Latent profile of the insomnia severity index: A longitudinal study.

STUDY OBJECTIVES: To identify the distinct classification of insomnia symptoms and to explore their association with sleep problems and depression. METHODS: Latent profile analysis was used to examine patterns of insomnia symptoms in two samples. Discovery and replication samples comprised 1043 (Mean age at baseline = 18.95 ± 0.93 years, 62.2% females) and 729 (Mean age at baseline = 18.71 ± 1.02 years, 66.4% females) college students, respectively. Participants completed measures of sleep problems (insomnia symptoms, sleep quality, susceptibility to insomnia, perceived consequences of insomnia, dream recall frequency, and percentage of recurring nightmares) and other psychological variables (rumination and depression). Binary logistic regression was used to analyze the effects of different types of insomnia symptoms at baseline on sleep problems and depression two years later. RESULTS: Four classes of insomnia symptoms were identified, and classified as "non-insomnia" (class 1, 45.7%), "mild subjective symptoms but severe subjective feelings" (class 2, 23.9%), "severe subjective symptoms but mild subjective feelings" (class 3, 22.0%), and "high insomnia risk" (class 4, 8.4%), respectively. Compared with the group classified as non-insomnia group, other classifications significantly predicted insomnia two years later, only class 4 significantly predicted depression, and class 3 significantly predicted susceptibility to insomnia, after adjusting gender, insomnia, depression, and susceptibility to insomnia at baseline. CONCLUSIONS: The findings highlighted the importance of identifying the patterns of insomnia symptoms, and the need for tailored intervention to improve sleep problems. Additionally, when screening for insomnia symptoms, simplified screening using Insomnia Severity Index (ISI) dimensions or items should be considered.

Response inhibition impairment related to altered frontal-striatal functional connectivity in insomnia disorder: A pilot and non-clinical study.

BACKGROUND: It is not clear whether and how insomnia disorder (ID) impairs response inhibition ability. Fronto-striatal functional connectivity (FC) plays a critical role in response inhibition and is found be abnormal in patients with ID. In this study, we examined whether insomnia symptoms impair response inhibition in a large non-clinical sample and whether impaired response inhibition is related to abnormal fronto-striatal FC. METHODS: One hundred and fifteen young ID patients and 160 age and sex-matched healthy controls (HC) underwent resting-state functional magnetic response imaging scans and performed the stop-signal task (SST). Performance of SST, Gray Matter Volumes (GMVs), and connections of brain regions related to fronto-striatal circuits was compared between groups. Further examined the association between response inhibition impairment and fronto-striatal FC. RESULTS: The behavioral results showed that patients with ID had significantly longer stop-signal reaction time (SSRT) compared with the HC, reflecting the impaired response inhibition among IDs. Brain imaging results showed IDs had decreased GMVs of the Right Superior Frontal (SFG) and left Supplementary Motor area (SMA). Seed-based FC results showed that compared to HC, the ID showed decreased FC between left SMA and left Paracentral lobule, left SMA and right SMA, and right SFG and right Orbital Middle Frontal gyrus, and increased FC between right SFG and right putamen. Meanwhile, the FC between right SFG and putamen was positively correlated with SSRT in IDs. CONCLUSIONS: The current study found significantly impaired response inhibition among ID and this impairment may be related to abnormal fronto-striatal FC in ID.

Functional Connectivity Between Default Mode and Ventral Attention Networks Mediates the Effects of Chronotype on Daily Physical Activity.

Daily physical activity (dPA) is closely related to circadian rhythm and chronotype. The functional connectivity (FC) within or between the default mode (DMN) and ventral attention network (vAN) were associated with dPA and chronotype. DMN-vAN FC was investigated for its role in chronotype and dPA. 153 participants completed the reduced version of the Morningness-Eveningness Questionnaire (rMEQ), dPA was measured via actigraphy (5-day), and then resting-state fMRI scans were performed. rMEQ scores and steps recorded by the actigraphic devices (with each hour as the time window to calculate steps for five consecutive days per hour, subsequently yielding the maximum number of steps and its corresponding time, ie, SM and SMT) represent chronotype and dPA respectively. The results found that the rMEQ scores were significantly negatively correlated with SMT. The positive correlation between the rMEQ scores and the DMN-vAN FC was significant. There were also significant positive correlations between SMT and DMN-vAN FC. Further analysis revealed that DMN-vAN mediates the relationship between chronotype and SMT. The FC of DMN-vAN may be the underlying neural mechanism through which chronotype influences dPA. These findings could support the development of reasonable activity schedules or specific intervention programs to improve physical health.

Subtypes of insomnia revealed by the heterogeneity of neuroanatomical patterns: A structural MRI study.

The current conflicting neuroimaging findings of insomnia disorder (ID) may be attributed to heterogeneity in ID. The present study aims to clarify the high heterogeneity in ID and examine the objective neurobiological subtypes of ID by using a novel machine learning method based on gray matter volumes (GMVs). We recruited 56 patients with ID and 73 healthy controls (HCs). The T1-weighted anatomical images were obtained for each participant. We investigated whether the ID has higher interindividual heterogeneity in GMVs. Then, we used a heterogeneous machine learning algorithm by discriminative analysis (HYDRA) to identify subtypes of ID with features of brain regional GMVs. We found that patients with ID have higher interindividual variability than HCs. HYDRA identified two distinct and reliable neuroanatomical subtypes of ID. Two subtypes showed significantly different aberrance in GMVs compared with HCs. Specifically, subtype 1 exhibited widespread decreased GMVs in some brain regions, including the right inferior temporal gyrus, left superior temporal gyrus, left precuneus, right middle cingulate, and right supplementary motor area. Subtype 2 only demonstrated increased GMVs in the right superior temporal gyrus. Additionally, the GMVs of altered brain regions in subtype 1 were significantly correlated with daytime functioning, but in subtype 2, they were significantly correlated with sleep disturbance. These results explain conflicting neuroimaging findings and propose a potential objective neurobiological classification contributing to ID's precise clinical diagnosis and treatment.