Advanced feature selection to study the internationalization strategy of enterprises

Firms face an increasingly complex economic and financial environment in which the access to international networks and markets is crucial. To be successful, companies need to understand the role of internationalization determinants such as bilateral psychic distance, experience, etc. Cutting-edge feature selection methods are applied in the present paper and compared to previous results to gain deep knowledge about strategies for Foreign Direct Investment. More precisely, evolutionary feature selection, addressed from the wrapper approach, is applied with two different classifiers as the fitness function: Bagged Trees and Extreme Learning Machines. The proposed intelligent system is validated when applied to real-life data from Spanish Multinational Enterprises (MNEs). These data were extracted from databases belonging to the Spanish Ministry of Industry, Tourism, and Trade. As a result, interesting conclusions are derived about the key features driving to the internationalization of the companies under study. This is the first time that such outcomes are obtained by an intelligent system on internationalization data.The work was conducted during the research stays of Álvaro Herrero and Roberto Alcalde at KEDGE Business School in Bordeaux (France

Support vector classification analysis of resting state functional connectivity fMRI

Since its discovery in 1995 resting state functional connectivity derived from functional MRI data has become a popular neuroimaging method for study psychiatric disorders. Current methods for analyzing resting state functional connectivity in disease involve thousands of univariate tests, and the specification of regions of interests to employ in the analysis. There are several drawbacks to these methods. First the mass univariate tests employed are insensitive to the information present in distributed networks of functional connectivity. Second, the null hypothesis testing employed to select functional connectivity dierences between groups does not evaluate the predictive power of identified functional connectivities. Third, the specification of regions of interests is confounded by experimentor bias in terms of which regions should be modeled and experimental error in terms of the size and location of these regions of interests. The objective of this dissertation is to improve the methods for functional connectivity analysis using multivariate predictive modeling, feature selection, and whole brain parcellation. A method of applying Support vector classification (SVC) to resting state functional connectivity data was developed in the context of a neuroimaging study of depression. The interpretability of the obtained classifier was optimized using feature selection techniques that incorporate reliability information. The problem of selecting regions of interests for whole brain functional connectivity analysis was addressed by clustering whole brain functional connectivity data to parcellate the brain into contiguous functionally homogenous regions. This newly developed famework was applied to derive a classifier capable of correctly seperating the functional connectivity patterns of patients with depression from those of healthy controls 90% of the time. The features most relevant to the obtain classifier match those previously identified in previous studies, but also include several regions not previously implicated in the functional networks underlying depression.Ph.D.Committee Chair: Hu, Xiaoping; Committee Co-Chair: Vachtsevanos, George; Committee Member: Butera, Robert; Committee Member: Gurbaxani, Brian; Committee Member: Mayberg, Helen; Committee Member: Yezzi, Anthon

Concordant spatio-temporal patterns of brain activation in zebrafish exposed to compounds with similar pharmacodynamics or with similar seizurogenic potential.

Abstract Drug development is a highly resource intensive process that uses large numbers of animals for assessing the safety and efficacy of drugs prior to clinical testing. Improving the efficiency of drug development in terms of financial expenditure and number of animals used is therefore of utmost concern, not only to industry, but also to animal welfare organisations such as the NC3Rs. Poor efficiency in drug development largely stems from drug attrition, particularly attrition in the latter stages of the testing due to the large amount of resources expended at the point of failure. It is therefore imperative that deleterious off-target effects are identified as early as possible. However, typically, identification of seizure as a side-effect of drugs is performed in the later stages of development due to the highly intensive and low-throughput nature of seizure assays. At which point, if a compound fails, a large amount of resources have been squandered. There therefore exists a need for high-throughput and relatively inexpensive seizure liability assays that can be used early in drug development to prevent compounds destined for failure undergoing unnecessary resource intensive testing. In this thesis we propose a refined approach using non-invasive imaging techniques in non-protected life stage zebrafish as a method for the detection of seizurogenic compounds early in drug development. In addition, we highlight its utility for elucidating the pharmacodynamics of compounds. In this study, a transgenic zebrafish line containing a GCaMP6s calcium sensor under the control of the pan-neuronal promoter elavl3 was used for functional profiling of compounds with varied pharmacologies. Light sheet microscopy was used to record fluorescent activity in three spatial dimensions over time (4-dimensions) from the zebrafish brain after exposure to forty-three different compounds with varied pharmacodynamics and seizure liability profiles. Hierarchical clustering was employed in order to assess if compounds with seizurogenic activity or similar pharmacodynamics elicited specific functional brain activity. It was found that compounds with dopaminergic and serotonergic mechanisms of action elicited highly specific and similar brain activity patterns and that non-seizurogenic drugs also clustered separately from seizurogenic ones. Subsequent analyses, focussed on the utilisation of machine learning techniques, developing a model that could be used to discriminate between compounds with and without potentially seizurogenic effects. It is clear, from the analyses presented here, that drugs do in fact elicit specific brain patterns in zebrafish and that these brain patterns are effectively detected using light sheet microscopy. This system is highly applicable for use within the drug industry and even in its relatively preliminary stages provided an accurate method of discriminating between compounds based on their physiological effects in zebrafish

Augmenting Structure/Function Relationship Analysis with Deep Learning for the Classification of Psychoactive Drug Activity at Class A G Protein-Coupled Receptors

G protein-coupled receptors (GPCRs) initiate intracellular signaling pathways via interaction with external stimuli. [1-5] Despite sharing similar structure and cellular mechanism, GPCRs participate in a uniquely broad range of physiological functions. [6] Due to the size and functional diversity of the GPCR family, these receptors are a major focus for pharmacological applications. [1,7] Current state-of-the-art pharmacology and toxicology research strategies rely on computational methods to efficiently design highly selective, low toxicity compounds. [9], [10] GPCR-targeting therapeutics are associated with low selectivity resulting in increased risk of adverse effects and toxicity. Psychoactive drugs that are active at Class A GPCRs used in the treatment of schizophrenia and other psychiatric disorders display promiscuous binding behavior linked to chronic toxicity and high-risk adverse effects. [16-18] We hypothesized that using a combination of physiochemical feature engineering with a feedforward neural network, predictive models can be trained for these specific GPCR subgroups that are more efficient and accurate than current state-of-the-art methods.. We combined normal mode analysis with deep learning to create a novel framework for the prediction of Class A GPCR/psychoactive drug interaction activities. Our deep learning classifier results in high classification accuracy (5-HT F1-score = 0.78; DRD F1-score = 0.93) and achieves a 45% reduction in model training time when structure-based feature selection is applied via guidance from an anisotropic network model (ANM). Additionally, we demonstrate the interpretability and application potential of our framework via evaluation of highly clinically relevant Class A GPCR/psychoactive drug interactions guided by our ANM results and deep learning predictions. Our model offers an increased range of applicability as compared to other methods due to accessible data compatibility requirements and low model complexity. While this model can be applied to a multitude of clinical applications, we have presented strong evidence for the impact of machine learning in the development of novel psychiatric therapeutics with improved safety and tolerability

Functional Organization of the Human Brain: How We See, Feel, and Decide.

The human brain is responsible for constructing how we perceive, think, and act in the world around us. The organization of these functions is intricately distributed throughout the brain. Here, I discuss how functional magnetic resonance imaging (fMRI) was employed to understand three broad questions: how do we see, feel, and decide? First, high-resolution fMRI was used to measure the polar angle representation of saccadic eye movements in the superior colliculus. We found that eye movements along the superior-inferior visual field are mapped across the medial-lateral anatomy of a subcortical midbrain structure, the superior colliculus (SC). This result is consistent with the topography in monkey SC. Second, we measured the empathic responses of the brain as people watched a hand get painfully stabbed with a needle. We found that if the hand was labeled as belonging to the same religion as the observer, the empathic neural response was heightened, creating a strong ingroup bias that could not be readily manipulated. Third, we measured brain activity in individuals as they made free decisions (i.e., choosing randomly which of two buttons to press) and found the activity within fronto-thalamic networks to be significantly decreased compared to being instructed (forced) to press a particular button. I also summarize findings from several other projects ranging from addiction therapies to decoding visual imagination to how corporations are represented as people. Together, these approaches illustrate how functional neuroimaging can be used to understand the organization of the human brain

Classification of patients with parkinsonian syndromes using medical imaging and artificial intelligence algorithms

The distinction of Parkinsonian Syndromes (PS) is challenging due to similarities of symptoms and signs at early stages of disease. Thus, the need of accurate methods for differential diagnosis at those early stages has emerged. To improve the evaluation of medical images, artificial intelligence turns out to be a useful tool. Parkinson’s Disease, the commonest PS, is characterized by the degeneration of dopamine neurons in the substantia nigra which is detected by the dopamine transporter scan (DaTscanTM), a single photon-emission tomography (SPECT) exam that uses of a radiotracer that binds dopamine receptors. In fact, by using such exam it was possible to identify a sub-group of PD patients known as “Scans without evidence of dopaminergic deficit” (SWEDD) that present a normal exam, unlike PD patients. In this study, an approach based on Convolutional Neural Networks (CNNs) was proposed for classifying PD patients, SWEDD patients and healthy subjects using SPECT and Magnetic Resonance Imaging (MRI) images. Then, these images were divided into subsets of slices in the axial view that contains particular regions of interest since 2D images are the norm in clinical practice. The classifier evaluation was performed with Cohen’s Kappa and Receiver Operating Characteristic (ROC) curve. The results obtained allow to conclude that the CNN using imaging information of the Basal Ganglia and the mesencephalon was able to distinguish PD patients from healthy subjects since achieved 97.4% accuracy using MRI and 92.4% accuracy using SPECT, and PD from SWEDD with 97.3% accuracy using MRI and 93.3% accuracy using SPECT. Nonetheless, using the same approach, it was not possible to discriminate SWEDD patients from healthy subjects (60% accuracy) using DaTscanTM and MRI. These results allow to conclude that this approach may be a useful tool to aid in PD diagnosis in the future

Ventral striatal fMRI in affective and psychotic disorders: a transdiagnostic approach using resting state and task functional resonance imaging, clinical and genetic data

The effective clinical management of psychotic and affective disorders still represents a major challenge in psychiatry. Due to the high prevalence of these disorders and the subjective suffering, they cause a massive burden for the health system and society, and improvement in diagnostic and treatment strategies is urgently sought. In consideration of the literature, there are two promising avenues for this endeavour: On the one hand, particularly regarding schizophrenia (SCZ), early detection of high risk states or disease manifestation is crucial for the eventual treatment success. On the other hand, the heterogeneity of psychotic and affective disorders as well as blurry boundaries between the associated clinical syndromes often leave the diagnosis, which is the foundation of an evidence based treatment selection, on shaky ground. At the neurobiological level, several lines of evidence underline the role of the ventral striatum, particularly the nucleus accumbens (NAcc), for the pathophysiology of psychosis and more generally reward processing. Disturbed reward processing in turn is related to anhedonia, a core symptom of major depressive disorder (MDD), bipolar disorder (BD) and also SCZ. Against this background, this thesis aimed to unravel the potential of ventral striatal brain circuits as a source of biomarkers of psychotic and affective disorders. For this purpose, two sub-studies were performed: Firstly, we studied the impact of a validated polygenic risk score (PGRS) for SCZ, childhood adversity (CA) as widespread environmental factor and their interaction on resting state (RS) fMRI measures and NAcc seed connectivity in 253 healthy controls (HC) and compared these patterns with fully expressed disease in 23 patients with SCZ. Consistent with previous reports, SCZ patients showed strong regional functional connectivity density (FCD) increases in subcortical nuclei, particularly in the NAcc, compared with HC. Furthermore, in the HC sample, a a positive association between the FCD of the NAcc and both the PGRS and the interaction between PGRS and CA was found. Fine-mapping exhibited increased connectivity between the NAcc and visual association cortices for high levels of both PGRS and the PGRS-by-CA interaction. Taken together, this study showed that in HC, high PGRS for SCZ affects both global and regionally specific connectivity of the NAcc in a similar pattern as observed in SCZ patients, and that this effect was already amplified even by a history of very mild CA. This latter observation strengthened the notion that environmental factors need consideration in imaging genetics studies. Secondly, we examined the neural underpinnings of reward anticipation (RA) in MDD, BD and SCZ as studied by fMRI. This study revealed that aberrantly low striatal activation during RA is typical of SCZ, whereas the response of this network appeared to be preserved in MDD and BD. Interestingly, two further large-scale brain networks involved in RA – the salience network and the default mode network showed both transdiagnostic and further disease-specific alterations: While the salience network was found to be impaired primarily in SCZ patients, all patient groups revealed deficits in the suppression of the default mode network. Among hub regions of all three networks that were further differentiated in an early and a late response period, levels of anhedonia were correlated with the extent of the (early) hippocampal deactivation failure across diagnostic boundaries. In sum, both investigations confirm the possibility to use fMRI to probe the functional status of the ventral striatum. The first study underlines the centrality of striatal regions in the pahophysiology of psychosis as these alterations already emerged in healthy individuals at high genetic risk for developing SCZ, particularly when including unspecific environmental risk to the model. Hyperconnectivity of this region in SCZ during the resting state matched with a blunted response during the RA task. The latter studyshowed that at least two further large-scale brain networks are impaired in both psychotic and affective disorders during RA, indicating a potential of reward processing as a source of imaging phenotypes or biomarkers to characterize patients of the respective disease spectrum

Statistical Neuroimage Modeling, Processing and Synthesis based on Texture and Component Analysis: Tackling the Small Sample Size Problem

The rise of neuroimaging in the last years has provided physicians and radiologist with the ability to study the brain with unprecedented ease. This led to a new biological perspective in the study of neurodegenerative diseases, allowing the characterization of different anatomical and functional patterns associated with them. CAD systems use statistical techniques for preparing, processing and extracting information from neuroimaging data pursuing a major goal: optimize the process of analysis and diagnosis of neurodegenerative diseases and mental conditions. With this thesis we focus on three different stages of the CAD pipeline: preprocessing, feature extraction and validation. For preprocessing, we have developed a method that target a relatively recent concern: the confounding effect of false positives due to differences in the acquisition at multiple sites. Our method can effectively merge datasets while reducing the acquisition site effects. Regarding feature extraction, we have studied decomposition algorithms (independent component analysis, factor analysis), texture features and a complete framework called Spherical Brain Mapping, that reduces the 3-dimensional brain images to two-dimensional statistical maps. This allowed us to improve the performance of automatic systems for detecting Alzheimer's and Parkinson's diseases. Finally, we developed a brain simulation technique that can be used to validate new functional datasets as well as for educational purposes