Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.

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Effects of Brain Parcellation on the Characterization of Topological Deterioration in Alzheimer's Disease.

Current brain network studies based on persistent homology mainly focus on the spatial evolution over multiple spatial scales, and there is little research on the evolution of a spatiotemporal brain network of Alzheimer’s disease (AD). This paper proposed a persistent homology-based method by combining multiple temporal windows and spatial scales to study the spatiotemporal evolution of brain functional networks. Specifically, a time-sliding window method was performed to establish a spatiotemporal network, and the persistent homology-based features of such a network were obtained. We evaluated our proposed method using the resting-state functional MRI (rs-fMRI) data set from Alzheimer’s Disease Neuroimaging Initiative (ADNI) with 31 patients with AD and 37 normal controls (NCs). In the statistical analysis experiment, most network properties showed a better statistical power in spatiotemporal networks than in spatial networks. Moreover, compared to the standard graph theory properties in spatiotemporal networks, the persistent homology-based features detected more significant differences between the groups. In the clustering experiment, the brain networks on the sliding windows of all subjects were clustered into two highly structured connection states. Compared to the NC group, the AD group showed a longer residence time and a higher window ratio in a weak connection state, which may be because patients with AD have not established a firm connection. In summary, we constructed a spatiotemporal brain network containing more detailed information, and the dynamic spatiotemporal brain network analysis method based on persistent homology provides stronger adaptability and robustness in revealing the abnormalities of the functional organization of patients with AD.

/pdf/a-spatiotemporal-brain-network-analysis-of-alzheimers-3jox41x1.pdf

A Spatiotemporal Brain Network Analysis of Alzheimer’s Disease Based on Persistent Homology

To explore the evaluation of white matter structural network analysis in the differentiation of Alzheimer's disease (AD) and subcortical ischemic vascular dementia (SIVD), 67 participants [31 AD patients, 19 SIVD patients, and 19 normal control (NC)] were enrolled in this study. Each participant underwent 3.0T MRI scanning. Diffusion tensor imaging (DTI) data were analyzed by graph theory (GRETNA toolbox). Statistical analyses of global parameters [gamma, sigma, lambda, global shortest path length (Lp), global efficiency (Eg), and local efficiency (Eloc)] and nodal parameters [betweenness centrality (BC)] were obtained. Network-based statistical analysis (NBS) was employed to analyze the group differences of structural connections. The diagnosis efficiency of nodal BC in identifying different types of dementia was assessed by receiver operating characteristic (ROC) analysis. There were no significant differences of gender and years of education among the groups. There were no significant differences of sigma and gamma in AD vs. NC and SIVD vs. NC, whereas the Eg values of AD and SIVD were statistically decreased, and the lambda values were increased. The BC of the frontal cortex, left superior parietal gyrus, and left precuneus in AD patients were obviously reduced, while the BC of the prefrontal and subcortical regions were decreased in SIVD patients, compared with NC. SIVD patients had decreased structural connections in the frontal, prefrontal, and subcortical regions, while AD patients had decreased structural connections in the temporal and occipital regions and increased structural connections in the frontal and prefrontal regions. The highest area under curve (AUC) of BC was 0.946 in the right putamen for AD vs. SIVD. White matter structural network analysis may be a potential and promising method, and the topological changes of the network, especially the BC change in the right putamen, were valuable in differentiating AD and SIVD patients.

/pdf/white-matter-structural-network-analysis-to-differentiate-3dxh5lwrrq.pdf

White Matter Structural Network Analysis to Differentiate Alzheimer's Disease and Subcortical Ischemic Vascular Dementia.

BACKGROUND
A minimum spanning tree (MST) is a unique efficient network comprising the necessary connections needed to connect all regions in a network while retaining the lowest possible cost of connection weight.


OBJECTIVE
This study aimed to utilize functional near-infrared spectroscopy (fNIRS) to analyze brain activity in different regions and then construct MST-based regions to characterize the brain topologies of participants with Alzheimer's disease (AD), mild cognitive impairment (MCI), and normal controls (NC).


METHODS
A 46 channel fNIRS setup was used on all participants, with correlation being calculated for each channel pair. An MST was constructed from the resulting correlation matrix, from which graph theory measures were calculated. The average number of connections within a lobe in the left versus right hemisphere was calculated to identify which lobes displayed and abnormal amount of connectivity.


RESULTS
Compared to those in the MCI group, the AD group showed a less integrated network structure, with a higher characteristic path length, but lower leaf fraction, maximum degree, and degree divergence. The AD group also showed a higher number of connections in the frontal lobe within the left hemisphere and a lower number between hemispheric frontal lobes as compared to MCI.


CONCLUSION
These results indicate a deviation in network structure and connectivity within patient groups that is consistent with the theory of dysconnectivity for AD. Additionally, the AD group showed strong correlations between the Hamilton depression rating scale and different graph metrics, suggesting a link between network organization and the recurrence of depression in AD.

Global Network Analysis of Alzheimer's Disease with Minimum Spanning Trees.

Objectives Recent resting-state functional magnetic resonance imaging (fMRI) studies have focused on glaucoma-related neuronal degeneration in structural and spontaneous functional brain activity. However, there are limited studies regarding the differences in the topological organization of the functional brain network in patients with glaucoma. In this study, we aimed to assess both potential alterations and the network efficiency in the functional brain networks of patients with primary angle-closure glaucoma (PACG). Methods We applied resting-state fMRI data to construct the functional connectivity network of 33 patients with PACG (54.21 ± 7.21 years) and 33 gender- and age-matched healthy controls (52.42 ± 7.80 years). The differences in the global and regional topological brain network properties between the two groups were assessed using graph theoretical analysis. Partial correlations between the altered regional values and clinical parameters were computed for patients with PACG. Results No significant differences in global topological measures were identified between the two groups. However, significant regional alterations were identified in the patients with PACG, including differences within visual and nonvisual (somatomotor and cognition-emotion) regions. The normalized clustering coefficient and normalized local efficiency of the right superior parietal gyrus were significantly correlated with the retinal fiber layer thickness (RNFLT) and the vertical cup to disk ratio (V C/D). In addition, the normalized node betweenness of the left middle frontal gyrus (orbital portion) was significantly correlated with the V C/D in the patients with PACG. Conclusions Our results suggest that regional inefficiency with decrease and compensatory increase in local functional properties of visual and nonvisual nodes preserved the brain network of the PACG at the global level.

/pdf/brain-functional-network-analysis-of-patients-with-primary-260oz9u7.pdf

Brain Functional Network Analysis of Patients with Primary Angle-Closure Glaucoma

Despite the severe social burden caused by Alzheimer's disease (AD), no drug than can change the disease progression has been identified yet. The structural brain network research provides an opportunity to understand physiological deterioration caused by AD and its precursor, mild cognitive impairment (MCI). Recently, persistent homology has been used to study brain network dynamics and characterize the global network organization. However, it is unclear how these parameters reflect changes in structural brain networks of patients with AD or MCI. In this study, our previously proposed persistent features and various traditional graph-theoretical measures are used to quantify the topological property of white matter (WM) network in 150 subjects with diffusion tensor imaging (DTI). We found significant differences in these measures among AD, MCI, and normal controls (NC) under different brain parcellation schemes. The decreased network integration and increased network segregation are presented in AD and MCI. Moreover, the persistent homology-based measures demonstrated stronger statistical capability and robustness than traditional graph-theoretic measures, suggesting that they represent a more sensitive approach to detect altered brain structures and to better understand AD symptomology at the network level. These findings contribute to an increased understanding of structural connectome in AD and provide a novel approach to potentially track the progression of AD.

https://www.mdpi.com/1420-3049/25/11/2472/pdf

And Xie Han

Papers

White Matter Brain Network Research in Alzheimer's Disease Using Persistent Features.