Hypothesis Testing For Network Data in Functional Neuroimaging

In recent years, it has become common practice in neuroscience to use networks to summarize relational information in a set of measurements, typically assumed to be reflective of either functional or structural relationships between regions of interest in the brain. One of the most basic tasks of interest in the analysis of such data is the testing of hypotheses, in answer to questions such as "Is there a difference between the networks of these two groups of subjects?" In the classical setting, where the unit of interest is a scalar or a vector, such questions are answered through the use of familiar two-sample testing strategies. Networks, however, are not Euclidean objects, and hence classical methods do not directly apply. We address this challenge by drawing on concepts and techniques from geometry, and high-dimensional statistical inference. Our work is based on a precise geometric characterization of the space of graph Laplacian matrices and a nonparametric notion of averaging due to Fr\'echet. We motivate and illustrate our resulting methodologies for testing in the context of networks derived from functional neuroimaging data on human subjects from the 1000 Functional Connectomes Project. In particular, we show that this global test is more statistical powerful, than a mass-univariate approach. In addition, we have also provided a method for visualizing the individual contribution of each edge to the overall test statistic.Comment: 34 pages. 5 figure

In praise of tedious anatomy

Functional neuroimaging is fundamentally a tool for mapping function to structure, and its success consequently requires neuroanatomical precision and accuracy. Here we review the various means by which functional activation can be localized to neuroanatomy and suggest that the gold standard should be localization to the individual’s or group’s own anatomy through the use of neuroanatomical knowledge and atlases of neuroanatomy. While automated means of localization may be useful, they cannot provide the necessary accuracy, given variability between individuals. We also suggest that the field of functional neuroimaging needs to converge on a common set of methods for reporting functional localization including a common “standard” space and criteria for what constitutes sufficient evidence to report activation in terms of Brodmann’s areas

The mechanisms of tinnitus: perspectives from human functional neuroimaging

In this review, we highlight the contribution of advances in human neuroimaging to the current understanding of central mechanisms underpinning tinnitus and explain how interpretations of neuroimaging data have been guided by animal models. The primary motivation for studying the neural substrates of tinnitus in humans has been to demonstrate objectively its representation in the central auditory system and to develop a better understanding of its diverse pathophysiology and of the functional interplay between sensory, cognitive and affective systems. The ultimate goal of neuroimaging is to identify subtypes of tinnitus in order to better inform treatment strategies. The three neural mechanisms considered in this review may provide a basis for TI classification. While human neuroimaging evidence strongly implicates the central auditory system and emotional centres in TI, evidence for the precise contribution from the three mechanisms is unclear because the data are somewhat inconsistent. We consider a number of methodological issues limiting the field of human neuroimaging and recommend approaches to overcome potential inconsistency in results arising from poorly matched participants, lack of appropriate controls and low statistical power

Functional Neuroimaging in Dementia

Dementia refers to a clinical syndrome of cognitive deterioration and difficulty in the performance of activities of daily living. The most common cause of dementia is Alzheimer’s disease (AD), followed by vascular dementia (VaD) at old age and frontotemporal dementia (FTD) at young onset. Most dementia subtypes show a gradual decline in clinical and cognitive symptomatology, which enables us to identify subjects in a prodromal stage of dementia, referred to as mild cognitive impairment (MCI). As functional neuroimaging techniques provide a means to investigate (early) alterations in brain functioning, these techniques can aid research regarding dementia subtypes and prodromal dementia stages. The focus of this thesis was twofold. In a first chapter I studied the effects of cerebral small vessel disease (CSVD) on brain functioning and structural connectivity in MCI using functional MRI and diffusion tensor imaging. CSVD is a fairly common condition in elderly, which affects the small vessels of the brain supplying the white matter and deeper grey matter regions, visible on MRI as white matter hyperintensities and lacunar infarcts. CSVD contributes to cognitive and clinical symptoms in MCI, but the underlying mechanisms are unclear. The presence of CSVD was found to have a large effect on microstructural white matter integrity throughout the brain. Important fiber tracts for brain network functioning were found to be affected mainly by CSVD in MCI, while the presence of atrophy caused by neurodegenerative conditions did not have a large effect. We found that the presence and extent of CSVD in MCI was furthermore related to decreased medial temporal lobe activation, a brain region important for memory functioning, and decreased deactivation within a region known to be highly involved in the default mode network. These multimodal neuroimaging findings suggest that CSVD affects the microstructure of the white matter, as well as neural functioning during cognitive performance. We postulate that the mechanism through which CSVD affects cognition, or contributes to cognitive deterioration in MCI is neural network interference, as a consequence of damaging the interconnecting fiber tracts. A second focus of this thesis was investigating brain functioning in FTD, AD and progressive supranuclear palsy, using single-photon emission computed tomography (SPECT). Early discrimination between these neurodegenerative conditions during life is hampered by the fact that clinical and cognitive symptomatology can overlap. The use of quantitative analysis of perfusion SPECT neuroimaging may aid addressing these issues. We identified the functional substrate of episodic memory fa

Brain mapping in cognitive disorders: a multidisciplinary approach to learning the tools and applications of functional neuroimaging

<p>Abstract</p> <p>Background</p> <p>With rapid advances in functional imaging methods, human studies that feature functional neuroimaging techniques are increasing exponentially and have opened a vast arena of new possibilities for understanding brain function and improving the care of patients with cognitive disorders in the clinical setting. There is a growing need for medical centers to offer clinically relevant functional neuroimaging courses that emphasize the multifaceted and multidisciplinary nature of this field. In this paper, we describe the implementation of a functional neuroimaging course focusing on cognitive disorders that might serve as a model for other medical centers. We identify key components of an active learning course design that impact student learning gains in methods and issues pertaining to functional neuroimaging that deserve consideration when optimizing the medical neuroimaging curriculum.</p> <p>Methods</p> <p>Learning gains associated with the course were assessed using polychoric correlation analysis of responses to the SALG (Student Assessment of Learning Gains) instrument.</p> <p>Results</p> <p>Student gains in the functional neuroimaging of cognition as assessed by the SALG instrument were strongly associated with several aspects of the course design.</p> <p>Conclusion</p> <p>Our implementation of a multidisciplinary and active learning functional neuroimaging course produced positive learning outcomes. Inquiry-based learning activities and an online learning environment contributed positively to reported gains. This functional neuroimaging course design may serve as a useful model for other medical centers.</p

Meta-analysis of functional neuroimaging data using Bayesian nonparametric binary regression

In this work we perform a meta-analysis of neuroimaging data, consisting of locations of peak activations identified in 162 separate studies on emotion. Neuroimaging meta-analyses are typically performed using kernel-based methods. However, these methods require the width of the kernel to be set a priori and to be constant across the brain. To address these issues, we propose a fully Bayesian nonparametric binary regression method to perform neuroimaging meta-analyses. In our method, each location (or voxel) has a probability of being a peak activation, and the corresponding probability function is based on a spatially adaptive Gaussian Markov random field (GMRF). We also include parameters in the model to robustify the procedure against miscoding of the voxel response. Posterior inference is implemented using efficient MCMC algorithms extended from those introduced in Holmes and Held [Bayesian Anal. 1 (2006) 145--168]. Our method allows the probability function to be locally adaptive with respect to the covariates, that is, to be smooth in one region of the covariate space and wiggly or even discontinuous in another. Posterior miscoding probabilities for each of the identified voxels can also be obtained, identifying voxels that may have been falsely classified as being activated. Simulation studies and application to the emotion neuroimaging data indicate that our method is superior to standard kernel-based methods.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS523 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Finding related functional neuroimaging volumes

We describe a content-based image retrieval technique for finding related functional neuroimaging experiments by voxelization of sets of stereotactic coordinates in Talairach space, comparing the volumes and reporting related volumes in a sorted list. Voxelization is accomplished by convolving each coordinate with a Gaussian kernel. The scheme allows us to compare experiments represented as either lists of coordinates or volumes, and we introduce alternative entrances to databases by image-based indices constructed via novelty measures and singular value decomposition