Structural connectivity networks in Alzheimer's disease and Lewy body disease

Disrupted Structural White Matter Network in Alzheimer's Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study

BACKGROUND

Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types.

OBJECTIVE

This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network.

METHODS

A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer's disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network.

RESULTS

Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately.

CONCLUSION

These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.

Structural brain splitting is a hallmark of Granulin-related frontotemporal dementia

Frontotemporal dementia associated with granulin (GRN) mutations presents asymmetric brain atrophy. We applied a Minimum Spanning Tree plus an Efficiency Cost Optimization approach to cortical thickness data in order to test whether graph theory measures could identify global or local impairment of connectivity in the presymptomatic phase of pathology, where other techniques failed in demonstrating changes. We included 52 symptomatic GRN mutation carriers (SC), 161 presymptomatic GRN mutation carriers (PSC) and 341 non-carriers relatives from the Genetic Frontotemporal dementia research Initiative cohort. Group differences of global, nodal and edge connectivity in (Minimum Spanning Tree plus an Efficiency Cost Optimization) graph were tested via Structural Equation Models. Global graph perturbation was selectively impaired in SC compared to non-carriers, with no changes in PSC. At the local level, only SC exhibited perturbation of frontotemporal nodes, but edge connectivity revealed a characteristic pattern of interhemispheric disconnection, involving homologous parietal regions, in PSC. Our results suggest that GRN-related frontotemporal dementia resembles a disconnection syndrome, with interhemispheric disconnection between parietal regions in presymptomatic phases that progresses to frontotemporal areas as symptoms emerge.

An Improved Fusion Paired Group Lasso Structured Sparse Canonical Correlation Analysis Based on Brain Imaging Genetics to Identify Biomarkers of Alzheimer’s Disease

Brain imaging genetics can demonstrate the complicated relationship between genetic factors and the structure or function of the humankind brain. Therefore, it has become an important research topic and attracted more and more attention from scholars. The structured sparse canonical correlation analysis (SCCA) model has been widely used to identify the association between brain image data and genetic data in imaging genetics. To investigate the intricate genetic basis of cerebrum imaging phenotypes, a great deal of other standard SCCA methods combining different interested structed have now appeared. For example, some models use group lasso penalty, and some use the fused lasso or the graph/network guided fused lasso for feature selection. However, prior knowledge may not be completely available and the group lasso methods have limited capabilities in practical applications. The graph/network guided approaches can use sample correlation to define constraints, thereby overcoming this problem. Unfortunately, this also has certain limitations. The graph/network conducted methods are susceptible to the sign of the sample correlation of the data, which will affect the stability of the model. To improve the efficiency and stability of SCCA, a sparse canonical correlation analysis model with GraphNet regularization (FGLGNSCCA) is proposed in this manuscript. Based on the FGLSCCA model, the GraphNet regularization penalty is imposed in our study and an optimization algorithm is presented to optimize the model. The structural Magnetic Resonance Imaging (sMRI) and gene expression data are used in this study to find the genotype and characteristics of brain regions associated with Alzheimer’s disease (AD). Experiment results shown that the new FGLGNSCCA model proposed in this manuscript is superior or equivalent to traditional methods in both artificially synthesized neuroimaging genetics data or actual neuroimaging genetics data. It can select essential features more powerfully compared with other multivariate methods and identify significant canonical correlation coefficients as well as captures more significant typical weight patterns which demonstrated its excellent ability in finding biologically important imaging genetic relations.

Structural connectivity networks in Alzheimer's disease and Lewy body disease

OBJECTIVE

We evaluated disruption of the white matter (WM) network related with Alzheimer's disease (AD) and Lewy body disease (LBD), which includes Parkinson's disease and dementia with Lewy bodies.

METHODS

We consecutively recruited 37 controls and 77 patients with AD-related cognitive impairment (ADCI) and/or LBD-related cognitive impairment (LBCI). Diagnoses of ADCI and LBCI were supported by amyloid PET and dopamine transporter PET, respectively. There were 22 patients with ADCI, 19 patients with LBCI, and 36 patients with mixed ADCI/LBCI. We investigated the relationship between ADCI, LBCI, graph theory-based network measures on diffusion tensor images, and cognitive dysfunction using general linear models after controlling for age, sex, education, deep WM hyperintensities (WMH), periventricular WMH, and intracranial volume.

RESULTS

LBCI, especially mixed with ADCI, was associated with increased normalized path length and decreased normalized global efficiency. LBCI was related to the decreased nodal degree of left caudate, which was further associated with broad cognitive dysfunction. Decreased left caudate nodal degree was associated with decreased fractional anisotropy (FA) in the brain regions vulnerable to LBD. Compared with the control group, the LBCI group had an increased betweenness centrality in the occipital nodes, which was associated with decreased FA in the WM adjacent to the striatum and visuospatial dysfunction.

CONCLUSION

Concomitant ADCI and LBCI are associated with the accentuation of LBCI-related WM network disruption centered in the left caudate nucleus. The increase of occipital betweenness centrality could be a characteristic biologic change associated with visuospatial dysfunction in LBCI.