Home > Publications database > Parcellation-based resting-state blood-oxygen-level-dependent (BOLD) signals of a healthy cohort (v1.0) > print

001     907618
005     20220512185434.0
024 7 _ |a 10.25493/F9DP-WCQ
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037 _ _ |a FZJ-2022-02107
100 1 _ |a Domhof, Justin W. M.
|0 P:(DE-Juel1)179582
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245 _ _ |a Parcellation-based resting-state blood-oxygen-level-dependent (BOLD) signals of a healthy cohort (v1.0)
260 _ _ |c 2022
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336 7 _ |a MISC
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336 7 _ |a Dataset
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520 _ _ |a Resting-state functional connectivity (FC) is frequently used to predict behavioral, clinical and demographic subject traits. This type of brain connectome can be derived from blood-oxygen-level-dependent (BOLD) signals that reflect the activation of individual brain regions parcellated according to a given brain atlas. Deriving FC from BOLD signals typically involves the estimation of the amount of synchronized coactivations between the BOLD time series of different brain regions. However, several measures of synchronization exist and which one of these metrics is suited best may deviate from study to study. In parallel, the appropriate selection of the brain parcellation is nowadays also still an open issue. This dataset hence comprises the region-based BOLD signals extracted from the resting-state functional magnetic resonance imaging (fMRI) data of 200 healthy subjects included in the Human Connectome Project. The time series were extracted for 20 different state-of-the-art parcellations. The neuroimaging community may use the data of this repository to study, for example, how different measures of synchronization affect the resting-state FC under various parcellation conditions.
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536 _ _ |a HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)
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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
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588 _ _ |a Dataset connected to DataCite
650 _ 7 |a Neuroscience
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700 1 _ |a Jung, Kyesam
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700 1 _ |a Eickhoff, Simon B.
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700 1 _ |a Popovych, Oleksandr V.
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773 _ _ |a 10.25493/F9DP-WCQ
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