Using advanced neuroimaging and bioinformatics methods to study brain-behaviour relationships
Bibliographic record
Abstract
Abstract Understanding the complex relationships between brain structure, function, and behavior is a central challenge in neuroscience. This presentation aims to showcase the transformative potential of neuroimaging and bioinformatics in bridging the gap between neural mechanisms and behavior, ultimately advancing our understanding of the human brain and informing precision medicine. Recent advancements in neuroimaging and bioinformatics enable researchers to explore these relationships with unprecedented precision and scale. This presentation will provide an overview of how neuroimaging modalities can be integrated with advanced bioinformatics tools, including machine learning to uncover novel brain-behavior associations. We will discuss key applications of these methods for neuropsychiatric disorders and specific examples will be used to highlight how combining neuroimaging data with bioinformatics pipelines enhances our ability to measure brain organization at the level of a single individual. Additionally, challenges such as data complexity, standardization, and interpretability will be addressed, alongside strategies to overcome them. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".