European Journal of Neuroscience, Vol. 40, pp. 3128–3135, 2014

doi:10.1111/ejn.12659

DISORDERS OF THE NERVOUS SYSTEM

Recognition memory is associated with altered restingstate functional connectivity in people at genetic risk for Alzheimer’s disease Silke Matura,1 David Prvulovic,1 Marius Butz,1 Daniel Hartmann,1 Beate Sepanski,1 Katja Linnemann,1 €chel,1 Tarik Karakaya,1 Fabian Fußer,1 Johannes Pantel2 and Vincent van de Ven3 Viola Oertel-Kno 1

Laboratory of Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, Goethe University, Frankfurt/Main, Germany 2 Institute of General Practice, Goethe University, Frankfurt/Main, Germany 3 Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands Keywords: apolipoprotein4, fMRI, functional connectivity, recognition memory

Abstract The apolipoprotein E e4 (ApoE e4) allele not only represents the strongest single genetic risk factor for sporadic Alzheimer’s disease, but also imposes independent effects on brain function in healthy individuals where it has been shown to promote subtle memory deficits and altered intrinsic functional brain network connectivity. Based on previous work showing a potential relevance of the default mode network (DMN) functional connectivity for episodic memory function, we hypothesized that the ApoE e4 genotype would affect memory performance via modulation of the DMN. We assessed 63 healthy individuals (50–80 years old), of which 20 carried the e4 allele. All participants underwent resting-state functional magnetic resonance imaging (fMRI), high-resolution 3D anatomical MRI imaging and neuropsychological assessment. Functional connectivity analysis of resting-state activity was performed with a predefined seed region located in the left posterior cingulate cortex (PCC), a core region of the DMN. ApoE e4 carriers performed significantly poorer than non-carriers in wordlist recognition and cued recall. Furthermore, e4 carriers showed increased connectivity relative to e4 non-carriers between the PCC seed region and left-hemispheric middle temporal gyrus (MTG). There was a positive correlation between recognition memory scores and resting-state connectivity in the left MTG in e4 carriers. These results can be interpreted as compensatory mechanisms strengthening the cross-links between DMN core areas and cortical areas involved in memory processing.

Introduction The apolipoprotein E e4 (ApoE e4) allele is a well-established genetic risk factor for sporadic Alzheimer’s disease (AD): carriers of the e4 allele are three to four times more likely to develop AD and have a younger mean age at onset than non-carriers (Corder et al., 1993). The ApoE e4 genotype modulates both memory performance and brain resting-state networks in healthy adults (Machulda et al., 2011; Wisdom et al., 2011). However, little is known about specific interactions between functional alterations in resting-state networks and episodic memory deficits. The presence of the ApoE e4 allele in non-demented elderly is associated with impaired episodic memory performance and with deficits in executive functions (Caselli et al., 2002; Blair et al., 2005; Bartzokis et al., 2006). In a meta-analysis of 77 studies in a total of 40 962 cognitively healthy adults (age range 28–82 years),

Correspondence: Dr S. Matura, as above. E-mail: [email protected] S.M. and D.P. are joint first authors. Received 11 February 2014, revised 3 May 2014, accepted 18 May 2014

e4 carriers were found to perform poorer in measures of episodic memory, global cognitive ability, executive functioning and perceptual speed (Wisdom et al., 2011). Furthermore, the effect of the ApoE e4 allele on cognition correlated with increasing age (Wisdom et al., 2011). Moreover, the ApoE e4 genotype is associated with altered resting-state network activity in healthy adults. Sheline et al. (2010a) found reduced functional connectivity between precuneus and left hippocampus/parahippocampus as well as right middle temporal cortex in e4 carriers. The pattern found here was similar to an intrinsic connectivity pattern in patients with AD (Sheline et al., 2010b). Fleisher et al. (2009) could show diminished connectivity between the posterior cingulate cortex (PCC) and precuneus, as well as gyrus rectus in e4 carriers. On the other hand, e4 carriers showed increased connectivity between PCC and a number of other brain regions (ventromedial prefrontal cortex, right dorsolateral cortex, left postcentral gyrus, middle superior temporal gyrus and the hippocampus; Fleisher et al., 2009). Evidence from functional neuroimaging studies suggests substantial topographic overlap between resting-state neural networks (default mode network, DMN) and networks underpinning episodic

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd

ApoE e4 and resting-state functional connectivity 3129 memory processing (Buckner et al., 2005; Spreng et al., 2009; Sestieri et al., 2011). Activity in the DMN during resting state seems to predict episodic memory performance in healthy adults (Yang et al., 2012) and in neurological patients (McCormick et al., 2013). Because we were specifically interested in DMN resting-state activity in e4 carriers and its relation to memory performance, we investigated the intrinsic functional connectivity of a region anatomically co-localized with the major posterior hub of the DMN (PCC). The PCC is an anatomical/computational hub in DMN and braingeneral (Hagmann et al., 2008; Greicius et al., 2009). Additionally to being an important component of the DMN, the PCC seems to be especially vulnerable towards AD pathology. Studies using fluorodeoxyglucose-positron emission tomography (FDG-PET) have consistently shown diminished resting-state glucose metabolism in the PCC of patients with early AD (Buckner et al., 2005), mild cognitive impairment (MCI; Ishii et al., 2003) and also in cognitively intact (Reiman et al., 1996, 2004; Small et al., 2000; Chen et al., 2012) e4 carriers, possibly reflecting synaptic decline in this region. The PCC seems to be an appropriate seed region to investigate DMN resting-state activity and its relation to memory performance because, additionally to being a major hub in the DMN, it is a key component of the episodic memory network and is involved in both retrieval and encoding processes (Rugg et al., 2002; Shannon & Buckner, 2004; Cabeza, 2008; Miller et al., 2008; Spaniol et al., 2009). The vulnerability of the PCC towards AD pathology, as well as its role for episodic memory processes and the fact that it is a key component of the DMN, made it an ideal candidate region for investigating e4-associated alterations in intrinsic functional connectivity and its relation to memory performance. The PCC seed region used for our study had shown the highest functional deactivation levels during an episodic memory task (see Materials and methods) in the same study population and was therefore likely to reflect a DMN core area. We hypothesized that the e4 allele may affect episodic memory function in healthy adults via modulation of resting-state functional connectivity in the DMN. Specifically, we hypothesized that both resting-state functional connectivity across areas of the DMN and measures of episodic memory performance would differ between healthy adult e4 carriers and non-carriers. Moreover, we hypothesized that the correlation between neuropsychological performance scores and DMN functional connectivity would be affected by the ApoE genotype.

Materials and methods Participants We examined 63 cognitively intact participants (mean age 65.9  6.7 years). Eighteen subjects who were heterozygote for ApoE e4 (e3/e4) and two subjects who were homozygote for ApoE e4 were included in the e4+ group. Forty-three e4-negative subjects (e3/e3 and e2/e3), matched for age, gender and education, were included into the e4 group. The local ethics committee of the Goethe-University Frankfurt approved the study. All subjects declared that they understood the experimental procedure and signed a written informed consent. The study was undertaken in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki, Rickham, 1964). All subjects were right-handed (Edinburgh Handedness Inventory, Oldfield, 1971), and had no history of neurological or psychiatric disorders. All participants underwent ApoE genotyping. DNA was extracted from whole-blood samples. The DNA extraction and genotyping

process was conducted at biologis laboratories (Frankfurt a.M., Germany). ApoE genotyping of the two determinating variants rs7412 and rs429358 was amplified using polymerase chain reaction and analysed with pyrosequencing. The resulting sequences were compared with established sequence variants of the ApoE allele. Verbal learning and memory were assessed using the German Version of the California Verbal Learning Test (CVLT; Delis et al., 1987; German version by Niemann et al., 2008) and three subtests (immediate and delayed wordlist recall, wordlist recognition) of the CERAD-NP (Consortium to Establish a Registry for Alzheimer’s Disease: Morris et al., 1988). Visual memory was tested with the Brief Visual Memory Test- R (Benedict, 1997) and one subtest of the CERAD (figure recall). Furthermore, measures of working memory and attention were obtained using the Letter Number Span (Gold et al., 1997), the Spatial Span of the Wechsler Memory Scale 3 (Wechsler, 1997) and Trail Making Test A (Spreen & Strauss, 1998). Speed and fluency of word production were assessed with two subtests of the CERAD-NP (semantic fluency and phonematic fluency). Object naming was assessed with a short version of the Boston Naming Test, also a subtest of the CERAD-NP. The verbal IQ was tested with a German verbal intelligence test (Mehrfachwahl Wortschatz Test–B; Lehrl, 2005). Screening for potential occurrence of depressive symptoms was performed using the German Version of the Beck Depression Inventory (BDI II; Beck et al., 1996; German adaptation by Hautzinger et al., 2006). Analysis of neuropsychological data Statistical analysis of behavioral data was carried out with SPSS 21 (SPSS, Chicago, IL, USA). A Kolgomorov–Smirnov test was used on all cognitive variables to test for the assumption of normality. To test for group differences (e4+ vs. e4 ) in normally distributed variables, a two-sample t-test was used. Group differences in variables that did not meet the assumption of normality were tested with a non-parametric test, the Mann–Whitney U-test. For all neuropsychological test scores that were significantly different between e4 carriers and non-carriers, we examined the relationship between these scores and PCC resting-state connectivity scores. Associations between functional connectivity and cognition were investigated with Kendall’s tau correlations as variables were not normally distributed. In a first step, Kendall’s tau correlation coefficients between neuropsychological test scores and areas showing ApoE e4-related differences in PCC resting connectivity scores were computed for a pooled total group consisting of both e4 carriers and non-carriers. In a second step, Kendall’s tau correlation coefficients were computed for each group separately. We applied the Bonferroni correction to control for false-positive results. MRI hardware and procedure All MR images were acquired using a Trio 3-T scanner (Siemens, Erlangen, Germany) with a standard head coil for radiofrequency transmission and signal reception. Participants were outfitted with protective earplugs to reduce scanner noise. For T1-weighted structural brain imaging, an optimized 3D modified driven equilibrium Fourier transform (3D MDEFT) sequence (Deichmann et al., 2004) with the following parameters was conducted: acquisition matrix = 256 9 256, repetition time (TR) = 7.92 ms, echo time (TE) = 2.48 ms, field of view (FOV) = 256 mm, 176 slices, 1.0 mm slice thickness. Functional resting-state images were acquired using a blood oxygen level-dependent-sensitive EPI sequence comprising the following parameters: 300 volumes, voxel

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3128–3135

3130 S. Matura et al. size: 3 9 3 9 3 mm3, TR = 2000 ms, TE = 30 ms, 30 slices, slice thickness = 3 mm, distance factor = 20%, flip angle = 90°, FOV = 192 mm. The resting-state measurements were part of a larger functional magnetic resonance imaging (fMRI) study on episodic memory in e4 carriers. After participants had to encode and recognize face–name pairs in the scanner, the resting-state measurements were subsequently assessed. For the resting-state measurements, all participants were instructed to keep their eyes open, to lie still, not to engage in any speech, to think of nothing special and to look at a white fixation cross presented in the center of the visual field during the whole scan. Image processing Brain Voyager QX 2.3 (Brain Innovation, Maastricht, the Netherlands) was used to analyse the fMRI data (Goebel et al., 2006). Anatomical data were preprocessed with intensity inhomogeneity correction and transformed into Talairach space. Preprocessing of functional data included 3D motion correction to overcome minor head movements during the scan, slice scan time correction, spatial smoothing with a 4-mm Gaussian kernel (full-width at half-maximum) to accommodate inter-subject anatomical variability, temporal high-pass filtering to remove low-frequency non-linear drifts of three or fewer cycles per time course (cut-off = 0.0075 Hz) and linear trend removal. Quality assurance measures of head motion showed no group differences. The complete set of functional data of each subject was co-registered to the anatomical scans, transformed into Talairach space and resampled to an iso-voxel size of 3 9 3 9 3 mm3. We computed a seed-correlation analysis with a seed region localized in the left PCC. The seed region was extracted from an event-related fMRI analysis of an episodic memory task (face–name association task: Sperling et al., 2001) with the same participants. The task consisted of three encoding runs and three retrieval runs.

During encoding, 30 face–name pairs were presented for 8 s each. During retrieval, the previously shown face–name pairs were presented together with three distractor names in a pseudorandomized order. Subjects were instructed to indicate the correct name by a button press. Each face was presented together with four names (one target name, three distractors) for 8 s. The interstimulus interval consisted of a checkerboard pattern. Each encoding and retrieval run lasted approximately 4 min. The duration of the interstimulus interval was kept variable (from 8 to 12 s) in order to ‘jitter’ the onset times of trials and thus minimize multi-colinearity of eventrelated fMRI analyses. The seed location that was used for the resting-state analysis (Fig. 1A; Talairach coordinates: x = 2, y = 56, z = 18, 134 anatomical voxels) was derived from the event-related fMRI analysis: it showed the strongest task-related deactivation during retrieval of face–name pairs and has been reported in the literature as a major hub of the DMN (Hagmann et al., 2008; Buckner et al., 2009; Greicius et al., 2009). The analysis of intrinsic functional connectivity with the PCC as seed region was done using Matlab software (MathWorks, Natick, MA, USA) and freely available toolboxes and custom-written routines developed by our working group. The computation of group differences of intrinsic PCC functional connectivity comprised a two-level random effects analysis (Fox et al., 2005; Oertel-Knochel et al., 2013). At the first level, we first regressed out fMRI nuisance signals from the functional time-series, which included the z-normalized signal sampled from the lateral ventricles, white matter, six motion correction parameters (Birn et al., 2006; Birn, 2012) and the global brain signal. We then sampled and z-normalized the temporal profile from the PCC seed region and correlated (Pearson’s r) it with the nuisance-corrected time-series (residuals) in a voxel-by-voxel manner. The correlation values were normalized using Fisher’s Z, and were then entered into the second level of the random effects

A

B

Fig. 1. (A) Functional connectivity maps of the entire cohort (n = 63) showing blood oxygen level-dependent fluctuations during rest correlated with the left posterior cingulate cortex (PCC) seed region (depicted in violet) demonstrating the default mode network (DMN) q(FDR) < 0.05. (B) Increased connectivity between the PCC seed region and left middle temporal gyrus (MTG) in ApoE e4 carriers (P < 0.01, cluster threshold corrected). © 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3128–3135

ApoE e4 and resting-state functional connectivity 3131 analysis using an ANCOVA with group as between-subjects factor, and age, sex and education as subject-level covariates. We performed a one-sample t-test to obtain a multi-subject map of overlapping spatial distributions of PCC-related connectivity across all subjects (one-sided to include only positive effects), and results were visualized using a false discovery rate of q = 0.05 (Genovese et al., 2002). False discovery rate corrects for the expected proportion of false-positives among those tests for which the null hypothesis was rejected. It has been shown to reliably reject false-positives, albeit being less conservative than family-wise error correction (Genovese et al., 2002). For visualization of the nuisance-corrected between-group effects, we set the initial uncorrected voxel-level threshold to P < 0.01. The ensuing map was then corrected for multiple comparisons at the voxel-cluster level, for which we simulated the statistical map (5000 Monte-Carlo simulations) based on its inherent spatial smoothness (Goebel et al., 2006), and then tabulating the surviving clusters at a false-positive probability of 5%. This resulted in a minimum clustersize of 269 mm3. The resulting statistical maps were visualized on a template that was created by averaging all participants’ brains.

Results Neuropsychological test results Table 1 summarizes the sociodemographic characteristics as well as the neuropsychological test results of all participants. e4 carriers and non-carriers did not differ with regards to age, education and gender distribution (P > 0.05). The t-tests revealed no significant group differences (P > 0.05) in e4 carriers vs. non-carriers in all memory tests requiring free recall of word lists (CVLT, CERAD). ApoE e4 carriers performed significantly poorer in wordlist recognition as tested with the CERAD (U = 307, z = 2.6, P = 0.01) and cued recall as tested with the CVLT (t61 = 2.10, P = 0.039). Resting-state correlation analysis Seed-correlation analysis of resting-state signal fluctuations for a pooled total group consisting of both carriers and non-carriers (onesample t-test, n = 63) revealed regions of significant connectivity [q (FDR) < 0.05], with the PCC seed region comprising extended areas of bilateral PCC and precuneus, bilateral medial frontal gyrus, bilateral superior frontal gyrus, bilateral parahippocampal gyrus/hippocampus and bilateral medial temporal gyrus (MTG; Fig. 1A). This pattern largely resembles cortical regions of the DMN in cognitively normal elderly individuals (Greicius et al., 2004; Spreng et al., 2009). The nuisance-corrected between-group comparison showed significantly increased connectivity (cluster level corrected at false-positive rate of 5%) in e4 carriers (mean connectivity strength = 0.11) relative to e4 non-carriers (mean connectivity strength = 0.056) between the PCC seed region and left-hemispheric MTG [Brodmann area (BA) 21, Talairach coordinates: x = 55, y = 20, z = 12, 314 voxel]. Figure 1B shows the areas marked in yellow-red. No other brain areas showed a significant group effect of PCC connectivity.

Table 1. Sociodemographic features and neuropsychological test results of the genotype groups

Gender male/female† Age‡ Years of education‡ MMSE§ CVLT immediate recall trial 1 list A‡ CVLT immediate recall trial 1 list B‡ CVLT total immediate recall list A‡ CVLT short delayed free recall‡ CVLT short delayed cued recall‡ CVLT long delayed free recall§ CVLT long delayed cued recall‡ CVLT recognition discriminability§ CERAD total immediate recall‡ CERAD delayed free recall§ CERAD recognition discriminability (%)§ CERAD delayed drawing figures‡ BVMT-R‡ Letter Number Span‡ Spatial Span‡ Trail Making Test A‡ CERAD verbal fluency (animals)‡ CERAD phonematic fluency (s-words)‡ MWTB‡ Boston Naming Test§ CERAD drawing figures§

Apoe4+ (n = 20)

Apoe4(n = 43)

P-value

8/12 65.00 14.80 28.85 7.00 5.75 56.40 11.40 12.00 11.75 12.30 15.42 22.95 8.20 0.97

(8.5) (2.9) (1.5) (2.2) (1.5) (8.9) (3.1) (2.3) (3.1) (2.9) (0.93) (3.3) (1.2) (0.03)

16/27 66.33 16.00 29.30 7.86 6.00 59.37 12.42 13.42 13.09 13.37 14.45 23.40 8.30 0.99

(5.9) (3.6) (0.8) (2.6) (2.1) (10.6) (3.0) (2.5) (3.4) (2.6) (2.01) (2.8) (1.6) (0.02)

0.832 0.533 0.172 0.244 0.205 0.637 0.283 0.224 0.039* 0.054 0.156 0.063 0.444 0.368 0.010**

11.40 21.17 15.42 15.16 34.40 22.95 15.90

(2.2) (6.5) (2.1) (2.3) (12.8) (4.6) (4.9)

11.40 24.05 15.91 16.16 38.26 23.81 15.50

(2.2) (6.6) (2.2) (2.5) (9.6) (5.4) (4.4)

0.939 0.124 0.423 0.149 0.191 0.543 0.834

33.56 (2.6) 14.67 (0.8) 10.77 (0.6)

0.105 0.630 0.071

32.16 (4.2) 14.70 (0.6) 10.60 (0.6)

Values denote mean (standard deviation) or number of subjects. (CVLT, California Verbal Learning Test; CERAD, Consortium to Establish a Registry for Alzheimer’s Disease; BVMT: Brief Visual Memory Test; MWTB, Mehrfachwahl Wortschatz Intelligenz Test B; MAC-Q, Memory Complaint Questionnaire; MMSE, Mini Mental State Examination; † = chi square test, values denote number of subjects; ‡ = two sample t-test, § = Mann–WhitneyU-test *: P < 0.05; **: P < 0.01.

these scores and PCC resting-state connectivity scores. In a first step, Kendall’s tau correlation coefficients between recognition discriminability scores and DMN resting connectivity scores in the left MTG were computed for a pooled total group consisting of both carriers and non-carriers. The analysis for both groups pooled together did not reveal a significant correlation (P = 0.273). In a second step, Kendall’s tau correlation coefficients were computed for each group separately, with P-values corrected for multiple comparisons (corrected alpha = 0.025). In e4 carriers, the association between recognition performance (CERAD) and PCC-left MTG connectivity showed a statistical trend (s = 0.39, P = 0.035), pointing to a moderate effect size. For non-carriers, there was no significant correlation (P = 0.112). Pearson product-moment correlations revealed no significant associations between cued recall scores (CVLT) and PCC resting connectivity scores for both groups pooled together (P = 1.00), nor in ApoE e4 carriers (P = 0.895) or non-carriers (P = 0.825). Figure 2 displays a scatterplot of memory performance (recognition dicriminability) as a function of resting-state connectivity.

Discussion Correlation between DMN connectivity and memory performance Because e4 carriers showed poorer recognition discriminability and cued recall performance, we examined the relationship between

In this study we investigated DMN functional connectivity in e4 carriers and controls, and its relation to episodic memory function. The first finding of our study was that individuals who carried the ApoE e4 genotype showed impaired verbal recognition and cued

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3128–3135

3132 S. Matura et al.

Fig. 2. Scatterplot showing the relationship between resting-state connectivity scores and recognition discriminability scores. Functional connectivity scores increase with increasing recognition discriminability scores in the left middle temporal gyrus (MTG) in ApoE e4 carriers.

recall memory performance compared with non-carriers. Successful recognition discriminability reflects successful encoding and storage of information, as it requires distinguishing memorized from novel items. Impaired recognition memory thus reflects deterioration of encoding/storage, while impaired free recall alongside proper recognition performance points to retrieval deficits (Taylor & Monsch, 2004). Similarly, impaired cued recall performance has also been proposed to reflect deficits in encoding and storage rather than retrieval (Tulving & Pearlstone, 1966). The result of diminished recognition and cued recall performance is in line with previous findings of episodic memory deficits associated with the ApoE e4 allele in healthy adults (Wisdom et al., 2011). However, with regards to recognition and cued recall memory performance as reflecting a specific subprocess of episodic memory (encoding/storage), existing evidence is less clear about the potential impact of the ApoE e4 genotype. A study by Gilbert & Murphy (2004) revealed that recognition memory was affected in the olfactory domain but not in the visual domain in healthy e4 carriers, supporting a specific course of deterioration that starts in the olfactory system and that may later spread to other systems during the development of AD. Troyer et al. (2012) found a significant inverse relationship between the number of ApoE e4 alleles and face–name associative recognition performance in subjects suffering from MCI. Such a correlation was not found for word–word associative recognition or for face–name item recognition performance. In a ‘novel image novel location’ object-recognition memory task, healthy elderly e4 carriers (age 62–92 years) performed significantly worse than non-carriers, while there were no performance differences in other cognitive tests comprising spatial span, face recognition and familiar objects tasks (Berteau-Pavy et al., 2007). In a relatively large sample (n = 98), Westlye et al. (2011) found reduced recognition discriminability scores in e4 carriers compared with non-carriers as measured with the CVLT. Possible underlying causes for impaired recognition memory in elderly e4 carriers include the formation of neurofibrillary tangles and amyloid plaques, resembling the neuropathology of AD. These neuropathological changes have been shown to be more prevalent and severe in e4 carriers than in non-carriers (Kok et al., 2009; Caselli et al., 2010). Significant differences in verbal recognition memory between e4 carrier and non-carrier groups were found in our study only when assessed with the CERAD, whereas the differences in the CVLT reached a non-significant trend (P = 0.063). This discrepancy between the CVLT and CERAD results may be explained by differences in test procedures: while the wordlist to be memorized is

presented only three times in the CERAD, there is a total of five learning runs in the CVLT. All things considered, the CERAD may thus be more sensitive in assessing subtle encoding and storage deficits when compared with the CVLT. This line of reasoning is partly supported by a study in 138 patients with AD performing both neuropsychological test batteries: participants revealed significantly fewer relative recognition hits in the CERAD when compared with the CVLT (Kaltreider et al., 2000). A larger sample size in our study would likely have revealed significant group differences in recognition discriminability assessed with the CVLT as well, as shown by Westlye et al. (2011). Our findings thus contribute to the existing literature, and extend the understanding of subtle and selective impairments in episodic memory subprocesses affected by ApoE e4. Verbal recognition memory is a sensitive marker for episodic memory deficits, and is placed among the CERAD subscales with highest diagnostic value differentiating healthy elderly subjects from individuals with very mild AD (AUC = 0.9; Sotaniemi et al., 2012). The potential clinical relevance of our results is further reflected by the findings of Crowell et al. (2006), who suggested that treatment with cholinesterase inhibitors may support recognition memory performance to a greater extent than performance in learning and free delayed recall. Our second major finding was a different resting-state connectivity pattern between e4 carriers and non-carriers. ApoE e4 carriers relative to non-carriers showed increased connectivity between the PCC seed region and left-hemispheric MTG (BA 21; Fig. 1B, areas marked yellow-red). Our results are consistent with a number of previous studies also demonstrating increased resting-state coactivation in non-clinical e4 carriers (Filippini et al., 2009; Fleisher et al., 2009; Westlye et al., 2011). So far, only one previous study has used a task-based seed region procedure similar to our study, and also showed increased connectivity between the PCC seed region and DMN regions in e4 carriers. Contrary to our results, the ApoE e4 genotype has also been associated with decreased DMN restingstate connectivity. Using a PCC seed region that was derived from independent component analysis (ICA), Machulda et al. (2011) showed decreased connectivity between the PCC and regions of the posterior DMN. The discrepant finding with regard to our study could be related to differences in participants’ age, which was higher in their study compared with our study. Previous studies in healthy controls showed that age may affect intrinsic functional connectivity of the DMN (Damoiseaux et al., 2008). The PCC is an important area within the DMN functional architecture, as it has been shown to display hypometabolism in young (Reiman et al., 2004) and middle-aged (Small et al., 2000; Chen et al., 2012) healthy e4 carriers, qualitatively resembling metabolic changes observed with FDG-PET in patients with AD. Moreover, PCCs, as well as other DMN regions, have been found to be predilection sites for early fibrillar amyloid-b accumulation in preclinical AD (Buckner et al., 2005, 2009). Thus, in combination with previous results, our findings support an ApoE e4 genotype-related imbalance within resting-state functional networks locked to the PCC. The third important finding of this study was a difference between e4 carriers and non-carriers with regards to the relationship between resting-state functional connectivity and recognition memory test scores. Specifically, for e4 carriers but not for non-carriers, we found positive correlations between recognition discriminability scores and the resting-state connectivity values in the left MTG (BA 21). The MTG (BA 21) has been shown to be activated by facial recognition tasks (Phillips et al., 1998; Sprengelmeyer et al., 1998). Considering an involvement of the MTG in memory-related

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3128–3135

ApoE e4 and resting-state functional connectivity 3133 processes, our results can be interpreted as potential compensatory strengthening of pathways interconnecting DMN core areas with other memory-associated cortical areas, partially making up for recognition memory deficits found in e4 carriers but not in non-carriers. Contrary to our results, Westlye et al. (2011) showed an inverse correlation of resting-state DMN connectivity and recognition discriminability scores (as assessed with CVLT). The authors interpreted their findings of increased functional DMN connectivity in e4 carriers as indicative of network disruption and of uncoupling deficits across the hippocampus and other key areas of the DMN. Increased resting-state activity was proposed to lead to increased metabolic burden, which in turn may increase the vulnerability for the development of cognitive deficits (Westlye et al., 2011). The discrepant findings with regard to our study could be due to different analytical approaches. Westlye et al. (2011) used a combined ICA and dual regression approach, while our study used a hypothesis-driven seed-based analysis (SBA) with an empiricalderived seed region from a behavioral paradigm. Unlike ICA, SBA explicitly models functional connectivity and is sensitive to local connectivity. Although our study is only partially comparable with the study of Westlye et al. (2011), the outcomes of both studies converge in: (a) ApoE e4-dependent aberrant functional network properties of the DMN; and (b) ApoE e4 genotype-related modulation of the association between DMN resting-state connectivity and recognition memory performance. However, by using a different analytical approach, our study might have captured some effects with entirely different pathophysiological relevance compared with the study by Westlye et al. (2011). While negative correlations between ApoE e4-modulated synchronization among DMN areas and recognition memory shown by Westlye et al. (2011) can be explained as either ineffective compensatory mechanisms (at best) or even as a potential driving factor of cognitive decline, our findings are compatible with compensatory processes actually counteracting those emerging memory performance deficits. Increased brain activity as a compensatory mechanism to counteract incipient cognitive decline in e4 carriers has been demonstrated by a number of studies (Bookheimer et al., 2000; Bondi et al., 2005; Wishart et al., 2006; Han & Bondi, 2008). More precisely, these studies have found an increase in both magnitude and extent of brain activation during memory tasks in memoryrelated brain regions in e4 carriers compared with non-carriers. Interestingly, Bookheimer et al. (2000) could show that increased brain activation during cognitively challenging memory tasks in e4 carriers was predictive for subsequent memory decline, thus lending further support to the hypothesis of a compensatory mechanism. It is not clear how compensatory brain functions during cognitive tasks are related to increased functional connectivity in the DMN during rest. Yet, the type of compensatory recruitment in e4 carriers seen during challenging cognitive tasks is similar to redistributed network connectivity during the resting state. A few issues deserve consideration. Firstly, the relatively moderate sample size and accordingly the lower statistical power might have prevented us from finding additional significant differences between groups in memory-relevant areas (such as the hippocampus). A second limitation lies in the non-representative sample with very high average education levels. According to the brain reserve hypothesis (for a recent review and meta-analysis, see Meng & D’Arcy, 2012), the very high education levels in our sample might mitigate cognitive deterioration caused either by independent effects of the ApoE e4 genotype, by preclinical accumulation of AD pathology, or both.

A third limitation can be found in the fMRI study design with a memory task preceding the resting-state measurements. Some studies have shown that a task prior to the rest period can influence activity in default network regions (Waites et al., 2005; Hasson et al., 2009). This effect seems to be especially strong if the preceding task is a memory task. More precisely, a memory task prior to the resting-state measurements has been shown to increase activity in the DMN (Hasson et al., 2009). This effect most likely occurs because the DMN is typically associated with elaborative thinking that mediates encoding of complex information to memory. A number of studies have implicated regions of the DMN in constructing both representations of past events and possible future events (Schacter et al., 2007). With regard to our study, the findings of altered posttask DMN activity reported by other studies might implicate that the DMN activity that we captured was increased by the prior memory task. Thus, the prior task might have aggravated e4-related effects on intrinsic functional connectivity. Another limitation is the fact that we found diminished recognition performance in e4 carriers only in a subtest of the CERAD, whereas genotype-related group differences in the CVLT recognition subtest only reached a non-significant trend (P = 0.063). Although there is some evidence that the CERAD might be more sensitive in assessing encoding and storage deficits when compared with the CVLT (Kaltreider et al., 2000), this assumption needs further support from other studies. Finally, some consideration has to be given to our decision on including the global signal as a confound variable in the regression model. There has been some debate on global signal regression. Murphy et al. (2013) have argued against global signal regression as it introduces anti-correlations that might be wrongly interpreted. On the other hand, Fox et al. (2009) have shown that global signal regression improves the neuroanatomical specificity of positive correlations and furthermore leads to an enhancement in detection of system-specific correlations. Differential analyses with and without global signal regression with our data showed that regressing out the global signal lead to increased statistical power in detecting ApoE e4-related differences in positive correlations. In conclusion, our results complement previous work by showing: (a) subtle and very specific deficits in recognition and cued recall memory performance in healthy elderly e4 carriers; (b) an ApoE e4related imbalance of resting-state neural synchronization across the DMN; and (c) an ApoE e4-specific association of recognition memory performance with functional connectivity between the PCC and a memory-related temporal cortical region. Further studies with larger sample sizes and more balanced educational levels are warranted to further explore the nature (byproduct or specific compensatory effect?) of the found correlations between resting-state connectivity and recognition memory. Longitudinal studies are necessary to unravel how far the observed changes in DMN functional synchronization represent independent factors of future cognitive deterioration.

Funding This work was supported by the Neurodegeneration & Alzheimer’s Disease Research grant of the LOEWE program ‘Neuronal Coordination Research Focus Frankfurt’ (NeFF), awarded to J.P. and D.P.

Conflict of interest The authors certify that there is no actual or potential conflict of interest in relation to this article.

© 2014 Federation of European Neuroscience Societies and John Wiley & Sons Ltd European Journal of Neuroscience, 40, 3128–3135

3134 S. Matura et al.

Abbreviations AD, Alzheimer’s disease; ApoE e4, apolipoprotein E e4; BA, Brodmann area; CERAD, Consortium to Establish a Registry for Alzheimers Disease; CVLT, California Verbal Learning Task; DMN, default mode network; FDG-PET, fluorodeoxyglucose-positron emission tomography; fMRI, functional magnetic resonance imaging; FOV, field of view; ICA, independent component analysis; MCI, mild cognitive impairment; MTG, medial temporal gyrus; PCC, posterior cingulate cortex; SBA, seed-based analysis; TE, echo time; TR, repetition time.

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