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Functional connectivity alterations in PTSD patients with suicidal ideation

2024· article· en· W4391933910 on OpenAlexafffund
Stephen J. Wood, Samuel Booth, Ji Hyun Ko

Bibliographic record

VenueBrain Research Bulletin · 2024
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsHealth Sciences CentreUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaWorkers Compensation Board of Manitoba
KeywordsDefault mode networkPsychologySuicidal ideationFunctional magnetic resonance imagingVoxelMoodMood disordersLearned helplessnessNeurosciencePsychiatryClinical psychologyAnxietyMedicinePoison controlInjury prevention

Abstract

fetched live from OpenAlex

Post-traumatic stress disorder (PTSD) is a highly prevalent psychological disorder characterized by intense feelings of fear or helplessness after experiencing a traumatic event. PTSD is highly comorbid with mood disorders and patients are at increased risk for suicide. The present study aimed to identify neural connectivity alterations associated with suicidal ideation (SI) in PTSD patients by using resting-state functional magnetic resonance imaging. Voxel-to-voxel intrinsic connectivity was compared between PTSD patients with no (N-SI; N = 26) and high (H-SI; N = 7) SI. Region-to-voxel functional connectivity analysis was performed to identify the regions that contributed to intrinsic connectivity changes. H-SI patients had increased connectivity to various brain regions representing the central executive network, salience network, and default mode network in the frontal, temporal, and occipital lobes as well as subcortical structures involved in executive and limbic functioning, and motor systems. These results suggest SI is associated with large network-level alterations in PTSD patients and is not the result of neuronal abnormalities in any one specific area.

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)0.002
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.082
GPT teacher head0.339
Teacher spread0.257 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations1
Published2024
Admission routes2
Has abstractyes

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