The subcortical correlates of self-reported sleep quality

Abstract

Study objectives: To assess the association between self-reported measures of sleep quality and cortical and subcortical local morphometry.

Methods: Sleep quality, operationalized with the Pittsburgh Sleep Quality Index (PSQI), and neuroanatomical data from the full release of the young adult Human Connectome Project dataset were analyzed (N=1,112; 46% female; mean age: 28.8 years old). Local cortical and subcortical morphometry was measured with subject-specific segmentations resulting in voxelwise gray matter difference (i.e., voxel based morephometry) measurements for cortex and local shape measurements for subcortical regions. Associations between the total score of PSQI, two statistical groupings of its subcomponents (obtained with a principal component analysis), and their interaction with demographic (i.e., sex, age, handedness, years of education) and biometric (i.e., BMI) variables were assessed using a general linear model and a nonparametric permutation approach.

Results: Sleep quality-related variance was significantly associated with subcortical morphometry, particularly in the bilateral caudate, putamen, and left pallidum, where smaller shape measures correlated with worse sleep quality. Notably, these associations were independent of demographic and biometric factors. In contrast, cortical morphometry, along with additional subcortical sites, showed no direct associations with sleep quality but demonstrated interactions with demographic and biometric variables.

Conclusions: This study reveals a specific link between self-reported sleep quality and subcortical morphometry, particularly within the striatum and pallidum, reinforcing the role of these regions in sleep regulation. These findings underscore the importance of considering subcortical morphology in sleep research and highlight potential neuromodulatory targets for sleep-related interventions.

Keywords: Caudate; Neuroanatomy; Neuroimaging; Pallidum; Putamen; Sleep and Brain.

Fig. 1.

PSQI data. (a) Distribution of PSQI total scores in the analyzed sample. The vertical black line indicates the conventional criterion discriminating “poor sleepers” (i.e., PSQI total score > 5) from “good sleepers” (i.e., PSQI total score ≤ 5; [54]). (b-d) Distribution of PSQI total score, PSQI PCA PC1, and PC2 by sex and age group. (No significant differences were observed in the distribution of PSQI total score, or either PCA component, across sex or age group.)

Fig. 2.

Methods. Left: Illustration of cortical and subcortical segmentations of the T1-weighted data. Right: Illustration of 3D subcortical (top) and cortical (bottom) reconstructed meshes. (Image partially adapted from [65])

Fig. 3.

Correlates of sleep quality measures. Depiction of all voxels significantly associated with PSQI total score regressor (left), in the PSQI total score analysis, and with the PSQI PC1 regressor (right), in the PSQI PC analysis. As shown, only negative associations were detected between subcortical shape and the two sleep quality variables. No significant associations were detected in cortex for any of the sleep quality measures, neither positive nor negative, and no significant associations were observed for the PC2 component, in either cortex or subcortical regions, either positive or negative. (Note: colored areas imply a significant association, corrected for multiplicity; gray areas imply no significant associations.) See Tabs. SOM3 and SOM4 for detailed localization of each area of significant association.

Fig. 4.

Subcortical correlates of interactions with sample characteristic variables. Left: Depiction of all voxels significantly associated with the interaction of PSQI total score with each demographic and biometric covariate (i.e., sample characteristics variables). Right: Depiction of all voxels significantly associated with the interaction of PC1 and PC2, respectively, with each demographic and biometric covariate. See Tab. SOM3 and SOM4, respectively, for anatomical localization and statistics of each cluster. (Areas shown in color indicate significant voxels after correction for multiple comparisons. Areas shown in gray imply no significant result. ‘+’ indicates to a positive association; ‘−’ indicates a negative association.)

Fig. 5.

Cortical correlates of interactions with sample characteristic variables. Depiction of all voxels significantly associated with the interaction of PSQI PC1 and PC2 with each demographic and biometric covariate (i.e., sample characteristics variables). (Note that no significant associations were found between cortical morphometry and the interaction of PSQI total score and sample characteristic variables.) See Tab. SOM5 for anatomical localization and statistics of each significant cluster. (Areas shown in color indicate significant voxels after correction for multiple comparisons. Areas shown in gray imply no significant result. ‘+’ indicates to a positive association; ‘−’ indicates a negative association.)