![]() ![]() The stroke patients exhibited significant increases in FMA scores and significant reductions in DNS between the two timepoints. Two sets of clinical scores, Fugl-Meyer motor assessment (FMA) and neurological deficit scores (NDS), were assessed for all patients on the day the image data were collected. Here, we applied graph theoretical analysis to investigate the longitudinal topological organization of white matter networks in 20 ischemic stroke patients with a one-month interval between two timepoints. However, few studies have examined the longitudinal reorganization in whole-brain structural networks in stroke. Previous studies have shown brain functional and structural alterations in adults with stroke. Stroke is one of the leading causes of substantial disability worldwide. This novel toolbox is expected to substantially simplify the image processing of DTI datasets and facilitate human brain structural connectome studies. As an open-source package, DCP is freely available at. 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. ![]() #Parcell octave download windows x64 series#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. The processing modules of a few developed packages, including Diffusion Toolkit, DiffusionKit, SPM and MRIcron, were employed in DCP. Here, we developed a MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for fully automated constructing brain structure network. While a number of post processing packages have been developed, fully automated processing of DTI datasets on Windows Operating System remains challenging. The structure network based diffusion tensor imaging is widely used in scientific research. 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. ![]()
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