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Record W4285742940 · doi:10.3389/fnins.2022.851111

Functional Connectivity Features of Resting-State Functional Magnetic Resonance Imaging May Distinguish Migraine From Tension-Type Headache

2022· article· en· W4285742940 on OpenAlexaboutno aff
Yajuan Wang, Yingshuang Wang, Lihong Bu, Shaoyang Wang, Xin‐hui Xie, Fuchun Lin, Zheman Xiao

Bibliographic record

VenueFrontiers in Neuroscience · 2022
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsnot available
FundersWuhan UniversityRenmin Hospital of Wuhan UniversityNational Natural Science Foundation of China
KeywordsResting state fMRIMigraineFunctional magnetic resonance imagingFunctional connectivityMagnetic resonance imagingFunctional imagingNeuroscienceMedicineNuclear magnetic resonancePsychologyRadiologyPhysicsAnesthesia

Abstract

fetched live from OpenAlex

Background Migraineurs often exhibited abnormalities in cognition, emotion, and resting-state functional connectivity (rsFC), whereas patients with tension-type headache (TTH) rarely exhibited these abnormalities. The aim of this study is to explore whether rsFC alterations in brain regions related to cognition and emotion could be used to distinguish patients with migraine from patients with TTH. Methods In this study, Montreal Cognitive Assessment (MoCA), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and rsFC analyses were used to assess the cognition, anxiety, and depression of 24 healthy controls (HCs), 24 migraineurs, and 24 patients with TTH. Due to their important roles in neuropsychological functions, the bilateral amygdala and hippocampus were chosen as seed regions for rsFC analyses. We further assessed the accuracy of the potential rsFC alterations for distinguishing migraineurs from non-migraineurs (including HCs and patients with TTH) by the receiver operating characteristic (ROC) analysis. Associations between headache characteristics and rsFC features were calculated using a multi-linear regression model. This clinical trial protocol has been registered in the Chinese Clinical Trial Registry (registry number: ChiCTR1900024307, Registered: 5 July 2019-Retrospectively registered, http://www.chictr.org.cn/showproj.aspx?proj=40817 ). Results Migraineurs showed lower MoCA scores (p = 0.010) and higher SAS scores (p = 0.017) than HCs. Migraineurs also showed decreased rsFC in the bilateral calcarine/cuneus, lingual gyrus (seed: left amygdala), and bilateral calcarine/cuneus (seed: left hippocampus) in comparison to HCs and patients with TTH. These rsFC features demonstrated significant distinguishing capabilities and got a sensitivity of 82.6% and specificity of 81.8% with an area under the curve (AUC) of 0.868. rsFC alterations showed a significant correlation with headache frequency in migraineurs (p = 0.001, Pc = 0.020). Conclusion The rsFC of amygdala and hippocampus with occipital lobe can be used to distinguish patients with migraine from patients with TTH. Clinical Trial Registration [ http://www.chictr.org.cn/showproj.aspx?proj=40817 ], identifier [ChiCTR1900024307].

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 score0.001
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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