Unveiling Sleep Dysregulation in Chronic Fatigue Syndrome with and without Fibromyalgia Through Bayesian Networks
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arXiv
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| Format: | Preprint |
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2025
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| author | Bechny, Michal Scutari, Marco van der Meer, Julia Faraci, Francesca Meystre, Stéphane Natelson, Benjamin H. Kishi, Akifumi |
| author_facet | Bechny, Michal Scutari, Marco van der Meer, Julia Faraci, Francesca Meystre, Stéphane Natelson, Benjamin H. Kishi, Akifumi |
| contents | Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM) often co-occur as medically unexplained conditions linked to disrupted physiological regulation, including altered sleep. Building on the work of Kishi et al. (2011), who identified differences in sleep-stage transitions in women with CFS and CFS+FM, we exploited the same strictly controlled clinical cohort using a Bayesian Network (BN) to quantify detailed patterns of sleep and its dynamics. Our BN confirmed that sleep transitions are best described as a second-order process (Yetton et al., 2018), achieving a next-stage predictive accuracy of 70.6%, validated on two independent data sets with domain shifts (60.1-69.8% accuracy). Notably, we demonstrated that sleep dynamics can reveal the actual diagnoses. Our BN successfully differentiated healthy, CFS, and CFS+FM individuals, achieving an AUROC of 75.4%. Using interventions, we quantified sleep alterations attributable specifically to CFS and CFS+FM, identifying changes in stage prevalence, durations, and first- and second-order transitions. These findings reveal novel markers for CFS and CFS+FM in early-to-mid-adulthood women, offering insights into their physiological mechanisms and supporting their clinical differentiation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10271 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unveiling Sleep Dysregulation in Chronic Fatigue Syndrome with and without Fibromyalgia Through Bayesian Networks Bechny, Michal Scutari, Marco van der Meer, Julia Faraci, Francesca Meystre, Stéphane Natelson, Benjamin H. Kishi, Akifumi Applications Chronic Fatigue Syndrome (CFS) and Fibromyalgia (FM) often co-occur as medically unexplained conditions linked to disrupted physiological regulation, including altered sleep. Building on the work of Kishi et al. (2011), who identified differences in sleep-stage transitions in women with CFS and CFS+FM, we exploited the same strictly controlled clinical cohort using a Bayesian Network (BN) to quantify detailed patterns of sleep and its dynamics. Our BN confirmed that sleep transitions are best described as a second-order process (Yetton et al., 2018), achieving a next-stage predictive accuracy of 70.6%, validated on two independent data sets with domain shifts (60.1-69.8% accuracy). Notably, we demonstrated that sleep dynamics can reveal the actual diagnoses. Our BN successfully differentiated healthy, CFS, and CFS+FM individuals, achieving an AUROC of 75.4%. Using interventions, we quantified sleep alterations attributable specifically to CFS and CFS+FM, identifying changes in stage prevalence, durations, and first- and second-order transitions. These findings reveal novel markers for CFS and CFS+FM in early-to-mid-adulthood women, offering insights into their physiological mechanisms and supporting their clinical differentiation. |
| title | Unveiling Sleep Dysregulation in Chronic Fatigue Syndrome with and without Fibromyalgia Through Bayesian Networks |
| topic | Applications |
| url | https://arxiv.org/abs/2503.10271 |