3 research outputs found
Multivariate decoding of brain images using ordinal regression.
Neuroimaging data are increasingly being used to predict potential outcomes or groupings, such as clinical severity, drug dose response, and transitional illness states. In these examples, the variable (target) we want to predict is ordinal in nature. Conventional classification schemes assume that the targets are nominal and hence ignore their ranked nature, whereas parametric and/or non-parametric regression models enforce a metric notion of distance between classes. Here, we propose a novel, alternative multivariate approach that overcomes these limitations - whole brain probabilistic ordinal regression using a Gaussian process framework. We applied this technique to two data sets of pharmacological neuroimaging data from healthy volunteers. The first study was designed to investigate the effect of ketamine on brain activity and its subsequent modulation with two compounds - lamotrigine and risperidone. The second study investigates the effect of scopolamine on cerebral blood flow and its modulation using donepezil. We compared ordinal regression to multi-class classification schemes and metric regression. Considering the modulation of ketamine with lamotrigine, we found that ordinal regression significantly outperformed multi-class classification and metric regression in terms of accuracy and mean absolute error. However, for risperidone ordinal regression significantly outperformed metric regression but performed similarly to multi-class classification both in terms of accuracy and mean absolute error. For the scopolamine data set, ordinal regression was found to outperform both multi-class and metric regression techniques considering the regional cerebral blood flow in the anterior cingulate cortex. Ordinal regression was thus the only method that performed well in all cases. Our results indicate the potential of an ordinal regression approach for neuroimaging data while providing a fully probabilistic framework with elegant approaches for model selection
Methods to assess changes in human brain structure across the lifecourse
Human brain structure can be measured across the lifecourse (“in vivo”) with
magnetic resonance imaging (MRI). MRI data are often used to create “atlases” and
statistical models of brain structure across the lifecourse. These methods may define
how brain structure changes through life and support diagnoses of increasingly
common, yet still fatal, age-related neurodegenerative diseases. As diseases such as
Alzheimer’s (AD) cast an ever growing shadow over our ageing population, it is
vitally important to robustly define changes which are normal for age and those which
are pathological. This work therefore assessed existing MR brain image data, atlases,
and statistical models. These assessments led me to propose novel methods for
accurately defining the distributions and boundaries of normal ageing and
pathological brain structure.
A systematic review found that there were fewer than 100 appropriately tested
normal subjects aged ≥60 years openly available worldwide. These subjects did not
have the range of MRI sequences required to effectively characterise the features of
brain ageing. The majority of brain image atlases identified in this review were found
to contain data from few or no subjects aged ≥60 years and were in a limited range of
MRI sequences. All of these atlases were created with parametric (mean-based)
statistics that require the assumptions of equal variance and Gaussian distributions.
When these assumptions are not met, mean-based atlases and models may not well
represent the distributions and boundaries of brain structure.
I tested these assumptions and found that they were not met in whole brain,
subregional, and voxel-based models of ~580 subjects from across the lifecourse (0-
90 years). I then implemented novel whole brain, subregional, and voxel-based
statistics, e.g. percentile rank atlases and nonparametric effect size estimates. The
equivalent parametric statistics led to errors in classification and inflated effects by up
to 45% in normal ageing-AD comparisons. I conclude that more MR brain image
data, age appropriate atlases, and nonparametric statistical models are needed to
define the true limits of normal brain structure. Accurate definition of these limits will
ultimately improve diagnoses, treatment, and outcome of neurodegenerative disease