Brain Structure and Function

, Volume 219, Issue 2, pp 641–656 | Cite as

Group-constrained sparse fMRI connectivity modeling for mild cognitive impairment identification

  • Chong-Yaw Wee
  • Pew-Thian Yap
  • Daoqiang Zhang
  • Lihong Wang
  • Dinggang ShenEmail author
Original Article


Emergence of advanced network analysis techniques utilizing resting-state functional magnetic resonance imaging (R-fMRI) has enabled a more comprehensive understanding of neurological disorders at a whole-brain level. However, inferring brain connectivity from R-fMRI is a challenging task, particularly when the ultimate goal is to achieve good control–patient classification performance, owing to perplexing noise effects, curse of dimensionality, and inter-subject variability. Incorporating sparsity into connectivity modeling may be a possible solution to partially remedy this problem since most biological networks are intrinsically sparse. Nevertheless, sparsity constraint, when applied at an individual level, will inevitably cause inter-subject variability and hence degrade classification performance. To this end, we formulate the R-fMRI time series of each region of interest (ROI) as a linear representation of time series of other ROIs to infer sparse connectivity networks that are topologically identical across individuals. This formulation allows simultaneous selection of a common set of ROIs across subjects so that their linear combination is best in estimating the time series of the considered ROI. Specifically, l 1-norm is imposed on each subject to filter out spurious or insignificant connections to produce sparse networks. A group-constraint is hence imposed via multi-task learning using a l 2-norm to encourage consistent non-zero connections across subjects. This group-constraint is crucial since the network topology is identical for all subjects while still preserving individual information via different connectivity values. We validated the proposed modeling in mild cognitive impairment identification and promising results achieved demonstrate its superiority in disease characterization, particularly greater sensitivity to early stage brain pathologies. The inferred group-constrained sparse network is found to be biologically plausible and is highly associated with the disease-associated anatomical anomalies. Furthermore, our proposed approach achieved similar classification performance when finer atlas was used to parcellate the brain space.


Mild cognitive impairment (MCI) Group-constrained sparse modeling Resting-state fMRI Sparse linear regression Inter-subject variability Multi-task learning 



This work was supported in part by National Institute of Health (NIH) grants EB006733, EB008374, AG041721, EB009634, MH088520, K23-AG028982, as well as a National Alliance for Research in Schizophrenia and Depression Young Investigator Award (L.W.). Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: Abbott; Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Amorfix Life Sciences Ltd.; AstraZeneca; Bayer HealthCare; BioClinica, Inc.; Biogen Idec Inc.; Bristol-Myers Squibb Company; Eisai Inc.; Elan Pharmaceuticals Inc.; Eli Lilly and Company; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; GE Healthcare; Innogenetics, N.V.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Medpace, Inc.; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Servier; Synarc Inc.; and Takeda Pharmaceutical Company. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of California, Los Angeles. This research was also supported by NIH grants P30 AG010129, K01 AG030514, and the Dana Foundation.


  1. Achard S, Bassett DS, Meyer-Lindenberg A, Bullmore ET (2008) Fractal connectivity of long-memory networks. Phys Rev E Stat Nonlin Soft Matter Phys 77(3 Pt 2), 036104Google Scholar
  2. Achard S, Salvador R, Whitcher B, Suckling J, Bullmore ET (2006) A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs. J Neurosci 26(1):63–72PubMedCrossRefGoogle Scholar
  3. Alzheimer’s Association (2012) Alzheimer’s disease facts and figgues. Alzheimers Dement 8(2):1–72Google Scholar
  4. American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders, Fourth Edition—text revision (DSMIV-TR). American Psychiatric Association (2000)Google Scholar
  5. Azari NP, Rapoport SI, Grady CL, Schapiro MB, Salerno JA, Gonzalez-Aviles A, Horwitz B (1992) Patterns of interregional correlations of cerebral glucose metabolic rates in patients with dementia of the Alzheimer type. Neurodegeneration 1:101–111Google Scholar
  6. Bain LJ, Jedrziewski K, Morrison-Bogorad M, Albert M, Cotman C, Hendrie H, Trojanowski JQ (2008) Healthy brain aging: a meeting report from the Sylvan M. Cohen annual retreat of the University of Pennsylvania Institute on aging. Alzheimers Dement 4(6):443–446PubMedCentralPubMedCrossRefGoogle Scholar
  7. Bajo R, Maestú F, Nevado A, Sancho M, Gutiérrez R, Campo P, Castellanos NP, Gil P, Moratti S, Pereda E, Del-Pozo F (2010) Functional connectivity in mild cognitive impairment during a memory task: implications for the disconnection hypothesis. J Alzheimers Dis 22(1):183–193PubMedGoogle Scholar
  8. Bell-McGinty S, Lopez OL, Meltzer CC, Scanlon JM, Whyte EM, Dekosky ST, Becker JT (2005) Differential cortical atrophy in subgroups of mild cognitive impairment. Arch Neurol 62(9):1393–1397PubMedCrossRefGoogle Scholar
  9. Benton AL (1962) The visual retention test as a constructional praxis task. Confin Neurol 22:141–155PubMedCrossRefGoogle Scholar
  10. Benton AL, Hamsher K (1976) Multilingual Aphasia examination manual. University of Iowa, Iowa City (1976)Google Scholar
  11. Bischkopf J, Busse A, Angermeyer MC (2002) Mild cognitive impairment—a review of prevalence, incidence and outcome according to current approaches. Acta Psychiatr Scand 106:403–414PubMedCrossRefGoogle Scholar
  12. Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM (2007) Forecasting the global burden of Alzheimer’s disease. Alzheimers Dement 3(3):186–191PubMedCrossRefGoogle Scholar
  13. Candés EJ, Wakin MB (2008) An introduction to compressive sampling—a sensing/sampling paradigm that goes against the common knowledge in data acquisition. IEEE Signal Process Mag 25(2):21–30CrossRefGoogle Scholar
  14. Convit A, de Asis J, de Leon MJ, Tarshish CY, De Santi S, Rusinek H (2000) Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimers disease. Neurobiol Aging 21(1):19–26PubMedCrossRefGoogle Scholar
  15. Cooper JA, Sagar HJ, Jordan N, Harvey NS, Sullivan EV (1991) Cognitive impairment in early, untreated parkinsons disease and its relationship to motor function. Brain Behav Evol 114(5): 2095–2122PubMedCrossRefGoogle Scholar
  16. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA, Moritz CH, Quigley MA, Meyerand ME (2001) Frequencies contributing to functional connectivity in the cerebral cortex in “resting-state” data. Am. J. Neuroradiol. 22:1326–1333PubMedGoogle Scholar
  17. Dai W, Lopez OL, Carmichael OT, Becker JT, Kuller LH, Gach HM (2009) Mild cognitive impairment and Alzheimer disease: patterns of altered cerebral blood flow at MR imaging. Radiology 250:856–866. doi:  10.1148/radiol.2503080751 Google Scholar
  18. Ding C, Peng H (2005) Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol 3(2):185–205PubMedCrossRefGoogle Scholar
  19. Fleisher AS, Sherzai A, Taylor C, Langbaum JB, Chen K, Buxton RB (2009) Resting-state BOLD networks versus task-associated functional mri for distinguishing Alzheimer’s disease risk groups. Neuroimage 47(4):1678–1690PubMedCentralPubMedCrossRefGoogle Scholar
  20. Folstein MF, Folstein SE, McHugh PR (1975) “Mini-mental state”. A practical method for grading the cognitive state of patient for the clinician. J Psychiatr Res 12(3):189–198PubMedCrossRefGoogle Scholar
  21. Fox MD, Zhang D, Snyder AZ, Raichle ME (2009) The global signal and observed anticorrelated resting state brain networks. J Neurophysiol 101(6):3270–3283PubMedCrossRefGoogle Scholar
  22. Friedman J, Hastie T, Tibshirani R (2008) Sparse inverse covariance estimation with the graphical lasso. Biostat 9(3):432–441CrossRefGoogle Scholar
  23. Friston KJ, Frith C, Frackowiak RSJ, Turner R (1995) Characterizing dynamic brain responses with fMRI: a multivariate approach. Neuroimage 2:166–172PubMedCrossRefGoogle Scholar
  24. Friston KJ, Frith CD, Liddle PF, Frackowiak RS (1993) Functional connectivity: the principal-component analysis of large (PET) data sets. J Cereb Blood Flow Metab 13:5–14PubMedCrossRefGoogle Scholar
  25. Gauthier S, Reisberg B, Zaudig M, Petersen RC, Ritchie K, Broich K, Belleville S, Brodaty H, Bennett D, Chertkow H, Cummings JL, de Leon M, Feldman H, Ganguli M, Hampel H, Scheltens P, Tierney MC, Whitehouse P, Winblad B, on behalf of the participants of the International Psychogeriatric Association Expert Conference on mild cognitive impairment (2006) Mild cognitive impairment. Lancet 367:1262–1270Google Scholar
  26. Gold BT, Jiang Y, Jicha GA, Smith CD (2010) Functional response in ventral temporal cortex differentiates mild cognitive impairment from normal aging. Hum Brain Mapp 31(8):1249–1259PubMedCentralPubMedGoogle Scholar
  27. Gould RL, Arroyo B, Brown RG, Owen AM, Bullmore ET, Howard RJ (2006) Brain mechanisms of successful compensation during learning in Alzheimer disease. Neurology 67(7):1011–1017PubMedCrossRefGoogle Scholar
  28. Grady CL, McIntosh AR, Beig S, Keightley ML, Burian H, Black SE (2003) Evidence from functional neuroimaging of a compensatory prefrontal network in Alzheimer’s disease. J Neurosci 23(3):986–993PubMedGoogle Scholar
  29. Grundman M, Petersen RC, Ferris SH, Thomas RG, Aisen PS, Bennett DA et al (2004) Mild cognitive impairment can be distinguished from Alzheimer’s disease and normal aging for clinical trials. Arch Neurol 61(1):59–66PubMedCrossRefGoogle Scholar
  30. Guyon I, Weston J, Barnhill S, Vapnik V (2004) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389–422Google Scholar
  31. Haller S, Missonnier P, Herrmann FR, Rodriguez C, Deiber MP, Nguyen D, Gold G, Lovblad KO, Giannakopoulos P (2012) Individual classification of mild cognitive impairment subtypes by support vector machine analysis of white matter dti. AJNR Am J Neuroradiol (2012). Epub ahead of printGoogle Scholar
  32. Horwitz B, Grady CL, Schlageter NL, Duara R, Rapoport SI (1987) Intercorrelations of regional cerebral glucose metabolic rates in Alzheimer’s disease. Brain Res Brain Res Rev 407(2):294–306PubMedCrossRefGoogle Scholar
  33. Huang S, Li J, Sun L, Ye J, Fleisher A, Wu T, Chen K, Reiman E, the Alzheimer’s Disease NeuroImaging Initiative (2010) Learning brain connectivity of Alzheimer’s disease by sparse inverse covariance estimation. Neuroimage 50(3):935–949 (2010)Google Scholar
  34. Lee H, Lee DS, Kang H, Kim BN, Chung MK (2011) Sparse brain network recovery under compressed sensing. IEEE Trans Med Imaging 30(5):1154–1165PubMedCrossRefGoogle Scholar
  35. Liu J, Ji S, Ye J (2009) SLEP: Sparse learning with efficient projections. Arizona State University.
  36. Lynall ME, Bassett DS, Kerwin R, McKenna PJ, Kitzbichler M, Muller U, Bullmore ET (2010) Functional connectivity and brain networks in schizophrenia. J Neurosci 30:9477–9487PubMedCentralPubMedCrossRefGoogle Scholar
  37. Matthews CG, Klove H (1964) Instruction manual for the adult neuropsychology test battery. University of Wisconsin Medical School, MadisonGoogle Scholar
  38. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS–ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34(7):939–944PubMedCrossRefGoogle Scholar
  39. Misra C, Fan Y, Davatzikos C (2009) Baseline and longitudinal patterns of brain atrophy in MCI patients, and their use in prediction of short-term conversion to AD: results from ADNI. Neuroimage 44:1414–1422CrossRefGoogle Scholar
  40. Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, Mellits ED, Clark C (1989) The Consortium to establish a registry for Alzheimer’s disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer’s disease. Neurology 39(9), 1159–1165 (1989)Google Scholar
  41. Murphy K, Birn RM, Handwerker DA, Jones TB, Bandettini PA (2009) The impact of global signal regression on resting state correlations: are anti-correlated networks introduced? Neuroimage 44(3):893–905PubMedCentralPubMedCrossRefGoogle Scholar
  42. Nobili F, Mazzei D, Dessi B, Morbelli S, Brugnolo A, Barbieri P, Girtler N, Sambuceti G, Rodriguez G, Pagani M (2010) Unawareness of memory deficit in amnestic mci: FDG-PET findings. J Alzheimers Dis 22(3):993–1003PubMedGoogle Scholar
  43. Nobili F, Salmaso D, Morbelli S, Girtler N, Piccardo A, Brugnolo A, Dessi B, Larsson SA, Rodriguez G, Pagani M (2008) Principal component analysis of FDG PET in amnestic MCI.. Eur J Nucl Med Mol Imaging 35(12):2191–2202PubMedCrossRefGoogle Scholar
  44. Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1285PubMedCrossRefGoogle Scholar
  45. Rakotomamonjy A (2003) Variable selection using SVM based criteria. J Mach Learn Res 3:1357–1370Google Scholar
  46. Ramsey JD, Hanson SJ, Glymour C (2011) Multi-subject search correctly identifies causal connections and most causal directions in the dcm models of the smith et al. simulation study. Neuroimage 58(3):838–848PubMedCrossRefGoogle Scholar
  47. Reitan RM (1958) Validity of the trail making test as an indicator of organic brain damage. Percept Mot Skills 8:271–276CrossRefGoogle Scholar
  48. Reitan RM, Wolfson D (1993) Halstead-Reitan neuropsychological test battery: theory and clinical interpretation. Neuropsychological Press, TucsonGoogle Scholar
  49. Romero-Garcia R, Atienza M, Clemmensen LH, Cantero JL (2012) Effects of network resolution on topological properties of human neocortex. Neuroimage 59(4):3522–3532PubMedCrossRefGoogle Scholar
  50. Rothman AJ, Bickel PJ, Levina E (2008) Sparse permutation invariant covariance estimation. Electron J Stat 2:494–515CrossRefGoogle Scholar
  51. Rubinov M., Sporns O.: Complex networks measures of brain connectivity: Uses and interpretations. Neuroimage 52(3), 1059–1069 (2010). doi: 10.1016/j.neuroimage.2009.10.003 Google Scholar
  52. Sachs GA, Carter R, Holtz LR, Smith F, Stump TE, Tu W, Callahan CM (2011) Cognitive impairment: an independent predictor of excess mortality: a cohort study. Ann Intern Med 155(5):300–308PubMedCrossRefGoogle Scholar
  53. Sanabria-Diaz G, Melie-García L, Iturria-Medina Y, Alemán-Gómez Y, Hernández-González G, Valdés-Urrutia L, Galán L, Valdés-Sosa P (2010) Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks. Neuroimage 50(4):1497–1510PubMedCrossRefGoogle Scholar
  54. Shen D, Davatzikos C (2002) HAMMER: Heirarchical attribute matching mechanism for elastic registration. IEEE Trans Med Imaging 21(11):1421–1439PubMedCrossRefGoogle Scholar
  55. Shipley WC (1946) Institute of living scale. Western Psychological Services, Los AngelesGoogle Scholar
  56. Smith A (1968) The symbol-digit modalities test: a neuropsychologic test of learning and other cerebral disorders. Learn Disord 3:83–91Google Scholar
  57. Smith CD, Chebrolu H, Wekstein DR, Schmitt FA, Jicha GA, Cooper G, Markesbery WR (2007) Brain structural alterations before mild cognitive impairment. Neurology 68(16):1268–1273PubMedCrossRefGoogle Scholar
  58. Smith SM, Miller KL, Salimi-Khorshidi G, Webster M, Beckmann CF, Nichols TE, Ramsey JD, Woolrich MW (2011) Network modelling methods for fMRI. Neuroimage 54(2):875–891PubMedCrossRefGoogle Scholar
  59. Squire LR, Zouzounis JA (1988) Self-ratings of memory dysfunction: different findings in depression and amnesia. J Clin Exp Neuropsychol 10(6):727–738PubMedCrossRefGoogle Scholar
  60. Stam CJ, de Haan W, Daffertshofer A, Jones BF, Manshanden I, van Cappelen van Walsum AM, Montez T, Verbunt JPA, de Munck JC, van Dijk BW, Berendse HW, Scheltens P (2009) Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimer’s disease. Brain Behav Evol 132:213–224PubMedCrossRefGoogle Scholar
  61. Stam CJ, Jones BF, Nolte G, Breakspear M, Scheltens P (2007) Small-world networks and functional connectivity in Alzheimer’s disease. Cereb Cortex 17:92–99PubMedCrossRefGoogle Scholar
  62. Stern Y (2006) Cognitive reserve and Alzheimer disease. Alzheimer Dis Assoc Disord 20(3 Suppl 2), S69–S74Google Scholar
  63. Supekar K, Menon V, Rubin D, Musen M, Greicius MD (2008) Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. PLoS Comput Biol 4: e1000,100Google Scholar
  64. Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Natl Acad Sci USA 107(21):9885–9890PubMedCrossRefGoogle Scholar
  65. Tsutsumi R, Hanajima R, Hamada M, Shirota Y, Matsumoto H, Terao Y, Ohminami S, Yamakawa Y, Shimada H, Tsuji S, Ugawa Y (2012) Reduced interhemispheric inhibition in mild cognitive impairment. Exp Brain Res 218(1):21–26PubMedCrossRefGoogle Scholar
  66. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 15(1):273–289PubMedCrossRefGoogle Scholar
  67. Van Dijk KRA, Hedden T, Venkataraman A, Evans KC, Lazar SW, Buckner RL (2010) Intrinsic functional connectivity as a tool for human connectomics: theory, properties and optimization. J Neurophysiol 103:297–321PubMedCrossRefGoogle Scholar
  68. Varoquaux G, Gramfort A, Poline JB, Thirion B (2010) Brain covariance selection: better individual functional connectivity models using population prior. In: NIPS’10, pp 2334–2342Google Scholar
  69. Wang K, Liang M, Wang L, Tian L, Zhang X, Li K, Jiang T (2007) Altered functional connectivity in early Alzheimer’s disease: a resting-state fMRI study. Hum Brain Mapp 28(10):967–978PubMedCrossRefGoogle Scholar
  70. Wechsler D (1981) Manual for the wechsler adult intelligence scale—revised. Psychological Corporation, New YorkGoogle Scholar
  71. Wechsler D (1987) WMS-R: Wechsler memory scale-revised manual. The Psychological Corporation, New YorkGoogle Scholar
  72. Wee CY, Yap PT, Denny K, Browndyke JN, Potter GG, Welsh-Bohmer KA, Wang L, Shen D (2012) Resting-state multi-spectrum functional connectivity networks for identification of MCI patients. PLoS ONE 7(5):e37828Google Scholar
  73. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc B 68(1):49–67CrossRefGoogle Scholar
  74. Zalesky A, Fornito A, Harding IH, Cocchi L, Yücel M, Pantelis C, Bullmore ET (2010) Whole-brain anatomical networks: does the choice of nodes matter. Neuroimage 50(3):970–983PubMedCrossRefGoogle Scholar
  75. Zanetti O, Solerte SB, Cantonni F (2009) Life expectancy in Alzheimer’s disease (AD). Arch Gerontol Geriatr 49:237–243PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chong-Yaw Wee
    • 1
  • Pew-Thian Yap
    • 1
  • Daoqiang Zhang
    • 1
    • 2
  • Lihong Wang
    • 3
  • Dinggang Shen
    • 1
    Email author
  1. 1.Image Display, Enhancement, and Analysis (IDEA) Laboratory, Biomedical Research Imaging Center (BRIC) and Department of RadiologyUniversity of North Carolina at Chapel HillChapel HillUSA
  2. 2.Department of Computer Science & EngineeringNanjing University of Aeronautics and AstronauticsNanjingPeople’s Republic of China
  3. 3.Brain Imaging and Analysis Center (BIAC)Duke University Medical CenterDurhamUSA

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