Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

Ye, Chenfei; Mori, Susumu; Chan, Piu; Ma, Ting

doi:10.1016/j.nicl.2019.101690

Cited by 27 publications

(21 citation statements)

References 78 publications

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“…Our differential expression analysis confirmed that there are significant differences in CaMK2A expression in the brains of AD patients compared with control subjects, especially in the HP and TC. It has been well documented that the HP and TC are significantly associated with cognitive deficits, such as learning and memory (Ezzyat et al, 2018), and substantial research has also revealed abnormalities of the HP and TC in AD (Ye et al, 2019; Savioz et al, 2016). The finding that CaMK2A expression is altered in the HP and TC in AD implies that CaMK2A may be involved in the pathogenesis of AD.…”

Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer’s Disease in the Han Chinese Population

Fang

Tang

Yang

et al. 2019

Front. Aging Neurosci.

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There is ample evidence suggesting that calcium/calmodulin-dependent protein kinase II alpha (CaMK2A) may play an important role in the pathophysiology of Alzheimer’s disease (AD). This genetic study aimed to investigate whether CaMK2A confers susceptibility to the development of AD in the Han Chinese population. A total of seven single nucleotide polymorphisms (SNPs) within CaMK2A were screened in two independent cohorts from southwestern China (333 AD patients and 334 controls) and eastern China (382 AD patients and 426 controls) to discern the potential association between this gene and AD. In addition, a cross-platform normalized expression resource was used to investigate whether CaMK2A is differentially expressed in the brain between individuals with AD and the controls. In addition, expression quantitative trait loci (eQTL) analysis was used to explore the differences in CaMK2A expression in the brain among different genotypes. The cross-platform normalized data showed significant differences in CaMK2A expression in the hippocampus, entorhinal cortex and temporal cortex between the AD patients and the control subjects (|log FC| > 0.1, P < 0.05); however, only the differences in the hippocampus and temporal cortex remained after the multiple comparisons correction [false discovery rate (FDR)-corrected, P < 0.05]. The frequency of the rs4958445 genotype was significantly different between the AD subjects and the controls from southwestern China ( P = 0.013, P = 0.034 after FDR correction). When the two samples were combined, rs4958445 still showed a significant association with AD ( P = 0.044). Haplotype analysis indicated that the T-A-C-A-T-C-C and T-G-C-A-T-C-C haplotypes in the southwestern cohort and the T-G-C-G-C-T-C haplotype in the eastern cohort, consisting of rs10051644, rs6869634, rs3797617, rs3756577, rs4958445, rs10515639 and rs6881743, showed a significant association with AD ( P = 0.037, P = 0.026 and P = 0.045, respectively). Furthermore, the brain eQTL analysis revealed a significant association between the rs4958445 polymorphism and CaMK2A expression in the inferior olivary nucleus ( P = 0.029). Our results suggest an important role for CaMK2A in the pathophysiology of AD in the Han Chinese population, especially the southwestern population.

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Section: Discussionmentioning

confidence: 99%

A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer’s Disease in the Han Chinese Population

Fang

Tang

Yang

et al. 2019

Front. Aging Neurosci.

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“…It is important to note that an increase in the measured structural connectivity could stem from factors other than an actual strengthening of the tract. WM atrophy, volume reduction (Ye et al, 2019), and other geometrical variabilities could make ROIs closer to each other, leading to elevated measured structural connectivity. Additionally, in regions with fiber crossing, selective axonal loss can increase the FA and subsequently measured structural connectivity (Ryan et al, 2013;Kim et al, 2015b;Douaud et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

“…It is important to note that an increase in the measured structural connectivity could stem from factors other than an actual strengthening of the tract. WM atrophy, volume reduction (Ye et al, 2019), and other geometrical variabilities could make ROIs closer to each other, leading to elevated measured structural connectivity. To mitigate this effect, we excluded subcortico-subcortical regions in our study of anti-correlated connections.…”

Section: Discussionmentioning

confidence: 99%

“…In fact, there is evidence of increased FA in the left caudate in pre-symptomatic familial AD subjects (Ryan et al, 2013), increased structural connectivity in the right insula (Ye et al, 2019), and increased functional connectivity between the frontal lobe and the corpus striatum (Supekar et al, 2008) in AD. In addition, because the conductance method accounts for indirect paths, a possible enhancement in the thalamus and putamen structural connectivity in AD (Ryan et al, 2013; Ye et al, 2019) might also have contributed to the increase in the caudate-insula connectivity. Furthermore, the fact that this negative correlation was observed consistently in the older adults and those on the dementia spectrum (ADNI-2 and OASIS-3), but not in young healthy adults (HCP), suggests that this significant anti-correlation might be due to progression of dementia or aging.…”

Section: Discussionmentioning

confidence: 99%

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Conductance-Based Structural Brain Connectivity in Aging and Dementia

Frau-Pascual

Augustinak

Varadarajan

et al. 2020

Preprint

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BackgroundStructural brain connectivity has been shown to be sensitive to the changes that the brain undergoes during Alzheimer’s disease (AD) progression.MethodsIn this work, we use our recently proposed structural connectivity quantification measure derived from diffusion MRI, which accounts for both direct and indirect pathways, to quantify brain connectivity in dementia. We analyze data from the ADNI-2 and OASIS-3 datasets to derive relevant information for the study of the changes that the brain undergoes in AD. We also compare these datasets to the HCP dataset, as a reference.ResultsOur 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.DiscussionResults 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 anti-correlation in structural connections.Impact statementThis 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 dMRI, 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 AD.

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“…Emerging evidence suggest that in the pre-clinical stage of AD, network changes are present in the form of a disconnection on a large scale (179,192,193). Indeed, FC and SC are now being investigated to better differentiate AD patients, MCI, and controls moving towards the goal of identifying prodromal AD patients and the possibility of developing early intervention strategies (187,(194)(195)(196). A recent study suggests altered FC corresponding to accelerated aging in preclinical AD (197).…”

Section: Ii53: Connectomicsmentioning

confidence: 99%

From genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain

Stefanovski¹,

Meier²,

Rk³

et al. 2020

Preprint

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While the knowledge in neuroscience and the possibilities in clinical neurology have improved for many years, neurogenerative diseases and associated dementia remain a growing problem. Alzheimer’s Disease (AD) is the most common cause of dementia and also represents the most prevalent type of neurodegenerative diseases. For AD, disease-modifying treatments are presently lacking and the understanding of disease mechanisms remain incomplete. In the present review, we consider candidate contributing factors leading to AD and we evaluate novel computational brain simulation methods to further disentangle their potential roles. We first discuss existing computational models of AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (TVB, www.thevirtualbrain.org), an open-source, multi- scale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics and treatment of AD.

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Connectome-wide network analysis of white matter connectivity in Alzheimer's disease

Cited by 27 publications

References 78 publications

A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer’s Disease in the Han Chinese Population

A Comprehensive Analysis of the CaMK2A Gene and Susceptibility to Alzheimer’s Disease in the Han Chinese Population

Conductance-Based Structural Brain Connectivity in Aging and Dementia

From genes to network models of Alzheimer's disease: Biological framework for multi-scale brain simulation with The Virtual Brain

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