Stochastic Rank Aggregation for the Identification of Functional Neuromarkers

  • Paola Galdi
  • Michele Fratello
  • Francesca Trojsi
  • Antonio Russo
  • Gioacchino Tedeschi
  • Roberto Tagliaferri
  • Fabrizio EspositoEmail author
Original Article


The main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets. Brain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation. We found parcels for which the rankings were different among control subjects and subjects affected by Parkinson’s disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity. The proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases.


Independent component analysis Clustering Stochastic rank aggregation Default mode network fMRI data analysis 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. Agosta, F., Canu, E., Valsasina, P., Riva, N., Prelle, A., Comi, G., & Filippi, M. (2013). Divergent brain network connectivity in amyotrophic lateral sclerosis. Neurobiology of Aging, 34, 419–427. Scholar
  2. Amboni, M., Tessitore, A., Esposito, F., Santangelo, G., Picillo, M., Vitale, C., Giordano, A., Erro, R., de Micco, R., Corbo, D., Tedeschi, G., & Barone, P. (2015). Resting-state functional connectivity associated with mild cognitive impairment in Parkinson’s disease. Journal of Neurology, 262, 425–434. Scholar
  3. Amelio, A., Pizzuti, C. (2015). Is normalized mutual information a fair measure for comparing community detection methods? In Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015 - ASONAM ‘15 (pp 1584–1585). New York: ACM Press.Google Scholar
  4. Beato, R., Levy, R., Pillon, B., Vidal, C., du Montcel, S. T., Deweer, B., Bonnet, A. M., Houeto, J. L., Dubois, B., & Cardoso, F. (2008). Working memory in Parkinson’s disease patients: Clinical features and response to levodopa. Arquivos de Neuro-Psiquiatria, 66, 147–151.CrossRefGoogle Scholar
  5. Bosch, O. G., Esposito, F., Dornbierer, D., Havranek, M. M., von Rotz, R., Kometer, M., Staempfli, P., Quednow, B. B., & Seifritz, E. (2018). Gamma-hydroxybutyrate increases brain resting-state functional connectivity of the salience network and dorsal nexus in humans. Neuroimage, 173, 448–459. Scholar
  6. Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The Brain’s default network. Annals of the New York Academy of Sciences, 1124, 1–38. Scholar
  7. Chiò, A., Pagani, M., Agosta, F., Calvo, A., Cistaro, A., & Filippi, M. (2014). Neuroimaging in amyotrophic lateral sclerosis: Insights into structural and functional changes. Lancet Neurology, 13, 1228–1240. Scholar
  8. Convit, A., De Asis, J., De Leon, M. J., et al. (2000). Atrophy of the medial occipitotemporal, inferior, and middle temporal gyri in non-demented elderly predict decline to Alzheimer’s disease. Neurobiology of Aging, 21, 19–26. Scholar
  9. Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006). Consistent resting-state networks across healthy subjects. Proceedings of the National Academy of Sciences of the United States of America, 103, 13848–13853. Scholar
  10. De Micco, R., Tessitore, A., Paccone, A., et al. (2013). Dopaminergic modulation of the resting-state sensori-motor network in drug-naive patients with Parkinson’s disease. Movement Disorders, 28, S66–S66.Google Scholar
  11. Di Rosa, E., Pischedda, D., Cherubini, P., et al. (2017). Working memory in healthy aging and in Parkinson’s disease: evidence of interference effects. Aging Neuropsychology and Cognition, 24, 281–298. Scholar
  12. Dirnberger, G., & Jahanshahi, M. (2013). Executive dysfunction in Parkinson’s disease: A review. Journal of Neuropsychology, 7, 193–224. Scholar
  13. Disbrow, E. A., Sigvardt, K. A., Franz, E. A., et al. (2013). Movement activation and inhibition in Parkinson’s disease: A functional imaging study. Journal of Parkinson's Disease, 3, 181–192. Scholar
  14. Eklund, A., Nichols, T. E., & Knutsson, H. (2016). Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates. Proceedings of the National Academy of Sciences, 113, 7900–7905. Scholar
  15. Esposito, F., & Goebel, R. (2011). Extracting functional networks with spatial independent component analysis: the role of dimensionality, reliability and aggregation scheme. Current Opinion in Neurology, 24, 378–385. Scholar
  16. Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi, G., Goebel, R., Seifritz, E., & di Salle, F. (2005). Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25, 193–205. Scholar
  17. Esposito, F., Aragri, A., Pesaresi, I., Cirillo, S., Tedeschi, G., Marciano, E., Goebel, R., & di Salle, F. (2008). Independent component model of the default-mode brain function: combining individual-level and population-level analyses in resting-state fMRI. Magnetic Resonance Imaging, 26, 905–913. Scholar
  18. Esposito, F., Pignataro, G., Di Renzo, G., et al. (2010). Alcohol increases spontaneous BOLD signal fluctuations in the visual network. Neuroimage, 53, 534–543. Scholar
  19. Esposito, F., Tessitore, A., Giordano, A., de Micco, R., Paccone, A., Conforti, R., Pignataro, G., Annunziato, L., & Tedeschi, G. (2013). Rhythm-specific modulation of the sensorimotor network in drug-naïve patients with Parkinson’s disease by levodopa. Brain, 136, 710–725. Scholar
  20. Galdi, P., Fratello, M., Trojsi, F., Russo, A., Tedeschi, G., Tagliaferri, R., & Esposito, F. (2017). Consensus-based feature extraction in rs-fMRI data analysis. Soft Computing, 22, 1–11. Scholar
  21. Gattellaro, G., Minati, L., Grisoli, M., Mariani, C., Carella, F., Osio, M., Ciceri, E., Albanese, A., & Bruzzone, M. G. (2009). White matter involvement in idiopathic Parkinson disease: a diffusion tensor imaging study. AJNR. American Journal of Neuroradiology, 30, 1222–1226. Scholar
  22. Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536, 171–178. Scholar
  23. Gorges, M., Müller, H.-P., Lulé, D., Ludolph, A. C., Pinkhardt, E. H., & Kassubek, J. (2013). Functional connectivity within the default mode network is associated with saccadic accuracy in Parkinson’s disease: a resting-state FMRI and videooculographic study. Brain Connectivity, 3, 265–272. Scholar
  24. Goutte, C. (1999). On clustering fMRI time series.Google Scholar
  25. Goutte, C., Hansen, L. K., Liptrot, M. G., & Rostrup, E. (2001). Feature-space clustering for fMRI meta-analysis. Human Brain Mapping, 13, 165–183. Scholar
  26. Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. PNAS, 100, 253–258. Scholar
  27. Greicius, M. D., Srivastava, G., Reiss, A. L., & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101, 4637–4642. Scholar
  28. Greicius, M. D., Flores, B. H., Menon, V., Glover, G. H., Solvason, H. B., Kenna, H., Reiss, A. L., & Schatzberg, A. F. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from Subgenual cingulate cortex and thalamus. Biological Psychiatry, 62, 429–437. Scholar
  29. Hall, P., & Schimek, M. G. (2012). Moderate-deviation-based inference for random degeneration in paired rank lists. Journal of the American Statistical Association, 107, 661–672. Scholar
  30. Hanakawa, T., Fukuyama, H., Katsumi, Y., Honda, M., & Shibasaki, H. (1999). Enhanced lateral premotor activity during paradoxical gait in parkinson’s disease. Annals of Neurology, 45, 329–336.<329::AID-ANA8>3.0.CO;2-S.CrossRefPubMedGoogle Scholar
  31. Hänggi, J., Streffer, J., Jäncke, L., & Hock, C. (2011). Volumes of lateral temporal and parietal structures distinguish between healthy aging, mild cognitive impairment, and Alzheimer’s disease. Journal of Alzheimer's Disease, 26, 719–734. Scholar
  32. Harrison, B. J., Pujol, J., Lopez-Sola, M., Hernandez-Ribas, R., Deus, J., Ortiz, H., Soriano-Mas, C., Yucel, M., Pantelis, C., & Cardoner, N. (2008). Consistency and functional specialization in the default mode brain network. Proceedings of the National Academy of Sciences, 105, 9781–9786. Scholar
  33. Hepp, D. H., Foncke, E. M. J., Olde Dubbelink, K. T. E., van de Berg, W. D. J., Berendse, H. W., & Schoonheim, M. M. (2017). Loss of functional connectivity in patients with Parkinson disease and visual hallucinations. Radiology, 285, 896–903. Scholar
  34. Herrington, T. M., Briscoe, J., & Eskandar, E. (2017). Structural and functional network dysfunction in Parkinson disease. Radiology, 285, 725–727. Scholar
  35. Huettel, S. A., Singerman, J. D., & McCarthy, G. (2001). The effects of aging upon the hemodynamic response measured by functional MRI. Neuroimage, 13, 161–175. Scholar
  36. Hyvarinen, A. (1999). Fast and robust fixed-point algorithm for independent component analysis. IEEE Transactions on Neural Networks, 10, 626–634.CrossRefGoogle Scholar
  37. Hyvärinen, A., & Oja, E. (2000). Independent component analysis: algorithms and applications. Neural Networks, 13, 411–430.CrossRefGoogle Scholar
  38. Iyer, P. M., Egan, C., Pinto-Grau, M., Burke, T., Elamin, M., Nasseroleslami, B., Pender, N., Lalor, E. C., & Hardiman, O. (2015). Functional connectivity changes in resting-state EEG as potential biomarker for amyotrophic lateral sclerosis. PLoS One, 10.
  39. Jahanshahi, M., Obeso, I., Baunez, C., Alegre, M., & Krack, P. (2015). Parkinson’s disease, the subthalamic nucleus, inhibition, and Impulsivity. Movement Disorders, 30, 128–140. Scholar
  40. Kiernan, M. C., Vucic, S., Cheah, B. C., Turner, M. R., Eisen, A., Hardiman, O., Burrell, J. R., & Zoing, M. C. (2011). Amyotrophic lateral sclerosis. Lancet, 377, 942–955. Scholar
  41. Lin, S. (2010). Rank aggregation methods. Wiley Interdisciplinary Reviews: Computational Statistics, 2, 555–570. Scholar
  42. Lin, S., Ding, J. (2009). Integration of Ranked Lists via Cross Entropy Monte Carlo with Applications to mRNA and microRNA Studies on JSTOR. In: Biometrics. Accessed 7 Jan 2016.
  43. Lomen-Hoerth, C., Murphy, J., Langmore, S., Kramer, J. H., Olney, R. K., & Miller, B. (2003). Are amyotrophic lateral sclerosis patients cognitively normal? Neurology, 60, 1094–1097. Scholar
  44. Luo, C., Chen, Q., Huang, R., Chen, X. P., Chen, K., Huang, X. Q., Tang, H. H., Gong, Q. Y., & Shang, H. F. (2012). Patterns of spontaneous brain activity in amyotrophic lateral sclerosis: A resting-state fMRI study. PLoS One, 7, e45470. Scholar
  45. Luo, C., Guo, X., Song, W., Chen, Q., Yang, J., Gong, Q. Y., & Shang, H. F. (2015). The trajectory of disturbed resting-state cerebral function in Parkinson’s disease at different Hoehn and Yahr stages. Human Brain Mapping, 36, 3104–3116. Scholar
  46. McKinlay, A., Grace, R. C., Dalrymple-Alford, J. C., & Roger, D. (2010). Characteristics of executive function impairment in Parkinsons disease patients without dementia. Journal of the International Neuropsychological Society, 16, 268–277. Scholar
  47. Meilă, M. (2007). Comparing clusterings—An information based distance. Journal of Multivariate Analysis, 98, 873–895. Scholar
  48. Menke, R. A. L., Agosta, F., Grosskreutz, J., Filippi, M., & Turner, M. R. (2017). Neuroimaging endpoints in amyotrophic lateral sclerosis. Neurotherapeutics, 14, 11–23. Scholar
  49. Mohammadi, B., Kollewe, K., Samii, A., Krampfl, K., Dengler, R., & Münte, T. F. (2009). Changes of resting state brain networks in amyotrophic lateral sclerosis. Experimental Neurology, 217, 147–153. Scholar
  50. Monchi, O., Petrides, M., Mejia-Constain, B., & Strafella, A. P. (2007). Cortical activity in Parkinson’s disease during executive processing depends on striatal involvement. Brain, 130, 233–244. Scholar
  51. Olde Dubbelink, K. T. E. E., Hillebrand, A., Stoffers, D., et al. (2014). Disrupted brain network topology in Parkinson’s disease: A longitudinal magnetoencephalography study. Brain, 137, 197–207. Scholar
  52. Pereira, J. B., Junqué, C., Martí, M. J., et al. (2009). Neuroanatomical substrate of visuospatial and visuoperceptual impairment in Parkinson’s disease. Movement Disorders, 24, 1193–1199. Scholar
  53. Poldrack, R. A., Mumford, J. A., Schonberg, T., Kalar, D., Barman, B., & Yarkoni, T. (2012). Discovering relations between mind, brain, and mental disorders using topic mapping. PLoS Computational Biology, 8, e1002707. Scholar
  54. Possin, K. L., Filoteo, J. V., Song, D. D., & Salmon, D. P. (2008). Spatial and object working memory deficits in Parkinson’s disease are due to impairment in different underlying processes. Neuropsychology, 22, 585–595. Scholar
  55. Raichle, M. E. (2015). The Brain’s default mode network. Annual Review of Neuroscience, 38, 433–447. Scholar
  56. Samuel, M., Ceballos-Baumann, A. O., Blin, J., et al. (1997). Evidence for lateral premotor and parietal overactivity in Parkinson’s disease during sequential and bimanual movements. A PET study. Brain, 120, 963–976. Scholar
  57. Schimek, M. G., Myšičková, A., & Budinská, E. (2012). An inference and integration approach for the consolidation of ranked lists. Communications in Statistics: Simulation and Computation, 41, 1152–1166. Scholar
  58. Schimek, M. G., Budinská, E., Kugler, K. G., Švendová, V., Ding, J., & Lin, S. (2015). TopKLists: A comprehensive R package for statistical inference, stochastic aggregation, and visualization of multiple omics ranked lists. Statistical Applications in Genetics and Molecular Biology, 14, 311–316. Scholar
  59. Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., & Beckmann, C. F. (2009). Correspondence of the brain’s functional architecture during activation and rest. Proceedings of the National Academy of Sciences, 106, 13040–13045. Scholar
  60. Suo, X., Lei, D., Li, N., Cheng, L., Chen, F., Wang, M., Kemp, G. J., Peng, R., & Gong, Q. (2017). Functional brain connectome and its relation to Hoehn and Yahr stage in Parkinson disease. Radiology, 285, 904–913. Scholar
  61. Tedeschi, G., & Esposito, F. (2009). Neuronal networks observed with resting state functional magnetic resonance imaging in clinical populations. Neuroimaging – Cognitive and Clinical Neuroscience.
  62. Tedeschi, G., Trojsi, F., Tessitore, A., Corbo, D., Sagnelli, A., Paccone, A., D'Ambrosio, A., Piccirillo, G., Cirillo, M., Cirillo, S., Monsurrò, M. R., & Esposito, F. (2012). Interaction between aging and neurodegeneration in amyotrophic lateral sclerosis. Neurobiology of Aging, 33, 886–898. Scholar
  63. Terada, T., Obi, T., Miyata, J., Kubota, M., Yoshizumi, M., Murai, T., Yamazaki, K., & Mizoguchi, K. (2016). Correlation of frontal atrophy with behavioral changes in amyotrophic lateral sclerosis. Neurol Clin Neurosci, 4, 85–92. Scholar
  64. Tessitore, A., Amboni, M., Esposito, F., Russo, A., Picillo, M., Marcuccio, L., Pellecchia, M. T., Vitale, C., Cirillo, M., Tedeschi, G., & Barone, P. (2012a). Resting-state brain connectivity in patients with Parkinson’s disease and freezing of gait. Parkinsonism & Related Disorders, 18, 781–787. Scholar
  65. Tessitore, A., Esposito, F., Vitale, C., Santangelo, G., Amboni, M., Russo, A., Corbo, D., Cirillo, G., Barone, P., & Tedeschi, G. (2012b). Default-mode network connectivity in cognitively unimpaired patients with Parkinson disease. Neurology, 79, 2226–2232. Scholar
  66. Tessitore, A., Giordano, A., De Micco, R., et al. (2014). Sensorimotor connectivity in Parkinson’s disease: The role of functional neuroimaging. Frontiers in Neurology, 5, 180. Scholar
  67. Thirion, B., Flandin, G., Pinel, P., Roche, A., Ciuciu, P., & Poline, J. B. (2006). Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets. Human Brain Mapping, 27, 678–693. Scholar
  68. Thirion, B., Varoquaux, G., Dohmatob, E., & Poline, J.-B. (2014). Which fMRI clustering gives good brain parcellations? Frontiers in Neuroscience, 8, 167. Scholar
  69. Trojsi, F., Esposito, F., de Stefano, M., Buonanno, D., Conforti, F. L., Corbo, D., Piccirillo, G., Cirillo, M., Monsurrò, M. R., Montella, P., & Tedeschi, G. (2015). Functional overlap and divergence between ALS and bvFTD. Neurobiology of Aging, 36, 413–423. Scholar
  70. Tzourio-Mazoyer, N., Landeau, B., Papathanassiou, D. et al (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain.Google Scholar
  71. van den Heuvel, M., Mandl, R., & Pol, H. H. (2008). Normalized cut group clustering of resting-state fMRI data. PLoS One, 3, e2001. Scholar
  72. Van Eimeren, T., Monchi, O., Ballanger, B., & Strafella, A. P. (2009). Dysfunction of the default mode network in Parkinson disease: a functional magnetic resonance imaging study. Archives of Neurology, 66, 877–883. Scholar
  73. Verstraete, E., & Foerster, B. R. (2015). Neuroimaging as a new diagnostic modality in amyotrophic lateral sclerosis. Neurotherapeutics, 12, 403–416. Scholar
  74. Verstraete, E., Veldink, J. H., van den Berg, L. H., & van den Heuvel, M. P. (2014). Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis. Human Brain Mapping, 35, 1351–1361. Scholar
  75. Vinh, N.X., Epps, J. (2009). A novel approach for automatic number of clusters detection in microarray data based on consensus clustering. In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering. (pp 84–91). IEEE.Google Scholar
  76. Vinh N.X., Epps, J., Bailey, J. (2009). Information theoretic measures for clusterings comparison. In Proceedings of the 26th Annual International Conference on Machine Learning - ICML ‘09. (pp 1–8).Google Scholar
  77. von Luxburg, U. (2010). Clustering stability: an overview. Foundations and Trends in Machine Learning, 2, 235–274. Scholar
  78. Whitfield-Gabrieli, S., & Ford, J. M. (2012). Default mode network activity and connectivity in psychopathology. Annual Review of Clinical Psychology, 8, 49–76. Scholar
  79. Wicks, P., Turner, M. R., Abrahams, S., Hammers, A., Brooks, D. J., Leigh, P. N., & Goldstein, L. H. (2008). Neuronal loss associated with cognitive performance in amyotrophic lateral sclerosis: An ( 11 C)-flumazenil PET study. Amyotrophic Lateral Sclerosis, 9, 43–49. Scholar
  80. Yarkoni, T., Poldrack, R. A., Nichols, T. E., van Essen, D. C., & Wager, T. D. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8, 665–670. Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.NeuRoNe Lab, Department of Management and Innovation SystemsUniversity of SalernoFiscianoItaly
  2. 2.Department of Medical, Surgical, Neurological, Metabolic and Aging SciencesUniversity of Campania “Luigi Vanvitelli”NaplesItaly
  3. 3.Department of Medicine, Surgery and Dentistry “Scuola Medica Salernitana”University of SalernoBaronissiItaly

Personalised recommendations