Contact Information

Scientific Coordinator
Jyrki Lötjönen
VTT Technical Research Centre of Finland
P.O. Box 1300
33101 Tampere
+358 20 722 3378
jyrki.lotjonen@vtt.fi

Publications

Peer-reviewed Journals

  1. K. Antila, J. Lötjönen, L. Thurfjell, J. Laine, M. Massimini, D. Rueckert, R. Zubarev, M. Oresic, M. van Gils, J. Mattila, A. Simonsen, G. Waldemar and H. Soininen, The PredictAD project: development of novel biomarkers and analysis software for early diagnosis of the Alzheimer’s disease. Interface Focus, in press, 2013.
  2. 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

  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. S. Casarotto, S. Määttä, S-K. Herukka, A. Pigorini, M. Napolitani, O. Gosseries, E. Niskanen, M. Könönen, E. Mervaala, M. Rosanova, H. Soininen and M. Massimini. Transcranial magnetic stimulation-evoked EEG/cortical potentials in physiological and pathological aging. Neuroreport 22(12): 592-597, 2011. doi:10.1097/WNR.0b013e328349433a

  5. K. Gray, R. Wolz, R. Heckemann, P. Aljabar, A. Hammers, D. Rueckert, for the Alzheimer’s Disease Neuroimging Initiative. Multi-region analysis of longitudinal FDG-PET for the classification of Alzheimer’s disease. NeuroImage 60: 221-229, 2012. doi:10.1016/j.neuroimage.2011.12.071
  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. 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 56: 1134-1144, 2011. doi:10.1016/j.neuroimage.2011.03.029

  8. J. Koikkalainen, H. Pölönen, J. Mattila, M. van Gils, H. Soininen and J. Lötjönen;, the Alzheimer's Disease Neuroimaging Initiative. Improved Classification of Alzheimer's Disease Data via Removal of NuisanceVariability. PLoS ONE 7(2): e31112. doi:10.1371/journal.pone.0031112

  9. Y. Liu, M. Munoz, J. Mattila, T. Paajanen, J. Koikkalainen, M. van Gils, S-K. Herukka, G. Waldemar, J. Lötjönen and H. Soininen. for the Alzheimer’s Disease Neurodegenerative Initiative. Predicting AD conversion: comparison between prodromal AD guidelines and computer assisted PredictAD tool. PLoSOne, in press, 2013. doi:10.1371/journal.pone.0055246
  10. 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

  11. 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

  12. 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 27: 163-176, 2011. doi:10.3233/JAD-2011-110365

  13. J. Mattila, J. Koikkalainen, A. Virkki, M. van Gils, and J. Lötjönen and the Alzheimer’s Disease Neuroimaging Initiative. Design and Application of a Generic Clinical Decision Support System for Multi-Scale Data. IEEE Trans. Biomed. Eng. 59(1) pp. 234 - 240 2012. doi:10.1109/TBME.2011.2170986

  14. J. Mattila, H. Soininen, J. Koikkalainen, D. Rueckert, R. Wolz, G. Waldemar, J. Lötjönen. Optimizing the diagnosis of early Alzheimer’s disease in mild cognitive impairment subjects. Journal of Alzheimer’s Disease 32: 969-979, 2012. doi:10.3233/JAD-2012-120934
  15. M. Muñoz-Ruiz, P. Hartikainen, J. Koikkalainen, R. Wolz, V. Julkunen, E. Niskanen, S-K. Herukka, M. Kivipelto, R. Vanninen, D. Rueckert, Y. Liu, J. Lötjönen and H. Soininen. Structural MRI in Frontotemporal Dementia: Comparisons between Hippocampal volumetry, Tensor-based morphometry and Voxel-based morphometry, PLosOne 7(12), e52531, 2012. doi:10.1371/journal.pone.0052531

  16. E. Niskanen, M. Könönen, S. Määttä, M. Hallikainen, M. Kivipelto, S. Casarotto, M. Massimini, R. Vanninen, E. Mervaala, J. Karhu, H. Soininen. New insights into Alzheimer's disease progression: a combined TMS and structural MRI study. PloS One. 2011;6(10):e26113. Epub 2011 Oct 12. doi:10.1371/journal.pone.0026113

  17. M. Orešič, 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

  18. M. Orešič, T. Hyötyläinen, S-K. Herukka, M. Sysi-Aho, I. Mattila,T. Seppänen-Laakso, V. Julkunen, P. Gopalacharyulu, M. Hallikainen, J. Koikkalainen, M. Kivipelto, S. Helisalmi, J. Lötjönen, H. Soininen. Metabolome in progression to Alzheimer’s disease. Translational Psychiatry 1: e57, 2011. doi:10.1038/tp.2011.55

  19. A. Pigorini, A. G. Casali, S. Casarotto, F. Ferrarelli, G. .Baselli, M. Mariotti, M. Massimini, M. Rosanova. Time-frequency spectral analysis of TMS-evoked EEG oscillations by means of Hilbert-Huang transform.Journal of Neuroscience Methods 198: 236-245, 2011. doi:10.1016/j.jneumeth.2011.04.013

  20. L. Risser, F-X. Vialard, R. Wolz, M. Murgasova, D. Holm, D. Rueckert and The Alzheimer’s Disease Neuroimaging Initiative. Simultaneous Multi-scale Registration Using Large Deformation Diffeomorphic Metric Mapping. IEEE Transactions on Medical Imaging 30(10): 1746-1759, 2011. doi:10.1109/TMI.2011.2146787
  21. 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

  22. A. Simonsen, J. Mattila, A. Hejl, K.S. Frederiksen, S-K. Herukka, M. Hallikainen, M. van Gils, J. Lötjönen, H. Soininen, G. Waldemar, for the Alzheimer’s Disease Neurodegenerative Initiative. Application of the PredictAD software tool to patients with mild cognitive impairment. Dementia and Geriatric Cognitive Disorders 34: 344-450, 2012. doi:10.1159/000345554
  23. H. Soininen, J. Mattila, J. Koikkalainen, M. van Gils, A. Hviid Simonsen, G. Waldemar , D. Rueckert, L. Thurfjell, J. Lötjönen. Software tool for improved prediction of Alzheimer’s disease. Neurodegenerative Diseases, pp. 1-4, 2011. doi:10.1159/000332600.

  24. H. Soininen, Y. Liu, D. Rueckert and J. Lötjönen. Hippocampal atrophy in Alzheimer’s disease. Neurodegenerative Disease Management, 2(2): 197-209, 2012. doi:10.2217/nmt.12.13

  25. L Thurfjell, J. Lötjönen,  R. Lundqvist, J. Koikkalainen, H. Soininen, G. Waldemar, R. Vandenberghe. Combination of biomarkers from PET [18F]flutemetamol amyloid imaging and structural MRI in dementia. Neurodegenerative Diseases,10:246–249, 2012. doi:/10.1159/000335381

  26. M. Vounou, E. Janousova, R. Wolz, J. L. Stein, P. M. Thompson, D. Rueckert and G. Montana. Sparse reduced-rank regression detects genetic associations with voxel-wise longitudinal phenotypes in Alzheimer's disease. NeuroImage 60: 700-716, 2012. doi:10.1016/j.neuroimage.2011.12.029
  27. 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

  28. 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

  29. 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. Medical Image Analysis, 16(4) 819-830, 2012. doi:10.1016/j.media.2011.12.003

  30. R. Wolz, V. Julkunen, J. Koikkalainen, E. Niskanen, DP. Zhang, D. Rueckert, H. Soininen, J. Lötjönen; the Alzheimer's Disease Neuroimaging Initiative. Multi-Method Analysis of MRI Images in Early Diagnostics of Alzheimer's Disease. PLoS One 6(10):e25446, 2011. Epub 2011 Oct 13 doi:10.1371/journal.pone.0025446

  31. H. Yang, Y. Lyutvinskiy, H. Soininen, and R. A. Zubarev. Alzheimer’s disease and mild cognitive impairment are associated with elevated levels of isoaspartyl residues in blood plasma proteins. Journal of Alzheimer’s Disease 21(1), pp. 113-118, 2011. doi:10.3233/JAD-2011-110626

Peer-reviewed Conferences

  1. K. Gray, P. Aljabar, R.A. Heckemann, A. Hammers and D. Rueckert. Random forest-based manifold learning for classification of imaging data in dementia. Medical Image Computing and Computer Assisted Intervention 2011, Machine Learning workshop, LNCS 7009, pp. 159-166, 2011. doi:10.1007/978-3-642-24319-6_20

  2. C. Ledig, R. Wolz, P. Aljabar, J. Lötjönen R. Heckemann, A. Hammers and D. Rueckert. Multi-Class Brain Segmentation using Atlas Propagation and EM-based Refinement. IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2012, pp. 896 - 899 doi:10.1109/ISBI.2012.6235693.

  3. 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 2009, pp. 21-24. doi:10.1109/ISBI.2009.5192973

  4. 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. doi:10.1109/ISBI.2011.5872765

  5. 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.

  6. 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

  7. 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

  8. 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

  9. 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. doi:10.1109/ISBI.2011.5872717

  10. R. Wolz, P. Aljabar, J. Hajnal, J. Lötjönen and D. Rueckert. Manifold-based classification incorporating subject metadata. Medical Image Understanding and Analysis 2011 – MIUA-2011.

Abstracts

  1. 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. 15th Annual Meeting of the Organization for Human Brain Mapping, 18-23 June 2009, San Francisco – CA, US.

  2. S. Casarotto, S. Määttä, A. G. Casali, S-K. Herukka, A. Pigorini, M. Rosanova, M. Mariotti, M. Massimini and H. Soininen. Characterization of the cortical response to TMS in Alzheimer’s disease. Proceedings of AAIC11: S311-312, 2011. Alzheimer’s Association International Conference, 16-21 Jun, Paris, France.

  3. S. Casarotto, S. Määttä, A.G. Casali, S-K. Herukka, A. Pigorini, M. Rosanova, K. Lankinen, M. Massimini, H. Soininen. Transcranial magnetic stimulation and electrophysiological biomarkers in diagnosis of AD. PredictAD Workshop Proceedings, p. 29, 2011.

  4. K.R. Gray, R.A. Heckemann, A. Hammers, D. Rueckert. Multi-region analysis of longitudinal FDG-PET enables accurate AD classification. ICAD 2011.

  5. 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. EFNS 2011.

  6. 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

  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. PredictAD Workshop Proceedings, p. 49, 2011.

  9. J. Koikkalainen, J. Lötjönen, L. Thurfjell, D. Rueckert, G. Waldemar, H. Soininen and the Alzheimer's Disease Neuroimaging Initiative. Multi-template tensor-based morphometry: Application to analysis of Alzheimer's disease. PredictAD Workshop Proceedings, p. 52, 2011.

  10. 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.

  11. J. Lötjönen and H. Soininen. Magneettikuvien hyödyntäminen Alzheimerin taudin diagnostiikassa. Synapsi 3/2010, pp. 7-9, 2010.

  12. 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.

  13. 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.

  14. J. Lötjönen,  L. Thurfjell, J. Laine, H. Soininen, D. Rueckert, M. Massimini, G. Waldemar and R. Zubarev. PredictAD project – Concepts and progress. PredictAD Workshop Proceedings, p. 25, 2011.

  15. J. Lötjönen, R. Wolz, J. Koikkalainen, L. Thurfjell, V. Julkunen, G. Waldemar, H. Soininen, D. Rueckert and the Alzheimer's Disease Neuroimaging Initiative. Robust and accurate segmentation of hippocampus for diagnostics of Alzheimer’s disease. PredictAD Workshop Proceedings, p. 55, 2011.

  16. 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.

  17. 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.

  18. D. Rueckert, D. Zhang, J. Lötjönen, J. Koikkalainen, L. Thurfjell, R. Lundqvist, G. Waldemar, V. Julkunen and H. Soininen. Options for MRI analysis methods for diagnosis of AD. PredictAD Workshop Proceedings, p. 30, 2011.

  19. A.H. Simonsen, J. Mattila, A.M. Hejl, K. S. Frederiksen, S-K Herukka, M Hallikainen, M. Van Gils, J. Lötjönen, H. Soininen, G. Waldemar. Application of the PredictAD software tool to the differentiation between stable and progressive MCI. ICAD 2011.

  20. 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.

  21. H. Soininen, J. Mattila, J. Koikkalainen, M. van Gils, A.H. Simonsen, G. Waldemar, D. Rueckert, M. Oresic, M. Massimini, J. Laine, R. Zubarev, L. Thurfjell, J. Lötjönen. Clinical validation of Software tool for predicting Alzheimer's disease -PREDICTAD project. PredictAD Workshop Proceedings, p. 32, 2011.

  22. 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.

  23. 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.

  24. L. Thurfjell, J. Lötjönen, R. Wolz, J. Koikkalainen, V. Julkunen, G. Waldemar, H. Soininen, D. Rueckert and The Alzheimer’s Disease Neuroimaging Initiative. Automatic segmentation of hippocampus for diagnostics of Alzheimer’s disease. Alzheimer’s & Dementia: The Journal of the Alzheimer's Association 7(4):  S226, 2011.

  25. L. Thurfjell, R. Lundqvist, J. Lilja, J. Lötjönen, G. Waldemar, H. Soininen and R. Vandenberghe. Automated Quantification of [18F]flutemetamol Amyloid Imaging Data. Alzheimer’s & Dementia: The Journal of the Alzheimer's Association 7(4):  S726, 2011.

  26. L. Thurfjell, J. Lötjönen, M. Niemelä, H. Soininen, A. H. Simonsen, G. Waldemar. Beyond current diagnostic protocols – Application requirements. PredictAD Workshop Proceedings, p. 27, 2011.

  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.

  28. M. van Gils, J. Mattila, J. Koikkalainen, L. Thurfjell, M. Niemelä, J. Lötjönen, H. Soininen and G. Waldemar. The PredictAD Software Tool. PredictAD Workshop Proceedings, p. 31, 2011.

  29. 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.

  30. 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.

  31. R. Wolz, R.A. Heckemann, P. Aljabar, J.V. Hajnal, A. Hammers, J. Lotjonen and D. Rueckert. Measurement of hippocampal atrophy using 4D graph-cut segmentation: Application to ADNI. PredictAD Workshop Proceedings, p, 57, 2011.

  32. R. Wolz, P. Aljabar, J. V. Hajnal, J. Lotjonen, D. Rueckert. Manifold Learning Combining Imaging with Non-Imaging Information. PredictAD Workshop Proceedings, p. 60, 2011.

  33. H. Yang, Y. Lyutvinskiy and R. A. Zubarev. Alzheimer’s disease and mild cognitive impairment are associated with elevated levels of isoaspartyl residues in blood plasma proteins. PredictAD Workshop Proceedings, p. 50, 2011.