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Neuroscience-guided bilateral thalamic MRI deep learning for obstructive sleep apnea diagnosis: a dual-center translational study
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DOI：10.1186/s12888-026-08472-5.png)
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Abstract
En 中文
Obstructive sleep apnea (OSA) remains substantially underdiagnosed, and recurrent intermittent hypoxia (IH) is a key pathophysiological feature of OSA and may affect thalamic function. We aimed to develop a neuroscience-guided MRI-based deep learning model for auxiliary OSA diagnosis. Male C57BL/6J mice were exposed to IH, and thalamic activation was assessed using immunofluorescence staining and in vivo calcium imaging. Clinical brain MRI and polysomnography (PSG) data were retrospectively collected from 760 adults across two centers. Center 1 was divided into training and validation cohorts, and center 2 served as an independent external test set. Based on the experimental findings, bilateral thalamic regions were segmented, and a dual-branch 3D CNN with Transformer-based feature fusion was developed. IH induced prominent thalamic activation in mice, and central lateral thalamic nucleus (CL) showed the strongest relative response among the examined thalamic subregions. Compared with the control group, the IH group showed a marked increase in c-Fos-positive cells in the CL (62.50 ± 11.67 vs. 9.67 ± 4.89, n = 6, P < 0.0001). Further calcium signal recordings showed that calcium activity in the CL was significantly increased within the 1-minute time window after stimulation. In the human MRI cohort, the bilateral thalamic deep learning model achieved AUCs of 0.944 (95% CI, 0.926–0.960), 0.928 (95% CI, 0.893–0.957), and 0.911 (95% CI, 0.856–0.957) in the training, validation, and external test sets, respectively, outperforming the unilateral models (P < 0.001). IH-induced thalamic activation, with CL serving as a representative highly responsive thalamic subregion, provided experimental support for thalamic ROI selection. The bilateral thalamic MRI model provides a biologically grounded and externally validated imaging-based approach that may assist OSA identification.
Keywords:
Obstructive sleep apnea
Intermittent hypoxia
Thalamus
Magnetic resonance imaging
Deep learning
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