Controlling Prefrontal Attention Circuits: Neuromodulation of Cortical Layer 6 and its Local Outputs
Bibliographic record
Abstract
The prefrontal cortex is critical for mediating attention. Neuromodulation by acetylcholine and serotonin exerts opposing effects on attention. Layer 6 of prefrontal cortex is an important source of cortico-thalamic and cortico-cortical output, and this layer expresses both cholinergic and serotonergic receptors. The capacity of prefrontal layer 6 to influence attention through its cortical and thalamic connections highlights the necessity to understand its neuromodulation and the underlying cellular mechanisms involved. This thesis examines the modulation of prefrontal layer 6 by acetylcholine and serotonin, and the consequences of layer 6 activity on its downstream cortical targets. Is the cholinergic modulation of layer 6 in medial prefrontal cortex distinct? Contrasting layer 6 neurons of associative and primary regions of cortex, I find significant differences in the receptor composition and strength of cholinergic responses. The stronger cholinergic response in medial prefrontal layer 6 appears driven by high affinity nicotinic receptors of the ι4β2ι5 subtype with a modest contribution by muscarinic receptors. Functional disruptions in specific nicotinic receptor subunits are linked to attentional disruption, but what are their consequences for cholinergic modulation of layer 6? In mice with genetic deletion of key nicotinic receptor subunits, I demonstrate a compensatory upregulation in the muscarinic response, inversely proportional to the impairment of nicotinic receptor function. This compensatory plasticity partially rescues the excitatory response to cholinergic stimulation at near-threshold membrane potentials. Unlike acetylcholine, serotonin impairs attention, yet the underlying mechanisms are unclear. In medial prefrontal cortex, I show, for the first time, that serotonin suppresses of layer 6 excitability through 5-HT1A and 5-HT2A receptors. Using transgenic mice that allow for light-mediated activation of layer 6, I identify a direct, excitatory, local connection between layer 6 and layer 5 interneurons of medial prefrontal cortex. Finally, I show that suppression of layer 6 activity by serotonin inhibits the activation of downstream layer 5 interneurons. The findings in this thesis probe the receptor mechanisms underlying the modulation of prefrontal layer 6 by acetylcholine and serotonin. Furthermore, new insight is provided into the consequences of modulating layer 6 activity on its downstream cortical targets.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".