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Record W2158436381 · doi:10.1093/cercor/12.1.98

Motor Map Expansion Following Repeated Cortical and Limbic Seizures Is Related to Synaptic Potentiation

2002· article· en· W2158436381 on OpenAlexaff
G. Campbell Teskey

Bibliographic record

VenueCerebral Cortex · 2002
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNeuroscienceForelimbMotor cortexCorpus callosumKindlingPsychologyMicrostimulationEpilepsyStimulation

Abstract

fetched live from OpenAlex

The effect of experimentally induced seizure activity on the functional reorganization of motor maps has not previously been investigated. Furthermore, while the functional reorganization of motor maps has been thought to involve increases in synaptic communication, there has yet to be a test of this hypothesis. Here we show that repeated seizure activity (kindling), that is accompanied by increased synaptic strength within adult rat motor cortex, results in a doubling of the caudal forelimb motor area. We measured neo-cortical evoked potentials in the right hemisphere prior to 25 days of electrical kindling of the medial frontal corpus callosum or amygdala and re-measured them either 1 or 21 days following the last kindling session. Then, using high resolution intracortical microstimulation (ICMS), the caudal forelimb area in the left hemisphere was mapped. This is the first report of any procedure causing a motor representation to double in size. Furthermore, this expansion was related to the enhanced area of a neocortical polysynaptic field potential and not the motor convulsions per se. Moreover, both the motor map and field potential enhancements were persistent in nature and could be driven from either cortical or limbic sites. The data have implications for human populations with epilepsy.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.037
GPT teacher head0.304
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations105
Published2002
Admission routes1
Has abstractyes

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