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Record W4225279875 · doi:10.1016/j.nicl.2022.103029

Spatial and chronic differences in neural activity in medicated and unmedicated schizophrenia patients

2022· review· en· W4225279875 on OpenAlexaff
Zachary Yaple, Serenella Tolomeo, Rongjun Yu

Bibliographic record

VenueNeuroImage Clinical · 2022
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsYork University
FundersHong Kong Baptist University
KeywordsSchizophrenia (object-oriented programming)PsychologyMedicinePsychiatryAudiologyNeuroscience

Abstract

fetched live from OpenAlex

A major caveat with investigations on schizophrenic patients is the difficulty to control for medication usage across samples as disease-related neural differences may be confounded by medication usage. Following a thorough literature search (632 records identified), we included 37 studies with a total of 740 medicated schizophrenia patients and 367 unmedicated schizophrenia patients. Here, we perform several meta-analyses to assess the neurofunctional differences between medicated and unmedicated schizophrenic patients across fMRI studies to determine systematic regions associated with medication usage. Several clusters identified by the meta-analysis on the medicated group include three right lateralized frontal clusters and a left lateralized parietal cluster, whereas the unmedicated group yielded concordant activity among right lateralized frontal-parietal regions. We further explored the prevalence of activity within these regions across illness duration and task type. These findings suggest a neural compensatory mechanism across these regions both spatially and chronically, offering new insight into the spatial and temporal dynamic neural differences among medicated and unmedicated schizophrenia patients.

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.

How this classification was reachedexpand

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.003
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 head0.129
GPT teacher head0.413
Teacher spread0.284 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2022
Admission routes1
Has abstractyes

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