480
Views
0
CrossRef citations to date
0
Altmetric
Applications and Case Studies

Heterogeneity Analysis on Multi-State Brain Functional Connectivity and Adolescent Neurocognition

, , ORCID Icon & ORCID Icon
Pages 851-863 | Received 22 Apr 2022, Accepted 22 Jan 2024, Published online: 08 Mar 2024

References

  • Alexander, G. E., DeLong, M. R., and Strick, P. L. (1986), “Parallel Organization of Functionally Segregated Circuits Linking Basal Ganglia and Cortex,” Annual Review of Neuroscience, 9, 357–381. DOI: 10.1146/annurev.ne.09.030186.002041.
  • Aoki, S., Smith, J. B., Li, H., Yan, X., Igarashi, M., Coulon, P., Wickens, J. R., Ruigrok, T. J., and Jin, X. (2019), “An Open Cortico-Basal Ganglia Loop Allows Limbic Control Over Motor Output via the Nigrothalamic Pathway,” Elife, 8, e49995. DOI: 10.7554/eLife.49995.
  • Arain, M., Haque, M., Johal, L., Mathur, P., Nel, W., Rais, A., Sandhu, R., and Sharma, S. (2013), “Maturation of the Adolescent Brain,” Neuropsychiatric Disease and Treatment, 9, 449–461. DOI: 10.2147/NDT.S39776.
  • Berger, J. O., Wang, X., and Shen, L. (2014), “A Bayesian Approach to Subgroup Identification,” Journal of Biopharmaceutical Statistics, 24, 110–129. DOI: 10.1080/10543406.2013.856026.
  • Blakemore, S.-J. (2012), “Imaging Brain Development: The Adolescent Bain,” Neuroimage, 61, 397–406. DOI: 10.1016/j.neuroimage.2011.11.080.
  • Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010), “The Horseshoe Estimator for Sparse Signals,” Biometrika, 97, 465–480. DOI: 10.1093/biomet/asq017.
  • Casey, B., Cannonier, T., Conley, M. I., Cohen, A. O., Barch, D. M., Heitzeg, M. M., Soules, M. E., Teslovich, T., Dellarco, D. V., Garavan, H., et al. (2018), “The Adolescent Brain Cognitive Development (ABCD) Study: Imaging Acquisition across 21 Sites,” Developmental Cognitive Neuroscience, 32, 43–54. DOI: 10.1016/j.dcn.2018.03.001.
  • Cohen, J. R., and D’Esposito, M. (2016), “The Segregation and Integration of Distinct Brain Networks and their Relationship to Cognition,” Journal of Neuroscience, 36, 12083–12094. DOI: 10.1523/JNEUROSCI.2965-15.2016.
  • Cole, M. W., Ito, T., and Braver, T. S. (2015), “Lateral Prefrontal Cortex Contributes to Fluid Intelligence through Multinetwork Connectivity,” Brain Connectivity, 5, 497–504. DOI: 10.1089/brain.2015.0357.
  • Cole, M. W., Yarkoni, T., Repovš, G., Anticevic, A., and Braver, T. S. (2012), “Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence,” Journal of Neuroscience, 32, 8988–8999. DOI: 10.1523/JNEUROSCI.0536-12.2012.
  • Cosgrove, K. T., McDermott, T. J., White, E. J., Mosconi, M. W., Thompson, W. K., Paulus, M. P., Cardenas-Iniguez, C., and Aupperle, R. L. (2022), “Limits to the Generalizability of Resting-State Functional Magnetic Resonance Imaging Studies of Youth: An Examination of ABCD Study[textregistered] Baseline Data,” Brain Imaging and Behavior, 16, 1919–1925. DOI: 10.1007/s11682-022-00665-2.
  • Fox, M. D., and Raichle, M. E. (2007), “Spontaneous Fluctuations in Brain Activity Observed with Functional Magnetic Resonance Imaging,” Nature Reviews Neuroscience, 8, 700–711. DOI: 10.1038/nrn2201.
  • Friston, K., Frith, C., Liddle, P., and Frackowiak, R. (1993), “Functional Connectivity: The Principal-Component Analysis of Large (PET) Data Sets,” Journal of Cerebral Blood Flow & Metabolism, 13, 5–14. DOI: 10.1038/jcbfm.1993.4.
  • Fruhwirth-Schnatter, S., Celeux, G., and Robert, C. P. (2019), Handbook of Mixture Analysis, Boca Raton, FL: CRC Press.
  • Gao, S., Greene, A. S., Constable, R. T., and Scheinost, D. (2019), “Combining Multiple Connectomes Improves Predictive Modeling of Phenotypic Measures,” Neuroimage, 201, 116038. DOI: 10.1016/j.neuroimage.2019.116038.
  • Garavan, H., Bartsch, H., Conway, K., Decastro, A., Goldstein, R., Heeringa, S., Jernigan, T., Potter, A., Thompson, W., and Zahs, D. (2018), “Recruiting the ABCD Sample: Design Considerations and Procedures,” Developmental Cognitive Neuroscience, 32, 16–22. DOI: 10.1016/j.dcn.2018.04.004.
  • Ghahramani, Z., and Beal, M. J. (2001), “Propagation Algorithms for Variational Bayesian Learning,” in Advances in Neural Information Processing Systems, pp. 507–513.
  • Gonzalez-Castillo, J., and Bandettini, P. A. (2018), “Task-based Dynamic Functional Connectivity: Recent Findings and Open Questions,” Neuroimage, 180, 526–533. DOI: 10.1016/j.neuroimage.2017.08.006.
  • Greene, A. S., Gao, S., Scheinost, D., and Constable, R. T. (2018), “Task-Induced Brain State Manipulation Improves Prediction of Individual Traits,” Nature Communications, 9, 1–13. DOI: 10.1038/s41467-018-04920-3.
  • Hagler Jr, D. J., Hatton, S., Cornejo, M. D., Makowski, C., Fair, D. A., Dick, A. S., Sutherland, M. T., Casey, B., Barch, D. M., Harms, M. P., et al. (2019), “Image Processing and Analysis Methods for the Adolescent Brain Cognitive Development Study,” Neuroimage, 202, 116091. DOI: 10.1016/j.neuroimage.2019.116091.
  • Hearne, L. J., Mattingley, J. B., and Cocchi, L. (2016), “Functional Brain Networks Related to Individual Differences in Human Intelligence at Rest,” Scientific Reports, 6, 1–8. DOI: 10.1038/srep32328.
  • Heaton, R. K., Akshoomoff, N., Tulsky, D., Mungas, D., Weintraub, S., Dikmen, S., Beaumont, J., Casaletto, K. B., Conway, K., Slotkin, J., et al. (2014), “Reliability and Validity of Composite Scores from the NIH Toolbox Cognition Battery in Adults,” Journal of the International Neuropsychological Society, 20, 588–598. DOI: 10.1017/S1355617714000241.
  • Heeringa, S. G., and Berglund, P. A. (2020), “A Guide for Population-based Analysis of the Adolescent Brain Cognitive Development (ABCD) Study Baseline Data,” BioRxiv.
  • Horien, C., Shen, X., Scheinost, D., and Constable, R. T. (2019), “The Individual Functional Connectome is Unique and Stable over Months to Years,” Neuroimage, 189, 676–687. DOI: 10.1016/j.neuroimage.2019.02.002.
  • Hubert, L., and Arabie, P. (1985), “Comparing Partitions,” Journal of Classification, 2, 193–218. DOI: 10.1007/BF01908075.
  • Im, Y., Huang, Y., Tan, A., and Ma, S. (2021), “Bayesian Finite Mixture of Regression Analysis for Cancer based on Histopathological Imaging–Environment Interactions,” Biostatistics, 24, 425–442. DOI: 10.1093/biostatistics/kxab038.
  • Ishwaran, H., and Zarepour, M. (2000), “Markov chain Monte Carlo in Approximate Dirichlet and Beta Two-Parameter Process Hierarchical Models,” Biometrika, 87, 371–390. DOI: 10.1093/biomet/87.2.371.
  • Joshi, A., Scheinost, D., Okuda, H., Belhachemi, D., Murphy, I., Staib, L. H., and Papademetris, X. (2011), “Unified Framework for Development, Deployment and Robust Tsting of Neuroimaging Algorithms,” Neuroinformatics, 9, 69–84. DOI: 10.1007/s12021-010-9092-8.
  • Karcher, N. R., and Barch, D. M. (2021), “The ABCD Study: Understanding the Development of Risk for Mental and Physical Health Outcomes,” Neuropsychopharmacology, 46, 131–142. DOI: 10.1038/s41386-020-0736-6.
  • Leisman, G., and Melillo, R. (2013), “The Basal Ganglia: Motor and Cognitive Relationships in a Clinical Neurobehavioral Context,” Reviews in the Neurosciences, 24, 9–25. DOI: 10.1515/revneuro-2012-0067.
  • Li, X., Xu, D., Zhou, H., and Li, L. (2018), “Tucker Tensor Regression and Neuroimaging Analysis,” Statistics in Biosciences, 10, 520–545. DOI: 10.1007/s12561-018-9215-6.
  • Marek, S., and Dosenbach, N. U. (2018), “The Frontoparietal Network: Function, Electrophysiology, and Importance of Individual Precision Mapping,” Dialogues in Clinical Neuroscience, 20, 133–140. DOI: 10.31887/DCNS.2018.20.2/smarek.
  • Mennes, M., Kelly, C., Colcombe, S., Castellanos, F. X., and Milham, M. P. (2013), “The Extrinsic and Intrinsic Functional Architectures of the Human Brain Are Not Equivalent,” Cerebral Cortex, 23, 223–229. DOI: 10.1093/cercor/bhs010.
  • Morita, T., Asada, M., and Naito, E. (2016), “Contribution of Neuroimaging Studies to Understanding Development of Human Cognitive Brain Functions,” Frontiers in Human Neuroscience, 10, 464. DOI: 10.3389/fnhum.2016.00464.
  • Neal, R. M., and Hinton, G. E. (1998), “A View of the EM Algorithm that Justifies Incremental, Sparse, and other Variants,” in Learning in Graphical Models, eds. M. I. Jordan, pp. 355–368, Dordrecht: Springer.
  • Nebel, M. B., Lidstone, D. E., Wang, L., Benkeser, D., Mostofsky, S. H., and Risk, B. B. (2022), “Accounting for Motion in Resting-State fMRI: What Part of the Spectrum Are We Characterizing in Autism Spectrum Disorder?” NeuroImage, 257, 119296. DOI: 10.1016/j.neuroimage.2022.119296.
  • Niendam, T. A., Laird, A. R., Ray, K. L., Dean, Y. M., Glahn, D. C., and Carter, C. S. (2012), “Meta-Analytic Evidence for a Superordinate Cognitive Control Network Subserving Diverse Executive Functions,” Cognitive, Affective, & Behavioral Neuroscience, 12, 241–268. DOI: 10.3758/s13415-011-0083-5.
  • Rapuano, K. M., Rosenberg, M. D., Maza, M. T., Dennis, N. J., Dorji, M., Greene, A. S., Horien, C., Scheinost, D., Constable, R. T., and Casey, B. (2020), “Behavioral and Brain Signatures of Substance Use Vulnerability in Childhood,” Developmental Cognitive Neuroscience, 46, 100878. DOI: 10.1016/j.dcn.2020.100878.
  • Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H., Gur, R. C., Gur, R. E., et al. (2013), “An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data,” Neuroimage, 64, 240–256. DOI: 10.1016/j.neuroimage.2012.08.052.
  • Sethuraman, J. (1994), “A Constructive Definition of Dirichlet Priors,” Statistica sinica, 4, 639–650.
  • Shen, J., and He, X. (2015), “Inference for Subgroup Analysis with a Structured Logistic-Normal Mixture Model,” Journal of the American Statistical Association, 110, 303–312. DOI: 10.1080/01621459.2014.894763.
  • Shen, X., Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., and Constable, R. T. (2017), “Using Connectome-based Predictive Modeling to Predict Individual Behavior from Brain Connectivity,” Nature Protocols, 12, 506–518. DOI: 10.1038/nprot.2016.178.
  • Shen, X., Tokoglu, F., Papademetris, X., and Constable, R. T. (2013), “Groupwise Whole-Brain Parcellation from Resting-State fMRI Data for Network Node Identification,” Neuroimage, 82, 403–415. DOI: 10.1016/j.neuroimage.2013.05.081.
  • Somerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018), “The Lifespan Human Connectome Project in Development: A Large-Scale Study of Brain Connectivity Development in 5–21 Year Olds,” Neuroimage, 183, 456–468. DOI: 10.1016/j.neuroimage.2018.08.050.
  • Stark, G. F., Avery, E. W., Rosenberg, M. D., Greene, A. S., Gao, S., Scheinost, D., Todd Constable, R., Chun, M. M., and Yoo, K. (2021), “Using Functional Connectivity Models to Characterize Relationships between Working and Episodic Memory,” Brain and Behavior, 11, e02105. DOI: 10.1002/brb3.2105.
  • Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
  • Tzikas, D. G., Likas, A. C., and Galatsanos, N. P. (2008), “The Variational Approximation for Bayesian Inference,” IEEE Signal Processing Magazine, 25, 131–146. DOI: 10.1109/MSP.2008.929620.
  • Van Den Heuvel, M. P., Stam, C. J., Kahn, R. S., and Pol, H. E. H. (2009), “Efficiency of Functional Brain Networks and Intellectual Performance,” Journal of Neuroscience, 29, 7619–7624. DOI: 10.1523/JNEUROSCI.1443-09.2009.
  • Vriend, C., Wagenmakers, M. J., Van den Heuvel, O. A., and Van der Werf, Y. D. (2020), “Resting-State Network Topology and Planning Ability in Healthy Adults,” Brain Structure and Function, 225, 365–374. DOI: 10.1007/s00429-019-02004-6.
  • Wig, G. S. (2017), “Segregated Systems of Human Brain Networks,” Trends in Cognitive Sciences, 21, 981–996. DOI: 10.1016/j.tics.2017.09.006.
  • Xu, D. (2020), “Sparse Symmetric Tensor Regression for Functional Connectivity Analysis,” arXiv preprint arXiv:2010.14700.
  • Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011), “The Organization of the Human Cerebral Cortex Estimated by Intrinsic Functional Connectivity,” Journal of Neurophysiology, 106, 1125–1165. DOI: 10.1152/jn.00338.2011.
  • You, C., Ormerod, J. T., and Mueller, S. (2014), “On Variational Bayes Estimation and Variational Information Criteria for Linear Regression Models,” Australian & New Zealand Journal of Statistics, 56, 73–87. DOI: 10.1111/anzs.12063.
  • Zhang, H., and Singer, B. H. (2010), Recursive Partitioning and Applications, New York: Springer.
  • Zhou, H., Li, L., and Zhu, H. (2013), “Tensor Regression with Aplications in Neuroimaging Data Analysis,” Journal of the American Statistical Association, 108, 540–552. DOI: 10.1080/01621459.2013.776499.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.