scholarly journals Characterization of Structural Connectivity of the Default Mode Network in Dogs using Diffusion Tensor Imaging

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Jennifer L. Robinson ◽  
Madhura Baxi ◽  
Jeffrey S. Katz ◽  
Paul Waggoner ◽  
Ronald Beyers ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Default Mode Network ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Default Mode

scholarly journals Constituents and functional implications of the rat default mode network

2016 ◽  
Vol 113 (31) ◽  
pp. E4541-E4547 ◽  
Author(s):  
Li-Ming Hsu ◽  
Xia Liang ◽  
Hong Gu ◽  
Julia K. Brynildsen ◽  
Jennifer A. Stark ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Functional Connectivity ◽  
Default Mode Network ◽  
Diffusion Tensor ◽  
Preclinical Models ◽  
Fiber Density ◽  
Default Mode ◽  
Disease States ◽  
Close Relationship ◽  
Neurocognitive Aging

The default mode network (DMN) has been suggested to support a variety of self-referential functions in humans and has been fractionated into subsystems based on distinct responses to cognitive tasks and functional connectivity architecture. Such subsystems are thought to reflect functional hierarchy and segregation within the network. Because preclinical models can inform translational studies of neuropsychiatric disorders, partitioning of the DMN in nonhuman species, which has previously not been reported, may inform both physiology and pathophysiology of the human DMN. In this study, we sought to identify constituents of the rat DMN using resting-state functional MRI (rs-fMRI) and diffusion tensor imaging. After identifying DMN using a group-level independent-component analysis on the rs-fMRI data, modularity analyses fractionated the DMN into an anterior and a posterior subsystem, which were further segregated into five modules. Diffusion tensor imaging tractography demonstrates a close relationship between fiber density and the functional connectivity between DMN regions, and provides anatomical evidence to support the detected DMN subsystems. Finally, distinct modulation was seen within and between these DMN subcomponents using a neurocognitive aging model. Taken together, these results suggest that, like the human DMN, the rat DMN can be partitioned into several subcomponents that may support distinct functions. These data encourage further investigation into the neurobiological mechanisms of DMN processing in preclinical models of both normal and disease states.

Abstract WP489: Disrupted Functional and Structural Connectivity Within Default Mode Network Contribute to WMH Related Cognitive Impairment

Stroke ◽  
2020 ◽  
Vol 51 (Suppl_1) ◽  
Author(s):  
Lili Huang ◽  
Qing Ye ◽  
Xin Chen ◽  
Dan Yang ◽  
Ruomeng Qin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cognitive Impairment ◽  
White Matter ◽  
Default Mode Network ◽  
Rating Scale ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Mean Diffusivity ◽  
White Matter Tracts ◽  
Default Mode

Aims: The prevalence of white matter hyperintensities (WMH) rises dramatically with aging. Both the progression of WMH and default mode network (DMN) have been proven to be closely associated with cognitive function. Thus, we hypothesized that changes in functional connectivity (FC) and structural connectivity (SC) of the DMN contributed to WMH related cognitive impairment. Methods: A total of 116 subjects were enrolled from the Cerebral Small Vessel Disease Register in Drum Tower Hospital of Nanjing University, and were distributed across three categories according to Fazekas rating scale: WMH I(n=57), WMH II(n=34), and WMH III (n=25). The clinical and neuropsychological data were collected, and all participants underwent 3D T1 weighted images, T2 weighted images, 3D fluid attenuated inversion recovery (FLAIR) images, diffusion tensor images (DTI), and diffusion weighted imaging (DWI). The alterations of FC and SC within the DMN were further explored in these subjects. Results: The study found that age and hypertension were risk factors for WMH progression. Subjects with a larger WMH burden displayed higher DMN FC in the medial frontal gyrus (MFG), while lower DMN FC in the thalamus. After adjustment for age, gender, and education, the increasing FC between the MFG, posterior cingulate cortex (PCC), and ascending mean diffusivity (MD) of the white matter tracts between the hippocampus and PCC were independent indicators of worse performance in memory. Moreover, the decreasing FC between the thalamus, PCC, and ascending MD of the white matter tracts between the thalamus and PCC were independent risk factors for a slower processing speed. Conclusion: The changes in FC and SC within the DMN attributed to WMH progression were responsible for the cognitive impairment.

scholarly journals Integrative Structural Brain Network Analysis In Diffusion Tensor Imaging

2017 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Node Degree ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Structural Brain Network ◽  
Structural Networks

AbstractIn diffusion tensor imaging, structural connectivity between brain regions is often measured by the number of white matter fiber tracts connecting them. Other features such as the length of tracts or fractional anisotropy (FA) are also used in measuring the strength of connectivity. In this study, we investigated the effects of incorporating the number of tracts, the tract length and FA-values into the connectivity model. Using various node-degree based graph theory features, the three connectivity models are compared. The methods are applied in characterizing structural networks between normal controls and maltreated children, who experienced maltreatment while living in post-institutional settings before being adopted by families in the US.

scholarly journals Real‐time strategy video game experience and structural connectivity – A diffusion tensor imaging study

2018 ◽  
Vol 39 (9) ◽  
pp. 3742-3758 ◽  
Author(s):  
Natalia Kowalczyk ◽  
Feng Shi ◽  
Mikolaj Magnuski ◽  
Maciek Skorko ◽  
Pawel Dobrowolski ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Real Time ◽  
Video Game ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Imaging Study ◽  
Diffusion Tensor Imaging Study
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