Neural underpinnings of background acoustic noise in normal aging and mild cognitive impairment

Neuroscience 310 (2015) 410–421

NEURAL UNDERPINNINGS OF BACKGROUND ACOUSTIC NOISE IN NORMAL AGING AND MILD COGNITIVE IMPAIRMENT INDRIT SINANAJ, a,b,c* MARIE-LOUISE MONTANDON, c CRISTELLE RODRIGUEZ, c FRANC¸OIS HERRMANN, d FRANCESCO SANTINI, e SVEN HALLER f AND PANTELEIMON GIANNAKOPOULOS c

ential impact on WMN activation in normal aging as a function of the cognitive status. Only louder noise has a disruptive effect on the usually observed DMN deactivation during WM task performance in HC. In contrast, MCI cases show altered DMN reactivity even in the presence of lower noise. Ó 2015 IBRO. Published by Elsevier Ltd. All rights reserved.

a Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, Switzerland b Swiss Center for Affective Sciences, University of Geneva, Switzerland c Division of General Psychiatry, Department of Mental Health and Psychiatry, University Hospitals of Geneva, Geneva, Switzerland

Key words: mild cognitive impairment (MCI), background acoustic noise, default mode network (DMN), working memory network (WMN), posterior cingulated cortex (PCC).

d

Division of Geriatrics, Department of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva and University of Geneva, Switzerland e

Division of Radiological Physics, Department of Radiology, Clinic of Radiology and Nuclear Medicine, University of Basel Hospital, Basel, Switzerland

INTRODUCTION Performing a memory task in a noisy environment occurs frequently, and may require additional cognitive effort to overcome distraction (Sorqvist, 2010). Working memory (WM) is a cognitive function that is highly dependent on attentional resource recruitment. Functionally, it has been proposed that efficient WM performance relies on at least two large-scale networks operating in concert: the default mode network (DMN) and working memory network (WMN) (Pihlajamaki and Sperling, 2009). The DMN is a set of regions that are usually more active during rest in externally directed tasks (Buckner et al., 2008). Main core regions of the DMN include midline brain structures such as the medial prefrontal cortex/anterior cingulate (BA6), precuneus, posterior cingulate, inferior parietal lobule, middle temporal gyrus/lateral temporal cortex as well as hippocampal formation (Buckner et al., 2008). The WMN comprises fronto – parietal regions such as the superior, middle and inferior frontal gyri in addition to the inferior parietal lobules, typically activated when individuals perform a WM task (Cabeza and Nyberg, 2000; Corbetta and Shulman, 2002; Haller et al., 2005; Owen et al., 2005). This network is formed by limitedcapacity brain circuits that may be saturated due to increased attention-related cognitive demands (Baddeley, 2003). Activation of the WMN and deactivation of DMN during task performance may reflect reallocation of cerebral resources in order to guarantee WM performance (Daselaar et al., 2004; Fox et al., 2005; Buckner et al., 2008; Kelly et al., 2008; Christoff et al., 2009; Pyka et al., 2009). An inability to deactivate the different components of the DMN may lead to decreased WM performances (Mayer et al., 2010). A modulation of the WMN due to an alternative demand for neurocognitive resources can be reliably induced by background acoustic noise, as demonstrated

f

Division of Neuroradiology, University Hospitals of Geneva, Geneva, Switzerland

Abstract—Previous contributions in younger cohorts have revealed that reallocation of cerebral resources, a crucial mechanism for working memory (WM), may be disrupted by parallel demands of background acoustic noise suppression. To date, no study has explored the impact of such disruption on brain activation in elderly individuals with or without subtle cognitive deficits. We performed a functional Magnetic Resonance Imaging (fMRI) study in 23 cases (mean age = 75.7 y.o., 16 men) with mild cognitive impairment (MCI) and 16 elderly healthy controls (HC, mean age = 70.1 y.o., three men) using a 2-back WM task, under two distinct MRI background acoustic noise conditions (louder vs. lower noise echo-planar imaging). General linear models were used to assess brain activation as a function of group and noise. In both groups, lower background noise is associated with increased activation of the working memory network (WMN). A decrease of the normally observed deactivation of the default mode network (DMN) is found under louder noise in both groups. Unlike HC, MCI cases also show decreased deactivation of the DMN under both louder and lower background noise. Under louder noise, this decrease is observed in anterior parts of the DMN in HC, and in the posterior cingulate cortex in MCI cases. Our results suggest that background acoustic noise has a differ-

*Correspondence to: I. Sinanaj, Department of Fundamental Neuroscience, Faculty of Medicine, University of Geneva, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland. E-mail address: [email protected] (I. Sinanaj). Abbreviations: ANCOVA, analysis of co-variance; DMN, default mode network; EPI, echo planar imaging; fMRI, functional Magnetic Resonance Imaging; HC, healthy elderly controls; MCI, mild cognitive impairment; PCC, posterior cingulate cortex; WM, working memory; WMN, working memory network. http://dx.doi.org/10.1016/j.neuroscience.2015.09.031 0306-4522/Ó 2015 IBRO. Published by Elsevier Ltd. All rights reserved. 410

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by previous reports in children and young cognitively preserved individuals (Papso and Blood, 1989; Ando, 2001; Tremblay et al., 2001; Haller et al., 2005, 2009; Tomasi et al., 2005; Soderlund et al., 2010). In their 3-back activation study, Tomasi et al. (2005) reported deactivation of midline structures such as the medial frontal and anterior cingulate gyri for the comparison ‘loud’ > ‘quiet’ coupled with larger BOLD signal in WM regions suggesting increased requirements for attentionnetwork resources during louder scans to compensate for the interference of scanner noise. Other studies applying the same paradigm with equivalent acoustic noise protocols as in the current investigation demonstrated an interruption of the WMN during the loud condition and increased WMN activity with a decrease of the expected deactivation in the DMN during the silent condition (Haller et al., 2005, 2009). The modified background acoustic noise in the above-mentioned studies is ‘less distractive’ compared with the ‘more distractive’ pulsating background noise during classic echo planar imaging (EPI). Differences between these acoustic noises arise not only from differences in sound pressure levels but also from the presence of pulsations in the frequency range of about 10 Hz for classic EPI, which is a physiologically particularly potent stimulus (Haller et al., 2005, 2009; Seifritz et al., 2006). Unlike younger cohorts, the impact of background acoustic noise on elderly individuals with or without cognitive deficits is still poorly understood. Although auditory activity is attenuated during a visually engaging task in all age groups, elderly individuals continue to show greater auditory processing than their younger counterparts (Hugenschmidt et al., 2009). Additionally, elderly populations are more easily distracted (Gazzaley et al., 2005) and this seems to be aggravated in those with mild cognitive impairment (MCI) (Pilcher, 2005). Previous work on recognition memory has indicated abnormalities in the functional deactivations of the DMN related to aging and Alzheimer dementia (Lustig et al., 2003). We aimed to explore the impact of two types of background MRI acoustic noise (lower modified EPI sound vs. higher classic EPI sound) on functional status of WMN and DMN in MCI as compared to cognitively intact healthy elderly controls (HC) during a 2-back WM task. The a priori hypothesis was that MCI relative to HC would display a decreased deactivation of DMN. Additionally, we hypothesized that under more silent scanning condition, all of the elderly subjects would preferentially activate the WMN, whereas noisier scanning conditions would interrupt the usually observed deactivation of the DMN. MCI cases would be more vulnerable to background noise displaying decreased DMN deactivation under both noise conditions.

EXPERIMENTAL PROCEDURES Population This study was approved from the Ethics Committee of the University Hospitals of Geneva and announced in local media. Extensive information on the background and goals of this study were provided to individuals who

411

contacted the study center. Assessment of major medical conditions (cancer, cardiac illness, trauma, psychiatric or neurological disorders, and alcohol or drug abuse) and MRI compatibility was subsequently performed via telephone screening. After this initial phase, 39 elderly individuals were included in the study after providing written informed consent prior to inclusion. All had normal or corrected-to-normal vision. Past hearing problems were identified as a part of the medical interview (including both patients and their proxies). All cases with such problems were a priori excluded. Audition was tested by standard audiologic tests including self-report and speech in noise perception in all the cases in routine medical examination (Pronk et al., 2013). Cases with self-report of hearing loss and altered speech in noise perception were addressed in specialized consultation and were not considered for further investigations. After neuropsychological testing, two group categories were considered: cognitively intact HC and MCI subjects. The final sample included 16 HC (mean age = 70.12 y.o., standard deviation (SD) = 1.4 y.o., three males) and 23 MCI (mean age = 75.69 y.o., SD = 6.27 y.o., 16 males). Neuropsychological classification of the HC and MCI cases A full battery of neuropsychological tests was used to assess cognitive functions in the present series. Participants were administered Mini-Mental State Examination (or MMSE) (Folstein et al., 1975), the Hospital Anxiety and Depression Scale (or HAD) (Zigmond and Snaith, 1983), and the Lawton Instrumental Activities of Daily Living (or IADL) (Barberger-Gateau et al., 1992). Cognitive assessment included (a) attention (Code (Wechsler, 1997), Trail-Making Test A (Reitan, 1958)), (b) WM (verbal: Digit Span Forward (Wechsler, 1997), visuospatial: Visual Memory Span [Corsi] (Wechsler, 1997)), (c) episodic memory (verbal: RI-48 Cued Recall Test (Adam et al., 2007); visual: Shapes test (Baddeley et al., 1994); executive functions: Trail-Making Test B (Reitan, 1958), Wisconsin Card Sorting Test [or WCST] (Heaton, 1981), and Phonemic Verbal Fluency test (Cardebat et al., 1990), (d) language (Boston Naming (Kaplan et al., 1983)), (e) visual gnosis (Ghent Overlapping Figures (Ghent, 1956)), (f) ideomotor (Schnider et al., 1997), (g) reflexive (Poeck, 1985), and (h) constructional praxis (Consortium to Establish a Registry for Alzheimer’s Disease [or CERAD] Figures copy (Welsh et al., 1994)). Education level was defined according to the Swiss scholar system, as follows: level 1, less than 9 years (primary school); level 2, between 9 and 12 years (high school); and level 3, more than 12 years (university). All individuals were also evaluated with the Clinical Dementia Rating scale (or CDR) (Hughes et al., 1982). In agreement with the criteria of Petersen (2004), participants with a Clinical Dementia Rating scale (Hughes et al., 1982) of 0.5 but no dementia and a score more than 1.5 SDs below the age-appropriate mean in any of the previously mentioned tests were confirmed to have MCI. Subjects with Clinical Dementia Rating score of 0 as well

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as scores within 1.5 SDs of the age-appropriate mean in all other tests were classified in the control group. WM task All participants performed a classical 2-back task (Owen et al., 2005) while lying in the MRI scanner. This task requires participants to react by pressing a button either each time a specific letter e.g. ‘‘x” is presented on the screen (0-back condition), or if the letter currently displayed on the screen was presented two items ago, e.g. ‘‘a m n m” (2-back condition). Whether the former condition requires mere identification of the target letter, hence engaging visual identification networks, in the latter subjects are required to maintain a history of the presentation of the items. Therefore, the 2-back condition engages remarkably the WMN in addition to the visual identification network. Participants received a training session outside the MRI scanner. The MRI session was organized in four runs of 8-min duration; two runs were conducted in the MOD and other two in the CLASSIC EPI sequence. The order of the type of noise was pseudo-randomized. Stimuli were pseudorandom sequences of consonants (randomly varying in case), presented in the center of the visual field (500-ms duration, 2500-ms stimulus onset asynchrony). Each run comprised 10 pseudo-randomized blocks of 35 s each (five per type of task). Blocks were separated by a 15-s period of rest to allow the stabilization of the hemodynamic response back to baseline. Blocks started with a cue screen announcing the type of judgment to perform in the upcoming trials e.g. ‘‘0 Back” or ‘‘2 Back”. Participants were instructed to emphasize equally accuracy or speed in task completion.

MR image acquisition We used exactly the same scanning protocol as described previously in detail (Haller et al., 2005, 2009). Functional imaging time series were acquired in a 3.0T MR scanner (TRIO, Siemens medical systems, Erlangen, Germany) through a classic echo-planar imaging (CLASSIC) and a modified-sound echo-planar imaging (MOD) sequence. Despite the fact that these sequences have identical fundamental parameters (echo spacing = 0.86 ms, bandwidth = 1280 Hz/px, TE = 61 ms, TR = 2.56 s, 122-ms per slice, flip angle = 90°) and spatial resolution, they differ in how they are perceived. Indeed, CLASSIC is perceived as a pulsating more distracting sound whereas MOD is a continuous and less distracting sound. The different sound envelope oscillations of MOD and CLASSIC are represented by pulsatile and wider spectral peak separations of the Fourier transform of their associated scanner noise (Fig. 1). Additionally, a two-sample T-test between the sound pressure level (SPL) between MOD (mean = 51.42, SD = 0.13) and CLASSIC (mean = 52.39, SD = 0.33) calculated per one fourth of a second window size revealed a significant difference (T-value = 4.39*103, p < 0.001). The matrix size was 64  64 (FOV 192 mm  192 mm) and 21 slices were acquired (5-mm slice thickness, 1-mm gap) that covered the whole brain. The first three volumes were discarded from further analysis to avoid non-steady-state saturation effects. After functional scanning, highresolution anatomical 3DT1 data were acquired (1 mm isotropic T1w MPRAGE, Matrix 256  256, 176 slices) which were used for co-registration and spatial normalization.

Fig. 1. Spectra and SPL plots of MOD (left panel) and CLASSIC (right panel) EPI noise.

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Behavioral analysis Behavioral analysis was performed with Matlab 2014 (MATLAB and Statistics Toolbox Release 2014b, MathWorks Inc., Natick, Massachusetts, United States). Reaction time, accuracy as well as functional Magnetic Resonance Imaging (fMRI) data were compared across the diagnostic groups using a repeated-measures analysis of co-variance (ANCOVA) with task (0-back, 2-back), noise (CLASSIC, MOD) as within-subject factors and group (HC, MCI) as between-subject factors. For all ANCOVA-tested variables, statistical threshold was set at p < 0.05 after Huynh–Feldt correction for non-sphericity when appropriate. Post-hoc analysis was made with p < 0.05 as significance threshold after Bonferroni correction for multiple comparisons.

comparison) was assessed individually. At the third level, the contrast HC > MCI (and the opposite comparison) was computed. Higher-level analysis was carried out using a fixed effects model, by forcing the random effects variance to zero in FLAME (FMRIB’s Local Analysis of Mixed Effects) (Beckmann et al., 2003; Woolrich et al., 2004; Woolrich, 2008). Z (Gaussianised T/F) statistic images were thresholded using clusters determined by Z > 2.3 and a corrected cluster significance threshold of p = 0.05. The same analysis was repeated using age and gender as non-explanatory coregressors. As results of this second analysis were almost identical to the first analysis without age and gender as non-explanatory co-regressors, we only report the activations of the first analysis. Anatomic location of the activation clusters was determined using ‘‘atlasquery”, part of FSL, and the Harvard–Oxford Cortical Structural Atlas.

GLM analysis of task-related activation Task-related GLM data processing was carried out using FEAT (FMRI Expert Analysis Tool) Version 5.98, part of FSL (FMRIB’s Software Library, www.fmrib.ox.ac.uk/fsl). At the first level, the contrast of 2-back vs. 0-back was calculated separately for each run of each participant. At the second level, the intra-participant difference in the two runs of CLASSIC vs. MOD (and the opposite

RESULTS Behavior Overall, no floor effect was observed in the performance for the two groups and two task conditions and two types of noise (group  task  noise). As expected, we found a main effect of task on RT and performance

Fig. 2. Bar plots of mean over subjects for accuracy (panel A) and reaction times (panel B) partitioned with respect to each of the experimental conditions (0-back, 0B vs. 2-back, 2B) per group (MCI, HC) and noise (MOD vs. CLASSIC). Red star represents significant differences (p < 0.05, Bonferroni corrected for multiple comparisons). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 1. (A) Accuracy per condition (in decimal). SD = standard deviation. (B) Summary of post hoc T-tests for accuracy (in decimal) (A) 0-back

2-back

MOD

CLASSIC

MOD

CLASSIC

HC

Mean = 0.98 SD = 0.02

Mean = 0.98 SD = 0.01

Mean = 0.86 SD = 0.05

Mean = 0.89 SD = 0.04

MCI

Mean = 0.77 SD = 0.27

Mean = 0.81 SD = 0.27

Mean = 0.70 SD = 0.23,

Mean = 0.74 SD = 0.24

T value

(B) Condition

Comparison

P Bonferroni corrected

CLASSIC 2-back MOD 2-back CLASSIC 0-back MOD 0 back

MCI MCI MCI MCI

0.015 0.03 0.02 0.0084

vs. vs. vs. vs.

CI CI CI CI

accuracy (FRT = 44.83, pRT < 0.001, Faccuracy = 10.96, paccuracy < 0.001, respectively). This was also the case for the effect of group with worst accuracy scores for MCI (FRT = 14.91, pRT < 0.001, Faccuracy = 39.54, paccuracy < 0.001, respectively). Additionally, there was a main effect of louder noise on RT (FRT = 9.62, pRT = 0.002) but not on performance accuracy (Fig. 2). No significant two- and three-way interactions were found between Task and Noise, Noise and Group, Group and Task and, between Group and Noise and Task. Post hoc testing revealed that MCI cases displayed lower performance accuracy in both noise conditions and for both tasks (all p values Bonferroni corrected <0.05). A summary of the behavioral results is presented in Table 1(A, B).

3.3 3.07 3.2 3.5

Functional MRI

Effect of acoustic background noise. For the HC group, the contrast MOD > CLASSIC yielded activations in the bilateral middle frontal gyrus (BA 9, 10), left superior frontal gyrus (BA 6), left fusiform gyrus (BA 37) (Fig. 3A). The opposite contrast, CLASSIC > MOD, revealed decreased deactivation in left anterior cingulate (BA 24), left caudate head, as well as different portions of the left middle frontal gyrus (BA 6,8,9) (Fig. 3B). For the MCI group, the contrast of MOD > CLASSIC revealed higher activations in right precuneus (BA 7), right superior frontal gyrus (BA 6), right medial frontal gyrus

Fig. 3. Brain activity maps for the second-level contrast MOD > CLASSIC in the top panel, (A) for HC and (C) for MCI. Brain activity maps for the second-level contrast CLASSIC > MOD in the bottom panel, (B) for HC and (D) for MCI.

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(BA 6) and right cingulate gyrus (BA 24), (Fig. 3C). The opposite contrast, CLASSIC > MOD, revealed decreased deactivations in midline brain structures such as the right medial frontal gyrus (BA 6), left middle frontal gyrus (BA 9), right anterior cingulate (BA 24), bilateral precuneus (BA 19), right middle frontal gyrus (BA 9, 46), left superior temporal gyrus (BA 38) and middle temporal gyrus (BA 21) (Fig. 3D). A summary of the results for these contrasts with the corresponding Z-scores, and MNI coordinates is presented in Table 2. Effect of group. HC displayed significantly higher fronto–parietal WMN activation compared to MCI cases under both MOD and CLASSIC noise. Under MOD

noise, there was higher activation of the left inferior frontal gyrus (Brodmann area 9), and left inferior parietal lobule (Brodmann area 40) in HC compared to MCI cases (Fig. 4A), whereas under CLASSIC noise such differences were found in the inferior parietal lobule (Brodmann area 40), left inferior frontal gyrus (Brodmann area 9) and right middle frontal gyrus (Brodmann area 9), (Fig. 4B). An illustration of the fMRI contrast HC > MCI, for CLASSIC (in blue) and MOD (in red) is presented in Fig. 4E. For both types of noise, the opposite contrast, MCI > HC revealed decreased deactivation of midline DMN structures. Under MOD noise, this decrease concerned the anterior subnetwork DMN nodes in left

Table 2. Summary of whole brain results (p < 0.05, corrected for multiple comparisons) for the second-level contrast MOD > CLASSIC in the top panel A: for HC and C: for MCI, as well as, for the second-level contrast CLASSIC > MOD in the bottom panel B: for HC and D: for MCI HC Z-value

MCI Coordinates (in mm) x

MOD > CLASSIC

CLASSIC > MOD

y

Anatomical label(aal)

Z-value

z

Coordinates (in mm) x

6.38

24

10

70

6.08

34

26

42

6.02

36

30

38

4.32

34

44

26

4.37

34

36

16

6

32

4

11

10

14

2

10.1

54

14

42

9.68

56

16

34

5.76

28

18

60

4.07

30

30

54

12.3

Side

y

Anatomical label(aal)

Side

z

Superior Frontal Gyrus (Brodmann area 6) Middle Frontal Gyrus (Brodmann area 9) Middle Frontal Gyrus (Brodmann area 9) Middle Frontal Gyrus (Brodmann area 10) Fusiform Gyrus (Brodmann area 37)

L

3.62

14

48

60

Precuneus (Brodmann area 7)

R

L

4.68

16

12

70

R

L

4.11

12

0

62

Superior Frontal Gyrus (Brodmann area 6) Medial Frontal Gyrus (Brodmann area 6)

R

3.73

8

12

44

Cingulate Gyrus (Brodmann area 24)

R

L

3.69

12

18

76

Superior Frontal Gyrus (Brodmann area 6)

R

Anterior Cingulate (Brodmann area 24) Caudate Head

L

9.17

6

32

40

R

L

9.09

48

32

36

Middle Frontal Gyrus (Brodmann area 8) Middle Frontal Gyrus (Brodmann area 9) Middle Frontal Gyrus (Brodmann area 6) Superior Frontal Gyrus (Brodmann area 8)

L

32

2

Medial Frontal Gyrus (Brodmann area 6) Middle Frontal Gyrus (Brodmann area 9) Anterior Cingulate (Brodmann area 24)

R

L R

L

7.56

6

18

32

Middle Frontal Gyrus (Brodmann area 9)

L

L

7.43

56

74

38

Precuneus (Brodmann area 19)

L

30

78

40

Precuneus (Brodmann area 19)

R

9.68

32

34

36

R

7.29

46

46

28

6.54

46

8

14

5.8

56

4

12

Middle Frontal Gyrus (Brodmann area 9) Middle Frontal Gyrus (Brodmann area 46) Middle Temporal Gyrus (Brodmann area 21) Superior Temporal Gyrus (Brodmann area 38)

L

11.8

R L

L

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caudate head (Meda et al., 2014), right anterior cingulate (Brodmann are 32) (Meda et al., 2014) left precuneus/posterior cingulate (Brodmann areas 7 and 23), in addition to the lentiform nucleus/putamen (Koch et al., 2010), left parahippocampal gyrus in MCI compared to controls (Fig. 4C). Under CLASSIC noise, group differences were present in the same cortical and subcortical areas yet at a lower extent (Fig. 4D). An overlap of these differences, for the contrast MCI > HC, for CLASSIC (in blue) and MOD (in red) is presented in Fig. 4F. A summary of the whole brain results for these contrasts with the corresponding Z-scores, and MNI coordinates is presented in Table 3.

Interaction: Background acoustic noise  group. The contrast (CLASSIC > MOD) – (MCI > HC) revealed higher activations in the right superior frontal gyrus (Brodmann area 6) at [18, 16, 60], Z = 5.28, part a cluster of 3388 voxels, left middle frontal gyrus (Brodmann area 9) at [ 38, 30, 34], Z = 5.2, part a cluster of 2076 voxels, left inferior parietal lobule (Brodmann area 40) at [ 40, 52, 48], Z = 7.01, and left superior parietal lobule (Brodmann area 7)at [ 34, 62, 56], Z = 6.17 both part of a cluster of 1598 voxels, as well as the right superior parietal lobule/right angular gyrus (Brodmann area 7) at [32, 64, 48], Z = 6.57 part of a cluster of 1524 voxels. The opposite contrast (CLASSIC > MOD) – (HC > MCI) revealed the anterior

Fig. 4. Brain activity maps for MOD noise, (A) for HC > MCI and (C) for MCI > HC. Brain activity maps for CLASSIC noise, (B) for HC > MCI and (D) for MCI > HC. (E) Overlap of statistical maps for HC > MCI under CLASSIC (in blue, in the foreground) and MOD (in red, in the background). (F) Overlap of statistical maps for MCI > HC under CLASSIC (in blue in the foreground) and MOD (in red, in the background). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Table 3. Summary of whole brain results (p < 0.05, corrected for multiple comparisons) for the second-level contrast HC > MCI in top row and the second-level contrast MCI > HC in the bottom row for: MOD noise in the left and CLASSIC noise in the right MOD Z - value

EPI Coordinates (in mm) x

y

Anatomical label(aal)

Side

Z - value

z

14.9

36

4

13.4

36

56

Coordinates (in mm) x

HC > MCI Inferior Frontal Gyrus (Brodmann area 9) 48 Inferior Parietal Lobule (Brodmann Area 40)

30

7.08

6

14

2

6.74

4

40

2

5.56 4.57

2 2

54 50

34 22

3.92 3.58

6 4

64 58

32 14

6.79 6.21

18 22

8 2

12 18

MCI > HC Caudate head Anterior Cingulate (Brodmann area 32) Precuneus (Brodmann area 7) Posterior Cingulate (Brodmann area 23) Precuneus (Brodmann area 7) Posterior Cingulate (Brodmann area 23) Lentiform Nucleus/Putamen Parahippocampal Gyrus

y

Anatomical label(aal)

Side

z

L

12.1

48

50

R

11.7

36

4

11.6

42

16

L

4.59

6

44

R

7.66

4

32

L L

7.26

6

12

HC > MCI Inferior Parietal Lobule (Brodmann area 40) 32 Inferior Frontal Gyrus (Brodmann area 9) 40 Middle Frontal Gyrus (Brodmann area 9) 52

MCI > HC Cingulate Gyrus (Brodmann area 31) 6 Anterior Cingulate (Brodmann area 32) 4 Caudate Head

38

L L R

L L L

L L L L

deactivate the midline structures of the DMN network at a greater spatial extent than MCI cases. Under louder noise the decrease of the normal DMN deactivation involved the anterior DMN nodes in healthy controls and posterior cingulate cortex (PCC) of MCI cases.

The effect of background acoustic noise on brain activation: HC vs. MCI cases

Fig. 5. Brain activity map for the interaction CLASSIC > MOD vs. HC > MCI at the anterior cingulate at [ 2, 32, 10], Z = 4.9.

cingulate at [ 2, 32, 10], Z = 4.9 part of a cluster with 547 voxels as well as the left inferior parietal lobule (Brodmann area 40) at [ 48, 50, 54], Z = 4.83 part of a cluster with 350 voxels (Figs. 5 and 6).

DISCUSSION This study analyzes the impact of distractive acoustic background noise on fMRI brain activation patterns related to WM activation in elderly controls and MCI. In both HC and MCI cases, lower noise was associated with the activation of key nodes of the WMN whereas louder noise affected the usually observed DMN deactivation during task performance. For lower noise condition, HC were able to activate the WMN and

Our GLM analysis further extends previous findings suggesting that less distraction from lower acoustic noise is accompanied by a wide activation of the WMN in HC, including the left superior frontal gyrus and bilateral middle frontal gyrus and recruitment of the right superior frontal gyrus in MCI cases (Yetkin et al., 2006; Teipel et al., 2014). Wider WMN recruitment for HC is in line with the better WM performance in this population (Haller et al., 2005, 2009). These findings suggest that for both groups, when background acoustic distraction is low, more cognitive resources may be available for task performance (Buckner et al., 2008). In both groups, louder acoustic noise affected DMN deactivation (anterior cingulate cortex (ACC) in HC and ACC, medial frontal cortex, precuneus in MCI cases). Growing evidence supports the idea that the deactivation of key nodes of the DMN is not only a by-product of cognitive resource reallocation, but may represent a critical step in WM activation (Daselaar et al., 2004; Kelly et al., 2008). Importantly in our study, when comparing groups independent of the type of noise, MCI cases displayed decreased deactivation of the midline structures of the DMN network at a greater spatial extent compared to HC. This observation should be interpreted in conjunction with the worse

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Fig. 6. Schematic representation of the fMRI on a brain diagram, for illustrative purposes. On the left panel: decreased deactivation (in blue) of the DMN was observed for CLASSIC vs. MOD noise for both groups, with MCI having a larger deactivation compared to controls, particularly in the PCC. On the right panel: increased activation (in red) of the WMN was observed for MOD vs. CLASSIC noise, with the HC having a larger activation of this network relative to MCI. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

n-back performance in MCI, suggesting, that altered DMN deactivation mainly in the posterior nodes may subserve task-disengagement. Alternatively, it may indicate a lower flexibility of network interactivity and a lower range of brain activity modulation in response to higher task demands due to distraction (Spreng and Schacter, 2012). A less prominent activation of the WMN nodes such as the middle frontal and superior frontal gyri was also observed in both groups under the louder condition. Of particular relevance to our study, Hampson et al. (2006) reported a positive correlation of brain activity between the DMN nodes and middle and superior frontal gyrus during WM performance suggesting a probable relay – switch role of the superior and middle frontal gyrus in cognitive resource reallocation during task performance. The increased activation of the WMN for the contrast HC vs. MCI and the contrast MOD vs. CLASSIC suggests that WM efficiency may rely on intact neurocognitive resource reallocation in HC and relay-switch regions, as well as on less distracting demands posed by the environment. Conversely, decreased reallocation capacities per additional environmental demands may subserve taskdisengagement in MCI. DMN deactivation in HC and MCI The interaction noise (louder vs. lower) – group (HC vs. MCI) reveals that the alteration of DMN deactivation under louder noise mainly concerns the anterior midline DMN regions such as the anterior cingulate gyrus in HC. This topography is consistent with previous observations showing a posterior anterior load-induced shift with increased deactivations in posterior midline

cortex but decreased deactivations in medial frontal cortex during task performance in normal aging (Mevel et al., 2011). The inverse pattern was observed in MCI with an increased deactivation in ACC and decreased deactivation in PCC. This pattern may be related to subjective task difficulty (Arsalidou et al., 2013) or specific load-induced task-disengagement of the PCC subserving stimulus-independent thought (Mason et al., 2007). Interestingly, recent studies pointed to the importance of DMN in relation to auditory scene analysis. In particular Goll et al. (2012) reported that AD is associated with impaired parsing of sound sources in the auditory environment due to structural deficits in posterior cortical areas. Using fMRI sequences, Golden et al. (2015) found that the interaction of own name identification with auditory object segregation processing led to enhanced activation of the right supramarginal gyrus in AD cases compared to controls. Our results also reveal that the superior and posterior portion of the PCC remains abnormally activated during louder background acoustic noise. The same was true for the inferior anterior portion of this area under lower background acoustic noise. A recent study (Leech et al., 2011) used a similar n-back WM task and showed a clear dissociation between the ventral and dorsal parts of the PCC with the more ventral portion representing a taskdisengagement and the more dorsal portion representing a higher connectivity with the task-positive WMN network. Conjointly, these findings point to a role for PCC in the modulation of the dynamic interaction between the large-scale networks implicated in WM performance in MCI. Previous studies have pointed to the early deposition of amyloid plaques in the key nodes of DMN particularly in the PCC cortex of MCI cases (Buckner et al., 2005;

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Pihlajamaki and Sperling, 2009; Sperling et al., 2009). Furthermore, recent neuroimaging studies in MCI have indicated an emerging deficiency to modulate activity in the PCC because of a predilection for amyloid deposition in these regions (see a review by Shin et al. (2011)). Other studies, however, have reported a ‘‘paradoxical activation” of the posterior nodes of the DMN in MCI. Whether this activity of the DMN reflects abnormalities in baseline synaptic activity with hypo-metabolism at rest in early stages of neurodegeneration (Meltzer et al., 1996; Silverman et al., 2001; Alexander et al., 2002) and MCI (Small et al., 2000; Reiman et al., 2004; Jagust et al., 2006) remains to be elucidated. An alternative technical approach for modification of the background auditory noise in fMRI is sparse sampling techniques. In principle, sparse sampling techniques make use of the hemodynamic delay of 6–7 s of the BOLD response. In our paradigm, the silent period is very short in the range of about 5 s. Moreover, sparse sampling is not really silent as during the scan periods, a standard EPI sequence is used that has the same loudness as the standard acquisition. Indeed, many participants perceive the ON–OFF noise at least as distractive as the continuous noise. Most importantly, the sparse sampling results in a limited number of data-points per time compared to classic sampling leading to reduced statistical power.

LIMITATIONS A main limitation of our study is the absence of longitudinal data addressing the evolution of MCI cases over time. We cannot thus identify whether the background noise-related differences in fMRI activation concerns stable or progressive MCI. Although valid from an experimental viewpoint, the choice of the n-back task does not allow for exploring whether or not the background noise effect is confined to WMN or also concerns other cortical networks. Moreover, the present experiment does not include an at rest fMRI condition (Barkhof et al., 2014). We cannot thus exclude that group differences observed may partly reflect intrinsic network alterations in MCI that are independent of output task. Although major hearing deficits were systematically excluded via the exclusion of cases with past hearing problems but also with positive self-report and speech in noise perception test alteration, audiometric tests and discrimination threshold analyses were not performed in the present series. Subtle hearing differences between MCI and controls that mainly concern the central auditory processing (Panza et al., 2015) may affect the observed fMRI activation patterns.

CONCLUSIONS The present findings shed new light on the impact of background acoustic noise on large-scale networks, such as the WMN and DMN in the course of brain aging. Future studies should focus on whether such differences in task disengagement under distractive conditions may serve as biomarkers predicting a further cognitive decline in MCI (Li et al., 2013). Finally, considering

that background noise is a common variable in neuroimaging tasks, fMRI studies should take into account any potential confounding impact of different noises due to different scanning protocols (Haller and Bartsch, 2009; Peelle, 2014).

FUNDING This work was supported by Swiss National Foundation grant SNF 3200B0-116193 and SPUM 33CM30-124111.

COMPETING INTERESTS The authors report no competing interests. Acknowledgments—The authors would like to thank the study participants as well as Noemie Bouchet, Simona Toma, Nikol Hiller, Raphaelle Martin-Casas, Tringa Ismaili, Marine Ackermann, Dina Zekry and Jeremy Hofmeister for valuable help with data acquisition, Holly Aleksonis for proof-reading and Dimitri Van De Ville for valuable comments on previous versions of the manuscript.

REFERENCES Adam S, Van der Linden M, Ivanoiu A, Juillerat AC, Bechet S, Salmon E (2007) Optimization of encoding specificity for the diagnosis of early AD: the RI-48 task. J Clin Exp Neuropsychol 29(5):477–487. Alexander GE, Chen K, Pietrini P, Rapoport SI, Reiman EM (2002) Longitudinal PET evaluation of cerebral metabolic decline in dementia: a potential outcome measure in Alzheimer’s disease treatment studies. Am J Psychiatry 159(5):738–745. Ando Y (2001) Differential effects of noise and music signals on the behavior of children. J Sound Vib 241(1):129–140. Arsalidou M, Pascual-Leone J, Johnson J, Morris D, Taylor MJ (2013) A balancing act of the brain: activations and deactivations driven by cognitive load. Brain Behav 3(3):273–285. Baddeley A (2003) Working memory: looking back and looking forward. Nat Rev Neurosci 4(10):829–839. Baddeley AD, Emslie H, Nimmo-Smith I (1994) The Doors and People Test: a test of visual and verbal recall and recognition. England, Bury St Edmunds: Thames Valley Test Company. Barberger-Gateau P, Commenges D, Gagnon M, Letenneur L, Sauvel C, Dartigues JF (1992) Instrumental activities of daily living as a screening tool for cognitive impairment and dementia in elderly community dwellers. J Am Geriatr Soc 40(11):1129–1134. Barkhof F, Haller S, Rombouts SARB (2014) Resting-state functional MR imaging: a new window to the brain. Radiology 272(1):28–48. Beckmann CF, Jenkinson M, Smith SM (2003) General multilevel linear modeling for group analysis in FMRI. Neuroimage 20 (2):1052–1063. Buckner RL, Snyder AZ, Shannon BJ, LaRossa G, Sachs R, Fotenos AF, Sheline YI, Klunk WE, Mathis CA, Morris JC, Mintun MA (2005) Molecular, structural, and functional characterization of Alzheimer’s disease: evidence for a relationship between default activity, amyloid, and memory. J Neurosci 25(34):7709–7717. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124:1–38. Cabeza R, Nyberg L (2000) Imaging cognition II: an empirical review of 275 PET and fMRI studies. J Cogn Neurosci 12(1):1–47. Cardebat D, Doyon B, Puel M, Goulet P, Joanette Y (1990) Formal and semantic lexical evocation in normal subjects. Performance and dynamics of production as a function of sex, age and educational level. Acta Neurol Belg 90(4):207–217.

420

I. Sinanaj et al. / Neuroscience 310 (2015) 410–421

Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. Proc Natl Acad Sci U S A 106(21):8719–8724. Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3 (3):201–215. Daselaar SM, Prince SE, Cabeza R (2004) When less means more: deactivations during encoding that predict subsequent memory. Neuroimage 23(3):921–927. Folstein MF, Folstein SE, McHugh PR (1975) ‘‘Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12(3):189–198. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A 102(27):9673–9678. Gazzaley A, Cooney JW, Rissman J, D’Esposito M (2005) Top-down suppression deficit underlies working memory impairment in normal aging. Nat Neurosci 8(10):1298–1300. Ghent L (1956) Perception of overlapping and embedded figures by children of different ages. Am J Psychol 69(4):575–587. Golden HL, Nicholas JM, Yong KXX, Downey LE, Schott JM, Mummery CJ, Crutch SJ, Warren JD (2015) Auditory spatial processing in Alzheimer’s disease. Brain 138:189–202. Goll JC, Kim LG, Ridgway GR, Hailstone JC, Lehmann M, Buckley AH, Crutch SJ, Warren JD (2012) Impairments of auditory scene analysis in Alzheimer’s disease. Brain 135:190–200. Haller S, Bartsch AJ (2009) Pitfalls in FMRI. Eur Radiol 19 (11):2689–2706. Haller S, Bartsch AJ, Radue EW, Klarhofer M, Seifritz E, Scheffler K (2005) Effect of fMRI acoustic noise on non-auditory working memory task: comparison between continuous and pulsed sound emitting EPI. MAGMA 18(5):263–271. Haller S, Homola GA, Scheffler K, Beckmann CF, Bartsch AJ (2009) Background MR gradient noise and non-auditory BOLD activations: a data-driven perspective. Brain Res 1282:74–83. Hampson M, Driesen NR, Skudlarski P, Gore JC, Constable RT (2006) Brain connectivity related to working memory performance. J Neurosci 26(51):13338–13343. Heaton RK (1981) Wisconsin card sorting test manual. Odessa, Fla: Psychological Assessment Resources. Hugenschmidt CE, Mozolic JL, Tan H, Kraft RA, Laurienti PJ (2009) Age-related increase in cross-sensory noise in resting and steady-state cerebral perfusion. Brain Topogr 21(3–4):241–251. Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL (1982) A new clinical scale for the staging of dementia. Br J Psychiatry 140:566–572. Jagust W, Gitcho A, Sun F, Kuczynski B, Mungas D, Haan M (2006) Brain imaging evidence of preclinical Alzheimer’s disease in normal aging. Ann Neurol 59(4):673–681. Kaplan EF, Goodglass H, Weintraub S (1983) The Boston naming test. Philadelphia: Lea and Febiger. Kelly AMC, Uddin LQ, Biswal BB, Castellanos FX, Milham MP (2008) Competition between functional brain networks mediates behavioral variability. Neuroimage 39(1):527–537. Koch W, Teipel S, Mueller S, Buerger K, Bokde ALW, Hampel H, Coates U, Reiser M, Meindl T (2010) Effects of aging on default mode network activity in resting state fMRI: does the method of analysis matter? Neuroimage 51(1):280–287. Leech R, Kamourieh S, Beckmann CF, Sharp DJ (2011) Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control. J Neurosci 31(9):3217–3224. Li YP, Qin YY, Chen X, Li W (2013) Exploring the functional brain network of Alzheimer’s disease: based on the computational experiment. PLoS One 8(9). Lustig C, Snyder AZ, Bhakta M, O’Brien KC, McAvoy M, Raichle ME, Morris JC, Buckner RL (2003) Functional deactivations: change with age and dementia of the Alzheimer type. Proc Natl Acad Sci U S A 100(24):14504–14509.

Mason MF, Norton MI, Van Horn JD, Wegner DM, Grafton ST, Macrae CN (2007) Wandering minds: the default network and stimulus-independent thought. Science 315(5810):393–395. Mayer JS, Roebroeck A, Maurer K, Linden DE (2010) Specialization in the default mode: task-induced brain deactivations dissociate between visual working memory and attention. Hum Brain Mapp 31(1):126–139. Meda SA, Ruano G, Windemuth A, O’Neil K, Berwise C, Dunn SM, Boccaccio LE, Narayanan B, Kocherla M, Sprooten E, Keshavan MS, Tamminga CA, Sweeney JA, Clementz BA, Calhoun VD, Pearlson GD (2014) Multivariate analysis reveals genetic associations of the resting default mode network in psychotic bipolar disorder and schizophrenia. Proc Natl Acad Sci U S A 111 (19). 6864-6864. Meltzer CC, Zubieta JK, Brandt J, Tune LE, Mayberg HS, Frost JJ (1996) Regional hypometabolism in Alzheimer’s disease as measured by positron emission tomography after correction for effects of partial volume averaging. Neurology 47(2):454–461. Mevel K, Chetelat G, Eustache F, Desgranges B (2011) The default mode network in healthy aging and Alzheimer’s disease. Int J Alzheimers Dis 2011:535816. Owen AM, McMillan KM, Laird AR, Bullmore E (2005) N-back working memory paradigm: a meta-analysis of normative functional neuroimaging studies 25(1):46–59. Panza F, Solfrizzi V, Logroscino G (2015) Age-related hearing impairment-a risk factor and frailty marker for dementia and AD. Nat Rev Neurol 11(3). Papso CF, Blood IM (1989) Word recognition skills of children and adults in background noise. Ear Hear 10(4):235–236. Peelle JE (2014) Methodological challenges and solutions in auditory functional magnetic resonance imaging. Front Neurosci 8:253. Petersen RC (2004) Mild cognitive impairment as a diagnostic entity. Intern Med 256(3):183–194. Pihlajamaki M, Sperling RA (2009) Functional MRI assessment of task-induced deactivation of the default mode network in Alzheimer’s disease and at-risk older individuals. Behav Neurol 21(1):77–91. Pilcher H (2005) Ability to tune out irrelevant information is impaired in AD. Lancet Neurol 4(11). 702-702. Poeck K (1985) Clues to the nature of disruption to limb praxis. In: Roy EA, editor. Neuropsychological studies of apraxia and related disorders. Amsterdam: Elsevier. p. 99–109. Pronk M, Deeg DJH, Kramer SE (2013) Hearing status in older persons: a significant determinant of depression and loneliness? Results from the longitudinal aging study Amsterdam. Am J Audiol 22(2):316–320. Pyka M, Beckmann CF, Schoning S, Hauke S, Heider D, Kugel H, Arolt V, Konrad C (2009) Impact of working memory load on FMRI resting state pattern in subsequent resting phases. PLoS One 4 (9):e7198. Reiman EM, Chen KW, Alexander GE, Caselli RJ, Bandy D, Osborne D, Saunders AM, Hardy J (2004) Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer’s dementia. Proc Natl Acad Sci U S A 101(1):284–289. Reitan RM (1958) Validity of the Trail Making Test as an indicator of organic brain damage. Percept Mot Skills 8:271–276. Schnider A, Hanlon RE, Alexander DN, Benson DF (1997) Ideomotor apraxia: behavioral dimensions and neuroanatomical basis. Brain Lang 58(1):125–136. Seifritz E, Di Salle F, Esposito F, Herdener M, Neuhoff JG, Scheffler K (2006) Enhancing BOLD response in the auditory system by neurophysiologically tuned fMRI sequence. Neuroimage 29 (3):1013–1022. Shin J, Kepe V, Small GW, Phelps ME, Barrio JR (2011) Multimodal imaging of Alzheimer pathophysiology in the brain’s default mode network. Int J Alzheimers Dis 2011:687945. Silverman DHS, Small GW, Chang CY, Lu CS, de Aburto MAK, Chen W, Czernin J, Rapoport SI, Pietrini P, Alexander GE, Schapiro MB, Jagust WJ, Hoffman JM, Welsh-Bohmer KA, Alavi A, Clark CM, Salmon E, de Leon MJ, Mielke R, Cummings JL, Kowell AP, Gambhir SS, Hoh CK, Phelps ME (2001) Positron emission

I. Sinanaj et al. / Neuroscience 310 (2015) 410–421 tomography in evaluation of dementia – regional brain metabolism and long-term outcome. JAMA 286(17):2120–2127. Small GW, Ercoli LM, Silverman DHS, Huang SC, Komo S, Bookheimer SY, Lavretsky H, Miller K, Siddarth P, Rasgon NL, Mazziotta JC, Saxena S, Wu HM, Mega MS, Cummings JL, Saunders AM, Pericak-Vance MA, Roses AD, Barrio JR, Phelps ME (2000) Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease. Proc Natl Acad Sci U S A 97 (11):6037–6042. Soderlund GB, Sikstrom S, Loftesnes JM, Sonuga-Barke EJ (2010) The effects of background white noise on memory performance in inattentive school children. Behav Brain Funct 6:55. Sorqvist P (2010) The role of working memory capacity in auditory distraction: a review. Noise Health 12(49):217–224. Sperling RA, Laviolette PS, O’Keefe K, O’Brien J, Rentz DM, Pihlajamaki M, Marshall G, Hyman BT, Selkoe DJ, Hedden T, Buckner RL, Becker JA, Johnson KA (2009) Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron 63(2):178–188. Spreng RN, Schacter DL (2012) Default network modulation and large-scale network interactivity in healthy young and old adults. Cereb Cortex 22(11):2610–2621. Teipel S, Ehlers I, Erbe A, Holzmann C, Lau E, Hauenstein K, Berger C (2014) Structural connectivity changes underlying altered

421

working memory networks in mild cognitive impairment: a threeway image fusion analysis. J Neuroimaging. Tomasi D, Caparelli EC, Chang L, Ernst T (2005) FMRI-acoustic noise alters brain activation during working memory tasks. Neuroimage 27(2):377–386. Tremblay S, MacKen WJ, Jones DM (2001) The impact of broadband noise on serial memory: changes in band-pass frequency increase disruption. Memory 9(4):323–331. Wechsler DA (1997) Wechsler memory scale. 3rd ed. San Antonio: Psychological Corporation. Welsh KA, Butters N, Mohs RC, Beekly D, Edland S, Fillenbaum G, Heyman A (1994) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part V. A normative study of the neuropsychological battery. Neurology 44(4):609–614. Woolrich M (2008) Robust group analysis using outlier inference. Neuroimage 41(2):286–301. Woolrich MW, Behrens TEJ, Beckmann CF, Jenkinson M, Smith SM (2004) Multilevel linear modelling for FMRI group analysis using Bayesian inference. Neuroimage 21(4):1732–1747. Yetkin FZ, Rosenberg RN, Weiner MF, Purdy PD, Cullum CM (2006) FMRI of working memory in patients with mild cognitive impairment and probable Alzheimer’s disease. Eur Radiol 16(1):193–206. Zigmond AS, Snaith RP (1983) The hospital anxiety and depression scale. Acta Psychiatr Scand 67(6):361–370.

(Accepted 10 September 2015) (Available online 29 September 2015)