Skip to main content
NCBI home page
Sleep logoLink to Sleep
. 2023 Oct 27;47(2):zsad274. doi: 10.1093/sleep/zsad274

Comprehensive assessment of sleep duration, insomnia, and brain structure within the UK Biobank cohort

Aleks Stolicyn 1, Laura M Lyall 2, Donald M Lyall 3, Nikolaj Kjær Høier 4,5, Mark J Adams 6, Xueyi Shen 7, James H Cole 8,9, Andrew M McIntosh 10, Heather C Whalley 11, Daniel J Smith 12,✉
PMCID: PMC10851840  PMID: 37889226

Abstract

Study Objectives

To assess for associations between sleeping more than or less than recommended by the National Sleep Foundation (NSF), and self-reported insomnia, with brain structure.

Methods

Data from the UK Biobank cohort were analyzed (N between 9K and 32K, dependent on availability, aged 44 to 82 years). Sleep measures included self-reported adherence to NSF guidelines on sleep duration (sleeping between 7 and 9 hours per night), and self-reported difficulty falling or staying asleep (insomnia). Brain structural measures included global and regional cortical or subcortical morphometry (thickness, surface area, volume), global and tract-related white matter microstructure, brain age gap (difference between chronological age and age estimated from brain scan), and total volume of white matter lesions.

Results

Longer-than-recommended sleep duration was associated with lower overall grey and white matter volumes, lower global and regional cortical thickness and volume measures, higher brain age gap, higher volume of white matter lesions, higher mean diffusivity globally and in thalamic and association fibers, and lower volume of the hippocampus. Shorter-than-recommended sleep duration was related to higher global and cerebellar white matter volumes, lower global and regional cortical surface areas, and lower fractional anisotropy in projection fibers. Self-reported insomnia was associated with higher global gray and white matter volumes, and with higher volumes of the amygdala, hippocampus, and putamen.

Conclusions

Sleeping longer than recommended by the NSF is associated with a wide range of differences in brain structure, potentially indicative of poorer brain health. Sleeping less than recommended is distinctly associated with lower cortical surface areas. Future studies should assess the potential mechanisms of these differences and investigate long sleep duration as a putative marker of brain health.

Keywords: sleep duration, insomnia, brain structure, grey matter, white matter

Graphical Abstract

Graphical Abstract.

Graphical Abstract

Open in a new tab

Statement of Significance.

Sleep is critical for mental health, but the relationship between sleep duration and brain structure is unclear. The current cross-sectional study of the large population-based UK Biobank cohort reveals that sleeping longer than recommended in public guidelines (>9 hours) is associated with differences in a wide range of brain structural measures that may be linked to poorer brain health (specifically lower global grey and white matter volumes, cortical thickness, white matter integrity, and hippocampal volume). Sleeping less than recommended in public guidelines (<7 hours), on the other hand, appears distinctly associated with lower global and regional cortical surface areas. Longer-than-recommended sleep should be studied further as a potential marker of brain health outcomes.

Introduction

Sleep is fundamentally important for physical and mental health. Sleep deprivation or insufficient sleep is associated with increased blood pressure [1], weight gain [2, 3], increased risk of diabetes and heart disease [4–6], and immune system dysfunction [7]. For mental health, problems with sleep (both hypersomnia or insomnia) are a feature of many psychiatric disorders, particularly dementia, schizophrenia, bipolar disorder, major depression, and anxiety [8–11].

Sleep has a multitude of functions, including the restoration of brain energy reserves [12, 13], clearance of metabolic byproducts of wakefulness [14–16], and the maintenance of attention and memory functions [17–20].

Several studies have investigated relationships between sleep and brain structure. Lower sleep quality and the presence of sleep problems were associated with reduced hippocampal volumes over the lifespan in the Lifebrain consortium (N = 1299) [21], while longer sleep duration was related to lower overall cerebral brain volumes in the Framingham Heart Study (N = 2060) [22]. For a subsample of the Human Connectome Project (N = 974), shorter sleep duration and poorer sleep quality were associated with lower intracortical myelin in parts of the cingulate, middle temporal, and orbitofrontal cortices [23]. In several smaller-scale studies, poor sleep quality and non-optimal sleep duration have been associated with reduced grey matter volumes in frontal, temporal, and parietal regions, as well as reduced white-matter integrity [24, 25]. Taken together, these findings indicate that sleep duration is non-linearly related to brain structure, with optimal sleep times associated with greater brain regional volumes [22–24, 26]. Poorer sleep quality, on the other hand, is likely related to lower cortical and subcortical volumes [21, 24].

Very few studies to date have looked at how sleep may be related to complex measures of brain structure which incorporate multiple imaging-derived phenotypes, such as brain-predicted age [27]. For example, Ramduny et al. found an association of poor sleep quality with accelerated brain aging in a small sample of older participants (N = 50) [28], while Chu et al. found an association of acute sleep deprivation with changes in brain morphology reminiscent of advanced brain aging (N = 134 young adults) [29]. These results have not yet been replicated in larger samples.

For insomnia, a recent ENIGMA consortium study found depression-specific insomnia severity to be associated with widespread decreases in cortical surface areas (N = 1053) [30]. In a Spanish study of risk for Alzheimer’s (ALFA, N = 330), diagnosis of insomnia was related to lower grey matter volume in the thalamus, cingulate, and temporal cortices [31]. The same study found associations with higher volume in the left caudate and widespread differences in white matter microstructure. These findings indicate that insomnia-related differences in the brain may depend on insomnia severity, as well as the presence of comorbid conditions such as depression or Alzheimer’s disease.

Overall, studies of sleep and brain structure have so far been somewhat inconsistent, and brain measures of both grey and white matter structure have only rarely been studied within the same sample [31]. We aimed to address these limitations by testing associations of sleep duration and insomnia with a comprehensive range of brain structural measures in the large UK Biobank cohort.

Methods

Study overview

We performed cross-sectional analyses to investigate associations of measures of grey matter structure and white matter microstructure with self-reported sleep variables in the UK Biobank [32]. Drawing on previous findings, we hypothesized that suboptimal sleep duration (either too long or too short) and insomnia would be associated with reductions in grey matter volumes and differences in white matter microstructure. We aimed to better characterize these differences by leveraging the wide range of brain measures and the large sample sizes available within UK Biobank.

Participant sample

All data analyzed in the current study were collected as part of the first imaging assessment conducted between April 2014 and March 2020. Participants were excluded from any analysis where they had missing sleep-related, covariate, or brain imaging data, or where they had outlier values in any brain measures. Exclusions were performed separately for each group of outcome (brain imaging) variables to maximize sample sizes. No further exclusion criteria were applied. All primary analyses had between 9K and 32K participants. Table 1 presents characteristics of the sample used in the analyses of global grey and white matter volumes in relation to sleep guideline adherence (N = 32 119). Supplementary section S1.6 and the accompanying CSV tables provide further details of participant exclusions and the summary sample demographic characteristics for all conducted analyses. UK Biobank received ethical approval from the North West Multi-center Research Ethics Committee in the UK (reference 11/NW/0382), and the current study received approval from the UKB Access Committee (application #4844). All participants gave written informed consent.

Table 1.

Characteristics of the sample in the analyses of global grey and white matter volumes in relation to NSF guideline adherence with the main statistical model (please see supplementary section S1.6 for characteristics of the samples in all other analyses)

Sample characteristic Participants sleeping as NSF recommended (7–9 hours per day) Participants sleeping less than NSF recommended (<7 hours per day) Participants sleeping more than NSF recommended (>9 hours per day)
Sample size (male/ female) 24057
(11557/12500)		 7682
(3338/4344)		 380
(182/198)
Age mean (SD) 63.65 (7.49) 62.81 (7.62)* 64.85 (7.53)*
Ethnic minority count (proportion) 1931 (8.03%) 742 (9.66%)* 32 (8.42%)
Townsend index mean (SD) −2.01 (2.65) −1.77 (2.77)* −1.53 (2.95)*
BMI mean (SD) 26.35 (4.26) 26.88 (4.66)* 28.37 (5.49)*
Vascular/heart problems-medicated count (proportion) 7892 (32.81%) 2519 (32.79%) 178 (46.84%)*
Diabetic count (proportion) 1172 (4.87%) 433 (5.64%)* 46 (12.11%)*
Smoking count (proportion) 8804 (36.60%) 2873 (37.40%) 153 (40.26%)
Alcohol consumer count (proportion) 17479 (72.66%) 5299 (68.98%)* 248 (65.26%)*
Lifetime depression count (proportion) 5508 (22.90%) 1980 (25.77%)* 150 (39.47%)*
Open in a new tab

Significant differences in groups when compared with participants sleeping the NSF recommended time (less than 7 hours per day / more than 9 hours compared to 7–9 hours) are marked with asterisks. Characteristics were compared between groups with two-sample t-tests (for continuous variables) or with chi-squared tests (for participant proportions). Age is measured in years.

Sleep measures

Drawing on previous work [21–26, 30, 31], we focused our main analyses on three binary sleep-related variables: (1) sleeping longer than recommended by the National Sleep Foundation (NSF) for adults [33] (>9 hours per day, as compared to 7–9 hours); (2) sleeping less than recommended by the NSF (<7 hours per day, as compared to 7–9 hours); and (3) insomnia/sleeplessness defined as usual trouble falling or staying asleep at night. As supplementary analyses, we also tested associations of sleep duration, longer compared to shorter than recommended sleep, late chronotype, and “ease of getting up” (supplementary section S2.2). All variables were based on self-report data from the UK Biobank touchscreen questionnaire, collected as part of the first imaging assessment [34]. Supplementary section S1.1 provides detailed definitions of sleep-related variables.

Brain structure measures

We tested a set of global brain measures, a set of regional grey matter morphometric measures, and a set of white matter microstructure measures for individual tracts.

Global grey matter measures included the overall grey matter volume (head-size normalized), and global cortical thickness, surface area and cortical volume measures. Global white matter measures included overall white matter volume (head-size normalized), overall volume of white matter hyperintensities (lesions), and five PCA-derived measures of global white matter microstructure: fractional anisotropy (FA), mean diffusivity (MD), intracellular volume fraction (ICVF), orientation dispersion (OD), and isotropic volume fraction (ISOVF). Finally, a brain age gap estimate was derived for a subset of participants as the difference between age estimated from T1-weighted MRI scan (brain-predicted age) and actual chronological age (additionally residualized for chronological age). Brain-predicted age measures were obtained with brainageR toolkit version 2.1, through application of a predictive model based on Gaussian processes regression to normalized voxel-wise measures of grey matter, white matter, and cerebrospinal fluid [27] (supplementary section S1.2.1).

Individual-region grey matter morphometric data consisted of cortical thickness, surface area, and volume measures for 5 bilateral lobes, 33 bilateral individual regions defined according to the Desikan-Killiany atlas, and volumes of 10 bilateral subcortical structures. White matter microstructure data consisted of FA and MD measures for three fiber bundles (association, projection and thalamic), and for 15 individual white matter tracts (12 bilateral and 3 unilateral). Supplementary analyses tested associations of fiber and tract-related ICVF, OD, and ISOVF measures (supplementary section S2.1). Supplementary section S1.2 provides further details of the analyzed brain measures.

Statistical models and covariates

Analyses of unilateral (singular) brain measures were performed using generalized linear regression models (GLM). Analyses of bilateral measures were performed with linear mixed effects models (LME), with data for left and right hemispheres treated as repeated measures. Functions “fitglm” and “fitlme” in MATLAB R2018a (http://www.mathworks.com/products/matlab/, Mathworks Inc, RRID:SCR_001622) were used, respectively, for GLM and LME analyses. Brain measures were entered as outcome variables, and sleep measures were entered as exposure variables. All data were standardized prior to analyses, and all effect sizes are reported as standardized beta coefficients.

All statistical analyses included correction for age, sex, ethnic background, Townsend deprivation index, a set of health-related covariates, and three head position coordinates in the scanner. Analyses of cortical thickness, surface area, and volume measures included an additional covariate of intracranial volume (ICV), standardized across the entire available sample, to correct for overall head size. Due to predominance of white British participants in the UK Biobank, ethnic background was defined as a binary variable indicating whether a participant was white British or not [35]. Townsend index was included as a proxy measure of socioeconomic status, which can be associated with sleep. Health-related covariates included body mass index (BMI), intake of medication for vascular/heart problems, diabetes, smoking status, alcohol consumption status, and lifetime experience of depression. Intake of vascular medication was included as a proxy variable to take into account heart problems. Health-related variables were corrected for because they can independently impact on both sleep and brain structure. As additional sensitivity analyses, we re-ran all statistical tests: (1) without lifetime depression covariate, and (2) without all health-related covariates. Please see supplementary sections S1.3 and S1.4 for definitions of each covariate variable, and for the details of the supplementary sensitivity analyses.

Of note, participants sleeping longer than recommended on average had higher BMI and were more likely to have self-reported diagnosis of diabetes, take medication for vascular or health problems, or have self-reported lifetime history of depression (supplementary sample information tables, all analyses with the main model). Participants sleeping less than recommended also had higher average BMI and were more likely to have self-reported diabetes or lifetime history of depression, but the differences in BMI and proportions of participants with health-related conditions were smaller. Participants sleeping both more or less than recommended came from areas with higher average Townsend deprivation indices.

Correction for false discovery rate (FDR) [36] was performed by adjusting the P-values according to the procedure described in Benjamini, Heller, and Yekutieli [37], implemented within “mafdr” function in MATLAB R2018a. Adjustment was performed separately for tests on each group of outcome variables and pFDR < 0.05 was taken as significant. Please see supplementary section S1.5 for the definition of outcome variable groups for correction for multiple comparisons.

Results

Brain structure differences associated with sleeping longer than recommended

Overview of brain structure differences

Sleep duration longer than the NSF recommendation was associated with differences in global brain measures, lobar and regional cortical thickness and volume measures. Differences were also found in fiber and tract-related MD, and the volume of the hippocampus. Please see top of Figure 1 for a Manhattan plot illustrating significant associations across all investigated brain measures.

Figure 1.

Figure 1.

Open in a new tab

Manhattan plots illustrating significance values of associations of longer than recommended sleep duration (top), and shorter than recommended sleep duration (bottom) with brain structural measures. Dashed lines indicate statistical significance threshold (p = 0.05).

Global brain structure differences

Sleeping longer than recommended was associated with lower overall grey and white matter volumes (βGM = −0.0207, p < 0.0001; βWM = −0.0136, p = 0.0232), lower global cortical thickness and volume measures (βCT = −0.0229, p < 0.0001; βCV = −0.0129, p = 0.000125), and higher brain age gap (βBAG = 0.0226, p = 0.0093). Analyses also revealed poorer white matter structure, including higher volume of white matter hyperintensities (βWMHV = 0.0270, p < 0.0001), and higher global MD and ISOVF measures (βgMD = 0.0188, p = 0.0016; βgISOVF = 0.0132, p = 0.0300). Left panel of Figure 2 illustrates effect sizes of significant associations of longer than recommended sleep with global brain measures.

Figure 2.

Figure 2.

Open in a new tab

Barchart illustration of standardized effect sizes (beta coefficients) of associations of longer than recommended sleep duration (left), and shorter than recommended sleep duration (right) with global brain structural measures. Stars indicate statistically significant associations (pFDR < 0.05).

Lobar and regional morphometry differences

Lower cortical thickness was associated with sleeping longer than recommended in four out of five lobes (frontal βCT = −0.0255, pFDR < 0.0001; temporal βCT = −0.0239, pFDR < 0.0001; parietal βCT = −0.0191, pFDR = 0.0013; cingulate βCT = −0.0160, pFDR = 0.0026), and in 25 out of 33 individual cortical regions. Largest associations were found in superior frontal cortex (βCT = −0.0256, pFDR < 0.0001), rostral and caudal middle frontal areas (βCT = −0.0225, pFDR < 0.0003 and βCT = −0.0222, pFDR = 0.0003), parahippocampal cortex (βCT = −0.0216, pFDR = 0.0003), pars orbitalis (βCT = −0.0215, pFDR = 0.0003), and insula (βCT = −0.0208, pFDR = 0.0006). Top left panels of Figures 3 and 4 and complete Figure 5 illustrate effect sizes related to lobar and regional cortical thickness measures.

Figure 3.

Figure 3.

Open in a new tab

Barchart illustration of standardized effect sizes (beta coefficients) of associations of longer than recommended sleep duration (left), and shorter than recommended sleep duration (right) with lobar morphometric measures. Stars indicate statistically significant associations (pFDR < 0.05).

Figure 4.

Figure 4.

Open in a new tab

Barchart illustration of standardized effect sizes (beta coefficients) of associations of longer than recommended sleep duration with cortical thickness and cortical volume measures for individual regions (left and middle), and standardized effect sizes of associations of shorter than recommended sleep duration with cortical surface area measures (right). Stars indicate statistically significant associations (pFDR < 0.05).

Figure 5.

Figure 5.

Open in a new tab

Heatmap illustration of lower cortical thickness in individual brain regions associated with sleeping longer than recommended.

Lower cortical volumes were found in all five lobes (frontal βCV = −0.0140, pFDR = 0.0009; temporal βCV = −0.0113, pFDR = 0.0048; parietal βCV = −0.0091, pFDR = 0.0248; cingulate βCV = −0.0105, pFDR = 0.0189; occipital βCV = −0.0103, pFDR = 0.0316), and in 11 of 33 individual regions. Largest associations were found in pars orbitalis (βCV = −0.0157, pFDR = 0.0041), parahippocampal (βCV = −0.0140, pFDR = 0.0362), frontal pole (βCV = −0.0130, pFDR = 0.0277), caudal anterior cingulate (βCV = −0.0120, pFDR = 0.0362), and middle temporal regions (βCV = −0.0119, pFDR = 0.0313). Figure 3 (bottom left) and Figure 4 (middle) illustrate effect sizes related to lobar and regional cortical volume measures.

Fiber and tract white matter microstructure differences

Sleeping longer than recommended was found to be associated with higher MD in association and thalamic fibers (βMD = 0.0162, pFDR = 0.0119 and βMD = 0.0230, pFDR = 0.0001), and in 7 of 15 individual tracts: posterior thalamic radiation (βMD = 0.0235, pFDR = 0.0003), anterior thalamic radiation (βMD = 0.0164, pFDR = 0.0211), superior thalamic radiation (βMD = 0.0125, pFDR = 0.0482), forceps minor (βMD = 0.0164, pFDR = 0.0385), inferior longitudinal fasciculus (βMD = 0.0144, pFDR = 0.0385), inferior frontooccipital fasciculus (βMD = 0.0143, pFDR = 0.0385), and uncinate fasciculus (βMD = 0.0135, pFDR = 0.0385). Posterior thalamic radiation was found to also have lower FA (βFA = −0.0197, pFDR = 0.0131).

Subcortical volume differences

Sleeping longer than recommended was found to be related to smaller hippocampal volume (β = −0.0201, pFDR = 0.0002). No other associations with subcortical volumes were found.

Brain structure differences associated with sleeping less than recommended

Overview of brain structure differences

Sleeping less than the NSF recommended duration was associated with differences in global brain structure, lobar and regional cortical surface areas and volumes, fiber and tract-related FA, and the volume of the cerebellar white matter. Figure 1 (bottom) illustrates significant associations across all investigated brain measures.

Global brain structure differences

Sleeping less than recommended was associated with higher overall white matter volume (βWM = 0.0152, p = 0.0037), lower global cortical surface area (βSA = −0.0080, p = 0.0035), lower global cortical volume (βCV = −0.0068, p = 0.0202), and higher global OD (βgOD = 0.0133, p = 0.0135). Figure 2 (right) illustrates effect sizes of significant associations of shorter than recommended sleep with global brain measures.

Lobar and regional morphometry differences

Sleeping less than recommended was associated with lower surface areas in the frontal lobe (βSA = −0.0073, pFDR = 0.0290), cingulate gyrus (βSA = −0.0105, pFDR = 0.0039), occipital lobe (βSA = −0.0134, pFDR = 0.0039), and 8 of 33 individual cortical regions. Individual regions with smaller surface areas were the lingual gyrus (βSA = −0.0132, pFDR = 0.0180), pericalcarine area (βSA = −0.0125, pFDR = 0.0400), caudal and rostral middle frontal areas (βSA = −0.0119, pFDR = 0.0180; βSA = −0.0119, pFDR = 0.0132), lateral occipital cortex (βSA = −0.0114, pFDR = 0.0253), cuneus (βSA = −0.0112, pFDR = 0.0442), isthmus cingulate (βSA = −0.0108, pFDR = 0.0180), and rostral anterior cingulate (βSA = −0.0093, pFDR = 0.0132). Figure 3 (right middle) and Figure 4 (right) illustrate effect sizes related respectively to lobar and regional surface areas.

Lower cortical volumes were found in the cingulate gyrus (βCV = −0.0113, pFDR = 0.0093), occipital lobe (βCV = −0.0120, pFDR = 0.01), and two individual regions: isthmus cingulate (βCV = −0.0146, pFDR = 0.0072) and rostral anterior cingulate (βCV = −0.0094, pFDR = 0.0418).

Fiber and tract white matter microstructure differences

Sleeping less than recommended was associated with lower FA in projection fibers (βFA = −0.0231, pFDR = 0.0001), and in three individual white matter tracts—corticospinal (βFA = −0.0227, pFDR = 0.0002), medial lemniscus (βFA = −0.0134, pFDR = 0.0324), and parahippocampal cingulate (βFA = −0.0145, pFDR = 0.0242). Conversely, higher FA was found in the cingulate gyrus (βFA = 0.0113, pFDR = 0.0364).

Subcortical volume differences

Sleeping less than recommended was related to higher cerebellar white matter volume (β = 0.0160, pFDR = 0.0027). No other associations were identified.

Brain structure differences associated with insomnia/sleeplessness

Insomnia/sleeplessness was associated with higher global grey matter and white matter volumes (βGM = 0.0162, p = 0.0049; βWM = 0.0198, p = 0.0067), and with higher volumes of the putamen (β = 0.0222, pFDR = 0.0016), hippocampus (β = 0.0206, pFDR = 0.0016), and the amygdala (β = 0.0167, pFDR = 0.0040).

Discussion

Summary and interpretation of findings

Sleeping longer than recommended

We found that longer than NSF-recommended sleep duration was associated with lower overall grey matter and white matter volumes, and widespread lower cortical thickness and volume measures. Associations of cortical thickness were more pronounced compared to cortical volumes (Figure 3). Furthermore, longer than recommended sleep was associated with advanced brain aging, higher volume of white matter hyperintensities, higher diffusivity in the thalamic and association fibers (indicating potentially worse white matter integrity), and lower hippocampal volume.

Grey and white matter volumes and cortical thickness gradually decrease with age in healthy populations [38]. Reductions in these measures have also been found in conditions which adversely affect both cognitive and emotional functions, including Alzheimer’s disease and dementia [39–41], cognitive impairment [42], depression [42–44], and bipolar disorder [45–49]. Increased diffusivity measures, indicating potential reductions in white matter integrity, have been observed after traumatic brain injury [50, 51] and in adult depression [52]. White matter hyperintensities typically represent lesions that are a hallmark feature of cerebral small vessel disease [53]. These lesions have also been found to be associated with depression [42, 54], poorer cognition [55], and increased risk of cognitive impairment or stroke [56–60]. Finally, the hippocampus has crucial roles in memory and learning [61]. Decreased hippocampal volume has been reported with aging [62], cognitive impairment [63, 64], depression [65, 66], and bipolar disorder [48, 67]. Taken together and in the context of the existing literature, our findings indicate that longer than recommended sleep duration is associated with advanced brain aging and potentially worse brain health.

Of note, association of longer sleep with brain age gap was similar in magnitude to associations of overall grey matter volume, global cortical thickness, and volume of white matter hyperintensities (Figure 2, left). This indicates that complex measures based on multiple aspects of brain structure may not be more sensitive to suboptimal sleep duration compared to simpler unimodal measures.

Sleeping less than recommended

Shorter than recommended sleep duration was related to higher global and cerebellar white matter volume, as well as lower global and regional cortical surface areas (primarily in the frontal, cingulate, and occipital lobes) and lower FA (indicating potentially worse white matter integrity) in projection fibers.

Previous studies indicate that relatively higher global white matter volumes may be related to higher levels of physical activity and fitness [68, 69]. The cerebellum is crucially involved in movement control, as well as a number of other cognitive functions [70]. It is clearly possible that shorter sleep times may lead to relatively higher levels of physical activity due to more time spent awake, which may in turn be linked to higher global and cerebellar white matter volumes.

Cortical surface area gradually decreases with age [38], with larger area measures associated with higher cognitive ability [71]. Decreased surface area specifically of the isthmus cingulate (Figure 4, right) may reflect vulnerability to Alzheimer’s disease [72].

Finally, lower FA of the projection fibers and the corticospinal tract (i.e. potentially reduced microstructural integrity) was previously identified in bipolar disorder, albeit in a small number of studies [73].

Overall, our findings indicate that the pattern of differences in brain structure associated with sleeping less than recommended is quite different from that of sleeping more than recommended, and less indicative of poorer brain health.

Insomnia/sleeplessness

Contrary to our initial hypothesis, we found that self-reported insomnia/sleeplessness was associated with higher (rather than lower) global grey and white matter volumes, with no evidence of changes in white matter microstructure. Furthermore, we observed higher volumes of three subcortical structures: amygdala, hippocampus, and putamen.

Global grey matter volume is correlated with general intelligence [74]. The hippocampus is critical for cognitive function [61], and relatively higher hippocampal volumes are associated with higher levels of education [75], and higher levels of physical activity [76, 77]. The putamen is a region likely to be involved in the circadian regulation of reward processing [78] and has previously been found to be enlarged in anxiety-related conditions and in bipolar disorder [79, 80]. Finally, the amygdala has critical roles in emotion and motivation [81], and higher amygdala volume has been identified in bipolar disorder [82], as well as genetic variants associated with risk for bipolar disorder [83].

Overall, our findings indicate that self-reported insomnia/sleeplessness was related to higher volumes of brain structures putatively associated with intelligence, cognition, motivation, and emotional processing. There is currently no robust evidence linking insomnia with higher intelligence; however, our findings parallel previous results in the UK Biobank which indicated better cognitive performance in participants with past moderately severe depression [84].

Study strengths and limitations

The two distinct advantages of our study are the large sample size and the wide range of structural brain measures, covering both grey and white matter. Previous studies of associations of sleep duration or insomnia and brain structure typically had N = 3000 and focused on relatively narrow sets of brain features. In contrast, the scope and power of the UK Biobank enabled sample sizes of between 9K and 33K (supplementary S1.6) and allowed us to uncover differences in brain structure distinctly associated with longer and shorter sleep, as well as insomnia/sleeplessness. Compared to previous work—including within the UK Biobank [85, 86]—we provide a more comprehensive characterization of the brain structure associations with self-reported non-adherence to NSF guidelines and insomnia/sleeplessness.

Our study and results are complementary to those of Schiel et al. [87], who have also investigated associations of regional grey matter volumes with longer or shorter sleep in the UK Biobank. To highlight the key differences, we studied regional volumes derived with the FreeSurfer toolkit rather than FAST, according to different brain atlases (Desikan-Killiany and Aseg vs. Harvard-Oxford) [88]. We also applied FDR rather than Bonferroni correction, grouped our statistical tests into smaller sets for correction (to facilitate independence between outcome variables, supplementary section S1.5), and combined data from left and right hemispheres as repeated measures to increase power. These differences most likely contributed to the higher numbers of significant associations found here as compared to Schiel et al. Together with Schiel et al. [87], our study represents an example of how different and independent investigations can analyze non-overlapping subsets of extensive UK Biobank data with different methods in order to address similar research questions.

Despite these advantages, several limitations are acknowledged. First, sleep variables were self-reported and may be subject to reporting biases. Future studies could confirm our findings by taking advantage of the actigraphy data collected within the UK Biobank from a subset of participants, albeit for the duration of only 1 week [89]. Second, we did not assess causal directionality in the associations between sleep and brain structure measures. Future work could seek to apply causal inference methods, such as Mendelian randomization [90], potentially focusing on brain measures identified as significant here. Third, we did not investigate changes in brain structure over time. Repeat brain imaging data collection is currently ongoing within the UK Biobank [91], and future data releases will enable high-powered longitudinal analyses. Fourth, we did not correct for intake of medications that can have an effect on sleep, such as antidepressants, sedatives, or hypnotics. Future studies can categorize the extensive self-reported medication data available within the UK Biobank and confirm if our findings hold when additionally correcting for psychotropic medication use. Finally, we did not address potential physiological mechanisms underlying the identified significant sleep and brain associations. These mechanisms could (at least in part) be addressed by using the rich proteomic data within the UK Biobank [92].

Conclusions

We made use of the breadth and scope of the UK Biobank dataset to identify that sleeping more than or less than the recommended NSF sleep time was associated with different patterns of altered brain structure. Specifically, longer than recommended sleep duration was related to differences in brain structural measures often associated with poorer brain health, while shorter sleep was associated with lower cortical surface areas. Future studies should seek to better characterize the mechanisms of these differences and define whether longer-than-recommended sleep duration represents a marker of clinical utility for population brain health.

Supplementary Material

zsad274_suppl_Supplementary_Material
Click here for additional data file. (166.1KB, pdf)
zsad274_suppl_Supplementary_Data
Click here for additional data file. (144.3KB, zip)

Acknowledgments

The current study was funded and supported through the Lister Institute of Preventive Medicine award (reference 173096) and the Wellcome-University of Edinburgh Institutional Strategic Support Fund (reference 204804/Z/16/Z). LML is supported by a Royal College of Physicians of Edinburgh John, Margaret, Alfred and Stewart Sim Fellowship and a University of Glasgow Lord Kelvin / Adam Smith (LKAS) Fellowship. MJA and XS are supported by the Wellcome Trust (grant reference 220857/Z/20/Z). The research was conducted using the UK Biobank resource, with application number 4844. Provision of access to the UK Biobank was supported by the Wellcome Trust Strategic Award “Stratifying Resilience and Depression Longitudinally” (STRADL) (reference 104036/Z/14/Z). The current work has made use of the resources provided by the Edinburgh Compute and Data Facility (ECDF) (http://www.ecdf.ed.ac.uk/).

Contributor Information

Aleks Stolicyn, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Laura M Lyall, School of Health & Wellbeing, University of Glasgow, Glasgow, UK.

Donald M Lyall, School of Health & Wellbeing, University of Glasgow, Glasgow, UK.

Nikolaj Kjær Høier, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK; Copenhagen Research Center for Mental Health CORE, Mental Health Center Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark.

Mark J Adams, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Xueyi Shen, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

James H Cole, Dementia Research Centre, Queen Square Institute of Neurology, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.

Andrew M McIntosh, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Heather C Whalley, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Daniel J Smith, Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.

Financial Disclosure Statement

AMM previously received research grant support from Pfizer, Eli Lilly, and Janssen, as well as speaker fees from Illumina. HCW previously received research grant support from Pfizer. None of these funding sources are connected to the present study. No other potential financial conflicts of interest are reported.

Non-Financial Disclosure Statement

The current manuscript was deposited to medRxiv preprint repository with DOI https://doi.org/10.1101/2023.06.19.23291496. No non-financial conflicts of interest are reported.

Data Availability

The current study used data from the UK Biobank resource, which is available for health-related research upon registration and application through the UK Biobank Access Management System (https://www.ukbiobank.ac.uk/register-apply/).

References

  • 1. Makarem N, Alcántara C, Williams N, Bello NA, Abdalla M.. Effect of sleep disturbances on blood pressure. Hypertension. 2021;77(4):1036–1046. doi: 10.1161/HYPERTENSIONAHA.120.14479 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2. Nielsen LS, Danielsen KV, Sørensen TIA.. Short sleep duration as a possible cause of obesity: critical analysis of the epidemiological evidence. Obesity Rev. 2011;12(2):78–92. doi: 10.1111/j.1467-789X.2010.00724.x [DOI] [PubMed] [Google Scholar]
  • 3. Ogilvie RP, Patel SR.. The epidemiology of sleep and obesity. Sleep Health. 2017;3(5):383–388. doi: 10.1016/j.sleh.2017.07.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. He Q, Zhang P, Li G, Dai H, Shi J.. The association between insomnia symptoms and risk of cardio-cerebral vascular events: a meta-analysis of prospective cohort studies. Eur J Prev Cardiol. 2017;24(10):1071–1082. doi: 10.1177/2047487317702043 [DOI] [PubMed] [Google Scholar]
  • 5. Antza C, Kostopoulos G, Mostafa S, Nirantharakumar K, Tahrani A.. The links between sleep duration, obesity and type 2 diabetes mellitus. J Endocrinol. 2021;252(2):125–141. doi: 10.1530/JOE-21-0155 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6. Ogilvie RP, Patel SR.. The epidemiology of sleep and diabetes. Curr Diab Rep. 2018;18(10):82. doi: 10.1007/s11892-018-1055-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Besedovsky L, Lange T, Haack M.. The sleep-immune crosstalk in health and disease. Physiol Rev. 2019;99(3):1325–1380. doi: 10.1152/physrev.00010.2018 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8. World Health Organisation. ICD-10 Classifications of Mental and Behavioural Disorder: Clinical Descriptions and Diagnostic Guidelines. Geneva: World Health Organisation; 1993. [Google Scholar]
  • 9. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. Fifth Edition. Arlington, VA: American Psychiatric Association; 2013. [Google Scholar]
  • 10. Palagini L, Hertenstein E, Riemann D, Nissen C.. Sleep, insomnia and mental health. J Sleep Res. 2022;31(4):e13628. doi: 10.1111/jsr.13628 [DOI] [PubMed] [Google Scholar]
  • 11. Cedernaes J, Osorio RS, Varga AW, Kam K, Schiöth HB, Benedict C.. Candidate mechanisms underlying the association between sleep-wake disruptions and Alzheimer’s disease. Sleep Med Rev. 2017;31:102–111. doi: 10.1016/j.smrv.2016.02.002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Schmidt MH. The energy allocation function of sleep: a unifying theory of sleep, torpor, and continuous wakefulness. Neurosci Biobehav Rev. 2014;47:122–153. doi: 10.1016/j.neubiorev.2014.08.001 [DOI] [PubMed] [Google Scholar]
  • 13. Dworak M, McCarley RW, Kim T, Kalinchuk AV, Basheer R.. Sleep and brain energy levels: ATP changes during sleep. J Neurosci. 2010;30(26):9007–9016. doi: 10.1523/JNEUROSCI.1423-10.2010 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Holth JK, Fritschi SK, Wang C, et al. The sleep-wake cycle regulates brain interstitial fluid tau in mice and CSF tau in humans. Science. 2019;363(6429):880–884. doi: 10.1126/science.aav2546 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Xie L, Kang H, Xu Q, et al. Sleep drives metabolite clearance from the adult brain. Science. 2013;342(6156):373–377. doi: 10.1126/science.1241224 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16. Dunwiddie TV, Masino SA.. The role and regulation of adenosine in the central nervous system. Annu Rev Neurosci. 2001;24:31–55. doi: 10.1146/annurev.neuro.24.1.31 [DOI] [PubMed] [Google Scholar]
  • 17. Anafi RC, Kayser MS, Raizen DM.. Exploring phylogeny to find the function of sleep. Nat Rev Neurosci. 2019;20(2):109–116. doi: 10.1038/s41583-018-0098-9 [DOI] [PubMed] [Google Scholar]
  • 18. Krueger JM, Frank MG, Wisor JP, Roy S.. Sleep function: toward elucidating an enigma. Sleep Med Rev. 2016;28:46–54. doi: 10.1016/j.smrv.2015.08.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19. Komaroff AL. Does sleep flush wastes from the brain? JAMA. 2021;325(21):2153–2155. doi: 10.1001/jama.2021.5631 [DOI] [PubMed] [Google Scholar]
  • 20. Boardman JM, Porcheret K, Clark JW, et al. The impact of sleep loss on performance monitoring and error-monitoring: a systematic review and meta-analysis. Sleep Med Rev. 2021;58:101490. doi: 10.1016/j.smrv.2021.101490 [DOI] [PubMed] [Google Scholar]
  • 21. Fjell AM, Sørensen O, Amlien IK, et al. Self-reported sleep relates to hippocampal atrophy across the adult lifespan: results from the Lifebrain consortium. Sleep. 2020;43(5). doi: 10.1093/sleep/zsz280 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Westwood AJ, Beiser A, Jain N, et al. Prolonged sleep duration as a marker of early neurodegeneration predicting incident dementia. Neurology. 2017;88(12):1172–1179. doi: 10.1212/WNL.0000000000003732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23. Toschi N, Passamonti L, Bellesi M.. Sleep quality relates to emotional reactivity via intracortical myelination. Sleep. 2021;44(1). doi: 10.1093/sleep/zsaa146 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. André C, Laniepce A, Chételat G, Rauchs G.. Brain changes associated with sleep disruption in cognitively unimpaired older adults: a short review of neuroimaging studies. Ageing Res Rev. 2021;66:101252. doi: 10.1016/j.arr.2020.101252 [DOI] [PubMed] [Google Scholar]
  • 25. Sexton CE, Zsoldos E, Filippini N, et al. Associations between self-reported sleep quality and white matter in community-dwelling older adults: a prospective cohort study. Hum Brain Mapp. 2017;38(11):5465–5473. doi: 10.1002/hbm.23739 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Kim REY, Abbott RD, Kim S, et al. Sleep duration, sleep apnea, and gray matter volume. J Geriatr Psychiatry Neurol. 2022;35(1):47–56. doi: 10.1177/0891988720988918 [DOI] [PubMed] [Google Scholar]
  • 27. Cole JH, Ritchie SJ, Bastin ME, et al. Brain age predicts mortality. Mol Psychiatry. 2018;23(5):1385–1392. doi: 10.1038/mp.2017.62 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Ramduny J, Bastiani M, Huedepohl R, Sotiropoulos SN, Chechlacz M.. The association between inadequate sleep and accelerated brain ageing. Neurobiol Aging. 2022;114:1–14. doi: 10.1016/j.neurobiolaging.2022.02.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29. Chu C, Holst SC, Elmenhorst EM, et al. Total sleep deprivation increases brain age prediction reversibly in multisite samples of young healthy adults. J Neurosci., 2023;43(12):2168–2177. doi: 10.1523/JNEUROSCI.0790-22.2023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Leerssen J, Blanken TF, Pozzi E, et al. Brain structural correlates of insomnia severity in 1053 individuals with major depressive disorder: results from the ENIGMA MDD Working Group. Transl Psychiatry. 2020;10(1):425. doi: 10.1038/s41398-020-01109-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Grau-Rivera O, Operto G, Falcón C, et al. Association between insomnia and cognitive performance, gray matter volume, and white matter microstructure in cognitively unimpaired adults. Alzheimers Res Ther. 2020;12(1):4. doi: 10.1186/s13195-019-0547-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Bycroft C, Freeman C, Petkova D, et al. The UK Biobank resource with deep phenotyping and genomic data. Nature. 2018;562(7726):203–209. doi: 10.1038/s41586-018-0579-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233–243. doi: 10.1016/j.sleh.2015.10.004 [DOI] [PubMed] [Google Scholar]
  • 34. UK Biobank. Touch Screen Questionnaire - Version 1.0. 2011. http://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/Touchscreen.pdf. Accessed May 27, 2019.
  • 35. Lyall DM, Quinn T, Lyall LM, et al. Quantifying bias in psychological and physical health in the UK Biobank imaging sub-sample. Brain Commun. 2022;4(3):fcac119. doi: 10.1093/braincomms/fcac119 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Benjamini Y, Hochberg Y.. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Statistical Soc Ser B. 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x [DOI] [Google Scholar]
  • 37. Benjamini Y, Heller R, Yekutieli D.. Selective inference in complex research. Philos Transact A Math Phys Eng Sci. 2009;367(1906):4255–4271. doi: 10.1098/rsta.2009.0127 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Bethlehem RI, Seidlitz J, White SR, et al. Brain charts for the human lifespan. Nature. 2022;604(7906):525–533. doi: 10.1038/s41586-022-04554-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39. Raimo S, Santangelo G, D’Iorio A, Trojano L, Grossi D.. Neural correlates of apathy in patients with neurodegenerative disorders: an activation likelihood estimation (ALE) meta-analysis. Brain Imaging Behav. 2019;13(6):1815–1834. doi: 10.1007/s11682-018-9959-0 [DOI] [PubMed] [Google Scholar]
  • 40. Sabuncu MR, Desikan RS, Sepulcre J, et al. The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch Neurol. 2011;68(8):1040–1048. doi: 10.1001/archneurol.2011.167 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41. Yin RH, Tan L, Liu Y, et al. Multimodal voxel-based meta-analysis of white matter abnormalities in alzheimer’s disease. J Alzheimer’s Dis. 2015;47(2):495–507. doi: 10.3233/JAD-150139 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Kim YK, Han KM.. Neural substrates for late-life depression: a selective review of structural neuroimaging studies. Prog Neuropsychopharmacol Biol Psychiatry. 2021;104:110010. doi: 10.1016/j.pnpbp.2020.110010 [DOI] [PubMed] [Google Scholar]
  • 43. Schmaal L, Hibar DP, Sämann PG, et al. Cortical abnormalities in adults and adolescents with major depression based on brain scans from 20 cohorts worldwide in the ENIGMA Major Depressive Disorder Working Group. Mol Psychiatry. 2017;22(6):900–909. doi: 10.1038/mp.2016.60 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44. Zhang H, Li L, Wu M, et al. Brain gray matter alterations in first episodes of depression: a meta-analysis of whole-brain studies. Neurosci Biobehav Rev. 2016;60:43–50. doi: 10.1016/j.neubiorev.2015.10.011 [DOI] [PubMed] [Google Scholar]
  • 45. Cahn AJ, Keramatian K, Frysch C, Yatham LN, Chakrabarty T.. Longitudinal grey matter changes following first episode mania in bipolar I disorder: a systematic review. J Affect Disord. 2021;291:198–208. doi: 10.1016/j.jad.2021.04.051 [DOI] [PubMed] [Google Scholar]
  • 46. Gong J, Wang J, Chen P, et al. Large-scale network abnormality in bipolar disorder: a multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies. J Affect Disord. 2021;292:9–20. doi: 10.1016/j.jad.2021.05.052 [DOI] [PubMed] [Google Scholar]
  • 47. Hanford LC, Nazarov A, Hall GB, Sassi RB.. Cortical thickness in bipolar disorder: a systematic review. Bipolar Disord. 2016;18(1):4–18. doi: 10.1111/bdi.12362 [DOI] [PubMed] [Google Scholar]
  • 48. Angelescu I, Brugger SP, Borgan F, Kaar SJ, Howes OD.. The magnitude and variability of brain structural alterations in bipolar disorder: a double meta-analysis of 5534 patients and 6651 healthy controls. J Affect Disord. 2021;291:171–176. doi: 10.1016/j.jad.2021.04.090 [DOI] [PubMed] [Google Scholar]
  • 49. Pezzoli S, Emsell L, Yip SW, et al. Meta-analysis of regional white matter volume in bipolar disorder with replication in an independent sample using coordinates, T-maps, and individual MRI data. Neurosci Biobehav Rev. 2018;84:162–170. doi: 10.1016/j.neubiorev.2017.11.005 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50. Hayes JP, Bigler ED, Verfaellie M.. Traumatic brain injury as a disorder of brain connectivity. J Int Neuropsychol Soc. 2016;22(2):120–137. doi: 10.1017/S1355617715000740 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51. Kim E, Yoo RE, Seong MY, Oh BM.. A systematic review and data synthesis of longitudinal changes in white matter integrity after mild traumatic brain injury assessed by diffusion tensor imaging in adults. Eur J Radiol. 2022;147:110117. doi: 10.1016/j.ejrad.2021.110117 [DOI] [PubMed] [Google Scholar]
  • 52. van Velzen LS, Kelly S, Isaev D, et al. White matter disturbances in major depressive disorder: a coordinated analysis across 20 international cohorts in the ENIGMA MDD working group. Mol Psychiatry. 2020;25:1511–1525. doi: 10.1038/s41380-019-0477-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53. Wardlaw JM, Smith C, Dichgans M.. Small vessel disease: mechanisms and clinical implications. Lancet Neurol. 2019;18(7):684–696. doi: 10.1016/S1474-4422(19)30079-1 [DOI] [PubMed] [Google Scholar]
  • 54. Wang L, Leonards CO, Sterzer P, Ebinger M.. White matter lesions and depression: a systematic review and meta-analysis. J Psychiatr Res. 2014;56:56–64. doi: 10.1016/j.jpsychires.2014.05.005 [DOI] [PubMed] [Google Scholar]
  • 55. Garnier-Crussard A, Bougacha S, Wirth M, et al. White matter hyperintensities across the adult lifespan: relation to age, Aβ load, and cognition. Alzheimers Res Ther. 2020;12(1):127. doi: 10.1186/s13195-020-00669-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56. Smith EE, Saposnik G, Biessels GJ, et al. Prevention of stroke in patients with silent cerebrovascular disease: a scientific statement for healthcare professionals from the american heart association/american stroke association. Stroke. 2017;48(2):e44–e71. doi: 10.1161/STR.0000000000000116 [DOI] [PubMed] [Google Scholar]
  • 57. Hu HY, Ou YN, Shen XN, et al. White matter hyperintensities and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 36 prospective studies. Neurosci Biobehav Rev. 2021;120:16–27. doi: 10.1016/j.neubiorev.2020.11.007 [DOI] [PubMed] [Google Scholar]
  • 58. Prins ND, Scheltens P.. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol. 2015;11(3):157–165. doi: 10.1038/nrneurol.2015.10 [DOI] [PubMed] [Google Scholar]
  • 59. Guo W, Shi J.. White matter hyperintensities volume and cognition: a meta-analysis. Front Aging Neurosci. 2022;14:949763. doi: 10.3389/fnagi.2022.949763 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60. Debette S, Markus HS.. The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ. 2010;341:c3666. doi: 10.1136/bmj.c3666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61. Rolls ET. The hippocampus, memory, and spatial function. In: Brain Computations. Oxford University Press; 2020:260–362. doi: 10.1093/oso/9780198871101.003.0009 [DOI] [Google Scholar]
  • 62. Fraser MA, Shaw ME, Cherbuin N.. A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. Neuroimage. 2015;112:364–374. doi: 10.1016/j.neuroimage.2015.03.035 [DOI] [PubMed] [Google Scholar]
  • 63. Arrondo P, Elía-Zudaire O, Martí-Andrés G, Fernández-Seara MA, Riverol M.. Grey matter changes on brain MRI in subjective cognitive decline: a systematic review. Alzheimers Res Ther. 2022;14(1):98. doi: 10.1186/s13195-022-01031-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64. Raine PJ, Rao H.. Volume, density, and thickness brain abnormalities in mild cognitive impairment: an ALE meta-analysis controlling for age and education. Brain Imaging Behav. 2022;16(5):2335–2352. doi: 10.1007/s11682-022-00659-0 [DOI] [PubMed] [Google Scholar]
  • 65. Boku S, Nakagawa S, Toda H, Hishimoto A.. Neural basis of major depressive disorder: beyond monoamine hypothesis. Psychiatry Clin Neurosci. 2018;72(1):3–12. doi: 10.1111/pcn.12604 [DOI] [PubMed] [Google Scholar]
  • 66. Schmaal L, Veltman DJ, van Erp TGM, et al. Subcortical brain alterations in major depressive disorder: findings from the ENIGMA Major Depressive Disorder working group. Mol Psychiatry. 2016;21(6):806–812. doi: 10.1038/mp.2015.69 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67. Phillips ML, Swartz HA.. A critical appraisal of neuroimaging studies of bipolar disorder: toward a new conceptualization of underlying neural circuitry and a road map for future research. Am J Psychiatry. 2014;171(8):829–843. doi: 10.1176/appi.ajp.2014.13081008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68. Maleki S, Hendrikse J, Chye Y, et al. Associations of cardiorespiratory fitness and exercise with brain white matter in healthy adults: a systematic review and meta-analysis. Brain Imaging Behav. 2022;16(5):2402–2425. doi: 10.1007/s11682-022-00693-y [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69. Sexton CE, Betts JF, Demnitz N, Dawes H, Ebmeier KP, Johansen-Berg H.. A systematic review of MRI studies examining the relationship between physical fitness and activity and the white matter of the ageing brain. Neuroimage. 2016;131:81–90. doi: 10.1016/j.neuroimage.2015.09.071 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70. Buckner RL. The cerebellum and cognitive function: 25 years of insight from anatomy and neuroimaging. Neuron. 2013;80(3):807–815. doi: 10.1016/j.neuron.2013.10.044 [DOI] [PubMed] [Google Scholar]
  • 71. Schnack HG, van Haren NEM, Brouwer RM, et al. Changes in thickness and surface area of the human cortex and their relationship with intelligence. Cerebral Cortex 2015;25(6):1608–1617. doi: 10.1093/cercor/bht357 [DOI] [PubMed] [Google Scholar]
  • 72. Wu BS, Zhang YR, Li HQ, et al. Cortical structure and the risk for Alzheimer’s disease: a bidirectional Mendelian randomization study. Transl Psychiatry. 2021;11(1):476. doi: 10.1038/s41398-021-01599-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 73. Duarte JA, de Araújo E, Silva JQ, Goldani AA, Massuda R, Gama CS.. Neurobiological underpinnings of bipolar disorder focusing on findings of diffusion tensor imaging: a systematic review. Rev Bras Psiquiatr Sao Paulo Braz 1999. 2016;38(2):167–175. doi: 10.1590/1516-4446-2015-1793 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74. Cox SR, Ritchie SJ, Fawns-Ritchie C, Tucker-Drob EM, Deary IJ.. Structural brain imaging correlates of general intelligence in UK Biobank. Intell. 2019;76:101376. doi: 10.1016/j.intell.2019.101376 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75. Nobis L, Manohar SG, Smith SM, et al. Hippocampal volume across age: nomograms derived from over 19,700 people in UK Biobank. NeuroImage Clin. 2019;23:101904. doi: 10.1016/j.nicl.2019.101904 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76. Firth J, Stubbs B, Vancampfort D, et al. Effect of aerobic exercise on hippocampal volume in humans: a systematic review and meta-analysis. Neuroimage. 2018;166:230–238. doi: 10.1016/j.neuroimage.2017.11.007 [DOI] [PubMed] [Google Scholar]
  • 77. Aghjayan SL, Lesnovskaya A, Esteban-Cornejo I, Peven JC, Stillman CM, Erickson KI.. Aerobic exercise, cardiorespiratory fitness, and the human hippocampus. Hippocampus. 2021;31(8):817–844. doi: 10.1002/hipo.23337 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 78. Byrne JEM, Tremain H, Leitan ND, Keating C, Johnson SL, Murray G.. Circadian modulation of human reward function: is there an evidentiary signal in existing neuroimaging studies? Neurosci Biobehav Rev. 2019;99:251–274. doi: 10.1016/j.neubiorev.2019.01.025 [DOI] [PubMed] [Google Scholar]
  • 79. Liu X, Klugah-Brown B, Zhang R, Chen H, Zhang J, Becker B.. Pathological fear, anxiety and negative affect exhibit distinct neurostructural signatures: evidence from psychiatric neuroimaging meta-analysis. Transl Psychiatry. 2022;12(1):405. doi: 10.1038/s41398-022-02157-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 80. Lu X, Zhong Y, Ma Z, et al. Structural imaging biomarkers for bipolar disorder: meta-analyses of whole-brain voxel-based morphometry studies. Depress Anxiety. 2019;36(4):353–364. doi: 10.1002/da.22866 [DOI] [PubMed] [Google Scholar]
  • 81. Janak PH, Tye KM.. From circuits to behaviour in the amygdala. Nature. 2015;517(7534):284–292. doi: 10.1038/nature14188 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 82. Bora E, Fornito A, Yücel M, Pantelis C.. Voxelwise meta-analysis of gray matter abnormalities in bipolar disorder. Biol Psychiatry. 2010;67(11):1097–1105. doi: 10.1016/j.biopsych.2010.01.020 [DOI] [PubMed] [Google Scholar]
  • 83. Lancaster TM, Foley S, Tansey KE, Linden DEJ, Caseras X.. CACNA1C risk variant is associated with increased amygdala volume. Eur Arch Psychiatry Clin Neurosci. 2016;266(3):269–275. doi: 10.1007/s00406-015-0609-x [DOI] [PubMed] [Google Scholar]
  • 84. Cullen B, Nicholl BI, Mackay DF, et al. Cognitive function and lifetime features of depression and bipolar disorder in a large population sample: cross-sectional study of 143,828 UK Biobank participants. Eur Psychiatry. 2015;30(8):950–958. doi: 10.1016/j.eurpsy.2015.08.006 [DOI] [PubMed] [Google Scholar]
  • 85. Li Y, Sahakian BJ, Kang J, et al. The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nat Aging. 2022;2(5):425–437. doi: 10.1038/s43587-022-00210-2 [DOI] [PubMed] [Google Scholar]
  • 86. Tai XY, Chen C, Manohar S, Husain M.. Impact of sleep duration on executive function and brain structure. Commun Biol. 2022;5(1):201. doi: 10.1038/s42003-022-03123-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 87. Schiel JE, Tamm S, Holub F, et al. Associations between sleep health and grey matter volume in the UK Biobank cohort (n = 33 356). Brain Commun. 2023;5(4):fcad200. doi: 10.1093/braincomms/fcad200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 88. UK Biobank. UK Biobank Brain Imaging Documentation. 2020. https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf. Accessed September 22, 2022.
  • 89. Brooks TG, Lahens NF, Grant GR, Sheline YI, FitzGerald GA, Skarke C.. Phenome-Wide Association Study of Actigraphy in the UK Biobank. Public and Global Health; 2021. doi: 10.1101/2021.12.09.21267558 [DOI] [Google Scholar]
  • 90. Shen X, Howard DM, Adams MJ, et al. A phenome-wide association and Mendelian Randomisation study of polygenic risk for depression in UK Biobank. Nat Commun. 2020;11(1):2301. doi: 10.1038/s41467-020-16022-0 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91. Alfaro-Almagro F, McCarthy P, Afyouni S, et al. Confound modelling in UK Biobank brain imaging. Neuroimage. 2021;224:117002. doi: 10.1016/j.neuroimage.2020.117002 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 92. Sun BB, Chiou J, Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature. 2023;622(7982):329–338. doi: 10.1038/s41586-023-06592-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

zsad274_suppl_Supplementary_Material
Click here for additional data file. (166.1KB, pdf)
zsad274_suppl_Supplementary_Data
Click here for additional data file. (144.3KB, zip)

Data Availability Statement

The current study used data from the UK Biobank resource, which is available for health-related research upon registration and application through the UK Biobank Access Management System (https://www.ukbiobank.ac.uk/register-apply/).

RESOURCES