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S. Van Huffel

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8 papers

1 author row

YNICL Journal 2018 Journal Article

Non-invasive assessment of disease progression and neuroprotective effects of dietary coconut oil supplementation in the ALS SOD1G93A mouse model: A 1H-magnetic resonance spectroscopic study

A. Weerasekera
D.M. Sima
T. Dresselaers
S. Van Huffel
P. Van Damme
U. Himmelreich

Amyotrophic Lateral Sclerosis (ALS) is an incurable neurodegenerative disease primarily characterized by progressive degeneration of motor neurons in the motor cortex, brainstem and spinal cord. Due to relatively fast progression of ALS, early diagnosis is essential for possible therapeutic intervention and disease management. To identify potential diagnostic markers, we investigated age-dependent effects of disease onset and progression on regional neurochemistry in the SOD1G93A ALS mouse model using localized in vivo magnetic resonance spectroscopy (MRS). We focused mainly on the brainstem region since brainstem motor nuclei are the primarily affected regions in SOD1G93A mice and ALS patients. In addition, metabolite profiles of the motor cortex were also assessed. In the brainstem, a gradual decrease in creatine levels were detected starting from the pre-symptomatic age of 70 days postpartum. During the early symptomatic phase (day 90), a significant increase in the levels of the inhibitory neurotransmitter γ- aminobutyric acid (GABA) was measured. At later time points, alterations in the form of decreased NAA, glutamate, glutamine and increased myo-inositol were observed. Also, decreased glutamate, NAA and increased taurine levels were seen at late stages in the motor cortex. A proof-of-concept (PoC) study was conducted to assess the effects of coconut oil supplementation in SODG93A mice. The PoC revealed that the coconut oil supplementation together with the regular diet delayed disease symptoms, enhanced motor performance, and prolonged survival in the SOD1G93A mouse model. Furthermore, MRS data showed stable metabolic profile at day 120 in the coconut oil diet group compared to the group receiving a standard diet without coconut oil supplementation. In addition, a positive correlation between survival and the neuronal marker NAA was found. To the best of our knowledge, this is the first study that reports metabolic changes in the brainstem using in vivo MRS and effects of coconut oil supplementation as a prophylactic treatment in SOD1G93A mice.

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JBHI Journal 2017 Journal Article

Nonnegative Canonical Polyadic Decomposition for Tissue-Type Differentiation in Gliomas

H. N. Bharath
D. M. Sima
N. Sauwen
U. Himmelreich
L. De Lathauwer
S. Van Huffel

Magnetic resonance spectroscopic imaging (MRSI) reveals chemical information that characterizes different tissue types in brain tumors. Blind source separation techniques are used to extract the tissue-specific profiles and their corresponding distribution from the MRSI data. We focus on automatic detection of the tumor, necrotic and normal brain tissue types by constructing a 3D MRSI tensor from in vivo 2D-MRSI data of individual glioma patients. Nonnegative canonical polyadic decomposition (NCPD) is applied to the MRSI tensor to differentiate various tissue types. An in vivo study shows that NCPD has better performance in identifying tumor and necrotic tissue type in glioma patients compared to previous matrix-based decompositions, such as nonnegative matrix factorization and hierarchical nonnegative matrix factorization.

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YNICL Journal 2016 Journal Article

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

N. Sauwen
M. Acou
S. Van Cauter
D.M. Sima
J. Veraart
F. Maes
U. Himmelreich
E. Achten

Tumor segmentation is a particularly challenging task in high-grade gliomas (HGGs), as they are among the most heterogeneous tumors in oncology. An accurate delineation of the lesion and its main subcomponents contributes to optimal treatment planning, prognosis and follow-up. Conventional MRI (cMRI) is the imaging modality of choice for manual segmentation, and is also considered in the vast majority of automated segmentation studies. Advanced MRI modalities such as perfusion-weighted imaging (PWI), diffusion-weighted imaging (DWI) and magnetic resonance spectroscopic imaging (MRSI) have already shown their added value in tumor tissue characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation. In this paper, we compare the performance of several unsupervised classification methods for HGG segmentation based on MP-MRI data including cMRI, DWI, MRSI and PWI. Two independent MP-MRI datasets with a different acquisition protocol were available from different hospitals. We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dice-scores for the pathologic tissue classes on both datasets.

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JBHI Journal 2014 Journal Article

An Evaluation of Cardiorespiratory and Movement Features With Respect to Sleep-Stage Classification

T. Willemen
D. Van Deun
V. Verhaert
M. Vandekerckhove
V. Exadaktylos
J. Verbraecken
S. Van Huffel
B. Haex

Polysomnography (PSG) is considered the gold standard to assess sleep accurately, but it can be expensive, time-consuming, and uncomfortable, specifically in long-term sleep studies. Actigraphy, on the other hand, is both cheap and user-friendly, but depending on the application lacks detail and accuracy. Our aim was to evaluate cardiorespiratory and movement signals in discriminating between wake, rapid-eye-movement (REM), light (N1N2), and deep (N3) sleep. The dataset comprised 85 nights of PSG from a healthy population. Starting from a total of 750 characteristic variables (features), problem-specific subsets of 40 features were forwardly selected using the combination of a wrapper method (Cohen's kappa statistic on radial basis function (RBF)-kernel support vector machine (SVM) classifier) and filter method (minimum redundancy maximum relevance criterion on mutual information). Final classification was performed using an RBF-kernel SVM. Non-subject-specific wake versus sleep classification resulted in a Cohen's kappa value of 0. 695, while REM versus NREM resulted in 0. 558 and N3 versus N1N2 in 0. 553. The broad pool of initial features gave insight in which features discriminated best between the different classes. The classification results demonstrate the possibility of making long-term sleep monitoring more widely available.

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YNIMG Journal 2011 Journal Article

The BOLD correlates of the visual P1 and N1 in single-trial analysis of simultaneous EEG-fMRI recordings during a spatial detection task

N. Novitskiy
J.R. Ramautar
K. Vanderperren
M. De Vos
M. Mennes
B. Mijovic
B. Vanrumste
P. Stiers

Simultaneous EEG-fMRI measurements can combine the high spatial resolution of fMRI with the high temporal resolution of EEG. Therefore, we applied this approach to the study of peripheral vision. More specifically, we presented visual field quadrant fragments of checkerboards and a full central checkerboard in a simple detection task. A technique called “integration-by-prediction” was used to integrate EEG and fMRI data. In particular, we used vectors of single-trial ERP amplitude differences between left and right occipital electrodes as regressors in an ERP-informed fMRI analysis. The amplitude differences for the regressors were measured at the latencies of the visual P1 and N1 components. Our results indicated that the traditional event-related fMRI analysis revealed mostly activations in the vicinity of the primary visual cortex and in the ventral visual stream, while both P1 and N1 regressors revealed activation of areas in the temporo-parietal junction. We conclude that simultaneous EEG-fMRI in a spatial detection task can separate visual processing at 100–200ms from stimulus onset from the rest of the information processing in the brain.

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YNIMG Journal 2007 Journal Article

Canonical decomposition of ictal scalp EEG reliably detects the seizure onset zone

M. De Vos
A. Vergult
L. De Lathauwer
W. De Clercq
S. Van Huffel
P. Dupont
A. Palmini
W. van Paesschen

Long-term electroencephalographic (EEG) recordings are important in the presurgical evaluation of refractory partial epilepsy for the delineation of the irritative and ictal onset zones. In this paper we introduce a new algorithm for an automatic, fast and objective localizing of the ictal onset zone in ictal EEG recordings. We extracted the potential distribution of the ictal activity from EEG using the higher order canonical decomposition method, also referred to as the CP model. The CP model decomposes in a unique way a higher order tensor in a minimal sum of rank-1 ‘atoms’. We showed that only one atom is related to the seizure activity. Simulation experiments demonstrated that the method correctly extracted the potential distribution of the ictal activity even with low signal-to-noise ratios. In 37 ictal EEGs, the CP method correctly localized the seizure onset zone in 34 (92%) and visual assessment in 21 cases (57%) (p=0. 00024). The CP method is a fast method to delineate the ictal onset zone in ictal EEGs and is more sensitive than visual interpretation of the ictal EEGs.

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AIIM Journal 2004 Journal Article

Brain tumor classification based on long echo proton MRS signals

L. Lukas
A. Devos
J.A.K. Suykens
L. Vanhamme
F.A. Howe
C. Majós
A. Moreno-Torres
M. Van Der Graaf

There has been a growing research interest in brain tumor classification based on proton magnetic resonance spectroscopy ( 1 H MRS) signals. Four research centers within the EU funded INTERPRET project have acquired a significant number of long echo 1 H MRS signals for brain tumor classification. In this paper, we present an objective comparison of several classification techniques applied to the discrimination of four types of brain tumors: meningiomas, glioblastomas, astrocytomas grade II and metastases. Linear and non-linear classifiers are compared: linear discriminant analysis (LDA), support vector machines (SVM) and least squares SVM (LS-SVM) with a linear kernel as linear techniques and LS-SVM with a radial basis function (RBF) kernel as a non-linear technique. Kernel-based methods can perform well in processing high dimensional data. This motivates the inclusion of SVM and LS-SVM in this study. The analysis includes optimal input variable selection, (hyper-) parameter estimation, followed by performance evaluation. The classification performance is evaluated over 200 stratified random samplings of the dataset into training and test sets. Receiver operating characteristic (ROC) curve analysis measures the performance of binary classification, while for multiclass classification, we consider the accuracy as performance measure. Based on the complete magnitude spectra, automated binary classifiers are able to reach an area under the ROC curve (AUC) of more than 0. 9 except for the hard case glioblastomas versus metastases. Although, based on the available long echo 1 H MRS data, we did not find any statistically significant difference between the performances of LDA and the kernel-based methods, the latter have the strength that no dimensionality reduction is required to obtain such a high performance.

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AIIM Journal 2003 Journal Article

Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

C. Lu
T. Van Gestel
J.A.K. Suykens
S. Van Huffel
I. Vergote
D. Timmerman

In this work, we develop and evaluate several least squares support vector machine (LS-SVM) classifiers within the Bayesian evidence framework, in order to preoperatively predict malignancy of ovarian tumors. The analysis includes exploratory data analysis, optimal input variable selection, parameter estimation, and performance evaluation via receiver operating characteristic (ROC) curve analysis. LS-SVM models with linear and radial basis function (RBF) kernels, and logistic regression models have been built on 265 training data, and tested on 160 newly collected patient data. The LS-SVM model with nonlinear RBF kernel achieves the best performance, on the test set with the area under the ROC curve (AUC), sensitivity and specificity equal to 0. 92, 81. 5% and 84. 0%, respectively. The best averaged performance over 30 runs of randomized cross-validation is also obtained by an LS-SVM RBF model, with AUC, sensitivity and specificity equal to 0. 94, 90. 0% and 80. 6%, respectively. These results show that the LS-SVM models have the potential to obtain a reliable preoperative distinction between benign and malignant ovarian tumors, and to assist the clinicians for making a correct diagnosis.

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