Long-term stability of the cortical volumetric profile and the functional human connectome throughout childhood and adolescence.
Connectome
children
fingerprint
neurodevelopment
resting-state
Journal
The European journal of neuroscience
ISSN: 1460-9568
Titre abrégé: Eur J Neurosci
Pays: France
ID NLM: 8918110
Informations de publication
Date de publication:
09 2021
09 2021
Historique:
revised:
18
08
2021
received:
03
03
2021
accepted:
28
08
2021
pubmed:
31
8
2021
medline:
25
9
2021
entrez:
30
8
2021
Statut:
ppublish
Résumé
There is compelling evidence showing that between-subject variability in several functional and structural brain features is sufficient for unique identification in adults. However, individuation of brain functional connectomes depends on the stabilization of neurodevelopmental processes during childhood and adolescence. Here, we aimed to (1) evaluate the intra-subject functional connectome stability over time for the whole brain and for large scale functional networks and (2) determine the long-term identification accuracy or 'fingerprinting' for the cortical volumetric profile and the functional connectome. For these purposes, we analysed a longitudinal cohort of 239 children and adolescents scanned in two sessions with an interval of approximately 3 years (age range 6-15 years at baseline and 9-18 years at follow-up). Corroborating previous results using short between-scan intervals in children and adolescents, we observed a moderate identification accuracy (38%) for the whole functional profile. In contrast, identification accuracy using cortical volumetric profile was 95%. Among the large-scale networks, the default-mode (26.8%), the frontoparietal (23.4%) and the dorsal-attention (27.6%) networks were the most discriminative. Our results provide further evidence for a protracted development of specific individual structural and functional connectivity profiles.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6187-6201Informations de copyright
© 2021 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.
Références
Biazoli, C. E., Salum, G. A., Pan, P. M., Zugman, A., Amaro, E., Rohde, L. A., Miguel, E. C., Jackowski, A. P., Bressan, R. A., & Sato, J. R. (2017). Commentary: Functional connectome fingerprint: Identifying individuals using patterns of brain connectivity. Frontiers in Human Neuroscience, 11, 47.
Chen, B., Xu, T., Zhou, C., Wang, L., Yang, N., Wang, Z., Dong, H. M., Yang, Z., Zang, Y. F., Zuo, X. N., & Weng, X. C. (2015). Individual variability and test-retest reliability revealed by ten repeated resting-state brain scans over one month. PLoS ONE, 10(12), e0144963. https://doi.org/10.1371/journal.pone.0144963
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., Albert, M. S., & Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31(3), 968-980. https://doi.org/10.1016/j.neuroimage.2006.01.021
Dosenbach, N. U., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., Barnes, K. A., Dubis, J. W., Feczko, E., Coalson, R. S., Pruett, J. R. Jr., Barch, D. M., Petersen, S. E., & Schlaggar, B. L. (2010). Prediction of individual brain maturity using fMRI. Science, 329(5997), 1358-1361. https://doi.org/10.1126/science.1194144
Dubois, J., & Adolphs, R. (2016). Building a science of individual differences from fMRI. Trends in Cognitive Sciences, 20(6), 425-443. https://doi.org/10.1016/j.tics.2016.03.014
Fair, D. A., Cohen, A. L., Dosenbach, N. U., Church, J. A., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2008). The maturing architecture of the brain's default network. Proceedings of the National Academy of Sciences of the United States of America, 105(10), 4028-4032. https://doi.org/10.1073/pnas.0800376105
Fair, D. A., Dosenbach, N. U., Church, J. A., Cohen, A. L., Brahmbhatt, S., Miezin, F. M., Barch, D. M., Raichle, M. E., Petersen, S. E., & Schlaggar, B. L. (2007). Development of distinct control networks through segregation and integration. Proceedings of the National Academy of Sciences of the United States of America, 104(33), 13507-13512. https://doi.org/10.1073/pnas.0705843104
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11), 1664-1671. https://doi.org/10.1038/nn.4135
Fischl, B. (2012). Freesurfer. NeuroImage, 62(2), 774-781. https://doi.org/10.1016/j.neuroimage.2012.01.021
Fuhrmann, D., Knoll, L. J., & Blakemore, S. J. (2015). Adolescence as a sensitive period of brain development. Trends in Cognitive Sciences, 19(10), 558-566. https://doi.org/10.1016/j.tics.2015.07.008
Giedd, J. N., Raznahan, A., Alexander-Bloch, A., Schmitt, E., Gogtay, N., & Rapoport, J. L. (2015). Child psychiatry branch of the National Institute of Mental Health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology, 40(1), 43-49.
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., & Smith, S. M. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171-178. https://doi.org/10.1038/nature18933
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and evaluation of a cortical area parcellation from resting-state correlations. Cerebral Cortex, 26(1), 288-303. https://doi.org/10.1093/cercor/bhu239
Grayson, D. S., & Fair, D. A. (2017). Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature. NeuroImage, 160, 15-31. https://doi.org/10.1016/j.neuroimage.2017.01.079
Gu, S., Satterthwaite, T. D., Medaglia, J. D., Yang, M., Gur, R. E., Gur, R. C., & Bassett, D. S. (2015). Emergence of system roles in normative neurodevelopment. Proceedings of the National Academy of Sciences of the United States of America, 112(44), 13681-13686. https://doi.org/10.1073/pnas.1502829112
Hallquist, M. N., Hwang, K., & Luna, B. (2013). The nuisance of nuisance regression: Spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity. NeuroImage, 82, 208-225. https://doi.org/10.1016/j.neuroimage.2013.05.116
Horien, C., Shen, X., Scheinost, D., & Constable, R. T. (2019). The individual functional connectome is unique and stable over months to years. NeuroImage, 189, 676-687. https://doi.org/10.1016/j.neuroimage.2019.02.002
Huang, L., Huang, T., Zhen, Z., & Liu, J. (2016). A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity. Science Data, 3, 160016. https://doi.org/10.1038/sdata.2016.16
Jalbrzikowski, M., Liu, F., Foran, W., Klei, L., Calabro, F. J., Roeder, K., Devlin, B., & Luna, B. (2020). Functional connectome fingerprinting accuracy in youths and adults is similar when examined on the same day and 1.5-years apart. Human Brain Mapping, 41(15), 4187-4199. https://doi.org/10.1002/hbm.25118
Kaufmann, T., Alnaes, D., Doan, N. T., Brandt, C. L., Andreassen, O. A., & Westlye, L. T. (2017). Delayed stabilization and individualization in connectome development are related to psychiatric disorders. Nature Neuroscience, 20(4), 513-515. https://doi.org/10.1038/nn.4511
Marin, O. (2016). Developmental timing and critical windows for the treatment of psychiatric disorders. Nature Medicine, 22(11), 1229-1238. https://doi.org/10.1038/nm.4225
Miranda-Dominguez, O., Feczko, E., Grayson, D. S., Walum, H., Nigg, J. T., & Fair, D. A. (2018). Heritability of the human connectome: A connectotyping study. Network Neuroscience, 2(2), 175-199. https://doi.org/10.1162/netn_a_00029 eCollection 2018
Miranda-Dominguez, O., Mills, B. D., Carpenter, S. D., Grant, K. A., Kroenke, C. D., Nigg, J. T., & Fair, D. A. (2014). Connectotyping: Model based fingerprinting of the functional connectome. PLoS ONE, 9(11), e111048. https://doi.org/10.1371/journal.pone.0111048
Mueller, S., Wang, D., Fox, M. D., Yeo, B. T., Sepulcre, J., Sabuncu, M. R., Shafee, R., Lu, J., & Liu, H. (2013). Individual variability in functional connectivity architecture of the human brain. Neuron, 77(3), 586-595. https://doi.org/10.1016/j.neuron.2012.12.028
Pan, P., Sato, J. R., Salum, G. A., Rohde, L. A., Gadelha, A., Zugman, A., Mari, J., Jackowski, A., Picon, F., Miguel, E. C., Pine, D. S., Leibenluft, E., Bressan, R. A., & Stringaris, A. (2017). Ventral striatum functional connectivity as a predictor of adolescent depressive disorder in a longitudinal community-based. The American Journal of Psychiatry, 174(11), 1112-1119. https://doi.org/10.1176/appi.ajp.2017.17040430
Paus, T., Keshavan, M., & Giedd, J. N. (2008). Why do many psychiatric disorders emerge during adolescence? Nature Reviews. Neuroscience, 9(12), 947-957. https://doi.org/10.1038/nrn2513
Poldrack, R. A., Barch, D. M., Mitchell, J. P., Wager, T. D., Wagner, A. D., Devlin, J. T., Cumba, C., Koyejo, O., & Milham, M. P. (2013). Toward open sharing of task-based fMRI data: The OpenfMRI project. Frontiers in Neuroinformatics, 7, 12.
Poldrack, R. A., Laumann, T. O., Koyejo, O., Gregory, B., Hover, A., Chen, M. Y., Gorgolewski, K. J., Luci, J., Joo, S. J., Boyd, R. L., Hunicke-Smith, S., Simpson, Z. B., Caven, T., Sochat, V., Shine, J. M., Gordon, E., Snyder, A. Z., Adeyemo, B., Petersen, S. E., … Mumford, J. A. (2015). Long-term neural and physiological phenotyping of a single human. Nature Communications, 6, 8885. https://doi.org/10.1038/ncomms9885
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage, 59(3), 2142-2154. https://doi.org/10.1016/j.neuroimage.2011.10.018
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018). Prediction complements explanation in understanding the developing brain. Nature Communications, 9(1), 589. https://doi.org/10.1038/s41467-018-02887-9
Salum, G. A., Gadelha, A., Pan, P. M., Moriyama, T. S., Graeff-Martins, A. S., Tamanaha, A. C., Alvarenga, P., Krieger, F. V., Fleitlich-Bilyk, B., Jackowski, A., Sato, J. R., Brietzke, E., Polanczyk, G. V., Brentani, H., Mari, J. D., Do Rosario, M. C., Manfro, G. G., Bressan, R. A., Mercadante, M. T., … Rohde, L. A. (2015). High risk cohort study for psychiatric disorders in childhood: Rationale, design, methods and preliminary results. International Journal of Methods in Psychiatric Research, 24(1), 58-73. https://doi.org/10.1002/mpr.1459
Sato, J. R., Salum, G. A., Gadelha, A., Picon, F. A., Pan, P. M., Vieira, G., Zugman, A., Hoexter, M. Q., Anes, M., Moura, L. M., Del'Aquilla, M. A. G., Amaro, E., McGuire, P., Crossley, N., Lacerda, A., Rohde, L. A., Miguel, E. C., Bressan, R. A., & Jackowski, A. P. (2014). Age effects on the default mode and control networks in typically developing children. Journal of Psychiatric Research, 58, 89-95. https://doi.org/10.1016/j.jpsychires.2014.07.004
Sato, J. R., Salum, G. A., Gadelha, A., Vieira, G., Zugman, A., Picon, F. A., Pan, P. M., Hoexter, M. Q., Anés, M., Moura, L. M., Del'Aquilla, M. A., Crossley, N., Amaro Junior, E., Mcguire, P., Lacerda, A. L., Rohde, L. A., Miguel, E. C., Jackowski, A. P., & Bressan, R. A. (2015). Decreased centrality of subcortical regions during the transition to adolescence: A functional connectivity study. NeuroImage, 104, 44-51. https://doi.org/10.1016/j.neuroimage.2014.09.063
Sato, J. R., White, T. P., & Biazoli, C. E. Jr. (2017). Commentary: A test-retest dataset for assessing long-term reliability of brain morphology and resting-state brain activity. Frontiers in Neuroscience, 11, 85.
Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., Roalf, D. R., Ryan Hopsona, K. P., Behr, M., Qiu, H., Mentch, F. D., Chiavacci, R., Sleiman, P. M., Gur, R. C., Hakonarson, H., & Gur, R. E. (2016). The Philadelphia Neurodevelopmental Cohort: A publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage, 124, 1115-1119.
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012). Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth. NeuroImage, 60(1), 623-632. https://doi.org/10.1016/j.neuroimage.2011.12.063
Somerville, L. H. (2016). Searching for signatures of brain maturity: What are we searching for? Neuron, 92(6), 1164-1167. https://doi.org/10.1016/j.neuron.2016.10.059
van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., Ugurbil, K., & Consortium, W. U.-M. H. (2013). The Wu-Minn Human Connectome Project: An overview. NeuroImage, 80, 62-79. https://doi.org/10.1016/j.neuroimage.2013.05.041
Vanderwal, T., Eilbott, J., Kelly, C., Frew, S. R., Woodward, T. S., Milham, M. P., & Castellanos, F. X. (2021). Stability and similarity of the pediatric connectome as developmental measures. NeuroImage, 226, 117537. https://doi.org/10.1016/j.neuroimage.2020.117537
Wachinger, C., Golland, P., Kremen, W., Fischl, B., Reuter, M., & Alzheimer's Disease Neuroimaging Initiative. (2015). BrainPrint: A discriminative characterization of brain morphology. NeuroImage, 09, 232-248.
Wachinger, C., Golland, P., & Reuter, M. (2014). BrainPrint: Identifying subjects by their brain. Medical Image Computing Computer Assisted Intervention, 17(3), 41-48.
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn: A functional connectivity toolbox for correlated and anticorrelated brain networks. Brain Connectivity, 2(3), 125-141. https://doi.org/10.1089/brain.2012.0073
Zuo, X. N., & Xing, X. X. (2014). Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective. Neuroscience and Biobehavioral Reviews, 45, 100-118. https://doi.org/10.1016/j.neubiorev.2014.05.009