Frontal Lobe Epilepsy Alters Functional Connections Within the Brain's Motor Network: A Resting-State fMRI Study
Bibliographic record
Abstract
Patients with frontal lobe epilepsy (FLE) often experience motor deficits, yet little is known of the impact of FLE on the activity of motor networks in the brain. Resting-state functional magnetic resonance imaging (rs-fMRI) has previously demonstrated an association between cognitive deficits in temporal lobe epilepsy patients and disruption of activity within pertinent brain networks. Hence, in the present study, rs-fMRI was used to determine whether FLE is associated with motor network disruption. Seven right-hemisphere FLE patients, six left-hemisphere FLE patients, and nine control subjects underwent rs-fMRI. Functional connectivity was computed between the sensorimotor cortex contralateral to the seizure focus and each voxel in the brain, and then compared voxel-by-voxel between patient groups and controls. A laterality index (LI) of connectivity between contralateral and ipsilateral sensorimotor cortices was calculated to investigate its association with epilepsy duration and seizure frequency. Positive laterality indices indicate reduced connectivity, and zero values indicate strong connectivity. Connectivity between the left and right sensorimotor cortices was significantly reduced in FLE patients compared with controls (p<0.05), and LI was positively correlated with the number of lifetime seizures (left FLE: rs=0.89, right FLE: rs=1.00). Patients with FLE exhibit decreased connectivity within the motor network, in correlation with the number of lifetime seizures, thus demonstrating a potential relationship between seizure activity and changes in motor network organization. These findings suggest that motor network disturbances may in part be responsible for the motor deficits observed in FLE patients.
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 | 0.000 | 0.001 |
| 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 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".