Clinical translation of neuroimaging research in mood disorders.
Bibliographic record
Abstract
BACKGROUND: Practitioners are increasingly presented with data procured from studies employing advanced neuroimaging techniques. The central role that neuroimaging occupies in contemporary psychiatric research highlights the need for practitioner familiarity with the neuroimaging technology and its clinical translation. METHODS: We conducted a PubMed search of all English-language articles published between January 1964 - October 2005. The search words were major depressive disorder, bipolar disorder, functional magnetic resonance imaging (fMRI), single-photon-emission computed tomography (SPECT), positron emission tomography (PET), voxel-based morphometry (VBM), region of interest (ROI), blood-oxygen-level-dependent (BOLD), glucose metabolism, blood flow, statistical parametric mapping (SPM), magnetic resonance spectroscopy (MRS), and diffusion-tensor imaging (DTI). The search was supplemented with a manual review of relevant references. The authors organize the review by addressing frequently asked questions on the topic of neuroimaging by mental healthcare providers. RESULTS: The localization of regional brain volumetric abnormalities with CT is enhanced with MRI techniques that allow for a separate assay of white and gray matter pathology (segmentation), cellular metabolism (MRS), and neurocircuitry (DTI). Positron emission tomography permits the quantification of brain glucose metabolism, regional blood flow, and receptor/transporter localization and function. Rapid changes in regional oxygen consumption may also be quantified with fMRI. CONCLUSIONS: Neuroimaging technology has helped refine pathophysiological models of disease activity in mood disorders and illuminate mechanisms of drug activity. A priority research vista in mood disorders is the integration of neuroimaging investigations with other research methods (e.g., genetics, endocrinology, etc.).
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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 | 0.035 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".