Longitudinal Imaging Data Research Articles

Rhesus monkey brain development during late infancy and the effect of phencyclidine: A longitudinal MRI and DTI study

Early brain development is a complex and rapid process, the disturbance of which may cause the onset of brain disorders. Based on longitudinal imaging data acquired from 6 to 16months postnatal, we describe a systematic trajectory of monkey brain development during late infancy, and demonstrate the influence of phencyclidine (PCP) on this trajectory. Although the general developmental trajectory of the monkey brain was close to that of the human brain, the development in monkeys was faster and regionally specific. Gray matter volume began to decrease during late infancy in monkeys, much earlier than in humans in whom it occurs in adolescence. Additionally, the decrease of gray matter volume in higher-order association regions (the frontal, parietal and temporal lobes) occurred later than in regions for primary functions (the occipital lobe and cerebellum). White matter volume displayed an increasing trend in most brain regions, but not in the occipital lobe, which had a stable volume. In addition, based on diffusion tensor imaging, we found an increase in fractional anisotropy and a decrease in diffusivity, which may be associated with myelination and axonal changes in white matter tracts. Meanwhile, we tested the influence of 14-day PCP treatment on the developmental trajectories. Such treatment tended to accelerated brain maturation during late infancy, although not statistically significant. These findings provide comparative information for the understanding of primate brain maturation and neurodevelopmental disorders.

Selecting a separable parametric spatiotemporal covariance structure for longitudinal imaging data.

Longitudinal imaging studies allow great insight into how the structure and function of a subject's internal anatomy changes over time. Unfortunately, the analysis of longitudinal imaging data is complicated by inherent spatial and temporal correlation: the temporal from the repeated measures and the spatial from the outcomes of interest being observed at multiple points in a patient's body. We propose the use of a linear model with a separable parametric spatiotemporal error structure for the analysis of repeated imaging data. The model makes use of spatial (exponential, spherical, and Matérn) and temporal (compound symmetric, autoregressive-1, Toeplitz, and unstructured) parametric correlation functions. A simulation study, inspired by a longitudinal cardiac imaging study on mitral regurgitation patients, compared different information criteria for selecting a particular separable parametric spatiotemporal correlation structure as well as the effects on types I and II error rates for inference on fixed effects when the specified model is incorrect. Information criteria were found to be highly accurate at choosing between separable parametric spatiotemporal correlation structures. Misspecification of the covariance structure was found to have the ability to inflate the type I error or have an overly conservative test size, which corresponded to decreased power. An example with clinical data is given illustrating how the covariance structure procedure can be performed in practice, as well as how covariance structure choice can change inferences about fixed effects.

Imaging proteomics for diagnosis, monitoring and prediction of Alzheimer's disease

Proteomic and imaging markers have been widely studied as potential biomarkers for diagnosis, monitoring and prognosis of Alzheimer's disease. In this study, we used Alzheimer Disease Neuroimaging Initiative dataset and performed parallel independent component analysis on cross sectional and longitudinal proteomic and imaging data in order to identify the best proteomic model for diagnosis, monitoring and prediction of Alzheimer disease (AD).We used plasma proteins measurement and imaging data from AD and healthy controls (HC) at the baseline and 1year follow-up. Group comparisons at baseline and changes over 1year were calculated for proteomic and imaging data. The results were fed into parallel independent component analysis in order to identify proteins that were associated with structural brain changes cross sectionally and longitudinally. Regression model was used to find the best model that can discriminate AD from HC, monitor AD and to predict MCI converters from non-converters.We showed that five proteins are associated with structural brain changes in the brain. These proteins could discriminate AD from HC with 57% specificity and 89% sensitivity. Four proteins whose change over 1year were associated with brain structural changes could discriminate AD from HC with sensitivity of 93%, and specificity of 92%. This model predicted MCI conversion to AD in 2years with 94% accuracy. This model has the highest accuracy in prediction of MCI conversion to AD within the ADNI-1 dataset. This study shows that combination of selected plasma protein levels and MR imaging is a useful method in identifying potential biomarker.

TU‐C‐12A‐02: Development of a Multiparametric Statistical Response Map for Quantitative Imaging

Purpose:Quantitative imaging biomarkers (QIB) are becoming increasingly utilized in early phase clinical trials as a means of non‐invasively assessing treatment response and associated response heterogeneity. The aim of this study was to develop a flexible multiparametric statistical framework to predict voxel‐by‐voxel response of several potential MRI QIBs.Methods:Patients with histologically proven glioblastomas (n=11) were treated with chemoradiation (with/without bevacizumab) and underwent one baseline and two mid‐treatment (3–4wks) MRIs. Dynamic contrast‐enhanced (3D FSPGR, 6.3sec/phase, 0.1 mmol/kg Gd‐DTPA), dynamic susceptibility contrast (2D GRE‐EPI, 1.5sec/phase, 0.2mmol/kg Gd‐DTPA), and diffusion tensor (2D DW‐EPI, b=0, 1200 s/mm2, 27 directions) imaging acquisitions were obtained during each study. Mid‐treatment and pre‐treatment images were rigidly aligned, and regions of partial response (PR), stable disease (SD), and progressive disease (PD) were contoured in consensus by two experienced radiation oncologists. Voxels in these categories were used to train ordinal (PR<SD<PD) and logistic (PR/SD<PD) models incorporating the apparent diffusion coefficient, fractional anisotropy, Ktrans, ve, vp, and relative cerebral blood volume imaging biomarkers, as well as treatment type. Leave‐one‐out cross‐validation was performed at the patient level to assess model prediction accuracy.Results:Ordinal regression resulted in model prediction accuracies of 60% (PR), 0% (SD), 81% (PD), and 69% (overall), with coefficients of variation (COV) of 9.4%, 9.6%, and 23.6%, respectively. Logistic regression resulted in accuracies of 82.0% (PR/SD), 46.2% (PD), and 76.2% (overall) with COVs of 22.4%, 45.7%, and 23.8%, respectively.Conclusion:Despite limited patient numbers, this feasibility pilot study demonstrates that ordinal and logistic regression models potentially provide a flexible statistical framework for incorporating longitudinal multiparametric quantitative imaging data and, for these particular data, can potentially distinguish PR from PD. Additional training data exhibiting a full range of treatment responses is expected to allow prediction of all three treatment outcomes. Moreover, these models are extensible to multi‐modality, multiparametric analyses.

Separability tests for high-dimensional, low-sample size multivariate repeated measures data

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Valid inference in longitudinal imaging requires enough flexibility of the covariance model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the covariance structure. Separable (Kronecker product) covariance models provide one such parameterization in which the spatial and temporal covariances are modeled separately. However, evaluating the validity of this parameterization in high dimensions remains a challenge. Here we provide a scientifically informed approach to assessing the adequacy of separable (Kronecker product) covariance models when the number of observations is large relative to the number of independent sampling units (sample size). We address both the general case, in which unstructured matrices are considered for each covariance model, and the structured case, which assumes a particular structure for each model. For the structured case, we focus on the situation where the within-subject correlation is believed to decrease exponentially in time and space as is common in longitudinal imaging studies. However, the provided framework equally applies to all covariance patterns used within the more general multivariate repeated measures context. Our approach provides useful guidance for high dimension, low-sample size data that preclude using standard likelihood-based tests. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrate the approaches appeal.

Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport.

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of continuous disease/treatment progression. Having the ability to produce a sensible time interpolation of the data can lead to improved analysis, such as intuitive visualizations of anatomical changes, or the creation of more samples to improve statistical analysis. In this work, we model interpolation of medical image data, in particular shape data, using the theory of optimal mass transport (OMT), which can construct a continuous transition from two time points while preserving "mass" (e.g., image intensity, shape volume) during the transition. The theory even allows a short extrapolation in time and may help predict short-term treatment impact or disease progression on anatomical structure. We apply the proposed method to the hippocampus-amygdala complex in schizophrenia, the heart in atrial fibrillation, and full head MR images in traumatic brain injury.

Kronecker Product Linear Exponent AR(1) Correlation Structures for Multivariate Repeated Measures

Longitudinal imaging studies have moved to the forefront of medical research due to their ability to characterize spatio-temporal features of biological structures across the lifespan. Credible models of the correlations in longitudinal imaging require two or more pattern components. Valid inference requires enough flexibility of the correlation model to allow reasonable fidelity to the true pattern. On the other hand, the existence of computable estimates demands a parsimonious parameterization of the correlation structure. For many one-dimensional spatial or temporal arrays, the linear exponent autoregressive (LEAR) correlation structure meets these two opposing goals in one model. The LEAR structure is a flexible two-parameter correlation model that applies to situations in which the within-subject correlation decreases exponentially in time or space. It allows for an attenuation or acceleration of the exponential decay rate imposed by the commonly used continuous-time AR(1) structure. We propose the Kronecker product LEAR correlation structure for multivariate repeated measures data in which the correlation between measurements for a given subject is induced by two factors (e.g., spatial and temporal dependence). Excellent analytic and numerical properties make the Kronecker product LEAR model a valuable addition to the suite of parsimonious correlation structures for multivariate repeated measures data. Longitudinal medical imaging data of caudate morphology in schizophrenia illustrates the appeal of the Kronecker product LEAR correlation structure.

Patient specific tumor growth prediction using multimodal images.

Personalized tumor growth model is valuable in tumor staging and therapy planning. In this paper, we present a patient specific tumor growth model based on longitudinal multimodal imaging data including dual-phase CT and FDG-PET. The proposed Reaction-Advection-Diffusion model is capable of integrating cancerous cell proliferation, infiltration, metabolic rate and extracellular matrix biomechanical response. To bridge the model with multimodal imaging data, we introduce Intracellular Volume Fraction (ICVF) measured from dual-phase CT and Standardized Uptake Value (SUV) measured from FDG-PET into the model. The patient specific model parameters are estimated by fitting the model to the observation, which leads to an inverse problem formalized as a coupled Partial Differential Equations (PDE)-constrained optimization problem. The optimality system is derived and solved by the Finite Difference Method. The model was evaluated by comparing the predicted tumors with the observed tumors in terms of average surface distance (ASD), root mean square difference (RMSD) of the ICVF map, average ICVF difference (AICVFD) of tumor surface and tumor relative volume difference (RVD) on six patients with pathologically confirmed pancreatic neuroendocrine tumors. The ASD between the predicted tumor and the reference tumor was 2.4±0.5mm, the RMSD was 4.3±0.4%, the AICVFD was 2.6±0.6%, and the RVD was 7.7±1.3%.

Longitudinal alignment of disease progression in fibrosing interstitial lung disease.

Generating disease progression models from longitudinal medical imaging data is a challenging task due to the varying and often unknown state and speed of disease progression at the time of data acquisition, the limited number of scans and varying scanning intervals. We propose a method for temporally aligning imaging data from multiple patients driven by disease appearance. It aligns follow- up series of different patients in time, and creates a cross-sectional spatio-temporal disease pattern distribution model. Similarities in the disease distribution guide an optimization process, regularized by temporal rigidity and disease volume terms. We demonstrate the benefit of longitudinal alignment by classifying instances of different fibrosing interstitial lung diseases. Classification results (AUC) of Usual Interstitial Pneumonia (UIP) versus non-UIP improve from AUC = 0.71 to 0.78 following alignment, classification of UIP vs. Extrinsic Allergic Alveolitis (EAA) improves from 0.78 to 0.88.

A cognitive chameleon: Lessons from a novel MAPT mutation case

We report a case of frontotemporal dementia caused by a novel MAPT mutation (Q351R) with a remarkably long amnestic presentation mimicking familial Alzheimer’s disease. Longitudinal clinical, neuropsychological and imaging data provide convergent evidence for predominantly bilateral anterior medial temporal lobe involvement consistent with previously established neuroanatomical signatures of MAPT mutations. This case supports the notion that the neural network affected in MAPT mutations is determined to a large extent by the underlying molecular pathology. We discuss the diagnostic significance of anomia in the context of atypical amnesia and the impact of impaired episodic and semantic memory systems on autobiographical memory.

Longitudinal Assessment of Amyloid Pathology in Transgenic ArcAβ Mice Using Multi-Parametric Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) can be used to monitor pathological changes in Alzheimer's disease (AD). The objective of this longitudinal study was to assess the effects of progressive amyloid-related pathology on multiple MRI parameters in transgenic arcAβ mice, a mouse model of cerebral amyloidosis. Diffusion-weighted imaging (DWI), T1-mapping and quantitative susceptibility mapping (QSM), a novel MRI based technique, were applied to monitor structural alterations and changes in tissue composition imposed by the pathology over time. Vascular function and integrity was studied by assessing blood-brain barrier integrity with dynamic contrast-enhanced MRI and cerebral microbleed (CMB) load with susceptibility weighted imaging and QSM. A linear mixed effects model was built for each MRI parameter to incorporate effects within and between groups (i.e. genotype) and to account for changes unrelated to the disease pathology. Linear mixed effects modelling revealed a strong association of all investigated MRI parameters with age. DWI and QSM in addition revealed differences between arcAβ and wt mice over time. CMBs became apparent in arcAβ mice with 9 month of age; and the CMB load reflected disease stage. This study demonstrates the benefits of linear mixed effects modelling of longitudinal imaging data. Moreover, the diagnostic utility of QSM and assessment of CMB load should be exploited further in studies of AD.

A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood

Cross-sectional analysis of longitudinal anatomical magnetic resonance imaging (MRI) data may be suboptimal as each dataset is analyzed independently. In this study, we evaluate how much variability can be reduced by analyzing structural volume changes in longitudinal data using longitudinal analysis. We propose a two-part pipeline that consists of longitudinal registration and longitudinal classification. The longitudinal registration step includes the creation of subject-specific linear and nonlinear templates that are then registered to a population template. The longitudinal classification step comprises a four-dimensional expectation-maximization algorithm, using a priori classes computed by averaging the tissue classes of all time points obtained cross-sectionally.To study the impact of these two steps, we apply the framework completely (“LL method”: Longitudinal registration and Longitudinal classification) and partially (“LC method”: Longitudinal registration and Cross-sectional classification) and compare these with a standard cross-sectional framework (“CC method”: Cross-sectional registration and Cross-sectional classification).The three methods are applied to (1) a scan–rescan database to analyze reliability and (2) the NIH pediatric population to compare gray matter growth trajectories evaluated with a linear mixed model.The LL method, and the LC method to a lesser extent, significantly reduced the variability in the measurements in the scan–rescan study and gave the best-fitted gray matter growth model with the NIH pediatric MRI database. The results confirm that both steps of the longitudinal framework reduce variability and improve accuracy in comparison with the cross-sectional framework, with longitudinal classification yielding the greatest impact.Using the improved method to analyze longitudinal data, we study the growth trajectories of anatomical brain structures in childhood using the NIH pediatric MRI database. We report age- and gender-related growth trajectories of specific regions of the brain during childhood that could be used as a reference in studying the impact of neurological disorders on brain development.

β-Propeller protein-associated neurodegeneration: a new X-linked dominant disorder with brain iron accumulation.

Neurodegenerative disorders with high iron in the basal ganglia encompass an expanding collection of single gene disorders collectively known as neurodegeneration with brain iron accumulation. These disorders can largely be distinguished from one another by their associated clinical and neuroimaging features. The aim of this study was to define the phenotype that is associated with mutations in WDR45, a new causative gene for neurodegeneration with brain iron accumulation located on the X chromosome. The study subjects consisted of WDR45 mutation-positive individuals identified after screening a large international cohort of patients with idiopathic neurodegeneration with brain iron accumulation. Their records were reviewed, including longitudinal clinical, laboratory and imaging data. Twenty-three mutation-positive subjects were identified (20 females). The natural history of their disease was remarkably uniform: global developmental delay in childhood and further regression in early adulthood with progressive dystonia, parkinsonism and dementia. Common early comorbidities included seizures, spasticity and disordered sleep. The symptoms of parkinsonism improved with l-DOPA; however, nearly all patients experienced early motor fluctuations that quickly progressed to disabling dyskinesias, warranting discontinuation of l-DOPA. Brain magnetic resonance imaging showed iron in the substantia nigra and globus pallidus, with a 'halo' of T1 hyperintense signal in the substantia nigra. All patients harboured de novo mutations in WDR45, encoding a beta-propeller protein postulated to play a role in autophagy. Beta-propeller protein-associated neurodegeneration, the only X-linked disorder of neurodegeneration with brain iron accumulation, is associated with de novo mutations in WDR45 and is recognizable by a unique combination of clinical, natural history and neuroimaging features.

From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs

Motivation: Imaging genetic studies typically focus on identifying single-nucleotide polymorphism (SNP) markers associated with imaging phenotypes. Few studies perform regression of SNP values on phenotypic measures for examining how the SNP values change when phenotypic measures are varied. This alternative approach may have a potential to help us discover important imaging genetic associations from a different perspective. In addition, the imaging markers are often measured over time, and this longitudinal profile may provide increased power for differentiating genotype groups. How to identify the longitudinal phenotypic markers associated to disease sensitive SNPs is an important and challenging research topic.Results: Taking into account the temporal structure of the longitudinal imaging data and the interrelatedness among the SNPs, we propose a novel ‘task-correlated longitudinal sparse regression’ model to study the association between the phenotypic imaging markers and the genotypes encoded by SNPs. In our new association model, we extend the widely used ℓ2,1-norm for matrices to tensors to jointly select imaging markers that have common effects across all the regression tasks and time points, and meanwhile impose the trace-norm regularization onto the unfolded coefficient tensor to achieve low rank such that the interrelationship among SNPs can be addressed. The effectiveness of our method is demonstrated by both clearly improved prediction performance in empirical evaluations and a compact set of selected imaging predictors relevant to disease sensitive SNPs.Availability: Software is publicly available at: http://ranger.uta.edu/%7eheng/Longitudinal/Contact: heng@uta.edu or shenli@inpui.edu

Stargardt macular dystrophy

AbstractInitiation of a number of upcoming clinical trials for patients with Stargardt disease emphasizes the urge of determining objective outcome measures derived from retinal imaging. Especially novel techniques such as fundus autofluorescence (AF) and quantitative AF imaging as well as spectral domain optical coherence tomography (SD‐OCT) now allow assessing the characteristic pathology of Stargardt disease at different disease stages. Fundus AF highlights lipofuscin accumulation in the retinal pigment epithelium (RPE), the phenotypic finding that defines Stargardt disease. Also, fundus AF delineates areas of RPE atrophy with high contrast, allowing to longitudinally monitoring respective changes with high accuracy. SD‐OCT, on the other hand, is a clinically meaningful outcome measures since is allows to delineate the photoreceptor layer of the retina. Structural alterations within this layer have been shown to correlate directly with retinal function. Relevant functional parameters include visual acuity and microperimetry. Disease phenotyping (including comparison of various imaging modalities) as well as analysis of longitudinal imaging data, structure‐function‐correlations and genotype‐phenotype correlations will be prudent. In a prospective, multicenter, non‐interventional, non‐randomized, unmasked, observational study the natural history of patients with Stargardt’s disease will be characterized, prognostic markers identified and microstructural alterations and functional variations determined. Allö probands will be ABCA4‐genotyped.

Multiscale Adaptive Marginal Analysis of Longitudinal Neuroimaging Data with Time‐Varying Covariates

Neuroimaging data collected at repeated occasions are gaining increasing attention in the neuroimaging community due to their potential in answering questions regarding brain development, aging, and neurodegeneration. These datasets are large and complicated, characterized by the intricate spatial dependence structure of each response image, multiple response images per subject, and covariates that may vary with time. We propose a multiscale adaptive generalized method of moments (MA-GMM) approach to estimate marginal regression models for imaging datasets that contain time-varying, spatially related responses and some time-varying covariates. Our method categorizes covariates into types to determine the valid moment conditions to combine during estimation. Further, instead of assuming independence of voxels (the components that make up each subject's response image at each time point) as many current neuroimaging analysis techniques do, this method "adaptively smoothes" neuroimaging response data, computing parameter estimates by iteratively building spheres around each voxel and combining observations within the spheres with weights. MA-GMM's development adds to the few available modeling approaches intended for longitudinal imaging data analysis. Simulation studies and an analysis of a real longitudinal imaging dataset from the Alzheimer's Disease Neuroimaging Initiative are used to assess the performance of MA-GMM. Martha Skup, Hongtu Zhu, and Heping Zhang for the Alzheimer's Disease Neuroimaging Initiative.

Abstract 5233: Abnormal lymphatic drainage and function from vascular endothelial growth factor (VEGF)-C overexpressing B16F10 melanoma

Abstract The lymphatic system provides a major transport route for initial cancer cell dissemination for many cancers. Tumor lymphangiogenesis (i.e., the new formation of tumor-associated lymphatic vessels) is associated with metastatic potential and is an emerging therapeutic target to arrest metastasis. Recent studies show that the major lymphangiogenic growth factor, vascular endothelial growth factor (VEGF)-C induces tumor lymphangiogenesis and enhances tumor spread to the regional lymph nodes (LNs) and distant sites in mouse models of breast cancer and melanoma. Yet despite the important role of lymphatics as a critical pathway for cancer metastasis, relatively little is known about lymphatic function during tumor progression and metastasis, due mainly to the lack of lymphatic imaging approaches. Recently, we have developed in vivo dynamic near-infrared (NIR) fluorescence imaging methods for quantifying propulsive lymph flow. Herein, we employed a dynamic, functional lymphatic imaging technique and longitudinally imaged spatial and temporal changes in lymphatic function and architecture in mice bearing vascular endothelial growth factor (VEGF)-C overexpressing B16F10 (VEGF-C-B16F10) or mock-transduced B16F10 (mock-B16F10) melanoma tumors. C57BL6 mice were injected with 10ul of indocyanine green (ICG) or FITC-Dextran in the base of the tail and were dynamically imaged for baseline information before implantation of VEGF-C-B16F10 or mock-B16F10 in the hindlimb every three to four days for up to 27 days after inoculation. Our dynamic NIR fluorescence imaging data show that ICG-laden lymph accumulates into a VEGF-C-B16F10 tumor at 3 days post implantation as confirmed with FITC-Dextran leakage from lymphatic vessels; thereafter lymphatic leakage is visualized only in peritumoral lymphatic vessels. Our longitudinal imaging data also show dilated and tortuous lymphatic vessels and reduced or loss of propulsive lymphatic flow in the process of VEGF-C-B16F10 growth. However, mock-B16F10 tumors do not show lymphatic vessel leakage during tumor progression. NIR fluorescence imaging technology can be used to non-invasively and quantitatively detect functional lymphatic changes associated with cancer, which may enable accurate nodal staging of cancer patients and provide new approaches to diagnose and treat cancer metastasis. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5233. doi:10.1158/1538-7445.AM2011-5233

UNDERSTANDING HETEROGENEITY IN NORMAL OLDER ADULT POPULATIONS VIA CLUSTERING OF LONGITUDINAL DATA.

Populations of healthy older individuals are often highly heterogeneous, as prevalence of various underlying pathologies increases with age. Finding coherent groups of normal older adults may allow to identify subpopulations that are at risk of developing Alzheimer's disease (AD). In this paper, we propose an approach that utilizes longitudinal magnetic resonance imaging (MRI) data to obtain natural groupings of older adult subjects via an unsupervised (i.e., clustering) technique. We develop a k-medoids-like clustering algorithm that simultaneously finds clusters of longitudinal images, as well as weights brain regions in such a way that the obtained clusters are maximally coherent. We propose a cluster-based measure that reflects the individual subject's cognitive decline. The proposed method is unsupervised and is suitable for analyzing AD at its very early stages.

Sex differences in grey matter atrophy patterns among AD and aMCI patients: Results from ADNI

We used longitudinal magnetic resonance imaging (MRI) data to determine whether there are any gender differences in grey matter atrophy patterns over time in 197 individuals with probable Alzheimer's disease (AD) and 266 with amnestic mild cognitive impairment (aMCI), compared with 224 healthy controls participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). While previous research has differentiated probable AD and aMCI groups from controls in brain atrophy, it is unclear whether and how sex plays a role in patterns of change over time. Using regional volumetric maps, we fit longitudinal models to the grey matter data collected at repeated occasions, seeking differences in patterns of volume change over time by sex and diagnostic group in a voxel-wise analysis. Additionally, using a region-of-interest approach, we fit longitudinal models to the global volumetric data of predetermined brain regions to determine whether this more conventional approach is sufficient for determining sex and group differences in atrophy. Our longitudinal analyses revealed that, of the various grey matter regions investigated, males and females in the AD group and the aMCI group showed different patterns of decline over time compared to controls in the bilateral precuneus, bilateral caudate nucleus, right entorhinal gyrus, bilateral thalamus, bilateral middle temporal gyrus, left insula, and right amygdala. As one of the first investigation to model more than two time points of structural MRI data over time, our findings add insight into how AD and aMCI males and females differ from controls and from each other over time.

Longitudinal volume changes of the pituitary gland in patients with schizotypal disorder and first-episode schizophrenia

An enlarged volume of the pituitary gland has been reported in the schizophrenia spectrum, possibly reflecting the hypothalamic–pituitary–adrenal (HPA) hyperactivity. However, it remains largely unknown whether the pituitary size longitudinally changes in the course of the spectrum disorders. In the present study, longitudinal magnetic resonance imaging (MRI) data were obtained from 18 patients with first-episode schizophrenia, 13 patients with schizotypal disorder, and 20 healthy controls. The pituitary volume was measured at baseline and follow-up (mean, 2.7years) scans and was compared across groups. The pituitary volume was larger in the schizophrenia patients than controls at baseline, and both patient groups had significantly larger pituitary volume than controls at follow-up. In a longitudinal comparison, both schizophrenia (3.6%/year) and schizotypal (2.7%/year) patients showed significant pituitary enlargement compared with controls (−1.8%/year). In the schizophrenia patients, greater pituitary enlargement over time was associated with less improvement of delusions and higher scores for thought disorders at the follow-up. These findings suggest that the pituitary gland exhibits ongoing volume changes during the early course of the schizophrenia spectrum as a possible marker of state-related impairments.