traumatic brain injury in acute stage. rsfMRI study. Medical Visualization. 2020;24(2):131–137. (In Russ.) EDN: OEKCXT doi: 10.24835/1607-0763-2020-2-131-137 |
| [6] |
Ублинский М.В., Семенова Н.А., Манжурцев А.В., и др. Исследование нарушений функциональных связей между сетью пассивного режима работы мозга и структурами мозжечка у пациентов с легкой черепно-мозговой травмой в острой стадии по данным фМРТ состояния покоя // Медицинская визуализация. 2020. Т. 24, № 2. С. 131–137. EDN: OEKCXT doi: 10.24835/1607-0763-2020-2-131-137 |
| [7] |
Friston K, Brown HR, Siemerkus J, Stephan KE. The dysconnection hypothesis. Schizophr Res. 2016;176(2–3):83–94. doi: 10.1016/j.schres.2016.07.014 |
| [8] |
Friston K., Brown H.R., Siemerkus J., Stephan K.E. The dysconnection hypothesis // Schizophr. Res. 2016. Vol. 176, N. 2–3. P. 83–94. doi: 10.1016/j.schres.2016.07.014 |
| [9] |
Menon V. Large-Scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10): 483–506. doi: 10.1016/j.tics.2011.08.003 |
| [10] |
Menon V. Large-scale brain networks and psychopathology: a unifying triple network model // Trends Cogn. Sci. 2011. Vol. 15, N. 10. P. 483–506. doi: 10.1016/j.tics.2011.08.003 |
| [11] |
Littow H, Huossa V, Karjalainen S, et al. Aberrant functional connectivity in the default mode and central executive networks in subjects with schizophrenia — a whole-brain resting-state ICA study. Front Psychiatry. 2015;6:26. doi: 10.3389/fpsyt.2015.00026 |
| [12] |
Littow H., Huossa V., Karjalainen S., et al. Aberrant functional connectivity in the default mode and central executive networks in subjects with schizophrenia — a whole-brain resting-state ICA study // Front. Psychiatry. 2015. Vol. 6. P. 26. doi: 10.3389/fpsyt.2015.00026 |
| [13] |
Bastos-Leite AJ, Ridgway GR, Silveira C, et al. Dysconnectivity within the default mode in first-episode schizophrenia: a stochastic dynamic causal modeling study with functional magnetic resonance imaging. Schizophr Bull. 2015;41(1):144–153. doi: 10.1093/schbul/sbu080 |
| [14] |
Bastos-Leite A.J., Ridgway G.R., Silveira C., et al. Dysconnectivity within the default mode in first-episode schizophrenia: a stochastic dynamic causal modeling study with functional magnetic resonance imaging // Schizophr. Bull. 2015. Vol. 41, N. 1. P. 144–153. doi: 10.1093/schbul/sbu080 |
| [15] |
Rong B, Huang H, Gao G, et al. Widespread intra- and inter-network dysconnectivity among large-scale resting state networks in schizophrenia. J Clin Med. 2023;12(9):3176. doi: 10.3390/jcm12093176 |
| [16] |
Rong B., Huang H., Gao G., et al. Widespread intra- and inter-network dysconnectivity among large-scale resting state networks in schizophrenia // J. Clin. Med. 2023. Vol. 12, N. 9. P. 3176. doi: 10.3390/jcm12093176 |
| [17] |
Kornelsen J, Wilson A, Labus JS, et al. Brain resting-state network alterations associated with crohn’s disease. Front Neurol. 2020;11:48. doi: 10.3389/fneur.2020.00048 |
| [18] |
Kornelsen J., Wilson A., Labus J.S., et al. Brain resting-state network alterations associated with crohn’s disease // Front. Neurol. 2020. Vol. 11. P. 48. doi: 10.3389/fneur.2020.00048 |
| [19] |
Bukkieva ТА, Chegina DS, Еfimtsev АYu, et al. Resting state functional MRI. General issues and clinical application. Russian Electronic Journal of Radiology. 2019;9(2):150–170. (In Russ.) EDN: IKLSOY doi: 10.21569/2222-7415-2019-9-2-150-170 |
| [20] |
Буккиева Т.А., Чегина Д.С., Ефимцев А.Ю., и др. Функциональная МРТ покоя. Общие вопросы и клиническое применение // Российский электронный журнал лучевой диагностики. 2019. Т. 9, № 2. С. 150–170. EDN: IKLSOY doi: 10.21569/2222-7415-2019-9-2-150-170 |
| [21] |
Nieto-Castanon A. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Boston, MA: Hilbert Press; 2020. doi: 10.56441/hilbertpress.2207.6598 |
| [22] |
Nieto-Castanon A. Handbook of functional connectivity Magnetic Resonance Imaging methods in CONN. Boston, MA: Hilbert Press, 2020. ISBN: 978–0–578–64400–4 doi: 10.56441/hilbertpress.2207.6598 |
| [23] |
Whitfield-Gabrieli S, Nieto-Castanon A. Conn: A Functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connect. 2012;2(3):125–141. doi: 10.1089/brain.2012.0073 |
| [24] |
Whitfield-Gabrieli S., Nieto-Castanon A. Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks // Brain Connect. 2012. Vol. 2, N. 3. P. 125–141. doi: 10.1089/brain.2012.0073 |
| [25] |
Behzadi Y, Restom K, Liau J, Liu TT. A Component based noise correction method (CompCor) for BOLD and perfusion based FMRI. Neuroimage. 2007;37(1):90–101. doi: 10.1016/j.neuroimage.2007.04.042 |
| [26] |
Behzadi Y., Restom K., Liau J., Liu T.T. A component based noise correction method (CompCor) for BOLD and perfusion based FMRI // Neuroimage. 2007. Vol. 37, N. 1. P. 90–101. doi: 10.1016/j.neuroimage.2007.04.042 |
| [27] |
Jafri MJ, Pearlson GD, Stevens M, Calhoun VD. A method for functional network connectivity among spatially independent resting state components in schizophrenia. Neuroimage. 2008;39(4): 1666–1681. doi: 10.1016/j.neuroimage.2007.11.001 |
| [28] |
Jafri M.J., Pearlson G.D., Stevens M., Calhoun V.D. A method for functional network connectivity among spatially independent resting state components in schizophrenia // Neuroimage, 2008. Vol. 39, N. 4. P. 1666–1681. doi: 10.1016/j.neuroimage.2007.11.001 |
| [29] |
Zalesky A, Fornito A, Bullmore ET. Network-based statistic: identifying differences in brain networks. Neuroimage. 2010;53(4): 1197–1207. doi: 10.1016/j.neuroimage.2010.06.041 |
| [30] |
Zalesky A., Fornito A., Bullmore E.T. Network-based statistic: identifying differences in brain networks // Neuroimage, 2010. Vol. 53, N. 4. P. 1197–1207. doi: 10.1016/j.neuroimage.2010.06.041 |
| [31] |
Bar-Joseph Z, Gifford DK, Jaakkola TS. Fast optimal leaf ordering for hierarchical clustering. Bioinformatics. 2001;17(suppl 1):S22–S29. EDN: ILDQBF doi: 10.1093/bioinformatics/17.suppl_1.s22 |
| [32] |
Bar-Joseph Z., Gifford D.K., Jaakkola T.S. Fast optimal leaf ordering for hierarchical clustering // Bioinformatics. 2001. Vol. 17, suppl. 1. S22–S29. EDN: ILDQBF doi: 10.1093/bioinformatics/17.suppl_1.s22 |
| [33] |
Smith SM, Nichols TE. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage, 2009;44(1):83–98. doi: 10.1016/j.neuroimage.2008.03.061 |
| [34] |
Smith S.M., Nichols T.E. Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference // Neuroimage, 2009. Vol. 44, N. 1. P. 83–98. doi: 10.1016/j.neuroimage.2008.03.061 |
Eco-Vector
/
| 〈 |
|
〉 |