Regular ArticleA Voxel-Based Method for the Statistical Analysis of Gray and White Matter Density Applied to Schizophrenia
Abstract
We describe a novel technique for characterizing regional cerebral gray and white matter differences in structural magnetic resonance images by the application of methods derived from functional imaging. The technique involves automatic scalp-editing of images followed by segmentation, smoothing, and spatial normalization to a symmetrical template brain in stereotactic Talairach space. The basic idea is (i) to convert structural magnetic resonance image data into spatially normalized images of gray (or white) matter density, effected by segmenting the images and smoothing, and then (ii) to use Statistical Parametric Mapping to make inferences about the relationship between gray (or white) matter density and symptoms (or other pathophysiological measures) in a regionally specific fashion. Because the whole brain sum of gray (or white) matter indices is treated as a confound, the analysis reduces to a characterization of relative gray (or white) matter density on a voxel by voxel basis. We suggest that this is a powerful approach to voxel-based statistical anatomy. Using the technique, we constructed maps of the regional cerebral gray and white matter density correlates of syndrome scores (distinct psychotic symptoms) in a group of 15 schizophrenic patients. There was a negative correlation between the score for the reality distortion syndrome and regional gray matter density in the left superior temporal lobe (P = 0.01) and regional white matter density in the corpus callosum (P < 0.001). These abnormalities may be associated with functional changes predisposing to auditory hallucinations and delusions. This method permits the detection of structural differences within the entire brain (as opposed to selected regions of interest) and may be of value in the investigation of structural gray and white matter abnormalities in a variety of brain diseases.
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Contributions of Polygenic Risk and Disease Status to Gray Matter Abnormalities in Major Depression
2024, Biological Psychiatry: Cognitive Neuroscience and NeuroimagingGray matter (GM) abnormalities in depression are potentially attributable to some combination of trait, state, and illness history factors. Here, we sought to determine the contributions of polygenic risk for depression, depressive disease status, and the interaction of these factors to these GM abnormalities.
We conducted a cross-sectional comparison using a 2 × 3 factorial design examining effects of polygenic risk for depression (lower vs. upper quartile) and depression status (never depressed, currently depressed, or remitted depression) on regional GM concentration and GM volume. Participants were a subset of magnetic resonance imaging–scanned UK Biobank participants comprising 2682 people (876 men, 1806 women) algorithmically matched on 16 potential confounders.
In women but not men, we observed that elevated polygenic risk for depression was associated with reduced cerebellar GM volume. This deficit occurred in salience and dorsal attention network regions of the cerebellum and was associated with poorer performance on tests of attention and executive function but not fluid intelligence. Moreover, in women with current depression compared to both women with remitted depression and women who never had depression, we observed GM reductions in ventral and medial prefrontal, insular, and medial temporal regions. These state-related abnormalities remained when accounting for antidepressant medication status.
Neuroanatomical deficits attributed broadly to major depression are more likely due to an aggregation of independent factors. Polygenic risk for depression accounted for cerebellar structural abnormalities that themselves accounted for cognitive deficits observed in this disorder. Medial and ventral prefrontal, insular, and temporal cortex deficits constituted a much larger proportion of the aggregate deficit and were attributable to the depressed state.
Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.
Abnormal white matter changes in Alzheimer's disease based on diffusion tensor imaging: A systematic review
2023, Ageing Research ReviewsAlzheimer's disease (AD) is a degenerative neurological disease in elderly individuals. Subjective cognitive decline (SCD), mild cognitive impairment (MCI) and further development to dementia (d-AD) are considered to be major stages of the progressive pathological development of AD. Diffusion tensor imaging (DTI), one of the most important modalities of MRI, can describe the microstructure of white matter through its tensor model. It is widely used in understanding the central nervous system mechanism and finding appropriate potential biomarkers for the early stages of AD. Based on the multilevel analysis methods of DTI (voxelwise, fiberwise and networkwise), we summarized that AD patients mainly showed extensive microstructural damage, structural disconnection and topological abnormalities in the corpus callosum, fornix, and medial temporal lobe, including the hippocampus and cingulum. The diffusion features and structural connectomics of specific regions can provide information for the early assisted recognition of AD. The classification accuracy of SCD and normal controls can reach 92.68% at present. And due to the further changes of brain structure and function, the classification accuracy of MCI, d‐AD and normal controls can reach more than 97%. Finally, we summarized the limitations of current DTI-based AD research and propose possible future research directions.
The measurement of quantitative, tissue-specific MR properties, e.g., water content, longitudinal relaxation time (T1) and effective transverse relaxation time (T2⁎), using quantitative MRI at a clinical field strength (1.5 T to 3T) is a well-explored topic. However, none of the commonly used standard brain atlases, such as MNI or JHU, provide quantitative information. Within the framework of quantitative MRI of the brain, this work reports on the development of the first quantitative brain atlas for tissue water content at 3T. A methodology to create this quantitative atlas of in vivo brain water content based on healthy volunteers is presented, and preliminary, practical examples of its potential applications are also shown.
Established methods for the fast and reliable measurement of the absolute water content were used to achieve high precision and accuracy. Water content and T2⁎ were mapped based on two different methods: an intermediate-TR, two-point method and a long-TR, single-scan method. Twenty healthy subjects (age 25.3 ± 2.5 years) were examined with these quantitative imaging protocols. The images were normalised to MNI stereotactic coordinates, and water content atlases of healthy volunteers were created for each method and compared. Regions-of-interest were generated with the help of a standard MNI template, and water content values averaged across the ROIs were compared to water content values from the literature.
Finally, in order to demonstrate the strength of quantitative MRI, water content maps from patients with pathological changes in the brain due to stroke, tumour (glioblastoma) and multiple sclerosis were voxel-wise compared to the healthy brain.
The water content atlases were largely independent of the method used to acquire the individual water maps. Global grey matter and white matter water content values between the methods agreed with each other to within 0.5 %. The feasibility of detecting abnormal water content in the brains of patients based on comparison to a healthy brain water content atlas was demonstrated.
In summary, the first quantitative water content brain atlas in vivo has been developed, and a voxel-wise assessment of pathology-related changes in the brain water content has been performed. These results suggest that qMRI, in combination with a water content atlas, allows for a quantitative interpretation of changes due to disease and could be used for disease monitoring.
Measuring variability of local brain volume using improved volume preserved warping
2022, Computerized Medical Imaging and GraphicsMeasuring local brain volume is clinically important in neuroimaging studies. Voxel preserved warping (VPW) and Jacobian determinant are effective methods for studying local brain volume changes and variations (LBVCV) across multiple brains. However, these LBVCV methods typically depend on the local deformation without using the global deformation, while both deformations are needed in co-registering the brains under examination so that the brains can be compared on a common and fair basis. However, instead of employing a uniformed strategy, different co-registration methods have developed their own unique strategy in performing global and local transformation of the co-registration of the brains, and how the global and local transformations may combine to achieve the final goal of co-registration is not their concern, as long as the final registration may accomplish the co-registering job satisfactorily. The aforementioned inconsistency thus makes the LBVCV measurement that relies on the registration methods for studying local brain volumes totally unstable and actually unreliable. To address the uncertainty in measuring local brain volume variability caused by the un-uniqueness of performing global and local deformations during co-registration, the present study proposes new VPW approaches (VPWα and VPWβ), which no longer require the separation of the global and local transformation components but employ only the general deformation concatenating both components, as long as the general registration may achieve the task of co-registering brain images. The new VPW methods are validated in theory and in practice, using both simulated and real-world imaging data, respectively, based on two registration methods popularly in use by the neuroimaging research community, i.e., the Automatic Registration Toolbox (ART) and Symmetric Image Normalization Method (SyN) registration methods. Experiments using simulated data demonstrated that the proposed new VPW methods may reliably measure local brain volume changes and variability. In contrast, traditional methods typically may result in LBVCV maps containing significantly inconsistent even false findings. In the experiments using real neuroimaging datasets from a schizophrenia study, the results based on the proposed new VPW methods were highly consistent, no matter which registration method was employed. Otherwise, the LBVCV results based on traditional approaches would show significant difference, depending on the individual registration method that the analysis employed. LBVCV assessments based on traditional methods appear to be unreliable. The proposed new VPW methods for measuring local volume changes is independent of registration methods, and therefore can serve as alternative approaches for assessing LBVCV reliably.
Brain patterns of pace – but not rhythm – are associated with vascular disease in older adults
2022, Cerebral Circulation - Cognition and BehaviorDistinct domains of gait such as pace and rhythm are linked to an increased risk for cognitive decline, falls, and dementia in aging. The brain substrates supporting these domains and underlying diseases, however, remain relatively unknown. The current study aimed to identify patterns of gray matter volume (GMV) associated with pace and rhythm, and whether these patterns vary as a function of vascular and non-vascular comorbidities.
A cross-sectional sample of 297 older adults (M Age = 72.5 years ± 7.2 years, 43% women) without dementia was drawn from the Tasmanian Study of Cognition and Gait (TASCOG). Factor analyses were used to reduce eight quantitative gait variables into two domains. The “pace” domain was primarily composed of gait speed, stride length, and double support time. The “rhythm” domain was composed of swing time, stance time, and cadence. Multivariate covariance-based analyses adjusted for age, sex, education, total intracranial volume, and presence of mild cognitive impairment identified gray matter volume (GMV) patterns associated with pace and rhythm, as well as participant-specific expression (or factor) scores for each pattern.
Pace was positively associated with GMV in the right superior temporal sulcus, bilateral supplementary motor areas (SMA), and bilateral cerebellar regions. Rhythm was positively associated with GMV in bilateral SMA, prefrontal, cingulate, and paracingulate cortices. The GMV pattern associated with pace was less expressed in participants with any vascular disease; this association was also found independently with hypertension, diabetes, and myocardial infarction.
Both pace and rhythm domains of gait were associated with the volume of brain structures that have been linked to controlled and automatic aspects of gait control, as well as with structures involved in multisensory integration. Only the brain structures associated with pace, however, were associated with vascular disease.