Skip to main content
SpringerLink
Log in

Introduction

  • Chapter
  • First Online:
Multimodal Neuroimaging Computing for the Characterization of Neurodegenerative Disorders

Part of the book series: Springer Theses ((Springer Theses))

  • 399 Accesses

Abstract

Modern neuroimaging technologies, such as magnetic resonance imaging (MRI), positron emission tomography (PET) and electro-/magneto-encephalography (EEG/MEG), have transformed the way we study the brain (Kikinis et al., 3D slicer: A platform for subject-specific image analysis, visualization, and clinical support, Intraoperative imaging and image-guided therapy, 277–289, 2014, [48]) by providing essential anatomical and functional information about the brain in unprecedented details.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Some content of this chapter has been reproduced with permission from [68, 71].

  2. 2.

    BLOD is closely related to cerebral blood flow (CBF), as brain function requires blood flow to supply oxygen for energy consumption by neurons.

  3. 3.

    www.neuroscienceblueprint.nih.gov/connectome.

  4. 4.

    DALYs is a summary metric of population health, which is the sum of years of life lost due to premature mortality and years lived with disability. DALYs represents a health gap, and as such, measures the state of a population’s health compared to a normative goal that is for individuals to live the standard life expectancy in full health [83].

References

  1. Abela, E., et al. (2014). Neuroimaging of epilepsy: Lesions, networks, oscillations. Clinical Neuroradiology, 24, 5–15.

    Article  Google Scholar 

  2. ABS. (2012). Australia Social Trends cat.no.4102.0 (Australian Bureau of Statistics, Canberra).

    Google Scholar 

  3. ABS. (2011). Causes of Death, Australia cat. no. 3303.0 (Australian Bureau of Statistics, Canberra).

    Google Scholar 

  4. ABS. (2009). Disability, Ageing and Carers, Australia: Summary of Findings cat. no. 4430.0 (Australian Bureau of Statistics, Canberra).

    Google Scholar 

  5. Agam, Y., Greenberg, J. L., Isom, M., Falkenstein, M. J., et al. (2014). Aberrant error processing in relation to symptom severity in obsessive-compulsive disorder: A multimodal neuroimaging study. NeuroImage: Clinical, 5, 141–151.

    Article  Google Scholar 

  6. Agam, Y., Vangel, M., Roffman, J. L., Gallagher, P. J., et al. (2014). Dissociable genetic contributions to error processing: A multimodal neuroimaging study. PLoS ONE, 9, e101784.

    Article  Google Scholar 

  7. Alzheimer’s Association. (2015). Changing the Trajectory of Alzheimer’s Disease: How a Treatment by 2025 Saves Lives and Dollars.

    Google Scholar 

  8. Amunts, K., Linder, A., & Zilles, K. (2014). The human brain project: Neuroscience perspectives and German contributions. e-Neuroforum, 5, 43–50.

    Article  Google Scholar 

  9. Anagnostou, E., & Taylor, M. J. (2011). Review of neuroimaging in autism spectrum disorders: What have we learnt and where we go from here. Moelcular Autism, 2, 1–9.

    Article  Google Scholar 

  10. Anderson, A., Douglas, P. K., Kerr, W. T., Haynes, V. S., et al. (2014). Non-negative matrix factorization of multimodal MRI, fMRI and phenotypic data reveals differential changes in default mode subnetworks in ADHD. NeuroImage, 102, 207–219.

    Article  Google Scholar 

  11. Aquino, D., Contarino, V., Albanese, A., et al. (2013). Substantia nigra in Parkinson’s disease: A multimodal MRI comparison between early and advanced stages of the disease. Neurological Sciences, 35, 753–758.

    Article  Google Scholar 

  12. Ashburner, J., & Friston, J. K. (2000). Voxel-based morphometry - The methods. NeuroImage, 11, 805–821.

    Article  Google Scholar 

  13. Avants, B., Cook, P., Ungar, L., Gee, J., & Grossman, M. (2010). Dementia induces correlated reduction in white matter integraty and cortical thickness: A multivariate neuroimaging study with sparse canonical correlation analysis. NeuroImage, 50, 1004–1016.

    Article  Google Scholar 

  14. Bagshaw, A. P., Rollings, D. T., Khalsa, S., & Cavanna, A. E. (2014). Multimodal neuroimaging investigations of alternations to consciousness: The relationship between absence epilepsy and sleep. Epilepsy & Behavior, 30, 33–37.

    Article  Google Scholar 

  15. Basser, P., Mattiello, J., & LeBihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66, 259–267.

    Article  Google Scholar 

  16. Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., et al. (2003). Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magnetic Resonance in Medicine, 50, 1077–1088.

    Article  Google Scholar 

  17. Beyer, T., et al. (2000). A combined PET/CT scanner for clinical oncology. Journal of Nuclear Medicine, 41, 1369–1379.

    Google Scholar 

  18. Bihan, D., Mangin, J., Poupon, C., Clark, C., et al. (2001). Diffusion tensor imaging: Concepts and applications. Journal of Magnetic Resonance Imaging, 13, 534–546.

    Article  MATH  Google Scholar 

  19. Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009). Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cerebral Cortex, 19, 2767–2796.

    Article  Google Scholar 

  20. Bisdas, S., et al. (2010). Switching on the lights for real-time multimodality tumor neuroimaging: The integrated positron-emission tomography/MR imaging system. American Journal of Neuroradiology (AJNR), 31, 610–614.

    Article  Google Scholar 

  21. Bonilha, L., & Keller, S. S. (2015). Quantitative MRI in refractory temporal lobe epilepsy: Relationship with surgical outcomes. Quantitative Imaging in Medicine and Surgery, 5, 204–224.

    Google Scholar 

  22. Bovenschulte, H., et al. (2012). MRI in patients with pacemakers - Overview and procedural management. Deutsches Arzteblatt International, 109, 270–275.

    Google Scholar 

  23. Brookmeyer, B., Johnson, E., Ziegler-Graham, K., & Arrighi, H. (2007). Forecasting the global burden of Alzheimer’s disease. Alzheimer’s & Dementia, 3, 186–191.

    Article  Google Scholar 

  24. Cai, W., et al. (2010). 3D Neurological image retrieval with localized pathology-centric CMRGlc patterns. In The 17th IEEE international conference on image processing (ICIP) (pp. 3201–3204). IEEE.

    Google Scholar 

  25. Cai, W., et al. (2014). A 3D difference of Gaussian based lesion detector for brain PET. In IEEE international symposium on biomedical imaging: From nano to macro (ISBI) (pp. 677–680). IEEE.

    Google Scholar 

  26. Che, H., et al. (2014). Co-neighbor multi-view spectral embedding for medical contentbased retrieval. In IEEE international symposium on biomedical imaging: From nano to macro (ISBI) (pp. 911–914). IEEE.

    Google Scholar 

  27. Chen, K., Ayutyanont, N., Langbaum, J. B., Fleisher, A. S., Reschke, C., et al. (2011). Characterizing Alzheimer’s disease using a hypometabolic convergence index. NeuroImage, 56, 52–60. ISSN: 1053-8119.

    Article  Google Scholar 

  28. Clark, C. M., et al. (2012). Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-OE \(\le \) plaques: A prospective cohort study. The Lancet Neurology, 11, 669–678. ISSN: 1474-4422.

    Article  Google Scholar 

  29. Cooper, D., Barker, V., Radua, J., Fusar-Poli, P., & Lawrie, S. M. (2014). Multimodal voxel-based meta-analysis of structural and functional magnetic resonance imaging studies in those at elevated genetic risk of developing schizophrenia. Psychiatry Research: Neuroimaging, 221, 69–77.

    Article  Google Scholar 

  30. Copen, W. A. (2015). Multimodal imaging in acute ischemic stroke. Current Treament Options in Cardiovascular Medicine, 17, 1–17.

    Article  Google Scholar 

  31. Dai, D., Wang, J., Hua, J., & He, H. (2012). Classification of ADHD children through multimodal magnetic resonance imaging. Frontiers in Systems Neuroscience, 6, 1–8.

    Article  Google Scholar 

  32. Durrieman, S., Pennec, X., Trouve, A., & Ayache, N. (2009). Statistical models of sets of curves and surfaces based on currents. Medical Image Analysis, 13, 793–808.

    Article  Google Scholar 

  33. Durst, C. R., Raghavan, P., Shaffrey, M. E., Schiff, D., et al. (2014). Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology, 56, 107–115.

    Article  Google Scholar 

  34. Essen, D. C. V., Smith, S. M., Barch, D. M., Behrens, T. E., Yacoub, E., et al. (2013). The WU-Minn human connectome project: An overview. NeuroImage, 80, 62–79.

    Article  Google Scholar 

  35. Fan, Y. (2011). In T. Liu, D. Shen, L. Ibanez, & X. Tao (Eds.), Ordinal ranking for detecting mild cognitive impairment and Alzheimer’s disease based on multimodal neuroimages and CSF biomarkers (Vol. 7012, pp. 44–51). Multimodal brain image analysis (MBIA) Berlin, Heidelberg: Springer. ISBN: 978-3-642-24445-2.

    Google Scholar 

  36. Fonov, V., Evans, A., Botteron, K., Almli, C., et al. (2010). Unbiased average age-approapriate atlases for pediatric studies. NeuroImage, 54, 313–327.

    Article  Google Scholar 

  37. Geffroy, D., et al. (2011). BrainVISA: A complete software platform for neuroimaging. In Python in neuroscience workshop.

    Google Scholar 

  38. Gotte, M., Russel, I., de Roest, G., Germans, T., Veldkamp, R., et al. (2010). Magnetic resonance imaging, pacemakers and implantable cardioverter-defibrillators: Current situation and clinical perspective. Netherlands Heart Journal, 18, 31–37.

    Google Scholar 

  39. Gross, J., Baillet, S., Barnes, G. R., Henson, R. N., Hillebrand, A., et al. (2013). Good practice for conducting and reporting MEG research. NeuroImage, 65, 349–363.

    Article  Google Scholar 

  40. Haldar, J. P., & Leahy, R. M. (2013). Linear transforms for fourier data on the sphere: Application to high angular resolution diffusion MRI of the brain. NeuroImage, 71, 233–247.

    Article  Google Scholar 

  41. Hasan, K. M., Walimuni, I. S., & Frye, R. E. (2013). Global cerebral and regional multimodal neuroimaging markers of the neurobiology of autism. Journal of Child Neurology, 28, 874–885.

    Article  Google Scholar 

  42. He, X., Qin, W., Liu, Y., Zhang, X., et al. (2013). Abnormal salience network in normal aging and in amnestic mild cognitive imapirment and Alzheimer’s disease. Human Brain Mapping, 35, 3446–3464.

    Article  Google Scholar 

  43. Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH BRAIN initiative. Science, 340, 687–688.

    Article  Google Scholar 

  44. Irimia, A., Chambers, M. C., Alger, J. R., Filippou, M., Prastawa, M. W., et al. (2011). Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. Journal of Neurotrauma, 28, 2287–2306.

    Article  Google Scholar 

  45. Jacobs, H. I., Gronenschild, E. H., Evers, E. A., et al. (2015). Visuospatial processing in early Alzheimer’s disease: A multimodal neuroimaging study. Cortex, 64, 394–406.

    Article  Google Scholar 

  46. Jiang, T. (2013). Brainnetome: A new-ome to understand the brain and its disorders. NeuroImage, 80, 263–272.

    Article  Google Scholar 

  47. Kalaria, R. N., et al. (2008). Alzheimer’s disease and vascular dementia in developing countries: Prevalence, management, and risk factors. The Lancet Neurology, 7, 812–826. ISSN: 1474-4422.

    Article  Google Scholar 

  48. Kikinis, R., Pieper, S. D., & Vosburgh, K. (2014). 3D slicer: A platform for subject-specific image analysis, visualization, and clinical support. In F. A. Jolesz (Ed.), Intraoperative imaging and image-guided therapy 3 (pp. 277–289). New York: Springer.

    Chapter  Google Scholar 

  49. Knopman, A. A., Wong, C. H., Stevenson, R. J., et al. (2015). The relationship between neuropsychological functioning and FDG-PET hypometabolism in intractable mesial temporal lobe epilepsy. Epilepsy & Behavior, 44, 136–142.

    Article  Google Scholar 

  50. Kochunov, P., Chiappelli, J., Wright, S. N., Rowland, L. M., et al. (2014). Multimodal white matter imaging to investigate reduced fractional anisotropy and its age- related decline in schizophrenia. Psychiatry Research: Neuroimaging, 223, 148–156.

    Article  Google Scholar 

  51. La Fougere, C., Rominger, A., Forster, S., Geisler, J., & Bartenstein, P. (2009). PET and SPECT in epilepsy: A critical review. Epilepsy & Behavior, 15, 50–55.

    Article  Google Scholar 

  52. Landau, S. M., et al. (2013). Comparing positron emission tomography imaging and cerebrospinal fluid measurements of beta-amyloid. Annals of Neurology, 74, 826–836. ISSN: 1531-8249.

    Article  Google Scholar 

  53. Liu, S. Q., et al. (2015). Content-based retrieval of brain diffusion magnetic resonance image. Multimodal retrieval in the medical domain (Vol. 9059). Switzerland: Springer.

    Chapter  Google Scholar 

  54. Liu, S. Q., et al. (2014). High-level feature based PET image retrieval with deep learning architecture. Journal of Nuclear Medicine, 55, 2018.

    Google Scholar 

  55. Liu, S. Q., et al. (2015). Longitudinal brain MR retrieval with diffeomorphic demons registration: What happened to those patients with similar changes?. In IEEE international symposium on biomedical imaging: From nano to macro (ISBI) (pp. 588–591). IEEE.

    Google Scholar 

  56. Liu, S. Q., et al. (2015). Multi-modal neuroimaging feature learning for multi-class diagnosis of Alzheimer’s disease. IEEE Transactions on Biomedical Engineering, 62, 1132–1140.

    Article  Google Scholar 

  57. Liu, S., Cai, W., Wen, L. & Feng, D. (2012). Multiscale and multiorientation feature extraction with degenerative patterns for 3D neuroimaging retrieval. In The 19th IEEE international conference on image processing (ICIP) (pp. 1249–1252). IEEE.

    Google Scholar 

  58. Liu, S., Cai, W., Wen, L. & Feng, D. (2013). Neuroimaging biomarker based prediction of Alzheimer’s disease severity with optimized graph construction. In IEEE international symposium on biomedical imaging: From nano to macro (ISBI) (pp. 1324–1327). IEEE.

    Google Scholar 

  59. Liu, S., Cai, W., Wen, L., & Feng, D. (2012). Semantic-word-based image retrieval for neurodegenerative disorders. Journal of Nuclear Medicine, 53, 2309.

    Article  Google Scholar 

  60. Liu, S., Cai,W.,Wen, L. & Feng, D. (2011). Volumetric congruent local binary patterns for 3D neurological image retrieval. In P. Delmas & B. Wuensche (Eds.), The 26th international conference on image and vision computing New Zealand (IVCNZ) (pp. 272–276). IVCNZ.

    Google Scholar 

  61. Liu, S., Liu, S. Q., Pujol, S., Kikinis, R. & Cai, W. (2014). Propagation graph fusion for multi-modal medical content-based retrieval. In The 13th annual international conference on control, automation, robotics and vision (ICARCV) (pp. 849–854). IEEE.

    Google Scholar 

  62. Liu, S., et al. (2010). A robust volumetric feature extraction approach for 3D neuroimaging retrieval. In The 32nd annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 5657–5660). IEEE.

    Google Scholar 

  63. Liu, S., et al. (2016). Cross-view neuroimage pattern analysis for Alzheimer’s disease staging. Frontiers in Aging Neuroscience.

    Google Scholar 

  64. Liu, S., et al. (2010). Localized multiscale texture based retrieval of neurological image. In The 23rd IEEE international symposium on computer-based medical systems (CBMS) (pp. 243–248). IEEE.

    Google Scholar 

  65. Liu, S., et al. (2013). Localized sparse code gradient in Alzheimer’s disease staging. In The 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC) (pp. 5398–5401). IEEE.

    Google Scholar 

  66. Liu, S., et al. (2014). Multi-channel neurodegenerative pattern analysis and its application in Alzheimer’s disease characterization. Computerized Medical Imaging and Graphics, 38, 436–444. ISSN: 0895-6111.

    Article  Google Scholar 

  67. Liu, S., et al. (2013). Multifold bayesian kernelization in Alzheimer’s diagnosis. In K. Mori, I. Sakuma, Y. Sato, C. Barillot & N. Navab (Eds.), The 16th international conference on medical image computing and computer-assisted intervention (MICCAI) (vol. 8150, pp. 303–310). Berlin, Heidelberg: Springer.

    Google Scholar 

  68. Liu, S., et al. (2015). Multimodal neuroimaging computing: A review of the applications in neuropsychiatric disorders. Brain Informatics, 2, 167–180.

    Article  Google Scholar 

  69. Liu, S., et al. (2015). Subject-centered multi-view neuroimaging analysis. In The 22nd IEEE international conference on image processing (ICIP). IEEE.

    Google Scholar 

  70. Liu, S., et al. (2011). Localized functional neuroimaging retrieval using 3D discrete curvelet transform. In IEEE international symposium on biomedical imaging: From nano to macro (ISBI) (pp. 1877–1880). IEEE.

    Google Scholar 

  71. Liu, S., et al. (2015). Multimodal neuroimaging computing: The workflows, methods and platforms. Brain Informatics, 2, 181–195.

    Article  Google Scholar 

  72. Liu, X., Lai, Y., Wang, X., Hao, C., et al. (2014). A combined DTI and structural MRI study in medicated-naive chronic schizophrenia. Magnetic Resonance Imaging, 32, 1–8.

    Article  Google Scholar 

  73. Liu, Z., Ding, L., & He, B. (2006). Integration of EEG/MEG with MRI and fMRI in functional neuroimaging. IEEE Engineering in Medicine and Biology Magazine, 25, 46–53.

    Google Scholar 

  74. Louapre, C., Perlbarg, V., Garcia-Lorenzo, D., Urbanski, M., et al. (2014). Brain networks disconnection in early multiple sclerosis cognitive deficits: An anatomofunctional study. Human Brain Mapping, 35, 4706–4717.

    Article  Google Scholar 

  75. Maier-Hein, K. H., et al. (2014). Widespread white matter degeneration preceding the onset of dementia. Alzheimer’s & Dementia, S1552–5260, 1–9.

    Google Scholar 

  76. Mazziotta, J., et al. (2001). A probabilistic atlas and reference system for the human brain: International consortium for brain mapping (ICBM). Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences, 356, 1293–1322.

    Article  Google Scholar 

  77. Medhi, B., Misra, S., Kumar, P., Kumar, P., & Singh, B. (2014). Role of neuroimaging in drug development. Reviews in the Neurosciences, 25, 663–673.

    Article  Google Scholar 

  78. Modat, M., Simpson, I., Cardoso, M., Cash, D., et al. (2014). Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data in medical image computing and computer-assisted intervention (MICCAI) (Vol. 8675). Berlin, Heidelberg: Springer.

    Google Scholar 

  79. Mori, S., & van Ziji, P. C. (2002). Fiber tracking: Principles and strategies - A technical review. NMR in Biomedicine, 15, 468–480.

    Article  Google Scholar 

  80. Morioka, H., Kanemura, A., Morimoto, S., Yoshioka, T., et al. (2013). Decoding spatial attention by using cortical currents estimated from electroencephalography with near-infrared spectroscopy prior information. NeuroImage, 90, 128–139.

    Article  Google Scholar 

  81. Mueller, S., Keeser, D., Samson, A. C., Kirsch, V., Blautzik, J., et al. (2013). Convergent findings of altered functional and structural briain connectivity in individuals with high functioning autism: A multimodal MRI study. PLoS ONE, 8(e67329), 31.

    Google Scholar 

  82. Murphy, M. A., O’Brien, T. J., Morris, K., & Cook, M. J. (2004). Multimodality image-guided surgery for the treatment of medically refractory epilepsy. Journal of Neurosurgery, 100, 452–462.

    Article  Google Scholar 

  83. Murray, C., Abraham, J., Ali, M., Alvarado, M., Atkinson, C., et al. (2013). The state of US health, 1990–2010: Burden of diseases, injuries, and risk factors. The Journal of the American Medical Association, 310, 591–608.

    Article  Google Scholar 

  84. Nettiksimmons, J., DeCarli, C., Susan Landau, & Beckett, L. (2014). Biological heterogeneity in ADNI amnestic mild cognitive impairment. Alzheimer’s & Dementia, 10, 511–521.

    Google Scholar 

  85. Neuner, I., Kaffanke, J. B., Langen, K.-J., Kops, E. R., Tellmann, L., et al. (2012). Multimodal imaging utilising integrated MR-PET for human brain tumor assessment. European Radiology, 22, 2568–2580.

    Article  Google Scholar 

  86. Nguyen, V. T., & Cunnington, R. (2014). The superior temporal sulcus and the N170 during face processing: Single trial analysis of concurrent EEG-fMRI. NeuroImage, 86, 492–502.

    Article  Google Scholar 

  87. NIH. BRAIN 2025 - A Scientific Vision 2014.

    Google Scholar 

  88. Nir, T. M., et al. (2013). Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage: Clinical, 3, 180–195. ISSN: 2213-1582.

    Article  Google Scholar 

  89. O’Donnell, L. J., Golby, A. J., & Westin, C.-F. (2013). Fiber clustering versus the parcellation-based connectome. NeuroImage, 80, 283–289.

    Article  Google Scholar 

  90. Okamoto, M., Dan, K., Shimizu, K., Takeo, K., et al. (2004). Multimodal assessment of cortical activation during apple peeling by NIRS and fMRI. NeuroImage, 21, 1275–1288.

    Article  Google Scholar 

  91. Okamura, N., Furumoto, S., Harada, R., Tago, T., et al. (2013). Novel 18F-labeled arylquinoline derivatives for noninvasive imaging of Tau pathology in Alzheimer disease. Journal of Nuclear Medicine, 54, 1420–1427.

    Article  Google Scholar 

  92. Phillips, M. L., & Swartz, H. A. (2014). A critical appraisal of neuroimaging studies of bipolar disorder: Toward a new conceptualization of underlying neural circuitry and a road map for future research. The American Journal of Psychiatry, 171, 829–843.

    Article  Google Scholar 

  93. Pomarol-Clotet, E., Canales-Rodriguez, E., Salvador, R., Sarro, S., et al. (2010). Medial prefrontal cortex pathology in schizophrenia as revealed by convergent findings from multimodal imaging. Molecular Psychiatry, 15, 823–830.

    Article  Google Scholar 

  94. Racine, A. M., Adluru, N., Alexander, A. L., Christian, B. T., et al. (2014). Associations between white matter microstructure and amyloid burden in precinical Alzheimer’s disease: A multmodal imaging investigation. NeuroImage: Clinical, 4, 604–614.

    Article  Google Scholar 

  95. Radua, J., Grau, M., van den Heuvel, O. A., de Schotten, M. T., et al. (2014). Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive- compulsive disorder. Neuropsychopharmacology, 39, 1547–1557.

    Article  Google Scholar 

  96. Rinck, P. (2014). Magnetic resonance: A critical peer-reviewed introduction in magnetic resonance in medicine. The basic textbook of the European magnetic resonance forum. Chap. 21.

    Google Scholar 

  97. Risacher, S. L., et al. (2009). Baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Current Alzheimer’s Research, 6, 347–361. ISSN: 1875-5828.

    Article  Google Scholar 

  98. Rodionov, R., Vollmar, C., Nowell, M., Miserocchi, A., et al. (2013). Feasibility of multimodal 3D neuroimaging to guide implantation of intracranial EEG electrodes. Epilepsy Research, 107, 91–100.

    Article  Google Scholar 

  99. Rydberg, J., Hammond, C., Grimm, R., Erickson, B., Jack, C. J., et al. (1994). Initial clinical experience in MR imaging of the brain with a fast fluid-attenuated inversion-recovery pulse sequence. Radiology, 193, 173–180.

    Article  Google Scholar 

  100. Savadjiev, P., Kindlemann, G., Bouix, S., Sheton, M., & Westin, C. (2010). Local white matter geometry from diffusion tensor gradients. NeuroImage, 49, 3175–3186.

    Article  Google Scholar 

  101. Savadjiev, P., Rathi, Y., Bouix, S., Smith, A. R., et al. (2014). Fusion of white and gray matter geometry: A framework for investigating brain development. Medical Image Analysis, 18, 1349–1360.

    Article  Google Scholar 

  102. Shah, N. J., Oros-Peusquens, A.-M., Arrbula, J., Zhang, K., Warbrick, T., et al. (2013). Advances in multimodal neuroimaging: Hybrid MR-PET and MR-PET-EEG at 3 T and 9.4 T. Journal of Magnetic Resonance, 229, 101–115.

    Article  Google Scholar 

  103. Shenton, M., Kubicki, M., & Makris, N. (2014). Understanding alterations in brain connectivity in attention-deficit/hyperactivity disorder using imaging connectomics. Biological Psychiatry, 76, 601–602.

    Article  Google Scholar 

  104. Sokoloff, L., Reivich, M., Kennedy, C., Des-Rosiers, M., et al. (1977). The [14C]Deoxy- glucose method for the measurement of local cerebral glucose utilization: Theory, procedure and normal values in the consicious and anesthetized albino rat. Journal of Neurochemistry, 28, 897–916.

    Article  Google Scholar 

  105. Stigler, K. A., McDonald, B. C., Anand, A., et al. (2011). Structural and functional megnetic resonance imaging of autism spectrum disorders. Brain Research, 1380, 146–161.

    Article  Google Scholar 

  106. Sui, J., Pearlson, G. D., Caprihan, A., Adali, T., Kiehl, K. A., et al. (2011). Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model. NeuroImage, 57, 839–855.

    Article  Google Scholar 

  107. Taylor, S. F., Stern, E. R., & Gehring, W. J. (2007). Neural systems for error monitoring - recent findings and theoretical perspectives. The Neuroscientist, 13, 160–172.

    Article  Google Scholar 

  108. Tempany, C. M., Jayender, J., Kapur, T., Bueno, R., et al. (2014). Multimodal imaging for improved diagnosis and treatment of cancers. Cancer, 121, 817–827.

    Article  Google Scholar 

  109. Tona, F., Petsas, N., Sbardella, E., Prosperini, L., et al. (2014). Multiple sclerosis: Altered thalamic resting-state functional connectivity and its effect on cognitive function. Radiology, 271, 814–821.

    Article  Google Scholar 

  110. Tong, E., Hou, Q., FFiebach, J. B., & Wintermark, M. (2014). The role of imaging in acute ischemic stroke. Neurosurgical Focus, 36, E3.

    Article  Google Scholar 

  111. Tournier, J. D., Calamante, F., & Connelly, A. (2007). Robust determination of the fiber orientation distribution in diffusion MRI: Non-negativity constrained super- resolved spherical deconvolution. NeuroImage, 35, 1459–1472.

    Article  Google Scholar 

  112. Townsend, D. W. (2001). A combined PET/CT scanner: The choices. Journal of Nuclear Medicine, 42, 533–534.

    Google Scholar 

  113. Tuch, D. S. (2004). Q-ball imaging. Magnetic Resonance in Medicine, 52, 1358–1372.

    Article  Google Scholar 

  114. Turken, A. U., Herron, T. J., Kang, X., Sorenson, D. J., O’Connor, L. E., et al. (2009). Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury. BMC Medical Imaging, 9, 111.

    Article  Google Scholar 

  115. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., et al. (2002). Automated anatomical labelling of activations in SPM using a macroscopy anatomical pacellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–289.

    Article  Google Scholar 

  116. Wedeen, V., Hagmann, P., Tseng, W., Reese, T., & Weisskoff, R. (2005). Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magnetic Resonance in Medicine, 54, 1377–1386.

    Article  Google Scholar 

  117. Wee, C.-Y., Yap, P.-T., Zhang, D., Denny, K., et al. (2012). Identification of MCI individuals using structural and functional connectivity networks. NeuroImage, 59, 2045–2056.

    Article  Google Scholar 

  118. Westin, C.-F., Szczepankiewicz, F., Pasternak, O., Ozarslan, E., Topgaard, D., et al. (2014). Measurement tensors in diffusion MRI: generalizing the concept of diffusion encoding. Medical image computing and computer-assisted intervention (MICCAI) (Vol. 8675, pp. 209–216). Switzerland: Springer.

    Google Scholar 

  119. Wong, D. F., Tauscher, J., & Grunder, G. (2009). The role of imaging in proof of concept for CNS drug discovery and development. Neuropsychopharmacology, 34, 187–203.

    Article  Google Scholar 

  120. Yeh, F., Wedeen, V., & Tseng, W. (2010). Generalized Q-sampling imaging (GQI). IEEE Transactions on Medical Imaging, 29, 1626–1635.

    Article  Google Scholar 

  121. Young, H., Baum, R., Cremerius, U., Herholz, K., et al. (1999). Measurement of clinical and subclinical tumour reponse using [18F]-Fluorodeoxyglucose and positron emission tomography: Review and 1999 EORTC recommendations. European organization of research and treatment of cancer (EORTC) PET study group. European Journal of Cancer, 35, 1773–1782.

    Article  Google Scholar 

  122. Zhang, D., Wang, Y., Zhou, L., Yuan, H., & Shen, D. (2011). Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage, 55, 856–867. ISSN: 1053-8119.

    Article  Google Scholar 

  123. Zhang, F., et al. (2014). Semantic association for neuroimaging classification of PET images. Journal of Nuclear Medicine, 55, 2029.

    Google Scholar 

  124. Zhang, W., Arteaga, J., Cashion, D. K., Chen, G., et al. (2013). A highly selective and specific PET tracer for imaging of Tau pathologies. Journal of Alzheimer’s Disease, 31, 601–612.

    Google Scholar 

  125. Zhu, D., Li, K., Terry, D. P., et al. (2014). Connectome-scale assessments of structural and functional connectivity in MCI. Human Brain Mapping, 35, 2911–2923.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Technologies, University of Sydney, Sydney, New South Wales, Australia

    Sidong Liu

Authors
  1. Sidong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sidong Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer Nature Singapore Pte Ltd.

About this chapter

Cite this chapter

Liu, S. (2017). Introduction. In: Multimodal Neuroimaging Computing for the Characterization of Neurodegenerative Disorders. Springer Theses. Springer, Singapore. https://doi.org/10.1007/978-981-10-3533-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-3533-3_1

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-3532-6

  • Online ISBN: 978-981-10-3533-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation