Mapping the Brain's Functional Architecture with Neuroimaging and Whole Brain Modelling: TMS Connectivity, Negative Correlations, and Structure-Function Relationships
Bibliographic record
Abstract
To understand brain function at a mesoscale level, integrating whole-brain models (WBM) of brain activity with fMRI-based functional connectivity (FC) is beneficial. This combination provides a robust framework to test hypotheses about neural pathways in resting-state and task-based brain dynamics. This approach allows for a more detailed exploration of both normal and pathological brain states and bridges different levels of analysis. This thesis combines functional neuroimaging with WBM to map the brain's functional architecture, focusing on the FC of transcranial magnetic stimulation (TMS) targets. It also examines the role of inhibition in resting-state brain dynamics and studies the interactions between canonical functional networks and how the brain’s structural connectivity influences them. In study one, the FC of two distinct TMS targets is examined to improve our understanding of how repetitive TMS exerts its therapeutic effects. Study two examines the role of inhibition in the occurrence of negative correlations (NCs) in resting-state fMRI data. Study three investigates the state-dependent interactions between functional networks in resting-state and task-based fMRI. Our results underscore the importance of personalized FC data to enhance psychiatric treatments, highlight the key role inhibition levels play in rs-fMRI brain dynamics, and help characterize the complex interplay between SC and FC across different brain states. The three studies in this thesis highlight the advantages of combining WBM methodology with fMRI FC to improve our knowledge of the brain's functional organization. This approach is key for capturing the complex interactions underlying brain dynamics and can provide a more comprehensive understanding of neural function. This integrative strategy not only enhances our knowledge of normal brain states but also holds significant potential for developing personalized clinical interventions and improving therapeutic outcomes for various neurological and psychiatric conditions.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".