Identifying the neuroanatomical basis of cognitive impairment in Alzheimer's disease by correlation- and nonlinearity-aware sparse Bayesian learning

Predicting cognitive performance of subjects from their magnetic resonance imaging (MRI) measures and identifying relevant imaging biomarkers are important research topics in the study of Alzheimer's disease. Traditionally, this task is performed by formulating a linear regression problem. Recently, it is found that using a linear sparse regression model can achieve better prediction accuracy. However, most existing studies only focus on the exploitation of sparsity of regression coefficients, ignoring useful structure information in regression coefficients. Also, these linear sparse models may not capture more complicated and possibly nonlinear relationships between cognitive performance and MRI measures. Motivated by these observations, in this work we build a sparse multivariate regression model for this task and propose an empirical sparse Bayesian learning algorithm. Different from existing sparse algorithms, the proposed algorithm models the response as a nonlinear function of the predictors by extending the predictor matrix with block structures. Further, it exploits not only inter-vector correlation among regression coefficient vectors, but also intra-block correlation in each regression coefficient vector. Experiments on the Alzheimer's Disease Neuroimaging Initiative database showed that the proposed algorithm not only achieved better prediction performance than state-of-the-art competitive methods, but also effectively identified biologically meaningful patterns

Temporally Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer's Disease

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers

Investigation of Multi-dimensional Tensor Multi-task Learning for Modeling Alzheimer's Disease Progression

Machine learning (ML) techniques for predicting Alzheimer's disease (AD) progression can significantly assist clinicians and researchers in constructing effective AD prevention and treatment strategies. The main constraints on the performance of current ML approaches are prediction accuracy and stability problems in medical small dataset scenarios, monotonic data formats (loss of multi-dimensional knowledge of the data and loss of correlation knowledge between biomarkers) and biomarker interpretability limitations. This thesis investigates how multi-dimensional information and knowledge from biomarker data integrated with multi-task learning approaches to predict AD progression. Firstly, a novel similarity-based quantification approach is proposed with two components: multi-dimensional knowledge vector construction and amalgamated magnitude-direction quantification of brain structural variation, which considers both the magnitude and directional correlations of structural variation between brain biomarkers and encodes the quantified data as a third-order tensor to address the problem of monotonic data form. Secondly, multi-task learning regression algorithms with the ability to integrate multi-dimensional tensor data and mine MRI data for spatio-temporal structural variation information and knowledge were designed and constructed to improve the accuracy, stability and interpretability of AD progression prediction in medical small dataset scenarios. The algorithm consists of three components: supervised symmetric tensor decomposition for extracting biomarker latent factors, tensor multi-task learning regression and algorithmic regularisation terms. The proposed algorithm aims to extract a set of first-order latent factors from the raw data, each represented by its first biomarker, second biomarker and patient sample dimensions, to elucidate potential factors affecting the variability of the data in an interpretable manner and can be utilised as predictor variables for training the prediction model that regards the prediction of each patient as a task, with each task sharing a set of biomarker latent factors obtained from tensor decomposition. Knowledge sharing between tasks improves the generalisation ability of the model and addresses the problem of sparse medical data. The experimental results demonstrate that the proposed approach achieves superior accuracy and stability in predicting various cognitive scores of AD progression compared to single-task learning, benchmarks and state-of-the-art multi-task regression methods. The proposed approach identifies brain structural variations in patients and the important brain biomarker correlations revealed by the experiments can be utilised as potential indicators for AD early identification

Individual Differences In Value-Based Decision-Making: Learning And Time Preference

Human decisions are strongly influenced by past experience or by the subjective values attributed to available choice options. Although decision processes show some common trends across individuals, they also vary considerably between individuals. The research presented in this dissertation focuses on two domains of decision-making, related to learning and time preference, and examines factors that explain decision-making differences between individuals. First, we focus on a form of reinforcement learning in a dynamic environment. Across three experiments, we investigated whether individual differences in learning were associated with differences in cognitive abilities, personality, and age. Participants made sequential predictions about an on-screen location in a video game. Consistent with previous work, participants showed high variability in their ability to implement normative strategies related to surprise and uncertainty. We found that higher cognitive ability, but not personality, was associated with stronger reliance on the normative factors that should govern learning. Furthermore, learning in older adults (age 60+) was less influenced by uncertainty, but also less influenced by reward, a non-normative factor that has substantial effects on learning across the lifespan. Second, we focus on delay discounting, the tendency to prefer smaller rewards delivered soon over larger rewards delivered after a delay. Delay discounting has been used as a behavioral measure of impulsivity and is associated with many undesirable real-life outcomes. Specifically, we examined how neuroanatomy is associated with individual differences in delay discounting in a large adolescent sample. Using a novel multivariate method, we identified networks where cortical thickness varied consistently across individuals and brain regions. Cortical thickness in several of these networks, including regions such as ventromedial prefrontal cortex, orbitofrontal cortex, and temporal pole, was negatively associated with delay discounting. Furthermore, this brain data predicted differences beyond those typically accounted for by other cognitive variables related to delay discounting. These results suggest that cortical thickness may be a useful brain phenotype of delay discounting and carry unique information about impulsivity. Collectively, this research furthers our understanding of how cognitive abilities, brain structure and healthy aging relate to individual differences in value-based decision-making

Machine Learning As Tool And Theory For Computational Neuroscience

Computational neuroscience is in the midst of constructing a new framework for understanding the brain based on the ideas and methods of machine learning. This is effort has been encouraged, in part, by recent advances in neural network models. It is also driven by a recognition of the complexity of neural computation and the challenges that this poses for neuroscience’s methods. In this dissertation, I first work to describe these problems of complexity that have prompted a shift in focus. In particular, I develop machine learning tools for neurophysiology that help test whether tuning curves and other statistical models in fact capture the meaning of neural activity. Then, taking up a machine learning framework for understanding, I consider theories about how neural computation emerges from experience. Specifically, I develop hypotheses about the potential learning objectives of sensory plasticity, the potential learning algorithms in the brain, and finally the consequences for sensory representations of learning with such algorithms. These hypotheses pull from advances in several areas of machine learning, including optimization, representation learning, and deep learning theory. Each of these subfields has insights for neuroscience, offering up links for a chain of knowledge about how we learn and think. Together, this dissertation helps to further an understanding of the brain in the lens of machine learning

Brain Blood Flow and Metabolism: Variable Relationships in Altered Metabolic States

Brain metabolism is usually thought of in terms of energy production. Decades of research has shown that the brain derives the majority of its energy from the oxidative phosphorylation of glucose transported from the blood into the brain. Because of this, cerebral blood flow (CBF), the cerebral metabolic rate of glucose consumption (CMRglc), and the cerebral metabolic rate of oxygen consumption (CMRO2) generally are tightly coupled. Indeed, the coupling between CBF, CMRglc, and CMRO2 is robust enough such that many investigators believe them to be equivalent measures of brain activity. Nevertheless, research over the last few decades has shown that cerebral metabolic coupling is not stoichiometrically exact. Perhaps the best example of metabolic uncoupling occurs during focal increases in brain activity. Sensory stimulation, for instance, increases CBF and CMRglc to a much greater extent than CMRO2. This response results in: 1) an increase in nonoxidative glucose consumption, and 2) an increase in oxygenated blood in the brain’s vasculature, the phenomenon which underlies blood oxygen dependent (BOLD) functional magnetic resonance imaging (fMRI). Importantly, metabolic uncoupling is not restricted to periods of increased neural activity. The primary goal of this thesis is to investigate other examples of uncoupling between CBF, CMRglc, and CMRO2. I performed four separate studies that all examine metabolic uncoupling from a different perspective. In the first study, I performed a meta-analysis of published papers to show that at rest, nearly 10% of the brain’s glucose consumption uses nonoxidative pathways that do not end in lactate efflux. If CMRglc and CMRO2 were completely coupled, then one would not expect to find any nonoxidative glucose consumption (NOglc). The second study expands upon the first by showing that there are regional differences in the amount of glucose consumed using nonoxidative pathways. In some brain regions, such as the precuneus and medial prefrontal cortex, NOglc accounts for nearly 20% of resting CMRglc. Conversely, there does not appear to by any NOglc in the cerebellum. The aim of the remaining two studies was to determine if changes in blood glucose concentration produce similar changes in CBF, CMRglc, and CMRO2. Although multiple studies have reported that hypoglycemia focally increases CBF in humans, it is not clear how it impacts regional CMRglc. Therefore, I examined both regional CBF and regional CMRglc during moderate hypoglycemia. Although hypoglycemia decreased CMRglc in every region of the brain, it only increased CBF significantly in the globus pallidus. This suggests that CBF does not increase during hypoglycemia to prevent a fall in CMRglc. Next, I examined regional changes in brain metabolism during hyperglycemia. Previous studies have established that acute hyperglycemia alters the topography of cerebral glucose metabolism. However, the impact of hyperglycemia on regional CBF and CMRO2 has not yet been determined. Therefore, I examined CBF, CMRglc, and CMRO2 in several brain regions during hyperglycemia. Hyperglycemia did not change CBF or CMRO2 in any brain region. However, hyperglycemia did increase CMRglc in white matter and in the brain stem by over 30%. CMRglc was not altered by hyperglycemia in any other region. Therefore, hyperglycemia appears to selectively increase NOglc in the brain stem and white matter. Taken together, the four studies that make up this thesis show that metabolic uncoupling, in particular NOglc, is an important part of brain metabolism. These results also highlight the need for future studies that can elucidate the mechanisms behind uncoupling in both health and disease

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

A Search For Principles of Basal Ganglia Function

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in humans. Different modes of basal ganglia dysfunction lead to Parkinson's disease and Huntington's disease, which have debilitating motor and cognitive symptoms. However, despite intensive study, both the internal computational mechanisms of the basal ganglia, and their contribution to normal brain function, have been elusive. The goal of this thesis is to identify basic principles that underlie basal ganglia function, with a focus on signal representation, computation, dynamics, and plasticity. This process begins with a review of two current hypotheses of normal basal ganglia function, one being that they automatically select actions on the basis of past reinforcement, and the other that they compress cortical signals that tend to occur in conjunction with reinforcement. It is argued that a wide range of experimental data are consistent with these mechanisms operating in series, and that in this configuration, compression makes selection practical in natural environments. Although experimental work is outside the present scope, an experimental means of testing this proposal in the future is suggested. The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering Framework (NEF), which provides an integrated theoretical account of computation, representation, and dynamics in large neural circuits. The NEF provides considerable insight into basal ganglia function, but its explanatory power is potentially limited by two assumptions that the basal ganglia violate. First, like most large-network models, the NEF assumes that neurons integrate multiple synaptic inputs in a linear manner. However, synaptic integration in the basal ganglia is nonlinear in several respects. Three modes of nonlinearity are examined, including nonlinear interactions between dendritic branches, nonlinear integration within terminal branches, and nonlinear conductance-current relationships. The first mode is shown to affect neuron tuning. The other two modes are shown to enable alternative computational mechanisms that facilitate learning, and make computation more flexible, respectively. Secondly, while the NEF assumes that the feedforward dynamics of individual neurons are dominated by the dynamics of post-synaptic current, many basal ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation, bursting, and hysterses. Of these, it is shown that the NEF theory of network dynamics applies fairly directly to certain cases of firing-rate adaptation. However, more complex dynamics, including nonlinear dynamics that are diverse across a population, can be described using the NEF equations for representation. In particular, a neuron's response can be characterized in terms of a more complex function that extends over both present and past inputs. It is therefore straightforward to apply NEF methods to interpret the effects of complex cell dynamics at the network level. The role of spike timing in basal ganglia function is also examined. Although the basal ganglia have been interpreted in the past to perform computations on the basis of mean firing rates (over windows of tens or hundreds of milliseconds) it has recently become clear that patterns of spikes on finer timescales are also functionally relevant. Past work has shown that precise spike times in sensory systems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that these computations do not require spike timing that is very precise. As a consequence, irregular and highly-variable firing patterns can drive behaviour with which they have no detectable correlation. Most of the projection neurons in the basal ganglia are inhibitory, and the effect of one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if each presynaptic neuron can both excite and inhibit its targets, but this is hardly ever the case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei. Finally, the relationship between population coding and synaptic plasticity is discussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning of neuron responses within the coded space. These results permit a straightforward interpretation of the effects of synaptic plasticity on information processing at the network level. Together with the NEF, these new results provide a rich set of theoretical principles through which the dominant physiological factors that affect basal ganglia function can be more clearly understood