Memory network activity flow failures in temporal and frontal lobe epilepsy
Bibliographic record
Abstract
Abstract Declarative memory deficits represent considerable challenges to adequate functioning and wellbeing in temporal lobe epilepsy (TLE) and frontal lobe epilepsy (FLE), two of the most common pharmaco-resistant epilepsies. TLE and FLE are impacted differently however, with TLE affecting primarily episodic, and mildly semantic memory, while FLE presenting with overall lesser declarative impairments and a greater involvement of language processes. Although functional magnetic resonance imaging (fMRI) studies are overall compatible with differential disruptions of medial temporal and fronto-limbic networks in both syndromes, direct comparisons of brain activity and connectivity remain scarce. The current study investigated how alterations in intrinsic functional brain organization, as mapped with resting-state connectivity (rsFC), shapes altered brain network activations in both episodic and semantic memory states. To this end, we acquired task- and rs-fMRI data in 28 TLE patients and 17 FLE patients and 87 age- and sex-matched healthy controls (HCs). We used activity flow mapping (AFM), a generative machine learning technique that derives plausible task activation patterns via individualized rsFC as well as normative task activation data. Previous studies in HC as well as patient populations have shown that this technique has been effective in identifying mechanisms contributing to atypical functional organization across different context. Overall, AFM reliably predicted task activations across HC, TLE, and FLE, but prediction accuracy was consistently reduced in both patient groups, indicating impaired propagation of task-relevant signals. These reductions co-occurred with abnormal episodic and semantic task activation patterns, atypical rsFC, and behavioral profiles marked by preserved semantic but impaired episodic memory performance in both patient groups. Importantly, although neither task-evoked abnormalities nor rsFC disruptions alone were associated with clinical variables, lower AFM accuracy in patients robustly tracked poorer episodic and semantic memory performance and longer disease duration. Prediction accuracy was driven more by intrinsic functional than structural network features, suggesting that altered network communication, rather than gross anatomy, constrains AFM in pharmaco-resistant epilepsy. Our work revealed syndrome-specific yet convergent disruptions in paralimbic and heteromodal association systems, linking intrinsic dysconnectivity to a shared episodic vulnerability and semantic resilience across TLE and FLE. By modelling how intrinsic connectivity shapes task-evoked responses, AFM provides a mechanistic account of imbalanced memory-state activations and isolates network-flow pathways as targets for intervention and rehabilitation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".