Topical Review: Magnetoencephalography and Magnetic Source Imaging in Children
Bibliographic record
Abstract
Magnetoencephalography is a technique that detects the magnetic fields associated with the intracellular current flow within neurons, unlike electroencephalography, which measures extracellular volume currents. Superconducting quantum interference devices are used to amplify these very small magnetic field signals. Magnetic source imaging is the combination of functional data derived from magnetoencephalographic recordings coregistered with structural magnetic resonance imaging (MRI). The utility of magnetic source imaging lies in the combination of the submillisecond temporal resolution of magnetoencephalography with the precise anatomic images provided by magnetic resonance imaging. As such, magnetic source imaging is a useful tool for noninvasive localization of the epileptogenic zone in children who are candidates for epilepsy surgery. Similarly, using magnetoencephalographic recordings with evoked and event-related potentials, magnetic source imaging holds great promise as a noninvasive method for precise localization of somatosensory, motor, language, visual, and auditory cortex. Finally, magnetic source imaging is proving a valuable research tool in the investigation of epilepsy, head trauma, brain plasticity, and disorders of language, memory, cognition, and executive function in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".