Identification of Cortical Targets for Modulating Function Supported by the Human Hippocampal Network
Bibliographic record
Abstract
Individualized transcranial magnetic stimulation (TMS) targeting using functional connectivity analysis of functional magnetic resonance imaging (fMRI) has been demonstrated to be advantageous in inducing neuroplasticity. However, how this approach can benefit modulating the episodic memory function supported by the hippocampal network remains elusive. We use the resting-state fMRI data from a large cohort to reveal tentative TMS targets at cortical regions within the hippocampal network. Functional MRI from 1,133 individuals in the Human Connectome Project was used to analyze the hippocampal network using seed-based functional connectivity. Using a weighted sum of time series at the cortex, we identified the average centroids of individualized targets at the medial prefrontal cortex (mPFC) and posterior parietal cortices (PPCs) at (-10, 49, 7) and (-40, -67, 30) in the left hemisphere, respectively. The mPFC and PPC coordinate at the right hemispheres are (11, 51, 6) and (48, -59, 24) in the right hemisphere, respectively. Centroids of the individualized functional connectivity at the mPFC and PPC were reproducible between sessions with separations in average about 2 and 4 mm, respectively. These separations were significantly smaller than the distance to average functional connectivity centroids (~10 mm) and atlas coordinate (~20 mm). These coordinates can be reliably identified (> 90% of individuals) using cortical "seedmaps." Our results suggest candidate TMS target coordinates to modulate the hippocampal function.
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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 | 0.000 | 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".