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Record W4283640577 · doi:10.1002/jmri.28317

Static and Dynamic Characteristics of Functional Network Connectivity in Neurologically Asymptomatic Patients Undergoing Maintenance Hemodialysis: A <scp>Resting‐State</scp> Functional <scp>MRI</scp> Study

2022· article· en· W4283640577 on OpenAlexaboutno aff
Die Zhang, Yingying Chen, Jing Shen, Qing Xie, Jing Li, Lin Lin, Qiong Wang, Jianlin Wu

Bibliographic record

VenueJournal of Magnetic Resonance Imaging · 2022
Typearticle
Languageen
FieldNeuroscience
TopicFunctional Brain Connectivity Studies
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsAsymptomaticMedicineDefault mode networkStatistical significanceHemodialysisNeuropsychologyInternal medicineCognitionFunctional magnetic resonance imagingRadiologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The characteristics of static functional network connectivity (sFNC) and dynamic FNC (dFNC) in neurologically asymptomatic patients undergoing maintenance hemodialysis are unknown. Elucidating these characteristics may improve our understanding of the mechanisms of neuropathological damage in these patients. PURPOSE: To explore the static and dynamic characteristics of FNC in neurologically asymptomatic patients undergoing maintenance hemodialysis and the relationship between FNC-related parameters with the neuropsychological scores and blood biomarkers. STUDY TYPE: Retrospective. POPULATION: A total of 23 neurologically asymptomatic patients undergoing maintenance hemodialysis and 25 healthy controls matched for age, sex, and years of education. FIELD STRENGTH/SEQUENCE: A 3.0 T MRI/functional MRI and three-dimensional-T1 structural imaging ASSESSMENT: Independent components; spatial map intensity; sFNC and dFNC strengths; and time attribute parameters (mean dwell time, fractional window, and number of transitions) were determined. Neuropsychological tests were performed. Blood biochemical tests were performed for the patients but not healthy controls. STATISTICAL TESTS: Chi-squared test, one-sample t-test, two-sample t-test, partial correlation analysis, and family-wise error and false discovery rate correction. P < 0.05 denoted statistical significance. RESULTS: Significant group differences in the strengths of sFNC and dFNC between networks were found. The sFNC strength between the visual and sensorimotor networks was significantly associated with the global cognitive function score (i.e. the Montreal Cognitive Assessment [MoCA]) (r = 0.606). The sFNC strength between the salience and default mode networks was significantly associated with anxiety scores (r = 0.458). In state 1, positive correlations were found between the mean dwell time and backward digital span task score (r = 0.562), fractional window and MoCA score (r = 0.576), and fractional window and backward digital span task score (r = 0.592). DATA CONCLUSION: Neurologically asymptomatic patients undergoing maintenance hemodialysis had defective sFNC and dFNC. Our results provide a new perspective on the mechanism of neuropathological damage in patients undergoing maintenance hemodialysis. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.223
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2022
Admission routes1
Has abstractyes

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