Dissociative contributions of the anterior cingulate ... - Semantic Scholar

Neuropsychologia 77 (2015) 10–18

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Dissociative contributions of the anterior cingulate cortex to apathy and depression: Topological evidence from resting-state functional MRI Keiichi Onoda n, Shuhei Yamaguchi Department of Neurology, Shimane University, 89-1, Enya-cho, Izumo, Shimane 693-8501, Japan

art ic l e i nf o

a b s t r a c t

Article history: Received 31 March 2015 Received in revised form 15 July 2015 Accepted 30 July 2015 Available online 31 July 2015

Apathy is defined as a mental state characterized by a lack of goal-directed behavior. However, the underlying mechanisms of apathy remain to be fully understood. Apathy shares certain symptoms with depression and both these affective disorders are known to be associated with dysfunctions of the frontal cortex-basal ganglia circuits. It is expected that clarifying differences in neural mechanisms between the two conditions would lead to an improved understanding of apathy. The present study was designed to investigate whether apathy and depression depend on different network properties of the frontal cortexbasal ganglia circuits, by using resting state fMRI. Resting-state fMRI measurement and neuropsychological testing were conducted on middle-aged and older adults (N ¼392). Based on graph theory, we estimated nodal efficiency (functional integration), local efficiency (functional segregation), and betweenness centrality. We conducted multiple regression analyses for the network parameters using age, sex, apathy, and depression as predictors. Interestingly, results indicated that the anterior cingulate cortex showed lower nodal efficiency, local efficiency, and betweenness centrality in apathy, whereas in depression, it showed higher nodal efficiency and betweenness centrality. The anterior cingulate cortex constitutes the so-called “salience network”, which detects salient experiences. Our results indicate that apathy is characterized by decreased salience-related processing in the anterior cingulate cortex, whereas depression is characterized by increased salience-related processing. & 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: Apathy Depression Graph theory Resting state functional MRI Anterior cingulate cortex Salience network

1. Introduction Apathy is defined as decreased, or diminished goal-directed behavior (Levy and Dubois, 2006; Marin, 1991). It is a feature in a number of neurological diseases, which generally serves to worsen prognoses (Hama et al., 2007; Harris et al., 2014; Starkstein et al., 2006). Therefore, apathy is a clinically important symptom, however its underlying neural mechanisms remain relatively poorly understood. One likely reason for this could be that apathy and depression show similar symptoms, including diminished interest and anhedonia. Moreover, dysfunctions of the frontal cortex-basal ganglia circuits could be related to both apathy and depression (Hama et al., 2011; Kostić and Filippi, 2011; Tagariello et al., 2009). Lesion and neuroimaging studies have provided evidence supporting the role of the frontal-striatum circuit in apathy (Levy and Dubois, 2006). It has been reported that patients with frontal lobe and basal ganglia lesions show post-stroke apathy (Caeiro n

Corresponding author. E-mail address: [email protected] (K. Onoda).

et al., 2013; Hama et al., 2011; Murakami et al., 2012). Moreover, hypoperfusion of these regions is associated with post-stroke apathy (Okada et al., 1997; Onoda et al., 2011). Neuroimaging studies in neurodegenerative disorders have reported hypoperfusion of the orbitofrontal cortex, anterior cingulate cortex (ACC), and thalamus (Benoit et al., 2004, 2002; Holthoff et al., 2005; Kang et al., 2012; Lanctôt et al., 2007; Marshall et al., 2007). Even healthy, apathetic elderly individuals show decreased frontal lobe gray matter volume (Grool et al., 2014). Information processing along the frontal cortex-basal ganglia circuits originates in the frontal cortex and then proceeds to the striatum, globus pallidus, and thalamus, returning through a final loop to the frontal cortex (Alexander et al., 1986). These circuits are known to be involved in generating motivation, and any dysfunctions in these circuits might result in the development of apathy (Levy and Dubois, 2006). However, deficits of the frontal cortex-basal ganglia circuits are also commonly observed in depression (Bora et al., 2012; Dutta et al., 2014; Kerestes et al., 2014). A new approach to exploring differences in neural mechanisms underlying these two affective disorders is therefore needed.

http://dx.doi.org/10.1016/j.neuropsychologia.2015.07.030 0028-3932/& 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

K. Onoda, S. Yamaguchi / Neuropsychologia 77 (2015) 10–18

In this study, we used resting-state functional MRI (rsfMRI) that can evaluate regional interactions occurring when a subject is not performing an explicit task, or during rest periods. The low-frequency range (o0.08 Hz) of fluctuations in blood oxygenation level dependent contrast shows strong correlations even with distant brain regions. Spontaneous neural activities are presumed to underlie these fluctuations (Van Dijk et al., 2010). Calculating the correlations among fluctuations in brain regions enable the quantification of functional connectivity between distant regions (Friston et al., 1993). Additionally, graph theory can be applied to rsfMRI data to estimate topological properties of global and local networks, including the efficiency of a network and the degree of centrality in a region (Rubinov and Sporns, 2010). RsfMRI techniques have been used for studying the pathophysiology of neurological and psychiatric diseases, including depression (Dutta et al., 2014). Recently, rsfMRI has been applied to investigations of apathy (Farb et al., 2013), and certain studies have suggested that apathy and depression are distinct entities (Skidmore et al., 2013). For instance, it has been demonstrated that apathy in Parkinson's disease is related to functional connectivity of frontal cortex-basal ganglia circuits, after controlling for depressive symptoms (Baggio et al., 2015). Moreover, depressive patients with apathy showed altered functional connectivity of these circuits compared to unapathetic patients (Alexopoulos et al., 2013). In a case report, a highly apathetic patient who had a stroke in the subcortical region showed remarkably aberrant functional connectivity of the ACC and basal ganglia and this change persisted for three years (Siegel et al., 2014). These studies suggest that the frontal cortex-basal ganglia network might play a crucial role in the development of apathy, independently of depression. A large sample would be required to detect differences in functional networks between apathy and depression, due to the similarity of the two affective states. The frequency and the degree of apathy increases with advancing age, even in normal elderly (Adams, 2001; Brodaty et al., 2010). In the present study, we examined the hypothesis that apathy and depression are associated with distinct alterations in functional connectivity within the frontal cortex-basal ganglia network system, by using a topological analysis of rsfMRI data in a large sample of normal middle-aged and elderly individuals. 2. Material and methods

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Assessment Battery (Dubois et al., 2000), the Kohs Test (Wigg and Duro, 1999), the Verbal Fluency Test (Bechtold et al., 1962), the Self-rating Depression Scale (SDS) (Zung, 1965), and the Apathy Scale (AS) (Okada et al., 1998; Starkstein et al., 1992). We used AS and SDS scores as indices of apathy and depression, respectively. The AS is a self-administrated questionnaire and shows less reliability in participants with cognitive impairments. Therefore, we excluded participants showing evidence of cognitive decline (MMSE scoreo 27 or WMS scoreo40). 2.3. Image acquisition Imaging data were acquired using a Siemens AG 1.5 T scanner (Symphony). Twenty seven axial slices parallel to the plane connecting the anterior and posterior commissures were measured using a T2*-weighted gradient-echo spiral pulse sequence (repetition time ¼2000 ms, echo time ¼ 30 ms, flip angle ¼ 90°, scan order ¼interleave, matrix size ¼64 64, field of view¼256 256 mm2, isotropic spatial resolution¼ 4 mm, slices¼ 27, slice thickness¼ 4.5 mm, no gap). All participants underwent a 5 min rs-fMRI scan after being instructed to remain awake with their eyes closed. After the functional scan, T1-weighted images of the entire brain were measured (192 sagittal slices, repetition time ¼ 2170 ms, echo time ¼ 3.93 ms, inversion time ¼ 1100 ms, flip angle ¼15°, matrix size¼ 256 256, field of view¼ 256 256 mm2, isotropic spatial resolution¼ 1 mm). 2.4. Functional image processing The procedure of the analysis is illustrated in Fig. 1. We used Statistical Parametric Mapping (SPM8, http://www.fil.ion.ucl.ac.uk/spm/) for spatial preprocessing. The first five functional volumes for each participant were discarded for magnetic field stabilization. The remaining 145 functional images were realigned to remove any artifacts from head movements (if head movement exceeded 2 mm or 2°, the participant was excluded from the study) and corrected for differences in image acquisition time between the slices. Next, the functional images were normalized to the standard space defined by a Montreal Neurological Institute (MNI) template and re-sliced with a voxel size of 3 3 3 mm3. Spatial smoothing was then applied with the full width at half maximum (FWHM) equal to 8 mm. After spatial preprocessing, we performed temporal preprocessing using the Functional Connectivity Toolbox (conn, http://www.alfnie.com/software). The head movement time series, white matter signal, and cerebral spinal fluid signal were regressed out from each voxel, based on CompCor Strategy (Behzadi et al., 2007). To check for the possible risk of head motion contamination, we performed correlation analyses between the head motion and AS/SDS, which indicated no significant correlations (| rs|o 0.07, ps40.26). Then, we divided voxels in the cerebrum (except cerebellum) into 90 regions based on an automated anatomical labeling atlas (AAL: Fig. 1B), and calculated the mean time course of the voxels within each atlas region (Fig. 1C). Temporal smoothing for the time course was performed using a band-pass filter (0.01–0.08 Hz). Pearson correlations for all time course pairs were computed for each participant and were transformed into z-scores via Fisher's transformation (Fig. 1D). To construct a graph network, each functional connectivity matrix was converted to an undirected binary network using a density threshold (K), which is equivalent to the ratio between the number of edges and all possible edges (Latora and Marchiori, 2001) (Fig. 1E). The density range in this study was from 0.10 to 0.30 (step: 0.01). We applied graph theory analysis to all the threshold networks of each individual, and all graph theory parameters were averaged over the density range.

2.1. Participants 2.5. Graph theory analysis The participants were selected from the database of the health examination system at the Shimane Institute of Health Science. This system includes a collection of medical, neurological, neuropsychological, MRI, and blood test data of individuals (N ¼392: 227 men and 165 women, age range: 29–91 years, mean age: 62.9 7 13.9 years) who had undergone resting-state fMRI scanning and neuropsychological testing. Among these individuals, those with any one of following characteristics were excluded from subsequent analyses: (1) a history of neurological or psychiatric disease with treatment (n¼ 39); (2) any cognitive impairments that are described in the neuropsychological test section below, (n¼ 98); (3) excessive head movements during resting-state fMRI (n ¼7); or (4) missing data (n¼ 1). Therefore, the sample consisted of cognitively normal participants with a wide range of apathy and depression scores, which allowed the examination of the separate correlates of apathy and depression. Two hundred sixty four participants were included in our analysis (161 men and 103 women, age range: 31–86 years, mean age: 59.7 7 13.7 years). All participants gave their informed consent before participation. The Shimane University medical ethics committee approved this study. 2.2. Neuropsychological assessment All participants were assessed using a neuropsychological test battery that included the Mini Mental State Examination (MMSE) (Folstein et al., 1975), the Wechsler Memory Scale concise version (WMS) (Kobayashi et al., 1987), the Frontal

There are many network parameters in graph theory, and local parameters have been categorized into at least three main types: functional integration, functional segregation, and centrality (Rubinov and Sporns, 2010). We selected nodal efficiency, local efficiency, and betweenness centrality from each category, which are free from connectedness restrictions (Latora and Marchiori, 2001). To examine differences in functional network organization between apathy and depression, we computed nodal efficiency, local efficiency, and betweenness centrality for each AAL region using the Brain Connectivity Toolbox (BCT, https://sites. google.com/site/bctnet/). Nodal efficiency is a measure of functional integration and is defined as the average inverse shortest path length (Latora and Marchiori, 2001)

Ei =

1 n−1

∑i,j ∈ N ,j ≠ i dij−1

where Ei is the efficiency of node i, n is the number of nodes, and dij is the shortest path length between nodes i and j. The path length represents the number of processing steps along the routes of information transfer among the brain regions (Rubinov and Sporns, 2010). Nodal efficiency measures the ability of information propagation between a given node i with the rest of the nodes in a network. Higher nodal efficiency is indicative of higher integration in the brain (Fig. 2A). Local efficiency is a measure of functional segregation, which is the ability for specialized processing to occur within densely interconnected groups of brain

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Fig. 1. Protocol for graph theoretical analysis on resting state functional MRI. (A) Initially, preprocessing was applied to the data. (B) 90 regions of interest (ROIs) were defined based on Automated Anatomical Labeling. (C) The mean time course of each ROI was extracted, band-pass filtered (0.01–0.08 Hz), and regressed out for nuisance covariates. (D) Pearson's correlation coefficients between all pairs of ROI time courses were computed, which resulted in a 90 90 correlation matrix representing functional connectivity. (E) The matrixes were thresholded at different network densities (0.10–0.30) and binarized. Then, topological parameters of graph theory were calculated for each ROI of the matrix.

regions. We computed local efficiency as follows (Latora and Marchiori, 2001):

Eloc,i =

1 k i(k i − 1)

∑j,h ∈ N,j ≠ i aihaih[dhj(Ni)]−1

3.1. Neuropsychological data

where Eloc,i is the local efficiency of node i, ki is the degree of node i, aih is connection status (1 or 0) between i and h, and dhj(Ni) is the length of the shortest path between h and j that contains only neighbors of i. Local efficiency measures the ability of information transmission of a network at the local level. Higher local efficiency is interpreted as the presence of sub-networks within a functional brain network (Fig. 2B). Additionally, we calculated betweenness centrality as follows:

bi = log 10(1 +

1 (n − 1)(n − 2)

∑h,j ∈,h ≠ i ≠ j

3. Results

P hj(i) P hj

)

where Pij is the number of shortest paths between h and j, and Phj(i) is the number of shortest paths between h and j that pass through i. To ensure a normal distribution, we performed logarithmic transformation. Betweenness centrality of a node captures the influence of the node over the information flow between all the other nodes in the network. Higher betweenness centrality indicates that the region functions as a hub (Fig. 2C).

Fig. 3 shows a scatter plot of AS and SDS scores. This plot indicates that 66 participants (25.0%) out of 264 exceeded the AS cut-off score ( Z16 in Japan), whereas 79 participants (29.9%) exceeded the SDS cut-off score ( Z40: mild depression). There were 33 participants with scores above the cut-off scores for both AS and SDS (12.5%). Moreover, AS and SDS scores were significantly correlated (r ¼0.51, p o0.001). Furthermore, there was neither a significant correlation between age and AS/SDS scores (|rs| o 0.02, ps40.8), nor any main effects of group (apathy and/or depression) for age (F(3260) o1.3, p ¼0.26). We have summarized the scores of each neuropsychological test and the relationships between them in Table 1. It can be seen from Table 1 that there were no significant correlations between AS/SDS scores and other cognitive test scores (rs o0.11, ps 40.1). Also, there were no main effects of group on any cognitive indices (F(3258) o1.6, ps40.21).

2.6. Statistical analysis

3.2. Graph theory analysis

To investigate the effects of apathy and depression on the graph theory parameters described above, we conducted multiple regression analysis of each parameter, with age, sex, AS scores, and SDS scores as independent variables. Multiple comparison corrections based on the false discovery ratio (FDR) were applied to both the analyses of each graph metric and each region. FDR for the analysis of each region was performed to examine whether apathy and depression had significant relationships with network parameters of each region. All the p-values obtained in a region were used for this FDR correction. The significance level for FDR-corrected criteria was set at p o 0.05. To illustrate the regions that showed significant coefficients with apathy or depression, brain maps were created using BrainNet Viewer (http://www.nitrc.org/projects/bnv/). This analysis indicated that regions showing aberrant network properties were related to both AS and SDS scores (bilateral ACC, see Section 3). Therefore, we also performed multiple regression analysis of each functional connectivity with ACC, by using the z-scores (transferred from the correlation) between the left/right ACC and the other regions were used as dependent variables.

We computed nodal efficiency, local efficiency, and betweenness centrality, and then conducted multiple regression analyses on graph theory measures of each region. Brain regions showing a significant relationship between topological measures and AS/SDS scores are summarized in Fig. 4 and Table 2. It can be seen that nodal efficiencies of the right ACC, left fusiform gyrus, and occipital cortex were negatively correlated with AS scores. On the other hand, nodal efficiencies of these regions were positively correlated with SDS scores. Moreover, local efficiencies of the right ACC and middle occipital gyrus were negatively correlated with AS scores. However, local efficiencies of the bilateral orbital cortex were positively correlated with AS scores. Furthermore, local efficiencies and SDS scores were positively correlated in the inferior


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Table 1 Mean scores and correlation coefficients of neuropsychological tests.

AS SDS MMSE WMS FAB Kohs VFT

Mean (SD)

SDS

MMSE

WMS

FAB

Kohs

VFT

11.1(6.0) 35.2(7.1) 29.4(1.0) 49.3(4.7) 16.9(1.2) 109.4(16.7) 26.2(6.2)

0.51**

0.06 0.08

0.05 0.03 0.32**

0.11 0.04 0.00 0.22**

0.06 0.04 0.05 0.27** 0.25**

0.06 0.04 0.02 0.22** 0.47** 0.14*

AS: Apathy Scale, SDS: Self-rating Depression Scale, MMSE: Mini Mental State Examination, WMS: Wechsler Memory Scale concise version, FAB: the Frontal Assessment Battery, VFT: Verbal Fluency Test, age was controlled as a covariate. n

p o 0.05 po 0.001

nn

Fig. 2. Topological parameters of graph theory in the present study. (A) Nodal efficiency: regions with high nodal efficiency indicate the existence of a higher efficiency in communicating with other brain regions. (B) Local efficiency: regions with higher local efficiency suggest a higher degree of interconnectedness in local networks consisting of direct neighbors of the region. (C) Betweenness centrality: regions with higher betweenness centrality have a role of hub for the network.

Our analysis indicated that network properties of the ACC were involved in both apathy and depression. Scatterplots between network properties of the right ACC and AS/SDS scores are shown in Fig. 5, which shows the opposite effects of the right ACC on AS and SDS scores. To further clarify details of this important result, we developed additional models with the AS score, the SDS score, the combined index of normalized AS and SDS scores, the interaction of AS and SDS scores, and the difference index (normalized AS minus SDS score) as independent variables. Standardized partial regression coefficient (r) and p-value of the independent variables in each model are summarized in Table 3. The combined index and the interaction between AS and SDS did not show any significant relationships with nodal, local efficiencies, or betweenness centrality. Moreover, both AS and SDS were inversely associated with network parameters when using AS and SDS scores independently and simultaneously as variables. The difference index was also negatively correlated with the three network properties in the right ACC, suggesting that increased apathy was more closely related to lower network properties compared to depression. In addition to the graph theory analysis, we performed multiple regression analyses of the functional connectivity between the bilateral ACC and other regions, in order to provide a more detailed examination of ACC network changes. Fig. 6 shows functional connectivity related to apathy and depression using loose statistical criteria (uncorrected p o0.05). It can be seen from Fig. 6A that the functional connectivity of the ACC with the bilateral medial prefrontal cortices, right putamen, globus pallidus, amygdala, left posterior cingulate cortex and precuneus, all tend to be negatively correlated with the AS scores. In contrast, Fig. 6B shows that SDS scores tend to be positively correlated with the functional connectivity of the ACC to the bilateral globus pallidus, right putamen and amygdala.

4. Discussion

Fig. 3. Scatter plot of apathy scale (cut-off Z 16) and self-rating depression scale (cut-off Z40) scores.

occipital gyrus, and negatively correlated in the orbital cortex and postcentral gyrus. Also, betweenness centrality of the right ACC was negatively correlated with AS scores, and that of the rectus gyrus was positively correlated with AS scores. In contrast, SDS scores were positively correlated with betweenness centrality for the bilateral ACC and right medial superior frontal gyrus, whereas SDS scores were negatively correlated with betweenness centrality for the left putamen.

The present study was designed to differentiate the neural mechanisms underlying apathy and depression in terms of their topological properties. The results of this study, similar to evidence from lesion and neuroimaging studies, supported the hypothesis that both apathy and depression are related to dysfunctions of the frontal cortex-basal ganglia circuits. Moreover, results suggested that network properties are differentially altered in these two conditions. Furthermore, results indicated that after controlling for the interaction between the two symptoms, certain regions, including the frontal cortex-basal ganglia circuits, were independently related to apathy and depression. Notably, the altered network properties of the ACC contributed to apathy and

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Fig. 4. Results of regression analyses of nodal efficiency, local efficiency, and betweenness centrality. Colored nodes indicate that the region had significant standardized partial regression coefficients for apathy or depression. Red and blue show positive and negative relationships, respectively. Size of colored node reflects absolute value of standardized partial regression coefficient. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) Table 2 List of regions showing significant correlations between graph theory indices and apathy/depression.

Nodal efficiency Apathy ACC (R) MOG (R) SOG (L) SOG (R) FFG (L) Depression ACC (L) ACC (R) MOG (R) FFG (L)

Local efficiency Apathy ORBinf (L) ORBinf (L) ACC (R) MOG (R) Depression ORBinf (L) IOG (R) PoCG (L)

Betweenness centrality Apathy REC (R) ACC (R) Depression SFGmed (R) ACC (L) ACC (R) PUT (L)

r

p

0.192 0.244 0.205 0.198 0.193

0.0073 0.0006 0.0043 0.0059 0.0071

0.178 0.162 0.201 0.206

0.0116 0.0224 0.0047 0.0038

0.196 0.264 0.251 0.182

0.0066 0.0002 0.0004 0.0116

0.184 0.192 0.211

0.0091 0.0071 0.0027

0.191 0.180

0.0078 0.0121

0.213 0.186 0.167 0.197

0.0028 0.0088 0.0187 0.0053

ACC: anterior cingulate cortex, MOG: middle occipital gyrus, SOG: superior occipital gyrus, FFG: fusiform gyrus, ORBinf: orbital part of inferior frontal gyrus, IOG: inferior occipital gyrus, PoCG: postcentral gyrus, REC: rectus gyrus, SFGmed: medial superior frontal gyrus, PUT: putamen, r shows standardized partial regression coefficients.

depression in different ways. These results support the view that apathy and depression are distinctive brain disorders. The evidence of this study indicated that the ACC plays a core

role in apathy, which corroborated previous rsfMRI studies (Alexopoulos et al., 2013; Siegel et al., 2014). The ACC, along with the anterior insula constitutes a salience network, which is well suited for identifying the most homeostatically relevant event among myriad inputs, as well as for integrating highly processed sensory data with visceral, autonomic, and hedonic markers, such that the organism can decide what to do next (Seeley et al., 2007). We propose that apathy is characterized by decreased salience integration in the ACC. In support of this contention, we found lower nodal efficiency in the ACC, which is suggestive of an increased functional distance between the ACC and other region. We also found lower local efficiency of the ACC, which is indicative of decreased contribution of the ACC to sub-networks. Furthermore, we found lower betweenness centrality in the ACC, which is suggestive of a decreased hub function in apathetic participants. An additional analysis revealed that the ACC in apathetic participants had decreased functional connectivity with the medial prefrontal cortex, posterior cingulate cortex, precuneus, amygdala, and basal ganglia. The medial prefrontal cortex, posterior cingulate cortex, and precuneus constitute the default mode network, which supports the construction of internal mental models based on mnemonic (limbic) systems (Buckner, 2013). The amygdala participates in emotional processing and also allows incoming information with either positive or negative valence to be processed more efficiently in distributed cerebral networks (Paz and Pare, 2013). Additionally, the amygdala has direct pathways to the ACC (Porrino et al., 1981). Based on the above evidence, we propose that the ACC of apathetic individuals is less efficient at integrating information from the internal mental model of the default mode network, as well as integrating ongoing emotional processing in the amygdala and as a result, less able to detect the most salience information. Furthermore, the ACC constitutes a portion of the frontal cortex-basal ganglia circuits (Alexander et al., 1986), which is involved in a variety of crucial brain functions including action selection, action gating, reward based learning, and motor preparation (Chakravarthy et al., 2010). Our findings suggest that such behavior-related processing of the basal ganglia is dissociated from the salience detection processing of the ACC in apathetic people. In sum, due to decreased ACC functioning, people with apathy, compared to non-apathetic individuals might be unable to detect and integrate information about the salience of ongoing internal and external processing, resulting in a failure to transmit the


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Fig. 5. Scatter plots between network properties (nodal efficiency, local efficiency, and betweenness centrality) of right anterior cingulate cortex (ACC) and apathy/depression scores. Apathy scores were adjusted for age, sex, and depression score, and depression scores also were adjusted for age, sex, and apathy score.

Table 3 Standardized partial regression coefficient (r) and p-value of each general liner model for network parameters of the right anterior cingulate cortex. Model

Nodal efficiency

Model 1 (Apathy þageþ sex) Apathy 0.110 (0.077) Model 2 (Depressionþ ageþsex) Depression 0.066 (0.284) Model 3 (Combined index þ ageþsex) Combined index 0.025 (0.693) Model 4 ((Apathy depression)þ ageþ sex) Apathy depression 0.073 (0.240) Model 5 (Apathy þdepression þageþ sex) Apathy 0.192 (0.007) Depression 0.162 (0.022) Model 6 (Apathy þdepression þ(apathy depression)þ age þsex) Apathy 0.191 (0.008) Depression 0.151 (0.035) Apathy depression 0.061 (0.323) Model 7 (Difference index þageþ sex) Difference index 0.175 (0.004)

Local efficiency

Betweenness centrality

0.206

(0.0009)

0.057

(0.361)

0.037

(0.553)

0.100

(0.106)

0.140

(0.024)

0.026

(0.683)

0.024

(0.697)

0.052

(0.400)

0.251 0.089

(0.0004) (0.209)

0.180 0.167

(0.012) (0.019)

0.251 0.084 0.028

(0.0005) (0.241) (0.646)

0.143 0.166 0.035

(0.046) (0.022) (0.575)

0.168

(0.007)

0.157

(0.011)

Apathy: Apathy score (AS), Depression: Self-rating Depression Scale score (SDS), Combined index: normalized AS þnormalized SDS, Apathy depression: interaction of apathy and depression, Difference index: normalized AS-normalized SDS. Italics denote significant coefficients and p-values (FDR corrected at region level).

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Fig. 6. Results of regression analyses for functional connectivity of the ACC with other regions. Red and blue show positive and negative relationships, respectively. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

results of processing to the basal ganglia. A number of voxel-based morphometric studies have reported that atrophy of the ACC is associated with apathy in patients with Alzheimer's disease (Apostolova et al., 2007; Bruen et al., 2008; Tunnard et al., 2011), traumatic brain injuries (Knutson et al., 2014), and progressive supranuclear palsy (Stanton et al., 2013). Furthermore, diffusion tensor imaging studies have demonstrated that fractional anisotropy of the ACC is decreased in apathetic Alzheimer's patients (Kim et al., 2011; Ota et al., 2012) and that reduced fractional anisotropy between the ACC and orbitofrontal cortex is associated with apathy in schizophrenia (Ohtani et al., 2014). Contributions of the orbitofrontal cortex to apathy have also been reported in studies based on several modalities (Benoit et al., 2004; Delmaire et al., 2013; Kamat et al., 2014; Massimo et al., 2009; Peters et al., 2006). Abnormal connectivity between the ACC and orbitofrontal cortex might result in a deficit of emotional processing (de Marco et al., 2006), and affect necessary interactions within the decision-making neural circuit (Rushworth et al., 2011). In the present study, we did not find a significant relationship between apathy and functional connectivity of the ACCorbitofrontal cortex, even though a part of the orbitofrontal cortex showed altered nodal and local efficiencies in apathetic individuals. A different approach might be necessary to demonstrate the specific functional disintegration between the ACC and orbitofrontal cortex in apathy. Levy has proposed that the underlying mechanisms of apathy can be divided into three subtypes according to the type of disrupted processing: emotional-affective, cognitive, and auto-activation (Levy and Dubois, 2006). In his model, lesions or dysfunctions of the limbic territories of the frontal lobes (the orbitalmesial prefrontal cortex including the ACC) and the basal ganglia (e.g., the ventral striatum, ventral pallidum) lead to apathy through difficulties in providing the affective value of a given behavioral context. Our results partially supported this model, but no significant effect of apathy on network properties was observed in the basal ganglia. This might be because the basal ganglia are divided into subregions based on anatomical connectivity (Alexander et al., 1986), which are functionally connected with different frontal cortices (Martino et al., 2008). Therefore, examining functional connectivity and network properties based on the subdivisions of the basal ganglia might prove useful. We found altered network properties of the temporal and occipital cortices in apathy, and some earlier studies have reported that apathy is associated with the temporal and occipital cortices (Ott et al., 1996; Spalletta et al., 2013; Yang et al., 2014). It remains unclear how these areas contribute to apathy. However, dysfunctions of the frontal cortex-basal ganglia circuit might not be the only cause of apathy, which might also result from alterations to the distribution networks that include the temporal and occipital cortices. Another finding of this study was that depression is associated with higher nodal efficiency and betweenness centrality in the

ACC, and increased functional connectivity of the ACC with the globus pallidus and amygdala. Numerous rsfMRI studies on depression have been published (Dutta et al., 2014). Certain of these studies have indicated that ACC of patients with depression shows increased amplitude of low-frequency fluctuations (Guo et al., 2012), increased regional homogeneity (Wu et al., 2011; Yao et al., 2009), high contribution to the default mode network (Sambataro et al., 2013; Zhu et al., 2012) and increased functional connectivity with the prefrontal cortex, insula, amygdala, basal ganglia, and thalamus (Anand et al., 2009; Bohr et al., 2012; Horn et al., 2010; Kenny et al., 2010; Ye et al., 2012). Such evidence, along with the results of this study indicates that excessive, or abnormal network properties of the ACC might contribute to symptoms of depression. It is suggested that the ACC of depressive people might excessively integrate and detect saliences of ongoing internal and external processing and cause a cognitive and attentional bias. There are some limitations to the present study. Unlike previous studies conducted with patient populations, participants in this study were otherwise normal middle-aged and elderly people, who presented with apathy and/or depression. This was because this study was designed to identify differences in neural networks between apathy and depression, without being confounded by factors associated with neurological, or psychological disorders per se. Apathy is highly prevalent in populations of normal, elderly people, with prevalence rates ranging from 6.0% to 15.8% (Brodaty et al., 2010). Interestingly, the prevalence of apathy in the current study was much higher than that indicated by Brodaty et al. No study to date has examined differences in apathy between normal adults and patients with neurological disorders. Although there are no reasons to believe that apathy is different between these two groups, because it has been defined as a quantitative reduction of goal-directed behavior, there is a need to replicate the findings of this study in patients with different neurological disorders. Another limitation of this study is that our sample was restricted to cognitively normal elderly, which was done to ensure the reliability of self-rating for apathy. Apathy caused by a disruption of cognitive processing might be associated with the lateral prefrontal cortex and the related subregions within basal ganglia (Levy and Dubois, 2006). Finally, the results of the multiple regression analysis in this study need to be interpreted carefully. We simultaneously used both AS and SDS scores as independent variables. Even though the two scores were highly correlated, apathy and depression showed respectively negative and positive relationships with network measures suggesting that network properties might have a significant relationship with aspects of apathy that is independent of depression, and vice versa. We also found that the relative difference between the two states was involved in the network properties of the ACC, suggesting that network function might represent a single dimension between apathy and depression. In summary, the present study, which applied graph theory analysis to rsfMRI data of normal adults revealed a remarkable


dissociation of network properties in the ACC between apathy and depression. This suggests that both apathy and depression could be associated with a dysfunction of ACC, but in opposite directions. Apathetic people might be unable to detect and integrate saliences in the ACC, whereas depressive people might excessively detect and integrate saliences in the ACC with a negative bias. It is suggested that the topological approach used in this study is useful for understanding neural mechanism underlying apathy.

Acknowledgments This study was supported by Grants-in-Aid for Scientific Research of Japan Society for the Promotion of Science (Grant number: 26870375). None of the authors have any conflict of interest regarding the findings presented in this manuscript.

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