Hostname: page-component-89b8bd64d-4ws75 Total loading time: 0 Render date: 2026-05-07T10:50:25.988Z Has data issue: false hasContentIssue false
  • English
  • Français

Alterations of Limbic Structure Volumes in Patients with Obstructive Sleep Apnea

Published online by Cambridge University Press:  17 October 2022

Kang Min Park Open the ORCID record for Kang Min Park [Opens in a new window]  and
Jinseung Kim Open the ORCID record for Jinseung Kim [Opens in a new window]
Kang Min Park
Affiliation:
Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Korea
Jinseung Kim*
Affiliation:
Department of Family medicine, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
*
Corresponding author: Jinseung Kim, MD, Department of Family medicine, Busan Paik Hospital, Inje University College of Medicine, 75, Bokji-ro, Busanjin-gu, Busan 47392, Republic of Korea. Email: jinseungkim@inje.ac.kr
Rights & Permissions [Opens in a new window]

Abstract:

Objectives:

We investigated the change in limbic structure volumes and intrinsic limbic network in patients with obstructive sleep apnea (OSA) compared to healthy controls.

Methods:

We enrolled 26 patients with OSA and 30 healthy controls. They underwent three-dimensional T1-weighted magnetic resonance imaging (MRI) on a 3 T MRI scanner. The limbic structures were analyzed volumetrically using the FreeSurfer program. We examined the intrinsic limbic network using the Brain Analysis with Graph Theory program and compared the groups' limbic structure volumes and intrinsic limbic network.

Results:

There were significant differences in specific limbic structure volumes between the groups. The volumes in the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens in patients with OSA were lower than those in healthy controls (right amygdala, 0.102 vs. 0.113%, p = 0.004; right hippocampus, 0.253 vs. 0.281%, p = 0.002; right hypothalamus, 0.028 vs. 0.032%, p = 0.002; right nucleus accumbens, 0.021 vs. 0.024%, p = 0.019; left amygdala, 0.089 vs. 0.098%, p = 0.007; left basal forebrain, 0.020 vs. 0.022%, p = 0.027; left hippocampus, 0.245 vs. 0.265%, p = 0.021; left hypothalamus, 0.028 vs. 0.031%, p = 0.016; left nucleus accumbens, 0.023 vs. 0.027%, p = 0.002). However, there were no significant differences in network measures between the groups.

Conclusion:

We demonstrate that the volumes of several limbic structures in patients with OSA are significantly lower than those in healthy controls. However, there are no alterations to the intrinsic limbic network. These findings suggest that OSA is one of the risk factors for cognitive impairments.

Résumé :

RÉSUMÉ :

Modifications du volume de structures limbiques chez des patients atteints d’apnée obstructive du sommeil.

Objectif :

L’étude visait à évaluer le changement de volume des structures limbiques et du réseau limbique intrinsèque chez des patients souffrant d’apnée obstructive du sommeil (AOS) comparativement à des témoins en bonne santé.

Méthode :

Au total, 26 patients souffrant d’AOS et 30 témoins en bonne santé ont participé à l’étude. Ils ont tous passé une IRM en trois dimensions, pondérée en T1, au moyen d’un appareil Tesla 3. Il y a eu une analyse volumétrique des structures limbiques à l’aide du programme FreeSurfer, et un examen du réseau limbique intrinsèque à l’aide du programme Brain Analysis with Graph Theory, après quoi il y a eu une comparaison du volume des structures limbiques et du réseau limbique intrinsèque entre les groupes.

Résultats :

Des différences importantes du volume de certaines structures limbiques ont été observées entre les groupes. Ainsi, le volume de l’amygdale droite, de l’hippocampe droit, de l’hypothalamus droit, du noyau accumbens droit, de l’amygdale gauche, du prosencéphale basal gauche, de l’hippocampe gauche, de l’hypothalamus gauche et du noyau accumbens gauche était plus petit chez les patients atteints d’AOS que chez les témoins en bonne santé (amygdale droite : 0,102 contre [c.] 0,113 %; p = 0,004; hippocampe droit : 0,253 c. 0,281 %; p = 0,002; hypothalamus droit : 0,028 c. 0,032 %; p = 0,002; accumbens nucléaire droit : 0,021 c. 0,024 %; p = 0,019; amygdale gauche : 0,089 c. 0,098 %; p = 0,007; prosencéphale basal gauche : 0,020 c. 0,022 %; p = 0,027; hippocampe gauche : 0,245 c. 0,265 %; p = 0,021; hypothalamus gauche : 0,028 c. 0,031 %; p = 0,016; accumbens nucléaire gauche : 0,023 c. 0,027 %; p = 0,002). Par contre, il n’y avait de différence importante entre les groupes quant aux mesures du réseau.

Conclusion :

Les résultats de l’étude ont démontré que le volume de plusieurs structures limbiques était passablement plus petit chez les patients atteints d’AOS que chez les témoins en bonne santé. Par contre, aucune modification du réseau limbique intrinsèque n’a été observée. Aussi les données recueillies donnent-elles à penser que l’AOS est l’un des facteurs de risque de troubles cognitifs.

Keywords

Limbic systemObstructive sleep apneaVolume

Information

Type
Original Article
Information
Canadian Journal of Neurological Sciences , Volume 50 , Issue 5 , September 2023 , pp. 730 - 737
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of Canadian Neurological Sciences Federation

Introduction

Obstructive sleep apnea (OSA) is characterized by episodic upper airway collapse, which is sleep state dependent, resulting in periodic reductions or cessations in ventilation, hypoxia, hypercapnia, or arousals from sleep.Reference Dempsey, Veasey, Morgan and O'Donnell1 OSA affects about 25% of adults in the USA and is a leading cause of excessive sleepiness, resulting in a lower quality of life, impaired work performance, and an increased risk of a car accident.Reference Punjabi2

OSA is also linked to various long-term health problems, including cardiovascular disease, metabolic disorders, and psychiatric problems.Reference Drager, Togeiro, Polotsky and Lorenzi-Filho3–Reference Wheaton, Perry, Chapman and Croft5 Furthermore, numerous reports have associated cognitive difficulties in memory and new learning, attention, and executive function.Reference Bédard, Montplaisir, Richer, Rouleau and Malo6–Reference Beebe and Gozal9 A meta-analysis of six prospective studies have found that 26% of patients with OSA experience significant cognitive decline or dementia.Reference Leng, McEvoy, Allen and Yaffe10 Another study has found that the combined prevalence of depressive symptom in patients with OSA is about 35%.Reference Garbarino, Bardwell, Guglielmi, Chiorri, Bonanni and Magnavita11 Sleep fragmentation, often seen in patients with OSA, may play a role in the cognitive impairments associated with OSA by disrupting neural networks, particularly in the frontal lobes.Reference Beebe and Gozal9 It contributes to cognitive impairments, particularly attention and memory problems.Reference Verstraeten12,Reference Daurat, Foret, Bret-Dibat, Fureix and Tiberge13 Decreased sleep efficiency also reduces the efficacy of restorative processes, resulting in cellular and biochemical stress.Reference Madsen14,Reference Maquet15 Another cause of cognitive impairments is the intermittent hypoxia associated with OSA.Reference Bartlett, Rae and Thompson16 These changes affect cell neurogenesis and the density in the hippocamus.Reference Bartlett, Rae and Thompson16,Reference Hopkins, Kesner and Goldstein17 These factors contribute to OSA, increasing the risk of mild cognitive impairment, Alzheimer’s disease (AD), and other types of dementia.Reference Mubashir, Abrahamyan and Niazi18–Reference Lutsey, Misialek and Mosley20

Brain magnetic resonance imaging (MRI) studies have found that atrophy of the hippocampus and amygdala in older adults with normal cognitive function is a risk factor for developing dementia.Reference den Heijer, Geerlings, Hoebeek, Hofman, Koudstaal and Breteler21 The amygdala’s and hippocampus’s asymmetric atrophy could also be a particularly sensitive indicator for detecting early cognitive impairments.Reference Yue, Wang and Wang22 Previous research has found that patients with OSA have impaired attention, memory, emotion, and executive functions linked to multiple brain regions, especially in the amygdala and hippocampus.Reference Torelli, Moscufo and Garreffa23 The basolateral amygdala/hippocampus are the regions of structural atrophy and functional disturbances in OSA, and these changes are linked to emotional, sensory, and limbic dysfunction.Reference Tahmasian, Rosenzweig and Eickhoff24 The limbic system is a network of interconnected cortical and subcortical structures that is responsible for connecting visceral states, emotion, and cognition to behavior.Reference Mesulam25 It is well known that the limbic system’s activation during sleep plays a crucial role in memory consolidation.Reference Luppi, Billwiller and Fort26 Thus, we could assume that the patients with OSA have abnormalities in the limbic system, which could be detected by brain MRI.

In the past, it was challenging to obtain volume automatically. Recently, machine learning techniques have become available to segment and determine the volumes of the limbic structures, including the hippocampus, amygdala, thalamus, mammary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens.Reference Greve, Billot and Cordero27 Furthermore, graph theory, which uses natural frameworks to handle large networks analytically, may quantify the topological configuration of brain connections and evaluate brain efficiency for information processing and network features.Reference Bullmore and Sporns28,Reference Pasemann29 Graph theory based on the limbic structure volumes can provide the state of the intrinsic limbic network. However, no studies have focused on limbic structure volumes and investigated the intrinsic limbic network in patients with OSA compared to healthy controls. Abnormalities in limbic structures in patients with OSA may suggest that OSA is associated with cognitive impairments.

We investigated the change in limbic structure volumes and intrinsic limbic network in patients with OSA in this study compared to healthy controls. We hypothesized that there were significant alterations of limbic structure volumes and intrinsic limbic networks in patients with OSA.

Methods

Participants: Patients with OSA and Healthy Controls

This study took place in a tertiary care hospital. The hospital’s institutional review approved this study board, which was conducted in accordance with the Declaration of Helsinki. We retrospectively identified patients who met the following criteria for OSA: Reference Kapur, Auckley and Chowdhuri30 1) a diagnosis of OSA based on laboratory polysomnography demonstrating an apnea-hypopnea index (AHI) >5 in addition to symptoms such as sleepiness or chronic snoring, 2) OSA was the only medical or neurological disorder, 3) no structural lesions on brain MRI on visual inspection, 4) no complaints of cognitive impairment, and 5) with three-dimensional T1-weighted MRI data, which were suitable for volumetric analysis. Patients with OSA did not complain of memory loss or problems in daily living. We collected clinical and polysomnographic data from patients with OSA, including their age, sex, Epworth sleepiness scale score, total sleep time, sleep efficiency, the ratio of sleep stages N1, N2, N3, and R during sleep, total AHI during sleep, AHI during stage N, AHI during stage R, and total respiratory disturbance index during sleep.

We calculated a value of %AHI and defined the patients with non-rapid eye movement (NREM)-predominant OSA (more than 66.7% of %AHI) and rapid eye movement (REM)-predominant OSA (less than 33.3% of %AHI).Reference Yamauchi, Fujita and Kumamoto31

Our control group was age and sex matched with study cases. They had been previously recruited from our study,Reference Jang, Lee, Lee and Park32 who did not have a history of medical or neurological disorders. They had a normal brain MRI on visual inspection. None complained of snoring or other OSA symptoms and, therefore, did not have polysomnography testing.

MRI Acquisition

All patients with OSA and controls underwent three-dimensional T1-weighted MRI on a 3 T MRI scanner with the following acquisition parameters: TI = 1300 ms, TR/TE = 8.6/3.96 ms, flip angle = 8°, and isotropic voxel size = 1 mm3. To rule out structural lesions, they were scanned using standard brain MRI protocols, including FLAIR and T2-weighted imaging.

Calculation of Limbic Structure Volumes

The limbic structures were analyzed volumetrically using the development version of FreeSurfer program with the following steps. First, we used the FreeSurfer “recon-all” commandReference Dale, Fischl and Sereno33 to process our three-dimensional T1-weighted MRI data. Using this command, we could obtain the volumes of the hippocampus, amygdala, and thalamus. Second, we used “mri_sclimbic_seg”Reference Greve, Billot and Cordero27 scripts to segment limbic structures and obtain their absolute volumes, including the mammary body, hypothalamus, basal forebrain, septal nuclei, fornix, and nucleus accumbens. This method used a U-net-based deep learning algorithm. All segmentations were visually inspected for accuracy prior to inclusion in the group analysis to correct for a potential error in the automated procedure. Figure 1 illustrates an example of segmentation in limbic structures in a patient. Third, we corrected the limbic structure volumes for their estimated intracranial volumes.

Figure 1:

The example of segmentation of subcortical limbic structures. The segmentations are overlaid onto a T1-weighted image in coronal, axial, and sagittal orientation and shown in volume rendering. HypoThal-noMB: hypothalamus, AntCom: anterior commissure, SeptalNuc: septal nucleus.

Calculation of Intrinsic Limbic Network

We examined the intrinsic limbic network in patients with OSA and healthy controls using the Brain Analysis with Graph Theory (BRAPH) program.Reference Mijalkov, Kakaei, Pereira, Westman and Volpe34 This software develops a collection of nodes representing brain regions (individual volumes within limbic structures) and edges representing their connections (calculated as partial correlation coefficients between each pair of brain regions while controlling for age and sex effects) for each group. Each group was assigned a weighted, undirected connection matrix. We applied graph theory to determine the differences in the intrinsic limbic network between the groups using network measures such as average degree, average strength, radius, diameter, eccentricity, characteristic path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, modularity, assortativity, and small-worldness index.Reference Farahani, Karwowski and Lighthall35–Reference Park, Lee and Shin37 These network parameters were compared between patients with OSA and healthy controls.

Statistical Analysis

The age and sex were compared using the chi-squared test and the Student’s t-test, respectively, between the patients with OSA and healthy controls. We used Student’s t-test to compare limbic structure volumes between the groups. We used nonparametric permutation tests with 1000 permutations to determine the statistical significance of the differences between the groups in the intrinsic limbic network, because we could obtain network measures at the group level through the BRAPH program data. We defined statistical significance as a p-value less than 0.05 for comparing baseline characteristics and correlations between the groups. All statistical analyses were carried out using MedCalc® Statistical Software, version 20.022 (MedCalc Software Ltd, Ostend, Belgium; https://www.medcalc.org; 2021).

Results

Clinical and Polysomnographic Characteristics

We enrolled 26 patients with OSA and 30 healthy controls. The groups did not differ by age and sex. Table 1 shows the clinical and polysomnographic characteristics of patients with OSA and healthy controls.

Table 1:

The clinical and polysomnographic characteristics in the patients with obstructive sleep apnea

SD=standard deviation; BMI=body mass index; AHI=apnea-hypopnea index; RDI=respiratory disturbance index.

The Differences in Limbic Structure Volumes Between Patients with OSA and Healthy Controls

Table 2 reveals the differences in limbic structure volumes between patients with OSA and healthy controls. Significant differences existed between the groups' volumes of some limbic structures. The volumes in the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens in patients with OSA were lower than those in healthy controls.

Table 2:

The differences in limbic structure volumes between patients with OSA and healthy controls

* p < 0.05.

OSA=obstructive sleep apnea.

The Differences in Limbic Structure Volumes Between Patients with NREM-Predominant OSA and REM-Predominant OSA

Eight patients were NREM-predominant OSA, whereas five patients were REM-predominant OSA. There were no significant differences in the limbic structures’ volumes, including right and left amygdala, basal forebrain, fornix, hippocampus, hypothalamus, mammary body, nucleus accumbens, septal nuclei, and thalamus between the groups (Suppl. 1).

The Differences in Intrinsic Limbic Network Between Patients with OSA and Healthy Controls

Table 3 shows the differences in the intrinsic limbic network between patients with OSA and healthy controls. There were no significant differences in network measures, including average degree, average strength, radius, diameter, eccentricity, characteristics path length, global efficiency, local efficiency, mean clustering coefficient, transitivity, modularity, assortativity, and small-worldness index, between the groups.

Table 3:

The differences in the intrinsic limbic network between patients with OSA and healthy controls

OSA=obstructive sleep apnea; CI=95% confidence interval of difference.

Correlation Between Clinical and Polysomnographic Characteristics and Limbic Structure Volumes

We conducted a correlation analysis between the limbic structure volumes, including the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens, and clinical and polysomnographic characteristics in patients with OSA. There was a significant negative correlation between volumes in the right amygdala, right nucleus accumbens, left amygdala, and left nucleus accumbens and age. However, there were no significant correlations between the limbic structure volumes, including the right hippocampus, right hypothalamus, left basal forebrain, left hippocampus, and left hypothalamus and the other clinical and polysomnographic characteristics (Table 4). Furthermore, we conducted a correlation analysis between the limbic structure volumes and age in the healthy controls, which showed no significant correlations between them (Suppl. 2.).

Table 4:

The results of correlation analysis between clinical and polysomnographic characteristics and limbic structures volumes in the patients with obstructive sleep apnea

* p < 0.05.

BMI=body mass index; AHI=apnea-hypopnea index; RDI=respiratory disturbance index.

Discussion

We found differences between cases and controls in the limbic structure volumes of the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens using a U-net-based deep learning algorithm.Reference Greve, Billot and Cordero27 We also found no alterations of the intrinsic limbic networks in patients with OSA compared to healthy controls, which was analyzed based on the graph theory.

The volumes of the right amygdala, right hippocampus, right hypothalamus, right nucleus accumbens, left amygdala, left basal forebrain, left hippocampus, left hypothalamus, and left nucleus accumbens were significantly lower in patients with OSA than in the controls. This finding was consistent with a previous meta-analysis, which showed structural atrophy in the basolateral amygdala, hippocampus, and insular cortex in patients with OSA.Reference Tahmasian, Rosenzweig and Eickhoff24 These findings suggest the important role of the amygdala, hippocampus, and insula in abnormal emotional and sensory processing in patients with OSA. The right amygdala is thought to mediate aversive conditioning to errors, while the left amygdala is believed to underpin negative performance affect.Reference Polli, Wright and Milad38 Synaptic plasticity in the basolateral amygdala is shown to mediate the acquisition of associative memories of both ends of emotional valences, and different populations of neurons in that complex may encode fearful or rewarding associations.Reference Namburi, Beyeler and Yorozu39 In major depressive disorder, abnormal functional connectivity of the amygdala and hippocampus may interact with dysfunctional intrinsic network activity, which could underlie emotional memory disturbances in patients with OSA.Reference Tahmasian, Knight and Manoliu40 Thus, the findings with a decrease in amygdala volume in patients with OSA may suggest that this role of amygdala may have declined in patients with OSA.Reference Tahmasian, Shao and Meng41

Furthermore, the hippocampus is particularly vulnerable to intermittent hypoxia, which could explain the high frequency of neurobehavioral deficits in patients with OSA.Reference Gozal, Row, Schurr and Gozal42 A recent study discovered a link between OSA and AD. Cognitive impairments observed in patients with OSA could be partly explained by hippocampal dysfunction, as previously demonstrated in patients with AD.Reference Tahmasian, Pasquini and Scherr43 In addition, another study discovered that patients with AD were five times more likely than healthy controls to develop OSA symptoms.Reference Emamian, Khazaie and Tahmasian19 The right hippocampus is known to be involved in memory tasks that require concentric spatial location processing, which could impair driving ability in patients with OSA.Reference Iglói, Doeller, Berthoz, Rondi-Reig and Burgess44 This is in a line with the finding of the present study showing the significant difference in hippocampus volume between patients with OSA and healthy controls.

The nucleus accumbens is one forebrain nuclei that play a crucial role in pain modulation and sleep-wake cycle regulation.Reference Oishi and Lazarus45 Dopaminergic activity at the inhibitory D2 receptor reduces nucleus accumbens output, increases arousal, and disrupts sleep status.Reference Qiu, Liu, Qu, Urade, Lu and Huang46 The nucleus accumbens is more activated during forced wakening than during uninterrupted sleep, according to a study on forced wakening by time division.Reference Seminowicz, Remeniuk and Krimmel47 In this study, the nucleus accumbens volume in patients with OSA was lower than that in the healthy controls, which may be related to poor sleep quality in OSA. Changes in the nucleus accumbens caused by forced awakening may be linked to sleep fragmentation, affecting cognitive impairments in patients with OSA. These findings suggested that changes in the limbic structure volumes in patients with OSA are related to developing cognitive impairments.

However, our study revealed no differences in the intrinsic limbic network between patients with OSA and a healthy control group. The present results differed from previous studies that analyzed the entire brain network. In one study, researchers investigated structural brain connectivity using diffusion tensor imaging and discovered that white matter abnormalities in patients with OSA caused changes in structural connectivity.Reference Lee, Yun and Min48 Another study found that OSA caused changes in global topological characteristics in the brain network, demonstrated by statistical cortical volume associations.Reference Y-g, Wang and Liu49 There are several reasons for different results. A plausible explanation is that the intrinsic limbic network is likely to differ from the global brain network, which we did not analyze. Another possibility is that our small sample size had insufficient power to detect a difference. Further research with larger sample sizes is needed to confirm our findings.

There were some limitations in this study. First, this study was limited to a single center and relatively small sample size, limiting generalizability. Second, a temporal relationship could not be determined because this was a retrospective study comparing patients with OSA and healthy controls. As a result, it was unclear whether the change in limbic structure volumes was the result or cause of OSA. Third, we included the control group without polysomnographic examination and may have included individuals with undiagnosed sleep apnea. Lastly, since limbic structures were very small, it was difficult to completely rule out the possibility of errors in segmentation. However, we used the toolbox based on the U-Net for segmentation of limbic structures, which was one of the recent machine learning algorithms. It had been trained using 39 manually labeled MRI data sets for spatial, intensity, contrast, and noise augmentation. Test–retest reliability of the tool was already proven.Reference Greve, Billot and Cordero27 Nevertheless, this was the first study to focus on changes in limbic structural volumes and intrinsic limbic networks based on the graph theory in patients with OSA compared to healthy controls. Significant volume changes in the several limbic structures were successfully confirmed.

Conclusion

We demonstrate that the volumes of several limbic structures in patients with OSA are significantly lower than those in healthy controls. However, there are no alterations to the intrinsic limbic network. These findings suggest that OSA is one of the risk factors for cognitive impairments.

Supplementary material

For supplementary material accompanying this paper visit https://doi.org/10.1017/cjn.2022.303

Acknowledgement

This work was supported by the 2022 Inje University Busan Paik Hospital Research Grant.

Conflict of Interest

The authors declare no conflict of interest.

Statement of Authorship

Conception and design: Kang Min Park and Jinseung Kim. Acquisition of data, analysis, and interpretation of data: Kang Min Park and Jinseung Kim.

References

Dempsey, JA, Veasey, SC, Morgan, BJ, O'Donnell, CP. Pathophysiology of sleep apnea. Physiol Rev. 2010;90:47–112.CrossRefGoogle ScholarPubMed
Punjabi, NM. The epidemiology of adult obstructive sleep apnea. In: Proceedings of the American Thoracic Society, 2008, 5, 136–43.CrossRefGoogle Scholar
Drager, LF, Togeiro, SM, Polotsky, VY, Lorenzi-Filho, G. Obstructive sleep apnea: a cardiometabolic risk in obesity and the metabolic syndrome. J Am Coll Cardiol. 2013;62:569–76.CrossRefGoogle ScholarPubMed
Olaithe, M, Bucks, RS, Hillman, DR, Eastwood, PR. Cognitive deficits in obstructive sleep apnea: insights from a meta-review and comparison with deficits observed in COPD, insomnia, and sleep deprivation. Sleep Med Rev. 2018;38:39–49.CrossRefGoogle ScholarPubMed
Wheaton, AG, Perry, GS, Chapman, DP, Croft, JB. Sleep disordered breathing and depression among US adults: National Health and Nutrition Examination Survey, 2005-2008. Sleep. 2012;35:461–7.CrossRefGoogle ScholarPubMed
Bédard, M-A, Montplaisir, J, Richer, F, Rouleau, I, Malo, J. Obstructive sleep apnea syndrome: pathogenesis of neuropsychological deficits. J Clin Exp Neuropsyc. 1991;13:950–64.CrossRefGoogle ScholarPubMed
Findley, LJ, Barth, JT, Powers, DC, Wilhoit, SC, Boyd, DG, Suratt, PM. Cognitive impairment in patients with obstructive sleep apnea and associated hypoxemia. Chest. 1986;90:686–90.CrossRefGoogle ScholarPubMed
Salorio, CF, White, DA, Piccirillo, J, Duntley, SP, Uhles, ML. Learning, memory, and executive control in individuals with obstructive sleep apnea syndrome. J Clin Exp Neuropsyc. 2002;24:93–100.CrossRefGoogle ScholarPubMed
Beebe, DW, Gozal, D. Obstructive sleep apnea and the prefrontal cortex: towards a comprehensive model linking nocturnal upper airway obstruction to daytime cognitive and behavioral deficits. J Sleep Res. 2002;11:1–16.CrossRefGoogle ScholarPubMed
Leng, Y, McEvoy, CT, Allen, IE, Yaffe, K. Association of sleep-disordered breathing with cognitive function and risk of cognitive impairment: a systematic review and meta-analysis. JAMA Neurol. 2017;74:1237–45.CrossRefGoogle ScholarPubMed
Garbarino, S, Bardwell, WA, Guglielmi, O, Chiorri, C, Bonanni, E, Magnavita, N. Association of anxiety and depression in obstructive sleep apnea patients: a systematic review and meta-analysis. Behav Sleep Med. 2020;18:35–57.CrossRefGoogle ScholarPubMed
Verstraeten, E. Neurocognitive effects of obstructive sleep apnea syndrome. Curr Neurol Neurosci. 2007;7:161–6.CrossRefGoogle ScholarPubMed
Daurat, A, Foret, J, Bret-Dibat, J-L, Fureix, C, Tiberge, M. Spatial and temporal memories are affected by sleep fragmentation in obstructive sleep apnea syndrome. J Clin Exp Neuropsyc. 2008;30:91–101.CrossRefGoogle ScholarPubMed
Madsen, PL. Blood flow and oxygen uptake in the human brain during various states of sleep and wakefulness. Acta Neurol Scand. 1993;148:3–27.Google ScholarPubMed
Maquet, P. Sleep function (s) and cerebral metabolism. Behav Brain Res. 1995;69:75–83.CrossRefGoogle ScholarPubMed
Bartlett, DJ, Rae, C, Thompson, CH, et al. Hippocampal area metabolites relate to severity and cognitive function in obstructive sleep apnea. Sleep Med. 2004;5:593–6.CrossRefGoogle ScholarPubMed
Hopkins, RO, Kesner, RP, Goldstein, M. Memory for novel and familiar spatial and linguistic temporal distance information in hypoxic subjects. J Int Neuropsych Soc. 1995;1:454–68.CrossRefGoogle ScholarPubMed
Mubashir, T, Abrahamyan, L, Niazi, A, et al. The prevalence of obstructive sleep apnea in mild cognitive impairment: a systematic review. Bmc Neurol. 2019;19:1–10.CrossRefGoogle ScholarPubMed
Emamian, F, Khazaie, H, Tahmasian, M, et al. The association between obstructive sleep apnea and Alzheimer’s disease: a meta-analysis perspective. Front Aging Neurosci. 2016;8:78.CrossRefGoogle ScholarPubMed
Lutsey, PL, Misialek, JR, Mosley, TH, et al. Sleep characteristics and risk of dementia and Alzheimer’s disease: the atherosclerosis risk in communities study. Alzheimer’s Dementia. 2018;14:157–66.CrossRefGoogle ScholarPubMed
den Heijer, T, Geerlings, MI, Hoebeek, FE, Hofman, A, Koudstaal, PJ, Breteler, MM. Use of hippocampal and amygdalar volumes on magnetic resonance imaging to predict dementia in cognitively intact elderly people. Arch Gen Psychiat. 2006;63:57–62.CrossRefGoogle ScholarPubMed
Yue, L, Wang, T, Wang, J, et al. Asymmetry of hippocampus and amygdala defect in subjective cognitive decline among the community dwelling Chinese. Front Psychiatry. 2018;9:226.CrossRefGoogle ScholarPubMed
Torelli, F, Moscufo, N, Garreffa, G, et al. Cognitive profile and brain morphological changes in obstructive sleep apnea. Neuroimage. 2011;54:787–93.CrossRefGoogle ScholarPubMed
Tahmasian, M, Rosenzweig, I, Eickhoff, SB, et al. Structural and functional neural adaptations in obstructive sleep apnea: an activation likelihood estimation meta-analysis. Neurosci Biobehav Rev. 2016;65:142–56.CrossRefGoogle ScholarPubMed
Mesulam, M-M. Behavioral neuroanatomy. Princ behav Cognit Neurol. 2000;2:1–120.Google Scholar
Luppi, P-H, Billwiller, F, Fort, P. Selective activation of a few limbic structures during paradoxical (REM) sleep by the claustrum and the supramammillary nucleus: evidence and function. Curr Opin Neurobiol. 2017;44:59–64.CrossRefGoogle ScholarPubMed
Greve, DN, Billot, B, Cordero, D, et al. A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. Neuroimage. 2021;244:118610.CrossRefGoogle ScholarPubMed
Bullmore, E, Sporns, O. Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci. 2009;10:186–98.Google ScholarPubMed
Pasemann, F. Complex dynamics and the structure of small neural networks. Netw Comput Neural Syst. 2002;13:195–216.CrossRefGoogle ScholarPubMed
Kapur, VK, Auckley, DH, Chowdhuri, S, et al. Clinical practice guideline for diagnostic testing for adult obstructive sleep apnea: an American Academy of Sleep Medicine Clinical Practice Guideline. J Clin Sleep Med. 2017;13:479–504.CrossRefGoogle ScholarPubMed
Yamauchi, M, Fujita, Y, Kumamoto, M, et al. Nonrapid eye movement-predominant obstructive sleep apnea: detection and mechanism. J Clin Sleep Med. 2015;11:987–93.CrossRefGoogle ScholarPubMed
Jang, H, Lee, JY, Lee, KI, Park, KM. Are there differences in brain morphology according to handedness? Brain Behav. 2017;7:e00730.CrossRefGoogle ScholarPubMed
Dale, AM, Fischl, B, Sereno, MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179–94.CrossRefGoogle ScholarPubMed
Mijalkov, M, Kakaei, E, Pereira, JB, Westman, E, Volpe, G. Alzheimer’s Disease Neuroimaging I. BRAPH: a graph theory software for the analysis of brain connectivity. PLoS One. 2017;12:e0178798.CrossRefGoogle ScholarPubMed
Farahani, FV, Karwowski, W, Lighthall, NR. Application of graph theory for identifying connectivity patterns in human brain networks: a systematic review. Front Neurosci. 2019;13:585.CrossRefGoogle ScholarPubMed
Thomas, J, Seo, D, Sael, L. Review on graph clustering and subgraph similarity based analysis of neurological disorders. Int J Mol Sci. 2016;17:862.CrossRefGoogle ScholarPubMed
Park, KM, Lee, BI, Shin, KJ, et al. Pivotal role of subcortical structures as a network hub in focal epilepsy: evidence from graph theoretical analysis based on diffusion-tensor imaging. J Clin Neurol. 2019;15:68–76.CrossRefGoogle ScholarPubMed
Polli, FE, Wright, CI, Milad, MR, et al. Hemispheric differences in amygdala contributions to response monitoring. Neuroreport. 2009;20:398–402.CrossRefGoogle ScholarPubMed
Namburi, P, Beyeler, A, Yorozu, S, et al. A circuit mechanism for differentiating positive and negative associations. Nature. 2015;520:675–8.CrossRefGoogle ScholarPubMed
Tahmasian, M, Knight, DC, Manoliu, A, et al. Aberrant intrinsic connectivity of hippocampus and amygdala overlap in the fronto-insular and dorsomedial-prefrontal cortex in major depressive disorder. Front Hum Neurosci. 2013;7:639.CrossRefGoogle ScholarPubMed
Tahmasian, M, Shao, J, Meng, C, et al. Based on the network degeneration hypothesis: separating individual patients with different neurodegenerative syndromes in a preliminary hybrid PET/MR study. J Nucl Med. 2016;57:410–5.CrossRefGoogle Scholar
Gozal, E, Row, BW, Schurr, A, Gozal, D. Developmental differences in cortical and hippocampal vulnerability to intermittent hypoxia in the rat. Neurosci Lett. 2001;305:197–201.CrossRefGoogle ScholarPubMed
Tahmasian, M, Pasquini, L, Scherr, M, et al. The lower hippocampus global connectivity, the higher its local metabolism in Alzheimer disease. Neurology. 2015;84:1956–63.CrossRefGoogle ScholarPubMed
Iglói, K, Doeller, CF, Berthoz, A, Rondi-Reig, L, Burgess, N. Lateralized human hippocampal activity predicts navigation based on sequence or place memory. In: Proceedings of the National Academy of Sciences, 2010, 107, 14466–71.CrossRefGoogle Scholar
Oishi, Y, Lazarus, M. The control of sleep and wakefulness by mesolimbic dopamine systems. Neurosci Res. 2017;118:66–73.CrossRefGoogle ScholarPubMed
Qiu, M-H, Liu, W, Qu, W-M, Urade, Y, Lu, J, Huang, Z-L. The role of nucleus accumbens core/shell in sleep-wake regulation and their involvement in modafinil-induced arousal. PLoS One. 2012;7:e45471.CrossRefGoogle ScholarPubMed
Seminowicz, DA, Remeniuk, B, Krimmel, SR, et al. Pain-related nucleus accumbens function: modulation by reward and sleep disruption. Pain. 2019;160:1196–1207.CrossRefGoogle ScholarPubMed
Lee, M-H, Yun, C-H, Min, A, et al. Altered structural brain network resulting from white matter injury in obstructive sleep apnea. Sleep. 2019;42:zsz120.CrossRefGoogle ScholarPubMed
Y-g, Luo, Wang, D, Liu, K, et al. Brain structure network analysis in patients with obstructive sleep apnea. PLoS One. 2015;10:e0139055.Google Scholar
Figure 0

Figure 1: The example of segmentation of subcortical limbic structures. The segmentations are overlaid onto a T1-weighted image in coronal, axial, and sagittal orientation and shown in volume rendering. HypoThal-noMB: hypothalamus, AntCom: anterior commissure, SeptalNuc: septal nucleus.

Figure 1

Table 1: The clinical and polysomnographic characteristics in the patients with obstructive sleep apnea

Figure 2

Table 2: The differences in limbic structure volumes between patients with OSA and healthy controls

Figure 3

Table 3: The differences in the intrinsic limbic network between patients with OSA and healthy controls

Figure 4

Table 4: The results of correlation analysis between clinical and polysomnographic characteristics and limbic structures volumes in the patients with obstructive sleep apnea

Supplementary material: File

Park and Kim supplementary material

Park and Kim supplementary material

Download Park and Kim supplementary material(File)
File 16.1 KB