Publications

  1. A. G. Casali, S. Casarotto, M. Rosanova, M. Mariotti, M. Massimini. General indices to characterize the electrical response of the cerebral cortex to TMS. NeuroImage 49: 1459-1468, 2010. doi:10.1016/j.neuroimage.2009.09.026
  2. S. Casarotto, A.G. Casali, M. Rosanova, M. Mariotti, M. Massimini. A data-driven procedure to characterize the electrophysiology of any cortical area using TMS/hd-EEG. NeuroImage 47: S2, 2009. doi:10.1016/S1053-8119(09)70555-5
  3. S. Casarotto, L. Romero Lauro, V. Bellina, A. Casali, M. Rosanova, A. Pigorini, S. Defendi, M. Mariotti, M. Massimini. EEG Responses to TMS Are Sensitive to Changes in the Perturbation Parameters and Repeatable over Time. Plos ONE 5(4): E10281, 2010. doi:10.1371/journal.pone.0010281
  4. A. Hviid-Simonsen, J. Mattila, A-M. Hejl, K.S. Frederiksen, S.K. Herukka, M. Hallikainen, M. van Gils, J. Lötjönen, H. Soininen and G. Waldemar. Application of the PredictAD software tool to the differentiation between stable and progressive MCI, submitted to EFNS, 2011.
  5. V. Julkunen, J. Koikkalainen, E. Niskanen, S-K. Herukka, M. Pihlajamäki, M. Hallikainen, M. Kivipelto, R. Vanninen, J. Lötjönen, H. Soininen. Combining cortical thickness analysis and clinical measures to predict Alzheimer’s disease. Alzheimer’s and Dementia 6(4): S37, 2010. doi:10.1016/j.jalz.2010.05.104
  6. V. Julkunen, E. Niskanen, J. Koikkalainen, S-K. Herukka, M. Pihlajamäki, M. Hallikainen, M. Kivipelto, S. Muehboeck, A.C. Evans, R. Vanninen, H. Soininen. Differences in cortical thickness in healthy controls, subjects with mild cognitive impairment and Alzheimer disease patients – a longitudinal study. Journal of Alzheimer’s Disease 21: 1141-1151, 2010. doi:10.3233/JAD-2010-100114
  7. V. Julkunen, E. Niskanen, S-K. Herukka, M. Pihlajamäki, M. Hallikainen, M. Kivipelto, S. Muehboeck, A.C. Evans, R. Vanninen, H. Soininen. Differences in cortical thickness in healthy controls, subjects with mild cognitive impairment and Alzheimer disease patients – a longitudinal study. 11th International Geneva/Springfield Symposium on Advances in Alzheimer Therapy, 2010.
  8. V. Julkunen, J. Koikkalainen, E. Niskanen, R. Wolz, M. Kivipelto, R. Vanninen, J. Lötjönen, H. Soininen and The Alzheimer’s Disease Neuroimaging Initiative. Decrease in cortical thickness predicts forthcoming Alzheimer’s disease – a two cohort study. submitted to NeuroImage.
  9. V. Julkunen, R. Wolz, J. Koikkalainen, J. Mattila, D. Rueckert, H. Soininen, J. Lötjönen. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer’s Disease. submitted to NeuroImage, 2011.
  10. J. Koikkalainen, J. Lötjönen, L. Thurfjell, D. Rueckert, G. Waldemar and H. Soininen, The Alzheimer’s Disease Neuroimaging Initiative. Multi-Template Tensor-Based Morphometry: Application to Analysis of Alzheimer’s Disease, NeuroImage, in press, 2011. doi:10.1016/j.neuroimage.2011.03.029
  11. J. Lötjönen, R. Wolz, J. Koikkalainen, L. Thurfjell, G. Waldemar, H. Soininen, D. Rueckert, The Alzheimer’s Disease Neuroimaging Initiative. Fast and robust multi-atlas segmentation of brain magnetic resonance images, NeuroImage, 49: 2352-2365, 2010. doi:10.1016/j.neuroimage.2009.10.026
  12. J. Lötjönen, L. Thurfjell, R. Zubarev, M. Massimini, J. Ruohonen, D. Rueckert. G. Waldemar and H. Soininen. PredictAD – From Patient Data to Personalised Healthcare in Alzheimer’s Disease. The 5th Kuopio Alzheimer Symposium (Mikko Hiltunen ed), pp. 58, 2009.
  13. J. Lötjönen, J. Koikkalainen, L. Thurfjell and D. Rueckert. Atlas-based Registration Parameters in Segmenting Sub-Cortical Regions From Brain MRI-Images. IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 21-24, 2009. doi:10.1109/ISBI.2009.5192973
  14. J. Lötjönen and H. Soininen. Magneettikuvien hyödyntäminen Alzheimerin taudin diagnostiikassa. Synapsi 3/2010, pp. 7-9, 2010.
  15. J. Lötjönen, R. Wolz, J. Koikkalainen, V. Julkunen, L. Thurfjell, R. Lundqvist, G. Waldemar, H. Soininen, D. Rueckert, The Alzheimer’s Disease Neuroimaging Initiative. Fast and robust extraction of hippocampus from MR images for diagnostics of Alzheimer’s disease. NeuroImage 56: 185-196, 2011. doi:10.1016/j.neuroimage.2011.01.062
  16. J. Lötjönen, R. Wolz, J. Koikkalainen, L. Thurfjell, R. Lundqvist, G. Waldemar,  H. Soininen, D. Rueckert. Fast and robust multi-atlas segmentation of magnetic resonance images: application to hippocampus. 1st Virtual Physiological Human Conference, September 30-October 1, Brussels, Belgium, pp. 238-239, 2010.
  17. J. Lötjönen, J. Koikkalainen, R. Wolz, L. Thurfjell, V. Julkunen, R. Lundqvist, D. Rueckert, G. Waldemar, H. Soininen and The Alzheimer’s Disease Neuroimaging Initiative. Fast and robust segmentation of hippocampus from magnetic resonance images. Neurodegenerative Diseases 8: S1, 2011. doi:10.1159/000327701
  18. J. Lötjönen, R. Wolz, J. Koikkalainen, L. Thurfjell, R. Lundqvist, G. Waldemar, H. Soininen, D. Rueckert. Improved Generation of Probabilistic Atlases for the Expectation Maximization Classification. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011, pp. 1839-1842, 2011.
  19. J. Mattila, J. Koikkalainen, D. Ververidis, M. van Gils, J. Lötjönen, G. Waldemar, A. Simonsen, D. Rueckert, L. Thurfjell, H. Soininen. Clinical decision support system based on statistical analysis of heterogeneous clinical data and Alzheimer’s disease biomarkers. Alzheimer’s and Dementia 6(4): S365, 2010. doi:10.1016/j.jalz.2010.05.1225
  20. J. Mattila, J. Koikkalainen, M. van Gils, J. Lötjönen, G. Waldemar, A. Simonsen, D. Rueckert, L. Thurfjell, H. Soininen. PredictAD – a clinical decision support system for early diagnosis of Alzheimer’s disease. 1st Virtual Physiological Human Conference, September 30-October 1, Brussels, Belgium, pp. 148-150, 2010.
  21. J. Mattila, J. Koikkalainen, A. Virkki, A. Hviid-Simonsen, M. van Gils, G. Waldemar, H. Soininen, J. Lötjönen, The Alzheimer’s Disease Neuroimaging Initiative. Disease State Fingerprint for Evaluating the State of Alzheimer’s Disease in Patients. Journal of Alzheimer’s Disease, submitted 2011.
  22. M. Oresic, J. Lötjönen and H. Soininen. Systems medicine and integration of bioinformatic tools for diagnostics of Alzheimer’s disease. Genome Medicine, 2:83: 1-5, 2010. doi:10.1186/gm204
  23. M. Rosanova, A. Casali, V. Bellina, F. Resta, M. Mariotti, M. Massimini. Natural frequencies of human corticothalamic circuits. The Journal of Neuroscience 29(24): 7679-7685, 2009. doi:10.1523/JNEUROSCI.0445-09.2009
  24. H. Soininen, J. Mattila, J. Koikkalainen, M. van Gils, G. Waldemar, A. Hviid Simonsen, D. Rueckert, L. Thurfjell, J. Lötjönen. Software tool for predicting Alzheimer’s disease – PREDICTAD project. Neurodegenerative Diseases 8: S1, 2011.
  25. L. Thurfjell, R. Lundqvist, J. Lötjönen, J. Koikkalainen, R. Wolz, D. Rueckert, R. Vandenberghe, H. Soininen and G. Waldemar. Comparison of biomarkers from PET[18F]flutemetamol amyloid imaging and structural MRI. Alzheimer’s and Dementia 6(4): S55, 2010. doi:10.1016/j.jalz.2010.05.155
  26. L. Thurfjell, J. Lötjönen, R. Wolz, J. Koikkalainen, G. Waldemar, H. Soininen, D. Rueckert, and The Alzheimer’s Disease Neuroimaging Initiative. Fast and robust segmentation of brain magnetic resonance images. Alzheimer’s and Dementia 6(4): S341, 2010. doi:10.1016/j.jalz.2010.05.1142
  27. M. van Gils, J. Koikkalainen, J. Mattila, S-K. Herukka, J. Lötjönen, H. Soininen. Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer’s disease. 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010. doi:10.1109/IEMBS.2010.5626311
  28. D. Ververidis, M. van Gils, J. Koikkalainen and J. Lötjönen. Feature selection and time regression software: application on predicting Alzheimer’s disease progress. European Signal Processing Conference EUSIPCO 2010.
  29. R. Wolz, P. Aljabar, J. Hajnal, A. Hammers, D. Rueckert, The Alzheimer’s Disease NeuroImaging Initiative. LEAP: Learning embeddings for atlas propagation. NeuroImage 49: 1316-1325, 2010. doi:10.1016/j.neuroimage.2009.09.069
  30. R. Wolz, P. Aljabar, D. Rueckert, R. Heckemann and A. Hammers. Segmentation of Subcortical Structures in Brain MRI Using Graph-Cuts and Subject-Specific A-Priori Information. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2009, pp. 474-477, 2009. doi:10.1109/ISBI.2009.5193086
  31. R. Wolz, R. Heckemann, P. Aljabar, J. Hajnal, A. Hammers, J. Lötjönen, and D. Rueckert. Measuring Atrophy by Simultaneous Segmentation of Serial MR Images using 4-D Graph-Cuts. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2010, pp. 960-963, 2010. doi:10.1109/ISBI.2010.5490147
  32. R. Wolz, R. Heckemann, P. Aljabar, J. Hajnal, A. Hammers, J. Lötjönen, D. Rueckert, and the Alzheimer’s Disease Neuroimaging Initiative. Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI. NeuroImage, 52:109-118, 2010. doi:10.1016/j.neuroimage.2010.04.006
  33. R. Wolz, R. Heckemann, P. Aljabar, J. Hajnal, A. Hammers, J. Lötjönen, D. Rueckert. Automatically determined hippocampal atrophy rates in ADNI: their usability to discriminate between clinical groups and to detect changes in atrophy rate. Alzheimer’s and Dementia 6(4): S284, 2010. doi:10.1016/j.jalz.2010.05.937
  34. R. Wolz, P. Aljabar, J. Hajnal and D. Rueckert. Manifold learning for biomarker discovery in MR imaging. F. Wang et al.: MLMI 2010, LNCS 6357, Springer Heidelberg, pp 116-123, 2010. doi:10.1007/978-3-642-15948-0_15
  35. R. Wolz, R. Heckemann,  P. Aljabar, J. Hajnal, A. Hammers, J. Lötjönen, D. Rueckert. Using automatically determined atrophy rates to discriminate between clinical groups and to detect atrophy changes in clinical trials. 1st Virtual Physiological Human Conference, September 30-October 1, Brussels, Belgium, pp. 421-423, 2010.
  36. R. Wolz, P. Aljabar, J. Hajnal, J. Lötjönen, D. Rueckert. Manifold Learning Combining Imaging with Non-Imaging Information. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011, pp. 1637-1640, 2011.
  37. R. Wolz, P. Aljabar, J. V. Hajnal, J. Lötjönen, D. Rueckert and The Alzheimer’s Disease NeuroImaging Initiative. Nonlinear Dimensionality Reduction Combining MR Imaging with Non-Imaging Information. submitted to Medical Image Analysis, 2011.
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