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Keywords:

  • cognitive reserve;
  • magnetic resonance imaging;
  • neuropsychology;
  • positron emission tomography;
  • resting state

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

Patients with obstructive sleep apnea (OSA) show neuropsychological impairments ranging from vigilance decrements, attentional lapses and memory gaps to decreased motor coordination, but their cognitive profile, and the origin of the impairments, remain unclear. We sought to establish the neuropsychological profile of 16 newly diagnosed apneics and to highlight both their morphological and functional brain abnormalities. We used an extensive neuropsychological test battery to investigate attention and vigilance, executive functions, episodic memory and motor domains. For brain imaging, we used the optimized voxel-based morphometry procedure for the MRI data, resting-state 18F-fluoro-2-deoxy-D-Glucose positron emission tomography (18FDG-PET) with correction for partial volume effects (PVEs) and voxel-based analyses. In terms of neurobehavioral performance, our patients displayed objective daytime somnolence but little impairment in memory and motor domains. Cerebral data revealed gray matter loss in the frontal and temporo–parieto–occipital cortices, the thalamus, hippocampal region, some basal ganglia and cerebellar regions, mainly in the right hemisphere. The decrease in brain metabolism was also right-lateralized, but more restricted than the gray matter density changes, and involved the precuneus, the middle and posterior cingulate gyrus, and the parieto–occipital cortex, as well as the prefrontal cortex. To conclude, despite the presence of only minor memory and motor impairments, our patients displayed significant cerebral changes in terms of both gray matter density and metabolic levels, and may have benefited from cognitive reserve and compensatory mechanisms. Thus, cerebral changes in OSA patients may precede the onset of notable neuropsychological consequences.


Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

Obstructive sleep apnea (OSA) is characterized by chronically repeated apneic and hypopneic events during sleep. It results in intermittent blood gas abnormalities, repetitive arousals and the disruption of normal sleep architecture. Furthermore, the respiratory disturbances are associated with dramatic changes in cerebral blood flow amount and velocity (Gillin et al., 2002). OSA patients show neuropsychological impairments ranging from vigilance decrements, attentional lapses and memory gaps to decreases in motor coordination (Aloia et al., 2004; Beebe et al., 2003). However, their cognitive profile remains difficult to grasp, given the use of a wide diversity of tests (Decary et al., 2000; Fulda and Schulz, 2001) and the lack of a clear-cut characterization of disease severity, which is often based on the respiratory disturbance index (Redline et al., 2000; Sauter et al., 2000; Veasey, 2006).

The possibility of structural impairment of the prefrontal cortex has been raised, given persistent neuropsychological deficits in apneics after treatment, especially in working memory and executive functions (Ferini-Strambi et al., 2003; Naëgeléet al., 1998). The first etiological model of OSA suggested a dysexecutive profile in apneics resulting from prefrontal cortex vulnerability to sleep fragmentation and blood gas abnormalities (Beebe and Gozal, 2002). Other authors, however, have assumed that apneics suffer instead from attentional problems due to lack of sleep, and that there is, therefore, no need to hypothesize structural brain damage (Verstraeten and Cluydts, 2004).

Apneics’ brain morphology has been investigated using magnetic resonance imaging (MRI), and hippocampal (Gale and Hopkins, 2004; Morrell et al., 2003) and parahippocampal atrophies (Morrell et al., 2004) have been observed. Using the voxel-based morphometry (VBM) procedure, which makes it possible to assess the whole brain, Macey et al. (2002) reported a significant reduction in the gray matter (GM) density in scattered areas in moderate-to-severe apneics. Conversely, O’Donoghue et al. (2005) initially failed to find any significant GM loss in severe apneics. However, when they adopted a more lenient statistical threshold, they observed a GM density loss in the posterior and mesial temporal lobe bilaterally and in the left insular region.

Functional neuroimaging methods can reveal subtle neural dysfunction more effectively than morphological MRI, but few studies have used such techniques so far to study apneics. An FDG-PET study of five patients showed hypometabolism in the right amygdala, left hippocampus and left medial parietal cortex (Dani et al., 1996). Another study of eight patients by the same authors revealed hypometabolism in the anterior cingulate gyrus and the right premotor area (Pietrini et al., 1998). To our knowledge, no PET study has been carried out on OSA patients, which performed an objective and comprehensive voxel-based analysis using spm software (Statistical Parametric Mapping, London, UK).

In this study, we sought to describe the profile of cognitive impairment in a group of mild-to-severe apneics using a comprehensive neuropsychological test battery, and to assess abnormalities in both GM density and resting-state brain glucose utilization in the same group of patients throughout the entire brain.

Methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

Subjects

Sixteen newly diagnosed patients (15 men and one woman; mean age = 54.75, SD = 5.71) with OSA were recruited (Table 1). The inclusion criteria were a clinical profile and subjective complaints of OSA, in accordance with the International Classification of Sleep Disorders (1997) and an apnea–hypopnea index (AHI) of ≥10 h. All patients were right-handed, had a mean AHI of 38.31 (SD = 14.33), at least 7 years of schooling and a normal score on the Mini Mental State Examination. Given the lack of information about the interval between the disease’s onset and its diagnosis, we asked our patients to estimate the date at which a significant decline had occurred in their quality of life and also when they experienced OSA symptoms for the first time. They estimated that the interval did not exceed a couple of years.

Table 1.   Obstructive sleep apnea patients and controls characteristics
CharacteristicsPatients (n = 16)Controls (n = 14)P-value
  1. REM, rapid eye movement; SAQLI, the Calgary sleep apnea quality of life index.

Age54.75 ± 5.7152.71 ± 7.01NS
Education (years)13.38 ± 4.5611.93 ± 3.50NS
Mini mental state examination28.88 ± 1.7529.07 ± 0.73NS
Apnea/apopnea index38.31 ± 14.335.71 ± 3.27<0.001
Desaturation index35.00 ± 13.535.29 ± 4.12<0.001
% desaturation time <90%12.63 ± 17.640.07 ± 0.27<0.001
Microarousal index29.69 ± 13.3916.86 ± 9.57  0.005
Sleep latency12.77 ± 9.3117.47 ± 9.67NS
Sleep efficiency85.63 ± 7.2781.14 ± 8.01  0.049
Total sleep time407.31 ± 40.97384.80 ± 54.00NS
% stages 1 + 2 (sleep period time)54.40 ± 8.9145.09 ± 9.36  0.0034
% stages 3 + 4 (sleep period time)19.49 ± 7.0525.69 ± 6.72  0.005
% REM stage14.52 ± 4.7714.52 ± 3.06NS
Epworth sleepiness scale12.50 ± 4.504.36 ± 3.61<0.001
Maintenance of wakefulness test20.40 ± 13.9837.00 ± 3.97<0.001
Beck depression inventory2.81 ± 2.372.43 ± 2.38NS
Calgary SAQLI, daily functioning4.80 ± 1.216.36 ± 0.84<0.001
Calgary SAQLI, social interactions4.80 ± 0.946.36 ± 0.74<0.001
Calgary SAQLI, emotional functioning5.73 ± 0.806.29 ± 0.61  0.037
Calgary SAQLI, pretreatment symptoms10.60 ± 4.882.43 ± 2.56<0.001

Fourteen healthy control subjects (13 men and one woman; mean age = 52.71, S.D = 7.01) matched with the OSA group for age, education and handedness underwent polysomnographic (PSG) recordings and a neuropsychological assessment. Each subject was submitted to full overnight PSG screening (mean AHI = 5.71, SD = 3.27). In addition to the exclusion criteria described above, no control had any history of snoring or sleep complaints, and none showed objective daytime somnolence. Brain imaging data were compared with those of another group of 19 strictly selected, healthy age-matched controls (mean age = 55.26, SD = 6.67) from a brain database at the Cyceron Neuroimaging Center in Caen, France. This set of controls had been screened for cerebrovascular risk factors, mental disorder, substance abuse, head trauma, significant MRI or biological abnormality and incipient dementia using a memory test battery (Baron et al., 2001). The study was approved by the Research and Ethics Committee. All participants gave informed written consent.

Protocol description

Participants underwent two full-night PSG recordings, the first one for familiarization. After the second night, a neuropsychological examination and a maintenance of wakefulness test (MWT) were carried out. Within a few days’ interval at most, each patient also underwent an MRI scan and a FDG-PET study. Before undergoing brain imaging scans, we excluded any volunteers experiencing claustrophobia or anxiety in a confined environment. During data acquisition, they were reassured, knowing that they could remain in contact with us throughout the process. Finally, as stated on the consent form they signed, they knew that they could interrupt the experiment at any time. During the PET data acquisition, the wakefulness of our patients was monitored with a portable electroencephalogram (EEG) device.

Sleep analysis

We conducted a standard PSG (data acquisition system: CID-102; Cidelec, Angers, France) including an EEG, left and right electrooculogram, chin electromyogram and electrocardiogram. EEG leads were used as follows: Fp1 and Fp2 (frontal), C3 and C4 (central), T3 and T4 (temporal), and O1 and O2 (occipital) according to the 10–20 International System, with ear reference. The sampling frequency was 64 Hz. Thoracoabdominal respiratory movements were recorded by strain gauges, nasal airflow by nasal cannula, snoring by tracheal microphone and oxygen saturation by finger pulse oximeter. The occurrence of respiratory efforts was validated by means of a suprasternal pressure transducer (Meslier et al., 2002). In addition, electromyographic recordings were made of the tibialis anterior muscles to detect periodic leg movements. Episodes of apnea were defined as complete cessations of airflow for 10 s or more, and episodes of hypopnea as decreases in nasal airflow of more than 50%, and lasting for at least 10 s, accompanied by desaturation ≥3% or microarousals. The mean AHI during total sleep time (TST) was used as a respiratory disturbance index. A decrease in oxygen saturation of 3% or more from the baseline level was regarded as clinically significant. A desaturation index (DI) during TST was calculated. We also took into account the percentage of sleep time spent in desaturation ≤90% (TSD). Recordings were scored off-line by an experienced neurologist according to Rechtshaffen and Kales’ (1968) criteria, with 30-s epochs. Microarousals were scored according to the criteria of the American Sleep Disorders Association (1992). Respiratory events and periodic leg movements were automatically detected and visually corrected when necessary. The microarousal index (MI) during TST was used as a sleep fragmentation index. The following variables were determined: sleep-onset latency, sleep efficiency, TST and the percentage of time spent in sleep stages (1 + 2) and (3 + 4), and rapid eye movement sleep.

Neuropsychological examination

Our patients underwent an extensive battery of neuropsychological tests. Attention and vigilance were assessed by means of four subtasks taken from the test battery for attentional performance (TAP; Zimmermann and Fimm, 1993), namely ‘bimodal vigilance’, ‘alertness’, ‘go/no-go’ and ‘visual scanning’, and two subtasks of the Wechsler memory scale (WMS3; Wechsler, 2001), namely forward digit and spatial spans. Working memory was assessed by means of the ‘working memory’ subtask (TAP) and three subtasks of the WMS3, namely backward digit, spatial spans and ‘letter-number sequencing’. Executive functions were measured with a verbal fluency test (Cardebat et al., 1990) and two TAP subtasks: ‘divided attention’ and ‘flexibility’. Episodic memory was gauged using four WMS3 subtasks: two verbal ones, namely ‘logical memory’ and ‘verbal paired associates’, and two visual ones, namely ‘faces’ and ‘family pictures’, while the Purdue pegboard test was used to test motor coordination in 11 patients.

Brain imaging

Data acquisition

A high-resolution T1-weighted volume MRI scan was conducted for each patient. This consisted of a set of 128 adjacent axial slices parallel to the anterior–posterior commissure (AC–PC line), with a slice thickness of 1.5 mm and a pixel size of 0.94 × 0.94 mm, using the spoiled gradient echo sequence [time repetition = 10.3 ms; echo time = 2.1 ms; field of view (FOV) = 24 × 18; matrix = 256 × 192]. All the MRI data sets were acquired on the same scanner (1.5 T Signa Advantage EchoSpeed; General Electric, Milwaukee, WI, USA) using the same parameters. Standard correction for field inhomogeneities was applied during acquisition.

Each subject also underwent a PET study. Data were collected using the high-resolution ECAT Exact HR+ device (Siemens AG, Munich, Germany) with an isotropic resolution of 4.6 × 4.2 × 4.2 mm (FOV = 158 mm). The patients were fasted for at least 4 h prior to scanning. To minimize anxiety, the PET procedure was explained in detail beforehand. The head was positioned on a headrest according to the canthomeatal line and gently restrained with straps. 18FDG uptake was measured in the resting condition, with eyes closed, in a dark, quiet environment. A catheter was introduced into a vein in the arm to inject the radiotracer. Following Ga transmission scans, three to five mCi of 18FDG were injected as a bolus at time 0 and a 10-min PET data acquisition period started at 50 min postinjection. Sixty-three planes were acquired with septa out (volume acquisition), using a voxel size of 2.2 × 2.2 × 2.43 mm (x y z). During the PET data acquisition, head motion was continuously monitored with laser beams projected onto ink marks drawn on the forehead and corrected whenever necessary.

Image processing and conversion

Magnetic resonance imaging data were analyzed using spm2 and the optimized VBM protocol, described in detail elsewhere (Ashburner and Friston, 2001; Good et al., 2001), and already in use in our laboratory (Chételat et al., 2003, 2005; Desgranges et al., 2007). Briefly, the procedure included customized template creation [of the whole brain and of the GM, white matter (WM) and cerebrospinal fluid (CSF) data sets] based on the MRI data of all the patients and controls (= 35), segmentation and normalization of the original (i.e. in native space) scans using these customized priors to determine optimum normalization parameters, application of these optimum parameters to the original scans, segmentation of the normalized data and smoothing of the resulting GM partitions, using a 12-mm Gaussian filter.

Glucose metabolic values measured using PET may sometimes be biased, due to partial volume effects (PVEs), i.e. radiotracer concentration in small structures being influenced by surrounding structures. For this reason, the PET data were first corrected for PVE due to both CSF and WM, using the optimum voxel-by-voxel method described in detail by Quarantelli et al. (2004) and already in use in our laboratory (Chételat et al., 2003; Desgranges et al., 2007). All image processing steps were carried out using ‘pve-lab’ software. Using spm2, corrected PET data were then subjected to coregistration onto their respective MRIs and normalized to the same customized template as that used for the normalization of MRI data, reapplying the optimum normalization parameters estimated by means of the VBM protocol. The corrected and spatially normalized PET data were then smoothed using a classic Gaussian kernel of 14 mm.

Statistical analysis

Mean differences in PSG and neuropsychological variables between patients and controls were calculated by means of the one-tailed Mann–Whitney U-test. Spearman’s rank correlation coefficients were then calculated in the patient group between the PSG variables: AHI, MI, DI, TSD, mean score on the MWT and the deficient neuropsychological scores. We used a statistical significance threshold of < 0.05, uncorrected for multiple comparisons.

Regarding the MRI and PET data, we assessed group differences to obtain maps of significant GM density decrease and significant hypometabolism in patients with OSA compared with controls. Two independent analyses were performed on the MRI and PET data, both using the ‘compare-populations: 1 scan/subject’ (two sample t-test) spm2 routine. To minimize ‘edge effects’, only those voxels with values above 60% of the mean for the whole brain were selected for statistical analysis. We used the ‘proportional scaling’ routine to control for individual variations in PET data. For these analyses, we used a statistical significance threshold of < 0.005 (uncorrected for multiple comparisons) for the voxels and P-corrected <0.05 for the clusters.

Lastly, using the ‘single-subject: covariates only’spm2 routine, with PSG variables (see above) and the MWT and deficient neuropsychological scores as covariates, correlations were computed across the 16 OSA patients with PET data. For each correlation, the influence of age was controlled by setting age as a confounding variable. For these correlative analyses, we used a threshold of < 0.005 uncorrected, with a minimum cluster size of 50 voxels, to limit the risk of false positives. A more liberal threshold in correlative analyses may not fully protect against results due to chance, but has previously been used with the same approach not only by our group (Chételat et al., 2003; Desgranges et al., 1998) but also by others (Teipel et al., 2006; Zahn et al., 2006).

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

Patients differed significantly from controls in terms of PSG variables, sleep architecture, daytime sleepiness and quality of life (Table 1), as well as several neuropsychological (episodic memory and motor domains) and cerebral findings (Tables 2 and 3).

Table 2.   Cognitive performances in obstructive sleep apnea patients and controls
Neuropsychological tasksPatients (n = 16)Controls (n = 14)P-value
  1. This table only features tests that revealed significant differences.

WMS3, paired associates, learning curve4.25 ± 1.445.50 ± 1.160.01
WMS3, family pictures, % retained94.56 ± 10.3099.53 ± 3.220.03
 Patients (= 11)Controls (= 14) 
Purdue pegboard, dominant hand11.64 ± 2.1114.29 ± 3.630.028
Purdue pegboard, assembly29.82 ± 8.2935.29 ± 5.430.025
Table 3.   Significant gray matter loss in the apneic group compared with controls
MNI coordinatest-valuekP cluster-levelLabel% Cluster
xyz
  1. Location and MNI coordinates of peaks of significant gray matter density loss (< 0.005 uncorrected for multiple comparisons for the voxels and P-corrected <0.05 for the clusters) in apneic patients compared with controls. Cluster size is indicated by k = number of voxels in the particular cluster. Labels and percentages were obtained using the aal toolbox (Tzourio-Mazoyer et al., 2002). Cerebral changes were considered to be significant when they represented at least 2% of the cluster. Results are listed in decreasing order of T score value.

  2. R, right; L, left; sup., superior; mid., middle; inf., inferior; MNI, Montreal Neurological Institute.

1266−156.4746 8140.000R. sup. temporal G12.5
R. supramarginal G11.9
R. mid. temporal G8.5
R. sup orbital frontal G5.5
R. inf. frontal G5.3
R. mid. frontal G5
R. inf. orbital frontal G4.1
R. rectus G3.9
R. inf. parietal G3.1
R. angular G2.2
−35−80435.3134350.01L. mid. occipital G52.6
L. inf. parietal_ G11.8
L. angular G2.8
24−48−474.9665680.001R. cerebellum_937.5
R. cerebellum_834.3
R. cerebellum_102.5
Vermis16
−1710−34.6053690.003L. putamen34.3
L. pallidum9.6
L. caudate nucleus9.3
L. olfactory7.6
39−51−154.4761180.001R. fusiform G50.4
R. lingual G14.5
R. inf. temporal G8.2
R. hippocampus8.1
R. parahippocampal G4.7
−3639254.3033590.01L. mid. frontal G66.6
L. inf. frontal G19.8
L. sup. frontal G13.5
11−354.0740940.007R. thalamus66.2

Neuropsychological outcomes

Apneics performed more poorly than controls on two of the WMS3 episodic memory subtests (‘verbal paired associates’ learning curve, percentage of retained items in ‘family pictures’) and on both components of the Purdue pegboard test (‘dominant hand’ and ‘assembly task’) (Table 2). The remaining neuropsychological test scores were within the normal range.

There were no significant correlations between these deficient neuropsychological scores, the PSG variables or the MWT score.

GM density changes

Significant GM loss (Fig. 1 and Table 3) was found in the bilateral prefrontal cortex (inferior, middle and superior gyri), bilateral inferior parietal gyrus, right temporal cortex (inferior, middle and superior temporal gyri), occipital cortex (left middle occipital gyrus, right fusiform and lingual gyri), right thalamus, some of the basal ganglia (left putamen, caudate nucleus and pallidum), right hippocampus and parahippocampal gyrus and cerebellum (right cerebellar hemisphere and vermis). Most of these abnormalities were right-lateralized.

image

Figure 1.  Significant gray matter loss (< 0.005 uncorrected for multiple comparisons for voxels and P-corrected <0.05 for clusters) in the apneic group compared with controls, as shown by superimposition onto axial slices of the customized template (right hemisphere corresponds to the right side of figure).

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Cerebral metabolic changes

Apneics only showed significant hypometabolism in the right hemisphere (Fig. 2 and Table 4), in the precuneus, middle and posterior cingulate gyrus, parietal cortex (supramarginal gyrus, inferior parietal gyrus, angular gyrus), occipital cortex (cuneus and superior occipital gyrus) and superior temporal gyrus. There was also a trend (P-corrected <0.08 for the clusters) towards hypometabolism in the prefrontal cortex (inferior and middle frontal gyri). Note that the peak located in this region was the most significant at the voxel level (Table 4).

image

Figure 2.  Significant hypometabolism (< 0.005 uncorrected for multiple comparisons for voxels and P-corrected <0.05 for clusters) in the apneic group compared with controls, as shown by superimposition onto axial slices of the customized template (right hemisphere corresponds to the right side of figure).

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Table 4.   Significant hypometabolism in the apneic group compared with controls
MNI coordinatest-valuekP cluster-levelLabel% Cluster
xyz
  1. Location and MNI coordinates of peaks of significant hypometabolism (< 0.005 uncorrected for multiple comparisons for voxels and P-corrected <0.05 for clusters) in apneic patients compared with controls.

  2. R, right; sup., superior; mid., middle; inf., inferior.

5832204.542780.08R. inf. frontal G71.22
R. mid. frontal G19.78
12−60304.174170.03R. precuneus72.66
R. cuneus10.55
R. sup. occipital G6.71
R. mid. cingulum4.08
R. posterior cingulum2.64
52−62443.687530.008R. angular G35.59
R. inf. parietal G22.58
R supramarginal G14.08
R. sup. temporal G4.78

Links between PSG/neuropsychological and brain metabolism

All the significant correlations went in the expected direction. Regarding PSG data, we found only a negative correlation between MI and the metabolism of the bilateral superior motor area and the left superior parietal cortex (Fig. 3 and Table 5).

image

Figure 3.  Significant positive correlations (< 0.005 uncorrected; k > 50 voxels) in the apneic group between brain metabolism and (a) microarousal index, (b) maintenance of wakefulness test, (c) paired associates, (d) Purdue pegboard test (dominant hand) and (e) Purdue pegboard test (assembly task), as shown by superimposition onto axial slices of the customized template (on the left) and by the ‘plots’ of the correlations with the main peak (on the right).

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Table 5.   Significant correlations in the apneic group between brain metabolism and (A) Microarousal index, (B) maintenance of wakefulness test, (C) paired associates, (D) Purdue pegboard test (dominant hand) and (E) Purdue pegboard test (assembly task)
MNI coordinatest-valuekP voxel levelrLabel% Cluster
xy z
  1. Location and MNI coordinates of significant (< 0.005 uncorrected; k > 50 voxels) correlation peaks (with the corresponding r values) in the patient group. See legend of Fig. 1.

  2. R, right; L, left; sup., superior; inf., inferior.

(A)
−4−8724.411590.000−0.77L. sup. motor area56.6
R. sup. motor area32.7
−34−46664.042040.001−0.74L sup. parietal66.7
L postcentral gyrus17.6
(B)
32−90−385.10630.0000.68R. cerebellum crus 246
R. cerebellum crus 13
−14−42−244.02640.0010.61L. cerebellum 4–570.3
L. cerebellum 314
−10−40−23.86550.0010.60L. lingual gyrus60
L. cerebellum 4–510.9
(C)
14−84−284.101630.0010.75R. cerebellum crus 183
R. cerebellum crus 217
(D)
−6−88 −364.67530.0010.78L. cerebellum crus 243.4
(E)
−6−66−126.721310.0000.89Vermis28.24
L. cerebellum 4–526
L. cerebellum 621.4
R. cerebellum 4–521.4

Objective daytime sleepiness, i.e. the MWT score, correlated positively with cerebellar metabolism, bilaterally (Fig. 3 and Table 5). The paired associates and Purdue pegboard (both measures) scores were also positively correlated with this region (Fig. 3 and Table 5).

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

We studied a group of patients suffering from mild-to-severe OSA, using a comprehensive neuropsychological test battery and, for the first time, both GM density and resting-state brain glucose utilization examinations in the same group of patients, including (i) an optimized VBM procedure using a customized template from both patients and controls; (ii) the correction of PET data for PVE and (iii) the same threshold for assessing both atrophy and hypometabolism statistics.

Neuropsychological performances

Compared with controls, our patients showed relatively preserved attentional performance. We did not find any group effect in our patients, although some displayed scattered attentional impairments (reaction time and/or errors). Mazza et al. (2005) reported vigilance and/or attentional impairment in the majority of their patients. These authors used an attentional test battery comprising three long-lasting tasks: (i) Oxford sleep resistance, which is an objective daytime somnolence test resembling the MWT condition; (ii) a driving simulator task and (iii) the continuous performance test. Our longest attention test was ‘bimodal vigilance’, which lasted 15 min, whereas all the attentional tasks in the study conducted by Mazza et al. (2005) lasted more than 20 min. In addition, our patient group fell asleep within approximately 20 min on average (MWT score), suggesting that potential attentional impairments might have been revealed by long-lasting tasks (Decary et al., 2000).

Alchanatis et al. (2005) showed that high-intelligence patients had the same attention/alertness performances as high-intelligence controls, and assumed that high intelligence offers some protection against OSA-related cognitive decline, possibly in the form of increased cognitive reserve. In our study, most of the patients were highly educated (graduate and postgraduate levels) and had professions involving permanent intellectual and/or attentional constraints (e.g. entrepreneurs, directors, a corporate lawyer, two medical professionals and three subjects occupying a position of responsibility). There is reason to believe that their professional experience may have allowed them to cope somewhat better with routine attentional requirements. However, they did acknowledge having to struggle to maintain their attention in monotonous and long-lasting situations.

In the executive function assessment, our patients performed similarly to controls, in agreement with some authors (Redline et al., 1997; Verstraeten et al., 2004) but in contrast to others (Bedard et al., 1991; Montplaisir et al., 1992). A critical review (Verstraeten and Cluydts, 2004) regarding the executive control of attention in apneics suggested that several findings in the literature, interpreted as evidence of dysexecutive performance, should be viewed with caution, given the lack of careful methodology (e.g. basic attentional performances were only infrequently monitored in executive tasks). Our findings, i.e. only vigilance decrements, as evidenced by MWT scores, suggest a probable decrease in the basic tonic attention level in apneics (Lis et al., 2008; Verstraeten and Cluydts, 2004).

In terms of memory, our patients displayed minor impairment in word list learning, confirming the findings of Salorio et al. (2002). They also displayed slightly decreased recall in a visual episodic memory task. In their group of patients, Naëgeléet al. (2006) observed a free recall deficit in episodic memory but normal maintenance, recognition and forgetfulness, suggesting that memory impairment in OSA is mild (Fulda and Schulz, 2001) and does not affect all memory processes (Naëgeléet al., 1995, 2006; Salorio et al., 2002). Thus, in our study, a memory task, namely ‘paired associates’, evidenced some learning difficulties and, another one, namely ‘family pictures’, showed a mild free recall impairment, in accordance with the literature, these two tasks being usually used to assess the same cognitive function, i.e. episodic memory.

Lastly, in the motor domain, the Purdue pegboard test evidenced both simple motor speed and fine coordination deficits, in accordance with several previous studies (Bedard et al., 1991; Beebe et al., 2003; Greenberg et al., 1987).

In sum, our apneic sample displayed only minor impairment when they underwent a comprehensive neuropsychological test battery, in agreement with other studies (Kim et al., 1997; Redline et al., 1997). Our findings do not corroborate the presumed dysexecutive profile (Beebe and Gozal, 2002).

It should be pointed out that OSA without notable cognitive impairments has been fortuitously discovered in about half a non-clinical population (Quan et al., 2006). The daytime neurobehavioral manifestations of OSA may simply be an epiphenomenon of respiratory disturbances during sleep, and many resilience factors may temporarily prevent apneics from sustaining substantial neurobehavioral deficits (Beebe, 2005). These factors include sleep deprivation tolerance (Mu et al., 2005) and cognitive reserve that allows patients to cope relatively well with daytime challenges (Stern, 2002).

The relationship between neuropsychological deficits and OSA features is rarely strong and consistent (Adams et al., 2001; Kingshott et al., 1998; Sauter et al., 2000), and there were no significant correlations in our sample between the deficient neuropsychological scores, the PSG and the MWT variables. Such links would, however, exist in more severely affected patients, as suggested by a number of studies highlighting the impact of nocturnal hypoxemia on vigilance, executive and psychomotor tasks and the impact of vigilance impairment on attention and memory functions (Bedard et al., 1991; Montplaisir et al., 1992). Unlike the aforementioned studies that comprised patients with profound hypoxemia, our sample did not seem to be severely hypoxemic (very few patients were under 80% in terms of the percentage of time spent under this degree of desaturation). Furthermore, our patients were mostly in their mid-50s, and possibly at disease onset. In most cases, they estimated that changes in their quality of life had occurred within the last couple of years. Hence, untreated OSA is likely to have a more substantial effect on cognitive functioning with advancing years. In fact, a recent study showed that aging apneics demonstrate cognitive decline, while younger patients with the same disease severity are somehow able to compensate for this effect (Alchanatis et al., 2008). Moreover, elderly patients may have a risk of dementia comorbidity (Bliwise, 2002; O’Hara et al., 2005).

Cerebral changes

Taken together, our findings revealed cerebral changes (mainly right-lateralized) regarding both GM density and metabolism. PET measurements of resting-state brain glucose utilization reflect local baseline integrated synaptic activity and therefore both neuronal lesions and synaptic dysfunction, whereas VBM only assesses GM loss. The latter was located in scattered sites, i.e. the frontal and temporo–parieto–occipital cortices, the thalamus, some of the basal ganglia and the cerebellar regions, whereas the decrease in brain metabolism was more localized than GM density changes, restricted to the cuneus and precuneus, temporo–parieto–occipital cortices and middle and posterior cingulate area, as well as the prefrontal cortex (as a statistical trend).

Our patients showed significant GM loss in several brain areas previously identified by Macey et al. (2002). Interestingly, some of the affected brain areas happen to be particularly vulnerable to anoxia, such as the basal ganglia, the cerebellum and the hippocampal region (Gozal et al., 2001; Konaka et al., 2007). Hypoxemia may cause brain structural changes, as shown in animal models (Gozal et al., 2001). That said, our patients did not seem to be severely hypoxemic and the controlled hypoxia of rodent models in a laboratory setting (e.g. Gozal and coworkers) cannot be likened to our patients’ condition, in terms of exposure to hypoxia. Thus, the decrease in GM density may also be induced by factors other than apneic events, i.e. brain lesions that are congenital in nature, acquired (Macey et al., 2002) or subsequent to cardiovascular comorbidities (Macey and Harper, 2005; O’Donoghue et al., 2005). As in the study conducted by Macey et al., it is noteworthy that our sample encompassed patients with cardiovascular comorbidities (controlled hypertension, dyslipidemia and prior but no recent history of myocardial infarction).

The GM density changes seen in our patients do partially corroborate the prefrontal hypothesis, given the changes found not only in the PFC region but also in the basal ganglia and thalamus that are linked to this region (Alexander and Crutcher, 1990; MacDonald et al., 2000).

These GM density changes may account for certain neurobehavioral impairments seen in our patients, as many of the atrophic sites (right frontoparietal circuit extending to basal ganglia) are involved in attentional abilities (Sturm and Willmes, 2001; Sturm et al., 1999). The cerebral damage (especially the prefrontal and thalamic ones) may also account for our patients’ sleepiness (Coull, 1998; Portas et al., 1998) and for the residual somnolence reported in treatment studies (Marshall et al., 2006).

In addition, some brain areas in which GM was significantly decreased in our patients (hippocampus and thalamus which belong to the Papez circuit, as well as frontoparietal regions) play an important role in episodic memory (Eustache and Desgranges, 2008), which is in agreement with the mild memory deficit seen in our patients. Lastly, the motor speed and fine motor coordination impairments displayed by our patients may be linked with cerebellar changes (Ramnani et al., 2001; Verleger et al., 1999).

Hypometabolism was more restricted than the GM density changes and was found in the precuneus, the parieto–occipital cortex and the cingulate gyrus (middle and posterior areas), as well as in the prefrontal cortex (as a statistical trend). Some of these structures were also atrophic, in particular the right inferior parietal cortex and prefrontal cortex, the latter finding reinforcing the frontal hypothesis. The functional impairment of the precuneus and cingulate gyrus, which were not atrophic, may have been caused partly by remote effects originating from morphologically impaired areas with decreased connectivity (Chételat et al., 2003), although we cannot exclude the possibility that minor structural changes in these areas may also have been partly responsible for this hypometabolism (at a lenient threshold of < 0.01 uncorrected, GM loss was observed in these structures).

Some of the brain areas found to be hypometabolic in our sample (i.e. the precuneus, cingulate areas and inferior parietal cortex) belong to the ‘default mode’ circuit, a ‘novel and only recently appreciated brain system that participates in internal modes of cognition’, i.e. a neural circuit of ‘normal’ mental activity in the conscious resting state (Buckner et al., 2008; Cavanna and Trimble, 2006; Raichle et al., 2001). Although the wakefulness of our patients was monitored during the PET data acquisition period using a portable EEG device, unlike healthy subjects, they probably had disturbed neural activity in brain regions that are usually active during the normal resting state. Similarly, using quantitative EEG analysis, Morisson et al. (1998) recorded an EEG slowing in awake apneics, in all the cortical regions they examined. This suggests that, during wakefulness, the apneics’ functional neural abnormalities may disturb the mental activity needed in the resting condition.

All the significant correlations between the PET data and MWT scores and the deficient neuropsychological scores were found in cerebellar regions. Our findings not only underline the well-known links between motor function and cerebellum, but also point to the involvement of this region in cognitive functions, notably in episodic memory (Andreasen et al., 1999; Leiner et al., 1991; Schmahmann and Pandya, 1997). In addition, we found significant correlations between the MI index and the metabolism of the bilateral superior motor area and the left superior parietal cortex, which may reflect a negative effect of sleep fragmentation, and could be linked to psychomotor vigilance decrements seen in healthy subjects after sleep deprivation (Drummond et al., 2005a).

Limitations

One of the limitations of this study is the relatively small sample of patients, which prevent us from generalizing our findings to all apneics, notably the neuropsychological results. Given the small size of this group, we chose not to correct for multiple comparisons as far as the neuropsychological data were concerned, in order to highlight cognitive abnormalities, even mild ones, and clarify their links with the brain imaging results. Regarding the structural imaging analysis, the whole-brain VBM method has sometimes been criticized because of the normalization process (inducing some deformations) and has been regarded as less sensitive than the region-of-interest method when it comes to detecting abnormalities in small subcortical structures. However, the use of an optimized VBM technique, i.e. obtaining a customized template from both samples (patients and their controls), has proved to be effective in studies of several pathologies (Chételat et al., 2003; Desgranges et al., 2007; Mevel et al., 2007; Salmon et al., 2008; for a review) as well as of healthy aging (Kalpouzos et al., 2009). Furthermore, this method presents notable advantages and only minor drawbacks. While the region-of-interest method leaves large brain areas unexplored and is very time-consuming and expertise-dependent, the automatic time-saving voxel-by-voxel method provides a prospective and fully comprehensive assessment of the brain, without bias. Regarding functional imaging, the use of a resting-state PET assessment allowed us to study the functional cerebral changes throughout the entire brain, while an activation study would have been informative in terms of the cerebral structures involved in a given neuropsychological function but would have been confined to that function.

In summary, our patients had significant metabolic and GM density changes, contrasting with minor neuropsychological deficits, suggesting that cognitive reserve may have averted a more significant impact of OSA. Some of these cerebral changes partially corroborate the prefrontal hypothesis. However, the neuropsychological assessment failed to reveal significant attentional or executive deficits. Nonetheless, physiological measurements (MWT latencies) did show decreased ability in our patients to resist sleepiness, evidencing to some extent a decline in vigilance. Thus, the relatively intact behavioral performances of our patients may have been due to compensatory mechanisms (Stern, 2002). In line with this idea, an fMRI study found that, relative to controls, apneics demonstrated intact performances on a verbal learning task, but with greater brain activation (Ayalon et al., 2006). Likewise, after sleep deprivation, healthy subjects have been found to display additional brain activation during demanding cognitive tasks (Drummond et al., 2005b). In the resting-state condition, our patients showed a metabolic defect in the default-mode circuit. Further studies are needed to improve our understanding of the neurobehavioral sequelae of this functional impairment. Our findings also highlight the contribution of cerebellar changes to impairments in the memory and motor domains.

To conclude, in apneics without notable cognitive impairments, cerebral imaging may be more sensitive when it comes to detecting the impact of silent OSA on brain morphology and function. The disease probably starts many years before it is clinically expressed, slowly becoming more severe and apparent in terms of significant neurobehavioral manifestations.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

The authors would like to thank the cyclotron staff (GIP Cyceron, Caen) for their assistance, the Sleep Unit and the EEG Department (Caen University Hospital) for their invaluable cooperation, the psychologist Alice Pélerin and Drs Vanessa Matuszewski, Pascale Piolino and Peggy Quinette for their help in conducting some of the neuropsychological investigations, and Philippe Conejero for producing the figures.

Disclosure statement

  1. Top of page
  2. Summary
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
  9. References

This was not an industry-supported study. Dr Yaouhi, Dr Bertran, Dr Clochon, Miss Mézenge, Dr Denise, Dr Foret, Dr Eustache and Dr Desgranges have indicated no financial conflicts of interest.

References

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  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. Disclosure statement
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