Distinct Patterns of Rich Club Organization in Alzheimer’s Disease and Subcortical Vascular Dementia: A White Matter Network Study

Multimodal imaging investigation of structural rich club alterations in Alzheimer's disease and mild cognitive impairment: Amyloid deposition, structural atrophy, and functional activation differences

Alzheimer's disease (AD) is characterized by significant cerebral dysfunction, including increased amyloid deposition, gray matter atrophy, and changes in brain function. The involvement of highly connected network hubs, known as the "rich club," in the pathology of the disease remains inconclusive despite previous research efforts. In this study, we aimed to systematically assess the link between the rich club and AD using a multimodal neuroimaging approach. We employed network analyses of diffusion magnetic resonance imaging (MRI), longitudinal assessments of gray matter atrophy, amyloid deposition measurements using positron emission tomography (PET) imaging, and meta-analytic data on functional activation differences. Our study focused on evaluating the role of both the structural brain network's core and extended rich club regions in individuals with mild cognitive impairment (MCI) and those diagnosed with AD. Our findings revealed that structural rich club regions exhibited accelerated gray matter atrophy and increased amyloid deposition in both MCI and AD. Importantly, these regions remained unaffected by altered functional activation patterns observed outside the core rich club regions. These results shed light on the connection between two major AD biomarkers and the rich club, providing valuable insights into AD as a potential disconnection syndrome.

The significance of structural rich club hubs for the processing of hierarchical stimuli

The brain's structural network follows a hierarchy that is described as rich club (RC) organization, with RC hubs forming the well-interconnected top of this hierarchy. In this study, we tested whether RC hubs are involved in the processing of hierarchically higher structures in stimulus sequences. Moreover, we explored the role of previously suggested cortical gradients along anterior-posterior and medial-lateral axes throughout the frontal cortex. To this end, we conducted a functional magnetic resonance imaging (fMRI) experiment and presented participants with blocks of digit sequences that were structured on different hierarchically nested levels. We additionally collected diffusion weighted imaging data of the same subjects to identify RC hubs. This classification then served as the basis for a region of interest analysis of the fMRI data. Moreover, we determined structural network centrality measures in areas that were found as activation clusters in the whole-brain fMRI analysis. Our findings support the previously found anterior and medial shift for processing hierarchically higher structures of stimuli. Additionally, we found that the processing of hierarchically higher structures of the stimulus structure engages RC hubs more than for lower levels. Areas involved in the functional processing of hierarchically higher structures were also more likely to be part of the structural RC and were furthermore more central to the structural network. In summary, our results highlight the potential role of the structural RC organization in shaping the cortical processing hierarchy.

Exploratory Correlation of The Human Structural Connectome with Non-MRI Variables in Alzheimer's Disease

INTRODUCTION

Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

METHODS

We used four diffusion-MRI databases, three related to Alzheimer's disease, to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

RESULTS

We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.

DISCUSSION

Our findings help to elucidate which structural brain networks are affected in Alzheimer's disease and aging and highlight the importance of including indirect connections.

Exploratory correlation of the human structural connectome with non-MRI variables in Alzheimer's disease

Introduction

Discovery of the associations between brain structural connectivity and clinical and demographic variables can help to better understand the vulnerability and resilience of the brain architecture to neurodegenerative diseases and to discover biomarkers.

Methods

We used four diffusion-MRI databases, three related to Alzheimer's disease (AD), to exploratorily correlate structural connections between 85 brain regions with non-MRI variables, while stringently correcting the significance values for multiple testing and ruling out spurious correlations via careful visual inspection. We repeated the analysis with brain connectivity augmented with multi-synaptic neural pathways.

Results

We found 85 and 101 significant relationships with direct and augmented connectivity, respectively, which were generally stronger for the latter. Age was consistently linked to decreased connectivity, and healthier clinical scores were generally linked to increased connectivity.

Discussion

Our findings help to elucidate which structural brain networks are affected in AD and aging and highlight the importance of including indirect connections.

Sex differences in the structural rich-club connectivity in patients with Alzheimer's disease

Background and objectives

Alzheimer's disease (AD) is more prevalent in women than in men; however, there is a discrepancy in research on sex differences in AD. The human brain is a large-scale network with hub regions forming a central core, the rich-club, which is vital to cognitive functions. However, it is unknown whether alterations in the rich-clubs in AD differ between men and women. We aimed to investigate sex differences in the rich-club organization in the brains of patients with AD.

Methods

In total, 260 cognitively unimpaired individuals with negative amyloid positron emission tomography (PET) scans, 281 with prodromal AD (mild cognitive impairment due to AD) and 285 with AD dementia who confirmed with positive amyloid PET scans participated in the study. We obtained high-resolution T1-weighted and diffusion tensor images and performed network analysis.

Results

We observed sex differences in the rich-club and feeder connections in patients with AD, suggesting lower structural connectivity strength in women than in men. We observed a significant group-by-sex interaction in the feeder connections, particularly in the thalamus. In addition, the connectivity strength of the thalamus in the feeder connections was significantly correlated with general cognitive function in only men with prodromal AD and women with AD dementia.

Conclusion

Our findings provide important evidence for sex-specific alterations in the structural brain network related to AD.

Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

Characterizing a particular neurodegenerative condition against others possible diseases remains a challenge along clinical, biomarker, and neuroscientific levels. This is the particular case of frontotemporal dementia (FTD) variants, where their specific characterization requires high levels of expertise and multidisciplinary teams to subtly distinguish among similar physiopathological processes. Here, we used a computational approach of multimodal brain networks to address simultaneous multiclass classification of 298 subjects (one group against all others), including five FTD variants: behavioral variant FTD, corticobasal syndrome, nonfluent variant primary progressive aphasia, progressive supranuclear palsy, and semantic variant primary progressive aphasia, with healthy controls. Fourteen machine learning classifiers were trained with functional and structural connectivity metrics calculated through different methods. Due to the large number of variables, dimensionality was reduced, employing statistical comparisons and progressive elimination to assess feature stability under nested cross-validation. The machine learning performance was measured through the area under the receiver operating characteristic curves, reaching 0.81 on average, with a standard deviation of 0.09. Furthermore, the contributions of demographic and cognitive data were also assessed via multifeatured classifiers. An accurate simultaneous multiclass classification of each FTD variant against other variants and controls was obtained based on the selection of an optimum set of features. The classifiers incorporating the brain's network and cognitive assessment increased performance metrics. Multimodal classifiers evidenced specific variants' compromise, across modalities and methods through feature importance analysis. If replicated and validated, this approach may help to support clinical decision tools aimed to detect specific affectations in the context of overlapping diseases.

The Diagnostic Value of the Combined 3D Pseudo-Continuous Arterial Spin Labeling and Diffusion Kurtosis Imaging in Patients With Binswanger’s Disease

Background and Purpose

The clinical diagnosis of Binswanger’s disease (BD), a chronic progressive form of subcortical vascular dementia, remains challenging. 3D pseudo-continuous arterial-spin-labeling (pcASL) and diffusion kurtosis imaging (DKI) can quantitatively reveal the microcirculation changes and heterogeneity of white matter (WM), respectively. We thus aimed to determine the diagnostic value of the combined 3D-pcASL and DKI in BD.

Materials and Methods

A total of 35 patients with BD and 33 healthy controls underwent 3D-ASL and DKI experiments. The perfusion parameter of cerebral blood flow (CBF), diffusion parameters of fractional anisotropy (FA), mean/axial/radial diffusivity (MD/Da/Dr), and kurtosis parameters of anisotropy fraction of kurtosis (FAk) and mean/axial/radial kurtosis MK/Ka/Kr were obtained to quantitatively measure the parametric distributions of functional brain subregions. One-way analysis of variance and post hoc t-test were applied to explore the different distributions of DKI/ASL-derived parameters among brain subregions of BD. In addition, all region-specific DKI/ASL parameters were separately analyzed in Pearson correlation analysis to investigate the relationship with Mini-Mental State Examination (MMSE), a typical clinical scale for cognitive function assessment in patients with BD.

Results

FA/FAk/MK/Ka/Kr was significantly declined in all WM hyperintensities (WMHs) of BD compared with healthy controls, while the corresponding MD/Da/Dr was significantly increased (all p < 0.005). In addition, significant changes, similar to the WMHs of patients with BD, were also observed in almost all DKI parameters in WM normal areas and genu/splenium of the corpus callosum (GCC/SCC) in BD (p < 0.005). Finally, CBF was significantly reduced in all of the above regions we measured in patients with BD (p < 0.005). For patients with BD, MMSE showed a negative correlation with MD/Da in thalamus (r = −0.42/−0.58; p < 0.05), and a positive correlation with CBF in PWM/TWM (r = 0.49/0.39; p < 0.05). Using receiver operating characteristic (ROC) analysis, FA/FAk/Kr in GCC, CBF/FA/Dr/FAk in SCC, MD/Da/Ka in thalamus, and the combined FA/MD/Dr/CBF in TWM showed high accuracy [area under the curves (AUCs) 0.957/0.946/0.942/0.986] in distinguishing patients with BD from healthy controls.

Conclusion

We found that combined DKI and 3D-ASL are helpful in diagnosing patients with BD, especially with FA, MD, Dr, and CBF in the temporal WM region. Additionally, the kurtosis parameters of DKI can sensitively monitor the potentially damaged WM areas in patients with BD patients, adding complementary clinical value.

Strengthened and posterior-shifted structural rich-club organization in people who use cocaine

BACKGROUND

People with cocaine use disorder (CUD) often have abnormal cognitive function and brain structure. Cognition is supported by brain networks that typically have characteristics like rich-club organization, which is a group of regions that are highly connected across the brain and to each other, and small worldness, which is a balance between local and long-distance connections. However, it is unknown whether there are abnormalities in structural brain network connectivity of CUD.

METHODS

Using diffusion-weighted imaging, we measured structural connectivity in 37 people with CUD and 38 age-matched controls. We identified differences in rich-club organization and whether such differences related to small worldness and behavior. We also tested whether rich-club reorganization was associated with caudate and putamen structural connectivity due to the relevance of the dopamine system to cocaine use.

RESULTS

People with CUD had a higher normalized rich-club coefficient than controls, more edges connecting rich-club nodes to each other and to non-rich-club nodes, and fewer edges connecting non-rich-club nodes. Rich-club nodes were shifted posterior and lateral. Rich-club reorganization was related to lower clustered connectivity around individual nodes found in CUD, to increased impulsivity, and to a decrease in caudate connectivity.

CONCLUSIONS

These findings are consistent with previous work showing increased rich-club connectivity in conditions associated with a hypofunctional dopamine system. The posterior shift in rich-club nodes in CUD suggests that the structural connectivity of posterior regions may be more impacted than previously recognized in models based on brain function and morphology.

Aberrant rich club organization in patients with obsessive-compulsive disorder and their unaffected first-degree relatives

Recent studies suggested that the rich club organization promoting global brain communication and integration of information, may be abnormally increased in obsessive-compulsive disorder (OCD). However, the structural and functional basis of this organization is still not very clear. Given the heritability of OCD, as suggested by previous family-based studies, we hypothesize that aberrant rich club organization may be a trait marker for OCD. In the present study, 32 patients with OCD, 30 unaffected first-degree relatives (FDR) and 32 healthy controls (HC) underwent diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). We examined the structural rich club organization and its interrelationship with functional coupling. Our results showed that rich club and peripheral connection strength in patients with OCD was lower than in HC, while it was intermediate in FDR. Finally, the coupling between structural and functional connections of the rich club, was decreased in FDR but not in OCD relative to HC, which suggests a buffering mechanism of brain functions in FDR. Overall, our findings suggest that alteration of the rich club organization may reflect a vulnerability biomarker for OCD, possibly buffered by structural and functional coupling of the rich club.

Conductance-Based Structural Brain Connectivity in Aging and Dementia

Background: Structural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer's disease (AD) progression. Methods: In this work, we used our recently proposed structural connectivity quantification measure derived from diffusion magnetic resonance imaging, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyzed data from the second phase of Alzheimer's Disease Neuroimaging Initiative and third release in the Open Access Series of Imaging Studies data sets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compared these data sets to the Human Connectome Project data set, as a reference, and eventually validated externally on two cohorts of the European DTI Study in Dementia database. Results: Our analysis shows expected trends of mean conductance with respect to age and cognitive scores, significant age prediction values in aging data, and regional effects centered among subcortical regions, and cingulate and temporal cortices. Discussion: Results indicate that the conductance measure has prediction potential, especially for age, that age and cognitive scores largely overlap, and that this measure could be used to study effects such as anticorrelation in structural connections. Impact statement This work presents a methodology and a set of analyses that open new possibilities in the study of healthy and pathological aging. The methodology used here is sensitive to direct and indirect pathways in deriving brain connectivity measures from diffusion-weighted magnetic resonance imaging, and therefore provides information that many state-of-the-art methods do not account for. As a result, this technique may provide the research community with ways to detect subtle effects of healthy aging and Alzheimer's disease.

Disruption of functional and structural networks in first-episode, drug-naïve adolescents with generalized anxiety disorder

BACKGROUND

Individuals with generalized anxiety disorder (GAD) tend to worry exaggeratedly and uncontrollably about various daily routines. Previous studies have demonstrated that the GAD patients exhibited widespread alternations in both functional networks (FN) and structural networks (SN). However, the simultaneous alternations of the topological organization of FN, SN, as well as their couplings in GAD still remain unknown.

METHODS

Using multimodal approach, we constructed FN from resting-state functional magnetic imaging (R-fMRI) data and SN from diffusion magnetic resonance imaging (dMRI) data of 32 adolescent GAD patients and 25 healthy controls (HC). Graph theory analysis was employed to investigate the topological properties of FN, SN, and FN-SN coupling.

RESULTS

Compared to HC, the GAD patients showed disruptions in global (i.e., decreased clustering coefficient, global, and local efficiency) and subnetwork (i.e., reduced intermodular connections, rich club, and feeder connections) levels in FN. Abnormal global level properties (i.e., increased characteristic path length and reduced global efficiency) were also observed in SN. Altered FN-SN couplings in normalized characteristic path length and feeder connections were identified in the GAD patients. The identified network measures were correlated with anxiety severity in the GAD patients.

LIMITATIONS

The sample size of the current study is small and the cross-sectional nature can not infer causal relationship.

CONCLUSIONS

Our findings identified GAD-related topological alternations in both FN and SN, together with the couplings between FN and SN, providing us with a novel perspective for understanding the pathophysiological mechanisms of GAD.

Disrupted Patterns of Rich-Club and Diverse-Club Organizations in Subjective Cognitive Decline and Amnestic Mild Cognitive Impairment

Background

Subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI) were considered to be a continuum of Alzheimer’s disease (AD) spectrum. The abnormal topological architecture and rich-club organization in the brain functional network can reveal the pathology of the AD spectrum. However, few studies have explored the disrupted patterns of diverse club organizations and the combination of rich- and diverse-club organizations in SCD and aMCI.

Methods

We collected resting-state functional magnetic resonance imaging data of 19 SCDs, 29 aMCIs, and 28 healthy controls (HCs) from the Alzheimer’s Disease Neuroimaging Initiative. Graph theory analysis was used to analyze the network metrics and rich- and diverse-club organizations simultaneously.

Results

Compared with HC, the aMCI group showed altered small-world and network efficiency, whereas the SCD group remained relatively stable. The aMCI group showed reduced rich-club connectivity compared with the HC. In addition, the aMCI group showed significantly increased feeder connectivity and decreased local connectivity of the diverse club compared with the SCD group. The overlapping nodes of the rich club and diverse club showed a significant difference in nodal efficiency and shortest path length (L_p) between groups. Notably, the L_p values of overlapping nodes in the SCD and aMCI groups were significantly associated with episodic memory.

Conclusion

The present study demonstrates that the network properties of SCD and aMCI have varying degrees of damage. The combination of the rich club and the diverse club can provide a novel insight into the pathological mechanism of the AD spectrum. The altered patterns in overlapping nodes might be potential biomarkers in the diagnosis of the AD spectrum.

Link-usage asymmetry and collective patterns emerging from rich-club organization of complex networks

In models of excitable dynamics on graphs, excitations can travel in both directions of an undirected link. However, as a striking interplay of dynamics and network topology, excitations often establish a directional preference. Some of these cases of "link-usage asymmetry" are local in nature and can be mechanistically understood, for instance, from the degree gradient of a link (i.e., the difference in node degrees at both ends of the link). Other contributions to the link-usage asymmetry are instead, as we show, self-organized in nature, and strictly nonlocal. This is the case for excitation waves, where the preferential propagation of excitations along a link depends on its orientation with respect to a hub acting as a source, even if the link in question is several steps away from the hub itself. Here, we identify and quantify the contribution of such self-organized patterns to link-usage asymmetry and show that they extend to ranges significantly longer than those ascribed to local patterns. We introduce a topological characterization, the hub-set-orientation prevalence of a link, which indicates its average orientation with respect to the hubs of a graph. Our numerical results show that the hub-set-orientation prevalence of a link strongly correlates with the preferential usage of the link in the direction of propagation away from the hub core of the graph. Our methodology is embedding-agnostic and allows for the measurement of wave signals and the sizes of the cores from which they originate.

Disrupted principal network organisation in multiple sclerosis relates to disability

Structural network-based approaches can assess white matter connections revealing topological alterations in multiple sclerosis (MS). However, principal network (PN) organisation and its clinical relevance in MS has not been explored yet. Here, structural networks were reconstructed from diffusion data in 58 relapsing-remitting MS (RRMS), 28 primary progressive MS (PPMS), 36 secondary progressive (SPMS) and 51 healthy controls (HCs). Network hubs' strengths were compared with HCs. Then, PN analysis was performed in each clinical subtype. Regression analysis was applied to investigate the associations between nodal strength derived from the first and second PNs (PN1 and PN2) in MS, with clinical disability. Compared with HCs, MS patients had preserved hub number, but some hubs exhibited reduced strength. PN1 comprised 10 hubs in HCs, RRMS and PPMS but did not include the right thalamus in SPMS. PN2 comprised 10 hub regions with intra-hemispheric connections in HCs. In MS, this subnetwork did not include the right putamen whilst in SPMS the right thalamus was also not included. Decreased nodal strength of the right thalamus and putamen from the PNs correlated strongly with higher clinical disability. These PN analyses suggest distinct patterns of disruptions in MS subtypes which are clinically relevant.

Mathematical Models to Shed Light on Amyloid-Beta and Tau Protein Dependent Pathologies in Alzheimer's Disease

The number of patients suffering from dementia due to Alzheimer's disease (AD) is constantly rising worldwide. This has accordingly resulted in huge burdens on the health systems and involved families. Lack of profound understanding of neural networking in normal brain and their interruption in AD makes the treatment of this neurodegenerative multifaceted disease a challenging issue. In recent years, mathematical and computational methods have paved the way towards a better understanding of the brain functional connectivity. Thus, much attention has been paid to this matter from both basic science researchers and clinicians with an interdisciplinary approach to determine what is not functioning properly in AD patients and how this malfunctioning can be addressed. In this review, a number of AD-related articles and well-studied pathophysiologic topics (e.g., amyloid-beta, neurofibrillary tangles, Ca²⁺ dysregulation, and synaptic plasticity alterations) has been literally surveyed from a computational and systems biology point of view. The neural networks were discussed from biological and mathematical point of views and their alterations in recent findings were further highlighted. Application of the graph theoretical analysis in the brain imaging was reviewed, depicting the relations between brain structure and function, without diving into mathematical details. Moreover, differential rate equations were briefly articulated, emphasizing the potential use of these equations in simplifying complex processes in relevance to pathologies of AD. Comprehensive insights were given into the AD progression from neural networks perspective, which may lead us towards potential strategies for early diagnosis and effective treatment of AD.