Compensation of intra-frame head motion in PET data with motion corrected independent component analysis (MCICA)
Bibliographic record
Abstract
Independent Component Analysis (ICA) has proved a powerful exploratory analysis method for fMRI. In the ICA model, the fMRI data at a given time point are modeled as the linear superposition of spatially independent (and spatially stationary) component maps. The ICA model has been recently applied to positron emission tomography (PET) data with some success (Human Brain Mapping 18:284-295(2003), IEEE Trans. BME, Naganawa et al, in press). However, in PET imaging each frame is, in fact, activity integrated over a relatively long period of time, making the assumption that the underlying component maps are spatially stationary (and hence no head movement has taken place during the frame collection) very tenuous. Here we extend the application of the ICA model to <sup>11</sup>C-methylphenidate PET data by assuming that each frame is actually composed of the superposition of rigidly transformed underlying spatial components. We first determine the “noisy” initial spatially independent components of a data set under the erroneous assumption of no intra or inter-frame motion. Aspects of the initial components that reliably track spatial perturbations of the data are then determined to produce the motion-compensated components. Initial components included ring-like spatial distributions, indicating that movement corrupts the statistical properties of the data. The final intra-frame motion-compensated components included more plausible symmetric and robust activity in the striatum as would be expected compared to the raw data and the initial components. We conclude that 1) intra-frame motion is a serious confound in PET imaging which affects the statistical properties of the data and 2) our proposed procedure ameliorates such motion effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".