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  • 1.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Gray-level invariant Haralick texture features2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S279-S280Article in journal (Other academic)
  • 2.
    Bylund, Mikael
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lundman, Josef
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Using deep learning to generate synthetic CTs for radiotherapy treatment planning2018In: Radiotherapy and Oncology, ISSN 0167-8140, E-ISSN 1879-0887, Vol. 127, p. S283-S283Article in journal (Other academic)
  • 3.
    de Pierrefeu, Amicie
    et al.
    NeuroSpin, CEA, Paris-Saclay, Gif-sur-Yvette, France.
    Fovet, Thomas
    Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille F- 59000, France; CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille F-59000, France.
    Hadj-Selem, Fouad
    Energy Transition Institute: VeDeCoM, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Ciuciu, Philippe
    NeuroSpin, CEA, Paris-Saclay, Gif-sur-Yvette, France; INRIA, CEA, Parietal team, Univ. Paris-Saclay, France.
    Lefebvre, Stephanie
    Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille F-59000, France; CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille F-59000, France.
    Thomas, Pierre
    Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille F-59000, France; CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille F-59000, France.
    Lopes, Renaud
    Imaging Dpt., Neuroradiology unit, CHU Lille, Lille F-59000, France; U1171 - Degenerative and Vascular Cognitive Disorders, Univ. Lille, INSERM, CHU Lille, Lille F-59000, France.
    Jardri, Renaud
    Univ. Lille, CNRS UMR 9193, Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab), PsyCHIC team, Lille F-59000, France; CHU Lille, Pôle de Psychiatrie, Unité CURE, Lille F-59000, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Paris-Saclay, Gif-sur-Yvette, France.
    Prediction of activation patterns preceding hallucinations in patients with schizophrenia using machine learning with structured sparsity2018In: Human Brain Mapping, ISSN 1065-9471, E-ISSN 1097-0193, Vol. 39, no 4, p. 1777-1788Article in journal (Refereed)
    Abstract [en]

    Despite significant progress in the field, the detection of fMRI signal changes during hallucinatory events remains difficult and time-consuming. This article first proposes a machine-learning algorithm to automatically identify resting-state fMRI periods that precede hallucinations versus periods that do not. When applied to whole-brain fMRI data, state-of-the-art classification methods, such as support vector machines (SVM), yield dense solutions that are difficult to interpret. We proposed to extend the existing sparse classification methods by taking the spatial structure of brain images into account with structured sparsity using the total variation penalty. Based on this approach, we obtained reliable classifying performances associated with interpretable predictive patterns, composed of two clearly identifiable clusters in speech-related brain regions. The variation in transition-to-hallucination functional patterns not only from one patient to another but also from one occurrence to the next (e.g., also depending on the sensory modalities involved) appeared to be the major difficulty when developing effective classifiers. Consequently, second, this article aimed to characterize the variability within the prehallucination patterns using an extension of principal component analysis with spatial constraints. The principal components (PCs) and the associated basis patterns shed light on the intrinsic structures of the variability present in the dataset. Such results are promising in the scope of innovative fMRI-guided therapy for drug-resistant hallucinations, such as fMRI-based neurofeedback.

  • 4.
    de Pierrefeu, Amicie
    et al.
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hadj-Selem, Fouad
    Energy Transition Institute: VeDeCoM, 78 000 Versailles, France.
    Dubois, Mathieu
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Jardri, Renaud
    Univ. Lille, CNRS UMR 9193, SCALab, CHU Lille, Pôle de Psychiatrie (unit CURE), 59000 Lille, France.
    Fovet, Thomas
    Univ. Lille, CNRS UMR 9193, SCALab, CHU Lille, Pôle de Psychiatrie (unit CURE), 59000 Lille, France.
    Ciuciu, Philippe
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Frouin, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Université Paris-Saclay, 91191 Gif-sur-Yvette, France.
    Structured Sparse Principal Components Analysis With the TV-Elastic Net Penalty2018In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 2, p. 396-407Article in journal (Refereed)
    Abstract [en]

    Principal component analysis (PCA) is an exploratory tool widely used in data analysis to uncover the dominant patterns of variability within a population. Despite its ability to represent a data set in a low-dimensional space, PCA’s interpretability remains limited. Indeed, the components produced by PCA are often noisy or exhibit no visually meaningful patterns. Furthermore, the fact that the components are usually non-sparse may also impede interpretation, unless arbitrary thresholding is applied. However, in neuroimaging, it is essential to uncover clinically interpretable phenotypic markers that would account for the main variability in the brain images of a population. Recently, some alternatives to the standard PCA approach, such as sparse PCA (SPCA), have been proposed, their aim being to limit the density of the components. Nonetheless, sparsity alone does not entirely solve the interpretability problem in neuroimaging, since it may yield scattered andunstable components. We hypothesized that the incorporation of prior information regarding the structure of the data may lead to improved relevance and interpretability of brain patterns. We therefore present a simple extension of the popular PCA framework that adds structured sparsity penalties on the loading vectors in order to identify the few stable regions in the brain images that capture most of the variability. Such structured sparsity can be obtained by combining, e.g., l1 and total variation (TV) penalties, where the TV regularization encodes information on the underlying structure of the data. This paper presents the structured SPCA (denoted SPCA-TV) optimization framework and its resolution. We demonstrate SPCA-TV’s effectiveness and versatility on three different data sets. It can be applied to any kind of structured data, such as, e.g., N-dimensional array images or meshes of cortical surfaces. The gains of SPCA-TV over unstructured approaches (such as SPCA and ElasticNet PCA) or structured approach (such as GraphNet PCA) are significant, since SPCA-TV reveals the variability within a data set in the form of intelligible brain patterns that are easier to interpret and more stable across different samples.

  • 5.
    de Pierrefeu, Amicie
    et al.
    NeuroSpin, CEA, Gif-sur-Yvette, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Laidi, C.
    NeuroSpin, CEA, Gif-sur-Yvette, France; Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Hadj-Selem, Fouad
    Energy Transition Institute: VeDeCoM, Versailles, France.
    Bourgin, Julie
    Department of Psychiatry, Louis-Mourier Hospital, AP-HP, Colombes, France; INSERM U894, Centre for Psychiatry and Neurosciences, Paris, France.
    Hajek, Tomas
    Department of Psychiatry, Dalhousie University, Halifax, NS, Canada; National Institute of Mental Health, Klecany, Czech Republic.
    Spaniel, Filip
    National Institute of Mental Health, Klecany, Czech Republic.
    Kolenic, Marian
    National Institute of Mental Health, Klecany, Czech Republic.
    Ciuciu, Philippe
    NeuroSpin, CEA, Gif-sur-Yvette, France; INRIA, CEA, Parietal team, University of Paris-Saclay, France.
    Hamdani, Nora
    Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Leboyer, Marion
    Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Fovet, Thomas
    Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France.
    Jardri, Renaud
    INRIA, CEA, Parietal team, University of Paris-Saclay, France; Laboratoire de Sciences Cognitives et Sciences Affectives (SCALab-PsyCHIC), CNRS UMR 9193, University of Lille; Pôle de Psychiatrie, Unité CURE, CHU Lille, Lille, France.
    Houenou, Josselin
    NeuroSpin, CEA, Gif-sur-Yvette, France; Institut National de la Santé et de la Recherche Médicale (INSERM), U955, Institut Mondor de Recherche Biomédicale, Psychiatrie Translationnelle, Créteil, France; Fondation Fondamental, Créteil, France; Pôle de Psychiatrie, Assistance Publique–Hôpitaux de Paris (AP-HP), Faculté, de Médecine de Créteil, DHU PePsy, Hôpitaux Universitaires Mondor, Créteil, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Gif-sur-Yvette, France.
    Identifying a neuroanatomical signature of schizophrenia, reproducible across sites and stages, using machine-learning with structured sparsity2018In: Acta Psychiatrica Scandinavica, ISSN 0001-690X, E-ISSN 1600-0447, Vol. 138, p. 571-580Article in journal (Refereed)
    Abstract [en]

    ObjectiveStructural MRI (sMRI) increasingly offers insight into abnormalities inherent to schizophrenia. Previous machine learning applications suggest that individual classification is feasible and reliable and, however, is focused on the predictive performance of the clinical status in cross‐sectional designs, which has limited biological perspectives. Moreover, most studies depend on relatively small cohorts or single recruiting site. Finally, no study controlled for disease stage or medication's effect. These elements cast doubt on previous findings’ reproducibility.

    MethodWe propose a machine learning algorithm that provides an interpretable brain signature. Using large datasets collected from 4 sites (276 schizophrenia patients, 330 controls), we assessed cross‐site prediction reproducibility and associated predictive signature. For the first time, we evaluated the predictive signature regarding medication and illness duration using an independent dataset of first‐episode patients.

    ResultsMachine learning classifiers based on neuroanatomical features yield significant intersite prediction accuracies (72%) together with an excellent predictive signature stability. This signature provides a neural score significantly correlated with symptom severity and the extent of cognitive impairments. Moreover, this signature demonstrates its efficiency on first‐episode psychosis patients (73% accuracy).

    ConclusionThese results highlight the existence of a common neuroanatomical signature for schizophrenia, shared by a majority of patients even from an early stage of the disorder.

  • 6.
    Dubois, Mathieu
    et al.
    NeuroSpin, I2BM, CEA.
    Hadj-Selem, Fouad
    NeuroSpin, I2BM, CEA.
    Löfstedt, Tommy
    NeuroSpin, I2BM, CEA.
    Perrot, Matthieu
    Centre d'Acquisition et de Traitement des Images (CATI).
    Fischer, Clara
    Centre d'Acquisition et de Traitement des Images (CATI).
    Frouin, Vincent
    NeuroSpin, I2BM, CEA.
    Duchesnay, Edouard
    NeuroSpin, I2BM, CEA.
    Predictive support recovery with TV-Elastic Net penalty and logistic regression: An application to structural MRI2014Conference paper (Refereed)
    Abstract [en]

    The use of machine-learning in neuroimaging offers new perspectives in early diagnosis and prognosis of brain diseases. Although such multivariate methods can capture complex relationships in the data, traditional approaches provide irregular (l12 penalty) or scattered (l1 penalty) predictive pattern with a very limited relevance. A penalty like Total Variation (TV) that exploits the natural 3D structure of the images can increase the spatial coherence of the weight map. However, TV penalization leads to non-smooth optimization problems that are hard to minimize. We propose an optimization framework that minimizes any combination of l1, l2, and TV penalties while preserving the exact l1 penalty. This algorithm uses Nesterov's smoothing technique to approximate the TV penalty with a smooth function such that the loss and the penalties are minimized with an exact accelerated proximal gradient algorithm. We propose an original continuation algorithm that uses successively smaller values of the smoothing parameter to reach a prescribed precision while achieving the best possible convergence rate. This algorithm can be used with other losses or penalties. The algorithm is applied on a classification problem on the ADNI dataset. We observe that the TV penalty does not necessarily improve the prediction but provides a major breakthrough in terms of support recovery of the predictive brain regions.

  • 7.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Kuess, Peter
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Georg, Dietmar
    Department of Radiotherapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Helbich, Thomas H.
    Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Medical Radiation Research for Radiation Oncology, Vienna, Austria.
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis2018In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 63, no 19, p. 9-15, article id 195017Article in journal (Refereed)
    Abstract [en]

    The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).

    The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.

    Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.

    The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about 20×20).

    In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.

  • 8.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Tommy, Löfstedt
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Parameter estimation using weighted total least squares in the two-compartment exchange model2018In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, no 1, p. 561-567Article in journal (Refereed)
    Abstract [en]

    Purpose

    The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account.

    Method

    To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator.

    Results

    The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio.

    Conclusion

    The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

  • 9.
    Hadj-Selem, Fouad
    et al.
    Energy Transition Institute VeDeCoM, Versailles, France.
    Löfstedt, Tommy
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Dohmatob, Elvis
    PARIETAL Team, INRIA/CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Frouin, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Dubois, Mathieu
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Guillemot, Vincent
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Duchesnay, Edouard
    NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France.
    Continuation of Nesterov's Smoothing for Regression With Structured Sparsity in High-Dimensional Neuroimaging2018In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 11, p. 2403-2413Article in journal (Refereed)
    Abstract [en]

    Predictive models can be used on high-dimensional brain images to decode cognitive states or diagnosis/prognosis of a clinical condition/evolution. Spatial regularization through structured sparsity offers new perspectives in this context and reduces the risk of overfitting the model while providing interpretable neuroimaging signatures by forcing the solution to adhere to domain-specific constraints. Total variation (TV) is a promising candidate for structured penalization: it enforces spatial smoothness of the solution while segmenting predictive regions from the background. We consider the problem of minimizing the sum of a smooth convex loss, a non-smooth convex penalty (whose proximal operator is known) and a wide range of possible complex, non-smooth convex structured penalties such as TV or overlapping group Lasso. Existing solvers are either limited in the functions they can minimize or in their practical capacity to scale to high-dimensional imaging data. Nesterov’s smoothing technique can be used to minimize a large number of non-smooth convex structured penalties. However, reasonable precision requires a small smoothing parameter, which slows down the convergence speed to unacceptable levels. To benefit from the versatility of Nesterov’s smoothing technique, we propose a first order continuation algorithm, CONESTA, which automatically generates a sequence of decreasing smoothing parameters. The generated sequence maintains the optimal convergence speed toward any globally desired precision. Our main contributions are: gap to probe the current distance to the global optimum in order to adapt the smoothing parameter and the To propose an expression of the duality convergence speed. This expression is applicable to many penalties and can be used with other solvers than CONESTA. We also propose an expression for the particular smoothing parameter that minimizes the number of iterations required to reach a given precision. Furthermore, we provide a convergence proof and its rate, which is an improvement over classical proximal gradient smoothing methods. We demonstrate on both simulated and high-dimensional structural neuroimaging data that CONESTA significantly outperforms many state-of-the-art solvers in regard to convergence speed and precision.

  • 10.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OnPLS: Orthogonal projections to latent structures in multiblock and path model data analysis2012Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The amounts of data collected from each sample of e.g. chemical or biological materials have increased by orders of magnitude since the beginning of the 20th century. Furthermore, the number of ways to collect data from observations is also increasing. Such configurations with several massive data sets increase the demands on the methods used to analyse them. Methods that handle such data are called multiblock methods and they are the topic of this thesis.

    Data collected from advanced analytical instruments often contain variation from diverse mutually independent sources, which may confound observed patterns and hinder interpretation of latent variable models. For this reason, new methods have been developed that decompose the data matrices, placing variation from different sources of variation into separate parts. Such procedures are no longer merely pre-processing filters, as they initially were, but have become integral elements of model building and interpretation. One strain of such methods, called OPLS, has been particularly successful since it is easy to use, understand and interpret.

    This thesis describes the development of a new multiblock data analysis method called OnPLS, which extends the OPLS framework to the analysis of multiblock and path models with very general relationships between blocks in both rows and columns. OnPLS utilises OPLS to decompose sets of matrices, dividing each matrix into a globally joint part (a part shared with all the matrices it is connected to), several locally joint parts (parts shared with some, but not all, of the connected matrices) and a unique part that no other matrix shares.

    The OnPLS method was applied to several synthetic data sets and data sets of “real” measurements. For the synthetic data sets, where the results could be compared to known, true parameters, the method generated global multiblock (and path) models that were more similar to the true underlying structures compared to models without such decompositions. I.e. the globally joint, locally joint and unique models more closely resembled the corresponding true data. When applied to the real data sets, the OnPLS models revealed chemically or biologically relevant information in all kinds of variation, effectively increasing the interpretability since different kinds of variation are distinguished and separately analysed.

    OnPLS thus improves the quality of the models and facilitates better understanding of the data since it separates and separately analyses different kinds of variation. Each kind of variation is purer and less tainted by other kinds. OnPLS is therefore highly recommended to anyone engaged in multiblock or path model data analysis.

  • 11.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Ahnlund, Olof
    Peolsson, Michael
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Dynamic ultrasound imaging: a multivariate approach for the analysis and comparison of time-dependent musculoskeletal movements2012In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 12, no 1, p. 29-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Muscle functions are generally assumed to affect a wide variety of conditions and activities, including pain, ischemic and neurological disorders, exercise and injury. It is therefore very desirable to obtain more information on musculoskeletal contributions to and activity during clinical processes such as the treatment of muscle injuries, post-surgery evaluations, and the monitoring of progressive degeneration in neuromuscular disorders.The spatial image resolution achievable with ultrasound systems has improved tremendously in the last few years and it is nowadays possible to study skeletal muscles in real-time during activity. However, ultrasound imaging has an inherent problem that makes it difficult to compare different measurement series or image sequences from two or more subjects. Due to physiological differences between different subjects, the ultrasound sequences will be visually different -- partly because of variation in probe placement and partly because of the difficulty of perfectly reproducing any given movement. METHODS: Ultrasound images of the biceps and calf of a single subject were transformed to achieve congruence and then efficiently compressed and stacked to facilitate analysis using a multivariate method known as O2PLS. O2PLS identifies related and unrelated variation in and between two sets of data such that different phases of the studied movements can be analysed. The methodology was used to study the dynamics of the Achilles tendon and the calf and also the Biceps brachii and upper arm. The movements of these parts of the body are both of interest in clinical orthopaedic research. RESULTS: This study extends the novel method of multivariate analysis of congruent images (MACI) to facilitate comparisons between two series of ultrasound images. This increases its potential range of medical applications and its utility for detecting, visualising and quantifying the dynamics and functions of skeletal muscle. CONCLUSIONS: The most important results of this study are that MACI with O2PLS is able to consistently extract meaningful variability from pairs of ultrasound sequences. The MACI method with O2PLS is a powerful tool with great potential for visualising and comparing dynamics between movements. It has many potential clinical applications in the study of muscle injuries, post-surgery evaluations and evaluations of rehabilitation, and the assessment of athletic training interventions.

  • 12.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Eriksson, Lennart
    Gunilla Wormbs, Gunilla
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Bi-modal OnPLS2012In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 26, no 6, p. 236-245Article in journal (Refereed)
    Abstract [en]

    This paper presents an extension to the recently published OnPLS data analysis method. Bi-modal OnPLS allows for arbitrary block relationships in both columns and rows and is able to extract orthogonal variation in both columns and rows without bias towards any particular direction or matrix: the method is fully symmetric with regard to both rows and columns.

    Bi-modal OnPLS extracts a minimal number of globally predictive score vectors that exhibit maximal covariance and correlation in the column space and a corresponding set of predictive loading vectors that exhibit maximal correlation in the row space. The method also extracts orthogonal variation (i.e. variation that is not related to all other matrices) in both columns and rows. The method was applied to two synthetic datasets and one real data set regarding sensory information and consumer likings of dairy products. It was shown that Bi-modal OnPLS greatly improves the intercorrelations between both loadings and scores while still finding the correct variation. This facilitates interpretation of the predictive components and makes it possible to study the orthogonal variation in the data.

  • 13.
    Löfstedt, Tommy
    et al.
    Brainomics Team, Neurospin, CEA Saclay, France.
    Guillemot, Vincent
    Brainomics Team, Neurospin, CEA Saclay, France.
    Frouin, Vincent
    Brainomics Team, Neurospin, CEA Saclay, France.
    Duchesnay, Edouard
    Brainomics Team, Neurospin, CEA Saclay, France.
    Hadj-Selem, Fouad
    Brainomics Team, Neurospin, CEA Saclay, France.
    Simulated Data for Linear Regression with Structured and Sparse Penalties: Introducing pylearn-simulate2018In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 87, no 3Article in journal (Refereed)
    Abstract [en]

    A currently very active field of research is how to incorporate structure and prior knowledge in machine learning methods. It has lead to numerous developments in the field of non-smooth convex minimization. With recently developed methods it is possible to perform an analysis in which the computed model can be linked to a given structure of the data and simultaneously do variable selection to find a few important features in the data. However, there is still no way to unambiguously simulate data to test proposed algorithms, since the exact solutions to such problems are unknown.

    The main aim of this paper is to present a theoretical framework for generating simulated data. These simulated data are appropriate when comparing optimization algorithms in the context of linear regression problems with sparse and structured penalties. Additionally, this approach allows the user to control the signal-to-noise ratio, the correlation structure of the data and the optimization problem to which they are the solution.

    The traditional approach is to simulate random data without taking into account the actual model that will be fit to the data. But when using such an approach it is not possible to know the exact solution of the underlying optimization problem. With our contribution, it is possible to know the exact theoretical solution of a penalized linear regression problem, and it is thus possible to compare algorithms without the need to use, e.g., cross-validation.

    We also present our implementation, the Python package pylearn-simulate, available at https://github.com/neurospin/pylearn-simulate and released under the BSD 3clause license. We describe the package and give examples at the end of the paper.

  • 14.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hadj-Selem, Fouad
    Guillemot, Vincent
    Philippe, Cathy
    Duchesnay, Edouard
    Frouin, Vincent
    Tenenhaus, Arthur
    Structured Variable Selection for Regularized Generalized Canonical Correlation Analysis2016In: MULTIPLE FACETS OF PARTIAL LEAST SQUARES AND RELATED METHODS / [ed] Abdi, H Vinzi, VE Russolillo, G Saporta, G Trinchera, L, SPRINGER INT PUBLISHING AG , 2016, Vol. 173, p. 129-139Conference paper (Refereed)
    Abstract [en]

    Regularized Generalized Canonical Correlation Analysis (RGCCA) extends regularized canonical correlation analysis to more than two sets of variables. Sparse GCCA(SGCCA) was recently proposed to address the issue of variable selection. However, the variable selection scheme offered by SGCCA is limited to the covariance (tau = 1) link between blocks. In this paper we go beyond the covariance link by proposing an extension of SGCCA for the full RGCCA model. (tau epsilon [0; 1]). In addition, we also propose an extension of SGCCA that exploits pre-given structural relationships between variables within blocks. Specifically, we propose an algorithm that allows structured and sparsity-inducing penalties to be included in the RGCCA optimization problem.

  • 15.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hanafi, Mohamed
    Unité de Recherches "Sensometrics and Chemometrics", ONIRIS, Site de la Géraudière, BP 82 225 Nantes 44322 Cedex 03, France.
    Mazerolles, Gérard
    INRA-UMR 1083 SPO, INRA, 2 Place Viala, 34060 Montpellier, France.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OnPLS path modelling2012In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 118, p. 139-149Article in journal (Refereed)
    Abstract [en]

    OnPLS was recently presented as a general extension of O2PLS to the multiblock case. OnPLS is equivalent to O2PLS in the case of two matrices, but generalises symmetrically to cases with more than two matrices, i.e. without giving preference to any one of the matrices.

    This article presents a straight-forward extension to this method and thereby also introduces the OPLS framework to the field of PLS path modelling. Path modelling links a number of data blocks to each other, thereby establishing a set of paths along which information is considered to flow between blocks, representing for instance a known time sequence, an assumed causality order, or some other chosen organising principle. Compared to existing methods for path analysis, OnPLS path modelling extracts a minimum number of predictive components that are maximally covarying with maximised correlation. This is a significant contribution to path modelling, because other methods may yield score vectors with variation that obstructs the interpretation. The method achieves this by extracting a set of "orthogonal" components that capture local phenomena orthogonal to the variation shared with all the connected blocks.

    Two applications will be used to illustrate the method. The first is based on a simulated dataset that show how the interpretation is improved by removing orthogonal variation and the second on a real data process for monitoring of protein structure changes during cheese ripening by analysing infrared data.

  • 16.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hanafi, Mohamed
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Multiblock and Path Modeling with OnPLS2013In: New Perspectives in Partial Least Squares and Related Methods (Part IV) / [ed] Herve Abdi, Wynne W. Chin, Vincenzo Esposito Vinzi, Giorgio Russolillo, Laura Trinchera, Springer Science+Business Media B.V., 2013, 56, , p. 209-220p. 209-220Conference paper (Refereed)
    Abstract [en]

    OnPLS was recently proposed as a general extension of O2PLS for applications in multiblock and path model analysis. OnPLS is very similar to O2PLS in the case with two matrices, but generalizes symmetrically to cases with more than two matrices without giving preference to any matrix.

    OnPLS extracts a minimal number of globally joint components that exhibit maximal covariance and correlation. A number of locally joint components are also extracted. These are shared between some matrices, but not between all. These components are also maximally covarying with maximal correlation. The variation that remains after the joint and locally joint variation has been extracted is unique to a particular matrix. This unique variation is orthogonal to all other matrices and captures phenomena specific in its matrix.

    The method's utility has been demonstrated by its application to synthetic datasets with very good results in terms of its ability to decompose the matrices. It has been shown that OnPLS affords a reduced number of globally joint components and increased intercorrelations of scores, and that it greatly facilitates interpretation of the models. Preliminary results in the application on real data has also given positive results. The results are similar to previous results using other multiblock and path model methods, but afford an increased interpretability because of the locally joint and unique components.

  • 17.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Hoffman, Daniel
    Umeå University, Faculty of Science and Technology, Department of Molecular Biology (Faculty of Science and Technology).
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Global, local and unique decompositions in OnPLS for multiblock data analysis2013In: Analytica Chimica Acta, ISSN 0003-2670, E-ISSN 1873-4324, Vol. 791, p. 13-24Article in journal (Other academic)
    Abstract [en]

    Background OnPLS is an extension of O2PLS that decomposes a set of matrices, in either multiblock or path model analysis, such that each matrix consists of two parts: a globally joint part containing variation shared with all other connected matrices, and another containing unique or locally joint variation, i.e. variation that is specific to a particular matrix or shared with some, but not all, other connected matrices.

    Results A further extension of OnPLS suggested here decomposes the non-globally joint parts into locally joint and unique parts, using the OnPLS method to first find and extract a globally joint model, and then applying OnPLS recursively to subsets of matrices containing the non-globally joint variation remaining after the globally joint variation has been extracted. This results in a set of locally joint models. The variation that is left after the globally joint and locally joint variation has been extracted is not related (by definition) to the other matrices and thus represents the strictly unique variation specific to each matrix. The method's utility is demonstrated by its application to both a simulated data set and a real data set acquired from metabolomic, proteomic and transcriptomic profiling of three genotypes of hybrid aspen.

    Conclusions The results show that OnPLS can successfully decompose each matrix into global, local and unique models, resulting in lower numbers of globally joint components and higher intercorrelations of scores. OnPLS also increases the interpretability of models of connected matrices, because of the locally joint and unique models it generates.

  • 18.
    Löfstedt, Tommy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    OnPLS—a novel multiblock method for the modelling of predictive and orthogonal variation2011In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 25, no 8, p. 441-455Article in journal (Refereed)
    Abstract [en]

    This paper presents a new multiblock analysis method called OnPLS, a general extension of O2PLS to the multiblock case. The proposed method is equivalent to O2PLS in cases involving only two matrices, but generalises to cases involving more than two matrices without giving preference to any particular matrix: the method is fully symmetric. OnPLS extracts a minimal number of globally predictive components that exhibit maximal covariance and correlation. Furthermore, the method can be used to study orthogonal variation, i.e. local phenomena captured in the data that are specific to individual combinations of matrices or to individual matrices. The method's utility was demonstrated by its application to three synthetic data sets. It was shown that OnPLS affords a reduced number of globally predictive components and increased intercorrelations of scores, and that it greatly facilitates interpretation of the predictive model.

  • 19. Peolsson, A.
    et al.
    Peolsson, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Jull, G.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    O'Leary, S.
    Preliminary evaluation of dorsal muscle activity during resisted cervical extension in patients with longstanding pain and disability following anterior cervical decompression and fusion surgery2015In: Physiotherapy, ISSN 0031-9406, E-ISSN 1873-1465, Vol. 101, no 1, p. 69-74Article in journal (Refereed)
    Abstract [en]

    Objectives To compare mechanical activity (deformation and deformation rate) of the dorsal neck muscles between individuals with longstanding symptoms after anterior cervical decompression and fusion (ACDF) surgery and healthy controls.

    Design Preliminary cross-sectional study.

    Setting Neurosurgery clinic.

    Participants Ten individuals {mean age 60 [standard deviation (SD) 7.111 who had undergone ACDF surgery 10 to 13 years previously and 10 healthy age- and sex-matched controls.

    Main outcomes Mechanical activity of the different layers of dorsal neck muscles, measured at the C4 segment using ultrasonography (speckle tracking analysis) during a standardised, resisted cervical extension task.

    Results A significant group x muscle interaction was found for muscle deformation (P<0.03) but not for deformation rate (P>0.79). The ACDF group showed significantly less deformation of the semispinalis capitis muscle during the extension task compared with the control group [mean 3.12 (SD 2.06) and 6.64 (SD 4.17), respectively; mean difference 3.34 (95% confidence interval 0.54 to 7.21)].

    Conclusions As the semispinalis capitis muscle is a powerful neck extensor, the finding of altered activation following ACDF surgery lends support to the inclusion of exercise to train neck muscle performance in the management of these patients.

  • 20. Peolsson, Anneli
    et al.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Peolsson, Michael
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Ultrasound imaging with speckle tracking of cervical muscle deformation and deformation rate: isometric contraction of patients after anterior cervical decompression and fusion for cervical disc disease and controls2012In: Manual Therapy, ISSN 1356-689X, E-ISSN 1532-2769, Vol. 17, no 6, p. 519-525Article in journal (Refereed)
    Abstract [en]

    There is currently a lack of information regarding neck muscle activity during specific exercises. The purpose of the present study was to investigate deformation and deformation rate in different layers of dorsal and ventral neck muscles during isometric neck muscle contraction in individuals after anterior cervical decompression and fusion and in healthy controls. This study included 10 individuals (mean age 60 years; SD 7.1) with a verified, long-standing neck disorder and 10 healthy, age- and sex-matched controls. Ultrasonography and post-process speckle tracking analysis was used to investigate the degree and the rate of neck muscles motions at the C4 segmental level during sub-maximal, isometric resistance of the head in a seated position. None of the analyses performed showed significant differences between groups (p > 0.05). In the dorsal muscles, both groups exhibited a higher deformation rate in the multifidus than in the trapezius, splenius, and semispinalis capitis (p ≤ 0.01). In the neck disorder group, the multifidus also showed a higher deformation rate compared to the semispinalis cervicis (p = 0.02). In the ventral muscles of patients with neck disorders, the longus colli had a higher deformation rate than the sternocleidomastoid (p = 0.02). Among the healthy controls, the multifidus showed a higher degree of deformation (p = 0.02) than the trapezius. In conclusion, our results showed no significant differences between the two groups in mechanical neck muscle activation. Larger studies with different exercises, preferably with a standardized measure of resistance, are needed to investigate whether patients and controls show differences in deformation and deformation rates in neck muscles.

  • 21. Peolsson, Michael
    et al.
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Vogt, Susanna
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Stenlund, Hans
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Arndt, Anton
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry.
    Modelling human musculoskeletal functional movements using ultrasound imaging2010In: BMC Medical Imaging, ISSN 1471-2342, E-ISSN 1471-2342, Vol. 10, no 9, p. 11 sidor-Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: A widespread and fundamental assumption in the health sciences is that muscle functions are related to a wide variety of conditions, for example pain, ischemic and neurological disorder, exercise and injury. It is therefore highly desirable to study musculoskeletal contributions in clinical applications such as the treatment of muscle injuries, post-surgery evaluations, monitoring of progressive degeneration in neuromuscular disorders, and so on.The spatial image resolution in ultrasound systems has improved tremendously in the last few years and nowadays provides detailed information about tissue characteristics. It is now possible to study skeletal muscles in real-time during activity. METHODS: The ultrasound images are transformed to be congruent and are effectively compressed and stacked in order to be analysed with multivariate techniques. The method is applied to a relevant clinical orthopaedic research field, namely to describe the dynamics in the Achilles tendon and the calf during real-time movements. RESULTS: This study introduces a novel method to medical applications that can be used to examine ultrasound image sequences and to detect, visualise and quantify skeletal muscle dynamics and functions. CONCLUSIONS: This new objective method is a powerful tool to use when visualising tissue activity and dynamics of musculoskeletal ultrasound registrations.

  • 22. Srivastava, Vaibhav
    et al.
    Obudulu, Ogonna
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University and Swedish University of Agricultural Sciences.
    Bygdell, Joakim
    Löfstedt, Tommy
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Rydén, Patrik
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Nilsson, Robert
    Ahnlund, Maria
    Johansson, Annika
    Jonsson, Pär
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Freyhult, Eva
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Umeå University, Faculty of Medicine, Department of Clinical Microbiology, Clinical Bacteriology. Computational life science cluster (CLiC), Umeå University.
    Qvarnström, Johanna
    Karlsson, Jan
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Melzer, Michael
    Moritz, Thomas
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University.
    Hvidsten, Torgeir R
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC). Umeå University, Faculty of Science and Technology, Department of Chemistry. Computational life science cluster (CLiC), Umeå University and Department of Chemistry, Biotechnology; Food Science, Norwegian, University of Life Sciences, Ås Norwegian, Norway.
    Wingsle, Gunnar
    OnPLS integration of transcriptomic, proteomic and metabolomic data shows multi-level oxidative stress responses in the cambium of transgenic hipI- superoxide dismutase Populus plants2013In: BMC Genomics, ISSN 1471-2164, E-ISSN 1471-2164, Vol. 14, article id 893Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Reactive oxygen species (ROS) are involved in the regulation of diverse physiological processes in plants, including various biotic and abiotic stress responses. Thus, oxidative stress tolerance mechanisms in plants are complex, and diverse responses at multiple levels need to be characterized in order to understand them. Here we present system responses to oxidative stress in Populus by integrating data from analyses of the cambial region of wild-type controls and plants expressing high-isoelectric-point superoxide dismutase (hipI-SOD) transcripts in antisense orientation showing a higher production of superoxide. The cambium, a thin cell layer, generates cells that differentiate to form either phloem or xylem and is hypothesized to be a major reason for phenotypic perturbations in the transgenic plants. Data from multiple platforms including transcriptomics (microarray analysis), proteomics (UPLC/QTOF-MS), and metabolomics (GC-TOF/MS, UPLC/MS, and UHPLC-LTQ/MS) were integrated using the most recent development of orthogonal projections to latent structures called OnPLS. OnPLS is a symmetrical multi-block method that does not depend on the order of analysis when more than two blocks are analysed. Significantly affected genes, proteins and metabolites were then visualized in painted pathway diagrams.

    RESULTS: The main categories that appear to be significantly influenced in the transgenic plants were pathways related to redox regulation, carbon metabolism and protein degradation, e.g. the glycolysis and pentose phosphate pathways (PPP). The results provide system-level information on ROS metabolism and responses to oxidative stress, and indicate that some initial responses to oxidative stress may share common pathways.

    CONCLUSION: The proposed data evaluation strategy shows an efficient way of compiling complex, multi-platform datasets to obtain significant biological information.

1 - 22 of 22
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