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Japanese Journal of Radiology

, Volume 37, Issue 1, pp 34–72 | Cite as

Machine learning studies on major brain diseases: 5-year trends of 2014–2018

  • Koji SakaiEmail author
  • Kei Yamada
Invited Review

Abstract

In the recent 5 years (2014–2018), there has been growing interest in the use of machine learning (ML) techniques to explore image diagnosis and prognosis of therapeutic lesion changes within the area of neuroradiology. However, to date, the majority of research trend and current status have not been clearly illuminated in the neuroradiology field. More than 1000 papers have been published during the past 5 years on subject classification and prediction focused on multiple brain disorders. We provide a survey of 209 papers in this field with a focus on top ten active areas of research; i.e., Alzheimer’s disease/mild cognitive impairment, brain tumor; schizophrenia, depressive disorders, Parkinson’s disease, attention-deficit hyperactivity disorder, autism spectrum disease, epilepsy, multiple sclerosis, stroke, and traumatic brain injury. Detailed information of these studies, such as ML methods, sample size, type of inputted features and reported accuracy, are summarized. This paper reviews the evidences, current limitations and status of studies using ML to assess brain disorders in neuroimaging data. The main bottleneck of this research field is still the limited sample size, which could be potentially addressed by modern data sharing models, such as ADNI.

Keywords

Artificial intelligence Machine learning Neurological disorder Neuroimaging Diagnosis 

Notes

Funding

One of the authors (K. Y.) was funded by following companies (within the past 12 months): Nihon Medi-Physics Co., Ltd., Daiichi Sankyo Co., Ltd., Fuji Pharma Co.,Ltd., Doctor-Net Inc, and Fujifilm RI Pharma Co., Ltd.

Compliance with ethical standards

Ethical statements

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Wang S, Summers RM. Machine learning and radiology. Med Image Anal. 2012;16:933–51.Google Scholar
  2. 2.
    Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. Neuroimage. 2017;145:137–65.Google Scholar
  3. 3.
    US national library of medicine national institutes of health. https://www.ncbi.nlm.nih.gov/pubmed/. Accessed Oct 2018.
  4. 4.
    Moher D. Preferred reporting items for systematic reviews and meta-analyses. J Clin Epidemiol. 2009;62:1006–12.Google Scholar
  5. 5.
    Jie B, Zhang D, Gao W, Wang Q, Wee CY, Shen D. Integration of network topological and connectivity properties for neuroimaging classification. IEEE Trans Biomed Eng. 2014;61(2):576–89.Google Scholar
  6. 6.
    Li M, Oishi K, He X, Qin Y, Gao F, Mori S, et al. An efficient approach for differentiating Alzheimer’s disease from normal elderly based on multicenter MRI using gray-level invariant features. PLoS One. 2014;9(8):e105563.Google Scholar
  7. 7.
    Li M, Qin Y, Gao F, Zhu W, He X. Discriminative analysis of multivariate features from structural MRI and diffusion tensor images. Magn Reson Imaging. 2014;32(8):1043–51.Google Scholar
  8. 8.
    Li S, Yuan X, Pu F, Li D, Fan Y, Wu L, et al. Abnormal changes of multidimensional surface features using multivariate pattern classification in amnestic mild cognitive impairment patients. J Neurosci. 2014;34(32):10541–53.Google Scholar
  9. 9.
    Liu M, Zhang D, Shen D, Alzheimer’s Disease Neuroimaging I. Identifying informative imaging biomarkers via tree structured sparse learning for AD diagnosis. Neuroinformatics. 2014;12(3):381–94.Google Scholar
  10. 10.
    Segovia F, Bastin C, Salmon E, Gorriz JM, Ramirez J, Phillips C. Combining PET images and neuropsychological test data for automatic diagnosis of Alzheimer’s disease. PLoS One. 2014;9(2):e88687.Google Scholar
  11. 11.
    Suk HI, Lee SW, Shen D, Alzheimer’s Disease Neuroimaging I. Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. Neuroimage. 2014;101:569–82.Google Scholar
  12. 12.
    Cheng B, Liu M, Suk HI, Shen D, Zhang D, Alzheimer’s Disease Neuroimaging I. Multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging Behav. 2015;9(4):913–26.Google Scholar
  13. 13.
    Dyrba M, Grothe M, Kirste T, Teipel SJ. Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Hum Brain Mapp. 2015;36(6):2118–31.Google Scholar
  14. 14.
    Farzan A, Mashohor S, Ramli AR, Mahmud R. Boosting diagnosis accuracy of Alzheimer’s disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res. 2015;290:124–30.Google Scholar
  15. 15.
    Jie B, Zhang D, Cheng B, Shen D, Alzheimer’s Disease Neuroimaging I. Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp. 2015;36(2):489–507.Google Scholar
  16. 16.
    Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Identifying patients with Alzheimer’s disease using resting-state fMRI and graph theory. Clin Neurophysiol. 2015;126(11):2132–41.Google Scholar
  17. 17.
    Liu M, Zhang D, Shen D, Alzheimer’s Disease Neuroimaging I. View-centralized multi-atlas classification for Alzheimer’s disease diagnosis. Hum Brain Mapp. 2015;36(5):1847–65.Google Scholar
  18. 18.
    Liu S, Liu S, Cai W, Che H, Pujol S, Kikinis R, et al. Multimodal neuroimaging feature learning for multiclass diagnosis of Alzheimer’s disease. IEEE Trans Biomed Eng. 2015;62(4):1132–40.Google Scholar
  19. 19.
    Moradi E, Pepe A, Gaser C, Huttunen H, Tohka J, Alzheimer’s Disease Neuroimaging I. Machine learning framework for early MRI-based Alzheimer’s conversion prediction in MCI subjects. Neuroimage. 2015;104:398–412.Google Scholar
  20. 20.
    Xu L, Wu X, Chen K, Yao L. Multi-modality sparse representation-based classification for Alzheimer’s disease and mild cognitive impairment. Comput Methods Progr Biomed. 2015;122(2):182–90.Google Scholar
  21. 21.
    Zhu X, Suk HI, Zhu Y, Thung KH, Wu G, Shen D. Multi-view classification for identification of Alzheimer’s disease. Mach Learn Med Imaging. 2015;9352:255–62.Google Scholar
  22. 22.
    Cattell L, Platsch G, Pfeiffer R, Declerck J, Schnabel JA, Hutton C, et al. Classification of amyloid status using machine learning with histograms of oriented 3D gradients. Neuroimage Clin. 2016;12:990–1003.Google Scholar
  23. 23.
    Hu C, Sepulcre J, Johnson KA, Fakhri GE, Lu YM, Li Q. Matched signal detection on graphs: theory and application to brain imaging data classification. Neuroimage. 2016;125:587–600.Google Scholar
  24. 24.
    Khazaee A, Ebrahimzadeh A, Babajani-Feremi A. Application of advanced machine learning methods on resting-state fMRI network for identification of mild cognitive impairment and Alzheimer’s disease. Brain Imaging Behav. 2016;10(3):799–817.Google Scholar
  25. 25.
    Liu M, Zhang D, Shen D. Relationship induced multi-template learning for diagnosis of Alzheimer’s disease and mild cognitive impairment. IEEE Trans Med Imaging. 2016;35(6):1463–74.Google Scholar
  26. 26.
    Ni H, Zhou L, Ning X, Wang L, Alzheimer’s Disease Neuroimaging I. Exploring multifractal-based features for mild Alzheimer’s disease classification. Magn Reson Med. 2016;76(1):259–69.Google Scholar
  27. 27.
    Suk HI, Wee CY, Lee SW, Shen D. State-space model with deep learning for functional dynamics estimation in resting-state fMRI. Neuroimage. 2016;129:292–307.Google Scholar
  28. 28.
    Yu G, Liu Y, Shen D. Graph-guided joint prediction of class label and clinical scores for the Alzheimer’s disease. Brain Struct Funct. 2016;221(7):3787–801.Google Scholar
  29. 29.
    Zu C, Jie B, Liu M, Chen S, Shen D, Zhang D, et al. Label-aligned multi-task feature learning for multimodal classification of Alzheimer’s disease and mild cognitive impairment. Brain Imaging Behav. 2016;10(4):1148–59.Google Scholar
  30. 30.
    An L, Adeli E, Liu M, Zhang J, Lee SW, Shen D. A hierarchical feature and sample selection framework and its application for Alzheimer’s disease diagnosis. Sci Rep. 2017;7:45269.Google Scholar
  31. 31.
    Beheshti I, Demirel H, Matsuda H, Alzheimer’s Disease Neuroimaging I. Classification of Alzheimer’s disease and prediction of mild cognitive impairment-to-Alzheimer’s conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm. Comput Biol Med. 2017;83:109–19.Google Scholar
  32. 32.
    Chen X, Zhang H, Zhang L, Shen C, Lee SW, Shen D. Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification. Hum Brain Mapp. 2017;38(10):5019–34.Google Scholar
  33. 33.
    Doan NT, Engvig A, Zaske K, Persson K, Lund MJ, Kaufmann T, et al. Distinguishing early and late brain aging from the Alzheimer’s disease spectrum: consistent morphological patterns across independent samples. Neuroimage. 2017;158:282–95.Google Scholar
  34. 34.
    Glozman T, Solomon J, Pestilli F, Guibas L, Alzheimer’s Disease Neuroimaging I. Shape-attributes of brain structures as biomarkers for Alzheimer’s disease. J Alzheimers Dis. 2017;56(1):287–95.Google Scholar
  35. 35.
    Guan H, Liu T, Jiang J, Tao D, Zhang J, Niu H, et al. Classifying MCI subtypes in community-dwelling elderly using cross-sectional and longitudinal MRI-Based biomarkers. Front Aging Neurosci. 2017;9:309.Google Scholar
  36. 36.
    Guo H, Zhang F, Chen J, Xu Y, Xiang J. Machine learning classification combining multiple features of a hyper-network of fMRI data in Alzheimer’s disease. Front Neurosci. 2017;11:615.Google Scholar
  37. 37.
    Hojjati SH, Ebrahimzadeh A, Khazaee A, Babajani-Feremi A, Alzheimer’s Disease Neuroimaging I. Predicting conversion from MCI to AD using resting-state fMRI, graph theoretical approach and SVM. J Neurosci Methods. 2017;282:69–80.Google Scholar
  38. 38.
    Kawaguchi A, Yamashita F, Alzheimer’s Disease Neuroimaging I. Supervised multiblock sparse multivariable analysis with application to multimodal brain imaging genetics. Biostatistics. 2017;18(4):651–65.Google Scholar
  39. 39.
    Khazaee A, Ebrahimzadeh A, Babajani-Feremi A, Alzheimer’s Disease Neuroimaging I. Classification of patients with MCI and AD from healthy controls using directed graph measures of resting-state fMRI. Behav Brain Res. 2017;322(Pt B):339–50.Google Scholar
  40. 40.
    Lama RK, Gwak J, Park JS, Lee SW. Diagnosis of Alzheimer’s disease based on structural MRI images using a regularized extreme learning machine and PCA features. J Healthc Eng. 2017;2017:5485080.Google Scholar
  41. 41.
    Li Q, Wu X, Xu L, Chen K, Yao L, Li R. Multi-modal discriminative dictionary learning for Alzheimer’s disease and mild cognitive impairment. Comput Methods Progr Biomed. 2017;150:1–8.Google Scholar
  42. 42.
    Long X, Chen L, Jiang C, Zhang L, Alzheimer’s Disease Neuroimaging I. Prediction and classification of Alzheimer disease based on quantification of MRI deformation. PLoS One. 2017;12(3):e0173372.Google Scholar
  43. 43.
    Min J, Moon WJ, Jeon JY, Choi JW, Moon YS, Han SH. Diagnostic efficacy of structural MRI in patients with mild-to-moderate Alzheimer disease: automated volumetric assessment versus visual assessment. AJR Am J Roentgenol. 2017;208(3):617–23.Google Scholar
  44. 44.
    Proitsi P, Kim M, Whiley L, Simmons A, Sattlecker M, Velayudhan L, et al. Association of blood lipids with Alzheimer’s disease: a comprehensive lipidomics analysis. Alzheimers Dement. 2017;13(2):140–51.Google Scholar
  45. 45.
    Sorensen L, Igel C, Pai A, Balas I, Anker C, Lillholm M, et al. Differential diagnosis of mild cognitive impairment and Alzheimer’s disease using structural MRI cortical thickness, hippocampal shape, hippocampal texture, and volumetry. Neuroimage Clin. 2017;13:470–82.Google Scholar
  46. 46.
    Suk HI, Lee SW, Shen D, Alzheimer’s Disease Neuroimaging I. Deep ensemble learning of sparse regression models for brain disease diagnosis. Med Image Anal. 2017;37:101–13.Google Scholar
  47. 47.
    Youssofzadeh V, McGuinness B, Maguire LP, Wong-Lin K. Multi-Kernel learning with Dartel improves combined MRI-PET classification of Alzheimer’s disease in AIBL data: group and individual analyses. Front Hum Neurosci. 2017;11:380.Google Scholar
  48. 48.
    Zhu X, Suk HI, Lee SW, Shen D. Discriminative self-representation sparse regression for neuroimaging-based Alzheimer’s disease diagnosis. Brain Imaging Behav. 2017.  https://doi.org/10.1007/s11682-017-9731-x.Google Scholar
  49. 49.
    Zhu X, Suk HI, Wang L, Lee SW, Shen D, Alzheimer’s Disease Neuroimaging I. A novel relational regularization feature selection method for joint regression and classification in AD diagnosis. Med Image Anal. 2017;38:205–14.Google Scholar
  50. 50.
    Amoroso N, Rocca M, Bellotti R, Fanizzi A, Monaco A, Tangaro S, et al. Alzheimer’s disease diagnosis based on the hippocampal unified multi-atlas network (HUMAN) algorithm. Biomed Eng Online. 2018;17(1):6.Google Scholar
  51. 51.
    Choi H, Jin KH, Alzheimer’s Disease Neuroimaging I. Predicting cognitive decline with deep learning of brain metabolism and amyloid imaging. Behav Brain Res. 2018;344:103–9.Google Scholar
  52. 52.
    Garali I, Adel M, Bourennane S, Guedj E. Histogram-Based features selection and volume of interest ranking for brain PET image classification. IEEE J Transl Eng Health Med. 2018;6:2100212.Google Scholar
  53. 53.
    Kim J, Lee B. Identification of Alzheimer’s disease and mild cognitive impairment using multimodal sparse hierarchical extreme learning machine. Hum Brain Mapp. 2018.  https://doi.org/10.1002/hbm.24207.Google Scholar
  54. 54.
    Liu M, Cheng D, Wang K, Wang Y, Alzheimer’s Disease Neuroimaging I. Multi-modality cascaded convolutional neural networks for Alzheimer’s disease diagnosis. Neuroinformatics. 2018;16(3–4):295–308.Google Scholar
  55. 55.
    Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, et al. Selecting the most relevant brain regions to discriminate Alzheimer’s disease patients from healthy controls using multiple kernel learning: a comparison across functional and structural imaging modalities and atlases. Neuroimage Clin. 2018;17:628–41.Google Scholar
  56. 56.
    Salvatore C, Castiglioni I. A wrapped multi-label classifier for the automatic diagnosis and prognosis of Alzheimer’s disease. J Neurosci Methods. 2018;302:58–65.Google Scholar
  57. 57.
    Sun Z, Qiao Y, Lelieveldt BPF, Staring M, Alzheimer’s Disease NeuroImaging I. Integrating spatial-anatomical regularization and structure sparsity into SVM: improving interpretation of Alzheimer’s disease classification. Neuroimage. 2018;178:445–60.Google Scholar
  58. 58.
    Wang SH, Phillips P, Sui Y, Liu B, Yang M, Cheng H. Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst. 2018;42(5):85.Google Scholar
  59. 59.
    Chen Y, Sha M, Zhao X, Ma J, Ni H, Gao W, et al. Automated detection of pathologic white matter alterations in Alzheimer’s disease using combined diffusivity and kurtosis method. Psychiatry Res Neuroimaging. 2017;264:35–45.Google Scholar
  60. 60.
    Schouten TM, Koini M, Vos F, Seiler S, Rooij M, Lechner A, et al. Individual classification of Alzheimer’s disease with diffusion magnetic resonance imaging. Neuroimage. 2017;152:476–81.Google Scholar
  61. 61.
    Sorensen L, Igel C, Liv Hansen N, Osler M, Lauritzen M, Rostrup E, et al. Early detection of Alzheimer’s disease using MRI hippocampal texture. Hum Brain Mapp. 2016;37(3):1148–61.Google Scholar
  62. 62.
    Varol E, Sotiras A, Davatzikos C, Alzheimer’s Disease Neuroimaging I. HYDRA: revealing heterogeneity of imaging and genetic patterns through a multiple max-margin discriminative analysis framework. Neuroimage. 2017;145(Pt B):346–64.Google Scholar
  63. 63.
    Cai K, Xu H, Guan H, Zhu W, Jiang J, Cui Y, et al. Identification of early-stage Alzheimer’s disease using sulcal morphology and other common neuroimaging indices. PLoS One. 2017;12(1):e0170875.Google Scholar
  64. 64.
    Jie B, Liu M, Liu J, Zhang D, Shen D. Temporally constrained group sparse learning for longitudinal data analysis in Alzheimer’s disease. IEEE Trans Biomed Eng. 2017;64(1):238–49.Google Scholar
  65. 65.
    Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R. Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol. 2018;39:208–16.Google Scholar
  66. 66.
    Imani F, Boada FE, Lieberman FS, Davis DK, Mountz JM. Molecular and metabolic pattern classification for detection of brain glioma progression. Eur J Radiol. 2014;83(2):e100–5.Google Scholar
  67. 67.
    Nachimuthu DS, Baladhandapani A. Multidimensional texture characterization: on analysis for brain tumor tissues using MRS and MRI. J Digit Imaging. 2014;27(4):496–506.Google Scholar
  68. 68.
    Sunwoo L, Kim YJ, Choi SH, Kim KG, Kang JH, Kang Y, et al. Computer-aided detection of brain metastasis on 3D MR imaging: observer performance study. PLoS One. 2017;12(6):e0178265.Google Scholar
  69. 69.
    Wu W, Chen AY, Zhao L, Corso JJ. Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int J Comput Assist Radiol Surg. 2014;9(2):241–53.Google Scholar
  70. 70.
    Fathi Kazerooni A, Mohseni M, Rezaei S, Bakhshandehpour G, Saligheh Rad H. Multi-parametric (ADC/PWI/T2-w) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA. 2015;28(1):13–22.Google Scholar
  71. 71.
    Szwarc P, Kawa J, Rudzki M, Pietka E. Automatic brain tumour detection and neovasculature assessment with multiseries MRI analysis. Comput Med Imaging Graph. 2015;46(Pt 2):178–90.Google Scholar
  72. 72.
    Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, et al. Brain tumor segmentation with deep neural networks. Med Image Anal. 2017;35:18–31.Google Scholar
  73. 73.
    Li Y, Liu X, Wei F, Sima DM, Van Cauter S, Himmelreich U, et al. An advanced MRI and MRSI data fusion scheme for enhancing unsupervised brain tumor differentiation. Comput Biol Med. 2017;81:121–9.Google Scholar
  74. 74.
    Sauwen N, Acou M, Sima DM, Veraart J, Maes F, Himmelreich U, et al. Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization. BMC Med Imaging. 2017;17(1):29.Google Scholar
  75. 75.
    AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 2018;45(3):1150–8.Google Scholar
  76. 76.
    Blanc-Durand P, Van Der Gucht A, Schaefer N, Itti E, Prior JO. Automatic lesion detection and segmentation of 18F-FET PET in gliomas: a full 3D U-Net convolutional neural network study. PLoS One. 2018;13(4):e0195798.Google Scholar
  77. 77.
    Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, et al. Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Progr Biomed. 2018;157:69–84.Google Scholar
  78. 78.
    Zhao Z, Yang G, Lin Y, Pang H, Wang M. Automated glioma detection and segmentation using graphical models. PLoS One. 2018;13(8):e0200745.Google Scholar
  79. 79.
    Inano R, Oishi N, Kunieda T, Arakawa Y, Yamao Y, Shibata S, et al. Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading. Neuroimage Clin. 2014;5:396–407.Google Scholar
  80. 80.
    Fetit AE, Novak J, Peet AC, Arvanitits TN. Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours. NMR Biomed. 2015;28(9):1174–84.Google Scholar
  81. 81.
    Hu LS, Ning S, Eschbacher JM, Gaw N, Dueck AC, Smith KA, et al. Multi-Parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma. PLoS One. 2015;10(11):e0141506.Google Scholar
  82. 82.
    Tsolaki E, Svolos P, Kousi E, Kapsalaki E, Fezoulidis I, Fountas K, et al. Fast spectroscopic multiple analysis (FASMA) for brain tumor classification: a clinical decision support system utilizing multi-parametric 3T MR data. Int J Comput Assist Radiol Surg. 2015;10(7):1149–66.Google Scholar
  83. 83.
    Li-Chun Hsieh K, Chen CY, Lo CM. Quantitative glioma grading using transformed gray-scale invariant textures of MRI. Comput Biol Med. 2017;83:102–8.Google Scholar
  84. 84.
    Zhang X, Yan LF, Hu YC, Li G, Yang Y, Han Y, et al. Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget. 2017;8(29):47816–30.Google Scholar
  85. 85.
    De Looze C, Beausang A, Cryan J, Loftus T, Buckley PG, Farrell M, et al. Machine learning: a useful radiological adjunct in determination of a newly diagnosed glioma’s grade and IDH status. J Neurooncol. 2018;139(2):491–9.Google Scholar
  86. 86.
    Fetit AE, Novak J, Rodriguez D, Auer DP, Clark CA, Grundy RG, et al. Radiomics in paediatric neuro-oncology: a multicentre study on MRI texture analysis. NMR Biomed. 2018.  https://doi.org/10.1002/nbm.3781.Google Scholar
  87. 87.
    Shofty B, Artzi M, Ben Bashat D, Liberman G, Haim O, Kashanian A, et al. MRI radiomics analysis of molecular alterations in low-grade gliomas. Int J Comput Assist Radiol Surg. 2018;13(4):563–71.Google Scholar
  88. 88.
    Zarinabad N, Abernethy LJ, Avula S, Davies NP, Rodriguez Gutierrez D, Jaspan T, et al. Application of pattern recognition techniques for classification of pediatric brain tumors by in vivo 3T (1) H-MR spectroscopy-A multi-center study. Magn Reson Med. 2018;79(4):2359–66.Google Scholar
  89. 89.
    Qian X, Tan H, Zhang J, Zhao W, Chan MD, Zhou X. Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation. Med Phys. 2016;43(11):5889.Google Scholar
  90. 90.
    Zhang J, Yu H, Qian X, Liu K, Tan H, Yang T, et al. Pseudo progression identification of glioblastoma with dictionary learning. Comput Biol Med. 2016;73:94–101.Google Scholar
  91. 91.
    Emblem KE, Due-Tonnessen P, Hald JK, Bjornerud A, Pinho MC, Scheie D, et al. Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI. J Magn Reson Imaging. 2014;40(1):47–54.Google Scholar
  92. 92.
    Emblem KE, Pinho MC, Zollner FG, Due-Tonnessen P, Hald JK, Schad LR, et al. A generic support vector machine model for preoperative glioma survival associations. Radiology. 2015;275(1):228–34.Google Scholar
  93. 93.
    Akbari H, Macyszyn L, Da X, Bilello M, Wolf RL, Martinez-Lage M, et al. Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery. 2016;78(4):572–80.Google Scholar
  94. 94.
    Chang K, Zhang B, Guo X, Zong M, Rahman R, Sanchez D, et al. Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol. 2016;18(12):1680–7.Google Scholar
  95. 95.
    Korfiatis P, Kline TL, Coufalova L, Lachance DH, Parney IF, Carter RE, et al. MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys. 2016;43(6):2835–44.Google Scholar
  96. 96.
    Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, et al. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol. 2016;18(3):417–25.Google Scholar
  97. 97.
    Akkus Z, Ali I, Sedlar J, Agrawal JP, Parney IF, Giannini C, et al. Predicting deletion of chromosomal arms 1p/19q in low-grade gliomas from MR images using machine intelligence. J Digit Imaging. 2017;30(4):469–76.Google Scholar
  98. 98.
    Chen L, Zhang H, Thung KH, Liu L, Lu J, Wu J, et al. Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients. Med Image Comput Comput Assist Interv. 2017;10434:450–8.Google Scholar
  99. 99.
    Zhang B, Chang K, Ramkissoon S, Tanguturi S, Bi WL, Reardon DA, et al. Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol. 2017;19(1):109–17.Google Scholar
  100. 100.
    Zhou M, Chaudhury B, Hall LO, Goldgof DB, Gillies RJ, Gatenby RA. Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J Magn Reson Imaging. 2017;46(1):115–23.Google Scholar
  101. 101.
    Li Y, Qian Z, Xu K, Wang K, Fan X, Li S, et al. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin. 2018;17:306–11.Google Scholar
  102. 102.
    Papp L, Potsch N, Grahovac M, Schmidbauer V, Woehrer A, Preusser M, et al. Glioma survival prediction with combined analysis of in vivo (11)C-MET PET features, ex vivo features, and patient features by supervised machine learning. J Nucl Med. 2018;59(6):892–9.Google Scholar
  103. 103.
    Anticevic A, Cole MW, Repovs G, Murray JD, Brumbaugh MS, Winkler AM, et al. Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness. Cereb Cortex. 2014;24(12):3116–30.Google Scholar
  104. 104.
    Bleich-Cohen M, Jamshy S, Sharon H, Weizman R, Intrator N, Poyurovsky M, et al. Machine learning fMRI classifier delineates subgroups of schizophrenia patients. Schizophr Res. 2014;160(1–3):196–200.Google Scholar
  105. 105.
    Castro E, Gupta CN, Martinez-Ramon M, Calhoun VD, Arbabshirani MR, Turner J. Identification of patterns of gray matter abnormalities in schizophrenia using source-based morphometry and bagging. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:1513–6.Google Scholar
  106. 106.
    Schnack HG, Nieuwenhuis M, van Haren NE, Abramovic L, Scheewe TW, Brouwer RM, et al. Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects. Neuroimage. 2014;84:299–306.Google Scholar
  107. 107.
    Cheng H, Newman S, Goni J, Kent JS, Howell J, Bolbecker A, et al. Nodal centrality of functional network in the differentiation of schizophrenia. Schizophr Res. 2015;168(1–2):345–52.Google Scholar
  108. 108.
    Chyzhyk D, Grana M, Ongur D, Shinn AK. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI. Int J Neural Syst. 2015;25(3):1550007.Google Scholar
  109. 109.
    Chyzhyk D, Savio A, Grana M. Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM. Neural Netw. 2015;68:23–33.Google Scholar
  110. 110.
    Koch SP, Hagele C, Haynes JD, Heinz A, Schlagenhauf F, Sterzer P. Diagnostic classification of schizophrenia patients on the basis of regional reward-related FMRI signal patterns. PLoS One. 2015;10(3):e0119089.Google Scholar
  111. 111.
    Chu WL, Huang MW, Jian BL, Hsu CY, Cheng KS. A correlative classification study of schizophrenic patients with results of clinical evaluation and structural magnetic resonance images. Behav Neurol. 2016;2016:7849526.Google Scholar
  112. 112.
    Kim J, Calhoun VD, Shim E, Lee JH. Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. Neuroimage. 2016;124(Pt A):127–46.Google Scholar
  113. 113.
    Lu X, Yang Y, Wu F, Gao M, Xu Y, Zhang Y, et al. Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images. Medicine (Baltimore). 2016;95(30):e3973.Google Scholar
  114. 114.
    Pinaya WH, Gadelha A, Doyle OM, Noto C, Zugman A, Cordeiro Q, et al. Using deep belief network modelling to characterize differences in brain morphometry in schizophrenia. Sci Rep. 2016;6:38897.Google Scholar
  115. 115.
    Pergola G, Trizio S, Di Carlo P, Taurisano P, Mancini M, Amoroso N, et al. Grey matter volume patterns in thalamic nuclei are associated with familial risk for schizophrenia. Schizophr Res. 2017;180:13–20.Google Scholar
  116. 116.
    Plaschke RN, Cieslik EC, Muller VI, Hoffstaedter F, Plachti A, Varikuti DP, et al. On the integrity of functional brain networks in schizophrenia, Parkinson’s disease, and advanced age: evidence from connectivity-based single-subject classification. Hum Brain Mapp. 2017;38(12):5845–58.Google Scholar
  117. 117.
    Qureshi MNI, Oh J, Cho D, Jo HJ, Lee B. Multimodal discrimination of schizophrenia using hybrid weighted feature concatenation of brain functional connectivity and anatomical features with an extreme learning machine. Front Neuroinform. 2017;11:59.Google Scholar
  118. 118.
    Skatun KC, Kaufmann T, Doan NT, Alnaes D, Cordova-Palomera A, Jonsson EG, et al. Consistent functional connectivity alterations in schizophrenia spectrum disorder: a multisite study. Schizophr Bull. 2017;43(4):914–24.Google Scholar
  119. 119.
    Zarogianni E, Storkey AJ, Johnstone EC, Owens DG, Lawrie SM. Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features. Schizophr Res. 2017;181:6–12.Google Scholar
  120. 120.
    Juneja A, Rana B, Agrawal RK. A novel fuzzy rough selection of non-linearly extracted features for schizophrenia diagnosis using fMRI. Comput Methods Progr Biomed. 2018;155:139–52.Google Scholar
  121. 121.
    Mikolas P, Hlinka J, Skoch A, Pitra Z, Frodl T, Spaniel F, et al. Machine learning classification of first-episode schizophrenia spectrum disorders and controls using whole brain white matter fractional anisotropy. BMC Psychiatry. 2018;18(1):97.Google Scholar
  122. 122.
    Orban P, Dansereau C, Desbois L, Mongeau-Perusse V, Giguere CE, Nguyen H, et al. Multisite generalizability of schizophrenia diagnosis classification based on functional brain connectivity. Schizophr Res. 2018;192:167–71.Google Scholar
  123. 123.
    Zeng LL, Wang H, Hu P, Yang B, Pu W, Shen H, et al. Multi-site diagnostic classification of schizophrenia using discriminant deep learning with functional connectivity MRI. EBioMedicine. 2018;30:74–85.Google Scholar
  124. 124.
    Papakostas GI. Managing partial response or nonresponse: switching, augmentation, and combination strategies for major depressive disorder. J Clin Psychol. 2009;70(Suppl. 6):16–25.Google Scholar
  125. 125.
    Cao L, Guo S, Xue Z, Hu Y, Liu H, Mwansisya TE, et al. Aberrant functional connectivity for diagnosis of major depressive disorder: a discriminant analysis. Psychiatry Clin Neurosci. 2014;68(2):110–9.Google Scholar
  126. 126.
    Qin J, Wei M, Liu H, Chen J, Yan R, Hua L, et al. Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification. Magn Reson Imaging. 2014;32(10):1314–20.Google Scholar
  127. 127.
    Zeng LL, Shen H, Liu L, Hu D. Unsupervised classification of major depression using functional connectivity MRI. Hum Brain Mapp. 2014;35(4):1630–41.Google Scholar
  128. 128.
    Lueken U, Straube B, Yang Y, Hahn T, Beesdo-Baum K, Wittchen HU, et al. Separating depressive comorbidity from panic disorder: a combined functional magnetic resonance imaging and machine learning approach. J Affect Disord. 2015;184:182–92.Google Scholar
  129. 129.
    Qin J, Wei M, Liu H, Chen J, Yan R, Yao Z, et al. Altered anatomical patterns of depression in relation to antidepressant treatment: evidence from a pattern recognition analysis on the topological organization of brain networks. J Affect Disord. 2015;180:129–37.Google Scholar
  130. 130.
    Sato JR, Moll J, Green S, Deakin JF, Thomaz CE, Zahn R. Machine learning algorithm accurately detects fMRI signature of vulnerability to major depression. Psychiatry Res. 2015;233(2):289–91.Google Scholar
  131. 131.
    Shimizu Y, Yoshimoto J, Toki S, Takamura M, Yoshimura S, Okamoto Y, et al. Toward probabilistic diagnosis and understanding of depression based on functional MRI data analysis with logistic group LASSO. PLoS One. 2015;10(5):e0123524.Google Scholar
  132. 132.
    Wu MJ, Wu HE, Mwangi B, Sanches M, Selvaraj S, Zunta-Soares GB, et al. Prediction of pediatric unipolar depression using multiple neuromorphometric measurements: a pattern classification approach. J Psychiatr Res. 2015;62:84–91.Google Scholar
  133. 133.
    Ramasubbu R, Brown MR, Cortese F, Gaxiola I, Goodyear B, Greenshaw AJ, et al. Accuracy of automated classification of major depressive disorder as a function of symptom severity. Neuroimage Clin. 2016;12:320–31.Google Scholar
  134. 134.
    Bhaumik R, Jenkins LM, Gowins JR, Jacobs RH, Barba A, Bhaumik DK, et al. Multivariate pattern analysis strategies in detection of remitted major depressive disorder using resting state functional connectivity. Neuroimage Clin. 2017;16:390–8.Google Scholar
  135. 135.
    Kautzky A, James GM, Philippe C, Baldinger-Melich P, Kraus C, Kranz GS, et al. The influence of the rs6295 gene polymorphism on serotonin-1A receptor distribution investigated with PET in patients with major depression applying machine learning. Transl Psychiatry. 2017;7(6):e1150.Google Scholar
  136. 136.
    Schnyer DM, Clasen PC, Gonzalez C, Beevers CG. Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder. Psychiatry Res Neuroimaging. 2017;264:1–9.Google Scholar
  137. 137.
    Wang J, Wei Q, Bai T, Zhou X, Sun H, Becker B, et al. Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder. Soc Cogn Affect Neurosci. 2017;12(12):1983–92.Google Scholar
  138. 138.
    Deng F, Wang Y, Huang H, Niu M, Zhong S, Zhao L, et al. Abnormal segments of right uncinate fasciculus and left anterior thalamic radiation in major and bipolar depression. Prog Neuropsychopharmacol Biol Psychiatry. 2018;81:340–9.Google Scholar
  139. 139.
    Milak MS, Pantazatos S, Rashid R, Zanderigo F, DeLorenzo C, Hesselgrave N, et al. Higher 5-HT1A autoreceptor binding as an endophenotype for major depressive disorder identified in high risk offspring—a pilot study. Psychiatry Res Neuroimaging. 2018;276:15–23.Google Scholar
  140. 140.
    Foland-Ross LC, Sacchet MD, Prasad G, Gilbert B, Thompson PM, Gotlib IH. Cortical thickness predicts the first onset of major depression in adolescence. Int J Dev Neurosci. 2015;46:125–31.Google Scholar
  141. 141.
    Johnston BA, Steele JD, Tolomeo S, Christmas D, Matthews K. Structural MRI-based predictions in patients with treatment-refractory depression (TRD). PLoS One. 2015;10(7):e0132958.Google Scholar
  142. 142.
    Patel MJ, Andreescu C, Price JC, Edelman KL, Reynolds CF 3rd, Aizenstein HJ. Machine learning approaches for integrating clinical and imaging features in late-life depression classification and response prediction. Int J Geriatr Psychiatry. 2015;30(10):1056–67.Google Scholar
  143. 143.
    Redlich R, Opel N, Grotegerd D, Dohm K, Zaremba D, Burger C, et al. Prediction of individual response to electroconvulsive therapy via machine learning on structural magnetic resonance imaging data. JAMA Psychiatry. 2016;73(6):557–64.Google Scholar
  144. 144.
    Yoshida K, Shimizu Y, Yoshimoto J, Takamura M, Okada G, Okamoto Y, et al. Prediction of clinical depression scores and detection of changes in whole-brain using resting-state functional MRI data with partial least squares regression. PLoS One. 2017;12(7):e0179638.Google Scholar
  145. 145.
    Adeli E, Shi F, An L, Wee CY, Wu G, Wang T, et al. Joint feature-sample selection and robust diagnosis of Parkinson’s disease from MRI data. Neuroimage. 2016;141:206–19.Google Scholar
  146. 146.
    Choi H, Ha S, Im HJ, Paek SH, Lee DS. Refining diagnosis of Parkinson’s disease with deep learning-based interpretation of dopamine transporter imaging. Neuroimage Clin. 2017;16:586–94.Google Scholar
  147. 147.
    Peng B, Wang S, Zhou Z, Liu Y, Tong B, Zhang T, et al. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson’s disease. Neurosci Lett. 2017;651:88–94.Google Scholar
  148. 148.
    Amoroso N, La Rocca M, Monaco A, Bellotti R, Tangaro S. Complex networks reveal early MRI markers of Parkinson’s disease. Med Image Anal. 2018;48:12–24.Google Scholar
  149. 149.
    Castillo-Barnes D, Ramirez J, Segovia F, Martinez-Murcia FJ, Salas-Gonzalez D, Gorriz JM. Robust ensemble classification methodology for I123-ioflupane SPECT images and multiple heterogeneous biomarkers in the diagnosis of Parkinson’s disease. Front Neuroinform. 2018;12:53.Google Scholar
  150. 150.
    Oliveira FPM, Faria DB, Costa DC, Castelo-Branco M, Tavares J. Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson’s disease based on [(123)I]FP-CIT SPECT images. Eur J Nucl Med Mol Imaging. 2018;45(6):1052–62.Google Scholar
  151. 151.
    Salvatore C, Cerasa A, Castiglioni I, Gallivanone F, Augimeri A, Lopez M, et al. Machine learning on brain MRI data for differential diagnosis of Parkinson’s disease and progressive supranuclear palsy. J Neurosci Methods. 2014;222:230–7.Google Scholar
  152. 152.
    Hirschauer TJ, Adeli H, Buford JA. Computer-Aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J Med Syst. 2015;39(11):179.Google Scholar
  153. 153.
    Huertas-Fernandez I, Garcia-Gomez FJ, Garcia-Solis D, Benitez-Rivero S, Marin-Oyaga VA, Jesus S, et al. Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson’s disease using [(123)I]FP-CIT SPECT. Eur J Nucl Med Mol Imaging. 2015;42(1):112–9.Google Scholar
  154. 154.
    Singh G, Samavedham L. Unsupervised learning based feature extraction for differential diagnosis of neurodegenerative diseases: a case study on early-stage diagnosis of Parkinson disease. J Neurosci Methods. 2015;256:30–40.Google Scholar
  155. 155.
    Liu L, Wang Q, Adeli E, Zhang L, Zhang H, Shen D. Feature selection based on iterative canonical correlation analysis for automatic diagnosis of Parkinson’s disease. Med Image Comput Comput Assist Interv. 2016;9901:1–8.Google Scholar
  156. 156.
    Du G, Lewis MM, Kanekar S, Sterling NW, He L, Kong L, et al. Combined diffusion tensor imaging and apparent transverse relaxation rate differentiate Parkinson disease and atypical parkinsonism. AJNR Am J Neuroradiol. 2017;38(5):966–72.Google Scholar
  157. 157.
    Ye Z, Rae CL, Nombela C, Ham T, Rittman T, Jones PS, et al. Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson’s disease with clinical and neuroimaging measures. Hum Brain Mapp. 2016;37(3):1026–37.Google Scholar
  158. 158.
    ADHD200 - international neuroimaging data-sharing initiative - NITRC. http://.fcon_1000.projects.nitrc.org/indi/adhd200. Accessed Oct 2018.Google Scholar
  159. 159.
    Qureshi MN, Min B, Jo HJ, Lee B. Multiclass classification for the differential diagnosis on the ADHD subtypes using recursive feature elimination and hierarchical extreme learning machine: structural MRI study. PLoS One. 2016;11(8):e0160697.Google Scholar
  160. 160.
    Qureshi MNI, Oh J, Min B, Jo HJ, Lee B. Multi-modal, multi-measure, and multi-class discrimination of ADHD with hierarchical feature extraction and extreme learning machine using structural and functional brain MRI. Front Hum Neurosci. 2017;11:157.Google Scholar
  161. 161.
    Tan L, Guo X, Ren S, Epstein JN, Lu LJ. A computational model for the automatic diagnosis of attention deficit hyperactivity disorder based on functional brain volume. Front Comput Neurosci. 2017;11:75.Google Scholar
  162. 162.
    Hart H, Chantiluke K, Cubillo AI, Smith AB, Simmons A, Brammer MJ, et al. Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD. Hum Brain Mapp. 2014;35(7):3083–94.Google Scholar
  163. 163.
    Johnston BA, Mwangi B, Matthews K, Coghill D, Konrad K, Steele JD. Brainstem abnormalities in attention deficit hyperactivity disorder support high accuracy individual diagnostic classification. Hum Brain Mapp. 2014;35(10):5179–89.Google Scholar
  164. 164.
    Ghiassian S, Greiner R, Jin P, Brown MR. Using functional or structural magnetic resonance images and personal characteristic data to identify ADHD and autism. PLoS One. 2016;11(12):e0166934.Google Scholar
  165. 165.
    Xiao C, Bledsoe J, Wang S, Chaovalitwongse WA, Mehta S, Semrud-Clikeman M, et al. An integrated feature ranking and selection framework for ADHD characterization. Brain Inform. 2016;3(3):145–55.Google Scholar
  166. 166.
    Chaim-Avancini TM, Doshi J, Zanetti MV, Erus G, Silva MA, Duran FLS, et al. Neurobiological support to the diagnosis of ADHD in stimulant-naive adults: pattern recognition analyses of MRI data. Acta Psychiatr Scand. 2017;136(6):623–36.Google Scholar
  167. 167.
    Riaz A, Asad M, Alonso E, Slabaugh G. Fusion of fMRI and non-imaging data for ADHD classification. Comput Med Imaging Graph. 2018;65:115–28.Google Scholar
  168. 168.
    Sen B, Borle NC, Greiner R, Brown MRG. A general prediction model for the detection of ADHD and Autism using structural and functional MRI. PLoS One. 2018;13(4):e0194856.Google Scholar
  169. 169.
    Kim JW, Sharma V, Ryan ND. Predicting methylphenidate response in ADHD using machine learning approaches. Int J Neuropsychopharmacol. 2015;18(11):pyv052.Google Scholar
  170. 170.
    Matson JL, Rieske RD, Williams LW. The relationship between autism spectrum disorders and attention-deficit/hyperactivity disorder: an overview. Res Dev Disabil. 2013;34:2475–84.Google Scholar
  171. 171.
    Lord C, Risi S, Lambrecht L, Cook EH Jr, Leventhal BL, DiLavore PC, et al. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30:205–23.Google Scholar
  172. 172.
    Uddin LQ, Dajani DR, Voorhies W, Bednarz H, Kana RK. Progress and roadblocks in the search for brain-based biomarkers of autism and attention-deficit/hyperactivity disorder. Transl Psychiatry. 2017;7:e1218.Google Scholar
  173. 173.
    Just MA, Cherkassky VL, Buchweitz A, Keller TA, Mitchell TM. Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS One. 2014;9(12):e113879.Google Scholar
  174. 174.
    Chen CP, Keown CL, Jahedi A, Nair A, Pflieger ME, Bailey BA, et al. Diagnostic classification of intrinsic functional connectivity highlights somatosensory, default mode, and visual regions in autism. Neuroimage Clin. 2015;8:238–45.Google Scholar
  175. 175.
    Gori I, Giuliano A, Muratori F, Saviozzi I, Oliva P, Tancredi R, et al. Gray matter alterations in young children with autism spectrum disorders: comparing morphometry at the voxel and regional level. J Neuroimaging. 2015;25(6):866–74.Google Scholar
  176. 176.
    Iidaka T. Resting state functional magnetic resonance imaging and neural network classified autism and control. Cortex. 2015;63:55–67.Google Scholar
  177. 177.
    Plitt M, Barnes KA, Martin A. Functional connectivity classification of autism identifies highly predictive brain features but falls short of biomarker standards. Neuroimage Clin. 2015;7:359–66.Google Scholar
  178. 178.
    Jahedi A, Nasamran CA, Faires B, Fan J, Muller RA. Distributed intrinsic functional connectivity patterns predict diagnostic status in large autism cohort. Brain Connect. 2017;7(8):515–25.Google Scholar
  179. 179.
    Kam TE, Suk HI, Lee SW. Multiple functional networks modeling for autism spectrum disorder diagnosis. Hum Brain Mapp. 2017;38(11):5804–21.Google Scholar
  180. 180.
    Akhavan Aghdam M, Sharifi A, Pedram MM. Combination of rs-fMRI and sMRI data to discriminate autism spectrum disorders in young children using deep belief network. J Digit Imaging. 2018;31(6):895–903.Google Scholar
  181. 181.
    Bi XA, Wang Y, Shu Q, Sun Q, Xu Q. Classification of autism spectrum disorder using random support vector machine cluster. Front Genet. 2018;9:18.Google Scholar
  182. 182.
    Heinsfeld AS, Franco AR, Craddock RC, Buchweitz A, Meneguzzi F. Identification of autism spectrum disorder using deep learning and the ABIDE dataset. Neuroimage Clin. 2018;17:16–23.Google Scholar
  183. 183.
    Li H, Parikh NA, He L. A novel transfer learning approach to enhance deep neural network classification of brain functional connectomes. Front Neurosci. 2018;12:491.Google Scholar
  184. 184.
    Moradi E, Khundrakpam B, Lewis JD, Evans AC, Tohka J. Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data. Neuroimage. 2017;144(Pt A):128–41.Google Scholar
  185. 185.
    Rudie JD, Colby JB, Salamon N. Machine learning classification of mesial temporal sclerosis in epilepsy patients. Epilepsy Res. 2015;117:63–9.Google Scholar
  186. 186.
    Hong SJ, Kim H, Schrader D, Bernasconi N, Bernhardt BC, Bernasconi A. Automated detection of cortical dysplasia type II in MRI-negative epilepsy. Neurology. 2014;83(1):48–55.Google Scholar
  187. 187.
    Kamiya K, Amemiya S, Suzuki Y, Kunii N, Kawai K, Mori H, et al. Machine learning of DTI structural brain connectomes for lateralization of temporal lobe epilepsy. Magn Reson Med Sci. 2016;15(1):121–9.Google Scholar
  188. 188.
    Del Gaizo J, Mofrad N, Jensen JH, Clark D, Glenn R, Helpern J, et al. Using machine learning to classify temporal lobe epilepsy based on diffusion MRI. Brain Behav. 2017;7(10):e00801.Google Scholar
  189. 189.
    Hong SJ, Bernhardt BC, Schrader DS, Bernasconi N, Bernasconi A. Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy. Neurology. 2016;86(7):643–50.Google Scholar
  190. 190.
    El Azami M, Hammers A, Jung J, Costes N, Bouet R, Lartizien C. Detection of lesions underlying intractable epilepsy on T1-weighted MRI as an outlier detection problem. PLoS One. 2016;11(9):e0161498.Google Scholar
  191. 191.
    Adler S, Wagstyl K, Gunny R, Ronan L, Carmichael D, Cross JH, et al. Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy. Neuroimage Clin. 2017;14:18–27.Google Scholar
  192. 192.
    Bernhardt BC, Hong SJ, Bernasconi A, Bernasconi N. Magnetic resonance imaging pattern learning in temporal lobe epilepsy: classification and prognostics. Ann Neurol. 2015;77(3):436–46.Google Scholar
  193. 193.
    Munsell BC, Wee CY, Keller SS, Weber B, Elger C, da Silva LA, et al. Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data. Neuroimage. 2015;118:219–30.Google Scholar
  194. 194.
    Pustina D, Avants B, Sperling M, Gorniak R, He X, Doucet G, et al. Predicting the laterality of temporal lobe epilepsy from PET, MRI, and DTI: a multimodal study. Neuroimage Clin. 2015;9:20–31.Google Scholar
  195. 195.
    Zhong J, Chen DQ, Nantes JC, Holmes SA, Hodaie M, Koski L. Combined structural and functional patterns discriminating upper limb motor disability in multiple sclerosis using multivariate approaches. Brain Imaging Behav. 2017;11(3):754–68.Google Scholar
  196. 196.
    Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, et al. Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls. Neuroimage Clin. 2018;17:169–78.Google Scholar
  197. 197.
    Zurita M, Montalba C, Labbe T, Cruz JP, Dalboni da Rocha J, Tejos C, et al. Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data. Neuroimage Clin. 2018;20:724–30.Google Scholar
  198. 198.
    Sacca V, Sarica A, Novellino F, Barone S, Tallarico T, Filippelli E, et al. Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data. Brain Imaging Behav. 2018.  https://doi.org/10.1007/s11682-018-9926-9.Google Scholar
  199. 199.
    Salem M, Cabezas M, Valverde S, Pareto D, Oliver A, Salvi J, et al. A supervised framework with intensity subtraction and deformation field features for the detection of new T2-w lesions in multiple sclerosis. Neuroimage Clin. 2018;17:607–15.Google Scholar
  200. 200.
    Zhao Y, Healy BC, Rotstein D, Guttmann CR, Bakshi R, Weiner HL, et al. Exploration of machine learning techniques in predicting multiple sclerosis disease course. PLoS One. 2017;12(4):e0174866.Google Scholar
  201. 201.
    Deshpande H, Maurel P, Barillot C. Classification of multiple sclerosis lesions using adaptive dictionary learning. Comput Med Imaging Graph. 2015;46(Pt 1):2–10.Google Scholar
  202. 202.
    Fartaria MJ, Bonnier G, Roche A, Kober T, Meuli R, Rotzinger D, et al. Automated detection of white matter and cortical lesions in early stages of multiple sclerosis. J Magn Reson Imaging. 2016;43(6):1445–54.Google Scholar
  203. 203.
    Lee Eun-Jae, Kim Yong-Hwan, Kim Namkug, Kanga Dong-Wha. Deep into the brain: artificial intelligence in stroke imaging. J Stroke. 2017;19(3):277–85.Google Scholar
  204. 204.
    Ashton EA, Takahashi C, Berg MJ, Goodman A, Totterman S, Ekholm S. Accuracy and reproducibility of manual and semiautomated quantification of MS lesions by MRI. J Magn Reson Imaging. 2003;17:300–8.Google Scholar
  205. 205.
    Bentley P, Ganesalingam J, Carlton Jones AL, Mahady K, Epton S, Rinne P, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin. 2014;4:635640.Google Scholar
  206. 206.
    Kim BJ, Kim YH, Kim N, Kwon SU, Kim SJ, Kim JS, et al. Lesion location-based prediction of visual field improvement after cerebral infarction. PLoS One. 2015;10:e0143882.Google Scholar
  207. 207.
    Guberina N, Dietrich U, Radbruch A, Goebel J, Deuschl C, Ringelstein A, et al. Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine. Neuroradiology. 2018;60(9):889–901.Google Scholar
  208. 208.
    Rehme AK, Volz LJ, Feis DL, Bomilcar-Focke I, Liebig T, Eickhoff SB, et al. Identifying neuroimaging markers of motor disability in acute stroke by machine learning techniques. Cereb Cortex. 2015;25(9):3046–56.Google Scholar
  209. 209.
    Asadi H, Dowling R, Yan B, Mitchell P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One. 2014;9(2):e88225.Google Scholar
  210. 210.
    Bentley P, Ganesalingam J, Carlton Jones AL, Mahady K, Epton S, Rinne P, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning. Neuroimage Clin. 2014;4:635–40.Google Scholar
  211. 211.
    Maier O, Schroder C, Forkert ND, Martinetz T, Handels H. Classifiers for ischemic stroke lesion segmentation: a comparison study. PLoS One. 2015;10(12):e0145118.Google Scholar
  212. 212.
    Griffis JC, Allendorfer JB, Szaflarski JP. Voxel-based Gaussian naive Bayes classification of ischemic stroke lesions in individual T1-weighted MRI scans. J Neurosci Methods. 2016;257:97–108.Google Scholar
  213. 213.
    Pustina D, Coslett HB, Turkeltaub PE, Tustison N, Schwartz MF, Avants B. Automated segmentation of chronic stroke lesions using LINDA: Lesion identification with neighborhood data analysis. Hum Brain Mapp. 2016;37(4):1405–21.Google Scholar
  214. 214.
    Douglas David B, Iv Michael, Douglas Pamela K, Ariana Anderson, Vos Sjoerd B, Bammer Roland, Zeineh Michael, Wintermark Max. Diffusion tensor imaging of TBI: potentials and challenges. Top Magn Reson Imaging. 2015;24(5):241–51.Google Scholar
  215. 215.
    Johansen-Berg H. Behavioural relevance of variation in white matter microstructure. Curr Opin Neurol. 2010;23(351–358):20581685.Google Scholar
  216. 216.
    Mitra J, Shen KK, Ghose S, Bourgeat P, Fripp J, Salvado O, et al. Statistical machine learning to identify traumatic brain injury (TBI) from structural disconnections of white matter networks. Neuroimage. 2016;129:247–59.Google Scholar
  217. 217.
    Peacock WFT, Van Meter TE, Mirshahi N, Ferber K, Gerwien R, Rao V, et al. Derivation of a three biomarker panel to improve diagnosis in patients with mild traumatic brain injury. Front Neurol. 2017;8:641.Google Scholar
  218. 218.
    Rangaprakash D, Deshpande G, Daniel TA, Goodman AM, Robinson JL, Salibi N, et al. Compromised hippocampus-striatum pathway as a potential imaging biomarker of mild-traumatic brain injury and posttraumatic stress disorder. Hum Brain Mapp. 2017;38(6):2843–64.Google Scholar
  219. 219.
    Vergara VM, Mayer AR, Damaraju E, Kiehl KA, Calhoun V. Detection of mild traumatic brain injury by machine learning classification using resting state functional network connectivity and fractional anisotropy. J Neurotrauma. 2017;34(5):1045–53.Google Scholar
  220. 220.
    Rangaprakash D, Dretsch MN, Venkataraman A, Katz JS, Denney TS Jr, Deshpande G. Identifying disease foci from static and dynamic effective connectivity networks: illustration in soldiers with trauma. Hum Brain Mapp. 2018;39(1):264–87.Google Scholar
  221. 221.
    Fagerholm ED, Hellyer PJ, Scott G, Leech R, Sharp DJ. Disconnection of network hubs and cognitive impairment after traumatic brain injury. Brain. 2015;138(Pt 6):1696–709.Google Scholar
  222. 222.
    Chong SL, Liu N, Barbier S, Ong ME. Predictive modeling in pediatric traumatic brain injury using machine learning. BMC Med Res Methodol. 2015;15:22.Google Scholar
  223. 223.
    OASIS brains - open access series of imaging studies. https://www.oasis-brains.org/. Accessed Oct 2018.
  224. 224.
    DZNE – longitudinal cognitive impairment and dementia study. https://www.dzne.de/en/research/studies/studien/delcode/. Accessed Oct 2018.
  225. 225.
    Seiler S, Schmidt H, Lechner A, Benke T. Driving cessation and dementia: results of the prospective registry on dementia in Austria PRODEM. PLoS One. 2012;7(12):1–6.Google Scholar
  226. 226.
    Ellis KA, Bush AI, Darby D, De Fazio D, Foster J, Hudson P, Lautenschlager NT, Lenzo N, Martins RN, Maruff P, et al. The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease. Int Psychogeriatr. 2009;21:672–87.Google Scholar
  227. 227.
    QIBA - quantitative imaging biomarkers alliance. https://www.linkedin.com/company/rsna-qiba. Accessed Oct 2018.
  228. 228.
    Shen Dinggang, Guorong Wu, Suk Heung-Il. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;21(19):221–48.Google Scholar
  229. 229.
    Vieira S, Pinaya WHL, Mechelli A. Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci Biobehav Rev. 2017;74:58–75.Google Scholar

Copyright information

© Japan Radiological Society 2018

Authors and Affiliations

  1. 1.Department of Radiology, Graduate School of Medical ScienceKyoto Prefectural University of MedicineKyotoJapan

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