{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T22:14:34Z","timestamp":1775081674461,"version":"3.50.1"},"reference-count":59,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,10,12]],"date-time":"2019-10-12T00:00:00Z","timestamp":1570838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Brain complexity estimated using sample entropy and multiscale entropy (MSE) has recently gained much attention to compare brain function between diseased or neurologically impaired groups and healthy control groups. Using resting-state functional magnetic resonance imaging (rfMRI) blood oxygen-level dependent (BOLD) signals in a large cohort (n = 967) of healthy young adults, the present study maps neuronal and functional complexities estimated by using MSE of BOLD signals and BOLD phase coherence connectivity, respectively, at various levels of the brain\u2019s organization. The functional complexity explores patterns in a higher dimension than neuronal complexity and may better discern changes in brain functioning. The leave-one-subject-out cross-validation method is used to predict fluid intelligence using neuronal and functional complexity MSE values as features. While a wide range of scales was selected with neuronal complexity, only the first three scales were selected with functional complexity. Fewer scales are advantageous as they preclude the need for long BOLD signals to calculate good estimates of MSE. The presented results corroborate with previous findings and provide a baseline for other studies exploring the use of MSE to examine changes in brain function related to aging, diseases, and clinical disorders.<\/jats:p>","DOI":"10.3390\/e21100995","type":"journal-article","created":{"date-parts":[[2019,10,14]],"date-time":"2019-10-14T03:54:13Z","timestamp":1571025253000},"page":"995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7628-1847","authenticated-orcid":false,"given":"Sreevalsan S.","family":"Menon","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0219-6153","authenticated-orcid":false,"given":"K.","family":"Krishnamurthy","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"666","DOI":"10.1016\/j.tics.2013.09.016","article-title":"Functional connectomics from resting-state fMRI","volume":"17","author":"Smith","year":"2013","journal-title":"Trends Cogn. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1093\/cercor\/bhr099","article-title":"Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns","volume":"22","author":"Shirer","year":"2011","journal-title":"Cereb. Cortex"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1866","DOI":"10.3174\/ajnr.A3263","article-title":"Resting-State fMRI: A Review of Methods and Clinical Applications","volume":"34","author":"Lee","year":"2013","journal-title":"Am. J. Neuroradiol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.neuroimage.2016.12.061","article-title":"The dynamic functional connectome: State-of-the-art and perspectives","volume":"160","author":"Preti","year":"2017","journal-title":"NeuroImage"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Filippi, M., Spinelli, E.G., Cividini, C., and Agosta, F. (2019). Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings. Front. Neurosci., 13.","DOI":"10.3389\/fnins.2019.00657"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1093\/cercor\/bhs352","article-title":"Tracking whole-brain connectivity dynamics in the resting state","volume":"24","author":"Allen","year":"2014","journal-title":"Cereb. Cortex"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"538","DOI":"10.1016\/j.neuroimage.2017.03.023","article-title":"Single or multiple frequency generators in on-going brain activity: A mechanistic whole-brain model of empirical MEG data","volume":"152","author":"Deco","year":"2017","journal-title":"NeuroImage"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1089\/brain.2011.0068","article-title":"Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity","volume":"2","author":"Glerean","year":"2012","journal-title":"Brain Connect."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neuroimage.2017.03.045","article-title":"Functional connectivity dynamically evolves on multiple time-scales over a static structural connectome: Models and mechanisms","volume":"160","author":"Cabral","year":"2017","journal-title":"NeuroImage"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1002\/hbm.23890","article-title":"Chronnectome fingerprinting: Identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns","volume":"39","author":"Liu","year":"2018","journal-title":"Hum. Brain Mapp."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5729","DOI":"10.1038\/s41598-019-42090-4","article-title":"A Comparison of Static and Dynamic Functional Connectivities for Identifying Subjects and Biological Sex Using Intrinsic Individual Brain Connectivity","volume":"9","author":"Menon","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"525","DOI":"10.3389\/fnins.2018.00525","article-title":"Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging","volume":"12","author":"Du","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100630","DOI":"10.1016\/j.dcn.2019.100630","article-title":"BOLD signal variability and complexity in children and adolescents with and without autism spectrum disorder","volume":"36","author":"Easson","year":"2019","journal-title":"Dev. Cogn. Neurosci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1126\/science.2916117","article-title":"Is it healthy to be chaotic?","volume":"243","author":"Pool","year":"1989","journal-title":"Science"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0197-4580(01)00247-0","article-title":"Changing complexity in human behavior and physiology through aging and disease","volume":"23","author":"Vaillancourt","year":"2002","journal-title":"Neurobiol. Aging"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"69","DOI":"10.3389\/fninf.2014.00069","article-title":"Sample entropy reveals high discriminative power between young and elderly adults in short fMRI data sets","volume":"8","author":"Sokunbi","year":"2014","journal-title":"Front. Neuroinform."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2297","DOI":"10.1073\/pnas.88.6.2297","article-title":"Approximate entropy as a measure of system complexity","volume":"88","author":"Pincus","year":"1991","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"H2039","DOI":"10.1152\/ajpheart.2000.278.6.H2039","article-title":"Physiological time-series analysis using approximate entropy and sample entropy","volume":"278","author":"Richman","year":"2000","journal-title":"Am. J. Physiol.-Heart Circ. Physiol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, Z., Li, Y., Childress, A.R., and Detre, J.A. (2014). Brain Entropy Mapping Using fMRI. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0089948"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"R789","DOI":"10.1152\/ajpregu.00069.2002","article-title":"Sample entropy analysis of neonatal heart rate variability","volume":"283","author":"Lake","year":"2002","journal-title":"Am. J. Physiol.-Regul. Integr. Comp. Physiol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yang, A.C., Tsai, S.J., Lin, C.P., and Peng, C.K. (2018). A strategy to reduce bias of entropy estimates in resting-state fMRI signals. Front. Neurosci.","DOI":"10.3389\/fnins.2018.00398"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"068102","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale Entropy Analysis of Complex Physiologic Time Series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1016\/j.neurobiolaging.2012.05.004","article-title":"Complexity of spontaneous BOLD activity in default mode network is correlated with cognitive function in normal male elderly: a multiscale entropy analysis","volume":"34","author":"Yang","year":"2013","journal-title":"Neurobiol. Aging"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Smith, R.X., Yan, L., and Wang, D.J. (2014). Multiple time scale complexity analysis of resting state FMRI. Brain Imaging Behav.","DOI":"10.1007\/s11682-013-9276-6"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"677","DOI":"10.3389\/fnins.2018.00677","article-title":"Dynamic Complexity of Spontaneous BOLD Activity in Alzheimer\u2019s Disease and Mild Cognitive Impairment Using Multiscale Entropy Analysis","volume":"12","author":"Niu","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"770","DOI":"10.3389\/fnins.2018.00770","article-title":"Default Mode Network Complexity and Cognitive Decmidrule in Mild Alzheimer\u2019s Disease","volume":"12","author":"Grieder","year":"2018","journal-title":"Front. Neurosci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wang, X., Zhang, Y., Han, S., Zhao, J., and Chen, H. (2017). Resting-State Brain Activity Complexity in Early-Onset Schizophrenia Characterized by a Multi-scale Entropy Method. Intelligence Science and Big Data Engineering, Springer.","DOI":"10.1007\/978-3-319-67777-4_52"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2327","DOI":"10.1007\/s11071-019-04924-8","article-title":"Identifying nonlinear dynamics of brain functional networks of patients with schizophrenia by sample entropy","volume":"96","author":"Jia","year":"2019","journal-title":"Nonlinear Dyn."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"7990","DOI":"10.1038\/s41598-017-08565-y","article-title":"Sample entropy reveals an age-related reduction in the complexity of dynamic brain","volume":"7","author":"Jia","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neuroimage.2013.05.041","article-title":"The WU-Minn Human Connectome Project: An overview","volume":"80","author":"Essen","year":"2013","journal-title":"NeuroImage"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3758\/CABN.5.2.182","article-title":"Affective personality differences in neural processing efficiency confirmed using fMRI","volume":"5","author":"Gray","year":"2005","journal-title":"Cogn. Affect. Behav. Neurosci."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1177\/1073191112446655","article-title":"Development of Abbreviated Nine-Item Forms of the Raven\u2019s Standard Progressive Matrices Test","volume":"19","author":"Bilker","year":"2012","journal-title":"Assessment"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1038\/nn1014","article-title":"Neural mechanisms of general fluid intelligence","volume":"6","author":"Gray","year":"2003","journal-title":"Nat. Neurosci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"McDonough, I.M., and Nashiro, K. (2014). Network complexity as a measure of information processing across resting-state networks: evidence from the Human Connectome Project. Front. Hum. Neurosci., 8.","DOI":"10.3389\/fnhum.2014.00409"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"089804","DOI":"10.1103\/PhysRevLett.92.089804","article-title":"Costa, Goldberger, and Peng Reply","volume":"92","author":"Costa","year":"2004","journal-title":"Phys. Rev. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, D.J.J., Jann, K., Fan, C., Qiao, Y., Zang, Y.F., Lu, H., and Yang, Y. (2018). Neurophysiological Basis of Multi-Scale Entropy of Brain Complexity and Its Relationship with Functional Connectivity. Front. Neurosci., 12.","DOI":"10.3389\/fnins.2018.00352"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.intell.2017.06.004","article-title":"Brain volume and intelligence: The moderating role of intelligence measurement quality","volume":"64","author":"Gignac","year":"2017","journal-title":"Intelligence"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.paid.2016.06.069","article-title":"Effect size guidelines for individual differences researchers","volume":"102","author":"Gignac","year":"2016","journal-title":"Personal. Individ. Differ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1038\/nn.4135","article-title":"Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity","volume":"18","author":"Finn","year":"2015","journal-title":"Nat. Neurosci."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"5415","DOI":"10.1093\/cercor\/bhx230","article-title":"Influences on the Test\u2013Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility","volume":"27","author":"Noble","year":"2017","journal-title":"Cereb. Cortex"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Saxe, G.N., Calderone, D., and Morales, L.J. (2018). Brain entropy and human intelligence: A resting-state fMRI study. PLoS ONE.","DOI":"10.1371\/journal.pone.0191582"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6405","DOI":"10.1523\/JNEUROSCI.3153-10.2011","article-title":"Variability of Brain Signals Processed Locally Transforms into Higher Connectivity with Brain Development","volume":"31","author":"Vakorin","year":"2011","journal-title":"J. Neurosci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1806","DOI":"10.1093\/cercor\/bht030","article-title":"Spatiotemporal Dependency of Age-Related Changes in Brain Signal Variability","volume":"24","author":"McIntosh","year":"2014","journal-title":"Cereb. Cortex"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8551","DOI":"10.1523\/JNEUROSCI.0358-16.2016","article-title":"Higher Intelligence Is Associated with Less Task-Related Brain Network Reconfiguration","volume":"36","author":"Schultz","year":"2016","journal-title":"J. Neurosci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1162\/NETN_a_00010","article-title":"Fluid and flexible minds: Intelligence reflects synchrony in the brain\u2019s intrinsic network architecture","volume":"1","author":"Ferguson","year":"2017","journal-title":"Netw. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"3110","DOI":"10.3390\/e17053110","article-title":"The Multiscale Entropy Algorithm and Its Variants: A Review","volume":"17","year":"2015","journal-title":"Entropy"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1768264","DOI":"10.1155\/2017\/1768264","article-title":"Efficient Computation of Multiscale Entropy over Short Biomedical Time Series Based on Linear State-Space Models","volume":"2017","author":"Faes","year":"2017","journal-title":"Complexity"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Faes, L., Marinazzo, D., and Stramaglia, S. (2017). Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes. Entropy, 19.","DOI":"10.3390\/e19080408"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"032115","DOI":"10.1103\/PhysRevE.99.032115","article-title":"Multiscale information storage of linear long-range correlated stochastic processes","volume":"99","author":"Faes","year":"2019","journal-title":"Phys. Rev. E"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kosciessa, J.Q., Kloosterman, N.A., and Garrett, D.D. (2019). Standard multiscale entropy reflects spectral power at mismatched temporal scales: What\u2019s signal irregularity got to do with it?. bioRxiv.","DOI":"10.1101\/752808"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.neuroimage.2013.04.127","article-title":"The minimal preprocessing pipelines for the Human Connectome Project","volume":"80","author":"Glasser","year":"2013","journal-title":"NeuroImage"},{"key":"ref_52","first-page":"46","article-title":"Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers","volume":"90","author":"Douaud","year":"2014","journal-title":"NeuroImage"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4492","DOI":"10.1093\/cercor\/bhw253","article-title":"Data quality influences observed links between functional connectivity and behavior","volume":"27","author":"Siegel","year":"2017","journal-title":"Cereb. Cortex"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"20170284","DOI":"10.1098\/rstb.2017.0284","article-title":"A distributed brain network predicts general intelligence from resting-state human neuroimaging data","volume":"373","author":"Dubois","year":"2018","journal-title":"Philos. Trans. R. Soc. Biol. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Feinberg, D.A., Moeller, S., Smith, S.M., Auerbach, E., Ramanna, S., Glasser, M.F., Miller, K.L., Ugurbil, K., and Yacoub, E. (2010). Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging. PLoS ONE, 5.","DOI":"10.1371\/journal.pone.0015710"},{"key":"ref_56","unstructured":"Bijsterbosch, J., Smith, S.M., and Beckmann, C.F. (2017). Introduction to Resting State FMRI Functional Connectivity, Oxford University Press."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Cabral, J., Vidaurre, D., Marques, P., Magalh\u00e3es, R., Silva Moreira, P., Miguel Soares, J., Deco, G., Sousa, N., and Kringelbach, M.L. (2017). Cognitive performance in healthy older adults relates to spontaneous switching between states of functional connectivity during rest. Sci. Rep., 7.","DOI":"10.1038\/s41598-017-05425-7"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1126\/science.1194144","article-title":"Prediction of Individual Brain Maturity Using fMRI","volume":"329","author":"Dosenbach","year":"2010","journal-title":"Science"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1656","DOI":"10.1093\/cercor\/bhx061","article-title":"Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume","volume":"28","author":"Cui","year":"2018","journal-title":"Cereb. Cortex"}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/10\/995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:25:33Z","timestamp":1760189133000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/21\/10\/995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,12]]},"references-count":59,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["e21100995"],"URL":"https:\/\/doi.org\/10.3390\/e21100995","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,12]]}}}