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Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis

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Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 140))

Abstract

Multi-modal magnetic resonance imaging (MRI) is increasingly used in neuroscience research, as it allowed the non-invasive investigation of structure and function of the human brain in health and pathology. One of the most important applications of multi-modal MRI is the provision of vital diagnostic data for neurologic and psychiatric disorders. As traditional MRI researches using univariate analyses can only reveal disease-related structural and functional alterations at group level which limited the clinical application, and recent attention has turned toward integrating multi-modal neuroimaging and computer-aided prognosis (CAD) technology, especially machine learning, to assist clinical disease diagnose. Research in this area is growing exponentially, and therefore it is meaningful to review the current and future development of this emerging area. Hence, in this paper, based on our own studies and contributions, we review the recent advances in multi-modal MRI and CAD technologies, and their applications to assist the clinical diagnosis of three common neurologic and psychiatric disorders, namely, Alzheimer’s disease, Attention deficit/hyperactivity disorder and Tourette syndrome. We extracted multi-modal features from structural, diffusion and resting-state functional MRI, then different feature selection methods and classifiers were applied. In addition, we applied different feature fusion schemes (e.g. multiple kernel learning) to combining multi-modal features for classification. Our experiments show that using feature fusion techniques to integrate multi-modal features can yield better classification results for diseases prediction, which may outline some future directions for multi-modal neuroimaging where researchers can design more advanced methods and models for neurologic and psychiatric research.

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References

  1. Friston, K.J.: Modalities, modes, and models in functional neuroimaging. Science 326, 399–403 (2009)

    Article  Google Scholar 

  2. Zakzanis, K.K., Graham, S.J., Campbell, Z.: A meta-analysis of structural and functional brain imaging in dementia of the Alzheimer’s type: a neuroimaging profile. Neuropsychol. Rev. 13, 1–18 (2003)

    Article  Google Scholar 

  3. Binnewijzend, M.A., Schoonheim, M.M., Sanz-Arigita, E., Wink, A.M., van der Flier, W.M., Tolboom, N., Adriaanse, S.M., Damoiseaux, J.S., Scheltens, P., van Berckel, B.N., Barkhof, F.: Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol. Aging 33, 2018–2028 (2012)

    Article  Google Scholar 

  4. Stein, D.J., Fontenelle, L.F., Reed, G.M.: Obsessive-compulsive and related disorders in ICD-11. Revista brasileira de psiquiatria 36(Suppl 1), 1–2 (2014)

    Article  Google Scholar 

  5. Zhong, Z., Zhao, T., Luo, J., Guo, Z., Guo, M., Li, P., Sun, J., He, Y., Li, Z.: Abnormal topological organization in white matter structural networks revealed by diffusion tensor tractography in unmedicated patients with obsessive-compulsive disorder. Prog. Neuropsychopharmacol. Biol. Psychiatry 51, 39–50 (2014)

    Article  Google Scholar 

  6. Hong, S.B., Zalesky, A., Fornito, A., Park, S., Yang, Y.H., Park, M.H., Song, I.C., Sohn, C.H., Shin, M.S., Kim, B.N., Cho, S.C., Han, D.H., Cheong, J.H., Kim, J.W.: Connectomic disturbances in attention-deficit/hyperactivity disorder: a whole-brain tractography analysis. Biol. Psychiatry 76, 656–663 (2014)

    Article  Google Scholar 

  7. Hart, H., Radua, J., Nakao, T., Mataix-Cols, D., Rubia, K.: Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry 70, 185–198 (2013)

    Article  Google Scholar 

  8. Liu, Y., Miao, W., Wang, J., Gao, P., Yin, G., Zhang, L., Lv, C., Ji, Z., Yu, T., Sabel, B.A., He, H., Peng, Y.: Structural abnormalities in early Tourette syndrome children: a combined voxel-based morphometry and tract-based spatial statistics study. PLoS ONE 8, e76105 (2013)

    Article  Google Scholar 

  9. Worbe, Y., Marrakchi-Kacem, L., Lecomte, S., Valabregue, R., Poupon, F., Guevara, P., Tucholka, A., Mangin, J.F., Vidailhet, M., Lehericy, S., Hartmann, A., Poupon, C.: Altered structural connectivity of cortico-striato-pallido-thalamic networks in Gilles de la Tourette syndrome. Brain 138, 472–482 (2015)

    Article  Google Scholar 

  10. Ellison-Wright, I., Bullmore, E.: Anatomy of bipolar disorder and schizophrenia: a meta-analysis. Schizophr. Res. 117, 1–12 (2010)

    Article  Google Scholar 

  11. Shergill, S.S., Brammer, M.J., Williams, S.C., Murray, R.M., Mcguire, P.K.: Mapping auditory hallucinations in schizophrenia using functional magnetic resonance imaging. Arch. Gen. Psychiatry 57, 1033–1038 (2000)

    Article  Google Scholar 

  12. Etkin, A., Wager, T.D.: Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. Am. J. Psychiatry 164, 1476–1488 (2007)

    Article  Google Scholar 

  13. Liao, M., Yang, F., Zhang, Y., He, Z., Su, L., Li, L.: White matter abnormalities in adolescents with generalized anxiety disorder: a diffusion tensor imaging study. BMC Psychiatry 14, 41 (2014)

    Article  Google Scholar 

  14. Orrù, G., Pettersson-Yeo, W., Marquand, A.F., Sartori, G., Mechelli, A.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36, 1140–1152 (2012)

    Article  Google Scholar 

  15. Cerasa, A., Cherubini, A., Peran, P.: Multimodal MRI in neurodegenerative disorders. Neurol. Res. Int. 2012 (2012)

    Google Scholar 

  16. Arimura, H., Magome, T., Yamashita, Y., Yamamoto, D.: Computer-aided diagnosis systems for brain diseases in magnetic resonance images. Algorithms 2, 925–952 (2009)

    Article  MathSciNet  Google Scholar 

  17. Dorrius, M.D., Weide, M.D., Ooijen, P., Pijnappel, R.M.: Computer-aided detection in breast MRI: a systematic review and meta-analysis. Int. J. Med. Radiol. 21, 1600–1608 (2011)

    Google Scholar 

  18. Hidetaka, A., Takashi, Y., Seiji, K., Kazuhiro, T., Hiroshi, K., Futoshi, M., Hiroshi, H., Shuji, S., Fukai, T., Yoshiharu, H.: Automated method for identification of patients with Alzheimer’s disease based on three-dimensional MR images. Acad. Radiol. 15, 274–284 (2008)

    Article  Google Scholar 

  19. Liu, F., Wee, C.Y., Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for Alzheimer’s Disease and mild cognitive impairment identification. Neuroimage 84, 466–475 (2014)

    Article  Google Scholar 

  20. Cherkassky, V.: The nature of statistical learning theory. IEEE Trans. Neural Netw./A Publication of the IEEE Neural Networks Council 8, 1564 (1997)

    Article  Google Scholar 

  21. Fjell, A.M., Walhovd, K.B., Fennema-Notestine, C., McEvoy, L.K., Hagler, D.J., Holland, D., Brewer, J.B., Dale, A.M., Alzheimer’s Disease Neuroimaging, I.: CSF biomarkers in prediction of cerebral and clinical change in mild cognitive impairment and Alzheimer’s disease. J. Neurosci. 30, 2088–2101 (2010)

    Google Scholar 

  22. Gerardin, E., Chetelat, G., Chupin, M., Cuingnet, R., Desgranges, B., Kim, H.S., Niethammer, M., Dubois, B., Lehericy, S., Garnero, L., Eustache, F., Colliot, O., Alzheimer’s Disease Neuroimaging, I.: Multidimensional classification of hippocampal shape features discriminates Alzheimer’s disease and mild cognitive impairment from normal aging. Neuroimage 47, 1476–1486 (2009)

    Google Scholar 

  23. Lanckriet, G.R., De Bie, T., Cristianini, N., Jordan, M.I., Noble, W.S.: A statistical framework for genomic data fusion. Bioinformatics 20, 2626–2635 (2004)

    Article  Google Scholar 

  24. Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement. J. Alzheimers Assoc. 3, 186–191 (2007)

    Article  Google Scholar 

  25. Petersen, R.C., Doody, R., Kurz, A., Mohs, R.C., Morris, J.C., Rabins, P.V., Ritchie, K., Rossor, M., Thal, L., Winblad, B.: Current concepts in mild cognitive impairment. Arch. Neurol. 58, 1985–1992 (2001)

    Article  Google Scholar 

  26. Grundman, M., Petersen, R.C., Ferris, S.H., Thomas, R.G., Aisen, P.S., Bennett, D.A., Foster Jr., N.L.,, J.C., Galasko, D.R., Doody, R.: Mild cognitive impairment can be distinguished from Alzheimer disease and normal aging for clinical trials. JAMA Neurol. 61, 59–66 (2004)

    Google Scholar 

  27. Jack Jr., C.R., Shiung, M.M., Weigand, S.D., O’Brien, P.C., Gunter, J.L., Boeve, B.F., Knopman, D.S., Smith, G.E., Ivnik, R.J., Tangalos, E.G., Petersen, R.C.: Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 65, 1227–1231 (2005)

    Article  Google Scholar 

  28. Detoledo, M.L., Stoub, T.M., Wilson, R.S., Bennett, D.A., Leurgans, S., Wuu, J., Turner, D.A.: MRI-derived entorhinal volume is a good predictor of conversion from MCI to AD. Neurobiol. Aging 25, 1197–1203 (2004)

    Article  Google Scholar 

  29. Thompson, P.M., Mega, M.S., Woods, R.P., Zoumalan, C.I., Lindshield, C.J., Blanton, R.E., Moussai, J., Holmes, C.J., Cummings, J.L., Toga, A.W.: Cortical change in Alzheimer’s disease detected with a disease-specific population-based brain atlas. Cereb. Cortex 11, 1–16 (2001)

    Article  Google Scholar 

  30. Du, A.T., Schuff, N., Kramer, J.H., Rosen, H.J., Gorno-Tempini, M.L., Rankin, K., Miller, B.L., Weiner, M.W.: Different regional patterns of cortical thinning in Alzheimer’s disease and frontotemporal dementia. Brain 130, 1159–1166 (2007)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Biederman, J., Mick, E., Faraone, S.V.: Age-dependent decline of symptoms of attention deficit hyperactivity disorder: impact of remission definition and symptom type. Am. J. Psychiatry 157, 816–818 (2000)

    Article  Google Scholar 

  33. Trull, T.J., Verges, A., Wood, P.K., Jahng, S., Sher, K.J.: The structure of diagnostic and statistical manual of mental disorders (4th edn., text revision) personality disorder symptoms in a large national sample. Pers. Disord. 3, 355–369 (2012)

    Google Scholar 

  34. Seidman, L.J., Valera, E.M., Makris, N.: Structural brain imaging of attention-deficit/hyperactivity disorder. Biol. Psychiatry 57, 1263–1272 (2005)

    Article  Google Scholar 

  35. Valera, E.M., Faraone, S.V., Murray, K.E., Seidman, L.J.: Meta-analysis of structural imaging findings in attention-deficit/hyperactivity disorder. Biol. Psychiatry 61, 1361–1369 (2007)

    Article  Google Scholar 

  36. Semrud-Clikeman, M., Steingard, R.J., Filipek, P., Biederman, J., Bekken, K., Renshaw, P.F.: Using MRI to examine brain-behavior relationships in males with attention deficit disorder with hyperactivity. J. Am. Acad. Child Adolesc. Psychiatry 39, 477–484 (2000)

    Article  Google Scholar 

  37. Overmeyer, S., Bullmore, E.T., Suckling, J., Simmons, A., Williams, S.C., Santosh, P.J., Taylor, E.: Distributed grey and white matter deficits in hyperkinetic disorder: MRI evidence for anatomical abnormality in an attentional network. Psychol. Med. 31, 1425–1435 (2001)

    Article  Google Scholar 

  38. Kates, W.R., Frederikse, M., Mostofsky, S.H., Folley, B.S., Cooper, K., Mazur-Hopkins, P., Kofman, O., Singer, H.S., Denckla, M.B., Pearlson, G.D., Kaufmann, W.E.: MRI parcellation of the frontal lobe in boys with attention deficit hyperactivity disorder or Tourette syndrome. Psychiatry Res. 116, 63–81 (2002)

    Article  Google Scholar 

  39. Bush, G., Frazier, J.A., Rauch, S.L., Seidman, L.J., Whalen, P.J., Jenike, M.A., Rosen, B.R., Biederman, J.: Anterior cingulate cortex dysfunction in attention-deficit/hyperactivity disorder revealed by fMRI and the Counting Stroop. Biol. Psychiatry 45, 1542–1552 (1999)

    Article  Google Scholar 

  40. Teicher, M.H., Anderson, C.M., Polcari, A., Glod, C.A., Maas, L.C., Renshaw, P.F.: Functional deficits in basal ganglia of children with attention-deficit/hyperactivity disorder shown with functional magnetic resonance imaging relaxometry. Nat. Med. 6, 470–473 (2000)

    Article  Google Scholar 

  41. Durston, S., Tottenham, N.T., Thomas, K.M., Davidson, M.C., Eigsti, I.M., Yang, Y., Ulug, A.M., Casey, B.J.: Differential patterns of striatal activation in young children with and without ADHD. Biol. Psychiatry 53, 871–878 (2003)

    Article  Google Scholar 

  42. Cao, Q., Zang, Y., Sun, L., Sui, M., Long, X., Zou, Q., Wang, Y.: Abnormal neural activity in children with attention deficit hyperactivity disorder: a resting-state functional magnetic resonance imaging study. NeuroReport 17, 1033–1036 (2006)

    Article  Google Scholar 

  43. Tian, L., Jiang, T., Wang, Y., Zang, Y., He, Y., Liang, M., Sui, M., Cao, Q., Hu, S., Peng, M., Zhuo, Y.: Altered resting-state functional connectivity patterns of anterior cingulate cortex in adolescents with attention deficit hyperactivity disorder. Neurosci. Lett. 400, 39–43 (2006)

    Article  Google Scholar 

  44. Zang, Y.F., He, Y., Zhu, C.Z., Cao, Q.J., Sui, M.Q., Liang, M., Tian, L.X., Jiang, T.Z., Wang, Y.F.: Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev. 29, 83–91 (2007)

    Article  Google Scholar 

  45. Liu, D., Yan, C., Ren, J., Yao, L., Kiviniemi, V.J., Zang, Y.: Using coherence to measure regional homogeneity of resting-state fMRI signal. Front. Syst. Neurosci. 4, 24 (2015)

    Google Scholar 

  46. Xavier Castellanos, F., Margulies, D.S., Clare, K., Uddin, L.Q., Manely, G., Andrew, K., David, S., Zarrar, S., Adriana, D.M., Bharat, B.: Cingulate-precuneus interactions: a new locus of dysfunction in adult attention-deficit/hyperactivity disorder. Biol. Psychiatry 63, 332–337 (2008)

    Google Scholar 

  47. Dai, D., He, H., Vogelstein, J.T., Hou, Z.: Accurate prediction of AD patients using cortical thickness networks. Mach. Vis. Appl. 24, 1445–1457 (2013)

    Article  Google Scholar 

  48. Cooper, J.: Diagnostic and statistical manual of mental disorders (4th edn., text revision) (DSM-IV-TR). Br. J. Psychiatry 179, 85–85 (2001)

    Google Scholar 

  49. Stokes, A., Bawden, H.N., Camfield, P.R., Backman, J.E., Dooley, J.M.: Peer problems in Tourettes disorder. Pediatrics 87, 936–942 (1991)

    Google Scholar 

  50. Lucas, A.R., Beard, C.M., Rajput, A.H., Kurland, L.T.: Tourette syndrome in Rochester, Minnesota, 1968–1979. Adv. Neurol. 35, 267–269 (1982)

    Google Scholar 

  51. Knight, T., Steeves, T., Day, L., Lowerison, M., Jette, N., Pringsheim, T.: Prevalence of tic disorders: a systematic review and meta-analysis. Pediatr. Neurol. 47, 77–90 (2012)

    Article  Google Scholar 

  52. Mason, A., Banerjee, S., Eapen, V., Zeitlin, H., Robertson, M.M.: The prevalence of Tourette syndrome in a mainstream school population. Dev. Med. Child Neurol. 40, 292–296 (1998)

    Google Scholar 

  53. Stern, J.S., Burza SRobertson, M.M.: Gilles de la Tourette’s syndrome and its impact in the UK. Postgrad. Med. J. 81, 12–19 (2005)

    Google Scholar 

  54. Smith, S.M., Jenkinson, M., Johansen-Berg, H., Rueckert, D., Nichols, T.E., Mackay, C.E., Watkins, K.E., Ciccarelli, O., Cader, M.Z., Matthews, P.M., Behrens, T.E.: Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31, 1487–1505 (2006)

    Article  Google Scholar 

  55. Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44, 625–632 (2000)

    Article  Google Scholar 

  56. Cheng, B., Braass, H., Ganos, C., Treszl, A., Biermann-Ruben, K., Hummel, F.C., Muller-Vahl, K., Schnitzler, A., Gerloff, C., Munchau, A., Thomalla, G.: Altered intrahemispheric structural connectivity in Gilles de la Tourette syndrome. NeuroImage Clin. 4, 174–181 (2014)

    Article  Google Scholar 

  57. Wen, H., Liu, Y., Wang, J., Zhang, J., Peng, Y., He, H.: Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children. In: SPIE Medical Imaging, pp. 97852Q–97852Q-97859. International Society for Optics and Photonics (Year)

    Google Scholar 

  58. Wen, H., Liu, Y., Wang, J., Zhang, J., Peng, Y., He, H.: A diagnosis model for early Tourette syndrome children based on brain structural network characteristics. In: SPIE Medical Imaging, pp. 97852R–97852R-97859. International Society for Optics and Photonics (Year)

    Google Scholar 

  59. Wen, H., Liu, Y., Wang, J., Rekik, I., Zhang, J., Zhang, Y., Tian, H., Peng, Y., He, H.: Combining tract- and atlas-based analysis reveals microstructural abnormalities in early Tourette syndrome children. Hum. Brain Mapp. 37, 1903–1919 (2016)

    Article  Google Scholar 

  60. Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17, 87–97 (1998)

    Article  Google Scholar 

  61. Hastreiter, P., Rezksalama, C., Tomandl, B., Eberhardt, K.E.W., Ertl, T.: BFb: Medical Image Computing and Computer-Assisted Intervention—MICCAI’98. Springer, Berlin (1998)

    Google Scholar 

  62. June Sic, K., Vivek, S., Jun Ki, L., Jason, L., Yasser, A.D.B., David, M.D., Jong Min, L., Sun, I., Kim, Evans, A.C.: Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification. Neuroimage 27, 210–221 (2005)

    Google Scholar 

  63. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D., Crivello, F., Etard, O., Delcroix, N., Mazoyer, B., Joliot, M.: Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15, 273–289 (2002)

    Article  Google Scholar 

  64. Shiva, K., Ryan, N.S., Malone, I.B., Marc, M., David, C., Ridgway, G.R., Hui, Z., Fox, N.C., Sebastien, O.: The importance of group-wise registration in tract based spatial statistics study of neurodegeneration: a simulation study in Alzheimer’s disease. PLoS ONE 7, e45996–e45996 (2012)

    Article  Google Scholar 

  65. Smith, S.M., Nichols, T.E.: Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44, 83–98 (2009)

    Article  Google Scholar 

  66. Bullmore, E., Sporns, O.: Complex brain networks: graph theoretical analysis of structural and functional systems. Nat. Rev. Neurosci. 10, 186 (2009)

    Google Scholar 

  67. Liu, Y., Duan, Y.Y., He, Y., Wang, J., Xia, M.R., Yu, C.S., Dong, H.Q., Ye, J., Butzkueven, H., Li, K.C., Shu, N.: Altered topological organization of white matter structural networks in patients with neuromyelitis optica. Mult. Scler. J. 19, 666–667 (2013)

    Article  Google Scholar 

  68. Mori, S., Crain, B.J., Chacko, V.P., van Zijl, P.C.: Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol. 45, 265–269 (1999)

    Article  Google Scholar 

  69. Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E.: Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012)

    Article  Google Scholar 

  70. Ashburner, J., Friston, K.J.: Unified segmentation. Neuroimage 26, 839–851 (2005)

    Article  Google Scholar 

  71. Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Essen, D.C.V., Raichle, M.E.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. USA 102, 9673–9678 (2005)

    Article  Google Scholar 

  72. Meng, L., Yuan, Z., Tianzi, J., Zhening, L., Lixia, T., Haihong, L., Yihui, H.: Widespread functional disconnectivity in schizophrenia with resting-state functional magnetic resonance imaging. Neuroreport 17, 209–213 (2006)

    Article  Google Scholar 

  73. Xiao-Wei, S., Zhang-Ye, D., Xiang-Yu, L., Su-Fang, L., Xi-Nian, Z., Chao-Zhe, Z., Yong, H., Chao-Gan, Y., Yu-Feng, Z.: REST: a toolkit for resting-state functional magnetic resonance imaging data processing. PLoS ONE 6, e25031 (2011)

    Article  Google Scholar 

  74. Zang, Y., Jiang, T., Lu, Y., He, Y., Tian, L.: Regional homogeneity approach to fMRI data analysis. Neuroimage 22, 394–400 (2004)

    Article  Google Scholar 

  75. Kohavi, R., John, G.H.: Wrappers for feature subset selection. Artif. Intell. 97, 273–324 (1997)

    Article  MATH  Google Scholar 

  76. Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: Proceedings of European Conference on Machine Learning, vol. 784, pp. 356–361 (1996)

    Google Scholar 

  77. Guyon, I., Weston, J., Barnhill, S., Vapnik, V.: Gene selection for cancer classification using support vector machines. Mach. Learn. 46, 389–422 (2002)

    Article  MATH  Google Scholar 

  78. Sun, Y., Todorovic, S., Goodison, S.: Local-learning-based feature selection for high-dimensional data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1610–1626 (2010)

    Article  Google Scholar 

  79. Wilson, S.M., Ogar, J.M., Laluz, V., Growdon, M., Jang, J., Glenn, S., Miller, B.L., Weiner, M.W., Gorno-Tempini, M.L.: Automated MRI-based classification of primary progressive aphasia variants. Neuroimage 47, 1558–1567 (2009)

    Article  Google Scholar 

  80. Dyrba, M., Ewers, M., Wegrzyn, M., Kilimann, I., Plant, C., Oswald, A., Meindl, T., Pievani, M., Bokde, A.L.W., Fellgiebel, A.: Combining DTI and MRI for the Automated Detection of Alzheimer’s Disease Using a Large European Multicenter Dataset. Springer, Berlin (2012)

    Google Scholar 

  81. Grana, M., Termenon, M., Savio, A., Gonzalez-Pinto, A., Echeveste, J., Perez, J.M., Besga, A.: Computer aided diagnosis system for Alzheimer disease using brain diffusion tensor imaging features selected by Pearson’s correlation. Neurosci. Lett. 502, 225–229 (2011)

    Article  Google Scholar 

  82. O’Dwyer, L., Lamberton, F., Bokde, A.L.W., Ewers, M., Faluyi, Y.O., Tanner, C., Mazoyer, B., O’Neill, D., Bartley, M., Collins, D.R., Coughlan, T., Prvulovic, D., Hampel, H.: Using support vector machines with multiple indices of diffusion for automated classification of mild cognitive impairment. PLoS ONE 7 (2012)

    Google Scholar 

  83. Church, J.A., Fair, D.A., Dosenbach, N.U.F., Cohen, A.L., Miezin, F.M., Petersen, S.E., Schlaggar, B.L.: Control networks in paediatric Tourette syndrome show immature and anomalous patterns of functional connectivity. Brain 132, 225–238 (2009)

    Article  Google Scholar 

  84. Neuner, I., Kupriyanova, Y., Stocker, T., Huang, R.W., Posnansky, O., Schneider, F., Shah, N.J.: Microstructure assessment of grey matter nuclei in adult Tourette patients by diffusion tensor imaging. Neurosci. Lett. 487, 22–26 (2011)

    Article  Google Scholar 

  85. Greene, D.J., Church, J.A., Dosenbach, N.U.F., Nielsen, A.N., Adeyemo, B., Nardos, B., Petersen, S.E., Black, K.J., Schlaggar, B.L.: Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI. Dev. Sci. (2016)

    Google Scholar 

  86. Wee, C.Y., Yap, P.T., Li, W., Denny, K., Browndyke, J.N., Potter, G.G., Welsh-Bohmer, K.A., Wang, L., Shen, D.: Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 54, 1812–1822 (2011)

    Article  Google Scholar 

  87. Werner, C.J., Stocker, T., Kellermann, T., Wegener, H.P., Schneider, F., Shah, N.J., Neuner, I.: Altered amygdala functional connectivity in adult Tourette’s syndrome. Eur. Arch. Psychiatry Clin. Neurosci. 260(Suppl 2), S95–S99 (2010)

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (91520202, 61271151), and Youth Innovation Promotion Association CAS.

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Authors and Affiliations

  1. Research Center for Brain-Inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Huiguang He, Hongwei Wen, Dai Dai & Jieqiong Wang

  2. University of Chinese Academy of Sciences, Beijing, China

    Huiguang He, Hongwei Wen, Dai Dai & Jieqiong Wang

  3. Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China

    Huiguang He

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  4. Jieqiong Wang
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Correspondence to Huiguang He .

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  1. Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, Illinois, USA

    Kenji Suzuki

  2. Key Laboratory of Machine Perception (Ministry of Education), School of Electronics Engineering and Computer Science, Peking University, Beijing, China

    Yisong Chen

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He, H., Wen, H., Dai, D., Wang, J. (2018). Computer-Aided Prognosis: Accurate Prediction of Patients with Neurologic and Psychiatric Diseases via Multi-modal MRI Analysis. In: Suzuki, K., Chen, Y. (eds) Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging. Intelligent Systems Reference Library, vol 140. Springer, Cham. https://doi.org/10.1007/978-3-319-68843-5_10

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  • DOI: https://doi.org/10.1007/978-3-319-68843-5_10

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  • Online ISBN: 978-3-319-68843-5

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