Functional neuroimaging of post-mortem tissue: lithium-pilocarpine seized rats express reduced brain mass and proportional reductions of left ventral cerebral theta spectral power
Bibliographic record
Abstract
Structural imaging tools can be used to identify neuropathology in post-mortem tissue whereas functional imaging tools including quantitative electroencephalography (QEEG) are thought to be restricted for use in living subjects. We are not aware of any study which has used electrophysiological methods decades after death to infer pathology. We therefore attempted to discriminate between chemically preserved brains which had incurred electrical seizures and those that did not using functional imaging. Our data indicate that modified QEEG technology involving needle electrodes embedded within chemically fixed neural tissue can be used to discriminate pathology. Forty (n = 40) rat brains preserved in ethanol-formalin-acetic acid (EFA) were probed by needle electrodes inserted into the dorsal and ventral components of the left and right cerebral hemispheres. Raw microvolt potentials were converted to spectral power densities within classical electroencephalographic frequency bands (1.5 Hz to 40 Hz). Brain mass differences were shown to scale with left hemispheric ventral theta-band spectral power densities in lithium-pilocarpine seized rats. This relationship was not observed in non-seized rats. A conspicuous absence of pathological indicators within dorsal regions as inferred by microvolt fluctuations was expected given the known localization of post-ictal damage in lithium-pilocarpine seized rats. Together, the data demonstrate that post-mortem neuroimaging is both possible and potentially useful as a means to identify neuropathology without structural imaging techniques or dissection.
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 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".