Wang, Yimeng (2022) Reorganization of functional hubs in sleep and in epilepsy. Masters thesis, Concordia University.
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Abstract
Resting-state functional Magnetic Resonance Imaging (rs-fMRI) is a non-invasive brain imaging technique that measures brain activity non-invasively. Functional connectivity (FC) quantifies how Blood-Oxygen-Level-Dependent (BOLD) signal of remote brain regions correlates with each other temporally. Using variety of methodologies such as Independent Component Analysis (ICA) or sparse dictionary learning, Resting-State Networks (RSNs) are consistently found in human brain connectome. Functional hubs denote the brain regions that exhibit connections denser than others, whereas connector hubs especially participate in inter-network communication. My Master thesis is based on a previously published methodology called Sparsity-based analysis of reliable k-hubness (SPARK), which estimates the functional hubs by counting the number of RSNs connected to each brain voxels. By acquiring simultaneous electroencephalogram (EEG)-fMRI, functional connectivity (FC) during sleep can also be investigated. In addition, functional connectivity has been commonly applied to find potential biomarkers for neurological disease, such as epilepsy. Therefore, in the first study of this thesis, we investigated functional segregation during a recovery nap after total sleep deprivation and its association with cognitive performance. We applied an algorithm called Hierarchical Segregation Index (HSI) based on the hubness estimated by SPARK. As a result, we found significant correlation between functional segregation during sleep and working memory performance after sleep. In the second study of this thesis, we investigated the different patterns of functional hub reorganization in temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE). By applying similar methods used in the first study, we found significant and exclusive functional hub alteration both in TLE and FLE. To conclude, in sleep, functional segregation during a whole night sleep and its association between cognitive performance can be further investigated. In TLE and FLE, further research of the hub alterations in subcortical structures will be of interest, and might serve as potential biomarkers for post-surgical outcomes.
Divisions: | Concordia University > Faculty of Arts and Science > Physics |
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Item Type: | Thesis (Masters) |
Authors: | Wang, Yimeng |
Institution: | Concordia University |
Degree Name: | M. Sc. |
Program: | Physics |
Date: | August 2022 |
Thesis Supervisor(s): | Grova, Christophe |
ID Code: | 991117 |
Deposited By: | yimeng wang |
Deposited On: | 27 Oct 2022 14:33 |
Last Modified: | 27 Oct 2022 14:33 |
References:
AASM. (2007). AASM Scoring Manual versión 2.5 - American Academy of Sleep Medicine. https://aasm.org/clinical-resources/scoring-manual/Achard, S., Delon-Martin, C., Vértes, P. E., Renard, F., Schenck, M., Schneider, F., Heinrich, C., Kremer, S., & Bullmore, E. T. (2012). Hubs of brain functional networks are radically reorganized in comatose patients. Proceedings of the National Academy of Sciences of the United States of America, 109(50), 20608–20613. https://doi.org/10.1073/PNAS.1208933109/SUPPL_FILE/PNAS.201208933SI.PDF
Acharya, U. R., Bhat, S., Faust, O., Adeli, H., Chua, E. C. P., Lim, W. J. E., & Koh, J. E. W. (2015). Nonlinear dynamics measures for automated EEG-based sleep stage detection. European Neurology, 74(5–6), 268–287. https://doi.org/10.1159/000441975
Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J.-S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., & Evans, A. C. (1998). The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research B-References for tools called upon by CIVET: Figure 2: Example screen shots of the CIVET GUI Figure 1: Diagrammatic Representation of The CIVET Pipeline Environment. IEEE Trans Med Imaging, 17(6), 95.
Aertsen, A. M. H. J., Gerstein, G. L., Habib, M. K., & Palm, G. (1989). Dynamics of neuronal firing correlation: modulation of “effective connectivity.” Journal of Neurophysiology, 61(5). https://doi.org/10.1152/JN.1989.61.5.900
Alizadeh, M., Kozlowski, L., Muller, J., Ashraf, N., Shahrampour, S., Mohamed, F. B., Wu, C., & Sharan, A. (2019). Hemispheric Regional Based Analysis of Diffusion Tensor Imaging and Diffusion Tensor Tractography in Patients with Temporal Lobe Epilepsy and Correlation with Patient outcomes. Scientific Reports 2019 9:1, 9(1), 1–8. https://doi.org/10.1038/s41598-018-36818-x
Allen, P. J., Josephs, O., & Turner, R. (2000). A method for removing imaging artifact from continuous EEG recorded during functional MRI. NeuroImage, 12(2), 230–239. https://doi.org/10.1006/NIMG.2000.0599
Bartolomei, F., Lagarde, S., Wendling, F., McGonigal, A., Jirsa, V., Guye, M., & Bénar, C. (2017). Defining epileptogenic networks: Contribution of SEEG and signal analysis. Epilepsia, 58(7), 1131–1147. https://doi.org/10.1111/EPI.13791
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005). Investigations into resting-state connectivity using independent component analysis. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 1001–1013. https://doi.org/10.1098/rstb.2005.1634
Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010a). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51(3), 1126–1139. https://doi.org/10.1016/J.NEUROIMAGE.2010.02.082
Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., & Evans, A. C. (2010b). Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51(3), 1126–1139. https://doi.org/10.1016/J.NEUROIMAGE.2010.02.082
Berry, R. B., Gamaldo, C. E., Harding, S. M., Brooks, R., Lloyd, R. M., Vaughn, B. v., & Marcus, C. L. (2015). AASM Scoring Manual Version 2.2 Updates: New Chapters for Scoring Infant Sleep Staging and Home Sleep Apnea Testing. Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 11(11), 1253. https://doi.org/10.5664/JCSM.5176
Bethlehem, R. A. I., Paquola, C., Seidlitz, J., Ronan, L., Bernhardt, B., Consortium, C. C. A. N., & Tsvetanov, K. A. (2020). Dispersion of functional gradients across the adult lifespan. NeuroImage, 222. https://doi.org/10.1016/J.NEUROIMAGE.2020.117299
Bettus, G., Guedj, E., Joyeux, F., Confort-Gouny, S., Soulier, E., Laguitton, V., Cozzone, P. J., Chauvel, P., Ranjeva, J. P., Bartolomei, F., & Guye, M. (2009). Decreased basal fMRI functional connectivity in epileptogenic networks and contralateral compensatory mechanisms. Human Brain Mapping, 30(5), 1580–1591. https://doi.org/10.1002/HBM.20625
Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006). Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI. NeuroImage, 31(4), 1536–1548. https://doi.org/10.1016/J.NEUROIMAGE.2006.02.048
Birn, R. M., Smith, M. A., Jones, T. B., & Bandettini, P. A. (2008). The Respiration Response Function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration. NeuroImage, 40(2), 644. https://doi.org/10.1016/J.NEUROIMAGE.2007.11.059
Biswal, B. B., Mennes, M., Zuo, X. N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., Dogonowski, A. M., Ernst, M., Fair, D., Hampson, M., Hoptman, M. J., Hyde, J. S., Kiviniemi, V. J., Kötter, R., Li, S. J., … Milham, M. P. (2010). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 107(10), 4734–4739. https://doi.org/10.1073/pnas.0911855107
Biswal, B. B., van Kylen, J., & Hyde, J. S. (1997). Simultaneous assessment of flow and BOLD signals in resting-state functional connectivity maps. NMR in Biomedicine, 10(4–5), 165–170. https://doi.org/10.1002/(sici)1099-1492(199706/08)10:4/5<165::aid-nbm454>3.0.co;2-7
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34(4), 537–541. https://doi.org/10.1002/MRM.1910340409
Boly, M., Perlbarg, V., Marrelec, G., Schabus, M., Laureys, S., Doyon, J., Pélégrini-Issac, M., Maquet, P., & Benali, H. (2012). Hierarchical clustering of brain activity during human nonrapid eye movement sleep. Proceedings of the National Academy of Sciences of the United States of America, 109(15), 5856–5861. https://doi.org/10.1073/PNAS.1111133109/SUPPL_FILE/PNAS.201111133SI.PDF
Bordier, C., Nicolini, C., & Bifone, A. (2017). Graph analysis and modularity of brain functional connectivity networks: Searching for the optimal threshold. Frontiers in Neuroscience, 11(AUG), 441. https://doi.org/10.3389/FNINS.2017.00441/BIBTEX
Buckner, R. L., & Vincent, J. L. (2007). Unrest at rest: default activity and spontaneous network correlations. NeuroImage, 37(4), 1091–1096. https://doi.org/10.1016/J.NEUROIMAGE.2007.01.010
Bullmore, E., & Sporns, O. (2009). Complex brain networks: graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience 2009 10:3, 10(3), 186–198. https://doi.org/10.1038/nrn2575
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional MRI data using independent component analysis. Human Brain Mapping, 14(3), 140–151. https://doi.org/10.1002/HBM.1048
Campos, B. M., Coan, A. C., Beltramini, G. C., Liu, M., Yassuda, C. L., Ghizoni, E., Beaulieu, C., Gross, D. W., & Cendes, F. (2015). White matter abnormalities associate with type and localization of focal epileptogenic lesions. Epilepsia, 56(1), 125–132. https://doi.org/10.1111/epi.12871
Chee, M. W. L., & Choo, W. C. (2004). Functional Imaging of Working Memory after 24 Hr of Total Sleep Deprivation. Journal of Neuroscience, 24(19), 4560–4567. https://doi.org/10.1523/JNEUROSCI.0007-04.2004
Collingnon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., & Marchal, G. (1995). Automated multi-modality image registration based on information theory. IPMI, 263–274.
Constable, R. T., Scheinost, D., Finn, E. S., Shen, X., Hampson, M., Winstanley, F. S., Spencer, D. D., & Papademetris, X. (2013). Potential Use and Challenges of Functional Connectivity Mapping in Intractable Epilepsy. Frontiers in Neurology, 0, 39. https://doi.org/10.3389/FNEUR.2013.00039
Cordes, D., Haughton, V. M., Arfanakis, K., Carew, J. D., Turski, P. A., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2001). Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. AJNR. American Journal of Neuroradiology, 22(7), 1326–1333.
Cordes, D., Haughton, V. M., Arfanakis, K., Wendt, G. J., Turski, P. A., Moritz, C. H., Quigley, M. A., & Meyerand, M. E. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR. American Journal of Neuroradiology, 21(9), 1636–1644.
Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173. https://doi.org/10.1006/cbmr.1996.0014
Cross, N. E., Pomares, F. B., Nguyen, A., Perrault, A. A., Jegou, A., Uji, M., Lee, K., Razavipour, F., Ka’b Ali, O. bin, Aydin, U., Benali, H., Grova, C., & Dang-Vu, T. T. (2021). An altered balance of integrated and segregated brain activity is a marker of cognitive deficits following sleep deprivation. PLOS Biology, 19(11), e3001232. https://doi.org/10.1371/JOURNAL.PBIO.3001232
Cross, N., Paquola, C., Pomares, F. B., Perrault, A. A., Jegou, A., Nguyen, A., Aydin, U., Bernhardt, B. C., Grova, C., & Dang-Vu, T. T. (2021a). Cortical gradients of functional connectivity are robust to state-dependent changes following sleep deprivation. NeuroImage, 226, 117547. https://doi.org/10.1016/J.NEUROIMAGE.2020.117547
Cross, N., Paquola, C., Pomares, F. B., Perrault, A. A., Jegou, A., Nguyen, A., Aydin, U., Bernhardt, B. C., Grova, C., & Dang-Vu, T. T. (2021b). Cortical gradients of functional connectivity are robust to state-dependent changes following sleep deprivation. NeuroImage, 226, 117547. https://doi.org/10.1016/J.NEUROIMAGE.2020.117547
Cruces, R. R., Royer, J., Herholz, P., Larivière, S., Wael, R. V. de, Paquola, C., Benkarim, O., Park, B., Degré-Pelletier, J., Nelson, M., DeKraker, J., Tardif, C., Poline, J.-B., Concha, L., & Bernhardt, B. C. (2022). Micapipe: A Pipeline for Multimodal Neuroimaging and Connectome Analysis. BioRxiv, 2022.01.31.478189. https://doi.org/10.1101/2022.01.31.478189
Culhane-Shelburne, K., Chapieski, L., Hiscock, M., & Glaze, D. (2002). Executive functions in children with frontal and temporal lobe epilepsy. Journal of the International Neuropsychological Society, 8(5), 623–632. https://doi.org/10.1017/S1355617702801308
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006a). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848–13853. https://doi.org/10.1073/PNAS.0601417103
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006b). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13848–13853. https://doi.org/10.1073/PNAS.0601417103
Dang-Vu, T. T., Cartwright, R. D., Mograss, M. A., Ellenbogen, J. M., & Foulkes, D. (2022, July 28). sleep. Encyclopedia Britannica. Https://Www.Britannica.Com/Science/Sleep.
Dang-Vu, T. T., Schabus, M., Desseilles, M., Albouy, G., Boly, M., Darsaud, A., Gais, S., Rauchs, G., Sterpenich, V., Vandewalle, G., Carrier, J., Moonen, G., Balteau, E., Degueldre, C., Luxen, A., Phillips, C., & Maquet, P. (2008). Spontaneous neural activity during human slow wave sleep. Proceedings of the National Academy of Sciences of the United States of America, 105(39), 15160–15165. https://doi.org/10.1073/PNAS.0801819105/SUPPL_FILE/0801819105SI.PDF
de Gennaro, L., & Ferrara, M. (2003). Sleep spindles: An overview. Sleep Medicine Reviews, 7(5), 423–440. https://doi.org/10.1053/smrv.2002.0252
de Havas, J. A., Parimal, S., Soon, C. S., & Chee, M. W. L. (2012). Sleep deprivation reduces default mode network connectivity and anti-correlation during rest and task performance. NeuroImage, 59(2), 1745–1751. https://doi.org/10.1016/J.NEUROIMAGE.2011.08.026
de Luca, C. J., Adam, A., Wotiz, R., Gilmore, L. D., & Nawab, S. H. (2006). Decomposition of surface EMG signals. Journal of Neurophysiology, 96(3), 1646–1657. https://doi.org/10.1152/JN.00009.2006/ASSET/IMAGES/LARGE/Z9K0090676200010.JPEG
Efron, B., & Tibshirani, R. J. (1994). An introduction to the bootstrap. CRC press.
Evangelista, E., Bénar, C., Bonini, F., Carron, R., Colombet, B., Régis, J., & Bartolomei, F. (2015). Does the thalamo-cortical synchrony play a role in seizure termination? Frontiers in Neurology, 6(SEP), 192. https://doi.org/10.3389/FNEUR.2015.00192/BIBTEX
Fan, J., McCandliss, B. D., Fossella, J., Flombaum, J. I., & Posner, M. I. (2005). The activation of attentional networks. NeuroImage, 26(2), 471–479. https://doi.org/10.1016/J.NEUROIMAGE.2005.02.004
Feng, L., Motelow, J. E., Ma, C., Biche, W., McCafferty, C., Smith, N., Liu, M., Zhan, Q., Jia, R., Xiao, B., Duque, A., & Blumenfeld, H. (2017). Seizures and Sleep in the Thalamus: Focal Limbic Seizures Show Divergent Activity Patterns in Different Thalamic Nuclei. The Journal of Neuroscience, 37(47), 11441. https://doi.org/10.1523/JNEUROSCI.1011-17.2017
Ferri, R., Manconi, M., Plazzi, G., Bruni, O., Vandi, S., Montagna, P., Ferini-Strambi, L., & Zucconi, M. (2008). A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder. Journal of Sleep Research, 17(1), 89–100. https://doi.org/10.1111/J.1365-2869.2008.00631.X
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 2015 18:11, 18(11), 1664–1671. https://doi.org/10.1038/nn.4135
Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011). Unbiased average age-appropriate atlases for pediatric studies. NeuroImage, 54(1), 313–327. https://doi.org/10.1016/j.neuroimage.2010.07.033
Fonov, V., Evans, A., McKinstry, R., Almli, C., & Collins, D. (2009). Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. NeuroImage, 47, S102. https://doi.org/10.1016/S1053-8119(09)70884-5
Fox, M. D., & Raichle, M. E. (2007). Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nature Reviews Neuroscience 2007 8:9, 8(9), 700–711. https://doi.org/10.1038/nrn2201
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673–9678. https://doi.org/10.1073/PNAS.0504136102
Fransson, P. (2005). Spontaneous low‐frequency BOLD signal fluctuations: An fMRI investigation of the resting‐state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15. https://doi.org/10.1002/HBM.20113
Friston, K. J., Frith, C. D., Liddle, P. F., & Frackowiak, R. S. J. (1993). Functional connectivity: the principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism : Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 13(1), 5–14. https://doi.org/10.1038/JCBFM.1993.4
Friston, K., Kilner, J., & Harrison, L. (2006). A free energy principle for the brain. Journal of Physiology, Paris, 100(1–3), 70–87. https://doi.org/10.1016/J.JPHYSPARIS.2006.10.001
Functional Imaging Laboratory. (2016). SPM12 Manual. https://web.archive.org/web/20170711224110/http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf
Grabner, G., Janke, A. L., Budge, M. M., Smith, D., Pruessner, J., & Collins, D. L. (2006). Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 9(Pt 2), 58–66. https://doi.org/10.1007/11866763_8
Gramfort, A., Luessi, M., Larson, E., Engemann, D. A., Strohmeier, D., Brodbeck, C., Goj, R., Jas, M., Brooks, T., Parkkonen, L., & Hämäläinen, M. (2013). MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 0(7 DEC), 267. https://doi.org/10.3389/FNINS.2013.00267/XML/NLM
Greve, D. N., & Fischl, B. (2009). Accurate and robust brain image alignment using boundary-based registration. NeuroImage, 48(1), 63–72. https://doi.org/10.1016/J.NEUROIMAGE.2009.06.060
Griffanti, L., Douaud, G., Bijsterbosch, J., Evangelisti, S., Alfaro-Almagro, F., Glasser, M. F., Duff, E. P., Fitzgibbon, S., Westphal, R., Carone, D., Beckmann, C. F., & Smith, S. M. (2017). Hand classification of fMRI ICA noise components. NeuroImage, 154, 188–205. https://doi.org/10.1016/J.NEUROIMAGE.2016.12.036
Griffanti, L., Salimi-Khorshidi, G., Beckmann, C. F., Auerbach, E. J., Douaud, G., Sexton, C. E., Zsoldos, E., Ebmeier, K. P., Filippini, N., Mackay, C. E., Moeller, S., Xu, J., Yacoub, E., Baselli, G., Ugurbil, K., Miller, K. L., & Smith, S. M. (2014). ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage, 95, 232–247. https://doi.org/10.1016/J.NEUROIMAGE.2014.03.034
He, X., Doucet, G. E., Sperling, M., Sharan, A., & Tracy, J. I. (2015). Reduced thalamocortical functional connectivity in temporal lobe epilepsy. Epilepsia, 56(10), 1571–1579. https://doi.org/10.1111/epi.13085
Heuvel, M. P. van den, & Sporns, O. (2011). Rich-Club Organization of the Human Connectome. Journal of Neuroscience, 31(44), 15775–15786. https://doi.org/10.1523/JNEUROSCI.3539-11.2011
Honey, C. J., Sporns, O., Cammoun, L., Gigandet, X., Thiran, J. P., Meuli, R., & Hagmann, P. (2009). Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 106(6), 2035–2040. https://doi.org/10.1073/PNAS.0811168106
Horovitz, S. G., Braun, A. R., Carr, W. S., Picchioni, D., Balkin, T. J., Fukunaga, M., & Duyn, J. H. (2009). Decoupling of the brain’s default mode network during deep sleep. Proceedings of the National Academy of Sciences of the United States of America, 106(27), 11376–11381. https://doi.org/10.1073/PNAS.0901435106
Huettel, S. A. (2004). Linking Hemodynamic and Electrophysiological Measures of Brain Activity: Evidence from Functional MRI and Intracranial Field Potentials. Cerebral Cortex, 14(2), 165–173. https://doi.org/10.1093/cercor/bhg115
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841. https://doi.org/10.1016/S1053-8119(02)91132-8
Jenkinson, M., & Smith, S. (2001). A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2), 143–156. https://doi.org/10.1016/S1361-8415(01)00036-6
Jovicich, J., Czanner, S., Greve, D., Haley, E., van der Kouwe, A., Gollub, R., Kennedy, D., Schmitt, F., Brown, G., MacFall, J., Fischl, B., & Dale, A. (2006). Reliability in multi-site structural MRI studies: effects of gradient non-linearity correction on phantom and human data. NeuroImage, 30(2), 436–443. https://doi.org/10.1016/J.NEUROIMAGE.2005.09.046
Klugah-Brown, B., Luo, C., Peng, R., He, H., Li, J., Dong, L., & Yao, D. (2019). Altered structural and causal connectivity in frontal lobe epilepsy. BMC Neurology, 19(1), 1–9. https://doi.org/10.1186/S12883-019-1300-Z/FIGURES/3
Knoop, M. S., de Groot, E. R., & Dudink, J. (2021). Current ideas about the roles of rapid eye movement and non–rapid eye movement sleep in brain development. Acta Paediatrica (Oslo, Norway : 1992), 110(1), 36. https://doi.org/10.1111/APA.15485
Le, N. H., Nguyen, K. N., & Nguyen, H. M. (2018). Comparison analysis of ICA versus MCA-KSVD blind source separation on task-related fMRI data. Proceedings - 9th Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2017, 2018-February, 1129–1135. https://doi.org/10.1109/APSIPA.2017.8282196
Lee, K., Horien, C., O’Connor, D., Garand-Sheridan, B., Tokoglu, F., Scheinost, D., Lake, E. M. R., & Constable, R. T. (2022). Arousal impacts distributed hubs modulating the integration of brain functional connectivity. NeuroImage, 258, 119364. https://doi.org/10.1016/J.NEUROIMAGE.2022.119364
Lee, K., Khoo, H. M., Fourcade, C., Gotman, J., & Grova, C. (2019). Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis. Magnetic Resonance Imaging, 58, 97–107. https://doi.org/10.1016/J.MRI.2019.01.019
Lee, K., Khoo, H. M., Lina, J. M., Dubeau, F., Gotman, J., & Grova, C. (2018). Disruption, emergence and lateralization of brain network hubs in mesial temporal lobe epilepsy. NeuroImage : Clinical, 20, 71. https://doi.org/10.1016/J.NICL.2018.06.029
Lee, K., Lina, J. M., Gotman, J., & Grova, C. (2016). SPARK: Sparsity-based analysis of reliable k-hubness and overlapping network structure in brain functional connectivity. NeuroImage, 134, 434–449. https://doi.org/10.1016/J.NEUROIMAGE.2016.03.049
Lee, K., Tak, S., & Ye, J. C. (2011). A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion. IEEE Transactions on Medical Imaging, 30(5), 1076–1089. https://doi.org/10.1109/TMI.2010.2097275
Leergaard, T. B., Hilgetag, C. C., & Sporns, O. (2012). Mapping the connectome: Multi-level analysis of brain connectivity. Frontiers in Neuroinformatics, 6(APRIL 2012), 14. https://doi.org/10.3389/FNINF.2012.00014/XML/NLM
Lichstein, K. L., Riedel, B. W., & Richman, S. L. (2000). The Mackworth Clock Test: a computerized version. The Journal of Psychology, 134(2), 153–161. https://doi.org/10.1080/00223980009600858
Liu, T. T., & Falahpour, M. (2020). Vigilance Effects in Resting-State fMRI. Frontiers in Neuroscience, 14, 321. https://doi.org/10.3389/FNINS.2020.00321/BIBTEX
Liu, W., Yue, Q., Tian, Y., Gong, Q., Zhou, D., & Wu, X. (2021). Neural functional connectivity in patients with periventricular nodular heterotopia-mediated epilepsy. Epilepsy Research, 170. https://doi.org/10.1016/J.EPLEPSYRES.2021.106548
Lund, T. E., Madsen, K. H., Sidaros, K., Luo, W. L., & Nichols, T. E. (2006). Non-white noise in fMRI: does modelling have an impact? NeuroImage, 29(1), 54–66. https://doi.org/10.1016/J.NEUROIMAGE.2005.07.005
Marrelec, G., Bellec, P., Krainik, A., Duffau, H., Pélégrini-Issac, M., Lehéricy, S., Benali, H., & Doyon, J. (2008a). Regions, systems, and the brain: hierarchical measures of functional integration in fMRI. Medical Image Analysis, 12(4), 484–496. https://doi.org/10.1016/J.MEDIA.2008.02.002
Marrelec, G., Bellec, P., Krainik, A., Duffau, H., Pélégrini-Issac, M., Lehéricy, S., Benali, H., & Doyon, J. (2008b). Regions, systems, and the brain: hierarchical measures of functional integration in fMRI. Medical Image Analysis, 12(4), 484–496. https://doi.org/10.1016/J.MEDIA.2008.02.002
Marrelec, G., Horwitz, B., Kim, J., Pélégrini-Issac, M., Benali, H., & Doyon, J. (2007). Using partial correlation to enhance structural equation modeling of functional MRI data. Magnetic Resonance Imaging, 25(8), 1181–1189. https://doi.org/10.1016/J.MRI.2007.02.012
Marrelec, G., Krainik, A., Duffau, H., Pélégrini-Issac, M., Lehéricy, S., Doyon, J., & Benali, H. (2006a). Partial correlation for functional brain interactivity investigation in functional MRI. NeuroImage, 32(1), 228–237. https://doi.org/10.1016/J.NEUROIMAGE.2005.12.057
Marrelec, G., Krainik, A., Duffau, H., Pélégrini-Issac, M., Lehéricy, S., Doyon, J., & Benali, H. (2006b). Partial correlation for functional brain interactivity investigation in functional MRI. NeuroImage, 32(1), 228–237. https://doi.org/10.1016/J.NEUROIMAGE.2005.12.057
Marshall, L., Helgadóttir, H., Mölle, M., & Born, J. (2006). Boosting slow oscillations during sleep potentiates memory. Nature, 444(7119), 610–613. https://doi.org/10.1038/NATURE05278
Martín-López, D., Jiménez-Jiménez, D., Cabañés-Martínez, L., Selway, R. P., Valentín, A., & Alarcón, G. (2017). The Role of Thalamus Versus Cortex in Epilepsy: Evidence from Human Ictal Centromedian Recordings in Patients Assessed for Deep Brain Stimulation. International Journal of Neural Systems, 27(7). https://doi.org/10.1142/S0129065717500101
Massimini, M., Ferrarelli, F., Huber, R., Esser, S. K., Singh, H., & Tononi, G. (2005). Breakdown of cortical effective connectivity during sleep. Science (New York, N.Y.), 309(5744), 2228–2232. https://doi.org/10.1126/SCIENCE.1117256
Minoshima, S., Berger, K., Lee, K., & Mintun, M. (1992). An automated method for rotation correction and centering of 3D functional brain images. Journal of Nuclear Medicine, 33(8), 1579–1585.
Moussa, M. N., Steen, M. R., Laurienti, P. J., & Hayasaka, S. (2012). Consistency of Network Modules in Resting-State fMRI Connectome Data. PLoS ONE, 7(8), 44428. https://doi.org/10.1371/JOURNAL.PONE.0044428
Ngo, H. V. v., Martinetz, T., Born, J., & Mölle, M. (2013). Auditory closed-loop stimulation of the sleep slow oscillation enhances memory. Neuron, 78(3), 545–553. https://doi.org/10.1016/J.NEURON.2013.03.006
Nguyen, H. M., Chen, J., & Glover, G. (2022). Morphological Component Analysis of functional MRI Brain Networks. IEEE Transactions on Bio-Medical Engineering, PP. https://doi.org/10.1109/TBME.2022.3162606
Nir, Y., Mukamel, R., Dinstein, I., Privman, E., Harel, M., Fisch, L., Gelbard-Sagiv, H., Kipervasser, S., Andelman, F., Neufeld, M. Y., Kramer, U., Arieli, A., Fried, I., & Malach, R. (2008). Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nature Neuroscience, 11(9), 1100–1108. https://doi.org/10.1038/NN.2177
Ogawa, S., Lee, T. M., Kay, A. R., & Tank, D. W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences, 87(24), 9868–9872. https://doi.org/10.1073/PNAS.87.24.9868
Orżanowski, T. (2016). Nonuniformity correction algorithm with efficient pixel offset estimation for infrared focal plane arrays. SpringerPlus, 5(1). https://doi.org/10.1186/S40064-016-3534-1
Perez, C., & Germon, R. (2016). Graph Creation and Analysis for Linking Actors: Application to Social Data. Automating Open Source Intelligence: Algorithms for OSINT, 103–129. https://doi.org/10.1016/B978-0-12-802916-9.00007-5
Perlbarg, V., Bellec, P., Anton, J. L., Pélégrini-Issac, M., Doyon, J., & Benali, H. (2007). CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. Magnetic Resonance Imaging, 25(1), 35–46. https://doi.org/10.1016/J.MRI.2006.09.042
Pittau, F., Grova, C., Moeller, F., Dubeau, F., & Gotman, J. (2012). Patterns of altered functional connectivity in mesial temporal lobe epilepsy. Epilepsia, 53(6), 1013. https://doi.org/10.1111/J.1528-1167.2012.03464.X
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320–341. https://doi.org/10.1016/J.NEUROIMAGE.2013.08.048
Power, J. D., Schlaggar, B. L., Lessov-Schlaggar, C. N., & Petersen, S. E. (2013). Evidence for hubs in human functional brain networks. Neuron, 79(4), 798–813. https://doi.org/10.1016/J.NEURON.2013.07.035
Raichle, M. E., & Mintun, M. A. (2006). Brain work and brain imaging. Annual Review of Neuroscience, 29, 449–476. https://doi.org/10.1146/ANNUREV.NEURO.29.051605.112819
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. NeuroImage, 61(4), 1402–1418. https://doi.org/10.1016/J.NEUROIMAGE.2012.02.084
Rolls, E. T., Huang, C. C., Lin, C. P., Feng, J., & Joliot, M. (2020). Automated anatomical labelling atlas 3. NeuroImage, 206, 116189. https://doi.org/10.1016/J.NEUROIMAGE.2019.116189
Royer, J., Bernhardt, B. C., Larivière, S., Gleichgerrcht, E., Vorderwülbecke, B. J., Vulliémoz, S., & Bonilha, L. (2022). Epilepsy and brain network hubs. In Epilepsia (Vol. 63, Issue 3, pp. 537–550). John Wiley and Sons Inc. https://doi.org/10.1111/epi.17171
Royer, J., Rodríguez-Cruces, R., Tavakol, S., Larivière, S., Li, Q., Vos De Wael, R., Paquola, C., Benkarim, O., Park, B.-Y., Lowe, A. J., Margulies, D., Smallwood, J., Bernasconi, A., Bernasconi, N., Frauscher, B., Bernhardt, B. C., & Royer, J. D. (2021). An Open MRI Dataset for Multiscale Neuroscience. BioRxiv, 2021.08.04.454795. https://doi.org/10.1101/2021.08.04.454795
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. NeuroImage, 52(3), 1059–1069. https://doi.org/10.1016/J.NEUROIMAGE.2009.10.003
Sahoo, D., Satterthwaite, T. D., & Davatzikos, C. (2019). Extraction of hierarchical functional connectivity components in human brain using resting-state fMRI. https://doi.org/10.1109/TMI.2020.3042873
Saito, N. (1994). Simultaneous Noise Suppression and Signal Compression Using a Library of Orthonormal Bases and the Minimum Description Length Criterion. Wavelet Analysis and Its Applications, 4(C), 299–324. https://doi.org/10.1016/B978-0-08-052087-2.50017-7
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014). Automatic denoising of functional MRI data: combining independent component analysis and hierarchical fusion of classifiers. NeuroImage, 90, 449–468. https://doi.org/10.1016/J.NEUROIMAGE.2013.11.046
Salvador, R., Suckling, J., Schwarzbauer, C., & Bullmore, E. (2005). Undirected graphs of frequency-dependent functional connectivity in whole brain networks. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 937. https://doi.org/10.1098/RSTB.2005.1645
Sämann, P. G., Wehrle, R., Hoehn, D., Spoormaker, V. I., Peters, H., Tully, C., Holsboer, F., & Czisch, M. (2011). Development of the brain’s default mode network from wakefulness to slow wave sleep. Cerebral Cortex (New York, N.Y. : 1991), 21(9), 2082–2093. https://doi.org/10.1093/CERCOR/BHQ295
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cerebral Cortex (New York, N.Y. : 1991), 28(9), 3095–3114. https://doi.org/10.1093/CERCOR/BHX179
Seghouane, A. K., & Iqbal, A. (2017). Sequential dictionary learning from correlated data: Application to fMRI data analysis. IEEE Transactions on Image Processing, 26(6), 3002–3015. https://doi.org/10.1109/TIP.2017.2686014
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143–155. https://doi.org/10.1002/HBM.10062
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences of the United States of America, 106(31), 13040–13045. https://doi.org/10.1073/PNAS.0905267106/SUPPL_FILE/0905267106SI.PDF
Smitha, K. A., Arun, K. M., Rajesh, P. G., Joel, S. E., Venkatesan, R., Thomas, B., & Kesavadas, C. (2018). Multiband fMRI as a plausible, time-saving technique for resting-state data acquisition: Study on functional connectivity mapping using graph theoretical measures. Magnetic Resonance Imaging, 53, 1–6. https://doi.org/10.1016/J.MRI.2018.06.013
Spoormaker, V. I., Czisch, M., Maquet, P., & Jäncke, L. (2011). Large-scale functional brain networks in human non-rapid eye movement sleep: Insights from combined electroencephalographic/functional magnetic resonance imaging studies. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 369(1952), 3708–3729. https://doi.org/10.1098/RSTA.2011.0078
Spoormaker, V. I., Schröter, M. S., Gleiser, P. M., Andrade, K. C., Dresler, M., Wehrle, R., Sämann, P. G., & Czisch, M. (2010). Development of a Large-Scale Functional Brain Network during Human Non-Rapid Eye Movement Sleep. Journal of Neuroscience, 30(34), 11379–11387. https://doi.org/10.1523/JNEUROSCI.2015-10.2010
Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science (New York, N.Y.), 262(5134), 679–685. https://doi.org/10.1126/SCIENCE.8235588
Stretton, J., & Thompson, P. J. (2012). Frontal lobe function in temporal lobe epilepsy. Epilepsy Research, 98(1), 1. https://doi.org/10.1016/J.EPLEPSYRES.2011.10.009
Tagliazucchi, E., von Wegner, F., Morzelewski, A., Brodbeck, V., Jahnke, K., & Laufs, H. (2013). Breakdown of long-range temporal dependence in default mode and attention networks during deep sleep. Proceedings of the National Academy of Sciences of the United States of America, 110(38), 15419–15424. https://doi.org/10.1073/PNAS.1312848110/SUPPL_FILE/SAPP.PDF
Taillard, J., Sagaspe, P., Berthomier, C., Brandewinder, M., Amieva, H., Dartigues, J. F., Rainfray, M., Harston, S., Micoulaud-Franchi, J. A., & Philip, P. (2019). Non-REM Sleep Characteristics Predict Early Cognitive Impairment in an Aging Population. Frontiers in Neurology, 10. https://doi.org/10.3389/FNEUR.2019.00197
Thomas 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., Fisch, B., Liu, H., & Buckner, R. L. (2011a). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/JN.00338.2011
Thomas 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., Fisch, B., Liu, H., & Buckner, R. L. (2011b). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125–1165. https://doi.org/10.1152/JN.00338.2011
Tononi, G., Sporns, O., & Edelman, G. M. (1994). A measure for brain complexity: relating functional segregation and integration in the nervous system. Proceedings of the National Academy of Sciences of the United States of America, 91(11), 5033. https://doi.org/10.1073/PNAS.91.11.5033
Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., & Joliot, M. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15(1), 273–289. https://doi.org/10.1006/nimg.2001.0978
Uji, M., Cross, N., Pomares, F. B., Perrault, A. A., Jegou, A., Nguyen, A., Aydin, U., Lina, J. M., Dang-Vu, T. T., & Grova, C. (2021). Data-driven beamforming technique to attenuate ballistocardiogram artefacts in electroencephalography-functional magnetic resonance imaging without detecting cardiac pulses in electrocardiography recordings. Human Brain Mapping, 42(12), 3993–4021. https://doi.org/10.1002/HBM.25535
Urchs, S., Armoza, J., Moreau, C., Benhajali, Y., St-Aubin, J., Orban, P., & Bellec, P. (2019). MIST: A multi-resolution parcellation of functional brain networks. MNI Open Research, 1, 3. https://doi.org/10.12688/mniopenres.12767.2
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010a). Exploring the brain network: A review on resting-state fMRI functional connectivity. European Neuropsychopharmacology, 20, 519–534. https://doi.org/10.1016/j.euroneuro.2010.03.008
van den Heuvel, M. P., & Hulshoff Pol, H. E. (2010b). Exploring the brain network: A review on resting-state fMRI functional connectivity. In European Neuropsychopharmacology (Vol. 20, Issue 8, pp. 519–534). https://doi.org/10.1016/j.euroneuro.2010.03.008
Vatansever, D., Schröter, M., Adapa, R. M., Bullmore, E. T., Menon, D. K., & Stamatakis, E. A. (2020). Reorganisation of Brain Hubs across Altered States of Consciousness. Scientific Reports 2020 10:1, 10(1), 1–11. https://doi.org/10.1038/s41598-020-60258-1
Velíšek, L. (2018). “Can You Hear Me Now?” AMPA Receptor-Mediated Tonotopy Disruption by Early Life Seizures. Epilepsy Currents, 18(6), 391–393. https://doi.org/10.5698/1535-7597.18.6.391
Vincent, J. L., Snyder, A. Z., Fox, M. D., Shannon, B. J., Andrews, J. R., Raichle, M. E., & Buckner, R. L. (2006). Coherent spontaneous activity identifies a hippocampal-parietal memory network. Journal of Neurophysiology, 96(6), 3517–3531. https://doi.org/10.1152/JN.00048.2006
Viviani, R., Grön, G., & Spitzer, M. (2005). Functional principal component analysis of fMRI data. Human Brain Mapping, 24(2), 109. https://doi.org/10.1002/HBM.20074
Výtvarová, E., Mareček, R., Fousek, J., Strýček, O., & Rektor, I. (2017). Large-scale cortico-subcortical functional networks in focal epilepsies: The role of the basal ganglia. NeuroImage : Clinical, 14, 28. https://doi.org/10.1016/J.NICL.2016.12.014
Waites, A. B., Briellmann, R. S., Saling, M. M., Abbott, D. F., & Jackson, G. D. (2006). Functional connectivity networks are disrupted in left temporal lobe epilepsy. Annals of Neurology, 59(2), 335–343. https://doi.org/10.1002/ANA.20733
Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature 1998 393:6684, 393(6684), 440–442. https://doi.org/10.1038/30918
Woods, R. P., Mazziotta, J. C., & Cherry, S. R. (1993). MRI-PET Registration with Automated Algorithm. Journal of Computer Assisted Tomography, 17, 536–546.
Woolrich, M. W., Jbabdi, S., Patenaude, B., Chappell, M., Makni, S., Behrens, T., Beckmann, C., Jenkinson, M., & Smith, S. M. (2009). Bayesian analysis of neuroimaging data in FSL. NeuroImage, 45(1 Suppl). https://doi.org/10.1016/J.NEUROIMAGE.2008.10.055
Worsley, K. J., Chen, J. I., Lerch, J., & Evans, A. C. (2005). Comparing functional connectivity via thresholding correlations and singular value decomposition. Philosophical Transactions of the Royal Society B: Biological Sciences, 360(1457), 913–920. https://doi.org/10.1098/RSTB.2005.1637
Xie, J., Douglas, P. K., Wu, Y. N., Brody, A. L., & Anderson, A. E. (2017). Decoding the encoding of functional brain networks: An fMRI classification comparison of non-negative matrix factorization (NMF), independent component analysis (ICA), and sparse coding algorithms. Journal of Neuroscience Methods, 282, 81–94. https://doi.org/10.1016/j.jneumeth.2017.03.008
Yeo, B. T. T., Krienen, F. M., Chee, M. W. L., & Buckner, R. L. (2014). Estimates of Segregation and Overlap of Functional Connectivity Networks in the Human Cerebral Cortex. NeuroImage, 88, 212. https://doi.org/10.1016/J.NEUROIMAGE.2013.10.046
Zhang, D., & Raichle, M. E. (2010). Disease and the brain’s dark energy. Nature Reviews. Neurology, 6(1), 15–28. https://doi.org/10.1038/NRNEUROL.2009.198
Zhou, Y., Milham, M. P., Lui, Y. W., Miles, L., Reaume, J., Sodickson, D. K., Grossman, R. I., & Ge, Y. (2012). Default-Mode Network Disruption in Mild Traumatic Brain Injury. Radiology, 265(3), 882. https://doi.org/10.1148/RADIOL.12120748
Zijdenbos, A. P., Forghani, R., & Evans, A. C. (2002). Automatic “pipeline” analysis of 3-D MRI data for clinical trials: application to multiple sclerosis. IEEE Transactions on Medical Imaging, 21(10), 1280–1291. https://doi.org/10.1109/TMI.2002.806283
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
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