Neuromagnetic correlates of fMRI signals in human primary somatosensory cortex
Bibliographic record
Abstract
Techniques that more directly measure neural activity can contribute substantially to such investigations. Electroencephalography (EEG) resolves electrical neural activity on the millisecond time-scale, and its relatively new magnetic counterpart, magnetoencephalography (MEG), provides equally fine temporal resolution with improved spatial localization. Brain regions near the cortical surface are excellent candidates for investigation with both MEG and EEG. Results from such investigations can then be compared with their fMRI counterparts. This thesis focuses on human primary somatosensory cortex (SI), a brain region that receives incoming touch stimuli, with the aim of understanding SI fMRI signals in terms of the underlying MEG activity in response to vibrotactile stimulation. A preliminary fMRI experiment is performed using a range of stimulus durations (2--20 s) and SI fMRI data are mathematically modelled based on postulated neural activity functions. Next, MEG is used to characterize the temporal features of the neuromagnetic activity to experimentally support the postulates of the previous fMRI study. Lastly, an fMRI experiment is designed using the same stimulus delivery over short stimulus durations ( le; 1 s). The mathematical modelling is extended to include experimentally-derived MEG waveforms, improving on the standard approach that relies on the envelope of the stimulus waveform to predict fMRI signals. The implications of this work for future MEG/fMRI investigations, and open questions about vibrotactile information processing in SI are discussed. The emergence of new imaging technology towards the end of the last century has allowed unprecedented noninvasive access to the brain, revolutionizing the field of neuroscience and motivating applications in clinical care. Functional magnetic resonance imaging (fMRI), now used in hundreds of centres worldwide, is leading the way. The sensitivity of fMRI allows it to probe the entire brain volume for hemodynamic changes that occur as an indirect result of neural activity. The mechanisms by which neural activity induces a hemodynamic response and shapes fMRI signals are actively researched areas essential for understanding the neurophysiological basis of fMRI and for using the method with understanding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".