Motivation of Neural Plasticity: Neural Mechanisms in the Self-Organization of Depression
Bibliographic record
Abstract
In this chapter we outline a theoretical framework for self-organization in the development of a neurological and cognitive-emotional vulnerability to major depression. We begin by outlining the basis for plasticity in embryonic and early infant neural systems. Early plasticity, we argue, forms the basis for learning and, by extension, may determine the organization of the cognitive and emotional schemas that drive behavior. Disruptions in the mechanisms of plasticity may lead to disruptions in emotional, motivational, and cognitive self-organization, rendering the person vulnerable to affective psychopathology throughout life. Disruptions at two levels of neurological control of plasticity and self-organization may lead to vulnerability to depression. First, arousal states, under the control of brain stem and thalamic centers, are necessary to support the neural plasticity underlying learning. We propose that deficits in arousal due to early neglect and/or loss experiences may impair normal developmental plasticity. Second, we propose that particularly traumatic events, such as childhood sexual abuse, may form enduring memory traces in cortical and limbic areas. These memories, and the emotional dysregulation they engender, may then sensitize (or “kindle”) individuals to such experiences in the future. In support of these two hypotheses, we review the mechanisms of arousal control and memory consolidation, and their likely roles in neural plasticity. We then theorize on how these may be related to the neurological and cognitive sequelae of early neglect and trauma both in animal models and in studies with humans.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".