Therapeutic dose prediction of α5-GABA receptor modulation from simulated EEG of depression severity

Abstract

Treatment for major depressive disorder (depression) often has partial efficacy and a large portion of patients are treatment resistant. Recent studies implicate reduced somatostatin (SST) interneuron inhibition in depression, and new pharmacology boosting this inhibition via positive allosteric modulators of α5-GABAA receptors (α5-PAM) offers a promising effective treatment. However, testing the effect of α5-PAM on human brain activity is limited, meriting the use of detailed simulations. We utilized our previous detailed computational models of human depression microcircuits with reduced SST interneuron inhibition and α5-PAM effects, to simulate EEG of individual microcircuits across depression severity and α5-PAM doses. We developed machine learning models that predicted optimal dose from EEG with high accuracy and recovered microcircuit activity and EEG. This study provides dose prediction models for α5-PAM administration based on EEG biomarkers of depression severity. Given limitations in doing the above in the living human brain, the results and tools we developed will facilitate translation of α5-PAM treatment to clinical use.

Fig 1. Simulating EEG of inhibition loss severity in depression and treatment response.

A. Simulated neuronal spiking and EEG in human cortical L2/3 microcircuits in health, depression and under application of α5-PAM. Microcircuits were comprised of 1000 detailed neuron models of four types: Pyr (black), SST (red), PV (green), and VIP (yellow). The connectivity schematic (top left) highlights the cell-specific connectivity, the mechanisms of depression (MDD; loss of SST tonic and synaptic inhibition onto all cell types) and α5-PAM doses (boosted SST tonic and synaptic inhibition to Pyr neurons). B. We simulate five levels of SST inhibition loss severity (0%, 10%… 40%) across 20 different microcircuits each, representing a total of 100 different individual microcircuits. For each microcircuit we simulated a dose-response of α5-PAM (0%, 25%, 50% … 150% relative to the reference dose) and identified ground-truth optimal dose and range.

Fig 2. Simulated EEG biomarkers of depression severity.

A. PSD of simulated EEG from each severity level of SST interneuron inhibition loss (bootstrapped mean and 95% confidence intervals across microcircuits). Inset–PSD plotted in log scale. B-D. PSD power in theta band (4–8 Hz, B), the alpha band (8–12 Hz, C), and broadband 1/f (3–30 Hz, D) for each level of SST interneuron inhibition loss (grey–healthy standard deviation; dashed line–healthy mean; error bars = mean and standard deviation). All asterisks denote significant paired t-tests (p < 0.05) with effect sizes greater than 1 when compared to healthy.

Fig 3. EEG biomarkers of depression severity predict α5-PAM dose accurately.

A. Schematic illustrating the approach—we found optimal α5-PAM dose for each individual microcircuit based on power spectral biomarkers of their simulated EEG, and used the optimal doses to develop dose prediction models for restoring the EEG metrics back to healthy ranges across microcircuits. B. Example PSD profiles for one individual microcircuit (with 30% reduced SST interneuron inhibition, magenta), and under application of 100% of the reference α5-PAM dose (blue). Healthy mean and full ranges are shown in grey. C. Dose-response for the same individual microcircuit as in B, with 30% reduced SST interneuron inhibition (circle outlined in magenta), plotted across three EEG features (1/f, θ, α). A fit of the response was used to obtain the optimal dose (diamond color) and range with respect to the healthy EEG mean (diamond position) and ranges (grey cube), respectively. D. Predicted doses for each individual microcircuit (including healthy) as a function of its EEG features at baseline (before α5-PAM application). E. Percent of correct dose prediction for test sets of individual microcircuits, for dose prediction models using either multivariate (MV) or single EEG biomarkers (50 permutations; blue = under-estimated errors; red = over-estimated errors). F. EEG metrics of all individual microcircuits before (magenta) and after applying the predicted optimal α5-PAM dose (blue).

Fig 4. Predicted α5-PAM dose using EEG biomarkers recovers microcircuit spiking and function.

A. Example raster plot of simulated baseline spiking and response to a brief stimulus. Dashed line indicates stimulus time. Cell type color code is the same as in Fig 1A. B. Distributions of pre-stimulus firing rates from an example microcircuit with 40% SST inhibition reduction before (magenta) and after (blue) application of predicted dose. Average post-stimulus firing rates were similar across conditions, and are shown in black solid line. The overlaps between pre- and post- stimulus curves indicate failed and false signal detection errors. C—E. Dose-response curves in terms of Pyr neuron spike rate (C), failed detection rates (D) and false detection rates (E) for an example individual microcircuit with severity 40% SST inhibition reduction. The predicted dose based on EEG and the corresponding functional metrics is shown by the blue dot. F. Mean and standard deviation of spike rate (left), failed detection rates (middle), and false detection rates (right) in simulated depression (MDD) microcircuits before and after applying the predicted optimal dose. Grey area shows the healthy range.

Fig 5. Dose prediction and recovery using microcircuit spike rates.

A. Mean and SD of spike rates (left), failed detection rates (middle), and false detection rates (right) in individual depression microcircuits (MDD) and after applying the predicted doses. Grey area shows the healthy range. B. EEG metrics of all individual microcircuits before (magenta) and after applying the predicted α5-PAM dose based on spike rate (blue). C. EEG metric (left: 1/f, middle: α, right: θ) correlations with Pyr neuron spike rate across all individual microcircuits (black: healthy; color indicates % reduced SST interneuron inhibition). R values are shown in the top right. D. Variance across individual microcircuits was larger than the variance due to state (10 healthy microcircuits across 10 activity states). Error bars show SD. Different microcircuits are denoted by different colors. E. Average SD of spike rate across microcircuits or states.