30. ULTRA HIGH-FIELD 7-TESLA NEUROIMAGING: NEW INSIGHTS INTO MECHANISMS OF PSYCHOSIS
Bibliographic record
Abstract
The ability to visualize alive human brain’s function, structure and chemical composition in ultra-high field 7-Tesla MRI has opened the doors for several neuroscientific advances in recent times. This symposium will focus on the data gathered from this high-resolution access to a patient’s brain tissue to question some of the existing views on the mechanisms linked to psychosis. The symposium has the following objectives each discussed by the presenters: (a) Dr. Frangou will demonstrate how the increased spatial resolution of 7T structural imaging enabled the development and validation of a new method for deriving lamina-specific measures of intracortical myelin. Using this measure, it is possible to identify brain regions of disrupted intracortical organization in schizophrenia that map to symptomatic severity and cognitive disruption. (b) Dr. Palaniyappan shows that the highly resolved, dynamic course of glutamate and glutathione changes using 7T functional spectroscopy is a promising marker of early prognostic course that advances our approach to identifying an oxidative stress-prone subgroup of patients with schizophrenia. (c) Dr. Lahti used magnetoencephalography (MEG), 7T Spectroscopy, and 7T fMRI during task performance to show that illness-related changes in MRI and MEG signals were correlated, while changes in the highly resolved glutamate and NAA represented an independent underlying pathological mechanism. (d) Dr. Hulshoff Pol will leverage the power of cortical layer-specific investigations using 7T as well as spectroscopy to demonstrate illness-related abnormalities in the GABAergic system. She will present data showing that patients with SCZ have significantly lower prefrontal GABA/Cr ratios that were inversely correlated with cognitive functioning. Dr. Laura Rowland will lead the discussion, proffering further means to probe mechanistic aspects of psychosis as well as highlighting the future of a discovery-led approach in 7T neuroimaging in psychotic disorders.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".