Structural and functional neural correlates of visuospatial information processing in normal aging and amnestic mild cognitive impairment

Neurobiology of Aging 33 (2012) 2782–2797 www.elsevier.com/locate/neuaging

Structural and functional neural correlates of visuospatial information processing in normal aging and amnestic mild cognitive impairment Karolina K. Alichniewicza, Florian Brunnera, Hans H. Klünemannb, Mark W. Greenleea,* b

a Institute of Experimental Psychology, University of Regensburg, Regensburg, Germany Department of Psychiatry, Psychosomatics and Psychotherapy, University of Regensburg, Regensburg, Germany

Received 12 July 2011; received in revised form 7 February 2012; accepted 10 February 2012

Abstract Our understanding of cognitive changes related to human aging and their underlying neural processes is challenged by the distinction between normal and pathological aging. In our study, the neural correlates of visuospatial working memory (VSWM) in young persons (YC), healthy older adults (HC) and patients with amnestic mild cognitive impairment (aMCI) were investigated. Effects of the genetic risk factor apolipoprotein E (ApoE) ␧4 on a VSWM task were analyzed for HC and aMCI patients. Higher cortical activation in extrastriate occipital regions and significantly decreased brain volumes in frontoparietal areas were observed in HC compared with young persons. Also, reduced cortical activation in the right middle frontal gyrus and superior frontal gyrus was observed in aMCI-patients compared with HC. Thus, attenuated cortical activation during VSWM tasks is related to the formation of aMCI and may serve as an early marker for cognitive decline. In contrast to previous studies, no significant apolipoprotein E-linked differences were found between HC and aMCI groups. © 2012 Elsevier Inc. All rights reserved. Keywords: Mild cognitive impairment; Alzheimer’s disease; Functional magnetic resonance imaging; Working memory; Apolipoprotein E; Aging

1. Introduction Human aging is associated with a cognitive impairment that is accompanied by structural and functional changes in the brain (Bishop et al., 2010; Hedden and Gabrieli, 2004). To advance our understanding about the neural processes involved in healthy and pathological aging, brain activation patterns in the context of degeneration and compensation across the lifespan in healthy persons and persons with neurodegenerative syndromes have become topics of increasing importance (Prvulovic et al., 2005). Additionally, a focus on individual differences in the trajectory of aging is a promising approach to understand and distinguish between normal and pathological origins of cognitive changes in elderly persons (Nagel et al., 2009). Recent studies focus

* Corresponding author at: Universität Regensburg, Psychologie, 93040 Regensburg, Germany. Tel.: ⫹49 (0)941 943 3281; fax: ⫹49 (0)941 943 3233. E-mail address: [email protected] (M. Greenlee). 0197-4580/$ – see front matter © 2012 Elsevier Inc. All rights reserved. 10.1016/j.neurobiolaging.2012.02.010

on the investigation of normal aging and age-related neurodegenerative changes such as Alzheimer’s disease and its prodromal stage called mild cognitive impairment (MCI) (Petersen, 2007; Petersen and Jack, 2009). The concept of MCI refers to the transitional clinical phase between normal aging and dementia and is assumed to be a high-risk state for conversion to Alzheimer’s disease (AD) (Dickerson et al., 2007; Petersen, 2004). The diagnostic criteria for MCI are characterized by a decline in memory and learning on cognitive tests which persist for at least 6 months (Artero et al., 2006; Chertkow et al., 2008; Petersen, 2004). Although the term MCI has been proposed and used in the literature for over a decade (Petersen, 2004), many controversies regarding to its characterization, definition, and application in clinical practice still remain unresolved (Chertkow et al., 2008). Traditionally, deficits in episodic and semantic memory are considered as the earliest signs in persons diagnosed with MCI, especially in the amnestic form of MCI (aMCI) (Chertkow et al., 2008; Fox et al., 1998; Frank et al., 2011; Petersen and Jack, 2009). However, several recent studies suggest that further cogni-

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tive domains might be impaired in aMCI patients (Dickerson et al., 2007; Economou et al., 2007; Tabert et al., 2006; Traykov et al., 2007). Thus, deficits in cognitive functions such as visuospatial ability (Bokde et al., 2008; Morris, 1997; Tabert et al., 2006), working memory (Yetkin et al., 2006), psychomotor speed (Lopez et al., 2006; Tabert et al., 2006), and visuospatial attention (Belleville et al., 2007; Parasuraman et al., 2002) can provide predictive value with respect to the conversion rate from MCI to AD. Furthermore, a wide range of studies on dementia reported a wide heterogeneity in patients with AD and presence of visuospatial deficits even at early stages of AD (Foster et al., 1983; Haxby et al., 1985; Martin et al., 1986). Hence, the evaluation of visuospatial processes seems to be a promising approach for early diagnosis of dementia of the Alzheimer type (Belleville et al., 2007; Dickerson et al., 2007; Iachini et al., 2009; Kochan et al., 2010; Parasuraman et al., 2002; Tabert et al., 2006). Working memory (WM) is the ability to temporary maintain and actively manipulate information and is thus essential for numerous cognitive operations performed by the human brain (Baddeley, 2010). Current studies suggest that numerous executive functions, including WM, are already impaired at a very early stage of AD (Lee and Lim, 2010). The extent of WM impairment has rarely been studied in patients with aMCI. The Baddeley and Hitch model consists of the central executive responsible for encoding, storing, and retrieving information, the 2 domain-specific systems that include the phonological loop and the visuospatial sketchpad, along with the episodic buffer that has the function of integrating information into unitary episodic representations (Baddeley, 2003, 2010; Baddeley and Hitch, 1974). Despite the fact that visuospatial working memory impairment in AD has been confirmed in variety of studies (Kensinger et al., 2003; Toepper et al., 2008), findings related to the neural correlates of working memory and their alteration in aMCI vary considerably. Some of the studies identified decreased cortical activation in persons diagnosed with AD in the inferior temporal gyrus, superior frontal gyrus, fusiform gyrus, precuneus, and pre- and postcentral gyri (Yetkin et al., 2006) whereas increased activation in frontal, temporal, and parietal lobes, superior frontal gyrus, bilateral middle temporal, middle frontal, anterior cingulate, and fusiform gyri has been reported by others (Bokde et al., 2008; Johnson et al., 2000; Thulborn et al., 2000). Furthermore, genetic risk factors for Alzheimer’s disease were shown to affect brain activity in healthy elderly individuals. Wishart et al. (2006) found greater activity during working memory in the medial frontal and parietal regions bilaterally and in the right dorsolateral prefrontal cortex in the group of elderly participants with heterozygous ApoE ␧3/␧4 allele status. Given the evidence that executive functions are impaired in AD, some studies have suggested that these deficits occur earlier or even coexist with the well-known epi-

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sodic memory deficits in MCI (Iachini et al., 2009; Saykin et al., 2004). Despite the fact that current studies report deficits in executive functions, including working memory, that already occur at a very early stage of AD (Alescio-Lautier et al., 2007; Baddeley et al., 1991; McGuinness et al., 2010), the extent of working memory impairment has rarely been studied in patients with aMCI. Furthermore, a recent body of literature provides inconsistent evidence about the influence of risk factors such as the ApoE on the extent of the decline that is associated with MCI. The present study thus investigates visuospatial working memory dysfunction and its neural correlates in persons diagnosed with aMCI. 2. Methods 2.1. Participants A total of 82 participants aged between 18 and 70 years, comprising 3 subgroups, were assessed: aMCI patients (24 females and 15 males; mean age: 62.31 years; SD ⫽ 8.6), healthy elderly controls (11 females and 13 males; mean age: 60.67 years; SD ⫽ 7.16), and healthy young persons (10 females and 9 males; mean age: 23.21 years; SD ⫽ 4.39). Within the aMCI group, 16 subjects (13 females and 3 males) were ApoE ␧3-allele carriers and 19 persons (10 females and 9 males) carried ApoE ␧4 allele. Four persons with aMCI refused to undergo the assessment of the genotype status. Within the healthy elderly subjects, 13 participants (6 females and 7 males) were carriers of the ApoE ␧3 allele and 7 participants (4 females and 3 males) were carriers of the ApoE ␧4 allele. Similar to the aMCI group, 4 healthy elderly subjects refused to undergo the assessment of the genotype status. For the analysis with regard to the role of the genotype 19 healthy elderly (13 ApoE ␧4 allele and 6 ApoE ␧3 allele) and 26 aMCI patients (12 ApoE ␧4 allele and 14 ApoE ␧3 allele) were included in the study. As mentioned above, for ethical reasons the genotype status of the young healthy persons has not been assessed. The healthy control group consisted of adults with no cognitive complaints or medical history of significance. Participants were recruited from the Memory Clinic of the Department of Psychiatry, Psychosomatics and Psychotherapy of the University of Regensburg (aMCI patients only), via advertising in print media seeking adults older than 50 years of age with and without memory problems and by word-of-mouth communication. Subjects underwent a 2-stage screening process to identify those with probable MCI. The first stage consisted of a structured telephone interview assessing evidence of subjective and informantcorroborated reports of: memory problems, no history of significant medical, neurological, or psychiatric condition, no history of major risk factors for vascular disease, no history of alcohol abuse or sensory impairment, and ability to enter the magnetic resonance imaging (MRI) environment. The second stage of the screening procedure involved

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Table 1 Demographic and psychometric data in the investigated subject groups Parameters Demographics n Age (y) Gender (male/female) Education (y) Mini Mental State Examination Clinical characteristics a Verbal fluency Boston Naming Test Word list learning Word list delayed recall Word list: savings Word list intrusions Word list recognition Phonemic fluency TMT-A TMT-B Constructional praxis Recall of constructional praxis Constructional praxis: savings

Healthy elderly

aMCI

t

p

24 60.67 (7.16) 13/11 13.67 (2.01) 29.21 (0.88)

39 62.31 (8.60) 15/24 13.10 (2.98) 28.59 (1.16)

⫺0.82 ⫺2.24

n.s. ⬍ 0.05

29.25 (7.20) 14.75 (0.44) 22.38 (2.67) 8.63 (1.10) 0.95 (0.09) 0.29 (0.75) 19.88 (0.34) 17.17 (4.01) 32.00 (7.91) 76.70 (27.67) 10.96 (0.20) 10.63 (0.77) 0.97 (0.06)

21.13 (5.83) 14.03 (0.96) 18.00 (3.30) 5.59 (1.67) 0.76 (0.21) 0.69 (1.17) 19.31 (0.98) 13.36 (5.68) 48.33 (19.31) 104.95 (56.70) 10.69 (0.77) 8.49 (2.38) 0.79 (0.20)

⫺4.91 ⫺4.07 ⫺5.48 ⫺7.92 ⫺4.70 1.49 ⫺3.32 ⫺2.87 4.66 2.23 ⫺2.05 ⫺5.18 ⫺5.03

⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001 n.s. ⬍ 0.05 ⬍ 0.05 ⬍ 0.001 ⬍ 0.05 ⬍ 0.05 ⬍ 0.001 ⬍ 0.001

0.78

n.s.

Denoted are mean values (SD), t- and p-values. Key: aMCI, amnestic mild cognitive impairment; n.s., not significant; TMT, Trail-Making Test. a Clinical characteristics are raw data from subtests of the neuropsychological test battery contained in the Consortium to Establish a Registry for Alzheimer’s Disease-Plus (CERAD-Plus).

a standardized diagnostic examination to assess the cognitive, neuropsychological, and psychological state of the participants. Diagnosis of MCI met the criteria for amnestic MCI according to Artero et al. (2006) and guidelines for diagnosis of Alzheimer’s disease and MCI of European Federation of Neurological Societies (EFNS) (Chertkow et al., 2008) which include (1) presence of a cognitive complaint from either the subject and/or a family member; (2) absence of dementia; (3) change from normal functioning; (4) decline in any area of cognitive functioning; and (5) preserved overall general functioning but possibly with increasing difficulty in the performance of activities of daily living (Artero et al., 2006; Chertkow et al., 2008). According to a variety of findings in the clinical practice, aMCI is a very heterogeneous disorder (Reischies and Wertenauer, 2002; Schönknecht et al., 2005) and most patients are not only selectively impaired in memory (amnestic MCI single domain; Petersen, 2004) but also show deficits in other cognitive domains (amnestic MCI multiple domain). Thus, in our study also patients with impairment beyond those related to memory were included. Neuropsychological assessment consisted of neuropsychological test battery contained in the Consortium to Establish a Registry for Alzheimer’s disease (CERAD⫹, German version; Memory Clinic, Basel) (Barth, et al. 2005; Ehrensperger et al., 2010). Clinical characteristics for the classification to MCI versus healthy elderly group were the z-scores from the neuropsychological test battery included in the CERADPlus that were automatically calculated by a program incor-

porated with CERAD for diagnosis and statistic purposes. The scores were estimated separately for each test for each participant and the classification was made on the basis of the outcomes, where critical range of the CERAD was a z-score below ⫺1.5 SD in at least 1 CERAD memory subtest for the amnestic MCI group and z-scores of at least above ⫺1.5 SD in all CERAD subtests for the healthy control subjects. Memory tests crucial for the diagnosis were Wordlist Delayed Recall and Savings, Trail-Making Test (TMT-A), Recall of Constructional Praxis, and Constructional Praxis: Savings. The raw results in all CERAD subtests but Word List Intrusions were significantly lower in the patient group than in the healthy controls (Table 1). Assessment of cognitive functioning was conducted with the computerized neuropsychological tests comprised in the Cambridge Neuropsychological Test Automated Battery (CANTAB). The results of the individual tests for the 2 groups are given in Table 2. The aMCI subjects demonstrated reduced skills in visual memory, spatial planning, spatial working memory, and sustained attention compared with healthy subjects. The performance of activities of daily living was tested with the Bayer activities of daily living (ADL)-scale (Hindmarch et al., 1998), and psychological state was assessed using the Symptom Checklist 90 R (Hardt et al., 2000), Beck Depression Inventory Revised (Kühner et al., 2007), and psychological diagnosis interview. To investigate the impact of the ApoE ␧4 status on cognitive performance in visuospatial working memory (VSWM) task the genotype status of healthy elderly subjects and aMCI patients was determined. The blood samples

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Table 2 CANTAB attention test performances in young persons, healthy elderly, and aMCI patients Parameters

Healthy elderly

Young

DMS: total correct SOC: minimum moves SRM: number correct SWM: between errors SWM: strategy RVP: A= Spatial span: forward Spatial span: backward

35.04 (2.97) 8.75 (1.60) 16.50 (1.67) 30.08 (20.64) 32.83 (7.01) 0.90 (.05) 8.00 (1.75) 7.88 (1.70)

37.89 (1.61) 10.68 (1.29) 18.00 (1.67) 8.42 (10.81) 26.00 (6.05) 0.94 (.03) 9.11 (2.28) 9.05 (1.35)

Healthy vs. young t

p

3.98 4.28 2.93 ⫺4.43 ⫺3.37 3.20 1.80 2.46

⬍ 0.001 ⬍ 0.001 ⬍ 0.05 ⬍ 0.001 ⬍ 0.05 ⬍ 0.05 ⬍ 0.05 ⬍ 0.05

aMCI 33.28 (3.10) 7.44 (2.00) 15.92 (2.01) 43.47 (16.92) 36.63 (3.44) 0.86 (0.05) 7.46 (1.82) 6.82 (1.43)

Healthy vs. aMCI t

p

⫺2.22 ⫺2.73 ⫺1.18 2.79 2.47 ⫺2.89 ⫺1.16 ⫺2.64

⬍ 0.05 ⬍ 0.05 n.s. ⬍ 0.05 ⬍ 0.05 ⬍ 0.05 n.s. ⬍ 0.05

Denoted are mean values (SD), t- and p-values. Key: A=, area under the curve; aMCI, amnestic mild cognitive impairment; CANTAB, Cambridge Neuropsychological Test Automated Battery; DMS, Delayed Matching to Sample; Healthy, healthy subjects; n.s., not significant; RVP, Rapid Visual Processing; SOC, Stockings of Cambridge; SRM, Spatial Recognition Memory; SWM, Spatial Working Memory; young, young subjects.

of all subjects were taken, labeled, and prepared for analysis at the Institute for Clinical Chemistry and Laboratory Medicine of the University Hospital, Regensburg. Apolipoprotein E genotyping was performed with the LightCycler real-time polymerase chain reaction (PCR) technology (Roche Diagnostics, Indianapolis, IN, USA) on genomic DNA isolated from whole ethylenediaminetetraacetic acid (EDTA) blood using a commercially available kit (LightCycler ApoE Mutation Detection Kit; Roche) allowing the analysis of the amino acid positions 112 and 158 within the sequence for the ApoE protein. As “carriers” were qualified persons who carried at least 1 ApoE ␧4 allele and as “noncarriers,” those who did not have the ␧4 allele. Exclusion criteria for entry into the study were acute neurological and psychiatric disorders or systemic diseases (e.g., stroke, depression, alcoholism) and the use of psychoactive medication. All patients and control subjects were able to understand, and follow verbal instructions, and concentrate on the task for the duration of the experiment. They also were righthanded (according to the Handedness Inventory of Raczkowski; Raczkowski et al., 1974), and had normal or corrected-to-normal vision. Participants provided written informed consent prior to the commencement of the study, in accordance with the requirements of the Code of Ethical Principles for Medical Research Involving Human Subjects of the World Medical Association (Declaration of Helsinki). The procedures used in this study were approved by the Ethical Committee of the University of Regensburg. 2.2. Study design 2.2.1. Procedure To study the effects of normal aging, comparisons between healthy older adults and young subjects were performed. For the investigation of pathological aging processes, the performance of aMCI subjects was compared with those of an age-matched control group. After the behavioral and functional magnetic resonance imaging (fMRI)

data had been completely collected, the information concerning ApoE genotype was received from the Institute for Clinical Chemistry and Laboratory Medicine of the University Hospital, Regensburg. 2.2.2. Visuospatial working memory task To investigate VSWM we used the 2-back task (Carlson et al., 1998; Owen et al., 2005). The experiment consisted of 3 two-back and 3 zero-back blocks presented in random order. In the 2-back task as the working memory condition subjects were asked to monitor the color (red, green, yellow, blue) and location of series of dots and to indicate whether the currently presented stimulus was identical (same color and position on the screen) to the one presented 2 trials previously. As control condition, in the 0-back task participants had to respond whenever a prespecified stimulus was presented (e.g., a red dot at a particular location). This condition does not require the manipulation of information within working memory. Each block included 20 trials in both conditions and was separated by blocks of fixation cross with duration of 15 seconds. Each trial began with the presentation of a fixation cross in the center of a screen for 3 seconds. Then a stimulus was shown for 200 ms at 1 of the 4 predefined screen locations. Stimuli were presented randomly in counterbalanced order. For stimuli presentation and response data collection we used Presentation 9.9 software (Neurobehavioral Systems, Inc., Albany, Canada) on a standard personal computer (PC), equipped with a standard graphics card and back projected via an liquid crystal display (LCD) video projector (JVC DLA-G20; Yokohama, Japan) onto a translucent circular screen (approximately 30 degrees in diameter), placed inside the scanner bore at 62 cm from the observer. The projector was running at 72 Hz with a resolution of 800 ⫻ 600 and a color resolution of 3 ⫻ 8 bit (RBG). During the fMRI measurement, subjects viewed the stimuli through a mirror located above their eyes. Responses were recorded with a serial response box (LUMItouch, Photon Control

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Inc., Burnaby, BC, Canada) which the participants operated with their right hand (index finger for “yes”). Both groups were pretested to ensure that all participants were able to understand and perform the task. All participants performed in a brief training session until they were able to perform the task without errors in 3 consecutive blocks.

2.2.5. Functional image analysis

Gaussian kernel of 8 mm full-width at half-maximum (FWHM). Statistical inference of effect differences between stimuli periods of 0-back and 2-back (block design) was conducted with the general linear model (GLM) (Friston et al., 1994) between groups using a random effects analysis. For this purpose, the convolution of a canonical hemodynamic response function (HRF) with square temporal onset profiles (box cars), representing the onsets and durations of the experimental conditions, was used to define the regressors. Motion parameters estimated during preprocessing of the functional images were included as additional regressors in the general linear model. Single subject contrast maps were created by t statistics derived from contrasts utilizing the hemodynamic response function. In a second level randomeffects analysis, between group differences in neural activation associated with the contrast 2-back ⬎ 0-back were assessed using a 1-way ANOVA. Within this ANOVA 2-sample t tests were computed to analyze effects between young healthy subjects and healthy elderly, as well as between healthy elderly and MCI patients. In addition, a repeated measurement ANOVA with a 2 ⫻ 2 factorial design: cognitive status (aMCI, control) ⫻ ApoE genotype (E4⫹, E4⫺) was used to examine the effect of gene status on neural activation. Clusters of k ⫽ 10 contiguous voxels large enough to pass a cluster-wise threshold of p ⬍ 0.001 were considered as significant. To control the problem of multiple comparisons, voxel-wise control of the false discovery rate (FDR) was used. Active brain areas were labeled with anatomical loci and Brodmann areas by using the SPM5 extension Wake Forest University (WFU) pick-atlas (Maldjian et al., 2003). The WFU pick-atlas was also used to convert MNI in Talairach coordinates. The Computerized Anatomical Reconstruction Toolkit (Caret5, Van Essen Laboratory, Department of Anatomy and Neurobiology, Washington University School of Medicine) with PALSB12 Atlas for both hemispheres was used for data visualization.

2.2.5.1. Functional MRI data. Preprocessing and statistical data analysis was performed with Statistical Parametric Mapping 5 (SPM, Wellcome Trust Centre for Neuroimaging, University College London, UK, www.fil.ion.ucl.ac.uk/ spm). To correct for differences in image acquisition time between slices, slice timing correction was conducted. Then, the movement artifacts in the time series of images were removed using a least squares approach and a 6 parameter (rigid body) spatial transformation. The images were also realigned to spatially match the first image. Head motion beyond 3 mm in 1 of the 3 motion planes led to exclusion of 2 participants from further analysis. The structural image was realigned to a mean image computed from the functional series. All images were then normalized to the Montreal Neurological Institute (MNI)-152 space. The realigned and normalized functional series were resampled to 2 ⫻ 2 ⫻ 2-mm resolution and spatially smoothed with a

2.2.5.2. Voxel-based morphometry data. The voxel based morphometry (VBM) toolbox within the SPM5 environment was used to examine the relation between age, aMCI status and gray matter (GM) density cross-sectionally (VBM5 Toolbox, Christian Gaser, University Jena, Institute for Psychiatry, dbm.neuro.uni-jena.de/vbm/). The details of the unified segmentation method performed in the VBM toolbox have been described elsewhere (Ashburner and Friston, 2000; Whitwell, 2009). No parameters were altered from the defaults. Gray matter differences between groups were assessed with 2-sample t tests within a 1-way ANOVA. The total intracranial volume (TIV; gray ⫹ white matter ⫹ cerebrospinal fluid [CSF]) was included as a covariate to correct for interindividual premorbid brain size. To account for possible variability resulting from gender imbalance, gender was included as a covariate in the analysis. A conservative significance threshold of p ⬍ 0.05

2.2.3. Magnetic resonance imaging fMRI with a 3-Tesla head scanner (Siemens Allegra, Erlangen, Germany) was used to investigate the neural correlates of VSWM. High-resolution, sagittal T1-weighted images were acquired with a magnetization prepared rapid gradient echo (MP-RAGE) sequence to obtain a 3-D anatomical model of the brain. The anatomical data set consisted of 160 sagittal slices, field of view (FoV) 256 mm, slice thickness 1.00 mm, time-to-repeat (TR) 2250 ms, time-to-echo (TE) 2.6 ms, flip angle 90°. For functional analysis a total of 280 functional volumes were acquired. Each scan contained thirty-four 3-mm slices, positioned oblique to the axial plane using a transverse relaxation time, T2*-weighted echo planar imaging (EPI) sequence (TR ⫽ 2.0 seconds, TE ⫽ 30 ms, 3 ⫻ 3 ⫻ 3 mm3 voxel size, flip angle 90°). 2.2.4. Task performance analysis Group comparisons of the behavioral data were conducted using t test for independent samples in SPSS 18.0 (SPSS, Inc., Chicago, IL, USA). All tests were 2-tailed, unadjusted for multiple comparisons and a value of p ⬍ 0.05 was used to determine statistical significance. To investigate the effect of the interactions between the factors “cognitive status” and “genotype” on performance in visuospatial working memory task, we conducted a multifactorial analysis of variance (ANOVA) with “cognitive status” (aMCI, control) and “ApoE genotype” (with the ␧4 allele [E4⫹], and without [E4⫺]) as factors of interests.

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Table 3 N-back performance of young persons, healthy elderly, and aMCI patients Parameters Hit rates (portion) Zero-back Two-back Reaction times (msec) Zero-back Two-back

Healthy elderly

Young

Healthy vs. young t

p

aMCI

Healthy vs. aMCI t

0.92 (0.18) 0.82 (0.10)

0.95 (0.10) 0.86 (0.11)

⫺0.134 ⫺0.565

n.s. n.s.

0.88 (0.18) 0.72 (0.23)

551.03 (163.72) 761.62 (202.47)

587.08 (177.20) 733.02 (224.77)

⫺1.285 ⫺1.061

n.s. n.s.

583.57 (241.40) 871.34 (411.49)

p 1.822 3.033

⫺0.518 ⫺1.093

n.s. ⬍ 0.05 n.s. n.s.

Denoted are mean values (SD), t- and p-values. Key: aMCI, amnestic mild cognitive impairment; Healthy, healthy subjects; n.s., not significant; young, young subjects.

(family wise error, FWE) was determined with a minimum cluster size of 100 contiguous voxel to avoid type I error in the VBM data. Similar to the analysis of the EPI data, brain areas showing significant gray matter differences were labeled with anatomical loci and Brodmann areas by using the SPM5 extension WFU pick-atlas and then converted from the MNI in Talairach coordinates. To investigate the question whether GM changes are related to the performance level in VSWM task in healthy elderly and aMCI subjects, a multivariate linear regression with performance as the predictor of interest was carried out using SPM. 3. Results 3.1. Behavioral task performance Hit rates and reaction times were calculated for the visuospatial working memory task. We excluded 2 healthy elderly subjects (ApoE status not determined) and 11 MCI patients (8 ApoE ␧4 carriers) from the study after the fMRI data were collected. These were participants who could not perform the VSWM task with accuracy that exceeded performance by chance. Hence, accuracy rates across groups exceeded chance performance in every participant. Furthermore, it is important to note that healthy elderly and MCI patients remained matched with respect to age (healthy older adults [HC], mean age ⫽ 59.75 years; SD ⫽ 7.90; MCI mean age ⫽ 60.82; SD ⫽ 7.63) after excluding those subjects who could not perform above chance levels. In order to correct the comparisons between groups for the possible variability resulting from gender imbalance, we included gender as a covariate in all analyses. There were no significant differences in accuracy rate or in reaction times between young healthy subjects and elderly healthy persons (Table 3). Patients with diagnosed aMCI performed significantly less accurately than agematched healthy controls in VSWM task. Reaction times did not differ between healthy elderly persons and subjects with diagnosed aMCI. After accounting for the gender imbalance, performance of aMCI patients in VSWM task remained significantly less accurate compared with agematched healthy elderly (F(1,45) ⫽ 9.691; p ⫽ 0.003). Regarding the genotype, reaction times and accuracy rates

did not differ between groups, neither in age-matched healthy subjects nor in aMCI patients (F(3,44) ⫽ 0.461; p ⫽ not significant). After including gender as a covariate in the analysis with regard to genotype, the results remained not significant (F(1,42) ⫽ 0.838; p ⫽ not significant). 3.2. Imaging results Functional MRI for each group revealed an increased activation of distributed network of frontal and parietal areas that were involved in the VSWM n-back task (2-back task compared with 0-back; see Table 4). The results of the between groups comparisons are given in Table 5. The group comparison of the fMRI data revealed a significantly increased activation in healthy elderly persons compared with healthy young subjects bilaterally in posterior cingulate cortex, precuneus, and cingulate gyrus (Fig. 1A). The MCI group showed less activation than the group of healthy elderly controls in superior parietal lobule, middle frontal gyrus, inferior parietal lobule (see Fig. 1B). The analysis of functional data revealed more prominent (higher t values) differences in neural activation in healthy controls compared with aMCI after accounting for gender. Regarding the genotype, no significant differences in brain activation were observed between the ApoE ␧4 carriers and the noncarriers, neither in healthy subjects nor in aMCI patients. Accounting for the possible influence of gender did not change these results. In order to make the interpretation of the fMRI findings more plausible, we used strict exclusion criteria and excluded from the analysis 11 aMCI patients (8 ApoE ␧4 carriers) that did not perform in the 2-back task above chance levels. Furthermore, an analysis of the activation for the 0-back condition was performed to investigate any possible differences between groups due to an easy visuospatial task. However, there are no statistically significant differences in the activation between the agematched healthy controls and the aMCI groups in 0-back condition. Also, there were no statistically significant differences in the activation between excluded participants and aMCI patients that performed above chance levels.

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Table 4 Brain areas showing significant activation associated with VSWM task (2-back ⬎ 0-back) in all subject groups (p ⬍ 0.05, FDR corrected) Coordinatesb

tc

pd

4935 2594

34, ⫺2, 62 ⫺38, –50, 44

7.89 7.64

⬍ 0.001 ⬍ 0.001

40

1660

38, –46, 42

6.13

⬍ 0.001

L L L⫹R

45, 47 10, 9 7, 40

831 303 9846

⫺16, 2, 20 ⫺36, 38, 28 ⫺38, –52, 44

5.91 5.12 9.73

⬍ 0.001 ⬍ 0.001 ⬍ 0.001

L⫹R L⫹R L L

6, 9 18, 17 40 6, 9

33,954 1274 529 1427

42, 38, 30 6, –76, 10 ⫺46, –52, 50 ⫺46, 24, 36

9.43 5.28 6.47 6.32

⬍ 0.001 ⬍ 0.001 ⬍ 0.001 ⬍ 0.001

Group

Brain region

Hemisphere

BA

Young persons

Middle frontal gyrus Inferior parietal lobule, precuneus, superior parietal lobule Inferior parietal lobule, precuneus, superior parietal lobule Inferior frontal gyrus Middle frontal gyrus Inferior parietal lobule, precuneus, superior parietal lobule Middle frontal gyrus Cuneus, lingual gyrus Inferior parietal lobule Middle frontal gyrus, precentral gyrus, inferior frontal gyrus Superior frontal gyrus, medial frontal gyrus Middle frontal gyrus Precuneus, superior parietal lobule Middle frontal gyrus,superior frontal gyrus

R⫹L L

6 40, 7

R

Healthy elderly

aMCI patients

Cluster sizea

L

6, 8

659

10, 20, 48

5.82

⬍ 0.001

R L⫹R R

9 7 6

371 357 744

42, 28, 36 ⫺14, –74, 56 34, ⫺4, 60

5.19 5.14 4.95

⬍ 0.001 ⬍ 0.001 ⬍ 0.001

Key: aMCI, amnestic mild cognitive impairment; BA, Brodmann area; FDR, false discovery rate; L, left; R, right; VSWM, visuospatial working memory. a Numbers of activated voxels per cluster. b Coordinates (in mm) of peak voxels in activated brain regions according to Talairach stereotactic space. c Peak t value in activated cluster. d Peak p value in activated cluster.

3.3. Results of voxel-based morphometry The VBM analysis revealed that healthy elderly compared with young subjects showed significant atrophy bilaterally in the frontal lobes as well as in left middle occipital gyrus, cuneus, and precuneus (Fig. 2, Table 6). Patients with aMCI did not show any additional significant volumetric changes in any cortical region compared with the healthy elderly controls. Also, the genetic status had no significant influence on gray matter density in cortical and subcortical regions in the age-matched controls and aMCI patients. In order to investigate whether there are any statistically significant changes in the gray matter density due to the performance level in the visuospatial

working memory task, we conducted a multivariate linear regression analysis on the functional data. The multivariate linear regression analysis yielded no significant relationship between volumetric changes and performance level on the visuospatial working memory task in any of the investigated subject groups. There was also no influence of gender on these results. 4. Discussion In the present study, we have investigated the neural correlates of visuospatial information processing, in particular that required for visuospatial working memory, in

Table 5 Brain areas showing significant activation associated with VSWM task (2-back ⬎ 0-back; p ⬍ 0.05; FDR corrected) Group

Brain region

Hemisphere

BA

Cluster sizea

Coordinatesb

tc

pd

Healthy elderly ⬎ young persons

Posterior cingulate cortex Cingulate gyrus Cuneus Superior parietal lobule Middle frontal gyrus Inferior frontal gyrus Middle frontal gyrus Inferior parietal lobule

L L L⫹R L R L R R

31 18 7 9, 10, 46 13, 47 6, 8 7, 19, 39

277 91 110 819 724 267 265 569

⫺14, ⫺54, 14 ⫺4, ⫺44, 34 0, ⫺88, 18 ⫺30, ⫺68, 50 34, 46, 30 ⫺38, 22, ⫺2 36, 6, 54 40, ⫺52, 18

4.83 4.54 4.36 4.80 4.64 4.48 4.14 4.10

⬍ 0.001 ⬍ 0.05 ⬍ 0.05 ⬍ 0.001 ⬍ 0.001 ⬍ 0.05 ⬍ 0.05 ⬍ 0.001

Healthy elderly ⬎ aMCI patients

Key: aMCI, amnestic mild cognitive impairment; BA, Brodmann area; FDR, false discovery rate; L, left; R, right; VSWM, visuospatial working memory. a Numbers of activated voxels per cluster. b Coordinates (in mm) of peak voxels in activated brain regions according to Talairach stereotactic space. c Peak t value in activated cluster. d Peak p value in activated cluster.

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Fig. 1. Differences in activation during visuospatial working memory (VSWM) task (2-back ⬎ 0-back between healthy older adults (HC) and young controls (A) and between healthy elderly controls and amnestic mild cognitive impairment (aMCI) patients (B). Compared with young controls, healthy elderly participants showed increased activation in bilateral posterior cingulate, cuneus, and precuneus. Compared with aMCI patients, healthy elderly controls showed increased activation in superior parietal lobule, middle frontal gyrus, and inferior parietal lobule. Color bars represent t values. Abbreviations: left, left hemisphere; right, right hemisphere.

normal and pathological aging. We were able to replicate previous findings and provide new insights into the neural and cognitive changes that occur in aging processes in the healthy elderly and persons with diagnosed aMCI. Moreover, our study reveals the relationship between cognitive abilities on visuospatial information processing along with its neural correlates as revealed by fMRI. No significant effect of the ApoE status on cognitive function was found for the age-matched healthy elderly and aMCI patients. 4.1. Working memory abilities in normal and pathological aging Regarding visuospatial information processing with age, we observed no significant differences in performance on visuospatial working memory task between healthy elderly and young subjects. However, the ability to actively maintain information which is used immediately for task-solving is thought to decline monotonically over the life span (Hedden and Gabrieli, 2004). These contradictory findings may be explained by differences in the difficulty of tasks used to

investigate working memory. For example, Cornoldi et al. (2007) highlighted the role of the inhibitory functioning in older adults, which may cause poorer performance on very demanding working memory tasks compared with young persons. Also, Kemps and Newson (2006) reported significant decline of working memory abilities only in very elderly (85–93 years) compared with young (18 –25 years) adults, whereas no such decline appeared in performance analysis between elderly (65–74 years), more elderly (75– 84 years), and young adults. Thus, our results are consistent with previous findings on effect of age and working memory abilities. The significantly poorer performance of aMCI patients on working memory task observed in our study is in line with various recent neuropsychological studies (Égerházi et al., 2007; Patalong-Ogiewa et al., 2009; Saunders and Summers, 2010). Findings from neuropsychological longitudinal investigations reported that executive functions begin to decline 2–3 years before diagnosis of AD (Johnson et al., 2009) and are associated with the conversion to AD at 1 year follow-up (Rozzini et al., 2007). Neverthe-

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Fig. 2. Regional cortical atrophy in healthy controls compared with young controls. Compared with young controls, healthy participants showed decreased gray matter (GM) density in frontal lobe, middle occipital gyrus, cuneus, and precuneus. Color bar represents t values. Abbreviations: left, left hemisphere; right, right hemisphere.

less, no such evidence for reduced performance in episodic memory tasks was found in aMCI patients (Johnson et al., 2009; Rozzini et al., 2007). Thus, our findings illustrate that assessment of working memory changes in aMCI might be sensitive to early manifestations of AD. Furthermore, no ApoE-dependent differences in performance on a working memory task in any of the groups were found in our study, in contrast to several studies that reported reduced performance of ApoE ␧4 carriers in working memory tasks in healthy elderly (Parasuraman et al., 2002; Reinvang et al., 2010; Rosen et al., 2002) and

in AD patients (Wolk et al., 2010). However, considering the observations from other investigations on age-related changes in cognition and neural processes, aging is a phenomenon determined by a various factors (Baltes and Carstensen, 1996; Bosch et al., 2010; Freund, 2008; Freund and Baltes, 1998; Ouwehand et al., 2007). Therefore, we suggest that the presence of ApoE ␧4 allele may show its effect in combination with other components modulating cognitive performance with age, possibly such as education levels, lifetime occupations in leisure and cognitively stimulating activities, physical activity, and social life.

Table 6 Regional cortical atrophy (t ⫽ 4.78; p ⬍ 0.05, FWE; extend threshold k ⫽ 100 voxels) Group

Brain region

Hemisphere

BA

Cluster sizea

Coordinatesb

tc

pd

Young persons ⬎ healthy elderly

Frontal lobe Middle occipital gyrus Cuneus, precuneus

L⫹R L L

6, 10, 40 18, 19 19

639,214 190 716

⫺2, –83, ⫺25 ⫺40, –95, 1 ⫺21, –83, 37

9.63 5.08 4.75

⬍ 0.001 ⬍ 0.05 ⬍ 0.05

Key: BA, Brodmann area; FWE, family-wise error; L, left; R, right. a Numbers of voxels per cluster. b Coordinates (in mm)of the peak values of brain regions according to Talairach stereotactic space. c Peak t value in cluster of significant regional brain atrophy. d Peak p value in cluster of significant regional brain atrophy.

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4.2. Neural correlates of visuospatial working memory in normal and pathological aging From the analysis of the neural activation of normal elderly compared with young subjects during the performance of our visuospatial working memory task, we found strong evidence for compensatory mechanism in occipital areas engaged in healthy elderly to cope with cognitive challenges related to a visuospatial working memory task. In contrast, Cabeza et al. (2004) reported weaker occipital activity and more pronounced prefrontal and parietal activity in elderly compared with younger adults with respect to working memory. In their study, Cabeza et al. (2004) argued that bilateral patterns of prefrontal activity in older adults may reflect functional compensation. Because our study showed bilateral neural activation pattern in both healthy elderly and young subjects during the performance of a visuospatial working memory task (Table 4), we assume that the increased activation in the occipital lobe (precuneus, cuneus) is evidence for a task-dependent compensatory mechanism in healthy elderly adults. In the study of Cabeza et al. (2004) a word delayed-response test was used to investigate working memory, whereas a more demanding visuospatial working memory task was applied in our study, which evoked a different pattern of activation. Furthermore, several studies have suggested an important role of occipitotemporal regions in the encoding and the online maintenance of spatial information (Berman and Colby, 2002; Bledowski et al., 2009; LaBar et al., 1999; Postle and D’Esposito, 1999). Hence, we believe that our results provide new insights in the role of cuneus and precuneus in the occipital lobe related to cognitive changes occurring with age. Compared with healthy elderly adults, the group of aMCI patients showed less activation in bilateral inferior frontal gyrus, middle frontal gyrus, and superior frontal gyrus as well as in left superior-inferior parietal lobe and thalamus together with significant lower performance on the visuospatial working memory task. These results contrast with findings of Yetkin et al. (2006) who reported an increased extent of activation in the right superior frontal gyrus, bilateral middle temporal, middle frontal, anterior cingulate, and fusiform gyri in MCI patients in comparison with healthy elderly controls. However, our findings are consistent with the recent observation by Kochan et al. (2010) where neural responses to a graded working memory challenge in MCI patients were investigated. Their findings suggest that the task difficulty may cause variability in the patterns of activation in patients with MCI and AD. Correspondingly, the difference in our results and those of Yetkin et al. (2006) study may be due to our use of a more demanding task. Furthermore, we postulate that the larger sample size employed in our study better reflects the heterogeneous nature of aMCI. There is also evidence that occipital areas as well as posterior cingulate are involved in

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spatial working memory (Mesulam et al., 2001; Michels et al., 2008; Vogt et al., 1992) and that metabolic pathology can occur in occipitoparietal regions at an earlier stage of AD than it is currently recognized (Caine et al., 2001; Nestor et al., 2003). Overall, our findings on performance and neural activity regarding working memory abilities in healthy elderly and in aMCI suggest that visuospatial working memory tasks may serve as a prognostic tool for early detection of AD. Once again, we note that 11 of our aMCI patients had to be excluded from the study owing to their poor performance on the 2-back task in the scanner. These patients showed significantly less activation in all regions noted above. In order to establish whether a severe visuospatial disability is related to an early stage of the AD, all poorly performing patients are included in the follow-up examination (Alichniewicz et al., unpublished observations). The absence of an influence of the genotype status on the neural activity related to the working memory abilities is inconsistent with findings of Wishart et al. (2006). However, our findings are in line with the study by Lahiri et al. (2004) who argued that ApoE ␧4 allele status has a relatively more selective effect on episodic memory than on decline in other cognitive systems in AD. In addition, a recent meta-analysis of the effect of the ApoE ␧3/␧4 allele status revealed no significant differences on global cognitive ability, including visuospatial skills, between cognitively healthy ApoE ␧4 carriers and noncarriers (Wisdom et al., 2011). Moreover, the study by Wisdom et al. (2011) was also unable to confirm previous findings supporting the relationship between zygosity, the so-called gene dose effect, and measures of global cognitive performance. Thus, we speculate that although the presence of ApoE ␧4 allele may modulate the AD phenotype, there are other factors that may have a major influence on the pathogenesis of AD and its prodromal stage. This is consistent with previous suggestions regarding contribution of the genotype status to developing AD, which underlined the possible involvement of several genes, other genetic polymorphisms, inflammatory processes, and a synergistic interaction with environmental risk factors (Van Broeckhoven et al., 1987; Corder et al., 1993; Kamboh et al., 2006; Lahiri et al., 2004; Sastre et al., 2006; Small and Duff, 2008; St. George-Hyslop et al., 1990). 4.3. Volumetric changes in brain associated with normal aging, aMCI, and genotype status The outcomes of the analysis of the volumetric changes associated with normal aging are consistent with previous studies of the effects of aging on structural changes in the brain (Fjell and Walhovd, 2010; Haug and Eggers, 1991; Kalpouzos et al., 2009; Raz et al., 2005; Resnick et al., 2003). We observed in healthy elderly compared with young participants an accelerated loss of gray matter in frontoparietal areas (Fig. 2), which are connected to the brain regions involved in working memory, problem solv-

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ing, decision making, judgment, and integration processes. Our findings support the idea represented by Grieve et al. (2005) that cortical regions, which mature late in individual development, are more vulnerable to age-related morphologic changes. Volumetric changes in temporal and posterior-parietal cortex were also reported by a variety of studies on neurostructural changes with age (Courchesne et al., 2000; Good et al., 2001; Tisserand et al., 2004). Clusters of less significant gray matter loss were found in occipital lobe which agrees with previous findings (Raz et al., 2005). Despite the fact that brain volume differences appear with age, there is no general consensus regarding the association between specific components of cognitive aging and age-related gray matter reduction. In a longitudinal study by Tisserand et al. (2004), a cognitive decline in 37 healthy elderly associated with decreased gray matter density in prefrontal cortex, temporal lobes, and posterior parietal cortex was reported whereas Staff et al. (2006) argued, based on findings from 98 nondemented subjects, that there is no evidence for a significant volume loss in any area which would be related to specific cognitive measures. Our findings from the regression analysis revealed no statistically significant relationship between the ability to perform VSWM task and gray matter loss in any of groups and thus support this idea. The lack of significant differences in structural gray matter volume between aMCI patients and the elderly control group disagrees with some previous studies (Frank et al., 2011; Hämäläinen et al., 2008; Whitwell et al., 2007) and confirms others (Karas et al., 2004). A factor which may explain these apparent contradictory findings is a small number of subjects which were investigated in our study compared with samples from investigations focused only on the volumetric changes in MCI in which samples consisted of 900 MCI patients (e.g., Nickl-Jockschat et al., 2012). The heterogeneity of the MCI disorder may also play an important role in the interpretation of our findings. Frisoni et al. (2007) point out that persons with early onset and late onset AD show differences in the typical topographic patterns of brain atrophy and suggested possible different predisposing or etiological factors involved. This also could be true for persons with MCI. Moreover, the investigation of substantial brain shrinkage is not a sufficient but supportive examination for diagnosis of Alzheimer’s disease and related disorders (Dubois et al., 2007; Wolf, 2009). Our findings indicate that pathological changes in gray matter may occur later in life than do the functional changes. The absence of an influence of the genotype status on the neurostructural changes in the brain found in our study is consistent with previous findings of neuroimaging studies (Adamson et al., 2010; Cherbuin et al., 2008). However, there is still controversy regarding the role of ApoE as a genetic risk factor for developing AD. For example, Wolk et al. (2010) compared phenotypic differences in psychometric tests and regional cortical atrophy of patients with mild AD

where they found that ApoE genotype modulates the clinical phenotype of this disease. In this study, carriers showed greater impairment on measures of memory retention and displayed greater middle temporal lobe atrophy, whereas noncarriers were more impaired on tests of working memory, executive control, and lexical access and had greater frontoparietal atrophy (Wolk et al., 2010). Further, Lemaître et al. (2005) studied the effect of ApoE genotype on gray matter atrophy on a cohort of 750 healthy elderly volunteers and argued that cortical atrophy and cognitive performances of healthy elderly are limited only to ApoE ␧4 homozygous subjects. Because our population included only 1 person with diagnosed aMCI and 1 healthy elderly person who are ApoE ␧4 homozygous, we cannot exclude that lack of the differences in the cortical atrophy could be due to our inability to distinguish between heterozygous and homozygous ApoE ␧4 carriers. Recently, it has been highlighted that the presence of biomarkers is linked to so-called AD pathology in clinically normal older individuals, which may cause a development of the disease later in life (Jack et al., 2011; Sperling et al., 2011). Because 1 of the key genetic factors to play the role in late life pathogenesis of AD is ApoE ␧4 (Raber et al., 2004), it cannot be ruled out that the lack of significant differences in the gray matter density between aMCI patients and age-matched healthy elderly in our study may be due to the presence of this biomarker in the group of healthy controls. It has to be noted, however, that ApoE ␧4 carrier status by itself does not predict cognitive decline or conversion to AD (Devanand et al., 2005; Marquis et al., 2002). Furthermore, the strongest anatomical correlate of the degree of clinical impairment associated with AD is synapse loss (Selkoe, 2002; Shankar et al., 2007). There is evidence from postmortem studies that the ApoE ␧4 allele dosage does not predict cholinergic activity or synapse loss in Alzheimer’s disease (Blennow et al., 1996; Corey-Bloom et al., 2000; Scheff et al., 2006). Moreover, current literature suggests that other neuropathologic changes and genes are required to unmask the effect of ApoE ␧4 on such factors like sterol or sphingolipid metabolism in the brain (Bandaru et al., 2009) or CSF biomarkers (Kim et al., 2011) which are also associated with AD. Recent genome-wide association studies have shown that causative and susceptibility genes (i.e., APP, A2M, ApoE, PSEN1, and PSEN2) tend to be interconnected in AD (Bertram and Tanzi, 2008, 2009; Bertram et al., 2007; Tanzi and Bertram, 2001) suggesting that molecular mechanisms implicated in this disorder are very complex. Therefore, the effect of ApoE ␧4 on gray matter density in our study may be masked by other genetic and environmental factors. Future studies need to determine whether a combination of biomarkers will prove useful in predicting structural changes in the brain, cognitive decline, and progression to clinical stages of AD.

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4.4. Strengths and limitations of the present study A great strength of this fMRI study is the large overall size of examined subject samples, which increases the statistical power and allows generalizing our findings to the broader population. Furthermore, given the fact that the diagnosis of MCI includes various subtypes which are possibly caused by different etiologies (Petersen, 2004), we narrowed our investigation to amnestic MCI, which has the highest likelihood of converting to AD amongst all of the MCI subtypes (Petersen, 2004). To avoid biasing effects, we controlled for potential confounding factors, such as demographical variables, neuropsychological status, and comorbidities. Also, the experiment was conducted as a double-blind study, which allowed us to eliminate the examiner-related biasing effects regarding genetic status of the subjects. A further strength of our study is related to our comparison among activity measurements during 2 cognitive tasks to account for possible differences in baseline activation observed during resting state between elderly and MCI patients (Greicius, 2004; Rombouts et al., 2005). There are also limitations to this study. The first point of criticism is the standard hemodynamic response function approach that we used to analyze fMRI data. Some recent studies demonstrated that the most commonly used general linear model analysis is not as sensitive to temporal change in activation and deactivation patterns in dementia as exploratory data analysis techniques such as independent component analysis (ICA) (Rombouts et al., 2009). Hence, we cannot eliminate the possibility that we may have not been able to detect additional unknown group differences in our neuroimaging data. Also, it can be criticized that we excluded from our study 11 aMCI patients (8 ApoE ␧4 allele carriers and 3 ApoE ␧4 allele noncarriers) who could not perform above chance in the visuospatial working memory task, which could lead to an incomplete picture of aMCI. Furthermore, it should be noted that our healthy elderly control subjects showed a higher rate of the ApoE ␧4 allele compared with reported population estimates. Such selection effects could have led to a slight bias in the elderly control group. This could have affected the power of our comparisons with respect to the effect of ApoE ␧4 allele on our results. As we mentioned above, individual differences in ability to perform a visuospatial working memory task among aMCI patients are in agreement with a number of studies illustrating the heterogeneous nature of this disorder. However, we used strict exclusion criteria in order to make the interpretation of the fMRI findings more plausible. Furthermore, owing to ethical reasons, we examined the effect of ApoE ␧4 allele only in healthy elderly and persons with diagnosed aMCI. Thus, we did not examine the possible role of the genotype status on cognitive and changes in young healthy persons. We also cannot exclude that the

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genotype-dependent differences might have been manifested if we used an even larger subject sample. 4.5. Conclusions Our study focused on the relationship between visuospatial working memory with its neural correlates in young persons, healthy elderly, and aMCI patients. In addition, we examined the effect of ApoE ␧4 status on our results. We have demonstrated that healthy elderly show an overactivation in occipital regions in case of limited cognitive declines on visuospatial working memory task. Pathological declines in performance and neural activity with regard to the same task were found within aMCI patients, which occurred in addition to well-known neuropsychological changes in this group. Hence, further investigation, especially in longitudinal studies, would be required to address the question whether assessment of visuospatial working memory abilities in persons with aMCI might be sensitive to detect early manifestations of AD. Furthermore, significantly decreased brain volumes in frontoparietal areas have been observed in healthy elderly compared with young persons, supporting the idea that later-maturing cortical regions are more vulnerable to agerelated morphologic changes. No such volumetric changes in the aMCI group indicate that altered patterns of neural activation in this group cannot be explained with structural changes. The lack of significant differences in working memory task performance, its neural correlates, and gray matter density between ApoE ␧4 carriers and noncarriers suggest that aging is a phenomenon determined by a combination of genetic and environmental factors. In conclusion, the present study contributes to our understanding of age-appropriate as well as pathological neural processes in the elderly and underlines the importance of working memory abilities as a hallmark to distinguish between pathological and normal aging. Disclosure statement The authors disclose no conflicts of interest. Participants provided written informed consent prior to the commencement of the study, in accordance with the requirements of the Code of Ethical Principles for Medical Research Involving Human Subjects of the World Medical Association (Declaration of Helsinki). The procedures used in this study were approved by the Ethical Committee of the University of Regensburg. Acknowledgements The authors thank Prof. G. Schmitz (Institut für Medizinische Chemie) for genetic testing of blood samples. K.K.A. gratefully acknowledges the Bavarian Academic Center for Central, Eastern and Southeastern Europe (BAY-

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