Simulating Human L2/3 Cortical Microcircuit Aging Cellular and Synaptic Mechanisms and Associated EEG Biomarkers
Bibliographic record
Abstract
This is the readme for the model associated with the paper: Guet-McCreight A, Tripathy S, Sibille E, Hay E. Linking age changes in human cortical microcircuits to impaired brain function and EEG biomarkers Network Simulations: Simulation code associated with the L2/3 circuit used throughout the manuscript is in the /Circuit_Simulation/ directory. Note that this circuit model is adapted from https://doi.org/10.5281/zenodo.5770999. To run simulations, install all of the necessary python modules (see lfpy_env.yml), compile the mod files within the mod folder, and submit the simulations in parallel (e.g., see job_multirun_loop.sh). In job_multirun_loop.sh, the number at the end of the mpiexec command (see below - 1234) controls the random seed used for both the circuit variance (i.e., connection matrix, synapse placement, etc.) and the stimulus variance (i.e. Ornstein Uhlenbeck noise and stimulus presynaptic spike train timing). mpiexec -n 400 python circuit.py 1234 Here are some of the different configurations of parameters in circuit.py that we change to look at different conditions and levels of analysis. EEG simulations: tstop = 25000. rec_LFP = True rec_DIPOLES = True stimulate = 0 Brief stimulus simulations: tstop = 4500. rec_LFP = False rec_DIPOLES = False stimulate = 1 Stronger stimulus simulations: tstop = 4500. rec_LFP = False rec_DIPOLES = False stimulate = 2 Aging Parameters: OLDER_NMDA = 0 or 20 # Alters NMDA conductance (enter age in years relative to 50yrs) OLDER_SynLoss = 0 or 20 # Reduces PN2PN connection probability (enter age in years relative to 50yrs) OLDER_Proportion = 0 or 20 # Alters proportions (interneuron loss; enter age in years relative to 50yrs) Analysis and Dose Prediction: All code used for analysis and plotting of the circuit simulation results is found in the /Analysis_and_Plotting/ directory. Creation of subfolders for plots and analysis results may be necessary to run this code. In addition to spike simulation analysis, this folder also includes analysis of EEG power spectra (requires PSD generation by first running Plot_PSD_SavePSD2NPY_nperseg_80000.py) and oscillatory events (requires oscillatory event analysis generation by first running Plot_PSD_OEvents_thresh4.py).
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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.
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".