The Impact of Electrophysiological Diversity on Pattern Completion in Lithium Nonresponsive Bipolar Disorder: A Computational Modelling Approach
Bibliographic record
Abstract
Abstract Patients with bipolar disorder (BD) demonstrate episodic memory deficits, which may be hippocampal-dependent and may be attenuated in lithium responders. Induced pluripotent stem-cell derived CA3 pyramidal cell-like neurons show significant hyperexcitability in lithium responsive BD patients, while lithium nonresponders show marked variance in hyperexcitability. We hypothesize that this variable excitability will impair episodic memory recall, as assessed by cued retrieval (pattern completion) within a computational model of the hippocampal CA3. We simulated pattern completion tasks using a computational model of the CA3 with different degrees of pyramidal cell excitability variance. Since pyramidal cell excitability variance naturally leads to a mix of hyperexcitability and hypoexcitability, we also examined what fraction (hyper-vs. hypoexcitable) was predominantly responsible for pattern completion errors in our model. Pyramidal cell excitability variance impaired pattern completion (linear model β =-1.94, SE=0.01, p<0.001). The effect was invariant to the number of patterns stored in the network, as well as general inhibitory tone and pyramidal cell sparsity in the network. Excitability variance, and more specifically hyperexcitability, increased the number of spuriously active neurons, increasing false alarm rates and producing pattern completion deficits. Excessive inhibition also induces pattern completion deficits by limiting the number of correctly active neurons during pattern retrieval. Excitability variance in CA3 pyramidal cell-like neurons observed in lithium nonresponders may predict pattern completion deficits in these patients. These cognitive deficits may not be fully corrected by medications that minimize excitability. Future studies should test our predictions by examining behavioural correlates of pattern completion in lithium responsive and nonresponsive BD patients. Author summary Patients with bipolar disorder experience debilitating cognitive impairments whose mechanisms are unknown, and these deficits may be greater in patients who do not respond to the mood stabilizer lithium. Studies using induced pluripotent stem cell (iPSC) derived neurons have suggested that CA3 pyramidal cells in lithium nonresponders may have wide diversity of excitability. Our study examines how this diversity of neuronal excitability would impact the computation of pattern completion in the CA3. In a computational model of the CA3, we found that variance in pyramidal cell excitability reliably impaired pattern completion abilities. Furthermore, we found that both the hyperexcitable and hypoexcitable fractions of cells were each responsible for distinct pattern completion errors, depending on the overall level of network inhibition. These results suggest that lithium nonresponsive patients with bipolar disorder will have worse performance on behavioural tasks that are sensitive to pattern completion, potentially including cued recall paradigms. Our results also suggest that mood stabilizers that simply reduce cellular hyperexcitability may not be sufficient to correct micro-circuit level computations in lithium nonresponsive bipolar disorder. Rather, these patients may require development of mood stabilizers that normalize the distribution of neuronal hyperexcitability among CA3 pyramidal cells.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".