Learning Vector Quantization with Applications in Neuroimaging and Biomedicine

An early clinical diagnosis of neurodegenerative diseases is complex and not always possible due to overlapping characteristics between the disorders. Biomarkers are necessary and functional brain imaging may provide a solution. Generally, machine learning can aid the diagnosis, but the decision process of some methods cannot always be understood. Furthermore, machine learning requires data. Therefore, functional brain scans from several neuroimaging centers are combined into a single dataset. We show that this leads to unwanted variation in the data that can inflate the performance of machine learning.Learning Vector Quantization is a type of machine learning that produces a prototypical representation (prototypes) of the classes in the data. Additionally, it weights the input space based on its relevance to the classification task. In one application example, we train a model on steroid measurements from patients with a benign or malignant adrenocortical tumor. In this case, the obtained models were directly interpretable and helped to decide between different measuring technologies.Due to the complex nature of the brain data, the models trained to diagnose neurodegenerative diseases are not directly understandable. Nonetheless, we show that prototypes and relevances can be reconstructed in the imaging space, increasing the interpretability of the models significantly. Additionally, we can produce easy-to-understand representations of the data that can visualize the diagnosis and progression over time of patients, leading to actionable scenarios. Lastly, we present a novel method to deal with center-related, unwanted variance in the data

sklvq:Scikit Learning Vector Quantization

The sklvq package is an open-source Python implementation of a set of learning vector quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ, GMLVQ, and LGMLVQ algorithms, sklvq is distinctive by putting emphasis on its modular and customizable design. Not only resulting in a feature-rich implementation for users but enabling easy extensions of the algorithms for researchers. The theory behind this design is described in this paper. To facilitate adoptions and inspire future contributions, sklvq is publicly available on Github (under the BSD license) and can be installed through the Python package index (PyPI). Next to being well-covered by automated testing to ensure code quality, it is accompanied by detailed online documentation. The documentation covers usage examples and provides an in-depth API including theory and scientific references

Cholesterol feeding strongly reduces hepatic VLDL-triglyceride production in mice lacking the liver X receptor alpha

The oxysterol-activated nuclear receptor liver X receptor alpha (LXR alpha) has been implicated in the control of both cholesterol and fatty acid metabolism. In this study, we have evaluated the effects of excess dietary cholesterol on hepatic cholesterol metabolism, lipogenesis, and VLDL production in homozygous (Lxr alpha(-/-)), heterozygous (Lxr alpha(+/-)), and wild-type mice. Mice were fed either chow or a cholesterol-enriched diet (1%, w/w) for 2 weeks. On the high-cholesterol diet, fractional cholesterol absorption was higher in Lxr alpha(-/-) mice than in controls, leading to delivery of more dietary cholesterol to the liver. Lxr alpha(-/-) mice were not able to induce expression of hepatic Abcg5/Abcg8, and massive accumulation of free cholesterol and cholesteryl esters (CEs) occurred. Interestingly, despite the inability to upregulate Abcg5/Abcg8, the highly increased hepatic free cholesterol content did stimulate biliary cholesterol output in Lxr alpha(-/-) mice. Hepatic cholesterol accumulation was accompanied by decreased hepatic expression of lipogenic genes, probably caused by impaired sterol-regulatory element binding protein 1c processing, lower hepatic triglyceride (TG) contents, strongly reduced plasma TG concentrations (290%), and reduced VLDL-TG production rates (-60%) in Lxr alpha(-/-) mice. VLDL particles were smaller and CE-enriched under these conditions. Lxr alpha deficiency did not affect VLDL formation under chow-fed conditions. Hepatic stearyl coenzyme A desaturase 1 expression was decreased dramatically in Lxr alpha(-/-) mice and did not respond to cholesterol feeding, but fatty acid profiles of liver and VLDL were only slightly different between Lxr alpha(-/-) and wild-type mice. Our data indicate that displacement of TGs by CEs during the VLDL assembly process underlies hypotriglyceridemia in cholesterol-fed Lxr alpha(-/-) mice. - van der Veen, J. N., R. Havinga, V. W. Bloks, A. K. Groen, and F. Kuipers. Cholesterol feeding strongly reduces hepatic VLDL-triglyceride production in mice lacking the liver X receptor a

Modelling Climate Change Impacts on Tropical Dry Forest Fauna

Tropical dry forests are among the most threatened ecosystems in the world, and those occurring in the insular Caribbean are particularly vulnerable. Climate change represents a significant threat for the Caribbean region and for small islands like Jamaica. Using the Hellshire Hills protected area in Jamaica, a simple model was developed to project future abundance of arthropods and lizards based on current sensitivities to climate variables derived from rainfall and temperature records. The abundances of 20 modelled taxa were predicted more often by rainfall variables than temperature, but both were found to have strong impacts on arthropod and lizard abundance. Most taxa were projected to decrease in abundance by the end of the century under drier and warmer conditions. Where an increase in abundance was projected under a low emissions scenario, this change was reduced or reversed under a high emissions climate change scenario. The validation process showed that, even for a small population, there was reasonable skill in predicting its annual variability. Results of this study show that this simple model can be used to identify the vulnerability of similar sites to the effects of shifting climate and, by extension, their conservation needs

FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Background and Objectives 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) combined with principal component analysis (PCA) has been applied to identify disease-related brain patterns in neurodegenerative disorders such as Parkinson’s disease (PD), Dementia with Lewy Bodies (DLB) and Alzheimer’s disease (AD). These patterns are used to quantify functional brain changes at the single subject level. This is especially relevant in determining disease progression in idiopathic REM sleep behavior disorder (iRBD), a prodromal stage of PD and DLB. However, the PCA method is limited in discriminating between neurodegenerative conditions. More advanced machine learning algorithms may provide a solution. In this study, we apply Generalized Matrix Learning Vector Quantization (GMLVQ) to FDG-PET scans of healthy controls, and patients with AD, PD and DLB. Scans of iRBD patients, scanned twice with an approximate 4 year interval, were projected into GMLVQ space to visualize their trajectory. Methods We applied a combination of SSM/PCA and GMLVQ as a classifier on FDG-PET data of healthy controls, AD, DLB, and PD patients. We determined the diagnostic performance by performing a ten times repeated ten fold cross validation. We analyzed the validity of the classification system by inspecting the GMLVQ space. First by the projection of the patients into this space. Second by representing the axis, that span this decision space, into a voxel map. Furthermore, we projected a cohort of RBD patients, whom have been scanned twice (approximately 4 years apart), into the same decision space and visualized their trajectories. Results The GMLVQ prototypes, relevance diagonal, and decision space voxel maps showed metabolic patterns that agree with previously identified disease-related brain patterns. The GMLVQ decision space showed a plausible quantification of FDG-PET data. Distance traveled by iRBD subjects through GMLVQ space per year (i.e. velocity) was correlated with the change in motor symptoms per year (Spearman’s rho =0.62, P=0.004). Conclusion In this proof-of-concept study, we show that GMLVQ provides a classification of patients with neurodegenerative disorders, and may be useful in future studies investigating speed of progression in prodromal disease stages