Temporal dynamics of early brain activity explored using EEG and computational models
Bibliographic record
Abstract
This thesis considers the temporal structure of burst dynamics in early brain activity through the analysis of human very preterm electroencephalograph (EEG) recordings and computational neural network models. A novel algorithm for the detection of the discontinuous bursts of activity in the preterm EEG is developed and the temporal structure of burst occurrence, size and duration are assessed. The dynamics are shown to exhibit long-range temporal correlations (LRTCs) indicating a temporal complexity within early brain activity not previously appreciated. This result is replicated in a larger population of preterm children and the effect of gestational age and postnatal age on the degree of LRTCs is examined. A possible mechanism underlying the generation of burst activity that exhibits LRTCs is investigated in a stochastic excitatory neural network model. It is shown that burst dynamics occur in the model when there is a balance between the activity of an individual neuron and the number of neurons it in turn activates. Furthermore, it is shown that correlations in the temporal statistics of these bursts exist over a wide range and extend across an infinite range in the limit of system size. The behaviour of the model with respect to different network topologies is also investigated. In summary, it is shown that complex temporal dynamics exist even in early brain activity and such dynamics can be observed in a simple model. In light of this, the evidence that the brain exhibits self-organised criticality - a theoretical framework suggested by previous authors as an explanation for LRTCs in a systems dynamics - is discussed. Overall, the observation of complex temporal structure of activity in the early developing brain suggests that the temporal organisation of this activity may play an important developmental role. This thesis therefore provides strong motivation for future work in this area.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".