Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer’s disease: Neuroimaging-genetic evidence

Accepted Manuscript Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer disease: Neuroimaging-genetic evidence Feng Bai, Yongmei Shi, Yonggui Yuan, Chunming Xie, Zhijun Zhang PII: DOI: Reference:

S0889-1591(16)30036-8 http://dx.doi.org/10.1016/j.bbi.2016.02.015 YBRBI 2809

To appear in:

Brain, Behavior, and Immunity

Received Date: Revised Date: Accepted Date:

27 November 2015 14 February 2016 15 February 2016

Please cite this article as: Bai, F., Shi, Y., Yuan, Y., Xie, C., Zhang, Z., Immunity factor contributes to altered brain functional networks in individuals at risk for Alzheimer disease: Neuroimaging-genetic evidence, Brain, Behavior, and Immunity (2016), doi: http://dx.doi.org/10.1016/j.bbi.2016.02.015

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Immunity factor contributes to altered brain functional networks in individuals

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at risk for Alzheimer disease: Neuroimaging-genetic evidence

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Feng Bai*, Yongmei Shi, Yonggui Yuan, Chunming Xie, Zhijun Zhang*

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Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine,

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Southeast University, Nanjing, 210009, China

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* Corresponding authors:

Dr. Zhijun Zhang: Tel: 0086-25-83262241, Fax: 0086-25-

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2583285132, E-mail: [email protected] or Dr.

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0086-25-83262243, Fax: 0086-25- 2583285132, E-mail: [email protected].

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Feng Bai, Tel:

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Clusterin (CLU) is recognized as a secreted protein that is related to the

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processes of inflammation and immunity in the pathogenesis of Alzheimer's disease

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(AD). The effects of the risk variant of the C allele at the rs11136000 locus of the

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CLU gene are associated with variations in the brain structure and function. However,

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the relationship of the CLU-C allele to architectural disruptions in resting-state

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networks in amnestic mild cognitive impairment (aMCI) subjects (i.e., individuals

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with elevated risk of AD) remains relatively unknown. Using resting-state functional

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magnetic resonance imaging and an imaging genetic approach, this study investigated

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whether individual brain functional networks, i.e., the default mode network (DMN)

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and the task-positive network, were modulated by the CLU-C allele (rs11136000) in

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50 elderly participants, including 26 aMCI subjects and 24 healthy controls.

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CLU-by-aMCI interactions were associated with the information-bridging regions

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between resting-state networks rather than with the DMN itself, especially in cortical

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midline regions. Interestingly, the complex communications between resting-state

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networks was enhanced in aMCI subjects with the CLU rs11136000 CC genotype and

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were modulated by the degree of memory impairment, suggesting a reconstructed

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balance of the resting-state networks in these individuals with an elevated risk of AD.

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The neuroimaging-genetic evidence indicates that immunity factors may contribute to

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alterations in brain functional networks in aMCI. These findings add to the evidence

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that the CLU gene may represent a potential therapeutic target for slowing disease

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progression in AD.

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Keywords: Alzheimer’s disease, Amnestic mild cognitive impairment, Clusterin,

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Default mode network, task-positive network

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Introduction

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Alzheimer’s disease (AD) initially develops as worsening memory

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impairment and progresses to a debilitating decline in all cognitive domains (Morra et

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al., 2008). Amnestic mild cognitive impairment (aMCI) often represents a transition

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state between normal aging and AD (Petersen and Negash, 2008). The protein

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composition of amyloid β (Aβ) plaques plays a crucial role in the pathogenesis of AD

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(Bertram and Tanzi, 2010), and specific genetic variants are associated with increased

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risk for late-onset AD (Wang et al., 2015).

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Genome-wide association studies of AD have confirmed the existence of a

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risk variant at a locus (rs11136000) of the clusterin (CLU, also known as

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apolipoprotein J) gene (Harold et al., 2009; Lambert et al., 2009; Carrasquillo et al.,

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2010). Specifically, clusterin has been demonstrated to have a high affinity for Aβ

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(Bertram and Tanzi, 2010). Aβ-clusterin complexes appear to be involved in the

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metabolism and regulation of Aβ both directly and indirectly via aggregation (i.e., by

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blocking synthetic Aβ42 peptides), clearance (i.e., different receptors mediate the

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clusterin-dependent clearance of Aβ into glial cells), and transport (i.e., by binding to

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megalin receptors in the blood for transport across the blood-brain barrier) (Wu et al.,

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2012). Importantly, clusterin is a secreted protein and is related to the processes of

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inflammation and immunity that occur during the pathogenesis of AD (Falgarone and

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Chiocchia, 2009; Eikelenboom et al., 2011). For example, interleukin-1β (IL-1β) and

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IL-2 increase the expression of clusterin in astrocytes (Zwain et al., 1994). Clusterin

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can inhibit activation of the complement system (Nuutinen et al., 2009) and is

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involved in the regulation of the nuclear factor-kappa B (NF-κB) pathway (NF-κB is

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recognized as a ubiquitous transcription factor that plays a key role in the immune

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response and in inflammation) (Essabbani et al., 2010). Intriguingly, the rs11136000

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single-nucleotide polymorphism (SNP) C allele within the CLU gene is associated

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with more rapid rates of decline in memory performance (Thambisetty et al., 2013),

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and individuals whose genomes contain this SNP have a greater risk of developing

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AD (Bertram and Tanzi, 2010). In a sense, the less commonly carried CLU- (the

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minor T allele) may be considered a protective form of the gene (Roussotte et al.,

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2014).

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Evidence to support plasma clusterin as a potential biomarker for AD is

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inconsistent. Mullan et al. (2013) found that plasma clusterin levels were higher in

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aMCI subjects than in control and AD patients and that genotype did not influence

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plasma clusterin levels in aMCI. The correlation of plasma clusterin levels with brain

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atrophy (Thambisetty et al., 2012) and cognitive decline (Jongbloed et al., 2015) in

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aMCI suggests that plasma clusterin levels may predict disease progression in AD. In

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contrast, several studies reported that plasma levels of clusterin did not differ

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significantly in aMCI (Meng et al., 2015) and AD patients (Silajdžić et al., 2012)

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compared to controls, although plasma clusterin levels were negatively correlated

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with cognitive scores independent of other AD risk factors (Meng et al., 2015).

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Neuroimaging-genetic studies, on the other hand, have provided increasing evidence

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for a role of CLU in brain function. The C risk allele at CLU rs11136000 is associated

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with lower fractional anisotropy (a widely accepted measure of white matter integrity)

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in multiple brain regions (Braskie et al., 2011), higher activity of memory task-related

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regions in prefrontal cortex, posterior cingulate cortex and limbic areas (Lancaster et

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al., 2011, 2015), and with greater activity of executive function and attention

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task-related regions in the right insula and the superior parietal cortex (Green et al.,

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2014) than in participants with the protective allele. It was also associated with

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atrophy (i.e., ventricular volume expansion) in the elderly (Roussotte et al., 2014). A

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recent follow-up study showed that the CLU-C allele is associated with significant

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longitudinal brain atrophy in aMCI participants (Thambisetty et al., 2012) and with

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longitudinal increases in regional cerebral blood flow in the hippocampus and anterior

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cingulate cortex in cognitively normal individuals who eventually develop aMCI or

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AD (Thambisetty et al., 2013). This evidence indicates the importance of exploring

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possible CLU gene-related alterations in brain structure and task-state function.

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In recent years, there has been a dramatic increase in the number of studies

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using resting state functional magnetic resonance imaging (fMRI), a recent addition to

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imaging analysis techniques. This technique has been used to demonstrate that subtle

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functional abnormalities in resting-state default mode network (DMN) regions are

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associated with a distinctive pattern of Aβ plaque deposition (Sheline and Raichle,

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2013; Raichle, 2015). The DMN network includes the posterior cingulate cortex

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(PCC), the medial prefrontal cortex (MFPC), the medial temporal lobe, and the

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bilateral inferior parietal lobule/temporoparietal junction as major hubs (Buckner et al.,

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2008; Jacobs et al., 2013; Domhoff and Fox, 2015). The DMN is of great interest to

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researchers in the AD field because it correlates with episodic memory functioning

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and attentional processing (Buckner et al., 2008; Jacobs et al., 2013), both of which

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are potential new biomarkers for preclinical AD (Greicius et al., 2004; He et al., 2007;

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Sorg et al., 2007).

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It is generally agreed that the resting-state brain is composed of two spatially

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distinct functional networks: the DMN network and task-positive networks (TPN)

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(Smith et al., 2009; Biswal et al., 2010; Di and Biswal, 2014). DMN has been

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described as a task-induced deactivation (or task-negative) network that is highly

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active in the resting state and becomes deactivated during external stimuli task states

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(Raichle, 2011). In other words, whenever the level of activity in the DMN network

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increases due to spontaneous signal fluctuations during rest, activity in the

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task-positive network regions decreases, and vice versa (Bai et al., 2012). TPN

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(task-induced activations) take place in regions of the sensorimotor and

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attention-related cortices that become activated during goal-directed tasks (Hui et al.,

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2009). The integrity of the DMN and TPN may be central to the balancing of brain

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functions and the maintenance of health (Hui et al., 2009). An impaired balance

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between these two networks might cause difficulties in reading, arithmetic and

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concentration (Hanes and McCollum, 2006) as well as cognitive deficits (Klingner et

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al., 2014). Moreover, the balance and complex communications between the DMN

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and the TPN are modulated by midline DMN regions that are thought to play an

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information-bridging role and that are among the most efficiently wired brain areas,

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serving as global “hubs” that bridge different functional systems within the brain

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(Hagmann et al., 2008; Buckner et al., 2009; van den Heuvel and Sporns, 2013; Di

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and Biswal, 2014; Elton and Gao, 2015). These information-bridging regions may not

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only support a "default" mode of brain function but may also play an important role in

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the execution of both internal and external tasks through their flexible coupling with

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task-relevant brain regions (Elton and Gao, 2015). Abnormal anatomical connectivity

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and functioning of information-bridging regions has been hypothesized to relate to

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behavioural and cognitive impairment in AD patients (Buckner et al., 2008; Bassett

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and Bullmore, 2009; van den Heuvel and Sporns, 2013).

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To our knowledge, this is the first study to address whether the immunity

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factor contributes to altered brain functional networks in aMCI. We hypothesized that

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the CLU rs11136000 variant may be associated with functional abnormalities in

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resting-state networks. The aims of this study, therefore, were: (i) to determine

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whether altered topological patterns of the DMN are associated with the CLU-C allele

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in subjects with aMCI and (ii) to identify whether and how the altered balance of the

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DMN and TPN via information-bridging regions is affected by the risk variant CLU-C

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allele in aMCI.

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Materials and methods

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Subjects

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This study employed 50 elderly participants (right-handed), including 26

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aMCI subjects and 24 healthy controls. It was approved by the Research Ethics

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Committee of Affiliated Zhong-Da Hospital, Southeast University, and written

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informed consent was obtained from all participants. Cognitive functioning was

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evaluated by a mini-mental state examination (MMSE), and the degree of dementia

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was determined using a clinical dementia rating scale (CDR). In addition, a

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neuropsychological battery that consisted of an auditory verbal memory test

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(AVMT)-delayed recall, the Rey-Osterrieth Complex Figure Test (RCFT)-delayed

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recall, Trail Making Test (TMT)-A and -B, the Symbol Digit Modalities Test (SDMT),

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a Clock Drawing Test (CDT) and a Digit Span Test (DST) was used to evaluate the

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functions of episodic memory, attention, psychomotor speed, executive function and

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visuospatial skills, respectively. The aMCI subjects included in the study were

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selected according to the recommendations of Petersen et al. (1999) and Winblad et al.

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(2004): (i) subjective memory impairment corroborated by the subject and an

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informant; (ii) objective memory performance documented by an AVLT-delayed recall

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score less than or equal to 1.5 SD of age- and education-adjusted norms (cut-off of ≤ 4

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correct responses on 12 items for patients with ≥ 8 years of education); (iii) MMSE

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score of 24 or higher; (iv) CDR of 0.5; (v) no or minimal impairment in daily

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activities; and (vi) absence of dementia or insufficient dementia to meet the

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NINCDS-ADRDA (National Institute of Neurological and Communicative Disorders

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and Stroke and the Alzheimer’s Disease and Related Disorders Association)

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Alzheimer’s Criteria. In addition, a CDR of 0, an MMSE score ≥ 26, and an

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AVLT-delayed recall score > 4 were required for control subjects with 8 or more years

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of education. Participants with a history of known stroke, alcoholism, head injury,

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Parkinson’s disease, epilepsy, major depression or other neurological or psychiatric

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illness, major medical illness, or severe visual or hearing loss were not included in the

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present study. The diagnostic process was conducted by an experienced

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neuropsychiatrist by structured interviews with the subjects and their informants.

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DNA isolation and SNP genotyping

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Investigators who were blinded to all participants’ identifiers and information

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performed the genotype analysis. Blood samples were obtained from the 50

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participants. The data were processed and analysed using MassARRAY TYPER 4.0

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software (Sequenom). Because many previous studies suggest a dominant model of

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minor T-allele effects (Zhou et al., 2010; Mengel-From, et al., 2011; Ling et al., 2012;

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Roussotte et al., 2014), we compared brain functional networks between C

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homozygotes and carriers of 1 or 2 T alleles. The subjects were genotyped for CLU

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rs11136000 (aMCI: TT/CT carriers =13, CC carriers =13; controls: TT/CT carriers

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=11, CC carriers =13). Hardy-Weinberg equilibrium was checked with the χ2-test.

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MRI data acquisition

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A General Electric 1.5 Tesla scanner (General Electric Medical Systems,

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USA) with a homogeneous birdcage head coil was employed in this study.

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Conventional axial Fast Relaxation Fast Spin Echo sequence T2-weighted anatomic

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MR images were first obtained to rule out major white matter changes, cerebral

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infarction or other lesions: repetition time (TR) = 3500 ms; echo time (TE) = 103 ms;

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flip angle (FA) = 90 degrees; acquisition matrix = 320 × 192; field of view (FOV) =

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240 mm×240 mm; thickness = 6.0 mm; gap = 0 mm; no. of excitations (NEX) = 2.0).

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Second, high-resolution T1-weighted axial images covering the whole brain were

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acquired using a 3D spoiled gradient echo sequence as follows: TR = 9.9 ms; TE =

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2.1ms; FA = 15 degrees; acquisition matrix = 256×192; FOV = 240 mm ×240 mm;

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thickness = 2.0 mm; gap = 0 mm. Finally, the functional scans (T2* weighted images)

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involved the acquisition of 30 contiguous axial slices using a GRE-EPI pulse

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sequence: TR = 3000 ms; TE = 40 ms; FA = 90 degrees; acquisition matrix = 64 ×64;

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FOV = 240 × 240 mm; thickness = 4.0 mm; gap = 0 mm and 3.75 × 3.75 mm2

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in-plane resolution parallel to the anterior commissure–posterior commissure line. In

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all, 142 functional volumes were generated in 7 min and 6 s. Two experienced

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radiologists executed the scans for the entire screening process.

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Resting-state fMRI data preprocessing

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Preprocessing

steps

were

performed

with

SPM5

software

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(http://www.fil.ion.ucl.ac.uk/spm). The first eight volumes of the scanning session

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were discarded to allow for magnetization equilibration effects. The remaining images

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were corrected for timing differences and motion effects. No translation or rotation

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parameters of head motion in any given data set exceeded ±3 mm or ±3°. The

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resulting images were then spatially normalized into the SPM5 Montreal Neurological

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Institute echo-planar imaging template using the default settings and resampling to 3

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× 3 ×3 mm3 voxels and smoothed with a Gaussian kernel of 8 × 8 × 8 mm. REST

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software (http://www.restingfmri.sourceforge.net) was used to remove the linear trend

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of the time courses and for temporal band-pass filtering (0.01–0.08 Hz).

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Network construction

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DMN: multiple region-of-interest-based approaches

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In the present study, multiple regions-of-interest (ROIs)-based connectivity

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analysis was performed to obtain measures of DMN. The 13 ROIs used to define the

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default network were similar to those used in previous studies (Fair et al., 2008; Liu et

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al., 2010) and included anterior medial prefrontal cortex (aMPFC), left superior

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frontal cortex (L.Sup.F), right superior frontal cortex (R.Sup.F), ventral medial

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prefrontal cortex (vMPFC), left inferior temporal cortex (L.IT), right inferior temporal

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cortex (R.IT), left parahippocampal gyrus (L.PHC), right parahippocamal gyrus

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(R.PHC), posterior cingulate cortex (PCC), retrosplenial cortex (Rsp), left lateral

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parietal cortex (L.LatP), right lateral parietal cortex (R.LatP) and cerebellar tonsils

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(Cereb). All ROIs were defined as spherical regions with a radius of 6 mm at the

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centre of the obtained coordinates of the specific ROI (details see Table 2). For each

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participant, cross-correlation analysis was carried out between the mean signal

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changes (i.e., a BOLD time series) in each of the 13 pairs of ROIs. A Fisher’s

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z-transform was applied to improve the normality of the correlation coefficients. To

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remove possible effects of head motion, six head motion parameters were introduced

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as covariates. Thus, we obtained a 13x13 matrix for each participant in which the

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edge between any two nodes represented the z-valued strength of the functional

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connectivity between the two corresponding brain regions within the default network.

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The functional connectivity analyses were performed using REST software

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(http://www.restingfmri.sourceforge.net).

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Other networks associated with the DMN: voxel-based approach

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The brain is organized into networks that display spontaneous and

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synchronous neuronal activity at rest, including the DMN and the TPN (Smith et al.,

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2009; Biswal et al., 2010; Di and Biswal, 2014). Moreover, the DMN may play a

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greater role in TPN through flexible coupling with task-relevant brain regions (Elton

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and Gao, 2015). To comprehensively evaluate the relationship between the DMN and

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other networks, the 13 previously defined ROIs were further selected as seeds to

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separately establish whole-brain functional connectivity maps. In detail, for each

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participant, a mean time series for each seed was computed as the reference time

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course. Cross-correlation analysis was then carried out between the mean signal

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change in the seed and the time series of every voxel in the rest of the brain. A

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Fisher’s z-transform was applied to improve the normality of the correlation

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coefficients. To remove possible effects of head motion, six head motion parameters

275

were introduced as covariates. The functional connectivity analyses were performed

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using REST software (http://www.restingfmri.sourceforge.net).

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Voxelwise-based grey matter volume correction

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To control for possible differences in the functional results that might be

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explained by between-participant differences in grey matter volume between subjects,

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we included estimates of a voxel’s likelihood of containing grey matter as a covariate

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(nuisance variable) in the analysis of the resting-state functional data using standard

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statistical techniques (Oakes et al., 2007). The purpose of this method is to isolate the

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components of functional changes that cannot be attributed to anatomical differences

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and are thus likely to be due to genuine functional differences. First, voxel-based

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morphometry (VBM) was used to explore grey matter volume maps of every subject.

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These maps were transformed into the same standard space as the resting-state fMRI

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images using affine linear registration. Because VBM results can be sensitive to the

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size of the smoothing kernel used to smooth the tissue segment images, the criterion

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used in this work was matching of the smoothness of the grey matter volume map data

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to that of the corresponding functional data (8 mm). Finally, the resulting voxelwise

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grey matter volume maps were input as covariates in the analysis of functional data.

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Statistical analysis

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DMN

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All 13 ROIs were marked out to describe an undirected weighted DMN with

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13 nodes and 78 edges that described the network connectivity patterns for each

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participant. Si was recruited as the node strength; it can be used to qualify the extent

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to which a given node is central in the DMN network and is defined as follows:

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Si   wij j

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where wij denotes the weighted edge that connects node i and node j; in other words, it

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is the z-value strength of the functional connectivity between brain region i and brain

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region j in the present study. The differences in Si between each of the four subgroups

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were then analysed (p < 0.05), including aMCI with CLU rs11136000 TT/CT

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genotype, aMCI with CLU rs11136000 CC genotype, controls with CLU rs11136000

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TT/CT genotype and controls with CLU rs11136000 CC genotype.

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Other networks associated with the DMN

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A general linear model was used to analyse Group X Genotype interaction in

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the whole-brain functional connectivity maps of each ROI-seed. In all, a

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mixed-effects ANOVA with subjects was used as the random factor, and group (i.e.,

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aMCI and controls) and genotype (i.e., rs11136000 TT/CT carriers and rs11136000

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CC carriers) were the fixed factors. The analyses were performed separately on the

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whole-brain functional connectivity maps of each ROI-seed. Furthermore, we tested

312

the relationship between the Group X Genotype interaction and DMN. PCC is served

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as a key hub of DMN. Therefore, the fMRI time series of PCC [Center (mm) = (-3,

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-48, 30); Radius = 6.00 mm] and the time series of the regions in the Group X

315

Genotype interaction were further analysed in the four subgroups. Finally, Pearson

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correlation analysis was employed to separately analyse the neuropsychological

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performance and the Group X Genotype interaction in the aMCI and control groups.

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The ANOVA threshold was set at an AlphaSim-corrected p < 0.05 as determined by

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Monte Carlo simulation (single voxel p value = 0.005, a minimum cluster size of 1296

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mm3, and FWHM = 8 mm with mask (see the AlphaSim program by D. Ward at

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http://afni.nimh.nih.gov/pub/dist/doc/manual/AlphaSim.pdf).

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Results

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Demographic and neuropsychological evaluations

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Memory performance was significantly lower for the aMCI subjects than for

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the healthy controls (p < 0.05), with the impairments occurring on AVMT-delayed

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recall and RCFT-delayed recall (Table 1). In addition, the aMCI group displayed a

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tendency towards lower total MMSE scores compared to controls (p = 0.054) (Table

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1). There were no significant differences between the subgroups of aMCI and the

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control groups (aMCI: CLU-CC vs. aMCI: CLU-TT/CT and Control: CLU-CC vs.

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Control: CLU-TT/CT) with regard to age, education, gender or performance on other

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neuropsychological tests (p > 0.05) (Table 1). It should be noted that the genotype

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frequencies did not deviate from the Hardy–Weinberg equilibrium in either group (p >

334

0.05).

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DMN reconstruction

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DMNs were constructed based on 13 ROIs in the present study and were

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marked out as undirected weighted networks with 13 nodes and 78 edges that

338

described the network connectivity patterns (Figure 1). A qualitative visual inspection

339

of the four subgroups showed similar patterns of the DMN in all four subgroups. It

340

was

341

http://www.nitrc.org/projects/bnv/). There were also no significant differences in Si

342

between CLU rs11136000 CC carriers and CLU rs11136000 TT/CT carriers observed

343

in either aMCI or controls (p > 0.05).

344

Identification of Group X Genotype interaction in other networks associated

345

with the DMN

visualized

with

the

BrainNet

Viewer

(Xia

et

al.,

2013,

346

To comprehensively evaluate the resting-state networks, the 13 ROIs (i.e.,

347

DMN nodes) were used separately as seeds to detect the functional connectivity maps

348

(for details, see Table 2). (1) The main effect of group was observed in the bilateral

349

cerebellum posterior lobe (i.e., the R.PHC, PCC and Rsp networks) and in

350

parietal-medial temporal regions (left supramarginal gyrus/angular/inferior parietal

351

lobule/posterior cingulate and right hippocampus) (i.e., Cereb networks). (2) The

352

main effect of genotype was found in the right cerebellum posterior lobe (i.e., R.IT

16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

353

networks) and the bilateral superior temporal gyrus (i.e., Cereb networks). It should

354

be noted that none of the other ROI networks retained significant main effects of

355

group or genotype at p < 0.05 after correction with a Monte Carlo simulation. (3) The

356

regions of Group X Genotype interaction were identified in 3 of these 13 ROI

357

networks, including the L.IT network, the R.IT network and the L.LatP network, after

358

an AlphaSim-corrected p < 0.05. In detail, significant Group X Genotype interaction

359

of the L.IT/R.IT networks was predominant in cortical midline regions (i.e., bilateral

360

anterior cingulate and bilateral medial frontal gyrus), whereas significant Group X

361

Genotype interactions of the L.LatP network were mainly associated with cortical

362

midline regions (i.e., left anterior cingulate), parietal cortex (i.e., left inferior parietal

363

lobule, left superior parietal lobule and left postcentral gyrus) and subcortical

364

structures (i.e., bilateral caudate). The results support the idea that interactions

365

between the DMN and TPN occur through flexible coupling with task-relevant brain

366

regions. The detailed patterns of the Group X Genotype interactions are shown in

367

Figure 2.

368

Characteristics of regions in Group X Genotype interaction in other networks

369

associated with the DMN

370

We further tested whether the association between the regions identified in

371

the Group X Genotype interaction and the DMN was disrupted in CLU-C allele

372

carriers. We found that the fMRI time series of the DMN (i.e., the PCC) was

373

positively correlated with the overwhelming major time series of regions in Group X

374

Genotype interaction both in aMCI and controls (Figure 3 and Figure 4). Compared to

17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

375

the CLU rs11136000 TT/CT genotype, the communications were significantly

376

enhanced in aMCI with the CLU rs11136000 CC genotype, while a weaken tendency

377

was observed in controls with the CLU rs11136000 CC genotype. In particular,

378

relationships between PCC and Group X Genotype interaction of the L.IT network

379

(aMCI: CC genotype: r = 0.500, p = 7.88×10-10 vs. TT/CT genotype: r = 0.105, p =

380

0.229; controls: CC genotype: r = -0.003, p = 0.971 vs. TT/CT genotype: r = 0.057, p

381

= 0.517), the R.IT network (aMCI: CC genotype: r = 0.688, p = 4.21×10-20 vs. TT/CT

382

genotype: r = 0.363, p = 1.62×10-5; controls: CC genotype: r = 0.250, p = 0.004 vs.

383

TT/CT genotype: r = 0.573, p = 4.518×10-13) and the L.LatP network (aMCI: CC

384

genotype: r = 0.771, p = 1.22×10-27 vs. TT/CT genotype: r = 0.603, p = 1.29×10-14;

385

controls: CC genotype: r = 0.689, p = 3.332×10-20 vs. TT/CT genotype: r = 0.855, p =

386

1.628×10-39) were revealed in the present study. The significant strength of

387

reconstruction of the L.IT network was highlighted in aMCI with the CLU

388

rs11136000 CC genotype (i.e., from no statistical significance to statistical

389

significance).

390

The time series of regions in Group X Genotype interaction of the L.IT

391

network was negatively correlated with memory performance (AVMT: r = -0.671, p =

392

0.012, Cohen's d = 1.810; RCFT: r = -0.619, p = 0.024, Cohen's d = 1.576) in aMCI

393

with the CLU rs11136000 CC genotype (Figure 5a). Although differences in

394

performance on non-memory neuropsychological tests between groups were not

395

observed, this study also revealed some negative correlations of non-memory

396

cognitive function, i.e., poor scores on MMSE (r = -0.580, p = 0.038, Cohen's d =

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

397

1.424)/SDMT (r = -0.584, p = 0.036, Cohen's d = 1.439)/DST (r = -0.651, p = 0.016,

398

Cohen's d = 1.715) and more time consumption of TMT-B (r = 0.565, p = 0.044,

399

Cohen's d = 1.370) were associated with stronger function of the R.IT network in

400

aMCI with the CLU rs11136000 TT/CT genotype (Figure 5b). It should be noted that

401

these correlations were no longer significant after correcting for multiple comparisons.

402

In addition, there were no significant correlations between these neuropsychological

403

data reported for any of the memory/non-memory tests and these Group X Genotype

404

interactions either in control CC carriers (i.e., AVMT: r = 0.352, p = 0.239, Cohen's d

405

= 0.752; RCFT: r = 0.169, p = 0.580, Cohen's d = 0.343) or in TT/CT carriers (MMSE:

406

r = 0.211, p = 0.533, Cohen's d = 0.434; TMT-B: r = 0.551, p = 0.079, Cohen's d =

407

1.321; SDMT: r = -0.206, p = 0.543, Cohen's d = 0.421; DST: r = -0.115, p = 0.737,

408

Cohen's d = 0.232).

409 410

Discussion

411

This study is the first to show that the immunity factor contributes to altered

412

brain functional networks in aMCI. Specifically, the effect of the risk variant of the

413

CLU-C allele was associated with functional communications in resting-state

414

networks rather than in the DMN itself in aMCI subjects, especially in cortical

415

midline regions. Interestingly, the complex modulatory communications between

416

resting-state networks was enhanced in aMCI with the CLU rs11136000 CC genotype,

417

suggesting a reconstructed balance of resting-state networks in these individuals with

418

an elevated risk of AD.

19

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

419

AD is a disease in which neural networks experience disruption before

420

cognitive deficits are detectable (Jacobs et al., 2013). Presently, studies of the DMN,

421

which are popularly referred to as resting-state studies, have come to play a major role

422

in studies of the human brain in health and disease (Fox et al., 2005; Uddin et al.,

423

2009; Raichle, 2015). Resting-state connectivity studies have consistently found

424

decreased connectivity between nodes within the DMN in aMCI compared with

425

controls (Sorg et al., 2007; Koch et al., 2012; Liang et al., 2012). However, no

426

significant effects of the CLU CC genotype (rs11136000) were directly associated

427

with the properties of the DMN network in this study, suggesting that the role of the

428

risk variant of the CLU CC genotype in aMCI may not be specific to the DMN itself.

429

A recent study found that cerebral small vessel disease (CSVD) contributes to the

430

impaired DMN deactivation in aMCI, whereas aMCI without CSVD is associated

431

with relatively preserved integrity of the DMN compared to aMCI with CSVD

432

(Papma et al., 2012). The present study employed T2-weighted anatomical MR

433

images to exclude participants with major white matter changes, cerebral infarction or

434

other lesions, supporting the idea that the effects observed do not reflect the effects of

435

vascular lesions on the DMN. We presumed that the effects of the CLU gene on the

436

DMN would be emphasized due to the significant impairment of this network in

437

aMCI subjects. Indeed, the present study found effects of the CLU CC genotype on

438

cortical midline regions (i.e., bilateral anterior cingulate and bilateral medial frontal

439

gyrus) when the communications of resting-state networks were measured in aMCI.

440

These midline DMN regions are considered to have an information-bridging role with

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

441

respect to their functional contribution to TPN (Elton and Gao, 2015). To our

442

knowledge, the physiological role of the CLU CC genotype in brain networks in

443

subjects with aMCI status remains relatively unknown. There is increasing evidence

444

that TPN is activated when the brain processes externally presented information

445

during stimulus processing, task execution, and monitoring of external environments

446

(Cabeza and Nyberg, 2000; Naghavi and Nyberg, 2005; Golland et al., 2008; Broyd et

447

al., 2009; Kim et al., 2010). Therefore, it seems from the present findings that the

448

impact of the CLU CC genotype may include involvement in the above-mentioned

449

mechanism that occurs in a population at high risk of AD.

450

In this study, the temporoparietal system (e.g., the L.IT, R.IT and L.LatP

451

networks) of the 13 studied ROI networks was highlighted. Previous studies have

452

shown that thinning and lower regional blood perfusion in the temporoparietal cortex

453

is associated with the prodromal stage in AD patients (Moretti, 2015). Furthermore,

454

measurement of the atrophy of the temporoparietal association cortices and the medial

455

temporal lobe showed reasonable performance as a means of classification, with

456

90.7% sensitivity and 84% specificity in AD (Vemuri et al., 2011). The present study

457

further indicates that the CLU CC genotype is associated with the function of the

458

temporoparietal system in aMCI. However, the balance between resting-state

459

networks with respect to the temporoparietal system may prove to be functionally

460

more important than DMN activity itself. Kelly et al. (2008) found that

461

inter-individual variation in the strength of the correlation between DMN and TPN

462

was significantly related to individual differences in task response time variability: the

21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

463

stronger the negative correlation, the less variable the behavioural performance. The

464

present findings are also associated with this apparent discrepancy, and it is suggested

465

that opposite trajectories of the reconstructed balance of resting-state networks

466

between aMCI (i.e., enhanced) and controls (i.e., weaken) exist in CC carriers

467

compared to CT/TT carriers. These weaken relationships of controls are consistent

468

with the homozygous C allele carriers associated with the worse brain function in

469

elderly populations (Stevens et al., 2014). The communications were significantly

470

enhanced in aMCI CC carriers compared to CT/TT carriers, supporting that the

471

strengthening connections may represent compensatory mechanisms (Lancaster et al.,

472

2011, 2015). It is plausible that sustaining the observed increments in resting-state

473

relationships over several years places an especially high burden on the brain's energy

474

resources, and the eventual failure of these presumed compensatory increments in

475

brain function with the progression of disease (Thambisetty et al., 2013). Effects of

476

functional plasticity in resting-state networks have been observed in some recent

477

studies (Soares et al., 2013; Vaisvaser et al., 2013). Importantly, the functional plastic

478

phenomenon has its cytological and molecular foundation in microglial pruning of

479

developing synapses and regulation of synaptic plasticity (Hong et al., 2015), and

480

changes in gene expression can alter synaptic excitability, leading to plasticity (Saura

481

et al., 2015). Given the fact that clusterin is highly expressed in microglia in AD

482

(Villegas-Llerena et al., 2016), we propose that CLU affects abnormal plasticity may

483

contribute

484

individuals compared to controls. However, studies in non-human primates and

to cognitive impairment through

22

dose-dependent

effects

in

aMCI

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

485

rodents permit direct experimental manipulations that will provide explicit detailed

486

explanations of the underlying mechanisms.

487

A negative correlation with memory performance was especially evident in

488

L.IT network-mediated communications in aMCI CC carriers, whereas a significantly

489

negative

490

network-mediated communications was predominant in aMCI TT/CT carriers. Brain

491

activity is synchronized between different regions that are widely distributed

492

throughout the brain, forming functional networks that correlate with the genes linked

493

to ion channel activity and synaptic function (Richiardi et al., 2015). Our data provide

494

empirical support for the existence of a gene-regulatory network influencing cognition

495

and aMCI. CLU is known to affect inflammation, immune responses, and amyloid

496

clearance, which in turn may result in neurodegeneration (Roussotte et al., 2014).

497

Thus, we suggest that the CLU CC genotype may initially impact the integrity of

498

resting-state networks and then lead to cognitive impairment in subjects, although the

499

underlying mechanisms for this are not fully understood. These negative correlations

500

support the idea that brain functional reserve confers increased capacity for

501

compensatory mechanisms that can be used to offset neuropsychological deficits.

502

There are multiple theories of compensatory networks in MCI and dementia, such as

503

the HAROLD (hemispheric asymmetry reduction in older adults) model (Cabeza

504

2002), the CRUNCH (compensation-related utilization of neural circuits hypothesis)

505

model (Berlingeri et al., 2013), Cognitive Reserve (Arenaza-Urquijo et al., 2015) and

506

Cognitive Scaffolding (Reuter-Lorenz and Park, 2014). However, we also presume

relationship

between

non-memory

23

cognitive

function

and

R.IT

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

507

that the underlying deficits would begin to surface only when the compensatory

508

mechanism becomes overextended, whereas once clinical symptoms appear the

509

pathology would be more advanced.

510

Despite high levels of education and an unusually high digit span, even

511

control participants had lower scores on the symbol digit modalities test (Kiely et al.,

512

2014) and longer times on Trail-making test A (Tombaugh, 2004) than would be

513

expected based upon normative data for healthy older adults. The diversity of

514

cognitive performance using these neuropsychological tests may be associated with

515

the use of Arabic numerals. The present study employed a Chinese Han sample, and

516

Arabic numerals and Chinese numerals are two different types of digital systems used

517

in China. Due to cultural or sociodemographic variables associated with different

518

populations, we speculate that Arabic numeral processing in Chinese speakers may be

519

different from that in Western speakers (Cao et al., 2010), at least with respect to some

520

of the digital attributes. In addition, sample composition, including exclusionary

521

criteria, gender and intelligence, may also influence the results (Periáñez et al., 2007).

522

The present study has technical and biological limitations that must be

523

acknowledged. First, 3-second TR is less than ideal for examining low-frequency

524

effects in blood oxygenation level-dependent (BOLD) data and may decrease

525

statistical power and make it more difficult to remove noise (Constable et al., 2013).

526

Therefore, the present data should be interpreted as suggestive, and replication of the

527

findings should be sought in additional studies. Second, the ApoEε4 allele conveys

528

the vast majority of genetic risk for late-onset dementia and interacts with CLU

24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

529

through multiple molecular pathways. However, in the present study, only five aMCI

530

and no controls with the APOEε4 risk allele were CLU rs11136000 CC carriers, while

531

one aMCI and one control with the APOEε4 risk allele were CLU rs11136000 TT/CT

532

carriers. The conclusions would be much stronger if APOE genotype was included as a

533

main effect (or interaction factor) in statistical models using a larger sample size.

534

Third, based on previous studies that suggested a dominant model of minor T-allele

535

effects (Zhou et al., 2010; Mengel-From, et al., 2011; Ling et al., 2012; Roussotte et

536

al., 2014), the present study integrated TT carriers and CT carriers into one subgroup.

537

However, future studies should focus on more complex genotypes, e.g., CC, TT and

538

CT. Finally, these gene-brain-behaviour correlations were no longer significant after

539

correcting for multiple comparisons. It is likely that this study did not have sufficient

540

power to assess these correlations, and a larger sample size will be needed to expand

541

upon these preliminary findings in the future.

542

In summary, in support of the growing evidence for a genetic ontogeny of

543

AD, this study provides neuroimaging-genetic evidence that the immunity factor may

544

contribute to altered brain functional networks in aMCI. These findings contribute to

545

better understanding the effects of the risk variant CLU rs11136000 CC genotype on

546

resting-state networks in aMCI subjects. The present study may open novel avenues

547

for testing the efficacy of preventive treatments in individuals at risk for AD.

548 549

Competing interests

550

The authors declare that they have no competing interests.

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

551 552

Authors’ contributions

553

Feng Bai and Zhijun Zhang contributed to the concept of the manuscript. Feng Bai

554

drafted the manuscript. Yongmei Shi, Yonggui Yuan and Chunming Xie helped to

555

analyze the data and revised the manuscript. All authors read and approved the final

556

manuscript.

557 558

Acknowledgments

559

This research was partly supported by the National Natural Science

560

Foundation of China (No. 91332104, 81201080, 91332118); Program for New

561

Century Excellent Talents in University (No. NCET-13-0117); Key Program for

562

Clinical Medicine and Science and Tochnology: Jiangsu Provence Clinical Medical

563

Research Center (No.BL2013025); Natural Science Foundation of Jiangsu Province

564

(No.BK2012337);

565

(No.2015AA020508) and Doctoral Fund of Ministry of Education of China (No.

566

20120092120068).

National

High-tech

567 568 569 570 571 572

26

R.D

Program

(863

Program)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

573

References

574

Arenaza-Urquijo, E.M., Wirth, M., Chételat, G., 2015. Cognitive reserve and lifestyle: moving

575

towards preclinical Alzheimer's disease. Front Aging Neurosci. 7, 134.

576

Bai, F., Watson, D.R., Shi, Y., Yuan, Y., Yu, H., Zhang, Z., 2012. Mobilization and redistribution of

577

default mode network from resting state to task state in amnestic mild cognitive impairment. Curr.

578

Alzheimer Res. 9, 944-952.

579

Bassett, D.S., Bullmore, E.T., 2009. Human brain networks in health and disease. Curr Opin

580

Neurol. 22, 340-347.

581

Berlingeri, M., Danelli, L., Bottini, G., Sberna, M., Paulesu, E., 2013. Reassessing the HAROLD

582

model: is the hemispheric asymmetry reduction in older adults a special case of

583

compensatory-related utilisation of neural circuits? Exp Brain Res. 224, 393-410.

584

Bertram, L., Tanzi, R.E., 2010. Alzheimer disease: New light on an old CLU. Nat. Rev. Neurol. 6,

585

11-13.

586

Biswal, B.B., Mennes, M., Zuo, X.N., Gohel, S., Kelly, C., Smith, S.M., Beckmann, C.F.,

587

Adelstein, J.S., Buckner, R.L., Colcombe, S., Dogonowski, A.M., Ernst, M., Fair, D., Hampson,

588

M., Hoptman, M.J., Hyde, J.S., Kiviniemi, V.J., Kötter, R., Li, S.J., Lin, C.P., Lowe, M.J., Mackay,

589

C., Madden, D.J., Madsen, K.H., Margulies, D.S., Mayberg, H.S., McMahon, K., Monk, C.S.,

590

Mostofsky, S.H., Nagel, B.J., Pekar, J.J., Peltier, S.J., Petersen, S.E., Riedl, V., Rombouts, S.A.,

591

Rypma, B., Schlaggar, B.L., Schmidt, S., Seidler, R.D., Siegle, G.J., Sorg, C., Teng, G.J., Veijola, J.,

592

Villringer, A., Walter, M., Wang, L., Weng, X.C., Whitfield-Gabrieli, S., Williamson, P.,

593

Windischberger, C., Zang, Y.F., Zhang, H.Y., Castellanos, F.X., Milham, M.P., 2010. Toward

594

discovery science of human brain function. Proc. Natl. Acad. Sci. U S A. 107, 4734-4739.

27

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

595

Braskie, M.N., Jahanshad, N., Stein, J.L., Barysheva, M., McMahon, K.L., de Zubicaray, G.I.,

596

Martin, N.G., Wright, M.J., Ringman, J.M., Toga, A.W., Thompson, P.M., 2011. Common

597

Alzheimer's disease risk variant within the CLU gene affects white matter microstructure in young

598

adults. J. Neurosci. 31, 6764-6770.

599

Broyd, S.J., Demanuele, C., Debener, S., Helps, S.K., James, C.J., Sonuga-Barke, E.J., 2009.

600

efault-mode brain dysfunction in mental disorders: a systematic review. Neurosci. Biobehav. Rev.

601

33, 79-296.

602

Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L., 2008. The brain's default network: anatomy,

603

function, and relevance to disease. Ann N Y Acad Sci. 1124: 1-38.

604

Cabeza, R., Nyberg, L., 2000. Imaging cognition II: An empirical review of 275 PET and fMRI

605

studies. J Cogn Neurosci. 12, 1-47.

606

Cabeza, R.,2002. Hemispheric asymmetry reduction in older adults: the HAROLD model. Psychol

607

Aging. 17, 85-100.

608

Cao, B., Li, F., Li, H., 2010. Notation-dependent processing of numerical magnitude:

609

electrophysiological evidence from Chinese numerals. Biol Psychol. 83, 47-55.

610

Carrasquillo, M.M., Belbin, O., Hunter, T.A., Ma, L., Bisceglio, G.D., Zou, F., Crook, J.E.,

611

Pankratz, V.S., Dickson, D.W., Graff-Radford, N.R., Petersen, R.C., Morgan, K., Younkin, S.G.,

612

2010. Replication of CLU, CR1, and PICALM associations with alzheimer disease. Arch. Neurol.

613

67, 961-964.

614

Constable, R.T., Scheinost, D., Finn, E.S., Shen, X., Hampson, M., Winstanley, F.S., Spencer,

615

D.D., Papademetris, X., 2013. Potential use and challenges of functional connectivity mapping in

616

intractable epilepsy. Front Neurol. 4, 39.

28

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

617

Di, X., Biswal, B.B., 2014. Modulatory interactions between the default mode network and task

618

positive networks in resting-state. Peer J. 2, e367.

619

Domhoff, G.W., Fox, K.C., 2015. Dreaming and the default network: A review, synthesis, and

620

counterintuitive research proposal. Conscious. Cogn. 33, 342-53.

621

Eikelenboom, P., Veerhuis, R., van Exel, E., Hoozemans, J.J., Rozemuller, A.J., van Gool, W.A.,

622

2011. The early involvement of the innate immunity in the pathogenesis of late-onset Alzheimer's

623

disease: neuropathological, epidemiological and genetic evidence. Curr. Alzheimer Res. 8,

624

142-150.

625

Elton, A., Gao, W., 2015. Task-positive functional connectivity of the default mode network

626

transcends task domain. J. Cogn. Neurosci. 27, 2369-2381.

627

Essabbani, A., Margottin-Goguet, F., Chiocchia, G., 2010. Identification of clusterin domain

628

involved in NF-kappaB pathway regulation. J Biol Chem. 285, 4273-4277.

629

Fair, D.A., Cohen, A.L., Dosenbach, N.U., Church, J.A., Miezin, F.M., Barch, D.M., Raichle,

630

M.E., Petersen, S.E., Schlaggar, B.L., 2008. The maturing architecture of the brain's default

631

network. Proc. Natl. Acad. Sci. U S A. 105, 4028-4032.

632

Falgarone, G., Chiocchia, G., 2009. Chapter 8: Clusterin: A multifacet protein at the crossroad of

633

inflammation and autoimmunity. Adv. Cancer Res.104, 139-170.

634

Fox, M.D., Snyder, A.Z., Vincent, J.L., Corbetta, M., Van Essen, D.C., Raichle, M.E., 2005. The

635

human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl.

636

Acad. Sci. U S A. 102, 9673-9678.

637

Golland, Y., Golland, P., Bentin, S., Malach, R., 2008. Data-driven clustering reveals a

638

fundamental subdivision of the human cortex into two global systems. Neuropsychologia. 46,

29

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

639

540-553.

640

Green, A.E., Gray, J.R., Deyoung, C.G., Mhyre, T.R., Padilla, R., Dibattista, A.M., William, R.G.,

641

2014. A combined effect of two Alzheimer's risk genes on medial temporal activity during

642

executive attention in young adults. Neuropsychologia. 56, 1-8.

643

Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V., 2004. Default-mode network activity

644

distinguishes Alzheimer's disease from healthy aging: evidence from functional MRI. Proc. Natl.

645

Acad. Sci. U S A. 101, 4637-4642.

646

Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C.J., Wedeen, V.J., Sporns, O., 2008.

647

Mapping the structural core of human cerebral cortex. PLoS Biol. 6(7), e159.

648

Hanes, D.A., McCollum, G., 2006. Cognitive-vestibular interactions: a review of patient

649

difficulties and possible mechanisms. J Vestib Res. 16, 75-91.

650

Harold, D., Abraham, R., Hollingworth, P., Sims, R., Gerrish, A., Hamshere, M.L., Pahwa, J.S.,

651

Moskvina, V., Dowzell, K., Williams, A., Jones, N., Thomas, C., Stretton, A., Morgan, A.R.,

652

Lovestone, S., Powell, J., Proitsi, P., Lupton, M.K., Brayne, C., Rubinsztein, D.C., Gill, M.,

653

Lawlor, B., Lynch, A., Morgan, K., Brown, K.S., Passmore, P.A., Craig, D., McGuinness, B., Todd,

654

S., Holmes, C., Mann, D., Smith, A.D., Love, S., Kehoe, P.G., Hardy, J., Mead, S., Fox, N., Rossor,

655

M., Collinge, J., Maier, W., Jessen, F., Schürmann, B., Heun, R., van den Bussche, H., Heuser, I.,

656

Kornhuber, J., Wiltfang, J., Dichgans, M., Frölich, L., Hampel, H., Hüll, M., Rujescu, D., Goate,

657

A.M., Kauwe, J.S., Cruchaga, C., Nowotny, P., Morris, J.C., Mayo, K., Sleegers, K., Bettens, K.,

658

Engelborghs, S., De Deyn, P.P., Van Broeckhoven, C., Livingston, G., Bass, N.J., Gurling, H.,

659

McQuillin, A., Gwilliam, R., Deloukas, P., Al-Chalabi, A., Shaw, C.E., Tsolaki, M., Singleton,

660

A.B., Guerreiro, R., Mühleisen, T.W., Nöthen, M.M., Moebus, S., Jöckel, K.H., Klopp, N.,

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

661

Wichmann, H.E., Carrasquillo, M.M., Pankratz, V.S., Younkin, S.G., Holmans, P.A., O'Donovan,

662

M., Owen, M.J., Williams, J., 2009. Genome-wide association study identifies variants at CLU

663

and PICALM associated with Alzheimer's disease. Nat. Genet. 41, 1088-1093.

664

He, Y., Wang, L., Zang, Y., Tian, L., Zhang, X., Li, K., Jiang, T., 2007. Regional coherence

665

changes in the early stages of Alzheimer's disease: a combined structural and resting-state

666

functional MRI study. Neuroimage 35, 488-500.

667

Hong, S., Dissing-Olesen, L., Stevens, B., 2015. New insights on the role of microglia in synaptic

668

pruning in health and disease. Curr Opin Neurobiol. 36, 128-134.

669

Hui, K.K., Marina, O., Claunch, J.D., Nixon, E.E., Fang, J., Liu, J., Li, M., Napadow, V., Vangel,

670

M., Makris, N., Chan, S.T., Kwong, K.K., Rosen, B.R., 2009. Acupuncture mobilizes the brain's

671

default mode and its anti-correlated network in healthy subjects. Brain Res. 1287, 84-103.

672

Jacobs, H.I., Radua, J., Lückmann, H.C., Sack, A.T., 2013. Meta-analysis of functional network

673

alterations in Alzheimer's disease: toward a network biomarker. Neurosci. Biobehav. Rev. 37,

674

753-765.

675

Jongbloed, W., van Dijk, K.D., Mulder, S.D., van de Berg, W.D., Blankenstein, M.A., van der Flier,

676

W., Veerhuis, R., 2015. Clusterin Levels in Plasma Predict Cognitive Decline and Progression to

677

Alzheimer's Disease. J Alzheimers Dis. 46, 1103-1110.

678

Kelly, A.M., Uddin, L.Q., Biswal, B.B., Castellanos, F.X., Milham, M.P., 2008. Competition

679

between functional brain networks mediates behavioral variability. Neuroimage 39, 527-537.

680

Kiely, K.M., Butterworth, P., Watson, N., Wooden, M., 2014. The Symbol Digit Modalities Test:

681

Normative data from a large nationally representative sample of Australians. Arch Clin

682

Neuropsychol. 29, 767-775.

31

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

683

Kim, H., Daselaar, S.M., Cabeza, R., 2010. Overlapping brain activity between episodic memory

684

encoding and retrieval: roles of the task-positive and task-negative networks. Neuroimage. 49,

685

1045-1054.

686

Klingner, C.M., Volk, G.F., Brodoehl, S., Witte, O.W., Guntinas-Lichius, O., 2014. Disrupted

687

functional connectivity of the default mode network due to acute vestibular deficit. Neuroimage

688

Clin. 6,109-114.

689

Koch, W., Teipel, S., Mueller, S., Benninghoff, J., Wagner, M., Bokde, A.L., Hampel, H., Coates,

690

U., Reiser, M., Meindl, T., 2012. Diagnostic power of default mode network resting state fMRI in

691

the detection of Alzheimer's disease. Neurobiol Aging. 33, 466-478.

692

Meng, Y., Li, H., Hua, R., Wang, H., Lu, J., Yu, X., Zhang, C., 2015. A correlativity study of

693

plasma APL1β28 and clusterin levels with MMSE/MoCA/CASI in aMCI patients. Sci Rep. 5,

694

15546.

695

Mengel-From, J., Christensen, K., McGue, M., Christiansen, L., 2011. Genetic variations in the

696

CLU and PICALM genes are associated with cognitive function in the oldest old. Neurobiol Aging.

697

32, 554.e7-11.

698

Moretti, D.V., 2015. Association of EEG, MRI, and regional blood flow biomarkers is predictive

699

of prodromal Alzheimer's disease. Neuropsychiatr Dis Treat. 11, 2779-2791.

700

Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Avedissian, C., Madsen, S.K., Parikshak, N.,

701

Toga, A.W., Jack, C.R.Jr., Schuff, N., Weiner, M.W., Thompson, P.M., Alzheimer's Disease

702

Neuroimaging Initiative., 2009. Automated mapping of hippocampal atrophy in 1-year repeat MRI

703

data from 490 subjects with Alzheimer's disease, mild cognitive impairment, and elderly controls.

704

Neuroimage 45, S3-15.

32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

705

Mullan, G.M., McEneny, J., Fuchs, M., McMaster, C., Todd, S., McGuinness, B., Henry, M.,

706

Passmore, A.P., Young, I.S., Johnston, J.A., 2013. Plasma clusterin levels and the rs11136000

707

genotype in individuals with mild cognitive impairment and Alzheimer's disease. Curr Alzheimer

708

Res. 10, 973-978.

709

Naghavi, H.R., Nyberg, L., 2005. Common fronto-parietal activity in attention, memory, and

710

consciousness: shared demands on integration? Conscious Cogn. 14, 390-425.

711

Nuutinen, T., Suuronen, T., Kauppinen, A., Salminen, A., 2009. Clusterin: a forgotten player in

712

Alzheimer's disease. Brain Res Rev. 61, 89-104.

713

Lambert, J.C., Heath, S., Even, G., Campion, D., Sleegers, K., Hiltunen, M., Combarros, O.,

714

Zelenika, D., Bullido, M.J., Tavernier, B., Letenneur, L., Bettens, K., Berr, C., Pasquier, F., Fiévet,

715

N., Barberger-Gateau, P., Engelborghs, S., De Deyn, P., Mateo, I., Franck, A., Helisalmi, S.,

716

Porcellini, E., Hanon, O., European Alzheimer's Disease Initiative Investigators., de Pancorbo,

717

M.M., Lendon, C., Dufouil, C., Jaillard, C., Leveillard, T., Alvarez, V., Bosco, P., Mancuso, M,

718

Panza, F., Nacmias, B., Bossù, P., Piccardi, P., Annoni, G., Seripa, D., Galimberti, D., Hannequin,

719

D., Licastro, F., Soininen, H., Ritchie, K., Blanché, H., Dartigues, J.F., Tzourio, C., Gut, I., Van

720

Broeckhoven, C., Alpérovitch, A., Lathrop, M., Amouyel, P., 2009. Genome-wide association

721

study identifies variants at CLU and CR1 associated with Alzheimer's disease. Nat. Genet. 41,

722

1094-1099.

723

Lancaster, T.M., Baird, A., Wolf, C., Jackson, M.C., Johnston, S.J., Donev, R., Thome, J., Linden,

724

D.E., 2011. Neural hyperactivation in carriers of the Alzheimer's risk variant on the clusterin gene.

725

Eur. Neuropsychopharmacol. 21, 880-884.

726

Lancaster, T.M., Brindley, L.M., Tansey, K.E., Sims, R.C., Mantripragada, K., Owen, M.J.,

33

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

727

Williams, J., Linden, D.E., 2015. Alzheimer's disease risk variant in CLU is associated with neural

728

inefficiency in healthy individuals. Alzheimers Dement. 11, 1144-1152.

729

Ling, I.F., Bhongsatiern, J., Simpson, J.F., Fardo, D.W., Estus, S., 2012. Genetics of clusterin

730

isoform expression and Alzheimer’s disease risk. PLoS One. 7, e33923.

731

Liang, P., Wang, Z., Yang, Y., Li, K., 2012. Three subsystems of the inferior parietal cortex are

732

differently affected in mild cognitive impairment. J Alzheimers Dis. 30, 475-487.

733

Liu, B., Song, M., Li, J., Liu, Y., Li, K., Yu, C., Jiang, T., 2010. Prefrontal-related functional

734

connectivities within the default network are modulated by COMT val158met in healthy young

735

adults. J. Neurosci. 30, 64-69.

736

Oakes, T.R., Fox, A.S., Johnstone, T., Chung, M.K., Kalin, N., Davidson, R.J., 2007. Integrating

737

VBM into the General Linear Model with voxelwise anatomical covariates. Neuroimage 34,

738

500-508.

739

Papma, J.M., den Heijer, T., de Koning, I., Mattace-Raso, F.U., van der Lugt, A., van der Lijn, F.,

740

van Swieten, J.C., Koudstaal, P.J., Smits, M., Prins, N.D., 2012. The influence of cerebral small

741

vessel disease on default mode network deactivation in mild cognitive impairment. Neuroimage

742

Clin. 2, 33-42.

743

Periáñez, J.A., Ríos-Lago, M., Rodríguez-Sánchez, J.M., Adrover-Roig, D., Sánchez-Cubillo, I.,

744

Crespo-Facorro, B., Quemada, J.I., Barceló, F., 2007. Trail Making Test in traumatic brain injury,

745

schizophrenia, and normal ageing: sample comparisons and normative data. Arch Clin

746

Neuropsychol. 22, 433-447.

747

Petersen, R.C., Smith, G.E., Waring, S.C., Ivnik, R.J., Tangalos, E.G., Kokmen, E., 1999. Mild

748

cognitive impairment: clinical characterization and outcome. Arch Neurol. 56, 303-308.

34

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

749

Petersen, R.C., Negash, S., 2008. Mild cognitive impairment: an overview. CNS Spectr. 13, 45-53.

750

Raichle, M.E., 2011. The restless brain. Brain Connect. 1, 3-12.

751

Raichle, M.E., 2015. The brain's default mode network. Annu Rev Neurosci.38, 433-447.

752

Reuter-Lorenz, P.A., Park, D.C., 2014. How does it STAC up? Revisiting the scaffolding theory of

753

aging and cognition. Neuropsychol Rev. 2014 Sep; 24, 355-370.

754

Richiardi, J., Altmann, A., Milazzo, A.C., Chang, C., Chakravarty, M.M., Banaschewski, T.,

755

Barker, G.J., Bokde, A.L., Bromberg, U., Büchel, C., Conrod, P., Fauth-Bühler, M., Flor, H.,

756

Frouin, V., Gallinat, J., Garavan, H., Gowland, P., Heinz, A., Lemaître, H., Mann, K.F., Martinot,

757

J.L., Nees, F., Paus, T., Pausova, Z., Rietschel, M., Robbins, T.W., Smolka, M.N., Spanagel, R.,

758

Ströhle, A., Schumann, G., Hawrylycz, M., Poline, J.B., Greicius, M.D., IMAGEN consortium,

759

BRAIN NETWORKS. Correlated gene expression supports synchronous activity in brain

760

networks. 2015. Science. 348, 1241-1244.

761

Roussotte, F.F., Gutman, B.A., Madsen, S.K., Colby, J.B., Thompson, P.M., Alzheimer's Disease

762

Neuroimaging Initiative., 2014. Combined effects of Alzheimer risk variants in the CLU and

763

ApoE genes on ventricular expansion patterns in the elderly. J. Neurosci. 34, 6537-6545.

764

Saura, C.A., Parra-Damas, A., Enriquez-Barreto, L., 2015. Gene expression parallels synaptic

765

excitability and plasticity changes in Alzheimer's disease. Front Cell Neurosci. 9, 318.

766

Sheline, Y.I., Raichle, M.E., 2013. Resting state functional connectivity in preclinical Alzheimer's

767

disease. Biol Psychiatry. 74, 340-347.

768

Silajdžić, E., Minthon, L., Björkqvist, M., Hansson, O. 2012. No diagnostic value of plasma

769

clusterin in Alzheimer's disease. PLoS One. 7, e50237.

770

Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N., Watkins,

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

771

K.E., Toro, R., Laird, A.R., Beckmann, C.F., 2009. Correspondence of the brain's functional

772

architecture during activation and rest. Proc. Natl. Acad. Sci. U S A. 106, 13040-13045.

773

Soares, J.M., Sampaio, A., Marques, P., Ferreira, L,M,, Santosm, N.C., Marques, F., Palha, J.A.,

774

Cerqueira, J.J., Sousa, N., 2013. Plasticity of resting state brain networks in recovery from stress.

775

Front. Hum. Neurosci. 7, 919.

776

Sorg, C., Riedl, V., Mühlau, M., Calhoun, V.D., Eichele, T., Läer, L., Drzezga, A., Förstl, H., Kurz,

777

A., Zimmer, C., Wohlschläger, A.M., 2007. Selective changes of resting-state networks in

778

individuals at risk for Alzheimer's disease. Proc. Natl. Acad. Sci. U S A 104, 18760-18765.

779

Stevens, B.W., DiBattista, A.M., William Rebeck, G., Green, A.E., 2014. A gene-brain-cognition

780

pathway for the effect of an Alzheimer‫׳‬s risk gene on working memory in young adults.

781

Neuropsychologia. 61,143-149.

782

Thambisetty, M., An, Y., Kinsey, A., Koka, D., Saleem, M., Güntert, A., Kraut, M., Ferrucci, L.,

783

Davatzikos, C., Lovestone, S., Resnick, S.M., 2012. Plasma clusterin concentration is associated

784

with longitudinal brain atrophy in mild cognitive impairment. Neuroimage. 59(1), 212-217.

785

Thambisetty, M., Beason-Held, L.L., An, Y., Kraut, M., Nalls, M., Hernandez, D.G., Singleton,

786

A.B., Zonderman, A.B., Ferrucci, L., Lovestone, S., Resnick, S.M., 2013. Alzheimer risk variant

787

CLU and brain function during aging. Biol. Psychiatry. 73, 399-405.

788

Tombaugh, T.N., 2004. Trail Making Test A and B: normative data stratified by age and education.

789

Arch Clin Neuropsychol. 19, 203-214.

790

Uddin, L.Q., Kelly, A.M., Biswal, B.B., Castellanos, F.X., Milham, M.P., 2009. Functional

791

connectivity of default mode network components: correlation, anticorrelation, and causality. Hum

792

Brain Mapp. 30, 625-637.

36

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

793

Vaisvaser, S., Lin, T., Admon, R., Podlipsky, I., Greenman, Y., Stern, N., Fruchter, E., Wald, I.,

794

Pine, D.S., Tarrasch, R., Bar-Haim, Y., Hendler, T., 2013. Neural traces of stress: cortisol related

795

sustained enhancement of amygdala-hippocampal functional connectivity. Front Hum Neurosci.

796

7,313.

797

van den Heuvel, M.P., Sporns, O., 2013. Network hubs in the human brain. Trends Cogn Sci. 17,

798

683-696.

799

Vemuri, P., Simon, G., Kantarci, K., Whitwell, J.L., Senjem, M.L., Przybelski, S.A., Gunter, J.L.,

800

Josephs, K.A., Knopman, D.S., Boeve, B.F., Ferman, T.J., Dickson, D.W., Parisi, J.E., Petersen,

801

R.C., Jack, C.R.Jr., 2011. Antemortem differential diagnosis of dementia pathology using

802

structural MRI: Differential-STAND. Neuroimage. 55, 522-531.

803

Villegas-Llerena, C., Phillips, A., Garcia-Reitboeck, P., Hardy, J., Pocock, J.M., 2016. Microglial

804

genes regulating neuroinflammation in the progression of Alzheimer's disease. Curr Opin

805

Neurobiol. 36,74-81.

806

Wang, Z., Lei, H., Zheng, M., Li, Y., Cui, Y., Hao, F., 2015. Meta-analysis of the Association

807

between Alzheimer Disease and Variants in GAB2, PICALM, and SORL1. Mol Neurobiol. [Epub

808

ahead of print].

809

Winblad, B., Palmer, K., Kivipelto, M., Jelic, V., Fratiglioni, L., Wahlund, L.O., Nordberg, A.,

810

Bäckman, L., Albert, M., Almkvist, O., Arai, H., Basun, H., Blennow, K., de Leon, M., DeCarli, C.,

811

Erkinjuntti, T., Giacobini, E., Graff, C., Hardy, J., Jack, C., Jorm, A., Ritchie, K., van Duijn, C.,

812

Visser, P., Petersen, R.C., 2004. Mild cognitive impairment--beyond controversies, towards a

813

consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med.

814

256, 240-246.

37

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

815

Wu, Z.C., Yu, J.T., Li, Y., Tan, L., 2012. Clusterin in Alzheimer's disease. Adv Clin Chem. 56,

816

155-173.

817

Xia, M., Wang, J., He, Y., 2013. BrainNet Viewer: a network visualization tool for human brain

818

connectomics. PLoS One 8, e68910.

819

Zhou, Y., Wang, J., Wang, K., Li, S., Song, X., Ye, Y., Wang, L., Ying, B., 2010. Association

820

analysis between the rs11136000 single nucleotide polymorphism in clusterin gene, rs3851179

821

single nucleotide polymorphism in clathrin assembly lymphoid myeloid protein gene and the

822

patients with schizophrenia in the Chinese population. DNA Cell Biol. 29, 745-751.

823

Zwain, I.H., Grima, J., Cheng, C.Y., 1994. Regulation of clusterin secretion and mRNA expression

824

in astrocytes by cytokines. Mol Cell Neurosci. 5, 229-237.

825 826 827 828 829 830 831 832 833 834 835 836

38

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

837

Figure legends

838

Figure 1. DMN were reconstructed based on regions of interest. A qualitative visual

839

inspection of the four sample groups showed similar patterns of the unidirectional

840

weighted networks. The correlation coefficient threshold was set to r > 0.3. The

841

networks were visualized with the BrainNet Viewer (Xia et al., 2013,

842

http://www.nitrc.org/projects/bnv/).

843 844

Figure 2. Association between disease status and the CLU genotype. Group X

845

Genotype interaction of L.IT/R.IT networks predominated in the cortical midline

846

regions (i.e., bilateral anterior cingulate and bilateral medial frontal gyrus), whereas

847

the parietal cortex (i.e., left inferior parietal lobule, left superior parietal lobule and

848

left postcentral gyrus) and subcortical structures (i.e., bilateral caudate) were mainly

849

associated with Group X Genotype interaction of the L.LatP network.

850 851

Figure 3. The communications between Group X Genotype interaction and PCC (an

852

important node of the DMN) were significantly enhanced in aMCI with the CLU

853

rs11136000 CC genotype. (1) L.IT network (CC genotype: r = 0.500, p = 7.88×10-10

854

vs. TT/CT genotype: r = 0.105, p = 0.229); (2) R.IT network (CC genotype: r = 0.688,

855

p = 4.21×10-20 vs. TT/CT genotype: r = 0.363, p = 1.62×10-5); (3) L.LatP network

856

(CC genotype: r = 0.771, p = 1.22×10-27 vs. TT/CT genotype: r = 0.603, p =

857

1.29×10-14). Interestingly, the significant strength of reconstruction of the L.IT

858

network was highlighted in aMCI with the CLU rs11136000 CC genotype (i.e., from

859

no statistical significance to statistical significance).

39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

860 861

Figure 4. Compared to control subjects with the CLU rs11136000 TT/CT genotype, a

862

tendency toward down-regulation was revealed in controls with the CLU rs11136000

863

CC genotype. (1) L.IT network (CC genotype: r = -0.003, p = 0.971 vs. TT/CT

864

genotype: r = 0.057, p = 0.517); (2) R.IT network (CC genotype: r = 0.250, p = 0.004

865

vs. TT/CT genotype: r = 0.573, p = 4.518×10-13); (3) L.LatP network (CC genotype: r

866

= 0.689, p = 3.332×10-20 vs. TT/CT genotype: r = 0.855, p = 1.628×10-39).

867 868

Figure 5. (a) In aMCI with the CLU rs11136000 CC genotype, the time series of

869

regions in Group X Genotype interaction of the L.IT network was negatively

870

correlated with AVMT scores (r = -0.671, p = 0.012) and RCFT scores (r = -0.619, p =

871

0.024). (b) In aMCI with the CLU rs11136000 TT/CT genotype, brain networks and

872

behavioural significance of the non-memory tests were observed in the R.IT network

873

(i.e., MMSE r = -0.580, p = 0.038; TMT-B r = 0.565, p = 0.044; SDMT r = -0.584, p =

874

0.036; DST r = -0.651, p = 0.016). It should be noted that the z scores for the

875

neuropsychological data for each subject were converted from the raw scores with

876

reference to the means and standard deviations of all of the subjects.

877

40

Table 1 Demographic and neuropsychological data Items

aMCI subjects (n=26) CLU rs11136000

CLU rs11136000

TT/CT genotype

p

controls (n=24)

p

p

CLU rs11136000

CLU rs11136000

CC genotype

TT/CT genotype

CC genotype

(n=13)

(n=13)

(n=11)

(n=13)

Age (years)

72.15±4.90

71.38±4.61

0.724

74.45±3.86

72.08±3.04

0.134

0.526

Education (years)

14.46±3.07

13.69±3.09

0.650

15.82±1.54

13.88±3.59

0.459

0.274

Gender (femal/male)

4/9

4/9

1

5/6

5/8

0.776

0.427

Clinical Dementia Rating

0.5

0.5

-

0

0

-

-

26.92±1.50

27.46±1.94

0.614

27.82±1.25

28.31±1.32

0.331

0.054

3.31±2.50

2.77±1.24

0.579

7.91±2.12

8.23±1.64

0.776

0.000*

11.38±7.72

11.31±8.24

0.840

16.18±7.09

17.50±7.25

0.776

0.023*

84.31±31.88

98.15±36.16

0.336

83.18±37.80

66.77±22.19

0.392

0.065

167.00±91.97

180.31±66.95

0.362

140.64±54.85

139.69±39.29

0.608

0.143

Symbol digit modalities test

29.54±11.98

27.08±8.37

0.579

32.55±10.40

33.38±11.68

0.531

0.145

Clock drawing test

8.69±1.03

8.00±1.83

0.511

8.73±1.49

9.00±0.82

0.975

0.164

Digit span test

12.23±2.59

11.85±1.63

0.418

12.18±1.94

13.08±2.33

0.207

0.216

(CDR) Mini Mental State Exam (MMSE) Auditory verbal memory test (AVMT)- delayed recall Rey-Osterrieth complex figure test (RCFT)-delayed recall Trail

making

test-A

(seconds) Trail making test-B (seconds)

Values are mean ± (SD); Notes: p value was obtained by Mann-Whitney U-test, * indicates had statistical difference between groups, p < 0.05.

Table 2 Descriptions of brain regions for Group X Genotype ANOVA Brain region

BA

Peak MNI Coordiates x, y, z (mm)

Peak F value

cluster size

Functional connectivity of aMPFC: ROI Center (mm) = (-3, 54, 18); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction None Functional connectivity of L.Sup.F: ROI Center (mm) = (-15, 54, 42); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction None Functional connectivity of R.Sup.F: ROI Center (mm) = (18, 42, 48); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction None Functional connectivity of vMPFC: ROI Center (mm) = (-6, 36, -9); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction None Functional connectivity of L.IT: ROI Center (mm) = (-60, -9, -24); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction Bilateral Anterior Cingulate/ 10 -3 51 3 20.95 Medial Frontal Gyrus Left Superior Frontal Gyrus 10 -9 66 -3 17.15 Functional connectivity of R.IT: ROI Center (mm) = (57, 0, -27); Radius = 6.00 mm Main effect of group None Main effect of genotype Right Cerebellum Posterior Lobe 18 -60 -48 18.40 Group X Genotype interaction Bilateral Anterior Cingulate 10 -3 48 9 12.70

2889 2052

2619 1701

Functional connectivity of L.PHC: ROI Center (mm) = (-24, -18, -24); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction None Functional connectivity of R.PHC: ROI Center (mm) = (27, -18, -24); Radius = 6.00 mm Main effect of group Bilateral Cerebellum Posterior 0 -57 -54 13.67 Lobe Main effect of genotype None Group X Genotype interaction None Functional connectivity of PCC: ROI Center (mm) = (-3, -48, 30); Radius = 6.00 mm Main effect of group Bilateral Cerebellum Posterior 9 -54 -48 Lobe Main effect of genotype None Group X Genotype interaction None Functional connectivity of Rsp: ROI Center (mm) = (9, -54, 12); Radius = 6.00 mm Main effect of group Bilateral Cerebellum Posterior 9 -54 -60 Lobe Main effect of genotype None Group X Genotype interaction None

18.90

3888

25.14

3996

Functional connectivity of L.LatP: ROI Center (mm) = (-48, -69, 39); Radius = 6.00 mm Main effect of group None Main effect of genotype None Group X Genotype interaction Left Inferior Parietal 7/40 -48 -54 42 17.04 Lobule/Superior Parietal Lobule Left Postcentral Gyrus 6 -57 -15 45 13.28 Left Anterior Cingulate 24 -6 36 15 16.10 Right Caudate 12 15 -3 19.51 Left Caudate -9 9 0 14.43 Functional connectivity of R. LatP: ROI Center (mm) = (48, -66, 36); Radius = 6.00 mm Main effect of group None Main effect of genotype

2187

18252 5562 2322 4644 4320

None Group X Genotype interaction None Functional connectivity of Cereb: ROI Center (mm) = (-6, -54, -48); Radius = 6.00 mm Main effect of group Left Supramarginal Gyrus/Angular/ 7/31/39 -39 -54 27 19.70 32265 Inferior Parietal Lobule/Posterior Cingulate Right Hippocampus 27 -21 -15 14.69 1755 Main effect of genotype Left Superior Temporal Gyrus 38 -48 12 -27 18.61 7128 Right Superior Temporal Gyrus 38 48 12 -18 17.19 9612 Group X Genotype interaction None Note: A corrected threshold by Monte Carlo simulation at P < 0.05. Abbreviations: BA= Brodmann’s area; Cluster size is in mm3; MNI: Montreal Neurological Institute; aMPFC: Medial prefrontal cortex (anterior); L.Sup.F: Left superior frontal cortex; R.Sup.F: Right superior frontal cortex; vMPFC: Medial prefrontal cortex (ventral); L.IT: Left inferior temporal cortex; R.IT: Right inferior temporal cortex; L.PHC: Left parahippocampal gyrus; R.PHC: Right parahippocamal gyrus; PCC: Posterior cingulate cortex; Rsp: Retrosplenial; L.LatP: Left lateral parietal cortex; R. LatP: Right lateral parietal cortex; Cereb: Cerebellar tonsils.