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 Mapping covariance in brain FDG uptake to structural connectivity

2021 ◽  
Author(s):  
Igor Yakushev ◽  
Isabelle Ripp ◽  
Min Wang ◽  
Alex Savio ◽  
Michael Schutte ◽  
...  
Keyword(s):  
White Matter ◽  
Fdg Pet ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Whole Brain ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts ◽  
Sparse Inverse Covariance Estimation ◽  
Structural Networks

Abstract Purpose Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks. Methods We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking. Results For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections. Conclusion Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.

scholarly journals Integrative Structural Brain Network Analysis in Diffusion Tensor Imaging

2017 ◽  
Vol 7 (6) ◽  
pp. 331-346 ◽  
Author(s):  
Moo K. Chung ◽  
Jamie L. Hanson ◽  
Nagesh Adluru ◽  
Andrew L. Alexander ◽  
Richard J. Davidson ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
Network Analysis ◽  
Diffusion Tensor ◽  
Brain Network ◽  
Structural Brain Network ◽  
Structural Brain

Diffusion Tensor Imaging and Fiber Tractography

2009 ◽  
pp. 229-246
Author(s):  
Evanthia E. Tripoliti ◽  
Dimitrios I. Fotiadis ◽  
Konstantia Veliou
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Cerebral White Matter ◽  
Tissue Microstructure ◽  
Diffusion Weighted Images ◽  
Fiber Tracts ◽  
Diffusion Weighted ◽  
Magnetic Resonance Imaging Mri

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging (MRI) modality which can significantly improve our understanding of the brain structures and neural connectivity. DTI measures are thought to be representative of brain tissue microstructure and are particularly useful for examining organized brain regions, such as white matter tract areas. DTI measures the water diffusion tensor using diffusion weighted pulse sequences which are sensitive to microscopic random water motion. The resulting diffusion weighted images (DWI) display and allow quantification of how water diffuses along axes or diffusion encoding directions. This can help to measure and quantify the tissue’s orientation and structure, making it an ideal tool for examining cerebral white matter and neural fiber tracts. In this chapter the authors discuss the theoretical aspects of DTI, the information that can be extracted from DTI data, and the use of the extracted information for the reconstruction of fiber tracts and the diagnosis of a disease. In addition, a review of known fiber tracking algorithms is presented.

scholarly journals On the brain networks organization of individuals with high versus average fluid intelligence: a combined DTI and MEG study

2021 ◽  
Author(s):  
Silvia EP Bruzzone ◽  
Massimo Lumaca ◽  
Elvira Brattico ◽  
Peter Vuust ◽  
Morten L Kringelbach ◽  
...  
Keyword(s):  
Degree Distribution ◽  
Fluid Intelligence ◽  
Diffusion Tensor ◽  
Functional Integration ◽  
Structural Connectivity ◽  
Brain Network ◽  
Opposite Pattern ◽  
Structural Brain Network ◽  
Fast Oscillations ◽  
The Brain

The neural underpinning of human fluid intelligence (Gf) has gathered a large interest in the scientific community. Nonetheless, previous research did not provide a full understanding of such intriguing topic. Here, we studied the structural (from diffusion tensor imaging, DTI) and functional (from magnetoencephalography (MEG) resting state) connectivity in individuals with high versus average Gf scores. Our findings showed greater values in the brain areas degree distribution and higher proportion of long-range anatomical connections for high versus average Gfs. Further, the two groups presented different community structures, highlighting the structural and functional integration of the cingulate within frontal subnetworks of the brain in high Gfs. These results were consistently observed for structural connectivity and functional connectivity of delta, theta and alpha. Notably, gamma presented an opposite pattern, showing more segregation and lower degree distribution and connectivity in high versus average Gfs. Our study confirmed and expanded previous perspectives and knowledge on the small-worldness of the brain. Further, it complemented the widely investigated structural brain network of highly intelligent individuals with analyses on fast-scale functional networks in five frequency bands, highlighting key differences in the integration and segregation of information flow between slow and fast oscillations in groups with different Gf.

STRUCTURAL BRAIN NETWORK ABNORMALITIES IN SUBJECTS WITH INTERNET ADDICTION

2017 ◽  
Vol 17 (07) ◽  
pp. 1740031 ◽  
Author(s):  
MIN-HEE LEE ◽  
AREUM MIN ◽  
YOON HO HWANG ◽  
DONG YOUN KIM ◽  
BONG SOO HAN ◽  
...  
Keyword(s):  
Internet Addiction ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Brain Network ◽  
Imaging Data ◽  
Structural Brain Network ◽  
Global And Local ◽  
The Impact ◽  
Structural Brain

Although problematic overuse of internet has increased, psychopathological characteristics and neurobiological mechanisms for internet addiction (IA) remain poorly understood. Therefore, it is necessary to investigate the impact of IA on the brain. The present study included 17 subjects with IA and 20 healthy subjects. We constructed the structural brain network from diffusion tensor imaging data and investigated alteration of structural connections in subjects with IA using the network analysis on the global and local levels. The subjects with IA showed increase of regional efficiency (RE) in bilateral orbitofrontal cortex (OFC) and decrease in right middle cingulate and middle temporal gyri ([Formula: see text]), whereas the global properties did not show significant changes. Young’s internet addiction test (IAT) scores and RE in left OFC showed positive correlation, and average time spent on internet per day was positively correlated with the RE in right OFC. This is the first study examining alterations of the structural brain connectivity in IA. We found that subjects with IA showed alterations of RE in some brain regions and RE was positively associated with the severity of IA and average time spent on internet per day. Therefore, RE may be a good property for IA assessment.

Postshunting corpus callosum swelling with depiction on tractography

2013 ◽  
Vol 11 (2) ◽  
pp. 178-180 ◽  
Author(s):  
Daniel T. Ginat ◽  
Sanjay P. Prabhu ◽  
Joseph R. Madsen
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Corpus Callosum ◽  
Posterior Margin ◽  
Diffusion Tensor ◽  
The Body ◽  
Transverse Orientation ◽  
Chronic Hydrocephalus ◽  
Fiber Tracts ◽  
White Matter Fiber Tracts

Alterations in the appearance of the corpus callosum occasionally occur following successful ventricular decompression in patients with chronic hydrocephalus. There are certain features on imaging that suggest the diagnosis of what the authors propose be termed “postshunting corpus callosum swelling,” including diffuse high T2 signal predominantly affecting the body of the corpus callosum, with transverse orientation along the crossing white matter tracts and scalloping along the posterior margin of the structure. In this report, the authors demonstrate preservation of the corpus callosum white matter fiber tracts by using diffusion tensor imaging with tractography.

Diffusion Tensor Imaging

2014 ◽  
Author(s):  
Elisabeth A. Wilde ◽  
Kareem W. Ayoub ◽  
Asim F. Choudhri
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Diffusion Tensor ◽  
Structural Connectivity ◽  
Reliability And Validity ◽  
Brain Regions ◽  
Functional Integrity ◽  
P Diffusion ◽  
Effects Of Aging ◽  
Normal White Matter

Diffusion tensor imaging (DTI) is a method of specifying and visualizing the functional integrity of white matter tracts that contribute to the functional and structural connectivity among different brain regions through the examination of water diffusion through tissue. It has gained rapid popularity in the past two decades, particularly for elucidating the process of normal white matter development and the effects of aging on it, as well as providing some insights into the possible neuroanatomical correlates of numerous psychiatric and neurologic disorders. This chapter outlines the instrumentation and the procedures employed in deriving estimates of the functional integrity of anatomical connections in the brain, and issues regarding the reliability and validity of the different DTI procedures are systematically addressed.

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