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Record W4362673579 · doi:10.1016/j.nicl.2023.103395

Functional connectivity analysis of childhood depressive symptoms

2023· article· en· W4362673579 on OpenAlexaff
Pei Huang, Shi Yu Chan, Zhen Ming Ngoh, Ranjani Nadarajan, Yap Seng Chong, Peter D. Gluckman, Helen Chen, Marielle V. Fortier, Ai Peng Tan, Michael J. Meaney

Bibliographic record

VenueNeuroImage Clinical · 2023
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsMcGill UniversityDouglas Mental Health University Institute
FundersMedical Research CouncilAgency for Science, Technology and ResearchNational Research Foundation SingaporeNestecSingapore Institute for Clinical SciencesHope for Depression Research FoundationNational University Health SystemJacobs FoundationNational Medical Research CouncilNational Research FoundationDanoneMinistry of Health -Singapore
KeywordsResting state fMRIPsychologyOrbitofrontal cortexNeuroimagingFunctional magnetic resonance imagingDepression (economics)Functional neuroimagingCohortEarly childhoodConnectomeInsulaClinical psychologyDevelopmental psychologyCognitionFunctional connectivityPsychiatryNeuroscienceMedicineInternal medicinePrefrontal cortex

Abstract

fetched live from OpenAlex

BACKGROUND: Childhood depression is a highly distinct and prevalent condition with an unknown neurobiological basis. We wish to explore the resting state fMRI data in children for potential associations between neural connectivity and childhood depressive symptoms. METHODS: A longitudinal birth cohort study with neuroimaging data obtained at 4.5, 6.0 and 7.5 years of age and the Children Depression Inventory 2 (CDI) administered between 8.5 and 10.5 years was used. The CDI score was used as the dependent variable and tested for correlation, both simple Pearson and network based statistic, with the functional connectivity values obtained from the resting state fMRI. Cross-validated permutation testing with a general linear model was used to validate that the identified functional connections were indeed implicated in childhood depression. RESULTS: Ten functional connections and four brain regions (Somatomotor Area B, Temporoparietal Junction, Orbitofrontal Cortex and Insula) were identified as significantly associated with childhood depressive symptoms for girls at 6.0 and 7.5 years. No significant functional connections were found in girls at 4.5 years or for boys at any timepoint. Network based statistic and permutation testing confirmed these findings. CONCLUSIONS: This study revealed significant sex-dependent associations of neural connectivity and childhood depressive symptoms. The regions identified are implicated in speech/language, social cognition and information integration and suggest unique pathways to childhood depressive symptoms.

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)0.061
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.101
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.061
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
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 head0.099
GPT teacher head0.358
Teacher spread0.259 · 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
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

Citations7
Published2023
Admission routes1
Has abstractyes

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