A challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux...Show moreA challenge in clinical neuroscience is to identify systematic differences between patients’ resting state networks (RSNs) that may relate to phenotypes and/or disease subtypes. Recently, Durieux et al. (2022) proposed a novel unsupervised learning method for multi-subject rs-fMRI data, called Clusterwise Independent Component Analysis (C-ICA), which enables automatic clustering of subjects based on differences and similarities in subjects’ RSNs. A drawback of C-ICA, however, is that the method assumes that subject clusters show large difference in the RSNs that characterizes them. This, however, is a quite restrictive assumption as it can be expected that there exists important RSNs that are common to all subjects (across clusters). Not accounting for these common RSNs may negatively affect C-ICA’s ability to identify the subject clustering and the RSNs for each cluster. Therefore, in order to tackle this problem, two extensions of C-ICA (CCD1 and CCD2) are presented and compared to each other that allow to distinguish between (and extract from the data) both common and distinctive components. Both extensions differ in the procedure to identify the common components, with CCD2 selecting the common components out of a wider pool of possible components than CCD1. In a simulation study, the two C-ICA extensions are compared to C-ICA while manipulating the following factors: number of clusters, number of common and distinctive components, degree of overlap among clusters and amount of noise. Performance was determined by computing cluster recovery and component recovery, where component recovery was studied separately for the common components, the distinctive components and all components. Both extensions clearly outperformed C-ICA, with CCD2 performing the best. A mixed analysis of variance (ANOVA) showed that, the degree of overlap and the amount of noise in the data were the most influential factors, with large amounts of overlap and noise decreasing cluster and component recovery. Moreover, both extensions showed a slower deterioration of cluster and component recovery -with increasing overlap and noise- than C-ICA. Specifically for intermediate amounts of noise and overlap the differences between both extensions and C-ICA were the largest.Show less
Clustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere...Show moreClustering algorithms are important for data mining, and K-means is one of the most well-known clustering algorithms currently available. In cases in which data are high-dimensional, however, mere application of K-means to a data set may fail to uncover clusters due to presence of masking variables, the curse of dimensionality, and difficulties in interpretation of the obtained solution. A commonly used work-around is to apply dimension reduction to the data prior to performing cluster analysis, a practice called Tandem Analysis (TA). A vulnerability of TA is that the applied dimension reduction is not guaranteed to preserve cluster structure present in the original data, jeopardising the usefulness of subsequent cluster analysis. Multiple authors have provided algorithms that reduce dimensionality of a data set and perform cluster analysis on the reduced data, either in a sequential fashion or a simultaneous fashion, all aiming to find suitable low-dimensional representations of data while also keeping cluster structures intact. In this thesis, a novel approach to reducing dimensionality and performing cluster analysis on the low dimensional representation of the data - called SICA - is described and thoroughly tested in two systematically manipulated simulation studies and applied to three empirical data applications. Results show that SICA is a computationally efficient algorithm well able to extract components from the original data that preserve cluster structures, but that performance depends on characteristics of the data and the model of data generation. In addition, the correctness and validity of the clusterings obtained through SICA is high, although it does not always outperform currently available methods in this regard and is dependent on the same characteristics of the data and model generation as the other algorithms. Limitations and implications for future research are discussed.Show less
Functional Magnetic Resonance Imaging (fMRI) data capturing the BOLD response for various voxels through time for a single subjectcan reveal idiosyncratic dynamic functional connectivity (dFC)...Show moreFunctional Magnetic Resonance Imaging (fMRI) data capturing the BOLD response for various voxels through time for a single subjectcan reveal idiosyncratic dynamic functional connectivity (dFC) patternsunderlying a subject’s brain responses. These dFC patterns are known to be related to mental disorders, likeschizophrenia (Lynall et al.,2010)and Alzheimer’s disease (AD; Gili et al., 2011). Current directions in neuroscience hope to identify possible types and subtypes of mental disorders. In this thesis, we make the assumption that heterogeneity in dFC patterns across subjects may be indicative of such mental disorder (sub)types. To detect these(sub)typesbased on dFC patterns, we propose the clusterwise INDSCAL modelto analyze multi-subject rs-fMRI data, which is a generalization of the K-INDSCAL model of Bocci andVichi (2011). In this model, subjects with similar dFC patterns are clustered together, whereas subjects with clearly different patterns are allocated to different clusters. As such, clusterwise INDSCAL captures heterogeneity between subjects in dFC patternsand is able to identify unknown disease (sub)types. An Alternating Least Squares (ALS) algorithm to estimate the model parameters is presented in which the clustering and the model parameters for each cluster are updated alternatingly. This algorithm, alongwith a model selection heuristic to determine the optimal number of clusters and dFC patterns,is evaluated in an extensive simulation study in which several data characteristics (e.g., signal-to-noise ratio, similarity of clusters) are manipulated.The results show that the CLINDSCAL algorithm can successfully identify the true clustering of the patients and their underlying dFCpatterns. Further, when the spatial overlap in dFC patterns between the clusters increases, the performance of the algorithm in terms of recovering the clustering of the patientsdecreases.It can be concluded that CLINDSCAL is an interesting tool to discover a natural subject clustering with subject clusters differing in the dFC patterns underlying their data. Such a clustering may point at the existence of (yet unknown) mental disorder (sub)types.Show less
Synchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween...Show moreSynchronization between brain signals can be quantifiedby mathematical approaches. Recent studies have proposed a large variety ofsynchrony methods tocapture the synchrony in brain activitybetween interacting subjects. However, there isno detailed overview ofhow each synchrony method performs under different data characteristics.Here we investigate four synchrony methods, corr-entropy, S-estimator, Globalfield synchrony (GFS) and Omega complexityand this under varying data characteristics. These four synchrony methods are applied to time series simulated by two data generation mechanisms: Roessler system and linear multivariate autoregressive (MVAR)process.The simulated time series represent the brain activity ofsubjects and several data characteristics have been manipulated.Theperformance of each synchrony method is evaluated by root mean square error (RMSE) and the correlation coefficient between true and estimated synchronyvalues. Besides, the ANOVA analysis and effect sizes are introduced to test the influence of data characteristicson the performance of synchrony methods.The results show that the S-estimator is always the first or the second best performing method and corr-entropy outperforms other methodswhen it is applied to data generated by Roessler system. Thecoupling strength and thelength of time series can interact with synchrony methodsand significantly influence the performanceof each method.Time series with high synchrony results in good performance of the S-estimator and poor performance of Corr-entropy. It turns out that the longer data length can lead to better performance of each synchrony method.Show less
Understanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying...Show moreUnderstanding the functioning of the brain and how it relates to behavior is one of theprimary objectives of neuroscience. The focus of neuroscience has evolved from a singlebrain to studying interactions between multiple brains. In several fields, synchrony inbrain responses between individuals has been proven to positively influence psychologicalprocesses and lead to better outcomes.Time-series data for each subject’s behavior or modality are obtained by measuringsynchrony. Comparative studies for synchrony methods have been carried out in orderto gain some insight into the similarities and differences between many measures forevaluating the synchrony between subjects using such time-series data. The research onlyprovides a partial picture of the performance of the synchrony methods in terms of captur-ing synchrony and the conditions in which these methods are optimal. It is still unknownhow well the synchrony methods perform when changing other data characteristics.The goal of this study is to evaluate the performance of several methods for capturingdifferent types of synchrony between a pair of time series. Two mechanisms are usedto generate a pair of time-series data with a known amount of synchrony between thetime series (1) two unidirectionally connected Hénon maps, and (2) bivariate von Misesdistribution. Correlations between the two time series are computed as another definitionof true synchrony to provide a different perspective on true synchrony. In addition, asystematical evaluation of the performance of the synchrony methods on simulated datawith various data characteristics is carried out.For the generated data coherence and phase synchrony are the two best performingmethods. Regarding the varied data characteristics, especially the amount of true syn-chrony has a large effect on recovery performance. These main effects between the datacharacteristics are qualified by several two-way and three-way interactions that almostalways include the synchrony methods and the amount of true synchrony. Under all ofthe different data characteristics, no synchrony method is perfect, and all of the synchronymethods in this study are not always stable. As a result, using a combination of differentsynchrony methods to detect synchrony is recommended.Show less
In neuroscience, recent research has seen the rise of complex network analysis methods to studybrain data from methods like EEG and fMRI. Among the many applications of complex networkanalysis, it...Show moreIn neuroscience, recent research has seen the rise of complex network analysis methods to studybrain data from methods like EEG and fMRI. Among the many applications of complex networkanalysis, it has been used to the study of interpersonal synchrony in brain data (M ̈uller andLindenberger, 2014; M ̈uller et al., 2013; S ̈anger et al., 2012). However, the use of complexnetwork analysis in this context is very limited, despite its high potential. To bridge this gap, inthis thesis, we present a simulation study allowing for a systematic comparison of the ability ofvarious network analysis measures to capture synchrony and this in the context of different inputparameters, functions to transform the data to be suited for network analysis and different typesof networks (topology). The results made clear that a lot of graph theory measures, mostlyfrom weighted and unweighted networks, were able to pick up on changes in synchrony. Theresults also showed the importance of lengthier EEG recordings, as that improved a lot theperformance of several graph theory measures. Moreover, the use of Pearson’s correlation andcircular correlation for transforming the data for network analysis appears to be a better choicethan using some other transform functions. Although further research must be done in the fieldof complex network measures for synchrony, the results are promising for the use of graph theorymeasures to detect changes in interpersonal synchrony.Show less
This paper describes the results of a study to test the efficiency of a set of estimating methodsused in Statistical Matching (SM) for categorical variables, in a variety of different conditions.SM...Show moreThis paper describes the results of a study to test the efficiency of a set of estimating methodsused in Statistical Matching (SM) for categorical variables, in a variety of different conditions.SM is a technique that integrates datasets that include different units, that are coming from thesame population and share some common variables. The goal of the technique is to estimate theassociation between the variables that are not common. The tested estimators include some exist-ing methods (i.e. the Direct estimator, the CIA estimator, the Combined estimator and the EMestimator), along with Iterative Proportional Fitting (IPF), which is applied for the first time inthe context of SM in the context of this research. The methods are tested in populations withdifferent levels of dependence between the target variables and also for the effect of using a selectiveoverlap of units (sampled from another, relevant population) to make these estimations. For thisreason synthetic populations are created and are used both to directly test the estimators for theirpredicting accuracy and as populations from which selective overlap sets could be sampled from.Furthermore, the accuracy, bias and variance of the cells of the estimated contingency table wereassessed. The results suggest that Direct and EM estimators remain almost unaffected by thechanges in the populations’ characteristics or the selective overlaps respectively. On the contrary,methods based on CIA estimator appear to have advantage when the conditional independenceassumption is met in the population.Show less
Cut-off scores can be useful guides for researchers, clinicians or respondents in interpretating responses on psychometric instruments. Statistical methods involved in the development of cut-off...Show moreCut-off scores can be useful guides for researchers, clinicians or respondents in interpretating responses on psychometric instruments. Statistical methods involved in the development of cut-off scores are predominantly based on univariate analysis and in the presence of an observable criterion variable or normative sample data. To date, little is known about statistical procedures in defining multiple cutting scores for several latent variables within a multidimensional construct (i.e., subscales of a questionnaire). This study aims to evaluate the discriminatory power of various univariate and multivariate methods in cutting score development, and to explore a model-based clustering approach in setting cut-off scores for (sub)scales of multidimensional questionnaires. The investigated approach aims to cluster the responses on multidimensional constructs by using model-based clustering to classify respondents into ordered groups. Subsequently, cut-off points were identified as the intersection points of kernel density curves between the clusters or as (sub)scale split-rules in predicting cluster membership by the use of classification trees. A simulation study was conducted to compare the methods under medium and large sample sizes, various psychometric circumstances (the magnitude of factor correlations and item loadings), and across simulated homogenous and heterogeneous populations. Results of a simulation study show this model-based cluster approach is often preferred over percentile norms and linear standard scores in terms of discriminatory power, especially in defining two cut-off points. Recommendations for model selection and strategies to obtain cut-off points are given. Future research should focus to integrate the presented methodology into a case study using existing questionnaires to examine substantive implications of the calculated cutting scores. This study gives an overview the commonly known statistical techniques involved in cut-off score attainment, provides a multivariate framework to evaluate the discriminatory power in setting cutting scores, and lays out a promising novel application of model-based clustering in developing cut-off points for multidimensional questionnaires.Show less
The ability to correctly classify an individual’s clinical status could help pave the way for early treatment programs for disorders and illnesses of mental- and physical nature alike. Neuroimaging...Show moreThe ability to correctly classify an individual’s clinical status could help pave the way for early treatment programs for disorders and illnesses of mental- and physical nature alike. Neuroimaging data could serve as a basis in reaching this goal of accurate classification. The use of said data, however, does come with challenges. A prominent one of which is the fact that neuroimaging data is highly dimensional, meaning that the amount of features largely exceeds the number of subjects within the data set. Furthermore, research has indicated that the use of heterogeneous, but complimentary, data derived from multiple modalities can be an asset to a model used in a classification setting (Zhang et al., 2010; Schouten et al., 2016). The challenge arises in how to combine the different modalities within a single model. A solution could be the use of machine learning algorithms which search for patterns in the data to draw conclusions. Current literature is, however, lacking meaningful comparisons between different machine learning techniques. Within this project, three different algorithms (support vector machines, Gaussian process classification and multiple kernel learning) have been selected to get insight into (1) whether or not machine learning is able to cope with challenges in the use of neuroimaging data, (2) the difference in performance between these methods and (3) whether or not the use of multiple modalities leads to better results in classification. To this end, 16 alcohol-dependent respondents have been selected along with 32 age-matched healthy controls and have been subjected to both MRI and PET. Models have been trained on data from both separate modalities and on data combining the two modalities. The performances of the models have been assessed by leave-one-subject-out cross-validation and expressed in balanced accuracy and area under the curve. Results indicate that the chosen methods are effective in overcoming challenges arising in the use of neuroimaging data as a means of classification. High balanced accuracies have been found ranging from 76.56% (GPC using PET data) to 100% (GPC using MRI data). Different situations are cause for different solutions and the right choice of algorithm seems to be dependent on, for instance, the fact if either unimodal- or multimodal data is used. Also, settings/optimization of parameters within the model can make a large impact on accuracy. It is therefore advised that researchers try different algorithms and settings before selecting a technique. Different options need to be weighed in order to receive the best possible outcome.Show less
An often-encountered challenge in neuroscience is the reliable prediction of a subject’s disease status based on brain data to, for example, disentangle Alzheimer’s disease (AD) patients from...Show moreAn often-encountered challenge in neuroscience is the reliable prediction of a subject’s disease status based on brain data to, for example, disentangle Alzheimer’s disease (AD) patients from healthy control (HC) subjects. In this regard, functional Magnetic Resonance Imaging (fMRI) has been applied successfully to study the functional and structural differences between the brains of AD patients and HC’s. However, when using fMRI data for AD classification, the complex multivariate nature of these data faces researchers with two main statistical challenges that need to be appropriately addressed to improve the AD classification performance: high dimensionality (i.e., the data containing many variables/voxels) and multimodality (i.e., the data consisting of different brain modalities, like structural and functional brain information). In this study, both statistical challenges are tackled. To address high dimensionality, dimension reduction is proposed. Regarding the multimodality challenge, a ‘concatenated strategy’, which consists of a simultaneous dimension reduction of the information present in all the modalities involved, is compared to a ‘separate strategy’, which reduces the data for each modality separately. Combined with these strategies, three common feature extraction methods are compared in terms of classification performance: (1) Principal Component Analysis (PCA), (2) Partial Least Squares Regression (PLS-R) and (3) Generalized Regularized Canonical Correlation Analysis (RGCCA). Testing these methods and strategies on multimodal data consisting of three different neuroimaging properties that are related to AD, it is found that the best classification accuracies are obtained with PLS-R (compared to PCA and CCA) and the concatenated strategy (compared to the separate strategy). PLS-R combined with the concatenated strategy, however, is outperformed by a whole-brain analysis applied to all data modalities simultaneously and performs at the same level as a whole-brain analysis applied to one of the brain modalities used (i.e., the structural one).Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
National statistical institutes (NSIs) try to construct datasets that are rich in information as efficiently and cost effectively as possible. This can be achieved by combining available data, such...Show moreNational statistical institutes (NSIs) try to construct datasets that are rich in information as efficiently and cost effectively as possible. This can be achieved by combining available data, such as administrative data or survey data. When datasets do not pertain to the same units, one can sometimes resort to statistical matching to integrate them. Statistical matching is a data fusion technique which can be used when different data sets contain different units, but with a set of common (background) variables. The main goal of statistical matching is to estimate the relationship between the non-common variables in the different datasets. This paper investigates how best to utilize a small overlap of units in a statistical matching situation where data only consists of categorical variables. A small overlap of units contains joint information on all variables for only a limited number of units. A new statistical matching method, namely the combined estimator, is developed in this paper employing an idea from small area estimation. The performance of the combined estimator was compared to a couple of pre-existing statistical matching methods for categorical data under various data conditions. The result shows that, even though the combined estimator itself does not perform better than the pre-existing statistical matching method (the EM algorithm), the usage of the combined estimator as the starting point of the EM algorithm helps increasing its accuracy under certain data circumstances. The improvement of accuracy was noticed in cases where the number of matching variables was large.Show less
Master thesis | Statistical Science for the Life and Behavioural Sciences (MSc)
open access
Association of neurological and psychological conditions with changes in coactivation patterns of brain regions in ’resting state’ is of recent interest in neuroscience. To uncover such latent...Show moreAssociation of neurological and psychological conditions with changes in coactivation patterns of brain regions in ’resting state’ is of recent interest in neuroscience. To uncover such latent functional connectivity, series of functional Magnetic Resonance Imaging (fMRI) scans are typically reduced by averaging activations in brain atlas regions. The averaged activations are further reduced to pairwise correlation in sliding fixed width time windows. Unfortunately such reduction in dimensions also reduces the scan resolution and complicates interpretation. Changing to a text mining perspective, this thesis interprets the high dimensional scans as documents with categorical words drawn from a study bag. Consecutive scans measure the activation in V discrete voxels of brain volumes. Activation series in each voxel are segmented into stationary subsequences. Similar correlated segments within voxels and from distinct voxels are then bagged as words. The words capture correlated activation both within- and between-voxels. Instead of being predefined in an atlas, regions emerge as neighbourhoods of voxels drawing the same word at the original scan resolution. The word counts that document voxels draw from the bag of categorical words defines the document state. Document state transition probabilities measure the dynamics in coactivated brain locations at the original fMRI resolution, as a possible marker for a neurological condition. This alternative fMRI activation reduction method avoids a-priori selection of regions, tuning of fixed time window widths, and selection of the number of principal components of the contrasted existing method; the alternative method allows a more direct interpretation of activations. However, the direct state switching interpretation of scan document voxels drawing categorical word counts, does not sufficiently separate subject groups for reliable classification of neurological conditions.Show less
