Dynamic thalamus parcellation from resting‐state fMRI data
نویسندگان
چکیده
منابع مشابه
Dynamic thalamus parcellation based on resting-state fMRI data
Dynamic thalamus parcellation based on resting-state fMRI data Bing Ji, Zhihao Li, Kaiming Li, Longchuan Li, and Xiaoping Hu Wallace H. Coulter Dept. of Biomedical Engineering, Emory University School of Medicine, Atlanta, GA, United States, University of Shanghai for Science & Technology, 200093, Shanghai, China, Department of Pediatrics, Marcus Autism Center, Children's Healthcare of Altanta,...
متن کاملData-driven fMRI data analysis based on parcellation
Functional Magnetic Resonance Imaging (fMRI) is one of the most popular neuroimaging methods for investigating the activity of the human brain during cognitive tasks. As with many other neuroimaging tools, the group analysis of fMRI data often requires a transformation of the individual datasets to a common stereotaxic space, where the different brains have a similar global shape and size. Howe...
متن کاملROI atlas generated from whole brain parcellation of resting state fMRI data
Introduction Connectivity analyses and computational modeling of human brain function from fMRI data require the specification of regions of interests to be employed in the analysis. Several methods have been used that either rely on a neuroanatomist’s ability to reliably identify targeted brain regions, or atlases derived from anatomical or cyto-architectonic boundaries. Neither of these appro...
متن کاملAutomatic Method for Thalamus Parcellation Using Multi-modal Feature Classification
Segmentation and parcellation of the thalamus is an important step in providing volumetric assessment of the impact of disease n brain structures. Conventionally, segmentation is carried out on T1-weighted magnetic resonance (MR) images and nuclear parcellation using diffusion weighted MR images. We present the first fully automatic method that incorporates both tissue contrasts and several der...
متن کاملHemodynamic-Informed Parcellation of fMRI Data in a Joint Detection Estimation Framework
Identifying brain hemodynamics in event-related functional MRI (fMRI) data is a crucial issue to disentangle the vascular response from the neuronal activity in the BOLD signal. This question is usually addressed by estimating the so-called hemodynamic response function (HRF). Voxelwise or region-/parcelwise inference schemes have been proposed to achieve this goal but so far all known contribu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Human Brain Mapping
سال: 2015
ISSN: 1065-9471,1097-0193
DOI: 10.1002/hbm.23079