Publications

Robin Wolz, Paul Aljabar, Joseph V. Hajnal and Daniel Rueckert. Manifold learning for biomarker discovery in MR imaging. MICCAI 2010 Workshop.

Abstract

We propose a framework for the extraction of biomarkers from low-dimensional manifolds representing inter- and intra-subject brain variation in MR image data. The coordinates of each image in such a low-dimensional space captures information about structural shape and appearance and, when a phenotype exists, about the subject’s clinical state. A key contribution is that we propose a method for incorporating longitudinal image information in the learned manifold. In particular, we compare simultaneously embedding baseline and follow-up scans into a single manifold with the combination of separate manifold representations for inter-subject and intra-subject variation. We apply the proposed methods to 362 subjects enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and classify healthy controls, subjects with Alzheimer’s disease (AD) and subjects with mild cognitive impairment (MCI). Learning manifolds based on both the appearance and temporal change of the hippocampus, leads to correct classification rates comparable with those provided by state-of-the-art automatic segmentation estimates of hippocampal volume and atrophy. The biomarkers identified with the proposed method are data-driven and represent a potential alternative to a-priori defined biomarkers derived from manual or automated segmentations.

Mark van Gils, Juha Koikkalainen, Jussi Mattila, Sanna-Kaisa Herukka, Jyrki Lötjönen, Hilkka Soininen and the Alzheimer's Disease Neuroimaging Initiative. Discovery and use of efficient biomarkers for objective disease state assessment in Alzheimer’s disease. EMBC 2010.

Abstract

Objective and early detection of Alzheimer’s disease (AD) is a demanding problem requiring consideration of many-modal observations. Potentially, many features could be used to discern between people without AD and those at different stages of the disease. Such features include results from cognitive and memory tests, imaging (MRI, PET) results, cerebral spine fluid data, blood markers etc. However, in order to define an efficient and limited set of features that can be employed in classifiers requires mining of data from many patient cases. In this study we used two databases, ADNI and Kuopio L-MCI, to investigate the relative importance of features and their combinations. Optimal feature combinations are to be used in a Clinical Decision Support System that is to be used in clinical AD diagnosis practice

Robin Wolz, Rolf A. Heckemann, Paul Aljabar, Joseph V. Hajnal, Alexander Hammers, Jyrki Lötjönen, Daniel Rueckert and The Alzheimer's Disease Neuroimaging Initiative. Measurement of hippocampal atrophy using 4D graph-cut segmentation Application to ADNI. NeuroImage 52 (2010) 109-118
http://dx.doi.org/10.1016/j.neuroimage.2010.04.006

Abstract

We propose a new method of measuring atrophy of brain structures by simultaneously segmenting longitudinal magnetic resonance (MR) images. In this approach a 4D graph is used to represent the longitudinal data: edges are weighted based on spatial and intensity priors and connect spatially and temporally neighboring voxels represented by vertices in the graph. Solving the min-cut/max-flow problem on this graph yields the segmentation for all timepoints in a single step. By segmenting all timepoints simultaneously, a consistent and atrophy-sensitive segmentation is obtained. The application to hippocampal atrophyAlzheimer's Disease Neuroimaging Initiative (ADNI) confirms previous findings for atrophy in Alzheimer's disease (AD) and healthy aging. Highly significant correlations between hippocampal atrophy and clinical variables (MiniDementia Rating, CDR) were found and atrophy rates differ significantly according to subjects' ApoE genotype. Based on one year atrophy rates, a correct classification rate of 82% between AD and control subjects is achieved. Subjects that converted from Mild Cognitive Impairment (MCI) to AD measurement in 568 image pairs (Baseline and Month 12 follow-up) as well as 362 image triplets (Baseline, Month 12, and Month 24) from the Mental State Examination, MMSE and Clinical after the period for which atrophy was measured (i.e., after the first 12 months) and subjects for whom conversion is yet to be identified were discriminated with a rate of 64%, a promising result with a view to clinical application. Power analysis shows that 67 and 206 subjects are needed for the AD and MCI groups respectively to detect a 25% change in volume loss with 80% power and 5% significance.

Casarotto S, Romero Lauro LJ, Bellina V, Casali AG, Rosanova M, et al. (2010) EEG Responses to TMS Are Sensitive to Changes in the Perturbation Parameters and Repeatable over Time. PLoS ONE 5(4): e10281
http://dx.doi.org/10.1371/journal.pone.0010281

Abstract

We have recently published a manuscript entitled “EEG responses to TMS are sensitive to changes in the perturbation parameters and repeatable over time” (PLoS One 2010, 5(4): e10281). This paper first quantifies the sensitivity of TMS/hd-EEG to changes in the stimulation parameters, showing that the spatiotemporal characteristics of TMS-evoked potentials are specifically affected by stimulation site, intensity and angle. Moreover, the paper reports that, in normal conditions, the morphology of brain responses to TMS keeps unaltered over repeated experimental sessions performed with the same stimulation parameters at different times. These results demonstrate that TMS-evoked potentials specifically reflect deterministic properties of brain circuits and may be used to track longitudinal changes of the state of cortical networks, e.g. different physiological conditions, learning , neurodegenerative processes, rehabilitation and treatment.

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 (2010) 1459–1468
http://dx.doi.org/10.1016/j.neuroimage.2009.09.026

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.
http://dx.doi.org/10.1016/S1053-8119(09)70555-5

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 (2010) 2352–2365
http://dx.doi.org/10.1016/j.neuroimage.2009.10.026

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.

J. Lötjönen, J. Koikkalainen, L. Thurfjell and D. Rueckert. Atlas-based Registration Parameters in Segmenting Sub-Cortical Regions From Brain MRI-Images. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 21-24, 2009.
http://dx.doi.org/10.1109/ISBI.2009.5192973

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.
http://dx.doi.org/10.1523/JNEUROSCI.0445-09.2009

R. Wolz, P. Aljabar, J. Hajnal, A. Hammers, D. Rueckert, The Alzheimer’s Disease NeuroImaging Initiative. LEAP: Learning embeddings for atlas propagation. NeuroImage 49 (2010) 1316–1325
http://dx.doi.org/10.1016/j.neuroimage.2009.09.069

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. 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 474-477, 2009.
http://dx.doi.org/10.1109/ISBI.2009.5193086