Outline 3.1 Data Acquisition & Preprocessing
- Core Argument dieses Kapitels definieren
To-Do’s
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3.1 Data Acquisition & Preprocessing
3.1.1 Dataset and Participants
This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) data from the S1200 release of the Human Connectome Project (HCP). To ensure high data quality and completeness, a strictly defined sub-group of healthy young adult subjects was selected. This specific cohort was chosen because these subjects completed all four rs-fMRI runs, and their data were reconstructed using the latest r227 reconstruction algorithm, which corrects for specific phase-encoding artifacts present in earlier releases.
3.1.2 Data Acquisition
Imaging data were acquired using a customized Siemens 3T Connectome Skyra scanner. The rs-fMRI data were collected utilizing a gradient-echo EPI sequence with the following standard HCP parameters: a repetition time (TR) of , an echo time (TE) of , a flip angle of , and an isotropic spatial resolution of . Each subject completed two sessions on separate days. Each session consisted of two 15-minute runs with opposing phase-encoding directions (Right-to-Left and Left-to-Right), resulting in 1200 frames per run and a total of 4800 time points per subject. During the acquisition, subjects were instructed to keep their eyes open and maintain fixation on a red crosshair on a dark background.
3.1.3 Preprocessing
The raw fMRI data underwent preprocessing via the standardized HCP Minimal Preprocessing Pipelines. This first pipeline performs spatial distortion correction, motion correction, and registration to the structural final brain mask by reducing the bias field and normalization. Subsequently, the data were transformed from native mesh to fs_LR registered 32k mesh (2mm average vertex spacing).
To account for noise and motion artifacts, the data were denoised using ICA-FIX (Independent Component Analysis-based X-noiseifier). This automated method decomposes the data into independent components and regresses out those identified as structured noise (e.g., cardiac, respiratory, and head movement artifacts) without applying aggressive global signal regression.
Notes & Scrapbook
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Quelle:
Wu-Minn HCP Consortium. (2018). WU-Minn HCP 1200 Subjects Data Release: Reference Manual (updated April 2018). Human Connectome Project. https://www.humanconnectome.org/storage/app/media/documentation/s1200/HCP_S1200_Release_Reference_Manual.pdf
see also
3.0 Methods
3.2 Selection of Regions of Interest (ROIs)
3.3 Matrix Construction