zelig · Matched Sampling Approaches for Statistical Inference in Large-Scale Clinical Neuroimaging Studies

Matched Sampling Approaches for Statistical Inference in Large-Scale Clinical Neuroimaging Studies

Observational neuroimaging studies undertaken at-scale can offer deep insight into the complexities underlying neurological and psychiatric disease. While neuroimaging studies have shown promise over the last decade in the investigation, diagnosis and in treatment monitoring of these disorders, causal inference methodology in population neuroimaging is relatively underdeveloped compared to areas where large datasets are common as, for example in epidemiological studies and the social sciences. According to the World Health Organisation, neurological disease in its various forms afflicts tens of millions of people worldwide and in some of its domains is forecast to rise significantly. In prevalence studies of Alzheimers Disease (AD) for example, figures are predicted to rise from 46 million today to over 130 million by 2050. While there are limited treatment options available for many of these afflictions, it is likely that future treatment strategies will be most effective if applied at the earlier stages of disease. This work seeks to improve techniques and models for early disease detection using existing large scale high potential neuroimaging datasets including the UK Biobank. The main project objective was to increase the clinical utility of large-scale neuroimaging datasets through improved statistical modelling of the underlying disease factors related to Alzheimers Disease. We sought to both enhance early disease detection and advance understanding of complex neurological mechanisms that foreshadow disease onset. Specifically we developed statistical and algorithmic techniques from the fields of Causal Inference and Machine Learning for application to observational neuroimaging studies. We wished to build statistical models that go beyond capturing simple associational models to richer classes of technique capable of providing definitive causal links between interventions or risk factors, nascent imaging markers of disease and health outcomes. Conclusions:This project investigated the use of matching techniques from causal inference in observational neuroimaging datasets. These techniques have theoretical and empirical properties that render them attractive to clinical application : a treatment effect observed to be significant with these methods is more likely to have a valid interpretation compared to a naive estimate. On a large neuroimaging dataset we saw strong evidence of a causal relationship between education and certain brain structures, some of which have not previously been implicated with educational attainment. Since there also exists evidence of a relationship between risk of developing cognitive impairment and/or dementia and education, this finding holds promise for its extension to more complex models of dementia. We are continuing work in these directions.

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