In vivo Magnetic Resonance Spectroscopy and its Application to Neuropsychiatric Disorders
Bibliographic record
Abstract
In vivo magnetic resonance spectroscopy (MRS) is the only noninvasive imaging technique that can directly assess the living biochemistry in localized brain regions. In the past decade, spectroscopy studies have shown biochemical alterations in various neuropsychiatric disorders. These first-generation studies have, in most cases, been exploratory but have provided insightful biochemical information that has furthered our understanding of different brain disorders. This review provides a brief description of spectroscopy, followed by a literature review of key spectroscopy findings in schizophrenia, affective disorders, and autism. In schizophrenia, phosphorus spectroscopy studies have shown altered metabolism of membrane phospholipids (MPL) during the early course of the illness, which is consistent with a neurodevelopmental abnormality around the critical period of adolescence when the illness typically begins. Children and adolescents who are at increased genetic risk for schizophrenia show similar MPL alterations, suggesting that schizophrenia subjects with a genetic predisposition may have a premorbid neurodevelopmental abnormality. Independent of medication status, bipolar subjects in the depressive state tended to have higher MPL precursor levels and a deficit of high-energy phosphate metabolites, which also is consistent with major depression, though these results varied. Further bipolar studies are needed to investigate alterations at the early stage. Lastly, associations between prefrontal metabolism of high-energy phosphate and MPL and neuropsychological performance and reduced N-acetylaspartate in the temporal and cerebellum regions have been reported in individuals with autism. These findings are consistent with developmental alterations in the temporal lobe and in the cerebellum of persons with autism. This paper discusses recent findings of new functions of N-acetylaspartate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".