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Record W2190920317 · doi:10.3390/diagnostics5040577

Role of Hybrid Brain Imaging in Neuropsychiatric Disorders

2015· review· en· W2190920317 on OpenAlex
Amer M. Burhan, Nicole E. Marlatt, Lena Palaniyappan, Udunna Anazodo, Frank S. Prato

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiagnostics · 2015
Typereview
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsLawson Health Research InstituteSt Joseph's Health CareParkwood InstituteWestern University
Fundersnot available
KeywordsNeuroscienceNeuroimagingScope (computer science)ModalitiesFunctional Brain ImagingBiomarkerFunctional imagingMedicinePsychologyComputer scienceBiology

Abstract

fetched live from OpenAlex

This is a focused review of imaging literature to scope the utility of hybrid brain imaging in neuropsychiatric disorders. The review focuses on brain imaging modalities that utilize hybrid (fusion) techniques to characterize abnormal brain molecular signals in combination with structural and functional changes that have been observed in neuropsychiatric disorders. An overview of clinical hybrid brain imaging technologies for human use is followed by a selective review of the literature that conceptualizes the use of these technologies in understanding basic mechanisms of major neuropsychiatric disorders and their therapeutics. Neuronal network abnormalities are highlighted throughout this review to scope the utility of hybrid imaging as a potential biomarker for each disorder.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.057
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.057
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.311
Teacher spread0.277 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it