Network Models in Neuroimaging : A Survey of Multimodal Applications

• Autorzy: Mancini M. , Cercignani M.

Identyfikatory

DOI

10.3233/FI-2018-1732

• Języki publikacji: EN

Abstrakty

EN

Mapping the brain structure and function is one of the hardest problems in science. Different image modalities, in particular the ones based on magnetic resonance imaging (MRI) can shed more light on how it is organised and how its functions unfold, but a theoretical framework is needed. In the last years, using network models and graph theory to represent the brain structure and function has become a major trend in neuroscience. In this review, we outline how network modelling has been used in neuroimaging, clarifying what are the underlying mathematical concepts and the consequent methodological choices. The major findings are then presented for structural, functional and multimodal applications. We conclude outlining what are still the current issues and the perspective for the immediate future.

Słowa kluczowe

EN

network models; graph theory; neuroimaging; brain connectivity; connectome

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2018
• Tom: Vol. 163, nr 1
• Strony: 63--91
• Opis fizyczny: Bibliogr. 149 poz., rys., wykr.

Twórcy

autor

Mancini M.

• Centre for Medical Image Computing, University College London, London, UK

autor

Cercignani M.

• Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, UK

Bibliografia

• [1] Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K, Consortium WMH. The WU-Minn Human Connectome Project: An overview. Neuroimage, 2013. 80:62-79. doi:10.1016/j.neuroimage.2013.05.041.
• [2] Rose N. The Human Brain Project: Social and Ethical Challenges. Neuron, 2014. 82(6):1212-1215. doi:10.1016/j.neuron.2014.06.001.
• [3] Jorgenson LA, Newsome WT, Anderson DJ, Bargmann CI, Brown EN, Deisseroth K, Donoghue JP, Hudson KL, Ling GS, MacLeish PR, Marder E, Normann RA, Sanes JR, Schnitzer MJ, Sejnowski TJ, Tank DW, Tsien RY, Ugurbil K, Wingfield JC. The BRAIN Initiative: developing technology to catalyse neuroscience discovery. Philos Trans R Soc Lond B Biol Sci, 2015. 370(1668). doi:10.1098/rstb.2014.0164.
• [4] Thompson PM, Stein JL, Medland SE, Hibar DP, Vasquez AA, Renteria ME, Toro R, Jahanshad N, Schumann G, Franke B, Wright MJ, Martin NG, Agartz I, Alda M, Alhusaini S, Almasy L, Almeida J, Alpert K, Andreasen NC, Andreassen OA, Apostolova LG, Appel K, Armstrong NJ, Aribisala B, Bastin ME, Bauer M, Bearden CE, Bergmann O, Binder EB, Blangero J, Bockholt HJ, Boen E, Bois C, Boomsma DI, Booth T, Bowman IJ, Bralten J, Brouwer RM, Brunner HG, Brohawn DG, Buckner RL, Buitelaar J, Bulayeva K, Bustillo JR, Calhoun VD, Cannon DM, Cantor RM, Carless MA, Caseras X, Cavalleri GL, Chakravarty MM, Chang KD, Ching CRK, Christoforou A, Cichon S, Clark VP, Conrod P, Coppola G, Crespo-Facorro B, Curran JE, Czisch M, Deary IJ, de Geus EJC, den Braber A, Delvecchio G, Depondt C, de Haan L, de Zubicaray GI, Dima D, Dimitrova R, Djurovic S, Dong HW, Donohoe G, Duggirala R, Dyer TD, Ehrlich S, Ekman CJ, Elvsashagen T, Emsell L, Erk S, Espeseth T, Fagerness J, Fears S, Fedko I, Fernandez G, Fisher SE, Foroud T, Fox PT, Francks C, Frangou S, Frey EM, Frodl T, Frouin V, Garavan H, Giddaluru S, Glahn DC, Godlewska B, Goldstein RZ, Gollub RL, Grabe HJ, et al. The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging and Behavior, 2014. 8(2):153-182. doi:10.1007/s11682-013-9269-5.
• [5] Petersen RC, Aisen PS, Beckett LA, Donohue MC, Gamst AC, Harvey DJ, Jack CR, Jagust WJ, Shaw LM, Toga AW, Trojanowski JQ, Weiner MW. Alzheimer’s Disease Neuroimaging Initiative (ADNI) Clinical characterization. Neurology, 2010. 74(3):201-209. doi:10.1212/WNL.0b013e3181cb3e25.
• [6] Sporns O. Networks of the Brain. MIT Press, 2010. ISBN-10: 0262014696, 13: 978-0262014694.
• [7] Estrada E, Knight P. A first course in network theory. 2015. ISBN-9780198726463.
• [8] Bullmore E, Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems (vol 10, pg 186, 2009). Nature Reviews Neuroscience, 2009. 10(4). doi:10.1038/nrn2575.
• [9] Zhu DJ, Zhang T, Jiang X, Hu XT, Chen HB, Yang N, Lv JL, Han JW, Guo L, Liu TM. Fusing DTI and fMRI data: A survey of methods and applications. Neuroimage, 2014. 102:184-191. doi:10.1016/j.neuroimage.2013.09.071.
• [10] Sporns O, Tononi G, Kotter R. The human connectome: A structural description of the human brain. Plos Computational Biology, 2005. 1(4):245-251. URL https://doi.org/10.1371/journal.pcbi.0010042.
• [11] Hagmann P. From diffusion MRI to brain connectomics. Ph.D. thesis, EPFL, 2005.
• [12] Bullmore ET, Sporns O. The economy of brain network organization. Nature Reviews Neuroscience, 2012. 13(5):336-349. doi:10.1038/nrn3214.
• [13] Fornito A, Bullmore E, Zalesky A. Fundamentals of brain network analysis. Elsevier, Amsterdam, 2016. ISBN-9780124079083.
• [14] Erdös P, Renyi A. On random graphs. Publicationes Mathematicae, 1959. 6:290.
• [15] Granovetter MS. The Strength of Weak Ties. American Journal of Sociology, 1973. 78(6):1360-1380. URL https://doi.org/10.1086/225469.
• [16] Euler L. Solutio Problemat is ad Geometriam Situs Pertinentis. Commentarii Academiae Scientiarum Imperialis Petropolitanae, 1741. 8:128-140.
• [17] von Warnsdorf H. Rsselsprungs einfachste und allgemeinste Lsung. Th G Fr Varnhagensehen Buchhandlung, 1823. URL https://www.bibsonomy.org/bibtex/253bc3d5b5ee9e509d076e75f204da23c/jimbotonic.
• [18] Börner K, Sanyal S, Vespignani A. Network science. Annual Review of Information Science and Technology, 2007. 41:537-607. URL https://doi.org/10.1002/aris.2007.1440410119.
• [19] Barabasi AL, Posfai M. Network Science. Cambridge University Press, Cambridge, 2016. ISBN-10:1107076269, 13: 978-1107076266.
• [20] Sayama H, Cramer C, Porter M, Sheetz L, Uzzo S. What are essential concepts about networks? Journal of Complex Networks, 2015. 4(3):457-474. URL https://doi.org/10.1093/comnet/cnv028.
• [21] Costa LD, Rodrigues FA, Travieso G, Boas PRV. Characterization of complex networks: A survey of measurements. Advances in Physics, 2007. 56(1):167-242. doi:10.1080/00018730601170527.
• [22] Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 2010. 52(3):1059-1069. doi:10.1016/j.neuroimage.2009.10.003.
• [23] Guimera R, Amaral LAN. Functional cartography of complex metabolic networks. Nature, 2005. 433(7028):895-900. doi:10.1038/nature03288.
• [24] Watts DJ, Strogatz SH. Collective dynamics of ’small-world’ networks. Nature, 1998. 393(6684):440-442. doi:10.1038/30918.
• [25] Barabasi AL, Albert R. Emergence of scaling in random networks. Science, 1999. 286(5439):509-512. doi:10.1126/science.286.5439.509.
• [26] van den Heuvel MP, Sporns O. Rich-Club Organization of the Human Connectome. Journal of Neuroscience, 2011. 31(44):15775-15786. doi:10.1523/JNEUROSCI.3539-11.2011.
• [27] Albert R, Jeong H, Barabasi AL. Error and attack tolerance of complex networks. Nature, 2000. 406(6794):378-382. doi:10.1038/35019019.
• [28] Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature, 2001. 412(6843):150-157. doi:10.1038/35084005.
• [29] Ogawa S, Lee TM, Nayak AS, Glynn P. Oxygenation-Sensitive Contrast in Magnetic-Resonance Image of Rodent Brain at High Magnetic-Fields. Magnetic Resonance in Medicine, 1990. 14(1):68-78. doi:10.1002/mrm.1910140108.
• [30] Biswal B, Yetkin FZ, Haughton VM, Hyde JS. Functional Connectivity in the Motor Cortex of Resting Human Brain Using Echo-Planar Mri. Magnetic Resonance in Medicine, 1995. 34(4):537-541. doi:10.1002/mrm.1910340409.
• [31] Sakkalis V. Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG. Computers in Biology and Medicine, 2011. 41(12):1110-1117. doi:10.1016/j.compbiomed.2011.06.020.
• [32] Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford University Press, 2006. ISBN-13:9780195104233.
• [33] LeBihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeanet M. MR imaging of intra-voxel inchoerent motions: application to diffusion and perfusion in neurologic disorders. Radiology, 1986. 161:401-407.
• [34] Moseley ME, Kucharczyk J, Asgari HS, Norman D. Anisotropy in Diffusion-Weighted Mri. Magnetic Resonance in Medicine, 1991. 19(2):321-326.
• [35] Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A. In vivo fiber tractography using DT-MRI data. Magnetic Resonance in Medicine, 2000. 44(4):625-632. URL https://doi.org/10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O.
• [36] Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen V, Sporns O. Mapping the structural core of human cerebral cortex. Plos Biology, 2008. 6(7):1479-1493. doi:10.1371/journal.pbio.0060159.
• [37] Jeurissen B, Leemans A, Tournier JD, Jones DK, Sijbers J. Investigating the Prevalence of Complex Fiber Configurations in White Matter Tissue with Diffusion Magnetic Resonance Imaging. Human Brain Mapping, 2013. 34(11):2747-2766. doi:10.1002/hbm.22099.
• [38] Assaf Y, Freidlin RZ, Rohde GK, Basser PJ. New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magnetic Resonance in Medicine, 2004. 52(5):965-978. doi:10.1002/mrm.20274.
• [39] Henkelman RM, Stanisz GJ, Graham SJ. Magnetization transfer in MRI: a review. Nmr in Biomedicine, 2001. 14(2):57-64. doi:10.1002/nbm.683.
• [40] Swanson LW, Lichtman JW. From Cajal to Connectome and Beyond. Annual Review of Neuroscience, Vol 39, 2016. 39:197-216. doi:10.1146/annurev-neuro-071714-033954.
• [41] Lichtman JW, Denk W. The Big and the Small: Challenges of Imaging the Brain’s Circuits. Science, 2011. 334(6056):618-623. doi:10.1126/science.1209168.
• [42] Lichtman JW, Pfister H, Shavit N. The big data challenges of connectomics. Nature Neuroscience, 2014. 17(11):1448-1454. doi:10.1038/nn.3837.
• [43] Jain V, Seung HS, Turaga SC. Machines that learn to segment images: a crucial technology for connectomics. Current Opinion in Neurobiology, 2010. 20(5):653-666. doi:10.1016/j.conb.2010.07.004.
• [44] Logothesis N. What we can do and what we cannot do with fMRI. Nature, 2008. 453(7191): 869-878.doi:10.1038/nature06976.
• [45] Van den Heuvel M, Hulshoffpol HE, Pol HEH. Exploring the brain network: A review on restingstate fMRI functional connectivity. European Neuropsychopharmacology, 2010. 20(8):519-534. doi:10.1016/j.euroneuro.2010.03.008.
• [46] Bullmore ET, Fadili J, Maxim V, Sendur L, Whitcher B, Suckling J, Brammer M, Breakspear M. Wavelets and functional magnetic resonance imaging of the human brain. Neuroimage, 2004. 23:S234-S249. doi:10.1016/j.neuroimage.2004.07.012.
• [47] van den Heuvel MP, Mandl RCW, Stam CJ, Kahn RS, Pol HEH. Aberrant Frontal and Temporal Complex Network Structure in Schizophrenia: A Graph Theoretical Analysis. Journal of Neuroscience, 2010. 30(47):15915-15926. doi:10.1523/JNEUROSCI.2874-10.2010.
• [48] van den Heuvel MP, Sporns O. Network hubs in the human brain. Trends in Cognitive Sciences, 2013. 17(12):683-696. doi:10.1016/j.tics.2013.09.012.
• [49] Collin G, Sporns O, Mandl RCW, van den Heuvel MP. Structural and Functional Aspects Relating to Cost and Benefit of Rich Club Organization in the Human Cerebral Cortex. Cerebral Cortex, 2014. 24(9):2258-2267. doi:10.1093/cercor/bht064.
• [50] Senden M, Deco G, de Reus MA, Goebel R, van den Heuvel MP. Rich club organization supports a diverse set of functional network configurations. Neuroimage, 2014. 96:174-182. URL https://doi.org/10.1016/j.neuroimage.2014.03.066.
• [51] van den Heuvel MP, Sporns O, Collin G, Scheewe T, Mandl RCW, Cahn W, Goni J, Pol HEH, Kahn RS. Abnormal Rich Club Organization and Functional Brain Dynamics in Schizophrenia. Jama Psychiatry, 2013. 70(8):783-792. doi:10.1001/jamapsychiatry.2013.1328.
• [52] Collin G, van den Heuvel MP, Abramovic L, Vreeker A, de Reus MA, van Haren NEM, Boks MPM, Ophoff RA, Kahn RS. Brain Network Analysis Reveals Affected Connectome Structure in Bipolar I Disorder. Human Brain Mapping, 2016. 37(1):122-134. doi:10.1002/hbm.23017.
• [53] Mallio CA, Schmidt R, de Reus MA, Vernieri F, Quintiliani L, Curcio G, Zobel BB, Quattrocchi CC, van den Heuvel MP. Epicentral Disruption of Structural Connectivity in Alzheimer’s Disease. Cns Neuroscience & Therapeutics, 2015. 21(10):837-845. doi:10.1111/cns.12397.
• [54] Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET. The hubs of the human connectome are generally implicated in the anatomy of brain disorders (vol 137, 2382, 2014). Brain, 2015. 138:E374-E374.
• [55] Irimia A, Van Horn JD. Systematic network lesioning reveals the core white matter scaffold of the human brain. Frontiers in Human Neuroscience, 2014. 8. doi:10.3389/fnhum.2014.00051.
• [56] De Reus MA, Van den Heuvel M. Simulated rich club lesioning in brain networks: a scaffold for communication integration? Frontiers in Human neuroscience, 2014. 8:647. doi:10.3389/fnhum.2014.00647.
• [57] Aerts H, Fias W, Caeyenberghs K, Marinazzo D. Brain networks under attack: robustness properties and the impact of lesions. Brain, 2016. 139:3063-3083. doi:10.1093/brain/aww194.
• [58] Duffau H. Brain plasticity: From pathophysiological mechanisms to therapeutic applications. Journal of Clinical Neuroscience, 2006. 13(9):885-897. doi:10.1016/j.jocn.2005.11.045.
• [59] Griffa A, Baumann PS, Thiran JP, Hagmann P. Structural connectomics in brain diseases. Neuroimage, 2013. 80:515-526. doi:10.1016/j.neuroimage.2013.04.056.
• [60] De Reus MA. An eccentric perspective on brain networks. Ph.D. thesis, UMC, 2015.
• [61] de Reus MA, Saenger VM, Kahn RS, van den Heuvel MP. An edge-centric perspective on the human connectome: link communities in the brain. Philosophical Transactions of the Royal Society B-Biological Sciences, 2014. 369(1653). doi:10.1098/rstb.2013.0527.
• [62] Zalesky A, Fornito A, Bullmore ET. Network-based statistic: Identifying differences in brain networks. Neuroimage, 2010. 53(4):1197-1207.
• [63] de Lange S, de Reus MA, Van den Heuvel M. The Laplacian spectrum of neural networks. Frontiers in Computational Neuroscience, 2014. 7:189. doi:10.3389/fncom.2013.00189.
• [64] Cocchi L, Zalesky A, Fornito A, Mattingley JB. Dynamic cooperation and competition between brain systems during cognitive control. Trends in Cognitive Sciences, 2013. 17(10):493-501. URL https://doi.org/10.1016/j.tics.2013.08.006.
• [65] Gu S, Pasqualetti F, Cieslak M, Telesford QK, Yu AB, Kahn AE, Medaglia JD, Vettel JM, Miller MB, Grafton ST, Bassett DS. Controllability of structural brain networks. Nature Communications, 2015. 6. doi:10.1038/ncomms9414.
• [66] Power JD, Schlaggar BL, Lessov-Schlaggar CN, Petersen SE. Evidence for Hubs in Human Functional Brain Networks. Neuron, 2013. 79(4):798-813. URL https://doi.org/10.1016/j.neuron.2013.07.035.
• [67] Achard S, Salvador R, Whitcher B, Suckling J, Bullmore ET. A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. Journal of Neuroscience, 2006. 26(1):63-72. doi:10.1523/JNEUROSCI.3874-05.2006.
• [68] Cole MW, Reynolds JR, Power JD, Repovs G, Anticevic A, Braver TS. Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 2013. 16(9):1348-U247. doi:10.1038/nn.3470.
• [69] Achard S, Delon-Martin C, Vertes PE, Renard F, Schenck M, Schneider F, Heinrich C, Kremer S, Bullmore ET. Hubs of brain functional networks are radically reorganized in comatose patients. Proceedings of the National Academy of Sciences of the United States of America, 2012. 109(50):20608-20613. URL https://doi.org/10.1073/pnas.1208933109.
• [70] Sanz-Arigita EJ, Schoonheim MM, Damoiseaux JS, Rombouts SARB, Maris E, Barkhof F, Scheltens P, Stam CJ. Loss of ’Small-World’ Networks in Alzheimer’s Disease: Graph Analysis of fMRI Resting-State Functional Connectivity. Plos One, 2010. 5(11). URL https://doi.org/10.1371/journal.pone.0013788.
• [71] Baggio HC, Sala-Llonch R, Segura B, Marti MJ, Valldeoriola F, Compta Y, Tolosa E, Junque C. Functional Brain Networks and Cognitive Deficits in Parkinson’s Disease. Human Brain Mapping, 2014. 35(9):4620-4634. doi:10.1002/hbm.22499.
• [72] Wen T, Hsieh S. Network-Based Analysis Reveals Functional Connectivity Related to Internet Addiction Tendency. Frontiers in Human Neuroscience, 2016. 10(6). doi:10.3389/fnhum.2016.00006.
• [73] Dipasquale O, Cercignani M. Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions. Functional Neurology, 2016. 31(4):191-203. doi:10.11138/FNeur/2016.31.4.191.
• [74] Bassett DS, Bullmore ET. Human brain networks in health and disease. Current Opinion in Neurology, 2009. 22(4):340-347. doi:10.1097/WCO.0b013e32832d93dd.
• [75] Sporns O, Betzel RF. Modular Brain Networks. Annual Review of Psychology, Vol 67, 2016. 67:613-640. doi:10.1146/annurev-psych-122414-033634.
• [76] Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, Vogel AC, Laumann TO, Miezin FM, Schlaggar BL, Petersen SE. Functional Network Organization of the Human Brain. Neuron, 2011. 72(4):665-678. doi:10.1016/j.neuron.2011.09.006.
• [77] Meunier D, Achard S, Morcom A, Bullmore ET. Age-related changes in modular organization of human brain functional networks. Neuroimage, 2009. 44(3):715-723. doi:10.1016/j.neuroimage.2008.09.062’.
• [78] Meunier D, Lambiotte R, Fornito A, Ersche K, Bullmore E. Hierarchical modularity in human brain functional networks. Frontiers in Neuroinformatics, 2009. 3:37. doi:10.3389/neuro.11.037.2009.
• [79] Damoiseaux JS, Greicius MD. Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Structure & Function, 2009. 213(6):525-533. doi:10.1007/s00429-009-0208-6.
• [80] Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P. Predicting human resting-state functional connectivity from structural connectivity. Proceedings of the National Academy of Sciences of the United States of America, 2009. 106(6):2035-2040. URL https://doi.org/10.1073/pnas.0811168106.
• [81] Goni J, van den Heuvel MP, Avena-Koenigsberger A, de Mendizabal NV, Betzel RF, Griffa A, Hagmann P, Corominas-Murtra B, Thiran JP, Sporns O. Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111(2):833-838. URL https://doi.org/10.1073/pnas.1315529111.
• [82] Misic B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR, Sporns O. Network-Level Structure-Function Relationships in Human Neocortex. Cerebral Cortex, 2016. 26(7):3285-3296. doi:10.1093/cercor/bhw089.
• [83] Hermundstad AM, Brown KS, Bassett DS, Aminoff EM, Frithsen A, Johnson A, Tipper CM, Miller MB, Grafton ST, Carlson JM. Structurally-Constrained Relationships between Cognitive States in the Human Brain. Plos Computational Biology, 2014. 10(5). URL https://doi.org/10.1371/journal.pcbi.1003591.
• [84] Schmidt R, Verstraete E, de Reus MA, Veldink JH, van den Berg LH, van den Heuvel MP. Correlation Between Structural and Functional Connectivity Impairment in Amyotrophic Lateral Sclerosis. Human Brain Mapping, 2014. 35(9):4386-4395. doi:10.1002/hbm.22481.
• [85] Wirsich J, Perry A, Ridley B, Proix T, Golos M, Benar C, Ranjeva JP, Bartolomei F, Breakspear M, Jirsa V, Guye M. Whole-brain analytic measures of network communication reveal increased structure function correlation in right temporal lobe epilepsy. Neuroimage-Clinical, 2016. 11:707-718. URL https://doi.org/10.1016/j.nicl.2016.05.010.
• [86] Rodrigues FA, Peron TKDM, Ji P, Kurths J. The Kuramoto model in complex networks. Physics Reports-Review Section of Physics Letters, 2016. 610:1-98. URL https://doi.org/10.1016/j.physrep.2015.10.008.
• [87] Breakspear M, Heitmann S, Daffertshofer A. Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model. Frontiers in Human Neuroscience, 2010. 4:190. doi:10.3389/fnhum.2010.00190.
• [88] Schmidt R, LaFleur KJR, de Reus MA, van den Berg LH, van den Heuvel MP. Kuramoto model simulation of neural hubs and dynamic synchrony in the human cerebral connectome. Bmc Neuroscience, 2015. 16(54). doi:10.1186/s12868-015-0193-z.
• [89] Cocchi L, Sale ML, Gollo L, Bell P, Nguyen V, Zalesky A. A Hierarchy of Timescales Explains Distinct Effects of Local Inhibition of primary Visual Cortex and frontal Eye Fields. eLife, 2016. 5. doi:10.7554/eLife.15252.
• [90] Tymofiyeva O, Connolly CG, Ho TC, Sacchet MD, Blom EH, LeWinn KZ, Xu D, Yang TT. DTI-based connectome analysis of adolescents with major depressive disorder reveals hypoconnectivity of the right caudate. Journal of Affective Disorders, 2017. 207:18-25. doi:10.1016/j.jad.2016.09.013.
• [91] Llufriu S, Martinez-Heras E, Solana E, Sola-Valls N, Sepulveda M, Blanco Y, Martinez-Lapiscina EH, Andorra M, Villoslada P, Prats-Galino A, Saiz A. Structural networks involved in attention and executive functions in multiple sclerosis. Neuroimage-Clinical, 2017. 13:288-296. doi:10.1016/j.nicl.2016.11.026.
• [92] Zhang Y, Li M, Wang R, Bi Y, Li Y, Yi Z. Abnormal brain white matter network in young smokers: a graph theory analysis study. Brain Imaging and Behavior, 2017. doi:0.1007/s11682-017-9699-7.
• [93] O’Donoghue S, Kilmartin L, O’Hora D, Emsell L, Langan C, McInerney S, Forde NJ, Leemans A, Jeurissen B, Barker GJ, McCarthy P, Cannon DM, McDonald C. Anatomical integration and rich-club connectivity in euthymic bipolar disorder. Psychological Medicine, 2017. 47(9):1609-1623. doi:10.1017/S0033291717000058.
• [94] Ballester-Plane J, Schmidt R, Laporta-Hoyos O, Junque C, Vazquez E, Delgado I, Zubiaurre-Elorza L, Macaya A, Poo P, Toro E, de Reus MA, van den Heuvel MP, Pueyo R. Whole-Brain Structural Connectivity in Dyskinetic Cerebral Palsy and Its Association With Motor and Cognitive Function. Human Brain Mapping, 2017. 38(9):4594-4612. doi:10.1002/hbm.23686.
• [95] Reijmer YD, Fotiadis P, Charidimou A, van Veluw SJ, Xiong L, Riley GA, Martinez-Ramirez S, Schwab K, Viswanathan A, Gurol ME, Greenberg SM. Relationship Between White Matter Connectivity Loss and Cortical Thinning in Cerebral Amyloid Angiopathy. Human Brain Mapping, 2017. 38(7):3723-3731. doi:10.1002/hbm.23629.
• [96] Fischer FU, Wolf D, Scheurich A, Fellgiebel A, Neuroimaging AD. Altered whole-brain white matter networks in preclinical Alzheimer’s disease. Neuroimage-Clinical, 2015. 8:660-666. doi:10.1016/j.nicl.2015.06.007.
• [97] Wierenga LM, van den Heuvel MP, van Dijk S, Rijks Y, de Reus MA, Durston S. The Development of Brain Network Architecture. Human Brain Mapping, 2016. 37(2):717-729. doi:10.1002/hbm.23062.
• [98] Lemkaddem A, Daducci A, Kunz N, Lazeyras F, Seeck M, Thiran JP, Vulliemoz S. Connectivity and tissue microstructural alterations in right and left temporal lobe epilepsy revealed by diffusion spectrum imaging. Neuroimage-Clinical, 2014. 5:349-358. URL https://doi.org/10.1016/j.nicl.2014.07.013.
• [99] Batalle D, Hughes EJ, Zhang H, Tournier JD, Tusor N, Aljabar P, Wali L, Alexander DC, Hajnal JV, Nosarti C, Edwards AD, Counsell SJ. Early development of structural networks and the impact of prematurity on brain connectivity. Neuroimage, 2017. 149:379-392. doi:10.1016/j.neuroimage.2017.01.065.
• [100] Mancini M, Giulietti G, Spanò B, Bozzali M, Cercignani M, Conforto S. Estimating multimodal brain connectivity in multiple sclerosis: an exploratory factor analysis. 2016 38th Annual International Conference of the Ieee Engineering in Medicine and Biology Society (Embc), 2016. pp. 1131-1134. doi:10.1109/EMBC.2016.7590903.
• [101] Tournier J, Calamante F, Gadian G, Connelly A. Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. NeuroImage, 2004. 23(3):1176-1185. doi:10.1016/j.neuroimage.2004.07.037.
• [102] Callaghan P, Eccles C, Xia Y. NMR microscopy displacements: k-space and q-space imaging. Journal of Physics E, 1988. 21:820.
• [103] De Santis S, Gabrielli A, Palombo M, Maraviglia B, Capuani S. Non-gaussian diffusion imaging: a brief practical review. Magnetic Resonance Imaging, 2011. 29(10):1410-1416. doi:10.1016/j.mri.2011.04.006.
• [104] Pardini M, Yaldizli O, Sethi V, Muhlert N, Liu Z, Samson RS, Altmann DR, Ron MA, Wheeler-Kingshott CAM, Miller DH, Chard DT. Motor network efficiency and disability in multiple sclerosis. Neurology, 2015. 85(13):1115-1122. doi:10.1212/WNL.0000000000001970.
• [105] Lin YC, Daducci A, Meskaldji DE, Thiran JP, Michel P, Meuli R, Krueger G, Menegaz G, Granziera C. Quantitative Analysis of Myelin and Axonal Remodeling in the Uninjured Motor Network After Stroke. Brain Connect, 2015. 5(7):401-12. doi:10.1089/brain.2014.0245.
• [106] Stikov N, Campbell JS, Stroh T, Lavelee M, Frey S, Novek J, Nuara S, Ho MK, Bedell BJ, Dougherty RF, Leppert IR, Boudreau M, Narayanan S, Duval T, Cohen-Adad J, Picard PA, Gasecka A, Cote D, Pike GB. In vivo histology of the myelin g-ratio with magnetic resonance imaging. Neuroimage, 2015. 118:397-405. doi:10.1016/j.neuroimage.2015.05.023.
• [107] Cercignani M, Giulietti G, Dowell NG, Gabel M, Broad R, Leigh PN, Harrison NA, Bozzali M. Characterizing axonal myelination within the healthy population: a tract-by-tract mapping of effects of age and gender on the fiber g-ratio. Neurobiol Aging, 2017. 49:109-118. doi:10.1016/j.neurobiolaging.2016.09.016.
• [108] Mancini M, Giulietti G, Dowell N, Spanò B, Harrison N, Bozzali M, Cercignani M. Introducing axonal myelination in connectomics: A preliminary analysis of g-ratio distribution in healthy subjects. Neuroimage, 2017. doi:10.1016/j.neuroimage.2017.09.018.
• [109] Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 2006. 31(3):968-980. URL https://doi.org/10.1016/j.neuroimage.2006.01.021.
• [110] Glasser MF, Van Essen DC. Mapping Human Cortical Areas In Vivo Based on Myelin Content as Revealed by T1- and T2-Weighted MRI. Journal of Neuroscience, 2011. 31(32):11597-11616. doi:10.1523/JNEUROSCI.2180-11.2011.
• [111] Buxton RB, Wong EC, Frank LR. Dynamics of blood flow and oxygenation changes during brain activation: The balloon model. Magnetic Resonance in Medicine, 1998. 39(6):855-864.
• [112] Turner R. How much cortex can a vein drain? Downstream dilution of activation-related cerebral blood oxygenation changes. Neuroimage, 2002. 16(4):1062-1067. URL https://doi.org/10.1006/nimg.2002.1082.
• [113] Kannurpatti SS, Motes MA, Rypma B, Biswal BB. Neural and vascular variability and the fMRI-BOLD response in normal aging. Magnetic Resonance Imaging, 2010. 28(4):466-476. doi:10.1016/j.mri.2009.12.007.
• [114] Hammett ST, Wall MB, Edwards TC, Smith AT. Dietary supplementation of creatine monohydrate reduces the human fMRI BOLD signal. Neuroscience Letters, 2010. 479(3):201-205. doi:10.1016/j.neulet.2010.05.054.
• [115] Griffeth VEM, Perthen JE, Buxton RB. Prospects for quantitative fMRI: Investigating the effects of caffeine on baseline oxygen metabolism and the response to a visual stimulus in humans. Neuroimage, 2011. 57(3):809-816. doi:10.1016/j.neuroimage.2011.04.064.
• [116] Van Dijk KRA, Sabuncu MR, Buckner RL. The influence of head motion on intrinsic functional connectivity MRI. Neuroimage, 2012. 59(1):431-438. doi:10.1016/j.neuroimage.2011.07.044.
• [117] Satterthwaite TD, Elliott MA, Gerraty RT, Ruparel K, Loughead J, Calkins ME, Eickhoff SB, Hakonarson H, Gur RC, Gur RE, Wolf DH. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage, 2013. 64:240-256. doi:10.1016/j.neuroimage.2012.08.052.
• [118] Pruim RHR, Mennes M, van Rooij D, Llera A, Buitelaar JK, Beckmann CF. ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data. Neuroimage, 2015. 112:267-277. doi:10.1016/j.neuroimage.2015.02.064.
• [119] Kundu P, Inati SJ, Evans JW, Luh WM, Bandettini PA. Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. Neuroimage, 2012. 60(3):1759-1770. doi:10.1016/j.neuroimage.2011.12.028.
• [120] Dipasquale O, Sethi A, Lagana MM, Baglio F, Baselli G, Kundu P, Harrison NA, Cercignani M. Comparing resting state fMRI de-noising approaches using multi- and single-echo acquisitions. Plos One, 2017. 12(3). URL https://doi.org/10.1371/journal.pone.0173289.
• [121] Jones DK, Cercignani M. Twenty-five Pitfalls in the Analysis of Diffusion MRI Data. Nmr in Biomedicine, 2010. 23(7):803-820. doi:10.1002/nbm.1543.
• [122] Johansen-Berg H, Behrens TEJ. Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Current Opinion in Neurology, 2006. 19(4):379-385. doi:10.1097/01.wco.0000236618.82086.01.
• [123] Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, Pierpaoli C. Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proceedings of the National Academy of Sciences of the United States of America, 2014. 111(46):16574-16579. doi:10.1073/pnas.1405672111.
• [124] Beaulieu C. The basis of anisotropic water diffusion in the nervous system - a technical review. Nmr in Biomedicine, 2002. 15(7-8):435-455. doi:10.1002/nbm.782.
• [125] Wheeler-Kingshott CAM, Cercignani M. About ”Axial” and ”Radial” Diffusivities. Magnetic Resonance in Medicine, 2009. 61(5):1255-1260. doi:10.1002/mrm.21965.
• [126] Jespersen SN, Lundell H, Sonderby CK, Dyrby TB. Orientationally invariant metrics of apparent compartment eccentricity from double pulsed field gradient diffusion experiments. Nmr in Biomedicine, 2013. 26(12):1647-1662. URL https://doi.org/10.1002/nbm.2999.
• [127] Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 2012. 61(4):1000-1016. doi:10.1016/j.neuroimage.2012.03.072.
• [128] Lampinen B, Szczepankiewicz F, Martensson J, van Westen D, Sundgren PC, Nilsson M. Neurite density imaging versus imaging of microscopic anisotropy in diffusion MRI: A model comparison using spherical tensor encoding. Neuroimage, 2017. 147:517-531. doi:10.1016/j.neuroimage.2016.11.053.
• [129] Stanisz GJ, Webb S, Munro CA, Pun T, Midha R. MR properties of excised neural tissue following experimentally induced inflammation. Magnetic Resonance in Medicine, 2004. 51(3):473-479. doi:10.1002/mrm.20008.
• [130] Harrison NA, Cooper E, Dowell NG, Keramida G, Voon V, Critchley HD, Cercignani M. Quantitative Magnetization Transfer Imaging as a Biomarker for Effects of Systemic Inflammation on the Brain. Biological Psychiatry, 2015. 78(1):49-57. doi:10.1016/j.biopsych.2014.09.023.
• [131] Graham D, Rockmore D. The Packet Switching Brain. Journal of Cognitive Neuroscience, 2011. 23(2):267-276. doi:10.1162/jocn.2010.21477.
• [132] Raj A. Graph Models of Brain Diseases. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 2015. pp. 1552-1555.
• [133] Fornito A, Zalesky A, Breakspear M. Graph analysis of the human connectome: Promise, progress, and pitfalls. Neuroimage, 2013. 80:426-444. doi:10.1016/j.neuroimage.2013.04.087.
• [134] Watts DJ. Everything is obvious. Crown Pub, New York, 2011.
• [135] Onnela JP, Saramaki J, Kertesz J, Kaski K. Intensity and coherence of motifs in weighted complex networks. Physical Review E, 2005. 71(6). URL https://doi.org/10.1103/PhysRevE.71.065103.
• [136] Bassett DS, Bullmore ET. Small-World Brain Networks Revisited. Neuroscientist, 2017. 23(5):499-516. doi:10.1177/1073858416667720.
• [137] Braun U, Schafer A, Walter H, Erk S, Romanczuk-Seiferth N, Haddad L, Schweiger JI, Grimm O, Heinz A, Tost H, Meyer-Lindenberg A, Bassett DS. Dynamic reconfiguration of frontal brain networks during executive cognition in humans. Proceedings of the National Academy of Sciences of the United States of America, 2015. 112(37):11678-11683. URL https://doi.org/10.1073/pnas.1422487112.
• [138] Chai LR, Mattar MG, Blank IA, Fedorenko E, Bassett DS. Functional Network Dynamics of the Language System. Cerebral Cortex, 2016. 26(11):4148-4159. URL https://doi.org/10.1093/cercor/bhw238.
• [139] Shafie T. A Multigraph Approach to Social Network Analysis. Journal of Social Structure, 2015. 16.
• [140] Betzel RF, Bassett DS. Multi-scale brain networks. Neuroimage, 2017. 160:73-83. URL https://doi.org/10.1016/j.neuroimage.2016.11.006.
• [141] Giusti C, Ghrist R, Bassett DS. Twos company, three (or more) is a simplex. Journal of Computational Neuroscience, 2016. 41(1):1-14. doi:10.1007/s10827-016-0608-6.
• [142] Preti MG, Bolton TAW, Van De Ville D. The dynamic functional connectome: State-of-the-art and perspectives. Neuroimage, 2017. 160:41-54. URL https://doi.org/10.1016/j.neuroimage.2016.12.061.
• [143] Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST. Dynamic reconfiguration of human brain networks during learning. Proceedings of the National Academy of Sciences of the United States of America, 2011. 108(18):7641-7646. doi: 10.1073/pnas.1018985108.
• [144] Telesford QK, Lynall ME, Vettel J, Miller MB, Grafton ST, Bassett DS. Detection of functional brain network reconfiguration during task-driven cognitive states. Neuroimage, 2016. 142:198-210. doi:10.1016/j.neuroimage.2016.05.078.
• [145] Huang W, Bolton A, Medaglia J, Bassett D, Ribeiro A, Van De Ville D. A graph signal processing perspective on functional brain imaging. Proceedings of the IEEE., 2018. 102:184-191.
• [146] Finotelli P, Dulio P. A mathematical model for evaluating the functional connectivity strongness in healthy people. Archives italiennes de biologie., 2015. 153:270-300. doi:10.12871/00039829201544.
• [147] Marblestone AH, Wayne G, Kording KP. Toward an Integration of Deep Learning and Neuroscience. Frontiers in Computational Neuroscience, 2016. 10. doi: 10.3389/fncom.2016.00094.
• [148] Yamins DLK, DiCarlo JJ. Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience, 2016. 19(3):356-365. doi:10.1038/nn.4244.
• [149] Hassabis D, Kumaran D, Summerfield C, Botvinick M. Neuroscience-Inspired Artificial Intelligence. Neuron, 2017. 95(2):245-258. doi:10.1016/j.neuron.2017.06.011.