Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus

abstract: In this paper, we develop a new automated surface registration system based on surface conformal parameterization by holomorphic 1-forms, inverse consistent surface fluid registration, and multivariate tensor-based morphometty (mTBM). First, we conformally map a surface onto a planar rectangle space with holomorphic 1-forms. Second, we compute surface conformal representation by combining its local conformal factor and mean curvature and linearly scale the dynamic range of the conformal representation to form the feature image of the surface. Third, we align the feature image with a chosen template image via the fluid image registration algorithm, which has been extended into the curvilinear coordinates to adjust for the distortion introduced by surface parameterization. The inverse consistent image registration algorithm is also incorporated in the system to jointly estimate the forward and inverse transformations between the study and template images. This alignment induces a corresponding deformation on the surface. We tested the system on Alzheimer's Disease Neuroimaging Initiative (ADNI) baseline dataset to study AD symptoms on hippocampus. In our system, by modeling a hippocampus as a 3D parametric surface, we nonlinearly registered each surface with a selected template surface. Then we used mTBM to analyze the morphometry difference between diagnostic groups. Experimental results show that the new system has better performance than two publicly available subcortical surface registration tools: FIRST and SPHARM. We also analyzed the genetic influence of the Apolipoprotein E(is an element of)4 allele (ApoE4), which is considered as the most prevalent risk factor for AD. Our work successfully detected statistically significant difference between ApoE4 carriers and non-carriers in both patients of mild cognitive impairment (MCI) and healthy control subjects. The results show evidence that the ApoE genotype may be associated with accelerated brain atrophy so that our work provides a new MRI analysis tool that may help presymptomatic AD research.NOTICE: this is the author’s version of a work that was accepted for publication in NEUROIMAGE. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neuroimage, 78, 111-134 [2013] http://dx.doi.org/10.1016/j.neuroimage.2013.04.01

The aging frontal lobe in health and disease : a structural magnetic resonance imaging study

Cortical and subcortical regions of the brain decrease in volume in normal as well as pathological aging. Previous studies indicate that certain parts of the brain, like the prefrontal cortex, may be particularly vulnerable to age-related processes which are manifested by significant volume loss in this region. Cortical volume loss may be further enhanced by different kinds of pathology in the brain. The purpose of this study was to further investigate regional volumetric changes of the frontal lobe in normal aging and in aging patients with dementia. In study I-III patients with frontotemporal lobar degeneration (FTLD), Alzheimer’s disease (AD) and healthy controls are investigated. Cortical atrophy is related to clinical symptoms (study I), discussed in relation to gross morphology and cytoarchitecture (study II), and compared with the atrophy in the hippocampus (study III). In study IV a large number of normal elderly participants are investigated. Age-related volume loss in the limbic system (the dorsal anterior cingulate cortex and the hippocampus) is compared with atrophy of a region of the prefrontal cortex (the orbitofrontal cortex). Volumetric data of frontal and temporal cortical regions and the hippocampus was acquired by manual delineation on structural magnetic resonance images. Results of study I and III reveal that the clinical symptoms displayed by the subtypes of FTLD are commonly reflected in a specific pattern of atrophy in frontotemporal cortices as well as in the hippocampus. Study II suggests that the surface morphology of sulci and gyri may be unreliable landmarks for cyto-architectonic regions of the frontal cortex. Study IV finally indicates that a common characteristic of limbic regions may be that age-related volume loss is delayed in comparison to regions of the prefrontal cortex. Results also suggest that the dorsal anterior cingulate is more resistant to age-related volume loss than hippocampus, which implies that age-related volume loss occurs at different rates for different regions also within the limbic system

Statistical shape analysis of neuroanatomical structures based on spherical wavelet transformation

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2008.Includes bibliographical references.Evidence suggests that morphological changes of neuroanatomical structures may reflect abnormalities in neurodevelopment, or relate to a variety of disorders, such as schizophrenia and Alzheimer's disease (AD). Advances in high-resolution Magnetic Resonance Imaging (MRI) techniques allow us to study these alterations of brain structures in vivo. Previous work in studying the shape variations of brain structures has provided additional localized information compared with traditional volume-based study. However, challenges remain in finding an accurate shape presentation and conducting shape analysis with sound statistical principles. In this work, we develop methods for automatically extracting localized and multi-scale shape features and conducting statistical shape analysis of neuroanatomical structures obtained from MR images. We first develop a procedure to extract multi-scale shape features of brain structures using biorthogonal spherical wavelets. Using this wavelet-based shape representation, we build multi-scale shape models and study the localized cortical folding variations in a normal population using Principal Component Analysis (PCA). We then build a shape-based classification framework for detecting pathological changes of cortical surfaces using advanced classification methods, such as predictive Automatic Relevance Determination (pred-ARD), and demonstrate promising results in patient/control group comparison studies. Thirdly, we develop a nonlinear temporal model for studying the temporal order and regional difference of cortical folding development based on this shape representation. Furthermore, we develop a shape-guided segmentation method to improve the segmentation of sub-cortical structures, such as hippocampus, by using shape constraints obtained in the wavelet domain.(cont.) Finally, we improve upon the proposed wavelet-based shape representation by adopting a newly developed over-complete spherical wavelet transformation and demonstrate its utility in improving the accuracy and stability of shape representations. By using these shape representations and statistical analysis methods, we have demonstrated promising results in localizing shape changes of neuroanatomical structures related to aging, neurological diseases, and neurodevelopment at multiple spatial scales. Identification of these shape changes could potentially lead to more accurate diagnoses and improved understanding of neurodevelopment and neurological diseases.by Peng Yu.Ph.D

Predicting Interrelated Alzheimer's Disease Outcomes via New Self-Learned Structured Low-Rank Model

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. As the prodromal stage of AD, Mild Cognitive Impairment (MCI) maintains a good chance of converting to AD. How to efficaciously detect this conversion from MCI to AD is significant in AD diagnosis. Different from standard classification problems where the distributions of classes are independent, the AD outcomes are usually interrelated (their distributions have certain overlaps). Most of existing methods failed to examine the interrelations among different classes, such as AD, MCI conversion and MCI non-conversion. In this paper, we proposed a novel self-learned low-rank structured learning model to automatically uncover the interrelations among different classes and utilized such interrelated structures to enhance classification. We conducted experiments on the ADNI cohort data. Empirical results demonstrated advantages of our model

Basal forebrain integrity and cognitive memory profile in healthy aging

Age-related dysfunctions in cholinergic and dopaminergic neuromodulation are assumed to contribute to age-associated impairment of explicit memory. Both neurotransmitters also modulate attention, working memory, and processing speed. To date, in vivo evidence linking structural age-related changes in these neuromodulatory systems to dysfunction within or across these cognitive domains remains scarce. Using a factor analytical approach in a cross-sectional study including 86 healthy older (aged 55 to 83 years) and 24 young (aged 18 to 30 years) adults, we assessed the relationship between structural integrity-as measured by magnetization transfer ratio (MTR)-of the substantia nigra/ventral tegmental area (SN/VTA), main origin of dopaminergic projections, basal forebrain (major origin of cortical cholinergic projections), frontal white matter (FWM), and hippocampus to neuro psychological and psychosocial scores. Basal forebrain MTR and FWM changes correlated with a factor combining verbal learning and memory and working memory and, as indicated by measures of diffusion, were most likely due to vascular pathology. These findings suggest that frontal white matter integrity and cholinergic neuromodulation provide clues as to why age-related cognitive decline is often correlated across cognitive domains. (C) 2009 Elsevier B.V. All rights reserved

Early Medial Temporal Atrophy Scale (EMTA)

186 p.[ES]La atrofia del lóbulo temporal medial puede ser medida a través del uso de escalas de atrofia visual tales como la escala de atrofia del lóbulo temporal medial (MTA). La escala MTA ha sido diseñada y validada para el estudio de pacientes con Enfermedad de Alzheimer moderada (EA). Sin embargo, la MTA no ha sido diseñada para medir los cambios de atrofia de bajo grado que ocurren en la etapa precoz y media del proceso de envejecimiento. El objetivo de este estudio fue desarrollar y validar una nueva MTA; La “Goiz” (en Euskera) GMTA o “Early” (en ingles) EMTA, una nueva escala diseñada para la valoración de la atrofia precoz del lóbulo temporal medial que tiene la capacidad de medir los cambios de atrofia de bajo grado.[EN]Medial temporal lobe atrophy can be measured through visual rating scales such us the medial temporal lobe atrophy scale (MTA). MTA has been designed and validated for the study of patients with mild to moderate Alzheimer disease (AD). However, MTA has not been designed to measure the low-grade atrophy changes that occur at the early and middle aging process. The aim of this study was develop and validate a new MTA; the early (“Goiz” in Basque language) medial temporal lobe atrophy scale (EMTA) that has the capability to measure the low-grade atrophy changes

Neurobiological mechanisms of hallucinations in schizophrenia

All perception is a construct of the brain. Yet occasionally, sensory constructions emerge without origin in the physical world and are experienced as hallucinations. Hallucinations occur transdiagnostically, cross-culturally, and in all sensory modalities. They are common in people with schizophrenia, presenting in 60-80% of patients. Despite over 20 years of active neuroimaging research on hallucinations, the neural systems supporting these anomalous perceptual experiences remain disputed. This dissertation investigates the neurobiology of hallucinations, integrating research across structural and functional magnetic resonance imaging (MRI) to elucidate how hallucinations, chiefly in the context of schizophrenia, are supported by the brain, drawing on MRI indices of neurodevelopment. I introduce the phenomenon of hallucinations and motivate the utility of MRI for studying hallucinations. Considering their prevalence in other medical conditions, I conduct a meta-analysis and systematic review of the structural brain basis of hallucinations across diagnoses, primarily schizophrenia spectrum disorders and Parkinson’s disease. This illustrated distinct neuroanatomical organizations of grey matter associated with hallucinations that occur in neurodevelopmental compared to neurodegenerative disorders, which I hypothesise constitute at least two distinct mechanisms. Focussing on the neurodevelopmental mechanism characterized by fronto-temporal and insular grey matter reductions, I turn to the contribution of cortical sulcation, a product of second and third trimester neurodevelopmental processes, which has been robustly implicated in schizophrenia pathology, and, more recently, in hallucinations. Sulcal patterns derived from structural MRI provide a proxy in adulthood for early brain development. I studied two independent datasets of patients with schizophrenia who underwent clinical assessment and 3T MRI from the United Kingdom and Shanghai, China, stratified into those with and without hallucinations, and healthy controls from Shanghai. I first replicate the finding that left hemisphere paracingulate sulcus (PCS) length is reduced in patients who experience hallucinations, then demonstrate similar associations for superior temporal sulcus depth. Length and depth alterations occurred with focal deviations in sulcal geometry. The interindividual and interhemispheric variability of the PCS necessitated the development of semi-automated methods to characterize its morphology and validation to a manual protocol. I used structural covariance networks of the local gyrification index to investigate how specific sulcal deviations relate to global neurodevelopmental coordination, demonstrating that hallucinations correspond to increased covariance within and between salience and auditory networks. Hypothesizing structure-function relationships, I analyse resting-state functional MRI data from the same datasets described, finding significant interactions between PCS length and hallucinations status, but no main effects. There were no effects of hallucination status on salience and auditory network connectivity or in graph theoretical measures of connectivity, suggesting that resting-state connectivity is not a trait marker for hallucinations. Together, the discovery of neurodevelopmental alterations contributing to hallucinations provides mechanistic insight into the pathological consequences of prenatal origins. The interaction of sulcal alterations and hallucination status are associated with connectivity, which may have a role in the pathophysiology of hallucinations. I provide clear predictions and recommendations for future research.Gates Cambridge Scholarshi

A resting state network in the motor control circuit of the basal ganglia

<p>Abstract</p> <p>Background</p> <p>In the absence of overt stimuli, the brain shows correlated fluctuations in functionally related brain regions. Approximately ten largely independent resting state networks (RSNs) showing this behaviour have been documented to date. Recent studies have reported the existence of an RSN in the basal ganglia - albeit inconsistently and without the means to interpret its function. Using two large study groups with different resting state conditions and MR protocols, the reproducibility of the network across subjects, behavioural conditions and acquisition parameters is assessed. Independent Component Analysis (ICA), combined with novel analyses of temporal features, is applied to establish the basis of signal fluctuations in the network and its relation to other RSNs. Reference to prior probabilistic diffusion tractography work is used to identify the basal ganglia circuit to which these fluctuations correspond.</p> <p>Results</p> <p>An RSN is identified in the basal ganglia and thalamus, comprising the pallidum, putamen, subthalamic nucleus and substantia nigra, with a projection also to the supplementary motor area. Participating nuclei and thalamo-cortical connection probabilities allow this network to be identified as the motor control circuit of the basal ganglia. The network was reproducibly identified across subjects, behavioural conditions (fixation, eyes closed), field strength and echo-planar imaging parameters. It shows a frequency peak at 0.025 ± 0.007 Hz and is most similar in spectral composition to the Default Mode (DM), a network of regions that is more active at rest than during task processing. Frequency features allow the network to be classified as an RSN rather than a physiological artefact. Fluctuations in this RSN are correlated with those in the task-positive fronto-parietal network and anticorrelated with those in the DM, whose hemodynamic response it anticipates.</p> <p>Conclusion</p> <p>Although the basal ganglia RSN has not been reported in most ICA-based studies using a similar methodology, we demonstrate that it is reproducible across subjects, common resting state conditions and imaging parameters, and show that it corresponds with the motor control circuit. This characterisation of the basal ganglia network opens a potential means to investigate the motor-related neuropathologies in which the basal ganglia are involved.</p