Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment

Zhao, Tengda; Sheng, Can; Bi, Qiuhui; Niu, Weili; Shu, Ni

doi:10.1016/j.neurobiolaging.2017.08.005

Cited by 26 publications

(28 citation statements)

References 68 publications

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“…We found that the NC had higher global efficiency in FA weighted network, FN weighted network and length weighted network. These results are consistent with previous findings (Zhao et al, 2017).…”

Section: Discussionsupporting

confidence: 94%

DCP: a pipeline toolbox for diffusion connectome

Huang

Shu

2020

Preprint

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The brain structure network constructed by diffusion tensor imaging (DTI) reflects the anatomical connections between brain regions, so the brain structure network can quantitatively describes the anatomical connectivity pattern of the entire brain. The structure network based diffusion tensor imaging is widely used in scientific research. While a number of post processing packages have been developed, fully automated processing of DTI datasets on Windows Operating System remains challenging. Here, we developed a MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for fully automated constructing brain structure network. The processing modules of a few developed packages, including Diffusion Toolkit, DiffusionKit, SPM and MRIcron, were employed in DCP. Using any number of raw DTI datasets from different subjects, in either DICOM or NIfTI format, DCP can automatically perform a series of steps to construct network. In addition, DCP has a friendly graphical user interface (GUI) running on the Windows Operating System, allowing the user to be interactive and to adjust the input/output settings, as well as the processing parameters. As an open-source package, DCP is freely available at https://www.nitrc.org/projects/dcp. This novel toolbox is expected to substantially simplify the image processing of DTI datasets and facilitate human brain structural connectome studies.

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Section: Discussionsupporting

confidence: 94%

DCP: a pipeline toolbox for diffusion connectome

Huang

Shu

2020

Preprint

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“…A decrease in degree and efficiency in the AD subjects consistent among the different levels of the pathology was described in a study based on the core network [ 18 ]. Likewise, decreased global efficiency, strength, degree, and clustering coefficient of the fiber number-weighted network were shown in patients with different types of MCI or preclinical AD [ 19 , 35 , 59 – 61 ], as well as decreased local efficiency in patients with early AD [ 62 ]. In populations with a genetic risk of AD, such as carriers of the apolipoprotein E ( APOE ) ε4 and rs405509 alleles, decreased global and local efficiency of FA-w networks have been described [ 63 , 64 ].…”

Section: Discussionmentioning

confidence: 99%

“…Accordingly, studies in human populations have revealed reduced nodal efficiency in medial frontal cortical structures of the right hemisphere in AD compared with healthy subjects [ 21 ]. Fiber number-weighted nodal efficiency and/or strength was also described to be decreased in patients with MCI in different regions, including the bilateral medial frontal gyrus [ 19 , 35 , 60 , 61 ]. This region is involved in executive function, attention, and memory [ 66 ], and therefore alterations in its connectivity might be associated with the AD-related cognitive impairments.…”

Section: Discussionmentioning

confidence: 99%

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer’s disease

et al. 2018

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BackgroundAnimal models of Alzheimer’s disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present.MethodsCognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats.ResultsThe results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes.ConclusionsIn this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics.Electronic supplementary materialThe online version of this article (10.1186/s13195-018-0346-2) contains supplementary material, which is available to authorized users.

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“…Daianu et al (36) suggested that structural network disruptions predominated in more remotely connected regions in patients. In our previous studies, the disturbances in rich-club organization dynamically and potently disrupted connectivities among peripheral regions in preclinical AD and MCI patients, and the disrupted connectivities spread to the rich-club regions in the brains of patients with AD (26,38). Compared with the rich-club connections, the feeder and local connections are demonstrated to be impaired earlier and more severely (36).…”

Section: Introductionmentioning

confidence: 91%

Abnormal Anatomical Rich-Club Organization and Structural–Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease

et al. 2020

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Emerging research indicates interruptions in the wiring organization of the brain network in Mild cognitive impairment (MCI) and Alzheimer's disease (AD). Due to the important role of rich-club organization in distinguishing abnormalities of AD patients and the close relationship between structural connectivity (SC) and functional connectivity (FC), our study examined whether changes in SC-FC coupling and the relationship with abnormal rich-club organizations during the development of diseases may contribute to the pathophysiology of AD. Structural diffusion-tensor imaging (DTI) and resting-state functional magnetic resonance imaging (fMRI) were performed in 38 normal controls (NCs), 40 MCI patients and 19 AD patients. Measures of the rich-club structure and its role in global structural-functional coupling were administered. Our study found decreased levels of feeder and local connectivity in MCI and AD patients, which were the main contributing factors to the lower efficiency of the brain structural network. Another important finding was that we have more accurately characterized the changing pattern of functional brain dynamics. The enhanced coupling between SC and FC in MCI and AD patients might be due to disruptions in optimal structural organization. More interestingly, we also found increases in the SC-FC coupling for feeder and local connections in MCI and AD patients. SC-FC coupling also showed significant differences between MCI and AD patients, mainly between the abnormal feeder connections. The connection density and coupling strength were significantly correlated with clinical metrics in patients. The present findings enhanced our understanding of the neurophysiologic mechanisms associated with MCI and AD.

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Age-related differences in the topological efficiency of the brain structural connectome in amnestic mild cognitive impairment

Cited by 26 publications

References 68 publications

DCP: a pipeline toolbox for diffusion connectome

DCP: a pipeline toolbox for diffusion connectome

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer’s disease

Abnormal Anatomical Rich-Club Organization and Structural–Functional Coupling in Mild Cognitive Impairment and Alzheimer's Disease

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