Morphological variability may limit single-cell specificity to electric field stimulation
Bibliographic record
Abstract
Abstract Non-invasive brain stimulation techniques are widely used for manipulating the behaviour of neuronal circuits and the excitability of the neurons therein. While the usage of these techniques is widely studied at the meso- and macroscopic scales, less is known about the specificity of such approaches at the level of individual cells. Here we use models based on the morphologies of real pyramidal and parvalbumin neurons from mouse primary visual cortex created by the Allen Institute for Brain Science to explore the variability and evoked response susceptibility of different morphologies to uniform electric fields. We devised a range of metrics quantifying various aspects of cellular morphology, ranging from whole cell attributes to net compartment length, branching, diameter to orientation. In supporting layer- and cell-type specific responses, none of these physical traits passed statistical significance tests. While electric fields can modulate somatic, dendritic and axonal compartments reliably and subtype-specific responses could be observed, the specificity of such stimuli was blurred by the variability in cellular morphology. These null results suggest that morphology alone may not account for the reported subtype specificity of brain stimulation paradigms, and question the extent to which such techniques may be used to probe and control neural circuitry. Author summary Over the last several decades there has been increased interest in the efficacy of non-invasive brain stimulation, particularly in determining the limits of specificity of such techniques. Despite this growing area of research, much remains unknown about the interactions of non-invasive techniques with neurons at the single-cell level, notably the importance of morphology to these interactions. We make use of detailed single-neuron models and simulate them in a uniform electric field and demonstrate that the high variability in neuron morphologies may limit how specifically single neurons can be targeted non-invasively. We confirmed this for neuron morphology characteristics at macro- and meso- scales and at varied orientations. Our work suggests that previously reported subtype specificities in non-invasive frameworks are not accounted for by considering only morphological factors.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".