Imaging-derived subtypes of insomnia symptoms and their association with anxiety and depression
Poster No:
379
Submission Type:
Abstract Submission
Authors:
Qianhui Jin1, Yongbin Wei1
Institutions:
1School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,China, Beijing, China
First Author:
Qianhui Jin
School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,China
Beijing, China
School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,China
Beijing, China
Co-Author:
Yongbin Wei
School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,China
Beijing, China
School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing,China
Beijing, China
Introduction:
Insomnia is a common sleep disorder affecting 26% of adults globally and is associated with cognitive impairments as well as increased risks of depression and anxiety(Brownlow, 2020). It is also common among children, where it is associated with difficulties in social-emotional development(Vermeulen, 2021). Neuroimaging studies have shown widespread structural changes in the brain of insomnia, including gray matter abnormalities in the prefrontal and orbitofrontal cortices(Bagherzadeh-Azbari, 2019; Schiel, 2020). However, the reported brain-wide changes in insomnia are highly heterogeneous(Gong, 2019; Hardwicke, 2022), emphasizing the importance of examining potential subtypes to gain a better understanding of the neurobiological pathophysiology(Button, 2013; Fortier-Brochu, 2012).
Methods:
This study utilized data from the Adolescent Brain Cognitive Development (ABCD) Study Data Release 5.1 (DAR ID: 16920), which included 6,416 children aged 8–11 years (mean = 9.92 ± 0.51). Of these, 772 subjects with insomnia symptoms (IS) and 5,644 subjects with no insomnia symptom (NIS) were included based on responses to a sleep questionnaire: "In the past two weeks, how often did you have trouble falling or staying asleep when tired?" Phenotypic data of the Child Behaviour Checklist (CBCL) were used as measures of depression and anxiety syndromes, and the NIH Toolbox was used as cognitive assessments. Tabulated gray matter volume (GMV) data of the 68 regions of the Desikan-Killiany atlas were used for subtyping.
To obtain IS subtypes, we employed Smile-GAN(Yang, 2021), a deep learning-based Generative Adversarial Network model designed to cluster participants based on multivariate differences between groups. Specifically, Smile-GAN was used to divide the IS group into subtypes based on GMV differences, by learning a mapping from the NIS data to the IS data. With respect to IS subtypes, one-way ANOVA with post-hoc multiple comparisons were performed to examine behavioral and emotional differences across IS subtypes and NIS, with the mixed-effects model used for brain-behavior interactions. Polygenic risk scores (PRS) for depression and anxiety were calculated using PRSice-2 and were also compared across IS subtypes and NIS.
To obtain IS subtypes, we employed Smile-GAN(Yang, 2021), a deep learning-based Generative Adversarial Network model designed to cluster participants based on multivariate differences between groups. Specifically, Smile-GAN was used to divide the IS group into subtypes based on GMV differences, by learning a mapping from the NIS data to the IS data. With respect to IS subtypes, one-way ANOVA with post-hoc multiple comparisons were performed to examine behavioral and emotional differences across IS subtypes and NIS, with the mixed-effects model used for brain-behavior interactions. Polygenic risk scores (PRS) for depression and anxiety were calculated using PRSice-2 and were also compared across IS subtypes and NIS.
Results:
We identified two insomnia subtypes (IS-I: N = 424, IS-II: N = 348; ARI = 0.38), which showed distinct brain volume differences: IS-I had decreased GMVs in occipital regions and increased GMVs in parietal areas, while IS-II showed the opposite pattern in temporal and cingulate regions (Fig. 1a). Using synthetic data showed an average ARI of 0.15 and higher cluster loss, indicating better learning of insomnia-specific features. These patterns of GMV differences were found to correlate to AHBA gene component C3 (r = 0.603, p < 0.001; r = -0.522, p = 0.003; Fig. 1d).
CBCL syndrome scores showed significant differences across groups for external, internal and total problems (F > 21.502, p < 0.001), with IS-II showing higher internal syndrome (t = -2.093, p = 0.032) and anxiety/depression scores (t = -2.194, p = 0.028) compared to IS-I (Fig. 1e). Significant interactions were found between brain volumes and syndrome scales, including internal syndrome with the right precuneus (β= 0.0006, p = 0.001) and anxiety/depression with the right cuneus (β= -0.0008, p = 0.038; Fig. 2a). Moreover, IS-II had higher PRS for MDD compared to NIS and IS-I (p < 0.050 across 5 examined p-value thresholds of PRS). Longitudinal analysis revealed the IS-II subtype to demonstrate greater GMV decrease, particularly in the right frontal pole (Fig. 2b), as well as poorer crystallized cognitive scores (t = 3.061, p = 0.003) and emotion regulation (t > 2.056, p < 0.041) (Fig. 2c).
CBCL syndrome scores showed significant differences across groups for external, internal and total problems (F > 21.502, p < 0.001), with IS-II showing higher internal syndrome (t = -2.093, p = 0.032) and anxiety/depression scores (t = -2.194, p = 0.028) compared to IS-I (Fig. 1e). Significant interactions were found between brain volumes and syndrome scales, including internal syndrome with the right precuneus (β= 0.0006, p = 0.001) and anxiety/depression with the right cuneus (β= -0.0008, p = 0.038; Fig. 2a). Moreover, IS-II had higher PRS for MDD compared to NIS and IS-I (p < 0.050 across 5 examined p-value thresholds of PRS). Longitudinal analysis revealed the IS-II subtype to demonstrate greater GMV decrease, particularly in the right frontal pole (Fig. 2b), as well as poorer crystallized cognitive scores (t = 3.061, p = 0.003) and emotion regulation (t > 2.056, p < 0.041) (Fig. 2c).
Conclusions:
Using the semi-supervised deep learning model, we identified two insomnia subtypes that showed differences in internalizing and depressive symptoms, as well as genetic liability to depression, providing new insights into the heterogeneous neurobiology of insomnia.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Genetics:
Genetic Association Studies
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Keywords:
Sleep
STRUCTURAL MRI
Other - Insomnia,subtype,deep-learning
1|2Indicates the priority used for review
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