Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology

Morphological Brain Networks of White Matter: Mapping, Evaluation, Characterization, and Application

Although white matter (WM) accounts for nearly half of adult brain, its wiring diagram is largely unknown. Here, an approach is developed to construct WM networks by estimating interregional morphological similarity based on structural magnetic resonance imaging. It is found that morphological WM networks showed nontrivial topology, presented good-to-excellent test-retest reliability, accounted for phenotypic interindividual differences in cognition, and are under genetic control. Through integration with multimodal and multiscale data, it is further showed that morphological WM networks are able to predict the patterns of hamodynamic coherence, metabolic synchronization, gene co-expression, and chemoarchitectonic covariance, and associated with structural connectivity. Moreover, the prediction followed WM functional connectomic hierarchy for the hamodynamic coherence, is related to genes enriched in the forebrain neuron development and differentiation for the gene co-expression, and is associated with serotonergic system-related receptors and transporters for the chemoarchitectonic covariance. Finally, applying this approach to multiple sclerosis and neuromyelitis optica spectrum disorders, it is found that both diseases exhibited morphological dysconnectivity, which are correlated with clinical variables of patients and are able to diagnose and differentiate the diseases. Altogether, these findings indicate that morphological WM networks provide a reliable and biologically meaningful means to explore WM architecture in health and disease.

Aberrant single-subject morphological brain networks in first-episode, treatment-naive adolescents with major depressive disorder

Background

Neuroimaging-based connectome studies have indicated that major depressive disorder (MDD) is associated with disrupted topological organization of large-scale brain networks. However, the disruptions and their clinical and cognitive relevance are not well established for morphological brain networks in adolescent MDD.

Objective

To investigate the topological alterations of single-subject morphological brain networks in adolescent MDD.

Methods

Twenty-five first-episode, treatment-naive adolescents with MDD and 19 healthy controls (HCs) underwent T1-weighted magnetic resonance imaging and a battery of neuropsychological tests. Single-subject morphological brain networks were constructed separately based on cortical thickness, fractal dimension, gyrification index, and sulcus depth, and topologically characterized by graph-based approaches. Between-group differences were inferred by permutation testing. For significant alterations, partial correlations were used to examine their associations with clinical and neuropsychological variables in the patients. Finally, a support vector machine was used to classify the patients from controls.

Results

Compared with the HCs, the patients exhibited topological alterations only in cortical thickness-based networks characterized by higher nodal centralities in parietal (left primary sensory cortex) but lower nodal centralities in temporal (left parabelt complex, right perirhinal ectorhinal cortex, right area PHT and right ventral visual complex) regions. Moreover, decreased nodal centralities of some temporal regions were correlated with cognitive dysfunction and clinical characteristics of the patients. These results were largely reproducible for binary and weighted network analyses. Finally, topological properties of the cortical thickness-based networks were able to distinguish the MDD adolescents from HCs with 87.6% accuracy.

Conclusion

Adolescent MDD is associated with disrupted topological organization of morphological brain networks, and the disruptions provide potential biomarkers for diagnosing and monitoring the disease.

Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI

Introduction

Current studies of structural covariance networks were focused on the gray matter in the human brain. The structural covariance connectivity in the white matter remains largely unexplored. This paper aimed to build novel metrics that can infer white matter structural covariance connectivity, and to explore the predictive power of the proposed features.

Methods

To this end, a cohort of 315 adult subjects with the anatomical brain MRI datasets were obtained from the publicly available Dallas Lifespan Brain Study (DLBS) project. The 3D wavelet transform was applied on the individual voxel-based morphology (VBM) volume to obtain the white matter structural covariance connectivity. The predictive models for cognitive functions were built using support vector regression (SVR).

Results

The predictive models exhibited comparable performance with previous studies. The novel features successfully predicted the individual ability of digit comparison (DC) (r = 0.41 ± 0.01, p < 0.01) and digit symbol (DSYM) (r = 0.5 ± 0.01, p < 0.01). The sensorimotor-related white matter system exhibited as the most predictive network node. Furthermore, the node strengths of sensorimotor mode were significantly correlated to cognitive scores.

Discussion

The results suggested that the white matter structural covariance connectivity was informative and had potential for predictive tasks of brain-behavior research.

Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer's Disease

Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer's disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration. In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer's Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer's disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer's disease.

A unified framework for mapping individual interregional high-order morphological connectivity based on regional cortical features from anatomical MRI

Building individual brain networks form the single volume of anatomical MRI is a challenging task. Furthermore, the high-order connectivity of morphological networks remains unexplored. This paper aimed to investigate the individual high-order morphological connectivity from anatomical MRI. Towards this goal, a unified framework based on six feature distances (euclidean, seuclidean, mahalanobis, cityblock, minkowski, and chebychev) was proposed to derive high-order interregional morphological features. The test-retest datasets and the healthy aging datasets were applied to analyze the reliability and the inter-subject variability of the novel features. In addition, the predictive models based on these novel features were established for age estimation. The proposed six neuroanatomical features exhibited significant high-to-excellent reliability. Certain connections were significantly correlated to biological age based on the six novel metrics (p < .05, FDR corrected). Moreover, the predicted age were significantly correlated to the original age in each regression task (r > 0.5, p < 10^-6). The results suggested that the novel high-order metrics were reliable and could reflect individual differences, which could be beneficial for current methods of individual brain connectomes.

Exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering

Multiscale signal processing techniques such as wavelet filtering have proved to be particularly successful in predicting exon sequences. Traditional wavelet predictor is domain filtering, and enforces exon features by weighting nucleotide values with coefficients. Such a measure performs linear filtering and is not suitable for preserving the short coding exons and the exon-intron boundaries. This paper describes a prediction framework that is capable of non-linearly processing DNA sequences while achieving high prediction rates. There are two key contributions. The first is the introduction of a genomic-inspired multiscale bilateral filtering (MSBF) which exploits both weighting coefficients in the spatial domain and nucleotide similarity in the range. Similarly to wavelet transform, the MSBF is also defined as a weighted sum of nucleotides. The difference is that the MSBF takes into account the variation of nucleotides at a specific codon position. The second contribution is the exploitation of inter-scale correlation in MSBF domain to find the inter-scale dependency on the differences between the exon signal and the background noise. This favourite property is used to sharp the important structures while weakening noise. Three benchmark data sets have been used in the evaluation of considered methods. By comparison with four existing techniques, the prediction results demonstrate that: the proposed method reveals at least improvement of 4.1%, 50.5%, 25.6%, 2.5%, 10.8%, 15.5%, 11.1%, 12.3%, 9.2% and 2.4% on the exons length of 1–24, 25–49, 50–74, 75–99, 100–124, 125–149, 150–174, 175–199, 200–299 and 300–300+, respectively. The MSBF of its nonlinear nature is good at energy compaction, which makes it capable of locating the sharp variations around short exons. The direct scale multiplication of coefficients at several adjacent scales obviously enhanced exon features while the noise contents were suppressed. We show that the non-linear nature and correlation-based property achieved in proposed predictor is greater than that for traditional filtering, which leads to better exon prediction performance. There are some possible applications of this predictor. Its good localization and protection of sharp variations will make the predictor be suitable to perform fault diagnosis of aero-engine.

Structural covariability hubs in old age

Studies have shown that inter-individual differences in grey matter, as measured by voxel-based morphometry, are coordinated between voxels. This has been done by studying covariance maps based on a limited number of seed regions. Here, we used GPU-based (Graphics Processing Unit) accelerated computing to calculate, for the first time, the aggregated map of the total structural topographical organisation in the brain on voxel level in a large sample of 960 healthy individuals in the age range 68-83 years. This map describes for each voxel the number of significant correlations with all other grey matter voxels in the brain. Voxels that correlate significantly with many other voxels are called hubs. A majority of these hubs were found in the basal ganglia, the thalamus, the brainstem, and the cerebellum; subcortical regions that have been preserved through vertebrate evolution, interact with large portions of the neocortex and play fundamental roles for the control of a wide range of behaviours. No significant difference in the level of covariability could be found with increasing age or between men and women in these hubs.