DCP: A pipeline toolbox for diffusion connectome

The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.

[Pattern recognition analysis of Alzheimer's disease based on brain structure network]

Alzheimer' s disease is the most common kind of dementia without effective treatment. Via early diagnosis, early intervention after diagnosis is the most effective way to handle this disease. However, the early diagnosis method remains to be studied. Neuroimaging data can provide a convenient measurement for the brain function and structure. Brain structure network is a good reflection of the fiber structural connectivity patterns between different brain cortical regions, which is the basis of brain's normal psychology function. In the paper, a brain structure network based on pattern recognition analysis was provided to realize an automatic diagnosis research of Alzheimer's disease and gray matter based on structure information. With the feature selection in pattern recognition, this method can provide the abnormal regions of brain structural network. The research in this paper analyzed the patterns of abnormal structural network in Alzheimer's disease from the aspects of connectivity and node, which was expected to provide updated information for the research about the pathological mechanism of Alzheimer's disease.

White Matter Integrity in Subcortical Vascular Cognitive Impairment: A Multimodal Structural MRI Study

OBJECTIVE

To investigate the disruption of white matter (WM) integrity in patients with subcortical vascular cognitive impairment (SVCI) with multimodal structural magnetic resonance imaging (MRI), and to explore the relationships between WM damage, structural network disruptions, and general cognitive function decline.

METHODS

Twenty-eight patients with SVCI and twenty control subjects underwent structural MRI scans and neuropsychological assessment. WM volume (WMV), WM hyperintensities (WMH), lacunar infarcts (LI), diffusion tensor imaging parameters and structural network characteristics were compared between two groups. Correlations between these parameters and general cognitive function were calculated.

RESULTS

WMV, WMH load, LI number, mean fractional anisotropy (FA), peak height and peak location of mean diffusivity (MD) of normal-appearing WM (NAWM) and structural network characteristics were significantly different between the two groups. Disruption of WM microstructure and network characteristics was widespread. WMV, WMH load, mean FA and peak location of MD of NAWM explained about 70% of the variance in structural network characteristics. Shortest path length with LI number explained 47.5% of the variance in z-scores.

CONCLUSION

SVCI was associated with widespread disruptions of WM integrity in the brain. Network characteristics may be a comprehensive reflection of WM integrity and a superior predictor of general cognitive function, while LI was an independent predictor of cognitive impairment.