Impact of scale space search on age- and gender-related changes in MRI-based cortical morphometry

Mapping Complex Brain Torque Components and Their Genetic Architecture and Phenomic Associations in 24,112 Individuals

BACKGROUND

The functional significance and mechanisms determining the development and individual variability of structural brain asymmetry remain unclear. Here, we systematically analyzed all relevant components of the most prominent structural asymmetry, brain torque (BT), and their relationships with potential genetic and nongenetic modifiers in a sample comprising 24,112 individuals from six cohorts.

METHODS

BT features, including petalia, bending, dorsoventral shift, brain tissue distribution asymmetries, and cortical surface positional asymmetries, were directly modeled using a set of automatic three-dimensional brain shape analysis approaches. Age-, sex-, and handedness-related effects on BT were assessed. The genetic architecture and phenomic associations of BT were investigated using genome- and phenome-wide association scans.

RESULTS

Our results confirmed the population-level predominance of the typical counterclockwise torque and suggested a first attenuating, then enlarging dynamic across the life span (3-81 years) primarily for frontal, occipital, and perisylvian BT features. Sex/handedness, BT, and cognitive function of verbal-numerical reasoning were found to be interrelated statistically. We observed differential heritability of up to 56% for BT, especially in temporal language areas. Individual variations of BT were also associated with various phenotypic variables of neuroanatomy, cognition, lifestyle, sociodemographics, anthropometry, physical health, and adult and child mental health. Our genomic analyses identified a number of genetic associations at lenient significance levels, which need to be further validated using larger samples in the future.

CONCLUSIONS

This study provides a comprehensive description of BT and insights into biological and other factors that may contribute to the development and individual variations of BT.

Neuroimaging PheWAS (Phenome-Wide Association Study): A Free Cloud-Computing Platform for Big-Data, Brain-Wide Imaging Association Studies

Large-scale, case-control genome-wide association studies (GWASs) have revealed genetic variations associated with diverse neurological and psychiatric disorders. Recent advances in neuroimaging and genomic databases of large healthy and diseased cohorts have empowered studies to characterize effects of the discovered genetic factors on brain structure and function, implicating neural pathways and genetic mechanisms in the underlying biology. However, the unprecedented scale and complexity of the imaging and genomic data requires new advanced biomedical data science tools to manage, process and analyze the data. In this work, we introduce Neuroimaging PheWAS (phenome-wide association study): a web-based system for searching over a wide variety of brain-wide imaging phenotypes to discover true system-level gene-brain relationships using a unified genotype-to-phenotype strategy. This design features a user-friendly graphical user interface (GUI) for anonymous data uploading, study definition and management, and interactive result visualizations as well as a cloud-based computational infrastructure and multiple state-of-art methods for statistical association analysis and multiple comparison correction. We demonstrated the potential of Neuroimaging PheWAS with a case study analyzing the influences of the apolipoprotein E (APOE) gene on various brain morphological properties across the brain in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Benchmark tests were performed to evaluate the system's performance using data from UK Biobank. The Neuroimaging PheWAS system is freely available. It simplifies the execution of PheWAS on neuroimaging data and provides an opportunity for imaging genetics studies to elucidate routes at play for specific genetic variants on diseases in the context of detailed imaging phenotypic data.

Association vs. Prediction: The Impact of Cortical Surface Smoothing and Parcellation on Brain Age

Association and prediction studies of the brain target the biological consequences of aging and their impact on brain function. Such studies are conducted using different smoothing levels and parcellations at the preprocessing stage, on which their results are dependent. However, the impact of these parameters on the relationship between association values and prediction accuracy is not established. In this study, we used cortical thickness and its relationship with age to investigate how different smoothing and parcellation levels affect the detection of age-related brain correlates as well as brain age prediction accuracy. Our main measures were resel numbers—resolution elements—and age-related variance explained. Using these common measures enabled us to directly compare parcellation and smoothing effects in both association and prediction studies. In our sample of N = 608 participants with age range 18–88, we evaluated age-related cortical thickness changes as well as brain age prediction. We found a negative relationship between prediction performance and correlation values for both parameters. Our results also quantify the relationship between delta age estimates obtained based on different processing parameters. Furthermore, with the direct comparison of the two approaches, we highlight the importance of correct choice of smoothing and parcellation parameters in each task, and how they can affect the results of the analysis in opposite directions.

Age-Related Differences in Brain Morphology and the Modifiers in Middle-Aged and Older Adults

Brain structural morphology differs with age. This study examined age-differences in surface-based morphometric measures of cortical thickness, volume, and surface area in a well-defined sample of 8137 generally healthy UK Biobank participants aged 45-79 years. We illustrate that the complexity of age-related brain morphological differences may be related to the laminar organization and regional evolutionary history of the cortex, and age of about 60 is a break point for increasing negative associations between age and brain morphology in Alzheimer's disease (AD)-prone areas. We also report novel relationships of age-related cortical differences with individual factors of sex, cognitive functions of fluid intelligence, reaction time and prospective memory, cigarette smoking, alcohol consumption, sleep disruption, genetic markers of apolipoprotein E, brain-derived neurotrophic factor, catechol-O-methyltransferase, and several genome-wide association study loci for AD and further reveal joint effects of cognitive functions, lifestyle behaviors, and education on age-related cortical differences. These findings provide one of the most extensive characterizations of age associations with major brain morphological measures and improve our understanding of normal structural brain aging and its potential modifiers.

Cortical and subcortical gray matter bases of cognitive deficits in REM sleep behavior disorder

OBJECTIVE

To investigate cortical and subcortical gray matter abnormalities underlying cognitive impairment in patients with REM sleep behavior disorder (RBD) with or without mild cognitive impairment (MCI).

METHODS

Fifty-two patients with RBD, including 17 patients with MCI, were recruited and compared to 41 controls. All participants underwent extensive clinical assessments, neuropsychological examination, and 3-tesla MRI acquisition of T1 anatomical images. Vertex-based cortical analyses of volume, thickness, and surface area were performed to investigate cortical abnormalities between groups, whereas vertex-based shape analysis was performed to investigate subcortical structure surfaces. Correlations were performed to investigate associations between cortical and subcortical metrics, cognitive domains, and other markers of neurodegeneration (color discrimination, olfaction, and autonomic measures).

RESULTS

Patients with MCI had cortical thinning in the frontal, cingulate, temporal, and occipital cortices, and abnormal surface contraction in the lenticular nucleus and thalamus. Patients without MCI had cortical thinning restricted to the frontal cortex. Lower patient performance in cognitive domains was associated with cortical and subcortical abnormalities. Moreover, impaired performance on olfaction, color discrimination, and autonomic measures was associated with thinning in the occipital lobe.

CONCLUSIONS

Cortical and subcortical gray matter abnormalities are associated with cognitive status in patients with RBD, with more extensive patterns in patients with MCI. Our results highlight the importance of distinguishing between subgroups of patients with RBD according to cognitive status in order to better understand the neurodegenerative process in this population.

Aberrant Topological Patterns of Structural Cortical Networks in Psychogenic Erectile Dysfunction

Male sexual arousal (SA) has been known as a multidimensional experience involving closely interrelated and coordinated neurobehavioral components that rely on widespread brain regions. Recent functional neuroimaging studies have shown relation between abnormal/altered dynamics in these circuits and male sexual dysfunction. However, alterations in the topological organization of structural brain networks in male sexual dysfunction are still unclear. Here, we used graph theory to investigate the topological properties of large-scale structural brain networks, which were constructed using inter-regional correlations of cortical thickness between 78 cortical regions in 40 patients with psychogenic erectile dysfunction (pED) and 39 normal controls. Compared with normal controls, pED patients exhibited a less optimal global topological organization with reduced global and increased local efficiencies. Our results suggest disrupted neural integration among distant brain regions in pED patients, consistent with previous reports of impaired white matter structure and abnormal functional integrity in pED. Additionally, disrupted global network topology in pED was observed to be primarily relevant to altered subnetwork and nodal properties within the networks mediating the cognitive, motivational and inhibitory processes of male SA, possibly indicating disrupted integration of these networks in the whole brain networks and might account for pED patients' abnormal cognitive, motivational and inhibitory processes for male SA. In total, our findings provide evidence for disrupted integrity in large-scale brain networks underlying the neurobehavioral processes of male SA in pED and provide new insights into the understanding of the pathophysiological mechanisms of pED.

Structural insights into aberrant cortical morphometry and network organization in psychogenic erectile dysfunction

Functional neuroimaging studies have revealed abnormal brain dynamics of male sexual arousal (SA) in psychogenic erectile dysfunction (pED). However, the neuroanatomical correlates of pED are still unclear. In this work, we obtained cortical thickness (CTh) measurements from structural magnetic resonance images of 40 pED patients and 39 healthy control subjects. Abnormalities in CTh related to pED were explored using a scale space search based brain morphometric analysis. Organizations of brain structural covariance networks were analyzed as well. Compared with healthy men, pED patients showed significantly decreased CTh in widespread cortical regions, most of which were previously reported to show abnormal dynamics of male SA in pED, such as the medial prefrontal, orbitofrontal, cingulate, inferotemporal, and insular cortices. CTh reductions in these areas were found to be significantly correlated with male sexual functioning degradation. Moreover, pED patients showed decreased interregional CTh correlations from the right lateral orbitofrontal cortex to the right supramarginal gyrus and the left angular cortex, implying disassociations between the cognitive, motivational, and inhibitory networks of male SA in pED. This work provides structural insights on the complex phenomenon of psychogenic sexual dysfunction in men, and suggests a specific vulnerability factor, possibly as an extra "organic" factor, that may play an important role in pED.

Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI

Permutation testing has been widely implemented in voxel-based morphometry (VBM) tools. However, this type of non-parametric inference has yet to be thoroughly compared with traditional parametric inference in VBM studies of brain structure. Here we compare both types of inference and investigate what influence the number of permutations in permutation testing has on results in an exemplar study of how gray matter proportion changes with age in a group of working age adults. High resolution T1-weighted volume scans were acquired from 80 healthy adults aged 25-64years. Using a validated VBM procedure and voxel-based permutation testing for Pearson product-moment coefficient, the effect sizes of changes in gray matter proportion with age were assessed using traditional parametric and permutation testing inference with 100, 500, 1000, 5000, 10000 and 20000 permutations. The statistical significance was set at P<0.05 and false discovery rate (FDR) was used to correct for multiple comparisons. Clusters of voxels with statistically significant (PFDR<0.05) declines in gray matter proportion with age identified with permutation testing inference (N≈6000) were approximately twice the size of those identified with parametric inference (N=3221voxels). Permutation testing with 10000 (N=6251voxels) and 20000 (N=6233voxels) permutations produced clusters that were generally consistent with each other. However, with 1000 permutations there were approximately 20% more statistically significant voxels (N=7117voxels) than with ≥10000 permutations. Permutation testing inference may provide a more sensitive method than traditional parametric inference for identifying age-related differences in gray matter proportion. Based on the results reported here, at least 10000 permutations should be used in future univariate VBM studies investigating age related changes in gray matter to avoid potential false findings. Additional studies using permutation testing in large imaging databanks are required to address the impact of model complexity, multivariate analysis, number of observations, sampling bias and data quality on the accuracy with which subtle differences in brain structure associated with normal aging can be identified.

Cortical thinning explains changes in sleep slow waves during adulthood

Sleep slow waves (SWs) change considerably throughout normal aging. In humans, SWs are generated and propagate on a structural backbone of highly interconnected cortical regions that form most of the default mode network, such as the insula, cingulate cortices, temporal lobe, parietal lobe, and medial frontal lobe. Regions in this network undergo cortical thinning and breakdown in structural and functional connectivity over the course of normal aging. In this study, we investigated how changes in cortical thickness (CT), a measure of gray matter integrity, are involved in modifications of sleep SWs during adulthood in humans. Thirty young (mean age = 23.49 years; SD = 2.79) and 33 older (mean age = 60.35 years; SD = 5.71) healthy subjects underwent a nocturnal polysomnography and T1 MRI. We show that, when controlling for age, higher SW density (nb/min of nonrapid eye movement sleep) was associated with higher CT in cortical regions involved in SW generation surrounding the lateral fissure (insula, superior temporal, parietal, middle frontal), whereas higher SW amplitude was associated with higher CT in middle frontal, medial prefrontal, and medial posterior regions. Mediation analyses demonstrated that thinning in a network of cortical regions involved in SW generation and propagation, but also in cognitive functions, explained the age-related decrease in SW density and amplitude. Altogether, our results suggest that microstructural degradation of specific cortical regions compromise SW generation and propagation in older subjects, critically contributing to age-related changes in SW oscillations.

Use of brain MRI atlases to determine boundaries of age-related pathology: the importance of statistical method

Introduction

Neurodegenerative disease diagnoses may be supported by the comparison of an individual patient’s brain magnetic resonance image (MRI) with a voxel-based atlas of normal brain MRI. Most current brain MRI atlases are of young to middle-aged adults and parametric, e.g., mean ±standard deviation (SD); these atlases require data to be Gaussian. Brain MRI data, e.g., grey matter (GM) proportion images, from normal older subjects are apparently not Gaussian. We created a nonparametric and a parametric atlas of the normal limits of GM proportions in older subjects and compared their classifications of GM proportions in Alzheimer’s disease (AD) patients.

Methods

Using publicly available brain MRI from 138 normal subjects and 138 subjects diagnosed with AD (all 55–90 years), we created: a mean ±SD atlas to estimate parametrically the percentile ranks and limits of normal ageing GM; and, separately, a nonparametric, rank order-based GM atlas from the same normal ageing subjects. GM images from AD patients were then classified with respect to each atlas to determine the effect statistical distributions had on classifications of proportions of GM in AD patients.

Results

The parametric atlas often defined the lower normal limit of the proportion of GM to be negative (which does not make sense physiologically as the lowest possible proportion is zero). Because of this, for approximately half of the AD subjects, 25–45% of voxels were classified as normal when compared to the parametric atlas; but were classified as abnormal when compared to the nonparametric atlas. These voxels were mainly concentrated in the frontal and occipital lobes.

Discussion

To our knowledge, we have presented the first nonparametric brain MRI atlas. In conditions where there is increasing variability in brain structure, such as in old age, nonparametric brain MRI atlases may represent the limits of normal brain structure more accurately than parametric approaches. Therefore, we conclude that the statistical method used for construction of brain MRI atlases should be selected taking into account the population and aim under study. Parametric methods are generally robust for defining central tendencies, e.g., means, of brain structure. Nonparametric methods are advisable when studying the limits of brain structure in ageing and neurodegenerative disease.

Reproducibility of brain-cognition relationships using three cortical surface-based protocols: An exhaustive analysis based on cortical thickness

People differ in their cognitive functioning. This variability has been exhaustively examined at the behavioral, neural and genetic level to uncover the mechanisms by which some individuals are more cognitively efficient than others. Studies investigating the neural underpinnings of interindividual differences in cognition aim to establish a reliable nexus between functional/structural properties of a given brain network and higher order cognitive performance. However, these studies have produced inconsistent results, which might be partly attributed to methodological variations. In the current study, 82 healthy young participants underwent MRI scanning and completed a comprehensive cognitive battery including measurements of fluid, crystallized, and spatial intelligence, along with working memory capacity/executive updating, controlled attention, and processing speed. The cognitive scores were obtained by confirmatory factor analyses. T1 -weighted images were processed using three different surface-based morphometry (SBM) pipelines, varying in their degree of user intervention, for obtaining measures of cortical thickness (CT) across the brain surface. Distribution and variability of CT and CT-cognition relationships were systematically compared across pipelines and between two cognitively/demographically matched samples to overcome potential sources of variability affecting the reproducibility of findings. We demonstrated that estimation of CT was not consistent across methods. In addition, among SBM methods, there was considerable variation in the spatial pattern of CT-cognition relationships. Finally, within each SBM method, results did not replicate in matched subsamples.

Cortical thickness and brain volumetric analysis in body dysmorphic disorder

Individuals with body dysmorphic disorder (BDD) suffer from preoccupations with perceived defects in physical appearance, causing severe distress and disability. Although BDD affects 1-2% of the population, the neurobiology is not understood. Discrepant results in previous volumetric studies may be due to small sample sizes, and no study has investigated cortical thickness in BDD. The current study is the largest neuroimaging analysis of BDD. Participants included 49 medication-free, right-handed individuals with DSM-IV BDD and 44 healthy controls matched by age, sex, and education. Using high-resolution T1-weighted magnetic resonance imaging, we computed vertex-wise gray matter (GM) thickness on the cortical surface and GM volume using voxel-based morphometry. We also computed volumes in cortical and subcortical regions of interest. In addition to group comparisons, we investigated associations with symptom severity, insight, and anxiety within the BDD group. In BDD, greater anxiety was significantly associated with thinner GM in the left superior temporal cortex and greater GM volume in the right caudate nucleus. There were no significant differences in cortical thickness, GM volume, or volumes in regions of interest between BDD and control subjects. Subtle associations with clinical symptoms may characterize brain morphometric patterns in BDD, rather than large group differences in brain structure.

Sex differences in neocortical structure and cognitive performance: A surface-based morphometry study

On average, men show larger brain volumes than women. Regional differences have been also observed, although most of the available studies apply voxel-based morphometry (VBM). Reports applying surface-based morphometry (SBM) have been focused mainly on cortical thickness (CT). Here we apply SBM for obtaining global and regional indices of CT, cortical surface area (CSA), and cortical gray matter volume (CGMV) from samples of men (N=40) and women (N=40) matched for their performance on four cognitive factors varying in their complexity: processing speed, attention control, working memory capacity, and fluid intelligence. These were the main findings: 1) CT and CSA produced very weak correlations in both sexes, 2) men showed larger values in CT, CSA, and CGMV, and 3) cognitive performance was unrelated to brain structural variation within sexes. Therefore, we found substantial group differences in brain structure, but there was no relationship with cognitive performance both between and within-sexes.

SGPP: spatial Gaussian predictive process models for neuroimaging data

The aim of this paper is to develop a spatial Gaussian predictive process (SGPP) framework for accurately predicting neuroimaging data by using a set of covariates of interest, such as age and diagnostic status, and an existing neuroimaging data set. To achieve a better prediction, we not only delineate spatial association between neuroimaging data and covariates, but also explicitly model spatial dependence in neuroimaging data. The SGPP model uses a functional principal component model to capture medium-to-long-range (or global) spatial dependence, while SGPP uses a multivariate simultaneous autoregressive model to capture short-range (or local) spatial dependence as well as cross-correlations of different imaging modalities. We propose a three-stage estimation procedure to simultaneously estimate varying regression coefficients across voxels and the global and local spatial dependence structures. Furthermore, we develop a predictive method to use the spatial correlations as well as the cross-correlations by employing a cokriging technique, which can be useful for the imputation of missing imaging data. Simulation studies and real data analysis are used to evaluate the prediction accuracy of SGPP and show that SGPP significantly outperforms several competing methods, such as voxel-wise linear model, in prediction. Although we focus on the morphometric variation of lateral ventricle surfaces in a clinical study of neurodevelopment, it is expected that SGPP is applicable to other imaging modalities and features.

Patterns of cortical thinning in idiopathic rapid eye movement sleep behavior disorder

Idiopathic rapid eye movement sleep behavior disorder is a parasomnia that is a risk factor for dementia with Lewy bodies and Parkinson's disease. Brain function impairments have been identified in this disorder, mainly in the frontal and posterior cortical regions. However, the anatomical support for these dysfunctions remains poorly understood. We investigated gray matter thickness, gray matter volume, and white matter integrity in patients with idiopathic rapid eye movement sleep behavior disorder. Twenty-four patients with polysomnography-confirmed idiopathic rapid eye movement sleep behavior disorder and 42 healthy individuals underwent a 3-tesla structural and diffusion magnetic resonance imaging examination using corticometry, voxel-based morphometry, and diffusion tensor imaging. In the patients with idiopathic rapid eye movement sleep behavior disorder, decreased cortical thickness was observed in the frontal cortex, the lingual gyrus, and the fusiform gyrus. Gray matter volume was reduced in the superior frontal sulcus only. Patients showed no increased gray matter thickness or volume. Diffusion tensor imaging analyses revealed no significant white matter differences between groups. Using corticometry in patients with idiopathic rapid eye movement sleep behavior disorder, several new cortical regions with gray matter alterations were identified, similar to those reported in dementia with Lewy bodies and Parkinson's disease. These findings provide some anatomical support for previously identified brain function impairments in this disorder.

Multiscale adaptive generalized estimating equations for longitudinal neuroimaging data

Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE is applicable to making statistical inference on regression coefficients in both balanced and unbalanced longitudinal designs and even in twin and familial studies, whereas standard software platforms have several major limitations in handling these complex studies. Specifically, conventional voxel-based analyses in these software platforms involve Gaussian smoothing imaging data and then independently fitting a statistical model at each voxel. However, the conventional smoothing methods suffer from the lack of spatial adaptivity to the shape and spatial extent of region of interest and the arbitrary choice of smoothing extent, while independently fitting statistical models across voxels does not account for the spatial properties of imaging observations and noise distribution. To address such drawbacks, we adapt a powerful propagation-separation (PS) procedure to sequentially incorporate the neighboring information of each voxel and develop a new novel strategy to solely update a set of parameters of interest, while fixing other nuisance parameters at their initial estimators. Simulation studies and real data analysis show that MAGEE significantly outperforms voxel-based analysis.