ArticleFrontiers in neuroscience2019
Abnormal Resting-State Functional Connectivity in the Whole Brain in Lifelong Premature Ejaculation Patients Based on Machine Learning Approach.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
18 citing papers in PubMed, 34 citations in OpenAlex.
- Progress in the research of artificial intelligence in andrology: a narrative review.Translational andrology and urology · 2026Review
- Genital-prioritized attentional bias in lifelong premature ejaculation: behavioral evidence from eye-tracking and machine learning.International journal of impotence research · 2026Article
- Abnormal grey matter in identifying primary intravaginal anejaculation patients from healthy controls: a voxel-based morphometry analysis.Sexual medicine · 2025Article
- Altered regional brain activity and functional connectivity in primary intravaginal anejaculation patients revealed by resting-state fMRI.Asian journal of andrology · 2024Article
- Topological differences of striato-thalamo-cortical circuit in functional brain network between premature ejaculation patients with and without depression.Brain and behavior · 2024Article
- Article
- Revolutionizing core muscle analysis in female sexual dysfunction based on machine learning.Scientific reports · 2024Article
- Abnormal cortical surface-based spontaneous and functional connectivity in the whole brain in lifelong premature ejaculation patients.Asian journal of andrology · 2023Article
- Functional Connectivity of Nucleus Accumbens Is Associated with Lifelong Premature Ejaculation in Male Adults : A Resting-state fMRI Study.Clinical neuroradiology · 2022Article
- Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists.The Urologic clinics of North America · 2022Review
- Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine.Frontiers in psychiatry · 2022Article
- Smaller volume and altered functional connectivity of the amygdala in patients with lifelong premature ejaculation.European radiology · 2021Article
- Machine-learning-based diagnosis of drug-naive adult patients with attention-deficit hyperactivity disorder using mismatch negativity.Translational psychiatry · 2021Article
- Higher inter-hemispheric homotopic connectivity in lifelong premature ejaculation patients: a pilot resting-state fMRI study.Quantitative imaging in medicine and surgery · 2021Article
- Abnormal degree centrality in lifelong premature ejaculation patients: an fMRI study.Brain imaging and behavior · 2021Article
- The Construction of a Risk Prediction Model Based on Neural Network for Pre-operative Acute Ischemic Stroke in Acute Type A Aortic Dissection Patients.Frontiers in neurology · 2021Article
- Reconfiguration of Dynamic Functional Connectivity States in Patients With Lifelong Premature Ejaculation.Frontiers in neuroscience · 2021Article
- Associations Between Altered Cerebral Activity Patterns and Psychosocial Disorders in Patients With Psychogenic Erectile Dysfunction: A Mediation Analysis of fMRI.Frontiers in psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent neuroimaging studies have indicated that abnormalities in brain structure and function may play an important role in the etiology of lifelong premature ejaculation (LPE). LPE patients have exhibited aberrant cortical structure, altered brain network function and abnormal brain activation in response to erotic pictures. However, it remains unclear whether resting-state whole brain functional connectivity (FC) is altered in LPE patients. Machine learning analysis has the advantage of screening the best classification features from high-throughput data (such as FC), which has the potential to identify the pathophysiological targets of disease by establishing classification indicators for patients and healthy controls (HCs). Therefore, the supported vector machine based classification model using FC as features was used in the present study to confirm the most specific FCs that distinguish LPE patients from healthy controls. After feature selection, the remained features were used to build the classification model, with an accuracy 0.85 ± 0.14, sensitivity of 0.92 ± 0.18, specificity of 0.72 ± 0.30, and recall index of 0.85 ± 0.17 across 1000 testing groups (100 times 10-folds cross validation). After that, two-sample
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.