Rich club disturbances of the human connectome from subjective cognitive decline to Alzheimer's disease

Abstract

Alzheimer's disease (AD) has a preclinical phase that can last for decades prior to clinical dementia onset. Subjective cognitive decline (SCD) is regarded as the last preclinical AD stage prior to the development of amnestic mild cognitive decline (aMCI) and AD dementia (d-AD). The analysis of brain structural networks based on diffusion tensor imaging (DTI) has identified the so-called 'rich club', a set of cortical regions highly connected to each other, with other regions referred to as peripheral. It has been reported that rich club architecture is affected by regional atrophy and connectivity, which are reduced in patients with aMCI and d-AD.Methods: We recruited 62 normal controls, 47 SCD patients, 60 aMCI patients and 55 d-AD patients and collected DTI data to analyze rich-club organization.Results: We demonstrated that rich club organization was disrupted, with reduced structural connectivity among rich club nodes, in aMCI and d-AD patients but remained stable in SCD patients. In addition, SCD, aMCI and d-AD patients showed similar patterns of disrupted peripheral regions and reduced connectivity involving these regions, suggesting that peripheral regions might contribute to cognitive decline and that disruptions here could be regarded as an early marker of SCD. This organization could provide the fundamental structural architecture for complex cognitive functions and explain the low prevalence of cognitive problems in SCD patients.Conclusions: These findings reveal a disrupted pattern of the AD connectome that starts in peripheral regions and then hierarchically propagates to rich club regions, when patients show clinical symptoms. This pattern provides evidence that disruptions in rich club organization are a key factor in the progression of AD that can dynamically reflect the progression of AD, thus representing a potential biomarker for early diagnosis.

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

“…Our previous researches con rmed that cognitively normal high-risk individuals of AD have already appeared alterations of brain functional networks (fMRI), white matter networks (DTI) or some re ned areas (sMRI) [46][47][48][49] , suggesting there may be more unmined data of MPMRI in the preclinical stage. As expected, the pure clinical data-based classi cation models were meaningless in this stage, and the traditional volumetric and functional indices are also not sensitive enough (details were presented in the Supplementary Table 5 and Material).…”

Altered Rich-Club Organization and Regional Topology Are Associated With Cognitive Decline in Patients With Frontal and Temporal Gliomas

“…The preprocessing steps used were as previously described (Bai et al, 2012;Wang X.N. et al, 2016) and were performed using PANDA software as follows: (Ostrom et al, 2014) format conversion of original data (DICOM); (Raysi Dehcordi et al, 2013) the extraction of brain tissue and structure; (Fornito et al, 2013) realignment; (Hart et al, 2016) Eddy current and motion artifact correction of DTI data by applying an affine alignment of each diffusion-weighted image to the b0 image; (Wang et al, 2019) fractional anisotropy (FA) calculation; (Yan et al, 2018) diffusion tensor tractography; and (Iturria-Medina et al, 2011) conduct of tractography to produce 3D streamlines representing fiber tract connectivity (Mori et al, 1999).…”

“…To characterize the topological organization of WM structural networks at a sparsity threshold, we calculated multiple network metrics of regional nodal characteristics and global network properties. We calculated several graph measures, including global efficiency (E glob ), local efficiency (E loc ), shortest path length (L p ), clustering coefficient (C p ), nodal betweenness (B nod ), nodal degree (K nodal ), nodal efficiency (E nodal ), nodal path length (N Lp ), small worldness (Sigma) (Rubinov and Sporns, 2010), and rich-club organization (Yan et al, 2018). In reference to a previous study (Rubinov and Sporns, 2010), the interpretations of these graph measures are found in Table 2 and Supplementary Material.…”

“…The development of complex network analysis using graph theory (i.e., connectome) in the brain offers the potential to identify the impacts of focal and diffuse pathologies in glioma patients (Fornito et al, 2013;Hart et al, 2016), such as rich-club organization (Yan et al, 2018;Wang et al, 2019), small-world characteristics (Iturria-Medina et al, 2011;Yan et al, 2018), betweenness (Iturria-Medina et al, 2011), and global and local network efficiency (Iturria-Medina et al, 2011;Shu et al, 2018). Recent studies have suggested that the human brain shares the same key organizational principles as common, natural, and manmade systems, that is to say, that the human brain is thought to be a network of unceasing communication (Hart et al, 2016).…”

The Motion of An Inv Nodal Cilium: a Realistic Model Revealing Dynein-Driven Ciliary Motion with Microtubule Mislocalization

“…This change in distance may be the mechanism of transmission of dynein activation in the doublet MTs [12,19,20]. These studies provide important contributions to our understanding of the ciliary motility as well as cilia-induced fluid motion [21][22][23][24][25][26][27][28]. However, most of the current studies are based on wild-type cilia, and investigations of the ultrastructure and motility of mutant cilia, especially mutant nodal cilia, are still lacking.…”