Journal of Alzheimer’s Disease 40 (2014) 993–1004 DOI 10.3233/JAD-131574 IOS Press

Resting State Executive Control Network Adaptations in Amnestic Mild Cognitive Impairment Liyong Wua,b,c , Ricardo Bernardi Sodera,b,d , Doroth´ee Schoemakera,b , Felix Carbonnelld , Viviane Sziklase , Jared Rowleya,b , Sara Mohadesa,b , Vladmir Fonovd , Pierre Bellecd , Alain Dagherd , Amir Shmueld , Jianping Jiac , Serge Gauthierb and Pedro Rosa-Netoa,b,d,∗ a Translational

Neuroimaging Laboratory, Douglas Hospital, McGill University, Montreal, QC, Canada Centre for Studies in Aging (MCSA), McGill University, Montreal, QC, Canada c Department of Neurology, Xuan Wu Hospital, Capital Medical University, Beijing, China d McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada e Department of Psychology, Neuropsychology Unit, McGill University, Montreal, QC, Canada b McGill

Accepted 5 January 2014

Abstract. Executive dysfunction is frequently associated with episodic memory decline in amnestic mild cognitive impairment (aMCI) patients. Resting state executive control network (RS-ECN) represents a novel approach to interrogate the integrity of brain areas underlying executive dysfunction. The present study aims to investigate RS-ECN in aMCI and examine a possible link between changes in brain functional connectivity and declines in executive function. aMCI individuals (n = 13) and healthy subjects (n = 16) underwent cognitive assessment including executive function and high field functional magnetic resonance imaging. Individual RS-ECN maps were estimated using a seed-based cross-correlation method. Between groups RS-ECN functional connectivity comparison was assessed using voxel-wise statistic parametric mapping. aMCI individuals had reduced RS-ECN connectivity in the anterior cingulate cortex (ACC) and dorsal lateral prefrontal cortex (DLPFC), bilaterally. In contrast, aMCI showed increased connectivity in ventral lateral and anterior prefrontal cortex, bilaterally. Connectivity strength was associated with executive function in the ACC (r = 0.6213, p = 0.023) and right DLPFC (r = 0.6454, p = 0.017). Coexistence between connectivity declines and recruitment of brain regions outside the RS-ECN as reported here fits a brain reserve conceptual framework in which brain networks undergo remodeling in aMCI individuals. Keywords: Executive control network, executive function, functional MRI, mild cognitive impairment, neural plasticity

INTRODUCTION Amnestic mild cognitive impairment (aMCI) is a cognitive syndrome characterized by memory deficits in the absence of dementia [1]. Although episodic memory impairment is the hallmark cognitive deficit in ∗ Correspondence

to: Pedro Rosa-Neto, MD, PhD, Translational Neuroimaging Laboratory, Douglas Hospital, McGill University, Montreal, QC, Canada; 6875 La Salle Blv, FBC Room 3149, Montreal, QC, H4H 1R3, Canada. Tel.: +1 514 761 6131/Ext. 3407; E-mail: [email protected]

aMCI, clinical studies have indicated in these patients coexistence of mild executive impairments on neuropsychological tests [2–5]. Executive deficits in aMCI include mild declines in response inhibition, action planning, cognitive flexibility, judgment, and feedback management [2–5]. In fact, aMCI individuals with deficits in psychomotor speed/executive function are at higher risk for progressing to dementia [6]. A recent body of literature linked cognitive declines in aMCI with abnormal brain connectivity, however the impact of brain networks abnormalities and executive dysfunction remains elusive in aMCI patients [7].

ISSN 1387-2877/14/$27.50 © 2014 – IOS Press and the authors. All rights reserved


L. Wu et al. / Resting State Executive Control Network of aMCI

Resting-state functional connectivity obtained by functional magnetic resonance imaging (fMRI) constitutes a powerful non-invasive method for investigating changes of brain networks underlying neurological conditions particularly in aMCI and AD. Most resting-state connectivity studies in aMCI have shown connectivity declines within the default mode network (DMN) [8–11]. Interestingly, a recent study using high field (3.0 Tesla) MRI suggests remodeling in aMCI brain functional networks by demonstrating the coexistence of increases and declines in brain connectivity within the DMN [8]. Compared to DMN, resting state executive control network (RS-ECN) is a less studied and undervalued network. Typically, the anterior cingulate cortex (ACC), anterior prefrontal cortex (aPFC), dorsolateral prefrontal cortex (DLPFC), ventrolateral prefrontal cortex (VLPFC), dorsomedial prefrontal cortex (dmPFC), and inferior parietal cortex (IPC), as well as the left fronto-insula are reported as brain regions part of RS-ECN in healthy subjects [13, 14]. Importantly, RS-ECN constitutes a novel approach to investigate the integrity of brain areas underlying executive function in aMCI patients. Sorg and colleagues [9] showed, using 1.5 T MRI and independent component analysis (ICA), that aMCI individuals have declines in RS-ECN connectivity in the superior frontal gyrus, ACC, posterior cingulate cortex, middle temporal gyrus, and IPC. However, it still remains unknown whether increased connectivity of RS-ECN exists in aMCI patients and whether changes of RS-ECN connectivity are related with executive deficits in aMCI. The present study aims to investigate RS-ECN in aMCI patients using 3.0 Tesla MRI and cross-correlation analysis (CCA) technique, and to evaluate whether changes in RS-ECN are correlated with executive function revealed by neuropsychological scales in aMCI individuals.

METHODS AND MATERIALS Subjects aMCI and healthy controls with no cognitive impairment were recruited by a neurological expert in dementia from the McGill Centre for Studies in Aging. Initial identification of patients was based on memory complaints substantiated by an informant. A subsequent interview was conducted with a full neurological examination including the standard Mini-Mental State

Examination (MMSE) [15]. Routine blood screening was done to rule out underlying metabolic disorder. All individuals had structural MRI including FLAIR sequences. aMCI was diagnosed using the Petersen criteria [16] which included: (i) memory complaint usually corroborated by an informant; (ii) objective memory impairment for age; (iii) essentially preserved general cognitive function; (iv) largely intact functional activities; and (v) not demented. Healthy controls were selected on basis of their neurological and clinical status. Exclusion criteria included co-morbidity with other neurological disease such as stroke, Parkinson’s disease, other neurodegenerative diseases, etc.; the presence of any major structural abnormalities or signs of major vascular pathology on the MRI evaluation; axis I psychiatric disorder or intellectual disability; use of psychoactive substance; and previous or present use of cholinesterase inhibitor. The study protocol was fully approved by McGill University Research Ethics Committee, and was conducted in accordance with the Helsinki Declaration [17]. All aMCI patients and healthy participants were enrolled in this study after signing a tri-council compliant consent form. Neuropsychological assessment All participants underwent cognitive assessment. Intelligence was estimated using the performance subscale of the Wechsler Abbreviated Intelligence Scale which included Block Design and Matrix Reasoning subtests [18]. Verbal and nonverbal learning and memory were assessed with the Rey Auditory Verbal Learning Test (RAVLT) [19] and Aggie Figures Learning Test (AFLT) [20], respectively. Delayed recall of the Rey Complex Figure was used to assess memory for dually-encodable material [21]. Expressive language was evaluated by the Boston Naming Test (BNT) [22]. Visuospatial construction ability was assessed by the Rey Complex Figure [21]. A number of tests usually associated with executive function were administered. Attention and short-term working memory were examined using the Digit Span subtest (forward and backward) of the Wechsler Adult Intelligence Scale-Third Edition [23]. Divergent thinking was measured by Verbal Fluency (phonemic and semantic fluency) of the Delis–Kaplan Executive Function System (DKEFS), response inhibition and switching between response modalities were assessed by DKEFS Color-Word Interference Test, and set shifting and mental tracking were tested with the DKEFS Trail Making Test [24, 25].

L. Wu et al. / Resting State Executive Control Network of aMCI

A composite executive score was calculated by taking the average of the z scores (generated using the healthy aging group as the reference) for backward of digit span, letter fluency, category fluency, inhibition of the Color-Word Interference Test, and letter-number shifting of Trail Making Test. Resting state functional MRI procedures fMRI data acquisition Structural and functional sequences were acquired on a 3.0T Siemens MAGNETOM Trio system, using a 32-channel head coil. Foam pads and earphones were used to reduce head motion and scanner noise. Functional data were collected by using a gradient echo EPI sequence with the following parameters: TR/TE = 2 s/30 ms, 64 × 64 matrix with a 3 × 3 mm2 resolution, 38 interleaved contiguous axial slices covering the whole brain, slice thickness = 3.6 mm, flip angle = 90◦ . Three functional runs (160 volumes each) were acquired for each subject under resting-state condition, i.e., to keep their eyes closed, to remain still, and to refrain from any mental activity. A high-resolution anatomical T1-weighted scan was also acquired with the following parameter: TR/TE = 23 ms/29 ms, 256 × 256 matrix with a 1 × 1 mm3 resolution, 176 contiguous sagittal slices covering the whole brain, flip angle = 9◦ . fMRI data preprocessing The fMRI data were pre-processed using the standard stereotaxic fMRI pre-processing pipeline implemented in the Neuroimaging Analysis Kit (NIAK, http://code.google.com/p/niak/). The first three volumes of each run were discarded to allow the magnetization to reach equilibrium. Each fMRI dataset was corrected for inter-slice differences in acquisition time, rigid body motion, ultra slow time drifts (high-pass filter with a 0.01 Hz cut-off), high temporal frequencies (low-pass filtering with a 0.1 Hz cut-off), and physiological noise [26]. Correction of structured noise using spatial independent component analysis (CORSICA) reduces the spatially structured noise with a highly preponderant influence in well-localized brain areas using a dedicated data driven procedure [26, 27]. This procedure started with a spatial ICA of the fMRI data using the infomax algorithm, initialized using all components of a principal component analysis [28]. The lateral ventricles and basilar artery were then segmented on the structural image of each subject using the non-linear transformation into stereotaxic space combined with tissue segmentation. These regions of


interest were used to extract time series predominantly influenced by cardiac, respiratory, and movementrelated noise, and clearly unrelated to neural activity. These time series were used to identify the independent components predominantly related to physiological noise, whose contribution was removed from the fMRI data set in the ICA mixture matrix. Afterwards, for each subject, the mean motion-corrected volume of all fMRI runs was co-registered with an individual T1 scan [29], which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) non-linear template using the CIVET pipeline [30]. The resulting functional volumes were re-sampled in the MNI space at a 2 mm isotropic resolution and spatially smoothed with a 6 mm isotropic Gaussian kernel. Regression of the time-courses of the six parameters resulting from rigid body motion correction was also applied. Functional connectivity analysis Seed based CCA of functional connectivity image analyses were performed with FMRISTAT toolbox (http://www.math.mcgill.ca/keith/fmristat/) [31]. Executive control seed region time course was derived from two distinct spherical seed points (radius = 6 mm) manually centered at the left aPFC (MNI space, x = −36, y = 57, z = 9) and right aPFC (MNI space, x = 34, y = 52, z = 10) as previously described [14]. CCA was then carried out within the General Linear Model (GLM) framework by considering the seed time course of interest as the covariate of main interest and the whole brain time voxel time-courses as the dependent variable. Seed-based t Statistical Parametric Maps (SPM) of single runs was then estimated in this model. Since the statistical inference for a (Pearson productmoment) correlation coefficient is equivalent to that for a regression coefficient [32], we can perfectly consider the resulting t SPMs as indices of the desired CCA maps. Besides, such alternative expressions (t SPMs) allow us to use hierarchical random effects analysis for defining CCA maps (again on t SPM scale) at subject level (combination of runs within subjects) and group level (combination of all subjects in the sample). In particular, we used the implementation of the hierarchical random effects analysis proposed in the fMRI Stat matlab toolbox (“multistat” function) [31], Thus, within the framework of the GLM and in conjunction with hierarchical random effects analysis, we are able to generate RS-ECN correlation maps (on t SPM scale) at group level for both aMCI patients and controls. Similarly, the GLM framework allows us to test the null hypothesis of no between-groups RS-ECN


L. Wu et al. / Resting State Executive Control Network of aMCI

connectivity, which also yields a t SPM. Finally, to assess statistical significance, all t SPM are submitted to a multiple comparison correction thresholding criterion by following the premises of the Random Field Theory [33]. Statistically significant correlations were detected after multiple comparisons correction with the threshold at t = 4.5. Statistical analysis Demographic and neuropsychological performances comparisons were carried out using SPSS 20.0 software, with statistical significance being set at p < 0.05. Independent sample t-test was used to compare demographic (except for gender) and neuropsychological data between groups, while chi-square was performed for group comparison of gender. Three increased and three decreased brain regions with significant group differences were selected based on the highest peak and cluster extension to conduct the correlation analysis. Correlation analyses were conducted between the functional connectivity of these selected brain regions and composite executive score (digit span, verbal fluency, DKEFS color-word interference test, and Trail-making test), delayed recall of RAVLT, as well as BNT in aMCI patients and controls, respectively. Statistical analyses were performed using Pearson correlation test with the RMINC software package, with statistical significance being set at p < 0.05. RESULTS Demographic and neuropsychological data Demographic data and neuropsychological test scores are shown in Table 1. No significant differences in gender, age, or education were noted between healthy elderly and aMCI groups. MMSE scores were significantly lower in aMCI patients compared to controls (Table 1). Performance IQ, comprised of the block design and matrix reasoning tests, showed borderline differences in aMCI (p = 0.054). aMCI individuals had declines in delayed recall of verbal and visual memory estimated by RAVLT, AFLT, and the delayed recall of the Rey Complex Figure. Language performance, measured by the BNT, was reduced in aMCI in comparison with the healthy elderly group. Visuospatial function, as measured by the copy of the Rey Complex Figure, was not impaired in the aMCI group compared to control subjects.

Executive dysfunction was evidenced by some but not all assessments. Digit span was significantly impaired in the aMCI. Moreover, aMCI individuals also showed declines in letter and category fluency. All primary measures comprising the Color-Word Interference Test (DKEFS) were significantly impaired in the aMCI group relative to controls, suggesting slower overall response latency across all conditions of this task. However, no significant differences between the two groups were observed on a number of contrast measures for the Color-Word Interference Test. In addition, Trail Making Test (DKEFS) performance was not affected in aMCI patients. Functional connectivity data The RS-ECN average maps of aMCI patients and controls are shown in Fig. 1. The RS-ECN encompassed bilateral ACC, aPFC, DLPFC and dmPFC, left lateral orbitofrontal cortex (LOFC), right insula, left precentral gyrus, bilateral IPC and precuneus, left superior temporal gyrus, bilateral middle temporal gyrus, right inferior temporal gyrus, bilateral fusiform gyrus, left entorhinal cortex, right lingual gyrus and occipital cortex, left thalamus, and pons, as well as cerebellum (Supplementary Tables 1 and 2). Despite similar architecture of RS-ECN, betweengroup differences (Fig. 2 and Table 2) illustrated aMCI individuals had most notably lower functional connectivity (cluster size >500 mm3 ) in bilateral ACC, DLPFC, IPC, and precuneus, as well as right aPFC. In contrast, aMCI individuals had increased functional connectivity (cluster size >500 mm3 ) in bilateral VLPFC and aPFC, left dmPFC, right DLPFC, and bilateral superior parietal cortex, as well as left middle occipital gyrus and occipital pole (Fig. 2 and Table 3). Association between executive function and functional connectivity In aMCI individuals, correlation analysis showed a positive correlation between composite executive score and functional connectivity strength in ACC (r = 0.6213, p = 0.023), as well as in the right DLPFC (r = 0.6454, p = 0.017) (Table 4 and Fig. 3). Importantly, no correlations between functional connectivity of RS-ECN and other neuropsychological measures (e.g., global cognition, memory, or language) were noticed in aMCI subjects (Table 4). In healthy controls, no correlations were observed between regional connectivity strength within the RS-ECN and neuropsychological scores (Supplementary Table 3).

L. Wu et al. / Resting State Executive Control Network of aMCI


Fig. 1. RS-ECN of controls and patients with aMCI. RS-ECN of controls (upper) and patients with aMCI (lower) revealed high connectivity among dorsal lateral prefrontal cortex, anterior dorsomedial prefrontal cortex/cingulated cortex, inferior parietal cortex, precuneus, and middle temporal gyrus.


L. Wu et al. / Resting State Executive Control Network of aMCI Table 1 Demographics and neuropsychology between controls and aMCI patients

Age Education (years) Gender (male/female) Mini-mental state examination Performance IQ Matrix reasoning (scaled score) Block design (scaled score) Delayed recall of RAVLT Delayed recall of AFLT Copy of Rey complex figure Delayed recall of Rey complex figure Boston naming test Digit span (scaled score) Verbal fluency (DKEFS) Letter fluency Category fluency Category switching Accuracy Color-word interference test (DKEFS)—scaled score Color naming Word reading Inhibition Inhibition/switching Color-word interference test (DKEFS) – contrast measures Color naming + word reading Color naming + word reading versus inhibition/switching Inhibition versus color naming Inhibition/switching versus inhibition Trail making test (DKEFS) –scaled score Visual scanning Letter sequencing Number sequencing Letter-number shifting

Controls (n = 16)

aMCI (n = 13)

p value

67.75 ± 5.64 14.94 ± 3.17 8/8 29.13 ± 1.09 107.56 ± 11.09 12.06 ± 2.98 11.13 ± 2.09 9.63 ± 2.36 7.31 ± 2.18 24.16 ± 2.39 11.28 ± 3.61 53.25 ± 3.28 12.19 ± 2.71

69.00 ± 5.69 13.15 ± 3.02 6/7 26.23 ± 2.05 97.85 ± 11.89 10.15 ± 3.53 9.15 ± 2.48 4.08 ± 2.84 3.09 ± 1.87 22.31 ± 5.26 5.54 ± 5.58 40.77 ± 12.62 9.62 ± 1.08

0.56 0.14 0.84 500 >500

z 30 14 18 36 66 68 52 −2 54

Note: The threshold for significance was set at t = 4.5 and p ≤ 0.05. k = cluster size (mm3 ). BA, Brodmann areas; ACC, anterior cingulated cortex; DLPFC, dorsal lateral prefrontal cortex; aPFC, anterior prefrontal cortex; IPC, inferior parietal cortex; L, left; R, right.

DISCUSSION This study characterized RS-ECN as well as explored associations between changes in this brain

network and executive dysfunction in aMCI. We found that aMCI individuals also had a modest but significant executive function decline measured by digit span and verbal fluency. In aMCI individuals, RS-ECN

L. Wu et al. / Resting State Executive Control Network of aMCI


Fig. 2. T-statistic contrasts between aMCI and controls overlaid in average surfaces. RS-ECN in aMCI group displayed increased connectivity in the bilateral ventral lateral prefrontal cortex and dorsal medial prefrontal cortex, with predominance in the left hemisphere. In addition, aMCI group demonstrated decreased connectivity mainly in the anterior cingulated cortex and dorsal lateral prefrontal cortex, bilaterally.


L. Wu et al. / Resting State Executive Control Network of aMCI Table 3 Increased functional connectivity of RS-ECN in aMCI

Brain region

Brain gyrus

BA x

VLPFC (L) dmPFC (L) aPFC (L) VLPFC (R) DLPFC (R) aPFC (R) DLPFC (R) DLPFC (R) Middle occipital gyrus (L) Occipital cortex pole (L) Superior parietal cortex (R) Superior parietal cortex (L)

inferior frontal gyrus superior frontal gyrus superior frontal gyrus middle frontal gyrus middle frontal gyrus medial frontal gyrus superior frontal gyrus middle frontal gyrus middle occipital gyrus middle occipital gyrus superior parietal cortex superior parietal cortex

47 8 11 10 9 10 8 9 19 18 40 7

−42 −12 −20 40 10 2 28 46 −30 −20 42 −22

Peak MNI coordinates y 32 50 62 56 60 64 28 30 −86 −102 −50 −56

t value


7.713 7.053 6.28 8.476 6.17 6.962 8.456 7.765 5.495 5.704 4.822 4.565

5816 5104 3568 2984 2456 1976 1736 1112 976 552 >500 >500

z −12 48 −14 2 18 −12 54 32 26 16 60 66

The threshold for significance was set at t = 4.5 and p < 0.05; k = cluster size (mm3 ). BA, Brodmann areas; DLPFC, dorsal lateral prefrontal cortex; VLPFC, ventral lateral prefrontal cortex; dmPFC, dorsal medial prefrontal cortex, aPFC, anterior prefrontal cortex; L, left; R, right.

Table 4 Correlation between functional connectivity of RS-ECN and neuropsychological scores in aMCI Functional connectivity compared to controls

Neuropsychological scores


p value

left DLPFC (−32,60,14)


ACC (0, 26, 30)


right DLPFC (34, 56, 18)


right VLPFC (40, 56, 2)


left VLPFC (−42, 32, 12)


right aPFC (2, 64, −12)


MMSE Composite executive score Delayed recall of RAVLT Boston naming test MMSE Composite executive score Delayed recall of RAVLT Boston naming test MMSE Composite executive score Delayed recall of RAVLT Boston naming test MMSE Composite executive score Delayed recall of RAVLT Boston naming test Boston naming test MMSE Composite executive score Delayed recall of RAVLT Boston naming test MMSE Composite executive score Delayed recall of RAVLT Boston naming test

0.0685 0.0774 0.0406 0.0782 0.0001 0.3860 0.0157 0.0143 0.2483 0.4165 0.0635 0.0687 0.0751 0.1838 0.0080 0.1645 0.2215 0.0298 0.1225 0.2893 0.0615 0.0070 0.1193 0.1663 0.0035

0.3879 0.3575 0.5092 0.3547 0.9761 0.0234 0.6838 0.6975 0.1337 0.0172 0.4064 0.3870 0.3651 0.1439 0.7711 0.1691 0.0658 0.5729 0.2411 0.1227 0.4140 0.7852 0.2477 0.1667 0.8484

Brain regions (MNI coordinate)

MNI, Montreal Neurological Institute; MMSE, Mini-Mental State Examination; DLPFC, dorsal lateral prefrontal cortex; RAVLT, Rey Auditory Verbal Learning Test; ACC, anterior cingulated cortex; VLPFC, ventral lateral prefrontal cortex; aPFC, anterior prefrontal cortex.

connectivity strength was reduced in brain regions part of ECN, such as ACC, DLPFC and IPC. In contrast, aMCI group showed remarkably increased functional connectivity strength in bilateral VLPFC and left dmPFC, which are brain regions marginally connected to the RS-ECN in controls. Finally, we observed positive correlation between executive performance

and functional connectivity strength of RS-ECN in aMCI. Executive dysfunction in aMCI In addition to memory deficits, aMCI individuals in our study showed declines in executive functions

L. Wu et al. / Resting State Executive Control Network of aMCI


Fig. 3. Correlation between composite executive score and functional connectivity of RS-ECN in aMCI. Correlation analysis showed the positive correlation between functional connectivity of anterior cingulated cortex (ACC) (peak MNI coordination: x = 0, y = 26, z = 30) and composite executive score (A), between right dorsal lateral prefrontal cortex (DLPFC) (peak MNI coordination: x = 34, y = 56, z = 18) and composite executive score (B). r, Pearson correlation coefficient.

(working memory measured by Digit Span and divergent thinking, tested by Verbal Fluency). Previous studies conducted in aMCI have described similar impairments [2–5], however the existence of a typical aMCI dysexecutive profile remains elusive. The diversity of aMCI dysexecutive profiles reported by the literature might be partially due to disease heterogeneity, wide range of approaches available to assess executive dysfunction as well as research criteria to define aMCI [2, 4]. For instance, in contrast with the present results, Traykov and collaborators reported normal verbal fluency in patients with aMCI [4]. In contrast, in concordance with Traykov et al. [4], but in disagreement with Chang et al. [5], aMCI Trail Making Test results were similar to controls. One might argue that both Trail making A and B may not specifically measure executive impairment in aMCI [5]. Furthermore, our DKEFS Color-Word interference results are consistent with those found by Traykov et al. [4], however, when response inhibition scores were adjusted for color naming and word reading conditions, the response inhibition group differences disappeared. As a whole, the magnitude of dysexecutive symptoms found in aMCI individuals support the concept that memory is the dominant but not the unique cognitive dysfunction in aMCI (see Table 1). As suggested by Peterson [34], aMCI encompasses a cognitively heterogeneous population, in which memory deficit has predominantly been emphasized over other cognitive domains particularly executive dysfunction. It has been previously proposed that aMCI, with multiple-domain cognitive impairment, repre-

sents a transitional stage between single domain aMCI and mild AD. In fact, the presence of an additional cognitive dysfunction such as executive deficits increases the risk of conversion from aMCI to AD [6]. Furthermore, Brambati [35] demonstrated a greater degree of brain atrophy in multiple-domain aMCI individuals as compared to single domain aMCI. The RS-ECN in aMCI patients RS-ECN has been previously investigated using either ICA or seed-based CCA techniques and designated as frontal-parietal control and ECN, respectively [13, 14]. The rationale for utilizing CCA technique in the present study was based on the stability of RSECN analysis across individual measures as previously reported [14, 36]. As such, DLPFC, ACC and IPC have been consistently reported as RS-ECN regions in both healthy controls and aMCI [9, 13, 14]. In addition to these three cortical areas, we found that dmPFC, LOFC, superior and middle temporal gyri, occipital cortex, cerebellum, and left thalamus as part of the RS-ECN. Interestingly, the presence of the cerebellum and thalamus suggest that procedural and skill learning may play a role in more complex executive functions in RS-ECN. Previous studies on functional connectivity in older adults [37], using RS-fMRI and positron emission tomography (PET) amyloid imaging, demonstrate a spatial overlap between amyloid load in AD and cortical network hubs. Furthermore, amyloid plaques have been shown to disrupt DMN connectivity in


L. Wu et al. / Resting State Executive Control Network of aMCI

cognitively normal elderly and MCI [38, 39]. Since RS-ECN hubs include brain regions typically affected by high amyloid load in aMCI or AD (e.g., DLPFC, ACC, and IPC), functional connectivity changes of RS-ECN in aMCI may also be interpreted as vulnerability of brain circuits to the presence of AD pathology [37, 40].

ECN on cognition remains uncertain given the reduced executive function present in aMCI. Notably, there is an increase of brain connectivity in transition nodes between ECN and DMN.

Coexistence of decreased and increased functional connectivity in RS-ECN of aMCI

Association between RS-ECN connectivity strength and executive function offers a unique opportunity to interrogate clinical aspects of resting brain networks as well as mechanisms underlying brain reorganization in aMCI. In the present study, decreased functional connectivity in ACC and right DLPFC was positively correlated with already impaired composite executive score, supporting the intuitive concept that decreased functional connectivity within the core of RS-ECN impacts on executive performance in aMCI patients. The DLPFC has been associated with executive functioning, a wide range of functions has been attributed to this area: verbal fluency, working memory (measured by backward of digit span), shifting processes (as measured by the Trail Making test), response inhibition (assessed by Color-Word Inference Test), planning and organizational skills, reasoning, problem-solving, and abstract thinking [51, 52]. ACC, another core for the executive function, has been known, among others, to play a critical role in the monitoring of conflict during the Color-Word Interference Test [53].

The present study showed in aMCI brain the coexistence of declines and increases of connectivity strength associated with RS-ECN. In agreement with Sorg [9], our study showed decrease of functional connectivity in aMCI patients within the core of executive control system, primarily affecting the ACC, DLPFC (middle frontal gyrus), IPC, and precuneus. Since ACC, DLPFC and IPC play an important role in numerous executive functions, it is plausible to assume that decreased functional connectivity within these regions could be linked to executive functional impairments [41]. Connectivity declines were also observed in the in lateral parietal and anterior insula, which are related with ventral attention network. To the best of our knowledge, increased of RSECN functional connectivity in aMCI has not been previously described. However, several studies have indicated regional connectivity increases as evidence of brain reserve and cognitive reserve in prodromal AD, MCI, amyloid positive individuals without cognitive impairment, and carriers of apolipoprotein E4 genotype (ApoE4) [7, 8, 39, 42, 43]. Instead, other authors argue that regional increased connectivity in resting state convey excitotoxicity and decrease of synaptic inhibition [7, 8, 39, 43]. Interestingly in this study, brain regions showing increased connectivity in aMCI were located mostly outside of typical RS-ECN areas part of the ventral attention network, dorsal attention network, and DMN (e.g., VLPFC, dmPFC, aPFC, superior parietal cortex, posterior IPC, and even occipital and temporal cortex). These results are possibly resultant from interactions between ECN with the ventral attention network, dorsal attention network, and DMN [13, 14, 44–47]. In fact, RS-ECN is anatomically interposed between the dorsal attention network and DMN, and has several overlapping areas [14]. Possibly, in aMCI ECN functionally serves as a cortical mediator, linking dorsal attention network and DMN [48–50]. However, the role of recruitment of brain regions marginal to the

Correlation between executive dysfunction and changed RS-ECN architecture in aMCI

Limitations of the study Interpretation of the present results should take into consideration several limitations. Regarding the study design, the absence of longitudinal data hampers further clinical interpretation of the present findings. The statistical analysis might be limited by the small sample size and restricted number of cognitive tests as well as absence of ApoE4 stratification. Correction for structural group differences were not formally applied since no group differences for cortical thickness were found in the brain regions related to RS-ECN. Thus we assume that these increased or decreased brain functional connectivity of RS-ECN in aMCI subjects could not be attributed to structural changes (see Supplementary Fig. 1). Finally, the absence of amyloid pathology status evaluated by either PET amyloid imaging or CSF A␤42 in the present study hampers further interpretation of RS-ECN changes in the context of AD pathophysiology.

L. Wu et al. / Resting State Executive Control Network of aMCI

CONCLUSIONS Our study indicated a partial impairment of executive function in aMCI patients. DLPFC, ACC, IPC, and precuneus constituted the main hubs of RS-ECN in healthy controls and aMCI. Despite the similar network architecture in the two groups, aMCI patients exhibited decreased functional connectivity within the typical RS-ECN regions (bilateral ACC, DLPFC and IPC) and increased activation in areas outside of typical brain areas of RS-ECN (bilateral VLPFC and VMPFC). Finally, the correlation between declines in RS-ECN and executive dysfunction in aMCI subjects supports the idea that decreased functional connectivity within RS-ECN may impact executive performance in aMCI. ACKNOWLEDGMENTS This work was supported in part by Alzheimer’s Association (NIRG-08-92090 to PR), Nussia & Andr´e Aisenstadt Foundation (to PR), Fonds de la recherche en sant´e du Qu´ebec (16326 to to PR), National Nature Science Foundation of China (30700241 to LW), the Beijing Scientific and Technological New Star Program (2007B069 to LW), Scholarship from Chinese Scholarship Council (to LW), and fellowship from Pfizer Canada (to LW), as well as Industry Canada/Montreal Neurological Institute Center of Excellence in Commercialization and Research grant (to AD and AS). Authors’ disclosures available online (http://www.jalz.com/disclosures/view.php?id=2090).












Supplementary material is available in the electronic version of this article: http://dx.doi.org/10.3233/JAD131574.







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Resting state executive control network adaptations in amnestic mild cognitive impairment.

Executive dysfunction is frequently associated with episodic memory decline in amnestic mild cognitive impairment (aMCI) patients. Resting state execu...
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