Dataset Information

Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning.

ABSTRACT:

Objectives

Insomnia disorder has been reclassified into short-term/acute and chronic subtypes based on recent etiological advances. However, understanding the similarities and differences in the neural mechanisms underlying the two subtypes and accurately predicting the sleep quality remain challenging.

Methods

Using 29 short-term/acute insomnia participants and 44 chronic insomnia participants, we used whole-brain regional functional connectivity strength to predict unseen individuals' Pittsburgh sleep quality index (PSQI), applying the multivariate relevance vector regression method. Evaluated using both leave-one-out and 10-fold cross-validation, the pattern of whole-brain regional functional connectivity strength significantly predicted an unseen individual's PSQI in both datasets.

Results

There were both similarities and differences in the regions that contributed the most to PSQI prediction between the two groups. Further functional connectivity analysis suggested that between-network connectivity was re-organized between short-term/acute insomnia and chronic insomnia.

Conclusions

The present study may have clinical value by informing the prediction of sleep quality and providing novel insights into the neural basis underlying the heterogeneity of insomnia.

SUBMITTER: Ma X

PROVIDER: S-EPMC7522804 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning.

Ma Xiaofen X Wu Dongyan D Mai Yuanqi Y Xu Guang G Tian Junzhang J Jiang Guihua G

NeuroImage. Clinical 20200918

<h4>Objectives</h4>Insomnia disorder has been reclassified into short-term/acute and chronic subtypes based on recent etiological advances. However, understanding the similarities and differences in the neural mechanisms underlying the two subtypes and accurately predicting the sleep quality remain challenging.<h4>Methods</h4>Using 29 short-term/acute insomnia participants and 44 chronic insomnia participants, we used whole-brain regional functional connectivity strength to predict unseen indivi ...[more]

PMID: 32980600

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Project description:BackgroundSleep disturbances are common in patients with functional dyspepsia. Our aim was to assess the relationship between subjective sleep and quality of life and to identify factors associated with impaired sleep in functional dyspepsia.MethodsOne thousand two hundred and twenty patients referred for functional gastrointestinal disorders at a single tertiary care center between end 2017 and June 2019 were studied using a self-administered questionnaire. 355 patients with Rome IV-based functional dyspepsia were identified. Sleep was assessed using both the Pittsburgh Sleep Quality Index (PSQI) and the Insomnia Severity Index (ISI). The severity of dyspeptic symptoms was assessed using the Total Symptom Score (TSS). Quality of life was assessed by the GastroIntestinal Quality of Life Index (GIQLI). Anxiety and depression levels were evaluated using the Hospital Anxiety and Depression (HAD) scale.Key resultsAmong the 355 patients with functional dyspepsia, 66 (18.6%) patients displayed normal sleep quality whereas 289 (81.4%) patients had altered sleep quality. Functional dyspepsia patients with sleep disturbances were older (48.1 ± 15.4 vs. 41.4 ± 16.0, p = 0.0009), had decreased quality of life (GIQLI: 75.3 ± 18.5 vs. 92.1 ± 15.4, p < 0.0001), greater severity of their symptoms (TSS: 18.9 ± 3.6 vs. 17.2 ± 3.9, p = 0.0007), and higher anxiety and depression scores (HADS: 17.7 ± 7.2 vs. 11.9 ± 5.1, p < 0.0001). A correlation was found between sleep quality and quality of life [r = -0.43 (95% CI: -0.51 to -0.34), p < 0.0001]. Independent factors predicting poor sleep quality were age [OR 1.03 (95% CI = 1.01-1.05), p = 0.006], depression level [OR 1.27 (95% CI = 1.16-1.39); p < 0.0001], and the severity of dyspeptic symptoms [OR 1.13 (95% CI = 1.04-1.22); p = 0.004].Conclusion and inferencesA high prevalence of sleep disturbances was found in patients suffering from functional dyspepsia, with 81% of them having altered sleep quality and 61% having insomnia based on subjective assessment. Altered sleep quality and insomnia were associated with altered quality of life, higher severity of symptoms, and higher anxiety and depression scores in this disorder.

Dataset Information

Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning.

Objectives

Methods

Results

Conclusions

Publications

Functional connectome fingerprint of sleep quality in insomnia patients: Individualized out-of-sample prediction using machine learning.

Similar Datasets

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