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Cited by 71Advanced Neuroimaging Techniques and ApplicationsAdvanced MRI Techniques and ApplicationsFunctional Brain Connectivity Studies

Whole mouse brain structural connectomics using magnetic resonance histology

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Wang, Nian, Anderson, Robert J., Badea, Alexandra, Cofer, Gary, Dibb, Russell, Qi, Yi, Johnson, G. Allan·2018·Brain Structure and Function
DOI 10.1007/s00429-018-1750-xPMC PMC7053251
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Abstract

Diffusion tensor histology holds great promise for quantitative characterization of structural connectivity in mouse models of neurological and psychiatric conditions. There has been extensive study in both the clinical and preclinical domains on the complex tradeoffs between the spatial resolution, the number of samples in diffusion q-space, scan time, and the reliability of the resultant data. We describe here a method for accelerating the acquisition of diffusion MRI data to support quantitative connectivity measurements in the whole mouse brain using compressed sensing (CS). The use of CS allows substantial increase in spatial resolution and/or reduction in scan time. Compared to the fully sampled results at the same scan time, the subtle anatomical details of the brain, such as cortical layers, dentate gyrus, and cerebellum, were better visualized using CS due to the higher spatial resolution. Compared to the fully sampled results at the same spatial resolution, the scalar diffusion metrics, including fractional anisotropy (FA) and mean diffusivity (MD), showed consistently low error across the whole brain (< 6.0%) even with 8.0 times acceleration. The node properties of connectivity (strength, cluster coefficient, eigenvector centrality, and local efficiency) demonstrated correlation of better than 95.0% between accelerated and fully sampled connectomes. The acceleration will enable routine application of this technology to a wide range of mouse models of neurologic diseases.

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  • Compression factors up to 8× could further reduce the acquisition time to 15.8 hours.

    “Since this work was started, we have demonstrated that compression factors up to 8× can be used with limited impact on the conventional DTI metrics which could further reduce the acquisition time to 15.8 hr (Wang et al. 2018a).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Wang et al. 2018a

    The Methods (CS Reconstruction) confirm an acceleration factor of 8×. But Methods (Animal Preparation) report 11.6 hours at that factor, and the Discussion (Tradeoffs between spatial resolution and angular resolution) says about 11 hours—not 15.8 hours. Page numbers are unavailable in our copy. (LLM, conf=0.91)

    Experimental — accuracy not yet measured

  • Compression factors up to 8× can be used with limited impact on conventional DTI metrics.

    “Since this work was started, we have demonstrated that compression factors up to 8× can be used with limited impact on the conventional DTI metrics which could further reduce the acquisition time to 15.8 hr (Wang et al. 2018a).”

    From Neurite orientation dispersion and density imaging of mouse brain microstructure (2019) · cites this paper as Wang et al. 2018a

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Research works printed but not linked: 54

  1. Adamantidis AR, Zhang F, Aravanis AM, Deisseroth K, de Lecea L (2007) Neural substrates of awakening probed with optogenetic control of hypocretin neurons. Nature 450 (7168): 420– 424. doi: 10.1038/nature06310 17943086 PMC6744371 · DOI · Paper page
  2. Adcock B, Hansen A, Roman B, Teschke G (2014) Generalized Sampling: Stable Reconstructions, Inverse Problems and Compressed Sensing over the Continuum. Adv Imag Elect Phys 182: 187– 279. doi: 10.1016/B978-0-12-800146-2.00004-7 · DOI
  3. Akil H, Martone ME, Van Essen DC (2011) Challenges and opportunities in mining neuroscience data. Science 331 (6018): 708– 712. doi: 10.1126/science.1199305 21311009 PMC3102049 · DOI
  4. Baldoli C, Scola E, Della Rosa PA, Pontesilli S, Longaretti R, Poloniato A, Scotti R, Blasi V, Cirillo S, Iadanza A, Rovelli R, Barera G, Scifo P (2015) Maturation of preterm newborn brains: a fMRI-DTI study of auditory processing of linguistic stimuli and white matter development. Brain Struct Funct 220 (6): 3733– 3751. doi: 10.1007/s00429-014-0887-5 25244942 · DOI
  5. Bargmann CI, Marder E (2013) From the connectome to brain function. Nat Methods 10 (6): 483– 490 23866325
  6. Barth M, Breuer F, Koopmans PJ, Norris DG, Poser BA (2016) Simultaneous multislice (SMS) imaging techniques. Magn Reson Med 75 (1): 63– 81. doi: 10.1002/mrm.25897 26308571 PMC4915494 · DOI
  7. Bilgic B, Setsompop K, Cohen-Adad J, Wedeen V, Wald LL, Adalsteinsson E (2012) Accelerated diffusion spectrum imaging with compressed sensing using adaptive dictionaries. Med Image Comput Comput Assist Interv 15 (Pt 3): 1– 9 PMC4679293 23286107
  8. Bilgic B, Ye H, Wald LL, Setsompop K (2017) Simultaneous Time Interleaved MultiSlice (STIMS) for Rapid Susceptibility Weighted acquisition. Neuroimage 155: 577– 586. doi: 10.1016/j.neuroimage.2017.04.036 28435102 PMC5511575 · DOI
  9. Boretius S, Michaelis T, Tammer R, Ashery-Padan R, Frahm J, Stoykova A (2009) In vivo MRI of altered brain anatomy and fiber connectivity in adult pax6 deficient mice. Cereb Cortex 19 (12): 2838– 2847. doi: 10.1093/cercor/bhp057 19329571 · DOI
  10. Bozzali M, Parker GJ, Serra L, Embleton K, Gili T, Perri R, Caltagirone C, Cercignani M (2011) Anatomical connectivity mapping: a new tool to assess brain disconnection in Alzheimer’s disease. Neuroimage 54 (3): 2045– 2051. doi: 10.1016/j.neuroimage.2010.08.069 20828625 · DOI
  11. Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10 (3): 186– 198. doi: 10.1038/nrn2575 19190637 · DOI · Paper page
  12. Calabrese E, Badea A, Cofer G, Qi Y, Johnson GA (2015) A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data. Cereb Cortex 25 (11): 4628– 4637. doi: 10.1093/cercor/bhv121 26048951 PMC4715247 · DOI · Paper page
  13. Cassady K, Koppelmans V, Reuter-Lorenz P, De Dios Y, Gadd N, Wood S, Castenada RR, Kofman I, Bloomberg J, Mulavara A, Seidler R (2016) Effects of a spaceflight analog environment on brain connectivity and behavior. Neuroimage 141: 18– 30. doi: 10.1016/j.neuroimage.2016.07.029 27423254 · DOI
  14. Chang HC, Sundman M, Petit L, Guhaniyogi S, Chu ML, Petty C, Song AW, Chen NK (2015) Human brain diffusion tensor imaging at submillimeter isotropic resolution on a 3 Tesla clinical MRI scanner. Neuroimage 118: 667– 675. doi: 10.1016/j.neuroimage.2015.06.016 26072250 PMC4554968 · DOI
  15. Chen H, Liu T, Zhao Y, Zhang T, Li Y, Li M, Zhang H, Kuang H, Guo L, Tsien JZ, Liu T (2015) Optimization of large-scale mouse brain connectome via joint evaluation of DTI and neuron tracing data. Neuroimage 115: 202– 213. doi: 10.1016/j.neuroimage.2015.04.050 25953631 PMC4461481 · DOI · Paper page
  16. Chu Y, Jin X, Parada I, Pesic A, Stevens B, Barres B, Prince DA (2010) Enhanced synaptic connectivity and epilepsy in C1q knockout mice. Proc Natl Acad Sci U S A 107 (17): 7975– 7980. doi: 10.1073/pnas.0913449107 20375278 PMC2867906 · DOI · Paper page
  17. Dai JK, Wang SX, Shan D, Niu HC, Lei H (2017) A diffusion tensor imaging atlas of white matter in tree shrew. Brain Struct Funct 222 (4): 1733– 1751. doi: 10.1007/s00429-0161304-z 27624528 · DOI
  18. Deshmane A, Gulani V, Griswold MA, Seiberlich N (2012) Parallel MR imaging. J Magn Reson Imaging 36 (1): 55– 72. doi: 10.1002/jmri.23639 22696125 PMC4459721 · DOI
  19. Dyrby TB, Baare WF, Alexander DC, Jelsing J, Garde E, Sogaard LV (2011) An ex vivo imaging pipeline for producing high-quality and high-resolution diffusion-weighted imaging datasets. Hum Brain Mapp 32 (4): 544– 563. doi: 10.1002/hbm.21043 20945352 PMC6870191 · DOI
  20. Guggisberg AG, Honma SM, Findlay AM, Dalal SS, Kirsch HE, Berger MS, Nagarajan SS (2008) Mapping functional connectivity in patients with brain lesions. Ann Neurol 63 (2): 193– 203. doi: 10.1002/ana.21224 17894381 PMC3646715 · DOI · Paper page
  21. Hubner NS, Mechling AE, Lee HL, Reisert M, Bienert T, Hennig J, von Elverfeldt D, Harsan LA (2017) The connectomics of brain demyelination: Functional and structural patterns in the cuprizone mouse model. Neuroimage 146: 1– 18. doi: 10.1016/j.neuroimage.2016.11.008 27845252 · DOI
  22. Insel TR, Landis SC, Collins FS (2013) Research priorities. The NIH BRAIN Initiative. Science 340 (6133): 687– 688. doi: 10.1126/science.1239276 23661744 PMC5101945 · DOI
  23. Kingwell K (2012) Brain imaging: measures of functional brain connectivity can be used to predict outcome after glioma surgery. Nat Rev Neurol 8 (10): 532. doi: 10.1038/nrneurol.2012.187 22964506 · DOI
  24. Liang D, Liu B, Wang J, Ying L (2009) Accelerating SENSE using compressed sensing. Magn Reson Med 62 (6): 1574– 1584. doi: 10.1002/mrm.22161 19785017 · DOI
  25. Lustig M, Donoho D, Pauly JM (2007) Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn Reson Med 58 (6): 1182– 1195. doi: 10.1002/mrm.21391 17969013 · DOI · Paper page
  26. Lustig M, Donoho DL, Santos JM, Pauly JM (2008) Compressed sensing MRI. Ieee Signal Proc Mag 25 (2): 72– 82. doi:Doi 10.1109/Msp.2007.914728 · DOI
  27. Maier-Hein KH, Neher PF, Houde JC, Cote MA, Garyfallidis E, Zhong J, Chamberland M, Yeh FC, Lin YC, Ji Q, Reddick WE, Glass JO, Chen DQ, Feng Y, Gao C, Wu Y, Ma J, Renjie H, Li Q, Westin CF, Deslauriers-Gauthier S, Gonzalez JOO, Paquette M, St-Jean S, Girard G, Rheault F, Sidhu J, Tax CMW, Guo F, Mesri HY, David S, Froeling M, Heemskerk AM, Leemans A, Bore A, Pinsard B, Bedetti C, Desrosiers M, Brambati S, Doyon J, Sarica A, Vasta R, Cerasa A, Quattrone A, Yeatman J, Khan AR, Hodges W, Alexander S, Romascano D, Barakovic M, Auria A, Esteban O, Lemkaddem A, Thiran JP, Cetingul HE, Odry BL, Mailhe B, Nadar MS, Pizzagalli F, Prasad G, Villalon-Reina JE, Galvis J, Thompson PM, Requejo FS, Laguna PL, Lacerda LM, Barrett R, Dell’Acqua F, Catani M, Petit L, Caruyer E, Daducci A, Dyrby TB, Holland-Letz T, Hilgetag CC, Stieltjes B, Descoteaux M (2017) The challenge of mapping the human connectome based on diffusion tractography. Nat Commun 8 (1): 1349. doi: 10.1038/s41467-017-01285-x 29116093 PMC5677006 · DOI
  28. Menzel MI, Tan ET, Khare K, Sperl JI, King KF, Tao X, Hardy CJ, Marinelli L (2011) Accelerated diffusion spectrum imaging in the human brain using compressed sensing. Magn Reson Med 66 (5): 1226– 1233. doi: 10.1002/mrm.23064 22012686 · DOI
  29. Moldrich RX, Pannek K, Hoch R, Rubenstein JL, Kurniawan ND, Richards LJ (2010) Comparative mouse brain tractography of diffusion magnetic resonance imaging. Neuroimage 51 (3): 1027– 1036. doi: 10.1016/j.neuroimage.2010.03.035 20303410 PMC2882245 · DOI
  30. Mukai J, Tamura M, Fenelon K, Rosen AM, Spellman TJ, Kang R, MacDermott AB, Karayiorgou M, Gordon JA, Gogos JA (2015) Molecular substrates of altered axonal growth and brain connectivity in a mouse model of schizophrenia. Neuron 86 (3):680695. doi: 10.1016/j.neuron.2015.04.003 PMC4603834 25913858 · DOI · Paper page
  31. Oh SW, Harris JA, Ng L, Winslow B, Cain N, Mihalas S, Wang Q, Lau C, Kuan L, Henry AM, Mortrud MT, Ouellette B, Nguyen TN, Sorensen SA, Slaughterbeck CR, Wakeman W, Li Y, Feng D, Ho A, Nicholas E, Hirokawa KE, Bohn P, Joines KM, Peng H, Hawrylycz MJ, Phillips JW, Hohmann JG, Wohnoutka P, Gerfen CR, Koch C, Bernard A, Dang C, Jones AR, Zeng H (2014) A mesoscale connectome of the mouse brain. Nature 508 (7495): 207– 214. doi: 10.1038/nature13186 24695228 PMC5102064 · DOI · Paper page
  32. Pandit AS, Robinson E, Aljabar P, Ball G, Gousias IS, Wang Z, Hajnal JV, Rueckert D, Counsell SJ, Montana G, Edwards AD (2014) Whole-brain mapping of structural connectivity in infants reveals altered connection strength associated with growth and preterm birth. Cereb Cortex 24 (9): 2324– 2333. doi: 10.1093/cercor/bht086 23547135 · DOI
  33. Pievani M, Filippini N, van den Heuvel MP, Cappa SF, Frisoni GB (2014) Brain connectivity in neurodegenerative diseases--from phenotype to proteinopathy. Nat Rev Neurol 10 (11): 620– 633. doi: 10.1038/nrneurol.2014.178 25287597 · DOI
  34. Poirier GL, Huang W, Tam K, DiFranza JR, King JA (2017) Awake whole-brain functional connectivity alterations in the adolescent spontaneously hypertensive rat feature visual streams and striatal networks. Brain Struct Funct 222 (4): 1673– 1683. doi: 10.1007/s00429-016-1301-2 27680743 · DOI
  35. Ragan T, Kadiri LR, Venkataraju KU, Bahlmann K, Sutin J, Taranda J, Arganda-Carreras I, Kim Y, Seung HS, Osten P (2012) Serial two-photon tomography for automated ex vivo mouse brain imaging. Nat Methods 9 (3): 255– 258. doi: 10.1038/nmeth.1854 22245809 PMC3297424 · DOI
  36. Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52 (3): 1059– 1069. doi: 10.1016/j.neuroimage.2009.10.003 19819337 · DOI · Paper page
  37. Sierra A, Laitinen T, Grohn O, Pitkanen A (2015) Diffusion tensor imaging of hippocampal network plasticity. Brain Struct Funct 220 (2): 781– 801. doi: 10.1007/s00429-013-0683-7 24363120 · DOI
  38. Sotiropoulos SN, Jbabdi S, Xu JQ, Andersson JL, Moeller S, Auerbach EJ, Glasser MF, Hernandez M, Sapiro G, Jenkinson M, Feinberg DA, Yacoub E, Lenglet C, Van Essen DC, Ugurbil K, Behrens TEJ, Consortium W-MH (2013) Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80: 125– 143. doi: 10.1016/j.neuroimage.2013.05.057 23702418 PMC3720790 · DOI
  39. Sporns O (2011) The human connectome: a complex network. Ann N Y Acad Sci 1224: 109– 125. doi: 10.1111/j.1749-6632.2010.05888.x 21251014 · DOI
  40. Sporns O, Bullmore ET (2014) From connections to function: the mouse brain connectome atlas. Cell 157 (4): 773– 775. doi: 10.1016/j.cell.2014.04.023 24813604 · DOI
  41. Stejskal EO, Tanner JE (1965) Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-Dependent Field Gradient. J Chem Phys 42 (1): 288-+. doi:Doi 10.1063/1.1695690 · DOI
  42. Thomas C, Ye FQ, Irfanoglu MO, Modi P, Saleem KS, Leopold DA, Pierpaoli C (2014) Anatomical accuracy of brain connections derived from diffusion MRI tractography is inherently limited. Proc Natl Acad Sci U S A 111 (46): 16574– 16579. doi: 10.1073/pnas.1405672111 25368179 PMC4246325 · DOI · Paper page
  43. Thompson PM, Ge T, Glahn DC, Jahanshad N, Nichols TE (2013) Genetics of the connectome. Neuroimage 80: 475– 488. doi: 10.1016/j.neuroimage.2013.05.013 23707675 PMC3905600 · DOI
  44. Tournier JD, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high-angular-resolution diffusion-weighted imaging. NMR Biomed 26 (12): 1775– 1786. doi: 10.1002/nbm.3017 24038308 · DOI · Paper page
  45. Ugwu ID, Amico F, Carballedo A, Fagan AJ, Frodl T (2015) Childhood adversity, depression, age and gender effects on white matter microstructure: a DTI study. Brain Struct Funct 220 (4): 1997– 2009. doi: 10.1007/s00429-014-0769-x 24744150 · DOI
  46. Volz LJ, Cieslak M, Grafton ST (2018) A probabilistic atlas of fiber crossings for variability reduction of anisotropy measures. Brain Struct Funct 223 (2): 635– 651. doi: 10.1007/s00429-017-1508-x 28905121 · DOI
  47. Vu AT, Auerbach E, Lenglet C, Moeller S, Sotiropoulos SN, Jbabdi S, Andersson J, Yacoub E, Ugurbil K (2015) High resolution whole brain diffusion imaging at 7T for the Human Connectome Project. Neuroimage 122: 318– 331. doi: 10.1016/j.neuroimage.2015.08.004 26260428 PMC4618066 · DOI
  48. Wang N, Badar F, Xia Y (2018) Compressed sensing in quantitative determination of GAG concentration in cartilage by microscopic MRI. Magn Reson Med 79 (6): 3163– 3171. doi: 10.1002/mrm.26973 29083096 PMC5843514 · DOI · Paper page
  49. Welsh CL, Dibella EV, Adluru G, Hsu EW (2013) Model-based reconstruction of undersampled diffusion tensor k-space data. Magn Reson Med 70 (2): 429– 440. doi: 10.1002/mrm.24486 23023738 PMC4469271 · DOI
  50. Wu Y, Zhu YJ, Tang QY, Zou C, Liu W, Dai RB, Liu X, Wu EX, Ying L, Liang D (2014) Accelerated MR diffusion tensor imaging using distributed compressed sensing. Magn Reson Med 71 (2): 763– 772. doi: 10.1002/mrm.24721. 23494999 · DOI
  51. Yeh FC, Verstynen TD, Wang Y, Fernandez-Miranda JC, Tseng WY (2013) Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One 8 (11):e80713. doi: 10.1371/journal.pone.0080713 PMC3858183 24348913 · DOI · Paper page
  52. Yeh FC, Wedeen VJ, Tseng WY (2010) Generalized q-sampling imaging. IEEE Trans Med Imaging 29 (9): 1626– 1635. doi: 10.1109/TMI.2010.2045126 20304721 · DOI
  53. Zhan Y, Paolicelli RC, Sforazzini F, Weinhard L, Bolasco G, Pagani F, Vyssotski AL, Bifone A, Gozzi A, Ragozzino D, Gross CT (2014) Deficient neuron-microglia signaling results in impaired functional brain connectivity and social behavior. Nat Neurosci 17 (3): 400– 406. doi: 10.1038/nn.3641 24487234 · DOI · Paper page
  54. Zingg B, Hintiryan H, Gou L, Song MY, Bay M, Bienkowski MS, Foster NN, Yamashita S, Bowman I, Toga AW, Dong HW (2014) Neural networks of the mouse neocortex. Cell 156 (5): 1096– 1111. doi: 10.1016/j.cell.2014.02.023 24581503 PMC4169118 · DOI · Paper page

Cited by

Sorted by citations

Authors
Wang, N., Anderson, R. J., Badea, A., Cofer, G., Dibb, R., Qi, Y., & Johnson, G. A.
Affiliations
  • Duke Medical Center (US)
  • Duke University (US)
Corresponding author
Johnson, G. Allan
ORCID
Venue
Brain Structure and Function
Publisher
Springer Science and Business Media LLC
Published
2018-09-17
Volume
223
Issue
9
Pages
4323-4335
ISSN
  • 1863-2653 (ISSN-L)
  • 1863-2661
DOI
10.1007/s00429-018-1750-x
PMC
PMC7053251
OpenAlex
W2890765734
Cited by
71
References
4
Citations by year
2026: 5 · 2025: 8 · 2024: 7 · 2023: 8 · 2022: 14 · 2021: 7 · 2020: 14 · 2019: 6 · 2018: 2
FWCI
5.01
Type
journal article
Language
en
Open access
Licence
Locations
  • Brain Structure and Function (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institute of Biomedical Imaging and Bioengineering (P41 EB015897)
  • National Institute on Aging (AG041211)
  • National Institute of Health (1S10OD010683-01)
  • National Institutes of Health (1R01NS096720-01A1)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
structural connectomics, mouse brain, diffusion MRI, compressed sensing, accelerated MRI acquisition, fractional anisotropy, mean diffusivity, spatial resolution
MeSH terms
Animals, Brain, Male, Mice, Inbred C57BL, Diffusion Magnetic Resonance Imaging, Diffusion Tensor Imaging, Connectome, White Matter

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