Multimodal neuroimaging computing: the workflows, methods, and platforms

Sidong Liu1, Weidong Cai1, Fan Zhang2,3, Michael Fulham4, Dagan Feng2,5, Sonia Pujol3, Ron Kikinis3
1School of IT, The University of Sydney, Sydney, Australia#TAB#
2School of IT, The University of Sydney, Sydney, Australia
3Surgical Planning Laboratory, Harvard Medical School, Boston, USA
4Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney Medical School, The University of Sydney, Sydney, Australia.
5Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China

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Kikinis R, Pieper SD, Vosburgh K (2014) 3D Slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoper Imaging Image-Guided Therapy 3(19):277–289

He B, Liu Z (2008) Multimodal functional neuroimaging: integrating functional MRI and EEG/MEG. IEEE Rev Biomed Eng 1:23–40

Knopman AA, Wong CH, Stevenson RJ et al (2015) The relationship between neuropsychological functioning and FDG–PET hypometabolism in intractable mesial temporal lobe epilepsy. Epilepsy Behav 44:136–142

Zhang D, Wang Y, Zhou L, Yuan H, Shen D (2011) Multimodal classification of Alzheimer’s disease and mild cognitive impairment. NeuroImage 55(3):856–867

Savadjiev P, Rathi Y, Bouix S, Smith AR et al (2014) Fusion of white and gray matter geometry: a framework for investigating brain development. Med Image Anal 18:1349–1360

Zhu D, Zhang T, Jiang X, Hu X et al (2014a) Fusing DTI and fMRI data: a survey of methods and applications. NeuroImage 102:184–191

Beyer T, Townsend DW, Brun T, Kinahan PE, Charron M, Robby R et al (2000) A combined PET/CT scanner for clinical oncology. J Nucl Med 41(8):1369–1379

Townsend DW (2001) A combined PET/CT scanner: the choices. J Nucl Med 42(3):533–534

Bisdas S, Nagele T, Schlemmer P, Boss A, Claussen C, Pichler B, Ernemann U (2010) Switching on the lights for real-time multimodality tumor neuroimaging: the integrated positron-emission tomography/MR imaging system. Am J Neuroradiol 31:610–614

Liu SQ, Liu S, Cai W, Che H, Pujol S, Kikinis R, Feng D (2015b) Multi-modal neuroimaging feature learning for multi-class diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng 62(4):1132–1140

Nir TM, Jahanshad N, Villalon-Reina JE, Toga AW, Jack CR, Weiner MW, Thompson PM (2013) Effectiveness of regional DTI measures in distinguishing Alzheimer’s disease, MCI, and normal aging. NeuroImage 3:180–195

Racine AM, Adluru N, Alexander AL, Christian BT et al. (2014) Associations between white matter microstructure and amyloid burden in precinical Alzheimer’s disease: a multmodal imaging investigation. NeuroImage 4:604–614

Cooper D, Barker V, Radua J, Fusar-Poli P, Lawrie SM (2014) Multimodal voxel-based meta-analysis of structural and functional magnetic resonance imaging studies in those at elevated genetic risk of developing schizophrenia. Psychiatry Res 221(1):69–77

Kochunov P, Chiappelli J, Wright SN, Rowland LM et al (2014) Multimodal white matter imaging to investigate reduced fractional anisotropy and its age-related decline in schizophrenia. Psychiatry Res 223(2):148–156

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

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. Mol Psychiatry 15:823–830

Bonilha L, Keller SS (2015) Quantitative MRI in refractory temporal lobe epilepsy: relationship with surgical outcomes. Quant Imaging Med Surg 5(2):204–224

Fernandez S, Donaire A, Seres E, Setoain X, Bargallo N et al (2015) PET/MRI and PET/MRI/SISCOM coregistration in the presurgical evaluation of refractory facol epilepsy. Epilepsy Res 111:1–9

Abela E, Rummel C, Hauf M, Weisstanner C, Schindler K, Wiest R (2014) Neuroimaging of epilepsy: lesions, networks, oscillations. Clin Neuroradiol 24(1):5–15

Agam Y, Vangel M, Roffman JL, Gallagher PJ et al (2014) Dissociable genetic contributions to error processing: a multimodal neuroimaging study. PLoS ONE 9(7):e101,784

Radua J, Grau M, van den Heuvel OA, de Schotten MT et al (2014) Multimodal voxel-based meta-analysis of white matter abnormalities in obsessive-compulsive disorder. Neuropsychopharmacology 39(7):1547–1557

Taylor SF, Stern ER, Gehring WJ (2007) Neural systems for error monitoring: recent findings and theoretical perspectives. Neuroscientist 13(2):160–172

Phillips ML, Swartz HA (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. Am J Psychiatry 171(8):829–843

Sui J, Pearlson GD, Caprihan A, Adali T, Kiehl KA et al (2011) Discriminating schizophrenia and bipolar disorder by fusing fMRI and DTI in a multimodal CCA+ joint ICA model. NeuroImage 57(3):839–855

Anderson A, Douglas PK, Kerr WT, Haynes VS 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(1):207–219

Dai D, Wang J, Hua J, He H (2012) Classification of ADHD children through multimodal magnetic resonance imaging. Front Syst Neurosci 6(63):1–8

Hong SB, Zalesky A, Fornito A, Park S, Yang YH et al (2014) Connectomic disturbances in attention-deficit/hyperactivity disorder: a whole-brain tractography analysis. Biol Psychiatry 76(8):656–663

Anagnostou E, Taylor MJ (2011) Review of neuroimaging in Autism spectrum disorders: what have we learnt and where we go from here. Mol Autism 2(4):1–9

Mueller S, Keeser D, Samson AC, 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(6):e67,329

Stigler KA, McDonald BC, Anand A et al (2011) Structural and functional magnetic resonance imaging of Autism spectrum disorders. Brain Res 1380:146–161

Cherubini A, Luccichenti G, Peran P, Hagberg GE et al (2007) Multimodal fMRI tractography in normal subjects and in clinically recovered traumatic brain injury patients. NeuroImage 34(4):1331–1341

Dean PJ, Sato JR, Vieira G, McNamara A, Sterr A (2014) Multimodal imaging of mild traumatic brain injury and persistent postconcussion syndrome. Brain Behav 5(1):45–61

Irimia A, Chambers MC, Alger JR, Filippou M, Prastawa MW et al (2011) Comparison of acute and chronic traumatic brain injury using semi-automatic multimodal segmentation of MR volumes. J Neurotrauma 28(11):2287–2306

Turken AU, Herron TJ, Kang X, O’Connor LE, Sorenson DJ et al. (2009) Multimodal surface-based morphometry reveals diffuse cortical atrophy in traumatic brain injury. BMC Medical Imaging 9(20)

Copen WA (2015) Multimodal imaging in acute ischemic stroke. Curr Treat Options Cardiovas Med 17(10):1–17

Tong E, Hou Q, Fiebach JB, Wintermark M (2014) The role of imaging in acute ischemic stroke. Neurosurg Focus 36(1):E3

Achiron A, Barak Y (2003) Cognitive impairment in probable multiple sclerosis. J Neurol Neurosurg Psychiatry 74:443–446

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

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(3):814–821

Durst CR, Raghavan P, Shaffrey ME, Schiff D et al (2014) Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 56(2):107–115

Neuner I, Kaffanke JB, Langen KJ, Kops ER, Tellmann L et al (2012) Multimodal imaging utilising integrated MR-PET for human brain tumor assessment. Eur Radiol 22:2568–2580

Tempany CM, Jayender J, Kapur T, Bueno R et al (2014) Multimodal imaging for improved diagnosis and treatment of cancers. Cancer 121(6):817–827

Liu S, Cai W, Liu SQ, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R (2015a) Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Info 2(3). doi: 10.1007/s40708-015-0019-x

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

Liu Z, Ding L, He B (2006) Integration of EEG/MEG with MRI and fMRI in functional neuroimaging. IEEE Eng Med Biol Mag 25(4):46–53

Binder JR, Desai RH, Graves WW, Conant LL (2009) Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb Cortex 19(12):2767–2796

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

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(4):1275–1288

Ashburner J, Friston JK (2000) Voxel-based morphometry: the methods. NeuroImage 11(6):805–821

Tustison NJ, Johnson HJ, Rohlfing T, Klein A et al. (2013) Instrumentation bias in the use and evaluation of scientific software: recommendations for reproducible practices in the computational sciences. Front Neurosci 7(162)

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

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. Neth Heart J 18(1):31–37

Bovenschulte H, Schluter-Brust K, Liebig T, Erdmann E, Eysel P, Zobel C (2012) MRI in patients with pacemakers: overview and procedural management. Deutsch Arztebl Int 109(15):270–275

Avants BB, Tustison NJ, Stauffer M, Song G, Wu B, Gee JC (2014) The insight toolkit image registration framework. Front Neuroinf 8(1):1–13

Yoo TS, Ackerman MJ, Lorensen WE, Schroeder W et al (2002) Engineering and algorithm design for an image processing API: a technical report on ITK: the Insight Toolkit. Stud Health Technol Inf 85:586–592

Tustison NJ, Avants BB, Cook PA, Zheng Y, Egan A, Yushkevich PA, Gee JC (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29(6):1310–1320

Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59(3):2142–2154

Pierpaoli C, Walker L, Irfanoglu M et al. (2010) TORTOISE: an integrated software package for processing of diffusion MRI data. In: The 18th ISMRM Annual Meeting, vol 1597

Behrens T, Woolrich M, Jenkinson M, Johansen-Berg H et al (2003) Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 50(5):1077–1088

Oguz I, Farzinfar M, Matsui J, Budin F, Liu Z, Gerig G et al (2014) DTIPrep: quality control of diffusion-weighted images. Front Neuroinf 8(4):1–11

Friston KJ, Williams S, Howard R, Frackwiak RSJ, Tumer R (1996) Movement-related effects in fMRI time-series. Magn Reson Med 35(3):346–355

Hricak H, Choi BI, Scott AM, Sugimura K et al (2010) Global trends in hybrid imaging. Radiology 257(2):498–506

Sureshbabu W, Mawlawi O (2005) PET/CT imaging artifacts. J Nucl Med Technol 33(3):156–161

Gross J, Ioannides A (1999) Linear transforms of data space in MEG. Phys Med Biol 44:2081–2097

Vigario R (1997) Extraction of ocular artifacts from EEG using independent component analysis. Electroencephalogr Clin Neurophysiol 103:295–404

Taulu S, Simola J (2006)Spatiotemporal signal separation method for rejecting nearby interference in MEG measurements. Phys Med Biol 51:1759–1768

Uusitalo M, Ilmoniemi R (1997) Signal-space projection method for separating MEG or EET into components. Med Biol Eng Comput 35:135–140

Geffroy D, Rivière D, Denghien I, Souedet N, Laguitton S, Cointepas Y (2011) BrainVISA: a complete software platform for neuroimaging. In: Python in neuroscience workshop

Fischl B, Dale AM (2000) Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci 97(20):11,050–11,055

Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis: I segmentation and surface reconstruction. NeuroImage 9(2):179–194

Talariach J, Tournoux P (1988) Co-planar stereotaxic atlas of the human brain 3-dimentional proportional system: an approach to cerebral imaging. Stuttgart, New York

Fonov V, Evans A, Botteron K, Almli C et al (2010) Unbiased average age-approapriate atlases for pediatric studies. NeuroImage 54(1):313–327

Mazziotta J, Toga A, Evans A, Fox P, Lancaster J, Zilles K et al (2001) A probabilistic atlas and reference system for the human brain: international consortium for brain mapping (ICBM). Philos Trans R Soc London Ser 356(1412):1293–1322

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(1):273–289

Schaer M, Cuadra M, Tamarit L, Lazeyras F, Eliez S, Thiran J (2008) AA surface-based approach to quantify local cortical gyrification. IEEE Trans Med Imaging 27(2):161–170

Awate SP, Yushkevich PA, Song Z, Licht DJ, Gee JC (2010) Cerebral cortical folding analysis with multivariate modeling and testing: studies on gender differences and neonatal development. NeuroImage 53(2):450–459

Cash DM, Melbourne A, Modat M, Cardoso MJ, Clarkson MJ, Fox NC, Ourselin S (2012) Cortical folding analysis on patients with Alzheimer’s disease and mild cognitive impairment. In: Ayache N, Delingette H, Golland P, Mori K (eds) Medical image computing and computer-assisted intervention (MICCAI), LNCS, vol 7512. Springer, Berlin, pp 289–296

Liu S, Cai W, Song Y, Pujol S, Kikinis R, Wen L, Feng D (2013a) 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), IEEE, pp 5398–5401

Liu S, Song Y, Cai W, Pujol S, Kikinis R, Wang X, Feng D (2013c) Multifold Bayesian kernelization in Alzheimer’s diagnosis. In: Mori K, Sakuma I, Sato Y, Barillot C, Navab N (eds) The 16th international conference on medical image computing and computer-assisted intervention (MICCAI), LNCS, vol 8150. Springer, Berlin Heidelberg, pp 303–310

Liu S, Zhang L, Cai W, Song Y, Wang Z, Wen L, Feng D (2013d) A supervised multiview spectral embedding method for neuroimaging classification. In: The 20th IEEE international conference on image processing (ICIP), pp 601–605

Cai W, Liu S, Wen L, Eberl S, Fulham MJ, Feng D (2010) 3D neurological image retrieval with localized pathology-centric CMRGlc patterns. In: The 17th IEEE international conference on image processing (ICIP), pp 3201–3204

Liu S, Cai W, Wen L, Eberl S, Fulham M, Feng D (2011a) Localized functional neuroimaging retrieval using 3D discrete curvelet transform. In: IEEE international symposium on biomedical imaging: from nano to macro (ISBI), pp 1877–1880

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

Mangin J, Jouvent E, Cachia A (2010) In-vivo measurement of cortical morphology: means and meanings. Curr Opin Neurol 23(4):359–367

Winkler AM, Kochunov P, Blangero J et al (2010) Cortical thickness or grey matter volume? the importance of selecting the phenotype for imaging genetics studies. NeuroImage 53(3):1135–1146

Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fiber orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. NeuroImage 35(4):1459–1472

Tuch DS (2004) Q-ball imaging. Magn Reson Med 52:1358–1372

Wedeen V, Hagmann P, Tseng W, Reese T, Weisskoff R (2005) Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 54(6):1377–1386

Yeh F, Wedeen V, Tseng W (2010) Generalized Q-sampling imaging (GQI). IEEE Trans Med Imaging 29:1626–1635

Haldar JP, Leahy RM (2013) Linear transforms for Fourier data on the sphere: application to high angular resolution diffusion MRI of the brain. NeuroImage 71:233–247

Wilkins B, Lee N, Gajawelli N, Law M, Lepore N (2015) Fiber estimation and tractography in diffusion MRI: development of simulated brain images and comparison of multi-fiber analysis methods at clinical b-values. NeuroImage 109:341–356

Maier-Hein KH, Westin CF, Shenton ME, Weiner MW, Raj A, Thomann P et al (2014) Widespread white matter degeneration preceding the onset of dementia. Alzheimer’s Dementia S1552–5260(14):1–9

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

Mori S, van Ziji PC (2002) Fiber tracking: principles and strategies: a technical review. NMR Biomed 15(7–8):468–480

Durrieman S, Pennec X, Trouve A, Ayache N (2009) Statistical models of sets of curves and surfaces based on currents. Med Image Anal 13(5):793–808

Zalesky A, Fornito A, Harding IH et al (2010) Whole-brain anatomical networks: does the choice of nodes matter? NeuroImage 50(3):970–983

O’Donnell LJ, Golby AJ, Westin CF (2013) Fiber clustering versus the parcellation-based connectome. NeuroImage 80:283–289

Jones DK, Knosche TR, Turner R (2013) White matter integrity, fiber count, and other fallacies: the do’s and don’ts of diffusion MRI. NeuroImage 73:239–254

Friston KJ (2003) Introduction: experimental design and statistical parametric mapping. In: Frackowiak RS et al (eds) Human brain function, 2nd edn. Elsevier, New York

Davison EN, Schlesinger KJ, Bassett DS, Lynall MR et al (2015) Brain network adaptability across task states. PLoS Comput Biol 11(1):e1004,029

Turk-Browne NB (2013) Functional interactions as big data in the human brain. Science 342(6158):580–584

Biswal B, Yetkin F, Haughton V, Hyde J (1995) Functional connectivity in motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

Raichle M, MacLeod A, Snyder A, Powers W et al (2001) A default mode of brain function. Proc Natl Acad Sci 98(2):676–682

Buckner R, Andrews-Hanna J, Schacter D (2008) The brain’s default network: anatomy, function and relevance to disease. Ann N Y Acad Sci 1124:1–38

Jiang T, Liu Y, Shi F, Shu N, Liu B et al (2008) Multimodal magnetic resonance imaging for brain disorders: advances and perspectives. Brain Imaging Behav 2:249–257

Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. NeuroImage 52:1059–1069

Carpenter AJ, Pontecorvo M, Hefti F, Skovronsky D (2009) The use of the exploratory IND in the evaluation and development of 18F-PET radiopharmaceuticals for amyloid imaging in the brain: a review of one company’s experience. Q J Nucl Med Mol Imaging 53(4):387–393

Ni R, Gillberg P, Bergfors A, Marutle A, Nordberg A (2013) Amyloid tracers detect multiple binding sites in Alzheimer’s disease brain tissue. Brain 136(7):2217–2227

Perrin RJ, Fagan AM, Holtzmann DM (2009) Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature 461:916–922

Thompson PM, Ye L, Morgenstem JL, Sue L, Beach TG et al (2009) Interaction of the amyloid imaging tracer FDDNP with hallmark Alzheimer’s disease pathologies. J Neurochem 109(2):623–630

Clark CM, Pontecorvo MJ, Beach TG, Bedell BJ, Coleman RE, Doraiswamy PM et al (2012) Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid- plaques: a prospective cohort study. Lancet Neurol 11(8):669–678

Landau SM, Lu M, Joshi AD, Pontecorvo M, Mintun MA, Trojanowski JQ, Shaw LM, Jagust WJ, Initiative Alzheimer’s Disease Neuroimaging (2013) Comparing positron emission tomography imaging and cerebrospinal fluid measurements of beta-amyloid. Ann Neurol 74(6):826–836

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. J Neurochem 28:897–916

Batty S, Clark J, Fryer T, Gao X (2008) Prototype system for semantic retrieval of neurological PET images. In: Gao X, Müller H, Loomes M, Comley R, Luo S (eds) Medical imaging and informatics, LNCS, vol 4987. Springer, Berlin Heidelberg, pp 179–188

Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE (1995) A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med 36(7):1238–1248

Chen K, Ayutyanont N, Langbaum JB, Fleisher AS, Reschke C et al (2011) Characterizing Alzheimer’s disease using a hypometabolic convergence index. NeuroImage 56(1):52–60

Cai W, Feng D, Fulton R (2000) Content-based retrieval of dynamic PET functional images. IEEE Trans Inf Technol Biomed 4(2):152–158

Cai W, Liu S, Song Y, Pujol S, Kikinis R, Feng D (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

Klein S, Staring M, Murphy K, Viergever MA, Pluim JP (2010) elastix: a toolbox for intensity-based medical image registration. IEEE Trans Med Imaging 29(1):196–205

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

Rueckert D, Sonoda L, Hayes C et al (1999) Non-rigid registration using free-form deformations: applications to breast MR images. IEEE Trans Med Imaging 18(8):712–721

Murphy K, van Ginneken B, Reinhardt J, Kabus S et al (2011) Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge. IEEE Trans Med Imaging 30:1901–1920

Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(Suppl. 1):S61–S72

Avants B, Epstein C, Grossman M, Gee J (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41

Heckemann RA, Keihaninejad S, Aljabar P, Gray KR, Nielsen C, Rueckert D et al (2011) Automatic morphometry in Alzheimer’s disease and mild cognitive impairment. NeuroImage 56(4):2024–2037

Hammers A, Allom R, Koepp M et al (2003) Three-dimentional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Hum Brain Map 19(4):224–247

Shattuck D, Mirza M, Adisetiyo V et al (2008) Construction of a 3D probabilistic atlas of human cortical structures. NeuroImage 39(3):1064–1080

Csernansky J, Wang L, Joshi S et al (2004) Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry and time-dependent change. NeuroImage Suppl 1(23):56–68

Rademacher J, Galaburda A, Kennedy D et al (1992)Human cerebral cortex: localization, parcellation, and morphometry with magnetic resonance imaging. J Cogn Neurosci 4(4):352–374

Yao Z, Hu B, Xie Y, Moore P, Zheng J (2015) A review of structural and functional brain networks: small world and atlas. Brain Inf 2(1):45–52

Heckemann R, Hajnal J, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1):115–126

Aljabar P, Heckemann R, Hammers A et al (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. NeuroImage 46:726–738

Heckemann R, Keihaninejad S, Aljabar P, Rueckert D et al (2010) Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. NeuroImage 51(1):221–227

Liu S, Cai W, Wen L, Eberl S, Fulham MJ, Feng D (2011b) Generalized regional disorder-sensitive-weighting scheme for 3D neuroimaging retrieval. In: The 33rd annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 7009–7012

Shen L, Kim S, Qi Y, Inlow M, Swaminathan S, Nho K, Wan J, Risacher S, Shaw L, Trojanowski J, Weiner M, Saykin A (2011) Identifying neuroimaging and proteomic biomarkers for MCI and AD via the elastic net. In: Liu T, Shen D, Ibanez L, Tao X (eds) Multimodal brain image analysis (MBIA), LNCS, vol 7012. Springer, Berlin Heidelberg, pp 27–34

Zhu X, Suk HI, Shen D (2014b) A novel matrix-similarity based loss function for joint regression and classification in AD diagnosis. NeuroImage 100:91–105

Liu S, Cai W, Wen L, Feng D (2013b) Multi-channel brain atrophy pattern analysis in neuroimaging retrieval. In: IEEE international symposium on biomedical imaging: from nano to macro (ISBI), pp 206–209

Liu S, Cai W, Wen L, Feng DD, Pujol S, Kikinis R, Fulham MJ, Eberl S (2014a) Multi-channel neurodegenerative pattern analysis and its application in Alzheimer’s disease characterization. Comput Med Imaging Graph 38:436–444

Xia T, Tao D, Mei T, Zhang Y (2010) Multiview spectral embedding. EEE Trans Syst Man Cybern Part B 40(6):1438–1446

Shen H, Tao D, Ma D (2013) Multiview locally linear embedding for effective medical image retrieval. PLoS ONE 8(12):e82,409

Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

Suk HI, Lee S, Shen D (2013) Latent feature representation with stacked auto-encoder for AD/MCI diagnosis. Brain Struct Funct 220(2):841–959

Suk H, Lee S, Shen D (2014) Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101:569–582

Hinrichs C, Singh V, Xu G, Johnson S (2009) MKL for robust multi-modality AD classification. In: Yang G (ed) Medical image computing and computer-assisted intervention (MICCAI), LNCS, vol 5762. Springer, New York, pp 786–794

Hinrichs C, Singh V, Xu G, Johnson S (2011) Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. NeuroImage 55:574–589

Liu SQ, Liu S, Zhang F, Cai W, Pujol S, Kikinis R, Feng D (2015c) 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), IEEE

Modat M, Simpson I, Cardoso M, Cash D et al. (2014) Simulating neurodegeneration through longitudinal population analysis of structural and diffusion weighted MRI data. Medical image computing and computer-assisted intervention (MICCAI), LNCS, vol 8675. Springer, Berlin, pp 57–64

Rubin D, Mongkolwat P, Kleper V et al (2009) Annotation and image markup: accessing and interoperating with the semantic content in medical imaging. IEEE Intell Syst 24(1):57–65

Tustison NJ, Cook PA, Klein A, Song G et al (2014) Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements. NeuroImage 99:166–179

Pierson R, Johnson HJ, Harris G, Keefe H et al (2011) Fully automated analysis using BRAINS: AutoWorkup. NeuroImage 54:328–336

Soares JM, Marques P, Alves V, Sousa N (2013) A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci 7:31

Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, Pujol S, Bauer C et al (2012) 3D Slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30(9):1323–1341

Pieper S, Lorensen B, Schroeder W, Kikinis R (2006) The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D Slicer as an open platform for the medical image computing community. In: The 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI), IEEE, pp 698–701