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Brain Topography

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03.06.2019 | Original Paper

Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

verfasst von: Young-Beom Lee, Kwangsun Yoo, Jee Hoon Roh, Won-Jin Moon, Yong Jeong

Erschienen in: Brain Topography | Ausgabe 5/2019

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Abstract

Spatial pattern of the brain network changes dynamically. This change is closely linked to the brain-state transition, which vary depending on a dynamic stream of thoughts. To date, many dynamic methods have been developed for decoding brain-states. However, most of them only consider changes over time, not the brain-state transition itself. Here, we propose a novel dynamic functional connectivity analysis method, brain-state extraction algorithm based on state transition (BEST), which constructs connectivity matrices from the duration of brain-states and decodes the proper number of brain-states in a data-driven way. To set the duration of each brain-state, we detected brain-state transition time-points using spatial standard deviation of the brain activity pattern that changes over time. Furthermore, we also used Bayesian information criterion to the clustering method to estimate and extract the number of brain-states. Through validations, it was proved that BEST could find brain-state transition time-points and could estimate the proper number of brain-states without any a priori knowledge. It has also shown that BEST can be applied to resting state fMRI data and provide stable and consistent results.

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Allen EA, Damaraju E, Plis SM et al (2014) Tracking whole-brain connectivity dynamics in the resting state. Cereb Cortex 24:663–676CrossRef

Arthur D, Vassilvitskii S (2007) K-means ++: the advantages of careful seeding. Proc Annu ACM-SIAM Symp Discret Algorithms. https://doi.org/10.1145/1283383.1283494 CrossRef

Baker AP, Brookes MJ, Rezek IA et al (2014) Fast transient networks in spontaneous human brain activity. Elife 2014:1–18. https://doi.org/10.7554/eLife.01867 CrossRef

Barnett I, Onnela JP (2016) Change point detection in correlation networks. Sci Rep 6:1–11. https://doi.org/10.1038/srep18893 CrossRef

Barttfeld P, Uhrig L, Sitt JD et al (2015) Signature of consciousness in the dynamics of resting-state brain activity. Proc Natl Acad Sci 112:887–892. https://doi.org/10.1073/pnas.1515029112 CrossRefPubMed

Bassett DS, Bullmore E (2006) Small-world brain networks. Neuroscientist 12:512–523. https://doi.org/10.1177/1073858406293182 CrossRefPubMed

Bassett DS, Wymbs NF, Porter MA et al (2011) Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci 108:7641–7646. https://doi.org/10.1073/pnas.1018985108 CrossRefPubMed

Beckmann CF, Deluca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc London B 360:1001–1013. https://doi.org/10.1098/rstb.2005.1634 CrossRef

Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541. https://doi.org/10.1002/mrm.1910340409 CrossRefPubMed

Chai LR, Mattar MG, Blank IA et al (2016) Functional network dynamics of the language system. Cereb Cortex. https://doi.org/10.1093/cercor/bhw238 CrossRefPubMedPubMedCentral

de Pasquale F, Della Penna S, Sporns O et al (2016) A dynamic core network and global efficiency in the resting human brain. Cereb Cortex 26:4015–4033CrossRef

Egiazarian K, Katkovnik V, Astola L (2001) Adaptive window size image denoising based on ICI rule. 2001 Proc IEEE Int Conf Acoust Speech Signal Process (Cat No. 01CH37221) 3:1869–1872. https://doi.org/10.1109/icassp.2001.941308 CrossRef

Finn ES, Shen X, Scheinost D et al (2015) Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nat Neurosci 18:1664–1671. https://doi.org/10.1038/nn.4135 CrossRefPubMedPubMedCentral

Finn ES, Scheinost D, Finn DM et al (2017) Can brain state be manipulated to emphasize individual differences in functional connectivity? Neuroimage 160:140–151. https://doi.org/10.1016/j.neuroimage.2017.03.064 CrossRefPubMed

Fraley C, Raftery AE (1998) How many clusters? Which clustering method? Answers via model-based cluster analysis. Comput J 41:578–588. https://doi.org/10.1093/comjnl/41.8.578 CrossRef

Gonzalez-Castillo J, Hoy CW, Handwerker DA et al (2015) Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns. Proc Natl Acad Sci 112:8762–8767. https://doi.org/10.1073/pnas.1501242112 CrossRefPubMed

Gordon EM, Laumann TO, Adeyemo B et al (2017) Individual-specific features of brain systems identified with resting state functional correlations. Neuroimage 146:918–939. https://doi.org/10.1016/j.neuroimage.2016.08.032 CrossRefPubMed

Gratton C, Laumann TO, Nielsen AN et al (2018) Functional brain networks are dominated by stable group and individual factors, not cognitive or daily variation. Neuron 98:439–452. https://doi.org/10.1016/j.neuron.2018.03.035 CrossRefPubMedPubMedCentral

Greicius MD, Srivastava G, Reiss AL, Menon V (2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proc Natl Acad Sci 101:4637–4642. https://doi.org/10.1073/pnas.0308627101 CrossRefPubMed

Hindriks R, Adhikari MH, Murayama Y et al (2016) Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI? Neuroimage 127:242–256. https://doi.org/10.1016/j.neuroimage.2015.11.055 CrossRefPubMedPubMedCentral

Hoekzema E, Carmona S, Ramos-Quiroga JA et al (2014) An independent components and functional connectivity analysis of resting state FMRI data points to neural network dysregulation in adult ADHD. Hum Brain Mapp 35:1261–1272. https://doi.org/10.1002/hbm.22250 CrossRefPubMed

Hutchison RM, Womelsdorf T, Gati JS et al (2013) Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum Brain Mapp 34:2154–2177CrossRef

Jeong SO, Pae C, Park HJ (2016) Connectivity-based change point detection for large-size functional networks. Neuroimage 143:353–363. https://doi.org/10.1016/j.neuroimage.2016.09.019 CrossRefPubMed

Kiviniemi V, Kantola J-H, Jauhiainen J et al (2003) Independent component analysis of nondeterministic fMRI signal sources. Neuroimage 19:253–260. https://doi.org/10.1016/S1053-8119(03)00097-1 CrossRefPubMed

Koch W, Teipel S, Mueller S et al (2012) Diagnostic power of default mode network resting state fMRI in the detection of Alzheimer’s disease. Neurobiol Aging 33:466–478. https://doi.org/10.1016/j.neurobiolaging.2010.04.013 CrossRefPubMed

Koenig T, Prichep L, Lehmann D et al (2002) Millisecond by millisecond, year by year: normative EEG microstates and developmental stages. Neuroimage 16:41–48. https://doi.org/10.1006/nimg.2002.1070 CrossRefPubMed

Leonardi N, Van De Ville D (2015) On spurious and real fluctuations of dynamic functional connectivity during rest. Neuroimage 104:430–436. https://doi.org/10.1016/j.neuroimage.2014.09.007 CrossRefPubMed

Lindenberg R, Nachtigall L, Meinzer M et al (2013) differential effects of dual and unihemispheric motor cortex stimulation in older adults. J Neurosci 33:9176–9183. https://doi.org/10.1523/JNEUROSCI.0055-13.2013 CrossRefPubMed

Lindquist MA, Waugh C, Wager TD (2007) Modeling state-related fMRI activity using change-point theory. Neuroimage 35:1125–1141. https://doi.org/10.1016/j.neuroimage.2007.01.004 CrossRefPubMed

Liu X, Duyn JH (2013) Time-varying functional network information extracted from brief instances of spontaneous brain activity. Proc Natl Acad Sci 110:4392–4397. https://doi.org/10.1073/pnas.1216856110 CrossRefPubMed

Liu F, Wang Y, Li M et al (2016) Dynamic functional network connectivity in idiopathic generalized epilepsy with generalized tonic-clonic seizure. Hum Brain Mapp. https://doi.org/10.1002/hbm.23430 CrossRefPubMedPubMedCentral

Lynall M-E, Bassett DS, Kerwin R et al (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci 30:9477–9487CrossRef

Mckeown MJ, Makeig S, Brown GG et al (1998a) Analysis of fMRI data by blind separation into independent spatial components. Hum Brain Mapp 6:160–188. https://doi.org/10.1002/(SICI)1097-0193(1998)6:3%3c160:AID-HBM5%3e3.0.CO;2-1 CrossRefPubMed

McKeown MJ, Jung T-P, Makeig S et al (1998b) Spatially independent activity patterns in functional MRI data during the Stroop color-naming task. Proc Natl Acad Sci 95:803–810. https://doi.org/10.1073/pnas.95.3.803 CrossRefPubMed

Meunier D, Achard S, Morcom A, Bullmore E (2009) Age-related changes in modular organization of human brain functional networks. Neuroimage 44:715–723CrossRef

Pelleg D, Moore A (2000) X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the 17th International Conference on Machine Learning, pp 727–734

Power JD, Barnes KA, Snyder AZ et al (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59:2142–2154CrossRef

Reineberg AE, Andrews-Hanna JR, Depue BE et al (2015) Resting-state networks predict individual differences in common and specific aspects of executive function. Neuroimage 104:69–78. https://doi.org/10.1016/j.neuroimage.2014.09.045 CrossRefPubMed

Reineberg AE, Banich MT (2016) Functional connectivity at rest is sensitive to individual differences in executive function: a network analysis. Hum Brain Mapp 37:2959–2975. https://doi.org/10.1002/hbm.23219 CrossRefPubMedPubMedCentral

Saggar M, Sporns O, Gonzalez-Castillo J et al (2018) Towards a new approach to reveal dynamical organization of the brain using topological data analysis. Nat Commun 9:1–14. https://doi.org/10.1038/s41467-018-03664-4 CrossRef

Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS et al (2010) Loss of “small-world” networks in Alzheimer’s disease: graph analysis of fMRI resting-state functional connectivity. PLoS ONE. https://doi.org/10.1371/journal.pone.0013788 CrossRefPubMedPubMedCentral

Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464. https://doi.org/10.1214/aos/1176344136 CrossRef

Shakil S, Lee C-H, Keilholz SD (2016) Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states. Neuroimage 133:111–128. https://doi.org/10.1016/j.neuroimage.2016.02.074 CrossRefPubMedPubMedCentral

Shen X, Tokoglu F, Papademetris X, Constable RT (2013) Groupwise whole-brain parcellation from resting-state fMRI data for network node identification. Neuroimage 82:403–415CrossRef

Shine JM, Bissett PG, Bell PT et al (2016) The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92:544–554. https://doi.org/10.1016/j.neuron.2016.09.018 CrossRefPubMedPubMedCentral

Skrandies W (1990) Global field power and topographic similarity. Brain Topogr 3:137–141CrossRef

Sohn WS, Yoo K, Lee YB et al (2015) Influence of ROI selection on resting functional connectivity: an individualized approach for resting fMRI analysis. Front Neurosci 9:1–10. https://doi.org/10.3389/fnins.2015.00280 CrossRef

Solodkin A, Hlustik P, Noll DC, Small SL (2001) Lateralization of motor circuits and handedness during finger movements. Eur J Neurol 8:425–434. https://doi.org/10.1046/j.1468-1331.2001.00242.x CrossRefPubMed

Supekar K, Menon V, Rubin D et al (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol. https://doi.org/10.1371/journal.pcbi.1000100 CrossRefPubMedPubMedCentral

Tagliazucchi E, Balenzuela P, Fraiman D, Chialvo (2012) Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis. Front Physiol. https://doi.org/10.3389/fphys.2012.00015 CrossRefPubMedPubMedCentral

Tavor I, Jones OP, Mars RB et al (2016) Task-free MRI predicts individual differences in brain activity during task performance. Science 352:216–220. https://doi.org/10.1126/science.aad8127 CrossRefPubMedPubMedCentral

Telesford QK, Lynall M-E, Vettel J et al (2016) Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage 142:198–210CrossRef

Thompson WH, Fransson P (2017) Spatial confluence of psychological and anatomical network constructs in the human brain revealed by a mass meta-analysis of fMRI activation. Sci Rep 7:1–11. https://doi.org/10.1038/srep44259 CrossRef

Thompson WH, Fransson P (2018) A common framework for the problem of deriving estimates of dynamic functional brain connectivity. Neuroimage 172:896–902. https://doi.org/10.1016/j.neuroimage.2017.12.057 CrossRefPubMed

Ting CM, Ombao H, Samdin SB, Salleh SH (2018) Estimating dynamic connectivity states in fMRI using regime-switching factor models. IEEE Trans Med Imaging 37:1011–1023. https://doi.org/10.1109/TMI.2017.2780185 CrossRefPubMed

Van De Ven VG, Formisano E, Prvulovic D et al (2004) Functional connectivity as revealed by spatial independent component analysis of fMRI measurements during rest. Hum Brain Mapp 22:165–178. https://doi.org/10.1002/hbm.20022 CrossRefPubMed

Vidaurre D, Quinn AJ, Baker AP et al (2016) Spectrally resolved fast transient brain states in electrophysiological data. Neuroimage 126:81–95. https://doi.org/10.1016/j.neuroimage.2015.11.047 CrossRefPubMedPubMedCentral

Vidaurre D, Abeysuriya R, Becker R et al (2017) Discovering dynamic brain networks from big data in rest and task. Neuroimage. https://doi.org/10.1016/j.neuroimage.2017.06.077 CrossRefPubMed

Wang L, Zang Y, He Y et al (2006) Changes in hippocampal connectivity in the early stages of Alzheimer’s disease: evidence from resting state fMRI. Neuroimage 31:496–504. https://doi.org/10.1016/j.neuroimage.2005.12.033 CrossRefPubMed

Wu X, Li R, Fleisher AS et al (2011) Altered default mode network connectivity in Alzheimer’s disease—a resting functional MRI and Bayesian network study. Hum Brain Mapp 32:1868–1881. https://doi.org/10.1002/hbm.21153 CrossRefPubMedPubMedCentral

Zalesky A, Fornito A, Cocchi L et al (2014) Time-resolved resting-state brain networks. Proc Natl Acad Sci 111:10341–10346CrossRef

Zhang HY, Wang SJ, Xing J et al (2009) Detection of PCC functional connectivity characteristics in resting-state fMRI in mild Alzheimer’s disease. Behav Brain Res 197:103–108. https://doi.org/10.1016/j.bbr.2008.08.012 CrossRefPubMed

Zhou Y, Dougherty JH Jr, Hubner KF et al (2008) Abnormal connectivity in the posterior cingulate and hippocampus in early Alzheimer’s disease and mild cognitive impairment. Alzheimers Dement 4:265–270CrossRef

Titel: Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study
verfasst von: Young-Beom Lee
Kwangsun Yoo
Jee Hoon Roh
Won-Jin Moon
Yong Jeong
Publikationsdatum: 03.06.2019
Verlag: Springer US
Erschienen in: Brain Topography / Ausgabe 5/2019
Print ISSN: 0896-0267
Elektronische ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-019-00719-7

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Brain-State Extraction Algorithm Based on the State Transition (BEST): A Dynamic Functional Brain Network Analysis in fMRI Study

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