Neurobiology of Aging 36 (2015) 2145e2152

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Resting-state synchrony between the retrosplenial cortex and anterior medial cortical structures relates to memory complaints in subjective cognitive impairment Fumihiko Yasuno a, b, *,1, Hiroaki Kazui c,1, Akihide Yamamoto b, Naomi Morita d, Katsufumi Kajimoto e, Masafumi Ihara e, Akihiko Taguchi e, f, Kiwamu Matsuoka a, Jun Kosaka a, Toshihisa Tanaka c, Takashi Kudo g, Masatoshi Takeda c, Kazuyuki Nagatsuka e, Hidehiro Iida d, Toshifumi Kishimoto a a

Department of Psychiatry, Nara Medical University, Kashihara, Japan Department of Investigative Radiology, National Cerebral and Cardiovascular Center, Suita, Japan c Department of Neuropsychiatry, Osaka University Medical School, Suita, Japan d Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Japan e Department of Neurology, National Cerebral and Cardiovascular Center, Suita, Japan f Department of Regenerative Medicine Research, Institute of Biomedical Research and Innovation, Kobe, Japan g Department of Psychiatry, Osaka University Health Care Center, Suita, Japan b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 December 2014 Received in revised form 7 March 2015 Accepted 9 March 2015 Available online 14 March 2015

Subjective cognitive impairment (SCI) is a clinical state characterized by subjective cognitive deficits without cognitive impairment. To test the hypothesis that this state might involve dysfunction of selfreferential processing mediated by cortical midline structures, we investigated abnormalities of functional connectivity in these structures in individuals with SCI using resting-state functional magnetic resonance imaging. We performed functional connectivity analysis for 23 individuals with SCI and 30 individuals without SCI. To reveal the pathophysiological basis of the functional connectivity change, we performed magnetic resonance-diffusion tensor imaging. Positron emission tomography-amyloid imaging was conducted in 13 SCI and 15 nonSCI subjects. Individuals with SCI showed reduced functional connectivity in cortical midline structures. Reduction in white matter connections was related to reduced functional connectivity, but we found no amyloid deposition in individuals with SCI. The results do not necessarily contradict the possibility that SCI indicates initial cognitive decrements, but imply that reduced functional connectivity in cortical midline structures contributes to overestimation of the experience of forgetfulness. Ó 2015 Elsevier Inc. All rights reserved.

Keywords: Subjective cognitive impairment (SCI) Resting-state functional MRI (fMRI) Diffusion tensor imaging (DTI) Amyloid imaging Self-referential processing Cortical midline structures

1. Introduction Subjective cognitive impairment (SCI) is a clinical state characterized by an individual’s own perception that their cognitive abilities, including memory, are declining; importantly, individuals with SCI do not have overt cognitive deficits and their cognitive performance tends to be within the general normal range. SCI is

* Corresponding author at: Department of Psychiatry, Nara Medical University, 840 Shijocho, Kashihara, Nara 634-8522, Japan. Tel.: þ81 744 22 3051; fax: þ81 744 22 3854. E-mail address: [email protected] (F. Yasuno). 1 These authors contributed equally to the work. 0197-4580/$ e see front matter Ó 2015 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neurobiolaging.2015.03.006

presently a topic of active debate, especially with respect to its implications in the early diagnosis of Alzheimer’s disease (AD). Epidemiological (Waldorff et al., 2012), clinical (Juncos-Rabadan et al., 2012), electrophysiological (Babiloni et al., 2010), and neuroimaging (Stewart et al., 2011) data suggest that a portion of SCI subjects is already on the path toward a neurodegenerative disease, mostly AD (Reisberg et al., 2008). However, other studies have found little or no correlation between subjective cognitive complaints and cognitive impairment (Jungwirth et al., 2004; Slavin et al., 2010), indicating that an individual’s subjective evaluation of his/her cognitive functioning may not provide an accurate appraisal of actual cognitive deficits (Roberts et al., 2009). The inconclusive results of previous studies may indicate that SCI could be due to various causes. Given that self-overestimation

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of cognitive disturbance is a major factor influencing SCI, dysfunction in self-referential processing might contribute to this phenomenon. Converging evidence suggests that self-referential processing is mediated by cortical midline structures such as the ventromedial and dorsomedial prefrontal cortex and the anterior and posterior cingulate cortex (Northoff et al., 2006). Therefore, we hypothesized that the resting-state functional connectivity among these regions in the cortical midline structures would be altered in SCI, and thought to underlie these patients’ subjective memory complaints. To test this hypothesis, we performed region of interest (ROI) seed-based functional connectivity analysis, to investigate the intrinsic neural network related to self-referential processing in individuals with SCI and in those without SCI (nSCI). In addition, to reveal the pathophysiological basis of any observed differences in functional connectivity, we performed magnetic resonancediffusion tensor imaging (MRI-DTI) and positron emission tomography (PET) imaging with the 11C-labeled Pittsburgh Compound-B ([11C]PIB), and examined the association of white matter connectivity and amyloid deposition with changes in resting-state functional connectivity in SCI.

2. Methods 2.1. Participants A total of 30 SCI and 38 nSCI individuals were recruited from the psychiatry unit of Osaka University Hospital. Individuals with SCI were included if they met the proposed Reisberg criteria for primary idiopathic SCI (Reisberg et al., 2008). This study was approved by the institutional review boards of all participating institutions, and all participants provided written informed consent. All participants were screened for comorbid medical and psychiatric conditions by means of clinical, physical, and neurological examinations. Cognitive function was assessed according to a standardized battery of cognitive tests, including Raven’s Colored Progressive Matrices (RCPM), Mini-Mental State Examination (MMSE), the Alzheimer’s Disease Assessment ScaleCognitive Component (ADAS-Cog), and the Logical Memory I/II subscale from the Wechsler Memory Scale (WMS-R LM I/II). To be classified into the SCI or nSCI groups, the individuals had to have normal memory function (scoring above the education-adjusted cutoff on WMS-R LM II, MMSE score 27), absence of significant levels of impairment in other cognitive domains, and essentially preserved activities of daily living. The education-adjusted cutoff scores of the WMS-R LM II (maximum score ¼ 25) for a definition of clinically normal with no cognitive impairment are as follows: (1) education years 16, LM II score 12; (2) education years 10e15, LM II score 10; and (3) education years 0e9, LM II score 7. After the medical examination and cognitive assessment, 7 individuals from the SCI group and 8 from the nSCI group were excluded because of the presence of overt cognitive deficits, and therefore 23 SCI and 30 nSCI individuals were included in the analysis. The presence of subjective memory deficit was evaluated with a standardized questionnaire system based on the everyday memory checklist (EMC) of Wilson et al. (1989). The EMC was translated into Japanese, and was slightly modified to fit Japanese culture. The EMC has been previously used to assess unawareness of memory impairment (Supplementary Table S1) (Kazui et al., 2003, 2006). The EMC scores for the subjects’ own ratings were analyzed. All individuals in the SCI group had EMC scores greater than the standardized cutoff score of 9.

2.2. Neuroimaging analysis 2.2.1. MR image acquisition All MRI examinations were performed using a 3-Tesla wholebody scanner (Signa Excite HD V12M4; GE Healthcare, Milwaukee, WI, USA) with an 8-channel phased-array brain coil. T1-weighted images were obtained using a 3-dimensional spoiled grass gradient recalled inversion-recovery sequence, and DT images were acquired with a locally modified single-shot echo-planar imaging sequence by using parallel acquisition at a reduction (ASSET) factor of 2 in the axial plane. The details are described in previous studies (Matsuoka et al., 2014; Yasuno et al., 2014). T2-weighted images were obtained using a fast-spin echo (TR ¼ 4800 ms; TE ¼ 101 ms; echo train length ¼ 8; field of view ¼ 256 mm; 74 slices in the transverse plane; acquisition matrix, 160  160, acquired resolution, 1 mm  1 mm  2 mm). To exclude subjects with significant microvascular disease, white matter hyperintensities and lacunar lesions were rated using the Scheltens scale, which is a semiquantitative visual rating scale (Scheltens et al., 1993). The resting-state functional MRI (fMRI) scan images were obtained by capturing 37 transverse slices of 4-mm thickness covering the entire brain with a temporal resolution of 3 seconds, using a T2*-weighted gradient echo-planar imaging pulse sequence (TR ¼ 3000 ms, TE ¼ 35 ms, flip angle ¼ 85 , time frames ¼ 88, number of images ¼ 3256; acquisition time ¼ 4 minutes 24 seconds). For resting-state image acquisition, participants were instructed to remain still with their eyes closed and to let their minds wander freely. To reduce blurring and signal loss arising from field inhomogeneity, an automated high-order shimming method based on spiral acquisitions (Kim et al., 2002) was used before acquiring DTI and fMRI scans. To correct for motion and distortion derived from the eddy current and B0 inhomogeneity, the FMRIB Software (v.5;//fsl.fmrib.ox.ac.uk/fsl) was used. 2.2.2. Image preprocessing for resting fMRI Standard image preprocessing methods were conducted using the Statistical Parametric Mapping 8 (SPM8) software (http://www. fil.ion.ucl.ac.uk/spm/) with the conn toolbox (http://www.nitrc.org/ projects/conn) for functional connectivity analysis. The functional images were corrected for slice time and motion, co-registered with a high-resolution anatomical scan, normalized into the Montreal Neurological Institute space, resampled at 2 mm3, and smoothed with a Gaussian kernel of 8 mm3 full-width half-maximum (Friston et al., 1995). In addition, the Artifact Detection Tool (http://www. nitro.org/projects/artifact_detect) was used to measure motion artifacts in all individuals of both groups (mean  SD; nSCI: 0.042  0.008, SCI: 0.047  0.013, p ¼ 0.13). Nonetheless, we controlled for any motion artifacts using realignment parameters detected by Artifact Detection Tool. 2.2.3. Seed region definition A series of ROIs were defined on the cortical midline structures (Northoff and Bermpohl, 2004). A mask for the following distinct subdomains of the cortical midline structures was provided by the conn toolbox: the orbitomedial prefrontal cortex (BA11; Montreal Neurological Institute coordinates for the centroid of the ROIs, right and left: 18, 46, 29 and 16, 45, 29); dorsal medial prefrontal cortex (DMPFC, BA10: 26, 62, 3 and 23, 62, 3); anterior cingulate cortex (ACC, BA32: 11, 34, 14, and 8, 33, 16); posterior cingulate cortex (PCC, BA31: 14, 45, 35 and 9, 45, 35); and retrosplenial cortex (RSC, BA29: 9, 46, 13 and 6, 45, 13) (Fig. 1A). 2.2.4. Functional connectivity analysis Following the preprocessing steps outlined in the previous section, the functional images were imported into the conn toolbox

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Fig. 1. Significantly reduced functional connectivity in individuals with SCI between group analysis. (A) Seed-based region of interest (ROI) drawn around the hubs within the cortical midline structures. Purple: orbitomedial prefrontal cortex (BA11), yellow: dorsal medial prefrontal cortex (DMPFC) (BA10), green: anterior cingulate cortex (ACC) (BA32), red: posterior cingulate cortex (PCC) (BA31), and blue: retrosplenial cortex (RSC) (BA29). (B) ROI-to-ROI in the cortical midline structure exhibited significantly reduced connectivity in individuals with SCI in the following ROI pairs: rRSC-lDMPFC, rRSC-rACC, lRSC-rDMPFC, lRS-r&lDMPFC, and lRSC-r&lACC. (C) Whole-brain connectivity from the seed of RSC (right RSC [upper] and left RSC [lower]) confirmed that SCI group had reduced connectivity between the RSC and bilateral cortical medial structures of the DMPFC and ACC, as indicated by the ROI-to-ROI analysis. We also found significantly reduced connectivity between the RSC and left angular gyrus in this group. Abbreviation: SCI, subjective cognitive impairment. (For interpretation of the references to color in this Figure, the reader is referred to the web version of this article.)

(http://www.nitrc.org/projects/conn) in SPM8 for further data correction. The blood oxygenation level dependent signal data were passed through a band pass filter (0.009e0.08 Hz). The mean blood oxygenation level dependent signal time course was then extracted from each of the predefined ROIs. The time course for each ROI was then correlated with the time course for each of the other ROIs, allowing for the calculation of a correlation coefficient for each ROI using Pearson’s product-moment calculation. These values were subsequently used for the statistical comparisons between the 2 groups. To reduce the probability of type 1 error, we controlled the false-discovery rate (p < 0.05) for comparisons within the cortical midline structure. We hypothesized that the resting-state functional connectivity among these regions in the cortical midline structures would be altered in SCI, and underlie these individuals’ subjective memory complaints. To test this hypothesis, a series of ROIs were defined on cortical midline structures, and performed an ROI-to-ROI seedbased functional connectivity analysis in the cortical midline area to compare the intrinsic neural networks related to self-referential processing between the SCI and nSCI subjects. The results of the ROI-to-ROI analysis were then verified at the whole-brain level with seed-to-voxel analysis to confirm the observed patterns in the ROI-to-ROI analysis and to reveal the extent

of these patterns inside and outside of the cortical midline area. Using a priori assumptions based on the ROI-to-ROI analysis, we used an uncorrected p-value height threshold of 0.25 (n ¼ 4) as the cutoff point for the determination of Ab-positive status (Amariglio et al., 2012). The group differences of the PIB-BPND values in the regions exhibiting a significant group

Table 1 Demographic statistics and neuropsychological performance of cognitively normal individuals with subjective cognitive impairments (SCI) and those without SCI (nSCI) t51 or c2

p

8.0 2.1 2.0 5.3 1.2 2.0

0.05 1.50 1.81 1.17 9.02 0.27 0.20

0.82 0.14 0.08 0.25 0.0003* 0.79 0.84

26.8  5.7 23.2  5.8

1.98 2.63

0.05 0.01*

Characteristic/test

nSCI

SCI

No. Sex, M/F Age, y Education, y RCPM Self-rated EMC MMSE ADAS-Cog WMS-R Logical memory I: immediate Logical memory II: delayed

30 16/14 72.2  12.5  33.1  5.3  29.3  3.6 

23 13/10 69.6  13.7  33.8  15.2  29.2  3.7 

4.8 2.7 2.4 2.6 1.0 1.5

24.0  4.5 19.3  5.0

Data are mean  SD. Key: ADAS-Cog, Alzheimer’s Disease Assessment Scale-Cognitive Component; ECM, everyday memory checklist; MMSE, Mini-Mental State Examination; RCPM, Raven’s Colored Progressive Matrices; WMS-R, Wechsler Memory Scale. * p < 0.05.

difference of DTI-FA were examined by ANCOVA using age, sex, and years of education as covariates. All statistical tests were 2tailed and reported at a < 0.05. Bonferroni correction was applied to avoid type 1 errors because of the multiplicity of statistical analyses. Statistical analysis of the data was performed using SPSS for Windows 22.0 (IBM Japan Inc, Tokyo, Japan). 3. Results 3.1. Demographics As shown in Table 1, there were no significant differences in age, gender, education level, and the scores of RCPM (range: 28e36), MMSE (27e30), and ADAS-Cog (0.7e8.0) between the SCI and nSCI groups. Individuals with SCI had a significantly higher delayed memory score of WMS-R LM II, compared with those of nSCI. We found no subjects with significant microvascular disease, and there was no significant difference (t ¼ 0.19, p ¼ 0.85) in the Scheltens scale scores (Scheltens et al., 1993) between the SCI (1.3  1.5; maximum score ¼ 5) and nSCI (1.4  2.0; maximum score ¼ 7) groups. 3.2. Between-group ROI-to-ROI and successive seed-to-voxel analysis Results from the between-group ROI-to-ROI analysis in the cortical midline structures are shown in Fig. 1B and Table 2a. Compared with the nSCI group, the SCI group exhibited significantly reduced connectivity in the following ROI pairs: rRSC-lDMPFC, rRSC-rACC, lRSC-rDMPFC, lRS-r&lDMPFC, and lRSC-r&lACC. Whole-brain connectivity from the right and left RSC was also assessed. Fig. 1C and Table 2b show the between-group contrasts. Consistent with the ROI-to-ROI results, significantly reduced connectivity between the RSC and bilateral cortical medial structures of the DMPFC and ACC was confirmed. We found substantial mean connectivity strength (Z-values) only in the nSCI group, reflecting a significant difference between groups in connectivity, except for that between the rRSC and cluster #3, in which the Z-values for the connection in both groups were close to zero. 3.3. Correlation between functional connectivity and EMC score/ subjective memory complaints Higher EMC scores correlated with more reduced functional connectivity between the rRSC and cluster #2 (r ¼ 0.40, p ¼ 0.003)

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Table 2 Functional connectivity showing significant difference between groups of SCI and nSCI Comparison

Seed region

Target region

Beta

t-score

FDR p-value

0.18 0.18 0.15 0.17 0.20 0.20 0.17

2.93 2.76 2.23 2.64 3.00 3.88 2.65

0.018 0.018 0.046 0.012 0.009 0.001 0.012

a) Functional connectivity showing significant difference between groups in the ROI-to-ROI analysis Non-SCI > SCI

R retrosplenial cortex (BA29)

L retrosplenial cortex (BA29)

L dorsolateral prefrontal cortex (BA 10) R Anterior cingulate cortex (BA32) L Anterior cingulate cortex (BA32) R dorsolateral prefrontal cortex (BA 10) L dorsolateral prefrontal cortex (BA 10) R Anterior cingulate cortex (BA32) L Anterior cingulate cortex (BA32)

b) Functional connectivity showing significant difference between groups in the seed-to-voxel analysis

BA

Cluster size Cluster Peak Peak MNI p-value p-value

NonSCI > SCI R retrosplenial Cluster#1: R and L anterior cortex (BA29) prefrontal/dorsal anterior cingulate cortex Cluster#2: L dorsal frontal cortex Cluster#3: L angular gyrus/associative visual cortex L retrosplenial Cluster#4: L dorsal cortex (BA29) frontal/premotor/dorsolateral prefrontal cortex Cluster#5: L anterior prefrontal/dorsolateral prefrontal/dorsal anterior cingulate cortex Cluster#6: L angular gyrus Cluster#7: R and L anterior prefrontal/dorsal anterior cingulate cortex

10/32

738

0.005

0.0003

2, 64, 6

8 39/19

650 391

0.007 0.030

1E-06 0.0001

987

0.001

10/9/32 396

39 10/32

8/6/9

368 330

Connectivity Connectivity strength of SCIa strength of nSCIa 0.11  0.17

0.33  0.25

22, 38, 54 24, 84, 46

0.03  0.21 0.10  0.23

0.31  0.17 0.07  0.26

3E-06

24, 40, 52

0.06  0.17

0.31  0.21

0.028

0.0002

18, 48, 4

0.01  0.18

0.20  0.22

0.033 0.042

0.0006 0.0002

42, 56, 36 0.01  0.21 6, 46, 14 0.07  0.16

0.19  0.25 0.26  0.20

Key: BA, Brodmann area; L, left; MNI, Montreal Neurological Institute; nSCI, nonSCI; R, right; ROI, region of interest; SCI, Subjective cognitive impairment. a Group mean z-values: Mean  SD.

and the lRSC and cluster #4 (r ¼ 0.49, p ¼ 0.0002) and #5 (r ¼ 0.36, p ¼ 0.007), which are included in the medial cortical structures of the frontal cortex and ACC (Fig. 2 and Supplementary Table S2). We found no significant correlation between the functional connectivity and any of the clinical variables, including age, education, and scores of the RCPM, MMSE, ADAS-Cog, and LM I and II of the WMS-R. When we added the LM II delayed recall scores as potentially confounding factors in the partial correlation analysis between the EMC score and the functional connectivity values showing a significant difference between groups, the connectivity of seed to cluster #4 was still significant (r ¼ 0.49, p ¼ 0.0002).

subjects among the 15 nSCI subjects and 1 among the 13 SCI subjects. There was no significant difference in the ratio of Ab-positive subjects between groups (c2 ¼ 0.86, p ¼ 0.35). We found no significant differences in PIB-BPND values between the SCI and nSCI in the whole-brain voxel-based analysis. In addition, there were no significant differences between BPND values in the regions of the left SLF and CG, where we found a significant change in FA, in both groups. There was no significant correlation between the BPND value at each voxel and the individual value of the functional connectivity of the seed-target regions showing a significant group difference.

3.4. FA changes and its correlation with functional connectivity

4. Discussion

Our results revealed significantly lower FA of the superior longitudinal fasciculus (SLF) at the left external capsule and higher FA in the left cingulum (CG) near the hippocampus in SCI subjects compared with nSCI subjects (Fig. 3A). We found no significant relationship between the LM I/II score and FA values of the SLF and CG. We found a significant positive correlation between the SLF-FA and functional connectivity of the left RSC and left dorsomedial prefrontal cortex (cluster #4, r ¼ 0.45, p ¼ 0.001), and negative correlations between the CG-FA and functional connectivity of (1) the right RSC and left dorsomedial prefrontal cortex (cluster #2, r ¼ 0.37, p ¼ 0.006), (2) the left RSC and left dorsomedial prefrontal cortex (cluster #4, r ¼ 0.49, p ¼ 0.0002), and (3) the left RSC and anterior cingulate/medial prefrontal cortex (cluster #7, r ¼ 0.44, p ¼ 0.001) (Fig. 3B and Supplementary Table S3).

This is the first study to demonstrate that individuals with SCI have reduced functional connectivity between the RSC and other cortical midline structures of the DMPFC and ACC. In addition, the reduced functional connectivity between these regions correlated well with higher subjective memory complaints determined by the self-rated EMC score. DTI-FA analysis revealed a significant decrease of FA in the left SLF, and an increase of FA in the left CG. The functional connectivity of RSC and cortical midline structures of DMPFC/ACC showed significant positive and negative correlations with the FA in the left SLF and left CG, respectively. Meta-analysis showed that the less functionally connected brain regions of the RSC, DMPFC, and ACC in SCI are all involved in selfreferential processing (Northoff et al., 2006); in particular, posterior cortical midline structures, including the RSC, are implicated in integrating self-referential stimuli in the context of one’s own viewpoint. Axonal tracing studies in monkeys have revealed that the RSC has reciprocal connections with the hippocampal formation and parahippocampal region, and the RSC is most likely to be involved in hippocampus-dependent functions within the PCC (Kobayashi and Amaral, 2003, 2007). The RSC also has reciprocal pathways to the prefrontal cortex, which provide an indirect route connecting the hippocampal regions and dorsolateral prefrontal

3.5. PIB-BPND and its correlation with functional connectivity We found no significant difference in global cortical mean PIBBPND values between the SCI and nSCI groups (BPND adjusted for age, sex, and years of education: SCI ¼ 0.20  0.16, nSCI ¼ 0.14  0.17; F1, 23 ¼ 0.95, p ¼ 0.34). There were 3 Ab-positive (BPND > 0.25)

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Fig. 2. Correlation between subjective memory complaints as reflected by the EMC score and functional connectivity. There was a negative correlation between the EMC score and functional connectivity of (A) rRSC-cluster #2 (L dorsal frontal cortex), (B) lRSC-cluster #4 (L dorsal frontal/premotor/dorsolateral prefrontal cortex), and (C) l-RSC-cluster #5 (L anterior prefrontal/dorsolateral prefrontal/dorsal anterior cingulate cortex). Abbreviations: EMC, everyday memory checklist; RSC, retrosplenial cortex.

Fig. 3. Group difference of FA values and its association with the functional connectivity. (A) Voxel-based comparison of FA values between the SCI and nSCI groups. Our results revealed significantly lower FA of the superior longitudinal fasciculus (SLF) at the left external capsule (x ¼ 34, y ¼ 10, z ¼ 8) and higher FA in the left cingulum (CG) near the hippocampus (x ¼ 20, y ¼ 38, z ¼ 8). (B) Association between the functional connectivity and FA values in regions showing group difference in functional connectivity. The figure showed the correlation plot of SLF-FA/CG-FA and the functional connectivity of RSC and cluster#4 (L dorsal frontal/premotor/dorsolateral prefrontal cortex). Abbreviations: FA, fractional anisotropy; nSCI, SCI, Subjective cognitive impairment.

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cortex. There are also reciprocal links with the anterior cingulate cortex (Morris et al., 1999). It has been proposed that the RSC transforms allocentric representations into egocentric ones, and vice versa (Vann et al., 2009). The hippocampus indexes the location that is embodied in an autobiographic memory, scene, or imagined event, and the RSC then translates this information into an egocentric representation so that the memory, scene, or imagined event can be viewed from a subjective, egocentric viewpoint (Burgess et al., 2001; Byrne et al., 2007). In other words, the RSC translates the objective representations of daily experience into a subjective, self-referential representation in the context of one’s own viewpoint. On the contrary, anterior cortical midline structures, including the ACC and DMPFC, are considered to be involved in the monitoring and evaluation of a self-referential stimulus (Northoff and Bermpohl, 2004). The ACC has been associated with monitoring and control functions such as response selection and inhibition, conflict monitoring, error detection, and performance monitoring (Bush et al., 2000; Carter et al., 1998; Devinsky et al., 1995; Paus, 2001). A variety of cognitive and emotional tasks revealed increased activity in the ACC to self-referential stimuli compared with non-selfreferential stimuli (Frith and Frith, 1999), and the monitoring function of the ACC is associated with a preference for self-referential stimuli. Likewise, subjects that were required to monitor and judge under conditions where auditory verbal feedback was their own or that of another person showed activation in the DMPFC (McGuire et al., 1996). Greater DMPFC activation was observed during evaluation of self-referential statements compared with memory retrieval trials (Zysset et al., 2002). The DMPFC was specifically activated during a self-referential evaluation task in which subjects had to judge whether positive or negative personality trait adjectives described themselves accurately (Fossati et al., 2003). The reduction in functional connectivity between the posterior cortical midline structures of the RSC and the anterior cortical midline structures of the ACC and DMPFC might contribute to dysfunction in self-referential processing. In particular, the subjective, self-referential representations of the daily experience of benign forgetfulness translated from the objective representations in the RSC cannot be efficiently and correctly monitored and evaluated in the ACC and DMPFC because of the fewer functional connections between these areas. As a result, individuals with SCI tend to incorrectly overestimate their experience of forgetfulness in spite of the lack of objective evidence. Functional connectivity measures are thought to reflect the sum of both direct and indirect synaptic connections between neural hubs (Dunn et al., 2014; Greicius et al., 2009; Sperling et al., 2010). The SLF is composed of long bundles of neurons connecting the front and the back of the cerebrum. In our study, the decrease of white matter connections in the SLF manifested as reduced functional connectivity between the RSC and ACC/DMPFC. On the contrary, this reduction in functional connectivity was related to an increase in white matter connectivity in the CG. From the viewpoint of memory function, the PCC/RSC has been identified as a key member of the posteromedial memory system that underlies the processing of “contextual” memory cues. The posterior medial system interacts with an anterior medial system, which processes the contents of episodic memory. The hippocampus is proposed to facilitate information transfer between the anterior and posterior medial memory systems, acting as a “dynamic integrator” of episodic memory (Ranganath and Ritchey, 2012). The increase in white matter connectivity in the CG near the hippocampus might facilitate the role of the hippocampus and compensate for the decreased functional connectivity between the 2 systems because of the reduction in white matter connectivity in the SLF in the SCI group.

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PET-amyloid imaging was conducted only in a subgroup of the enrolled subjects, namely 15 nSCI subjects among the 30 enrolled patients and 13 SCI subjects among the 23 enrolled patients. Previous studies with larger samples suggested a role of Ab in SCI (Amariglio et al., 2012; Perrotin et al., 2012; Rowe et al., 2010; Visser et al., 2009), which indicated the possibility that SCI is a very early at-risk period in the continuum of the dementia due to AD (Reisberg and Gauthier, 2008). Our results do not necessarily contradict the possibility that SCI indicates initial cognitive decrements because of AD pathology. However, the results of this study do suggest the existence of other factors related to SCI and raise questions about the pathophysiology underlying the functional and white matter disconnection in SCI. Whether the cognitive changes in SCI are innate or acquired, and fixed or progressive remain unknown. Therefore, longitudinal studies with larger samples that explore the evolving nature of other pathological features will be required to resolve these issues. Of particular note, the SCI group showed a significantly higher LM II delayed recall score compared with the nSCI group. However, this does not imply that nSCI individuals have some cognitive decline/deficits. From the standardized data in the Japanese version of the WMS-R, the mean LM II score of nSCI subjects (19.3/50) was equivalent to the 87th percentile of the 70 to 74-year-old group, while that of SCI subjects (23.2/50) was equivalent to the 90th percentile of the same group. Individuals of both the nSCI and SCI groups showed good memory ability. Therefore, the lack of subjective memory complaints in nSCI individuals with good memory function cannot necessarily be considered to result from an individual’s inability to recognize their own memory deficits. In summary, our study demonstrates that individuals with SCI have less functional connectivity between the RSC and other cortical midline structures, namely, the DMPFC and ACC. This might contribute to the overestimation of their experience of benign forgetfulness, because of dysfunction of self-referential processing. A reduction in white matter connection was shown to be related to less functional connectivity, but we found no relationship between amyloid deposition and reduced functional and white matter connections in SCI subjects. Our results do not necessarily contradict the possibility that SCI indicates initial cognitive decrements, but they do indicate that reduced functional connectivity in cortical midline structures contribute to SCI. Further studies are needed to understand the pathophysiological mechanisms underlying SCI.

Disclosure statement The authors have no conflicts of interest to disclose. The sponsor had no role in either the analysis or interpretation of these data or the content of the article. Appropriate approval procedures were used concerning human subjects.

Acknowledgements This research was supported by Grants-in-Aid for Scientific Research (C) 24591740 and 24591708 from the Japan Society for the Promotion of Science, and by the Health and Labor Sciences Research Grants, Research on Dementia (H25-01).

Appendix A. Supplementary Data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.neurobiolaging. 2015.03.006.

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