Genetic variation in the KIBRA gene (rs17070145) has been associated with episodic memory, with T-allele non-carriers exhibiting poorer performance than T-allele carriers. In a recent positron emission tomography study, KIBRA T-allele non-carriers exhibited hypometabolism in brain regions previously shown to be metabolically affected by Alzheimer’s disease (AD). Further, non-carriers have been shown to be at increased risk for late-onset AD. To further investigate the impact of this KIBRA variant on brain structures associated with AD, we utilized diffusion-weighted imaging to determine whether T-allele non-carriers exhibit altered white matter microstructure in a cohort of late-middle-aged, cognitively normal adults. 98 participants (age=62.36±5.96 years) from the Wisconsin Registry for Alzheimer’s Prevention underwent multi-shell diffusion-weighted magnetic resonance imaging. Neurite Orientation Dispersion and Density Imaging was applied to produce the intracellular volume fraction of axons and dendrites, termed the neurite density index (NDI). Analysis of covariance, controlling for age and sex, was used to test for the genetic effect of non-carriers on NDI on a voxel-wise basis in SPM12. Significance was inferred at p<0.001 uncorrected with cluster extent >100 voxels. Compared with T-allele carriers, non-carriers demonstrated decreased NDI in the right precuneal and lateral occipital white matter, the left temporal part of the superior longitudinal fascicle, and bilaterally in the corpus callosum and superior occipito-frontal fascicles. This study provides novel diffusion MRI based evidence of altered white matter integrity in KIBRA T-allele non-carriers in brain areas known to be affected in AD. Specifically, decreased neurite density in the occipito-frontal fascicles and corpus callosum correspond with white matter pathology associated with the progression of AD. Furthermore, the reductions in white matter integrity in the superior longitudinal fascicle and precuneal cerebral white matter pose a possible mechanism underlying the shared metabolic signatures previously demonstrated in AD and KIBRA T-allele non-carriers. Our findings further strengthen KIBRA’s connection to the neurobiological processes involved in memory and susceptibility to AD, and suggest a potential protective role of KIBRA’s T-allele against the pathophysiological processes of AD.
(Abstracted from JAMA Pediatr 2018;172(10):973–981) Depression and anxiety are common during pregnancy. These disorders are estimated to affect 7% to 20% of pregnant women; however, many women reach only a subclinical threshold and are not formally diagnosed.
Alzheimer's disease (AD) is associated with widespread gross cortical atrophy that can be measured on T1-weighted MRI; however, in vivo neuroimaging of gray matter (GM) microstructural changes in AD has been relatively limited. Recent advances in diffusion MRI have provided new techniques to study brain microstructure, which may provide additional information on neurodegenerative processes. In this study, we used neurite orientation dispersion and density imaging (NODDI) modelling along with gray-matter based spatial statistics (GBSS) to examine cortical microstructure in AD dementia. 29 individuals with AD dementia (mean age 71.8±9.9yrs, 62% female) and 87 age- and sex-matched cognitively-unimpaired individuals (mean age 71.7±9.5years, 67% female) underwent multi-shell diffusion MRI (9xb=500s/mm2, 18xb=800s/mm2, 36xb=2000s/mm2; 2mm3 isotropic voxel resolution) and NODDI modeling with optimized parameters for GM (intracellular intrinsic parallel diffusivity = 1.1μm2/ms). Images were further processed using a GBSS pipeline (Nazeri et al., 2015). Briefly, pseudo T1-weighted images were generated from diffusion-space tissue (WM, GM, CSF) segmentation maps, which were then used to create a study-specific population template. GM probability maps were aligned to template space and skeletonized. Neurite density index (NDI) and orientation dispersion index (ODI) maps were then projected onto the GM skeleton. FSL's 'randomise' with 5000 permutations and threshold-free cluster enhancement was used to generate family-wise error (FWE)-corrected statistical maps showing group differences between AD and cognitively-unimpaired individuals. Compared to age- and sex-matched cognitively healthy participants, individuals with AD dementia show widespread lower cortical NDI and ODI (PFWE<0.05, Fig1). Specifically, NDI is lower bilaterally in the inferior and medial temporal lobes, as well as in the angular gyrus and posterior cingulate cortex (Fig1A). ODI difference showed a similar distribution, but with additional differences in frontal regions, including in the superior frontal gyrus (Fig1B).
Improved understanding of neuroimaging signal changes and their relation to patient outcomes after ischemic stroke is needed to improve ability to predict motor improvement and make therapy recommendations. The posterior limb of the internal capsule (PLIC) is a hub of afferent and efferent motor signaling and this work proposes new, image-based methods for prognosis based on interhemispheric differences in the PLIC. In this work, nine acute supratentorial ischemic stroke patients with motor impairment received a baseline, 203-direction diffusion brain MRI and a clinical assessment 3-12 days post-stroke and were compared to nine age-matched healthy controls. Asymmetries based on the mean and Kullback-Leibler divergence in the ipsilesional and contralesional PLIC were calculated for diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) measures from the baseline MRI. Predictions of upper extremity Fugl-Meyer (FM) scores at 5-weeks follow-up from baseline measures of PLIC asymmetry in diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI) models were evaluated. For the stroke participants, the baseline asymmetry measures in the PLIC for the orientation dispersion index of the neurite orientation dispersion and density imaging (NODDI) model were highly correlated with upper extremity FM outcomes (r2 = 0.83). Use of DSI and the NODDI orientation dispersion index parameter shows promise of being more predictive of stroke recovery and to help better understand white matter changes in stroke, beyond DTI measures. The new finding that baseline interhemispheric differences in the PLIC calculated from the orientation dispersion index of the NODDI model are highly correlated with upper extremity functional outcomes may lead to improved image-based motor-outcome prediction after middle cerebral artery ischemic stroke.
We present a unified statistical approach to modeling 3D anatomical objects extracted from medical images. Due to image acquisition and preprocessing noises, it is expected the imaging data is noisy. Using the Laplace-Beltrami (LB) eigenfunctions, we smooth out noisy data and perform statistical analysis. The method is applied in characterizing the 3D growth pattern of human hyoid bone between ages 0 and 20 obtained from computed tomography (CT). We detected a significant age effect on localized parts of the hyoid bone.
Statistical data analysis plays a major role in discovering structural and functional imaging phenotypes for mental disorders such as Alzheimer's disease (AD). The goal here is to identify, ideally early on, which regions in the brain show abnormal variations with a disorder. To make the method more sensitive, we rely on a multi-resolutional perspective of the given data. Since the underlying imaging data (such as cortical surfaces and connectomes) are naturally represented in the form of weighted graphs which lie in a non-Euclidean space, we introduce recent work from the harmonics literature to derive an effective multi-scale descriptor using wavelets on graphs that characterize the local context at each data point. Using this descriptor, we demonstrate experiments where we identify significant differences between AD and control populations using cortical surface data and tractography derived graphs/networks.
Abstract Background By reinterpreting the difference between a model prediction and calendar age, machine learning algorithms allow for estimation of “brain age” using MRI. There is growing evidence for sex differences in the influence of APOE genotype (evaluated as APOE ε4 carrier status) on Alzheimer’s disease (AD) endophenotypes at various stages of disease. The purpose of this study is to examine the influence of an APOE neuropathology‐based score (APOE npscore) on the brain aging estimated from deep learning. Methods Brain age from 274 participants ( Fig. 1 ) in the Wisconsin‐ADRC was estimated from T1w‐MRI using a publicly available deep learning model called two‐stage‐age‐network (TSAN) that was pre‐trained on over four thousand scans from OASIS, ADNI‐I and PAC‐2019. TSAN uses a novel rank‐based loss along with mean squared loss and makes predictions in two steps which does not require a posteriori bias correction. This is a significant improvement from prior deep learning models in reducing the “regression‐to‐mean bias” typically present in brain age models. APOE genotypes (ε2ε2, ε2ε3, ε3ε3, ε2ε4, ε3ε4, ε4ε4) were weighted by the log(odds‐ratio) from a large study of autopsy‐confirmed AD cases/controls, providing a variable representing the relative amount of risk across APOE genotypes. Mood’s median test was used to examine the sex differences between the excess brain aging (EBA) as a function of the APOE npscore. Results Fig. 2 shows the brain age predictions. Fig. 3 shows that there are significant differences between females and males in the EBA for the APOE npscore <0. Median EBA for both males and females is <0 when APOE npscore <0 suggesting protective effects of ε2 on brain aging. The median EBA >0 for both the sexes when APOE npscore ≥0. Conclusion We report sex differences in the influence of APOE genotype on brain aging. There is heterogeneity in the differences within the ε4+ (APOE npscore >0) and ε4‐ (APOE npscore ≤0) groups. These sex differences may not be observed when using just ε4 carrier status instead of the APOE npscore. This provides evidence for a shift in paradigm for the way we analyze APOE genotype because of latent nuances of the ε4‐status influence on AD outcomes.
Abstract Conscious awareness of negative cues is thought to enhance emotion-regulatory capacity, but the neural mechanisms underlying this effect are unknown. Using continuous flash suppression (CFS) in the MRI scanner, we manipulated visual awareness of fearful faces during an affect misattribution paradigm, in which preferences for neutral objects can be biased by the valence of a previously presented stimulus. The amygdala responded to fearful faces independently of awareness. However, when awareness of fearful faces was prevented, individuals with greater amygdala responses displayed a negative bias toward unrelated novel neutral faces. In contrast, during the aware condition, inverse coupling between the amygdala and prefrontal cortex reduced this bias, particularly among individuals with higher structural connectivity in the major white matter pathway connecting the prefrontal cortex and amygdala. Collectively, these results indicate that awareness promotes the function of a critical emotion-regulatory network targeting the amygdala, providing a mechanistic account for the role of awareness in emotion regulation.
Purpose NODDI is widely used in parameterizing microstructural brain properties. The model includes three signal compartments: intracellular, extracellular, and free water. The neurite compartment intrinsic parallel diffusivity (d∥) is set to 1.7 μm2⋅ms−1, though the effects of this assumption have not been extensively explored. This work investigates the optimality of d∥ = 1.7 μm2⋅ms−1 under varying imaging protocol, age groups, sex, and tissue type in comparison to other biologically plausible values of d∥. Methods Model residuals were used as the optimality criterion. The model residuals were evaluated in function of d∥ over the range from 0.5 to 3.0 μm2⋅ms−1. This was done with respect to tissue type (i.e., white matter versus gray matter), sex, age (infancy to late adulthood), and diffusion-weighting protocol (maximum b-value). Variation in the estimated parameters with respect to d∥ was also explored. Results Results show d∥ = 1.7 μm2⋅ms−1 is appropriate for adult brain white matter but it is suboptimal for gray matter with optimal values being significantly lower. d∥ = 1.7 μm2⋅ms−1 was also suboptimal in the infant brain for both white and gray matter with optimal values being significantly lower. Minor optimum d∥ differences were observed versus diffusion protocol. No significant sex effects were observed. Additionally, changes in d∥ resulted in significant changes to the estimated NODDI parameters. Conclusion The default (d∥) of 1.7 μm2⋅ms−1 is suboptimal in gray matter and infant brains.
When a brain network is constructed by an existing parcellation method, the topological structure of the network changes depending on the scale of the parcellation. To avoid the scale dependency, we propose to construct a nested hierarchical structural brain network by subdividing the existing parcellation hierarchically. The method is applied in diffusion tensor imaging study of 111 twins in characterizing the topology of the brain network. The genetic contribution of the whole brain structural connectivity is determined and shown to be robustly present over different network scales.
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