Amnestic Mild Cognitive Impairment: Topological Reorganization of the Default-Mode Network 1

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1 Note: This copy is for your personal non-commercial use only. To order presentation-ready copies for distribution to your colleagues or clients, contact us at Liang Wang, PhD He Li, PhD Ying Liang, MS Junying Zhang, MS Xin Li, MS Ni Shu, PhD Yongyan Y. Wang, BS Zhanjun Zhang, MD Amnestic Mild Cognitive Impairment: Topological Reorganization of the Default-Mode Network 1 Purpose: Materials and Methods: To investigate the topologic reorganization of the defaultmode network (DMN) in patients with mild cognitive impairment (MCI) and whether, relative to healthy control subjects, patients with MCI would be more likely to show disrupted functional connectivity and altered topological configuration of the DMN during the memory task compared with that observed during the resting state. This study was approved by the institutional review board of Beijing Normal University Imaging Center for Brain Research. Written informed consent was obtained from each participant. Healthy control subjects (n = 26) and patients with amnestic MCI (amci) (n = 25) performed an episodic memory task and also rested while undergoing functional magnetic resonance imaging. Task-induced deactivations were identified and parcellated into different regions associated with the DMN. Functional connectivity across all pairs of regions was computed to construct the DMN architecture. Graph theoretical approaches were used to characterize topological properties of this network. Original Research n Neuroradiology 1 From the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 19 Xinjiekouwai St, Beijing , P.R. China (L.W., Y.L., J.Z., X.L., N.S., Z.Z.); Princeton Neuroscience Institute, Princeton University, Princeton, NJ (L.W.); and Institute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, P.R. China (H.L., Y.W.). Received July 18, 2012; revision requested September 11; revision received December 5; accepted December 16; final version accepted January 2, Supported by the Natural Science Foundation of China (grant nos , , and ), Fundamental Research Funds for the Central Universities (grant no ), Program for New Century Excellent Talents in University (grant no. NCET ), Program for Excellent Doctoral Dissertation Foundation (grant no. 2007B7), and Project of the Institute of Basic Research in Clinical Medicine (grant no. Z0175). Address correspondence to Z.Z. ( zhang_rzs@ bnu.edu.cn). q RSNA, 2013 Results: Conclusion: Patients with amci showed similar deactivation in the DMN to that observed in healthy control subjects (P..05) but showed significantly decreased anterior-to-posterior functional connectivity only during the task (P,.05). Significant increases in local efficiency (P,.05), but not in global efficiency (P..05), were observed in amci only during the task. Decreased functional connectivity was predictive of increased local efficiency (r = 20.35, P =.015). Significant correlations between these network measures and cognitive performance (P,.05) indicated their potential use as early markers to assess the risk of Alzheimer disease (AD). This study suggests the early onset functional reorganization of the DMN toward a nonoptimized regularity configuration in amci and expands the understanding of dynamic functional reorganization in brain networks along the continuum from normal aging to AD dementia. q RSNA, 2013 Supplemental material: /suppl/doi: /radiol /-/dc1 Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 501

2 Mild cognitive impairment (MCI) is the intermediate state between healthy aging and Alzheimer disease (AD) (1,2). Subjects with MCI, particularly amnestic MCI (amci), have a higher risk of progressing to clinical AD dementia (1,3,4). Evidence from functional magnetic resonance (MR) imaging suggests that individuals with MCI may have altered brain function (5 7), in particular, deficits in episodic memory function (8 11). Thus, episodic memory tasks could function to identify individuals with prodromal AD (12 15). Thus far, functional alterations linked to memory function have been reported in patients with MCI in several brain regions, such as the medial temporal lobe (11,16) and the hippocampus (17,18). However, the performance of a myriad of cognitive tasks, including memory tasks, does not rely on unitary specialized brain regions but rather complex neural networks (19). In this view, the regions with functional alterations need to be seen as parts of a functional network, which suggests that Advances in Knowledge nn Relative to brain activation that showed nonsignificant differences between the two groups, functional connectivity and network topological properties may provide more powerful and sensitive measurements to distinguish patients with mild cognitive impairment (MCI) from healthy elderly subjects. nn The decreased anterior-to-posterior functional connectivity was predictive of increased local efficiency (r = 20.35, P =.015), and the reorganized topological pattern fitted a trend toward a nonoptimized configuration observed in Alzheimer disease. nn Compared with the resting state, a cognitive task actively requiring subject performance can amplify the sensitivity to detect differences in functional connectivity and network efficiency between patients with MCI and healthy subjects (P,.05). patients with MCI likely demonstrate abnormalities in a distributed network of brain regions (20). Accumulating evidence has demonstrated that patients with MCI and AD show abnormal functional connectivity not only during episodic memory tasks, but also during the resting state (ie, task free) (21 26). In particular, many resting functional MR imaging studies have focused on altered functional connectivity between cortical regions, collectively known as the default-mode network (DMN) (27 34). This network usually involves the medial prefrontal cortex, the anterior cingulate cortex, the inferior temporal regions, angular gyrus, and the posterior cingulate cortex and precuneus (35 37). A study with amyloid imaging demonstrated that high levels of amyloid deposition are associated with aberrant activity in the DMN (30). Importantly, functional alterations in the DMN have been proposed as a quantitative MR imaging assessment that may aid in the clinical diagnosis and prognosis of AD (7). In addition to impaired functional connectivity, growing evidence has suggested that brain disorders are associated with an altered topological architecture of a distributed network of brain regions (38,39). For instance, patients with AD showed a disrupted topological organization in a large-scale brain network (40 44). For studies of MCI, however, several questions remain unanswered: (a) whether functional connectivity across all the regions in the DMN would be impaired, or if there are some specific connections that are more susceptible in this disorder; (b) whether the topological configuration of the DMN in MCI maintains a normal configuration or is disturbed in some way; and (c) whether functional alterations during cognitive tasks may be easier to detect than alterations in the resting state to differentiate patients with MCI from healthy control subjects. In this study, we addressed these questions by investigating changes in taskinduced deactivation, functional connectivity, and topological properties of the DMN between patients with amci and healthy control subjects during a resting state and a picture-encoding recall task that has previously been used to measure episodic memory (14,45). Furthermore, we examined the relationship between our neuroimaging measures and cognitive performance. We hypothesized that, relative to healthy control subjects, patients with MCI would be more likely to show disrupted functional connectivity and altered topological configuration of the DMN during the memory task than during the resting state (ie, cognitive demands may amplify the sensitivity to detect differences between the groups). Materials and Methods Participants This study was approved by the institutional review board of Beijing Normal University Imaging Center for Brain Research. Written informed consent was obtained from each participant. The participants in this prospective study were recruited from the research center for cognitive aging and brain health at Beijing Normal University. They were all right-handed and native Chinese speakers. Clinical and neuropsychologic assessments were performed by expert neurologists (Z.J.Z. and Y.Y.W., with 13 and 35 years of experience in clinical neurology, respectively) blinded to the results of MR imaging. Participants were Published online before print /radiol Content code: Radiology 2013; 268: Abbreviations: AD = Alzheimer disease amci = amnestic MCI DMN = default-mode network MCI = mild cognitive impairment Author contributions: Guarantors of integrity of entire study, L.W., Y.Y.W., Z.J.Z.; study concepts/study design or data acquisition or data analysis/interpretation, all authors; manuscript drafting or manuscript revision for important intellectual content, all authors; manuscript final version approval, all authors; literature research, L.W., H.L., Y.L.; clinical studies, L.W., H.L., Y.L., J.Z., X.L., Y.Y.W., Z.J.Z.; statistical analysis, L.W., Z.J.Z.; and manuscript editing, L.W., Z.J.Z. Conflicts of interest are listed at the end of this article. 502 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013

3 included if they met the following criteria: (a) aged years old; (b) at least 8 years of education; (c) Chinese version of the Mini-Mental State Examination (46) score of 24 or higher; (d) no history of neurologic, psychiatric, or systemic illnesses known to influence cerebral function, including serious vascular diseases, head trauma, tumor, current depression, alcoholism, and epilepsy; (e) no prior history of taking psychoactive medications; and (f) able to cope with the physical demands of the MR imager. The exclusion criteria were as follows: (a) structural abnormalities other than cerebrovascular lesions, such as tumors, subdural hematomas, and contusions due to previous head trauma that could impair cognitive function; (b) history of addictions, neurologic or psychiatric diseases, or treatments that would affect cognitive function; (c) large vessel disease, such as cortical or subcortical infarcts and watershed infarcts; and (d) diseases with white matter lesions, such as normal-pressure hydrocephalus and multiple sclerosis. All MCI was diagnosed according to previously established criteria for amci (1), including subjective memory complaints, cognitive impairment in memory (scoring more than 1.5 standard deviations below the age- and education-adjusted norm on the Auditory Verbal Learning Test), and preserved activities of daily living (scoring 0 on the activities of daily living test) (47). The Mini-Mental State Examination was used as a measure of general cognitive function. All participants also underwent a battery of neuropsychologic tests to assess other cognitive functions such as processing speed, verbal and nonverbal episodic memory, working memory, executive function, reasoning, and language ability. Specifically, processing speed was assessed with the Digit Symbol Coding subtest of the Wechsler Adult Intelligence Scale-Chinese revision (48). Verbal and nonverbal episodic memory tests included the Auditory Verbal Learning Test (49) and the Rey-Osterrieth Complex Figure Test (50), while verbal working memory was assessed with the Digit Span subtest of the Wechsler Adult Intelligence Scale-Chinese revision. Executive function was assessed with the Trail Making Test (51). Verbal reasoning and abstract thinking were assessed with the Similarities subtest of the Wechsler Adult Intelligence Scale-Chinese revision. Finally, language ability was assessed with the Boston Naming Test (52). We are conducting a 5-year longitudinal project called Beijing Brain-Health and Cognitive-Aging Initiative that was started in October 2008; the goal of the program is to identify neuroimaging biomarkers for MCI and AD. The brain imaging data acquired from October 2010 to September 2011 was analyzed in the present study. A total of 70 participants fulfilled the inclusion criteria in this study, including 40 patients with amci and 30 age- and sex-matched healthy control subjects. Of the 40 patients with amci, 10 were excluded because of cerebral tumor, type 2 diabetes, cerebral infarct, or leukoencephalopathy; two participants were excluded because they did Table 1 Demographic Data and Cognitive Performance not perform the encoding memory task properly during the MR imaging session, and three were excluded because of poor image quality. Of the 30 healthy control subjects, two dropped out during MR imaging, and two were excluded because of poor image quality. Finally, 25 patients with amci and 26 control subjects were left for final analyses. Of those participants, 20 patients with MCI and 24 healthy control subjects also underwent resting-state functional MR imaging. The demographic information for each group is presented in Table 1. Experimental Paradigm The episodic memory task consisted of an encoding task with two conditions (encoding and fixation) and a recognition task during the functional MR imaging acquisition. The task started with a 12-second presentation of a fixation Parameter amci Group Control Group P Value* Demographic data No. of participants Age (y) M:F ratio 12:13 12:14.89 Education (y) MMSE score ,.001 Neuropsychologic test AVLT ,.001 ROCFT, recall ROCFT, copy TMT part A TMT part B ,.001 TMT BA Digit Span, forward Digit Span, backward Digit Symbol Coding Similarity BNT Episodic memory task Encoding accuracy (%) (36.8/40) (38.8/40).003 Reaction time (msec) Recall accuracy (%) (30.4/40) (34.4/40).01 Note. Unless otherwise indicated, data are means 6 standard deviations. AVLT = Auditory Verbal Learning Test (a total score of five times reported), BA = difference in time between parts B and A, BNT = Boston Naming Test, MMSE = Mini-Mental State Examination, ROCFT = Rey-Osterrieth Complex Figure Test (delayed recall and immediately recall), TMT = Trail Making Test. * Unless otherwise indicated, P value was obtained with two-sample t tests (two-tailed and unequal variance). P value was obtained with a two-tailed x 2 test. Numbers in parentheses are the mean nominators and denominators over the group for all percentages provided. Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 503

4 cross. Then four encoding blocks of 40 seconds were interleaved with four fixation blocks of 24 seconds. There were 10 trials per encoding block including five natural and five artificial (ie, manmade) stimuli (each picture lasted 3.5 seconds, followed by a fixation cross for 0.5 second) in two runs per subject. The participants were instructed to indicate their response by pressing the button on the MR imaging compatible response box in their left or right hand (left, natural stimuli; right, artificial stimuli). For the fixation condition, a white cross on a black background was presented for the entire trial, and no response was required during this condition. Recall accuracy was assessed by using a recognition task immediately after the encoding run. Subjects saw 40 pictures presented sequentially in random order, of which 20 pictures were previously shown during the encoding runs and the other 20 were new. Subjects were instructed to indicate whether a presented picture had been shown previously by pressing the button on the response box in either the left or right hand (left, yes; right, no). Summing the number of correct responses divided by the total number of responses calculated recall and encoding accuracy during the recognition and encoding task, respectively. Data Acquisition Subjects were imaged with a 3.0-T imaging unit (Trio 3.0; Siemens, Erlangen, Germany) in the Imaging Center for Brain Research at Beijing Normal University. For both the episodic memory task and the resting state, functional images were acquired by using an echoplanar imaging sequence: 33 axial sections, repetition time of 2000 msec, echo time of 30 msec, section thickness of 3.5 mm, flip angle of 90, field of view of mm, and acquisition matrix of A stimulus computer was used to present the task stimuli, and the response box recorded the reaction of the subjects. For the episodic memory task imaging, each of the two functional runs had 221 volumes. During the singlerun resting acquisition, subjects were instructed to keep awake, relax with their eyes closed, and remain as motionless as possible. The resting acquisition lasted for 8 minutes, and 240 image volumes were obtained. T1-weighted structural images were acquired by using three-dimensional magnetization-prepared rapid gradient-echo sequences: 176 sagittal sections, repetition time of 1900 msec, echo time of 3.44 msec, section thickness of 1 mm, flip angle of 9, field of view of mm, and acquisition matrix of Data Preprocessing Functional MR imaging data were preprocessed with software (Functional Magnetic Resonance Imaging of the Brain [FSL]; University of Oxford, Oxford, England, uk/fsl) by an author (L.W., 6 years of experience in functional brain networks). The first five volumes were discarded because of T1 saturation effects. The 216 volumes per run for the memory task and 235 volumes for the resting state underwent section timing and motion correction and were spatially smoothed with a 6-mm fullwidth at half-maximum Gaussian kernel. The data were then registered to the individual s structural acquisition, normalized to Montreal Neurological Institute stereotaxic space by using optimal 12-parameter affine and nonlinear transformations, and resampled to 2-mm isotropic voxels. Finally, the task functional MR imaging data were highpass filtered with a cutoff frequency of 0.01 Hz, whereas the resting functional MR imaging data were band-pass filtered ( Hz). Localization of the DMN One event (ie, encoding) was defined for each subject. The regressor of this event was modeled as a predictor variable by convolving the onset times of trials where the subject responded correctly (53) with a canonical hemodynamic response function and its temporal derivative. Then, the regression coefficient was estimated by using a general linear model. The contrast of interest was correct encoding versus fixation, whereby the deactivated regions (ie, those more active during fixation than encoding) were assumed to constitute the DMN (36) during the episodic memory task. A higher-level analysis was further performed by using software (FMRIB Local Analysis of Mixed Effects [FLAME]). Statistical images were analyzed by using clusters determined by a threshold of z greater than 2.3 and a corrected cluster-based significance threshold of P less than.05. Age, sex, and the interaction of age and sex (ie, age 3 sex) were added as covariates in this analysis to eliminate the influence of these confounds on brain deactivations. All of the comparisons were marked by significant deactivations in each group; therefore, a conjunction analysis was used to identify the regions common to both groups for subsequent analyses. To investigate whether patients with amci demonstrated a functional disconnection in this task-negative network, we first defined the regions of interest by parcellating the common activation map by using the automated anatomic labeling template (54) and then computed interregional functional interaction. Generation of Functional Brain Networks The application of the parcellation processing method to the common grouplevel deactivations generated 56 brain regions overall. To eliminate the effect of small clusters inducing a large variance in the time series, as well as the false-positive rate due to multiple comparisons, we excluded 30 regions that included fewer than 200 voxels; the resulting 26 regions were used for functional connectivity analysis. In addition, to validate our results independent of the selection of regions of interest, we also applied the same analysis procedures mentioned below to the defaultmode regions reported in Franco et al (55) and provided by software (Group ICA of fmri Toolbox, or GIFT; MIALAB, Albuquerque, NM, org/software/gift/index.html) (56). The time series of all voxels in each region of interest were extracted and averaged to obtain a representative time series. By using a multiple linear regression model, spurious variance of spontaneous blood oxygen level dependent signal unlikely 504 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013

5 reflecting neuronal activity was removed from the mean time series (the dependent variable) by regressing out the signal attributable to head movements, white matter, and cerebrospinal fluid. We did not remove the global signal in terms of the relevant discussions from studies (57,58). The residuals of this regression were then used to substitute for the spontaneous raw mean time series of the corresponding regions (no changes for the averaged task-based time series). For each session of the memory task or the resting state for each subject, we computed Pearson correlation coefficients between the time series of all possible pairs of 26 regions, yielding one symmetric correlation matrix (ie, functional connectivity matrices). Task-related correlation matrices were then obtained by averaging the connectivity matrices over the two runs for each subject. To investigate whether the integral functional organization of the DMN in patients with amci became abnormal because of impairments in functional connectivity, we used graph theory approaches to identify any topological changes in this functional network. Several previous studies have demonstrated that both whole-brain and subbrain functional networks possess efficient small-world properties at a low cost (59). Our previous studies have demonstrated that network efficiency is susceptible to both normal aging (60) and brain disorders (61 63). Here, we briefly describe the computations involved in this analysis. (Detailed methods are given in other articles [60 64].) A sparsity measure thresholding connectivity matrix into a binary graph G is defined as a percentage of the number of existing edges to all the possible edges in the network. The efficiency of the binary graph is defined as the inverse of the harmonic mean of the shortest path length l i,j from node i to node j (65) as follows: 1 1 EG ( ) =. ( 1) i N N j G l G G ij When G represents a whole graph, this metric measures the efficiency over the whole graph, which is known as global efficiency E glob ; N G denotes the number of nodes in the whole network. In contrast, when G is a subgraph, as the set of nodes that are immediate neighbors of a node i (ie, directly connected to a node with an edge), this index measures the efficiency of the subgraph, known as the local efficiency of the node i, where N G denotes the number of nodes in the subgraph. Thus, local efficiency E loc of the whole graph is defined as the average of the local efficiency of the subgraphs. Overall, the local efficiency quantifies the extent of local cliquishness of information transfer of a network, whereas the global efficiency quantifies the ability of parallel information propagation of a network. The higher local and global efficiency indicates more efficient information transmission over a local and global network, respectively. The two network efficiency measures are relative to each other in a graph. A regular graph where the nodes only are connected to their neighbors shows high local efficiency but low global efficiency. In contrast, a random graph where the nodes are randomly connected to other nodes has high global efficiency but low local efficiency. Recent studies (59) have indicated that the global and local efficiency of the human brain networks locate between the extreme efficiency curves of the regular and random networks; that is, the human brain networks show higher global efficiency but lower local efficiency compared with a regular network, and higher local efficiency but lower global efficiency compared with a random network. The relative balance in global and local efficiency properties of the normal human brain networks could be impaired by brain disorders, which could shift the normal network configuration toward one of two extreme topological organizations. Structural Image Analysis This analysis was to examine if the whole-brain gray matter volume was changed in patients with amci compared with the healthy control subjects. Structural images were brain extracted by using software (Brain Extraction Tool in FSL), followed by tissue-type segmentation with a segmentation tool (FMRIB Automated Segmentation Tool) in native space. The whole-brain gray matter volume was obtained from the segmented gray matter tissue for each subject and then was normalized to total intracranial volume, followed by a statistical test. Statistical Analysis Functional connectivity. Correlation coefficients were first converted into z values by using Fisher r-to-z transformation to correct for nonnormality. To test the between-group differences, twosample t tests were performed on each transformed z value. The initial comparison (P,.01, uncorrected) showed that the altered connectivity resulted from the connections between the anterior and posterior regions relative to the anterior cingulate cortex (10 anterior and 16 posterior regions). As a result, we set a statistical significance level of P less than.006 for further comparison of each connection, such that = 0.96, 1, which means that it was likely that there was less than one false-positive result, known as false discovery rate corrected. Therefore, if significantly functional connectivity differences were observed between both groups, we would consider it to be a true abnormality in patients with amci and not resulting from a false-positive result due to performing multiple tests. Network efficiency. Two-sample t tests were applied to determine the statistical significance of the differences in network parameters (global and local efficiency) between the two groups for each level of sparsity and for the overall sparsity. The between-group difference was significant if P was less than.05. The association between the averaged z value across the abnormal functional connectivity and the averaged network efficiency over the sparsities was also examined across the subjects. The two neuroimaging measures were then separately used to evaluate the correlations with cognitive performance, regressing out the influence of age, sex, education, and group-level difference. These associations were significant if P was less than.05 uncorrected. Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 505

6 Figure 1 Figure 1: MR images show functional brain deactivations during the episodic memory task. The mapping of the deactivated regions corresponds to the contrast between the encoding and fixation conditions in, A, patients with MCI and, B, control subjects (NC). No significant differences in deactivations were found between the two groups (P,.05, cluster-based correction). C, Deactivated regions common to both groups. The left side of each section represents the right side of the brain; the z-coordinate in Montreal Neurological Institute space is indicated in A. Results Demographic Data and Behavioral Results Demographic data, performance levels on the neuropsychologic tests, and results from the episodic memory task are shown in Table 1. No differences in age, sex, or level of education were found between the two diagnostic groups (Table 1). Compared with healthy control subjects, patients with amci showed a significant decrease in Mini-Mental State Examination score (P,.001) and specific cognitive deficits in working memory (Auditory Verbal Learning Test, P,.001; recall Rey-Osterrieth Complex Figure Test, P =.01; forward Digit Span, P =.003; backward Digit Span, P =.007), executive function (Trail Making Test part B, P,.001; Trail Making Test, difference in time between part B and A, P =.002), processing speed (Digit Symbol Coding, P =.02), reasoning (Similarity test, P =.003), and language ability (Boston Naming Test, P =.004). During performance of the encoding and recognition tasks, the patients with amci showed significantly decreased encoding accuracy (P =.003) and recall accuracy (P =.01). However, we found no significant difference in copy Rey-Osterrieth Complex Figure Test (P =.99), Trail Making Test part A (P =.10), and reaction time (P =.24) between the two groups. Deactivated Regions during Task Performance and Change in Gray Matter Volume For each group, contrasting the encoding condition with the fixation condition revealed consistency in the deactivated brain regions; these areas included the medial prefrontal cortex, the anterior cingulate cortex, the inferior and middle temporal regions, angular gyrus, the posterior cingulate cortex, and the precuneus (Fig 1). A comparison of the deactivation in the two groups did not reveal any significant differences at the established threshold (P,.05, cluster-based correction). The clusters representing shared activations (conjunctions) across the two groups included the majority of the regions found individually in each group (Fig 1). The common activations were then categorized into 26 large regions of interest (Table 2, Fig 2) by using the automated anatomic labeling template (54). In addition, we measured the wholebrain gray matter volume normalized to total intracranial volume (patients with MCI, ml [standard deviation]; control subjects, ml ) 506 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013

7 and did not find significant difference between two groups with a two-sample t test (P =.14). Functional Connectivity and Network Topological Properties The functional connectivity analysis was performed on the connectivity matrix obtained from the parcellated regions. The mean functional connectivity for each group (ie, amci and control) and each condition (ie, task and rest) is shown in Figure 3. After regressing out the effect of age, sex, and education, we observed 10 anterior-to-posterior connections (Fig 3, A1 and A2) that were significantly decreased in the amci group compared with the healthy control subjects during the task after correcting for the false-positive rate. All of these alterations in connectivity involved anterior frontal areas and several posterior areas consisting of parietal and temporal regions (Figs 4, 5). We did not find any regions showing increased connectivity in patients with amci compared with healthy control subjects. In contrast, we did not observe any significant changes in restingstate functional connectivity between the two groups after a correction for false-positive rate. The application of graph theoretical approaches demonstrates that the DMN of healthy control subjects and patients with amci show small-world topological organization with high global (Fig 6, A1 and B1) and local (Fig 6, A2 and B2) efficiency at a low sparsity range (0.06 sparsity 0.4). However, only during task performance (as opposed to resting state) were significant increases observed in the local efficiency of patients with amci over a wide range of thresholds (0.14 sparsity 0.32) by using two-sample t tests (P,.05). In contrast, a significant reduction in global efficiency was found in patients at a sparsity of 0.08, although patients with amci showed consistent nonsignificant decreases in global efficiency over all the sparsities. For the overall sparsity, only local efficiency showed a significant increase (P =.006) in patients with amci (inset in Fig 6, A2), only during task performance. In Table 2 Regions of Interest and Number of Voxels in Left and Right Hemispheres Region Size* Percentage (%) Left hemisphere Superior medial frontal gyrus Medial orbital frontal gyrus Rectus Anterior cingulate gyrus Middle cingulate cortex Posterior cingulate cortex Calcarine Cuneus Middle occipital gyrus Angular gyrus Precuneus Middle temporal gyrus Right hemisphere Superior frontal gyrus Middle frontal gyrus Superior medial frontal gyrus Medial orbital frontal gyrus Rectus Anterior cingulate gyrus Middle cingulate cortex Calcarine Cuneus Angular gyrus Precuneus Superior temporal pole Middle temporal gyrus Middle temporal pole * The voxel resolution is mm. Data are percentage of voxels used relative to the number voxels in the automated anatomic labeling region of interest. addition, we found a significant inverse correlation (r = 20.35, P =.015) between the averaged functional connectivity and the averaged local efficiency over the sample (Fig 7). To validate the robustness of our findings, we repeated our analysis on the DMN constructed by the areas as found by the Franco et al study (55) (Fig E1 [online]). The results from the reconstructed network were consistent with our aforementioned findings (ie, significant increases in local efficiency and no significant changes in global efficiency only during the memory task) (Fig E2 [online]). In addition, some potential confounds need to be considered, because they may bias the sensitivity to detect the group difference toward the task. In this study, the task-based connectivity matrices were created by using an average of two task runs, while the rest matrices were created from one rest session. A recent study (66) has demonstrated that the amount of time that one acquires data could distinguish an individual from the group on the largescale organization of the brain. To deal with this issue, we used only one task run and recomputed the network measures used previously. Besides that, the sample size for task (25 patients with MCI and 26 control subjects) was higher than that for rest (20 MCI and 24 control subjects) because of the absence of functional MR imaging data for some subjects. To match the sample across the task and rest runs, we removed seven subjects from the task runs. Solving both issues required the Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 507

8 Figure 2 Figure 2: Regions of interest. The common group-level brain deactivations are parcellated by using an a priori brain atlas into different brain regions. Each color-coded cluster corresponds to a region depicted by using the BrainNet Viewer program ( Table 2 lists these regions. L = left hemisphere, R = right hemisphere. recalculation of the network measures on the resulting data. All analyses were recomputed, and new results justified the same conclusion as the original analysis. Correlation with Behavioral Measures of Cognitive Function The functional MR imaging measures (both functional connectivity and local efficiency) were correlated with the patients recall accuracy and performance on neuropsychologic tests. Across the diagnostic groups, linear correlation analyses indicated that lower anteriorposterior functional connectivity was associated with worse test performance on recall accuracy (r = 0.28, P =.05), Auditory Verbal Learning Test recall (r = 0.31, P =.03), Trail Making Test part B (r = 20.26, P =.06) and Trail Making Test difference in time between parts B and A (r = 20.32, P =.02), forward Digit Span (r = 0.26, P =.07), and backward Digit Span (r = 0.24, P =.09). In addition, local efficiency was found to be significantly correlated with Mini-Mental State Examination scores (r = 20.36, P =.009). Within the control group, we found a significant correlation between functional connectivity and scores for forward Digit Span test (r = 0.40, P =.04) and between local efficiency and Mini-Mental State Examination scores (r = 20.47, P =.02). Within the amci group, significant correlations were found between functional connectivity and performance on Auditory Verbal Learning Test (r = 0.49, P =.01), Trail Making Test part B (r = 20.37, P =.07), and Trail Making Test difference between parts B and A (r = 20.45, P =.03). Discussion The focus of this study was to evaluate disease-induced changes in brain connectivity and network topology among default-mode regions in patients with amci. We found that the deactivated regions during task performance were similar between the two groups. Although no significant differences in resting-state functional connectivity were found between patients with amci and healthy control subjects, task-related functional connectivity showed significant decreases in anterior-posterior brain connections in amci during the episodic memory task. Furthermore, we found an increased local efficiency of the DMN and a trend toward decreased global efficiency in patients with amci only during the task. Given a significant association between decreased functional connectivity and increased local efficiency, the disrupted anterior-posterior functional connectivity may contribute to the topological reconfiguration in amci. Thus, 508 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013

9 Figure 3 Figure 3: Mean functional connectivity in groups and conditions. The linear correlations between all possible pairs of the representative time courses derived from the parcellated regions are computed for each subject during the memory task (TASK) and resting state (REST). A symmetrical averaged correlation matrix within each group is shown for patients with amci (A1, B1) and control subjects (NC) (A2, B2) during the task and rest conditions. The elements marked by a red box (in A1, A2) denote anterior-to-posterior functional connectivity. Compared with the rest condition under which two groups show a similar connectivity pattern, group differences appear under the task condition mainly in anterior-to-posterior functional connectivity. the present study provided evidence for disrupted anterior-to-posterior functional connectivity and topological reorganization of the DMN in amci during an episodic memory task. The deactivation pattern found in our subjects was consistent with those found in previous studies (35 37,67,68). However, we did not find significant differences in the deactivated regions between the two groups (corrected for multiple comparisons), which indicates that both groups were effective at inhibiting intrinsic brain activity during the performance of this episodic memory task (69). This result differed from those in previous studies where changes in the deactivation of brain regions in the patients with MCI were reported (12 14,70). In contrast to the current study, previous restingstate functional MR imaging studies have found that patients with MCI show disrupted connectivity involving the DMN (21,28,31,33). In addition, we did not find significant atrophy in the whole-brain gray matter volume in the patients with amci, which was consistent with the finding at the baseline point in a longitudinal study (71). The lack of significant differences in brain deactivation, resting-state functional connectivity, and gray matter volume may be accounted for by the fact that the majority of the patients in this study were likely to be in a relatively early stage of MCI, because all of them were recruited from health screenings of healthy individuals from the community, rather than from a medical center or hospital, as was done in many previous studies. A study of normal aging, MCI, and AD demonstrated a nonlinear trajectory of functional MR imaging activation across the continuum of impairment (8), which suggests that patients in the late stage of MCI or patients with mild AD are more likely to show distinct patterns of task-related deactivation. It has been suggested that MR imaging assessed functional and structural changes might occur in a specific temporal order (7,72). For instance, functional alterations in MCI likely precede gray matter atrophy detectable with structural MR imaging (33,73), which suggests that structural abnormalities reflect a long-term effect of altered brain function. Bokde et al (24) claimed that changes in functional connectivity may precede changes in brain activation. No significant differences in resting-state functional connectivity in this study indicate that functional disconnections may be dependent on cognitive demands. Increasing the cognitive load would amplify the ability to detect between-group differences that reflect aberrant organization of the DMN (74,75). Our findings indicate that the disconnection of the DMN triggered by external stimuli could be a highly sensitive endophenotype in patients with amci. It has been suggested that episodic memory is subserved by a large-scale neural network (76) and that defective connections between the different regions of this network may lead to episodic memory deficits in early AD (77). Although recent studies have demonstrated that patients with MCI exhibited reduced connectivity of the DMN (29,33,34), it is still unclear whether the impairments in connectivity were related to specific types of connections within this network. The current study found that significant decreases in functional connectivity in patients with amci were primarily found in anterior-posterior brain connections involving the prefrontal, temporal, and Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 509

10 Figure 4 Figure 4: Altered functional connectivity in amci. Significantly decreased connections in amci are shown (in blue). There were no increased connections observed in amci. All the altered connections link anterior to posterior regions, particularly in the right hemisphere (R). The regions (in red) are shown in a different view of the brain by locating their x, y, and z centroid coordinates in each region shown in Figure 2. Details about the connections and abbreviations are in Figure 5. L = left hemisphere. parietal regions. Similarly, several studies also reported an anterior-posterior disconnection phenomenon in patients with AD and MCI (43,78 82). An increasing number of studies have used graph theory to investigate the effect of brain diseases on functional brain networks and demonstrated that cognitive deficits in patients are accompanied by disruptions in the coordination of large-scale brain functional networks (38,83). Recently, many studies have shown that patients with AD have an abnormal small-world architecture in both functional (40,41) and structural (84,85) brain networks, both of which indicate an increased shortest path length and cluster coefficient in AD. However, even though MCI is considered to be the transitional stage between normal aging and AD, the characteristics of these functional networks in Figure 5 Figure 5: Decreased task-related functional connectivity in amci (P,.05, false discovery rate corrected). Data are anterior-to-posterior connections. Abbreviations (abbr) used in Figure 4 are in parentheses. L = left hemisphere. R = right hemisphere. MCI are still largely unexplored. A study characterized the topological properties of the cortical network in patients with MCI and found a larger clustering coefficient and a longer absolute path length compared with healthy control subjects (44). In agreement with these studies, we also found that patients with amci showed an increased local efficiency and a slightly decreased global efficiency of 510 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013

11 Figure 6 Figure 6: Graphs show small-world properties of the DMN. Global (A1, B1) and local (A2, B2) efficiency during task (A1, A2) and rest (B1, B2) are shown as a function of sparsity for random and real (amci and control [NC]) networks. For all networks, global and local efficiency increased with the threshold. Independent of condition (task and rest), the global efficiency curves of the real networks are less than those of random networks, but the local efficiency profiles of the real networks are greater than those of random networks over the whole threshold range (ie, 0.06 sparsity 0.4), which is known as a small-world regimen. Significant increases in local efficiency (P,.05) are found in amci over the indicated threshold range ( in A2) only during the memory task, whereas a significant decrease in global efficiency is found at a single sparsity ( in A1). The average local efficiency (inset in A2) over the sparsity range shows a significant increase in amci (P =.006), whereas there is no difference in the average global efficiency (not shown). The random networks were generated by 50 random rewirings of the edges across nodes while keeping the same number of nodes and degree distribution as in the real networks. Error bars = standard error of the mean. Figure 7 Figure 7: Graph shows relationship between functional connectivity and local efficiency over the sample. The values (x-axis) were obtained by averaging the correlation coefficients of the 10 pairs of decreased long-range connections in amci. The values (y-axis) correspond to the averaged local efficiency over the sparsities ( in Fig 6, A2). Both amci and control (NC) groups were combined in this analysis to increase statistical power. the DMN, which indicates a less optimal organization of the network (ie, more regularity in the network configuration). The reorganized topological pattern seen in MCI fits a trend toward a nonoptimized regularity configuration that is also observed in AD (41,85). In this study, the disrupted anteriorposterior functional connectivity may contribute to the topological reconfiguration in amci because a significant association was observed between those two measures. Although the underlying reason for the appearance of network regularity remains unclear, a shifting network topology could be associated with the cause and pathogenesis of brain diseases. Taken together, our findings indicate that task-related changes in functional connectivity and topological properties, compared with functional deactivation and structural impairment, might be more likely to occur at the preclinical stage (24,33,73). In addition, we found a significant association between local efficiency and Mini-Mental State Radiology: Volume 268: Number 2 August 2013 n radiology.rsna.org 511

12 Examination score, which suggests that this network measurement may not reflect domain-specific deficits of cognitive function. Moreover, the altered anteriorposterior functional connectivity within the DMN was also associated with attention and working memory, as assessed by Digit Span, and executive function, as assessed by the Trail Making Test. This evidence indicates that patients with amci who exhibited impaired anteriorposterior connectivity when encoding pictures might be unable to properly coordinate the cognitive resources mediated by the DMN; this deficit could have led to poorer performance in the subsequent recall test. There were several limitations of this study. First, this study was conducted with a small sample size, which may attenuate the statistical power in detecting the between-group differences in some of our measures, such as resting-state functional connectivity. The relevant findings should be interpreted with caution. Although studies have demonstrated that spontaneous activity can be used to predict the variance in the task (86,87), resting-state blood oxygen level dependent signals could have larger variance than those in the cognitive tasks. Thus, future studies would need to use an increased number of subjects to validate our findings. Second, in this study, the participants were recruited from the local community in China. Studies have demonstrated ethnic-related differences in brain structure (88 90) and neuropsychiatric symptoms of AD (91,92) between Asian and Occidental populations. Thus, caution should be taken when comparing this study with previous studies of other populations. To our knowledge, no functional MR imaging studies have examined differences in brain responses to external stimuli between Chinese and white population samples afflicted with AD. Future studies would be helpful to clarify this issue. Third, this study emphasized changes in the functional integration of the tasknegative network (ie, the DMN) in patients with amci. A recent study (93) indicated that AD is associated with an alteration of large-scale functional brain networks, which extends well beyond the DMN. In this study, we did observe a number of task-positive regions, such as the hippocampus, which were activated by the episodic memory task. One of our previous studies demonstrated altered topological configuration in the task-related network with normal aging (60). Future studies would be useful in examining whether patients with amci also show aberrant coordination in the task-positive network. Fourth, this was a cross-sectional study. Recent longitudinal studies (29,34) have reported that functional connectivity measures could be used to distinguish patients with MCI converting to AD from those remaining cognitively stable after a mean follow-up of 3 years. Longitudinal imaging studies are essential in identifying the pattern of changes in brain activations, functional connectivity, and network properties, as well as gray matter volume in patients with MCI as the disease develops. In addition, a longitudinal study can verify our hypothesis that the deficits found by using the functional MR imaging measures would be more detectable in the later stages of MCI, which are approaching AD. In conclusion, patients with amci showed decreased anterior-posterior functional connectivity across the regions in the DMN, and this disrupted connectivity contributed to a nonoptimized network configuration in patients with amci. Compared with the resting state, these abnormalities were much more apparent when measured while the subjects performed a cognitively demanding task. Both decreased connectivity strength and increased local network properties were predictive of cognitive dysfunction. This study suggests that functional network measures may be useful as early markers to distinguish AD and MCI from healthy aging and expands our understanding of dynamic functional reorganization in brain networks along the continuum from normal aging to AD dementia. Acknowledgments: We thank the participating sites in the Clinic at the Neurology Department of Beijing Hospital and the Clinic at the China Academy of Chinese Medicine, all the volunteers and patients for their participation in our study, and Sabine Kastner, PhD, at Princeton University and Paul D. Metzak, MS, at University of British Columbia for helpful suggestions. Disclosures of Conflicts of Interest: L.W. No relevant conflicts of interest to disclose. H.L. No relevant conflicts of interest to disclose. Y.L. No relevant conflicts of interest to disclose. J.Z. No relevant conflicts of interest to disclose. X.L. No relevant conflicts of interest to disclose. N.S. No relevant conflicts of interest to disclose. Y.Y.W. No relevant conflicts of interest to disclose. Z.J.Z. No relevant conflicts of interest to disclose. References 1. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256(3): Petersen RC, Doody R, Kurz A, et al. Current concepts in mild cognitive impairment. Arch Neurol 2001;58(12): Petersen RC, Morris JC. Mild cognitive impairment as a clinical entity and treatment target. Arch Neurol 2005;62(7): ; discussion Mitchell J, Arnold R, Dawson K, Nestor PJ, Hodges JR. Outcome in subgroups of mild cognitive impairment (MCI) is highly predictable using a simple algorithm. J Neurol 2009;256(9): Pihlajamäki M, Jauhiainen AM, Soininen H. Structural and functional MRI in mild cognitive impairment. Curr Alzheimer Res 2009;6(2): Ries ML, Carlsson CM, Rowley HA, et al. Magnetic resonance imaging characterization of brain structure and function in mild cognitive impairment: a review. J Am Geriatr Soc 2008;56(5): Ewers M, Sperling RA, Klunk WE, Weiner MW, Hampel H. Neuroimaging markers for the prediction and early diagnosis of Alzheimer s disease dementia. Trends Neurosci 2011;34(8): Celone KA, Calhoun VD, Dickerson BC, et al. Alterations in memory networks in mild cognitive impairment and Alzheimer s disease: an independent component analysis. J Neurosci 2006;26(40): Dickerson BC, Salat DH, Bates JF, et al. Medial temporal lobe function and structure in mild cognitive impairment. Ann Neurol 2004;56(1): Dickerson BC, Salat DH, Greve DN, et al. Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD. Neurology 2005;65(3): Dickerson BC, Sperling RA. Functional abnormalities of the medial temporal lobe memory system in mild cognitive impairment and Alzheimer s disease: insights from 512 radiology.rsna.org n Radiology: Volume 268: Number 2 August 2013