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  4. Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyses of Grey-Matter

Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyses of Grey-Matter

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IEEE ISBI 2020 Virtual Conference April 2020
We evaluated the statistical power, family wise error rate (FWER) and precision of several competing methods that perform mass-univariate vertex-wise analyses of grey-matter (thickness and surface area). In particular, we compared several generalised linear models (GLMs, current state of the art) to linear mixed models (LMMs) that have proven superior in genomics. We used phenotypes simulated from real vertex-wise data and a large sample size (N=8,662) which may soon become the norm in neuroimaging. No method ensured a FWER
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Linear Mixed Models Minimise False Positive Rate and Enhance Precision of Mass Univariate Vertex-Wise Analyses of Grey-Matter

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We evaluated the statistical power, family wise error rate (FWER) and precision of several competing methods that perform mass-univariate vertex-wise analyses of grey-matter (thickness and surface area). In particular, we compared several generalised linear models (GLMs, current state of the art) to linear mixed models (LMMs) that have proven superior in genomics. We used phenotypes simulated from real vertex-wise data and a large sample size (N=8,662) which may soon become the norm in neuroimaging. No method ensured a FWER