Loss of Binocular Vision in Monocularly Blind Patients Causes Selective Degeneration of the Superior Lateral Occipital Cortices

PURPOSE. Chronic ocular pathology, such as glaucoma and macular degeneration, is associated with neuroanatomic changes in the visual pathways. It is a challenge to determine the mechanism responsible for these changes. This could be functional deprivation or transsynaptic degeneration. Acquired monocular blindness provides a unique opportunity to establish which mechanism underlies neuroanatomic changes in ocular pathology in general, since the loss of input is well defined, and it causes selective functional deprivation due to the loss of stereopsis. Here, we assessed whether acquired monocular blindness is associated with neuroanatomic changes, and if so, where these changes are located. METHODS. High-resolution T1-weighted magnetic resonance images were obtained in 15 monocularly blind patients and 18 healthy controls. We used voxel-and surface-based morphometry to compare gray and white matter volume, cortical thickness, mean curvature, and surface area between these groups. RESULTS. The gray matter volume in the bilateral superior lateral occipital cortices was decreased in the monocular blind patients, in the absence of volumetric differences in their early visual cortex. CONCLUSIONS. The volumetric decrease in the superior lateral occipital cortices is consistent with specific functional deprivation, as the superior lateral occipital cortices play an important role in depth perception. Moreover, in the absence of differences in the early visual cortex, the decrease is inconsistent with transsynaptic degeneration propagating from the degenerated retinal axons

Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis

Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated "eigenconnectivity" patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra-and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.Peer reviewe

The Manifold of Neural Responses Informs Physiological Circuits in the Visual System

The rapid development of multi-electrode and imaging techniques is leading to a data explosion in neuroscience, opening the possibility of truly understanding the organization and functionality of our visual systems. Furthermore, the need for more natural visual stimuli greatly increases the complexity of the data. Together, these create a challenge for machine learning. Our goal in this thesis is to develop one such technique. The central pillar of our contribution is designing a manifold of neurons, and providing an algorithmic approach to inferring it. This manifold is functional, in the sense that nearby neurons on the manifold respond similarly (in time) to similar aspects of the stimulus ensemble. By organizing the neurons, our manifold differs from other, standard manifolds as they are used in visual neuroscience which instead organize the stimuli. Our contributions to the machine learning component of the thesis are twofold. First, we develop a tensor representation of the data, adopting a multilinear view of potential circuitry. Tensor factorization then provides an intermediate representation between the neural data and the manifold. We found that the rank of the neural factor matrix can be used to select an appropriate number of tensor factors. Second, to apply manifold learning techniques, a similarity kernel on the data must be defined. Like many others, we employ a Gaussian kernel, but refine it based on a proposed graph sparsification technique—this makes the resulting manifolds less sensitive to the choice of bandwidth parameter. We apply this method to neuroscience data recorded from retina and primary visual cortex in the mouse. For the algorithm to work, however, the underlying circuitry must be exercised to as full an extent as possible. To this end, we develop an ensemble of flow stimuli, which simulate what the mouse would \u27see\u27 running through a field. Applying the algorithm to the retina reveals that neurons form clusters corresponding to known retinal ganglion cell types. In the cortex, a continuous manifold is found, indicating that, from a functional circuit point of view, there may be a continuum of cortical function types. Interestingly, both manifolds share similar global coordinates, which hint at what the key ingredients to vision might be. Lastly, we turn to perhaps the most widely used model for the cortex: deep convolutional networks. Their feedforward architecture leads to manifolds that are even more clustered than the retina, and not at all like that of the cortex. This suggests, perhaps, that they may not suffice as general models for Artificial Intelligence

Proceedings of the Fourth International Workshop on Mathematical Foundations of Computational Anatomy - Geometrical and Statistical Methods for Biological Shape Variability Modeling (MFCA 2013), Nagoya, Japan

International audienceComputational anatomy is an emerging discipline at the interface of geometry, statistics and image analysis which aims at modeling and analyzing the biological shape of tissues and organs. The goal is to estimate representative organ anatomies across diseases, populations, species or ages, to model the organ development across time (growth or aging), to establish their variability, and to correlate this variability information with other functional, genetic or structural information. The Mathematical Foundations of Computational Anatomy (MFCA) workshop aims at fostering the interactions between the mathematical community around shapes and the MICCAI community in view of computational anatomy applications. It targets more particularly researchers investigating the combination of statistical and geometrical aspects in the modeling of the variability of biological shapes. The workshop is a forum for the exchange of the theoretical ideas and aims at being a source of inspiration for new methodological developments in computational anatomy. A special emphasis is put on theoretical developments, applications and results being welcomed as illustrations. Following the first edition of this workshop in 2006, second edition in New-York in 2008, the third edition in Toronto in 2011, the forth edition was held in Nagoya Japan on September 22 2013

On the neurobiology of apathy and depression in cerebral small vessel disease

Cerebral small vessel disease (SVD) is a cerebrovascular pathology that affects the small vessels of the brain, resulting in heterogeneous brain tissue changes. These can lead to neuropsychiatric symptoms such as apathy, a loss of motivation, and depression, which is characterised by low mood and a loss of pleasure. Apathy and depression are both prevalent symptoms in SVD, but an understanding of the relationship between underlying disease processes and the expression of these neuropsychiatric symptoms remains poor. This thesis uses magnetic resonance imaging techniques to examine the neurobiological basis of apathy and depression in SVD. We show that apathy is related to focal grey matter damage and distributed white matter microstructural change. These microstructural changes underlie large-scale white matter network disruption, which is related to apathy, but not depression. We then show that depression, as a construct, can be dissociated into distinct symptoms which are associated with overlapping and distinct areas of cortical atrophy over time. This suggests that depression as a general syndrome may be characterised by atrophy in core structures, while different symptoms are associated with atrophy in more specialised areas. Consistent with these patterns of overarching tissue damage, we find that apathy, but not depression, predicts conversion to dementia in patients with SVD. Our findings suggest that different types of SVD-related pathology lead to apathy and depression. Diffuse white matter damage may lead to widespread network disruption, resulting in apathy and cognitive impairment. In contrast, depressive symptoms are associated with focal patterns of grey matter atrophy over time. This highlights the importance of differentiating neuropsychiatric symptoms, and paves the way for targeted treatment approaches.Cambridge International Scholarship (Cambridge Trust)