Functional Connectivity of Default Mode Network Subsystems in the Presymptomatic Stage of Autosomal Dominant Alzheimer’s Disease

Abstract

Background:

The default mode network (DMN) could be divided into subsystems, the functional connectivity of which are different across the Alzheimer’s disease (AD) spectrum. However, the functional connectivity patterns within the subsystems are unknown in presymptomatic autosomal dominant AD (ADAD).

Objective:

To investigate functional connectivity patterns within the subsystems of the DMN in presymptomatic subjects carrying PSEN1, PSEN2, or APP gene mutations.

Methods:

Twenty-six presymptomatic mutation carriers (PMC) and twenty-nine cognitively normal non-carriers as normal controls (NC) from the same families underwent resting state functional MRI and structural MRI. Seed-based analyses were done to obtain functional connectivity of posterior and anterior DMN. For the regions that showed significant connectivity difference between PMC and NC, volumes were extracted and compared between the two groups. Connectivity measures were then correlated with cognitive tests scores.

Results:

The posterior DMN showed connectivity decrease in the PMC group as compared with the NC group, which was primarily the connectivity of left precuneus with right precuneus and superior frontal gyrus; the anterior DMN showed significant connectivity decrease in the PMC group, which was the connectivity of medial frontal gyrus with middle frontal gyrus. In the brain regions showing connectivity changes in the PMC group, there was no group difference in volume. A positive correlation was observed between the precuneus connectivity value and Mini-Mental State Examination total score.

Conclusion:

Functional connectivity within both posterior and anterior DMN were disrupted in the presymptomatic stage of ADAD. Connectivity disruption within the posterior DMN may be useful for early identification of general cognitive decline and a potential imaging biomarker for early diagnosis.

Keywords

Autosomal dominant Alzheimer’s disease default mode network functional connectivity structural imaging

INTRODUCTION

Alzheimer’s disease (AD) is a continuous neurodegenerative disorder with a progressive loss of cognitive function. The presence of a sequence of pathophysiological abnormalities precedes overt clinical symptoms by decades [1, 2]. A proportion of AD (less than 1%) occurs in individuals with autosomal dominant gene mutations, which is called autosomal dominant Alzheimer’s disease (ADAD) [3]. The three known pathogenic gene mutations are presenilin 1 (PSEN1) [4], amyloid precursor protein (APP) [5], and presenilin 2 (PSEN2) [6]. Individuals with proven ADAD mutation genes who are absent of any clinical phenotype of AD are defined as the presymptomatic stage of AD according to the International Working Group (IWG)-2 diagnostic criteria [7]. The mutation carriers have nearly full penetrance, hence study of these mutation carriers in the presymptomatic stage can provide a unique opportunity to characterize the preclinical changes of AD [8].

Imaging studies based on blood oxygenation-level dependent functional magnetic resonance imaging (fMRI) showed that the functional connectivity of brain networks was changed in sporadic AD patients. The default mode network (DMN) was the first system shown to be disrupted in AD [9]. It is a brain system containing a number of anatomically connected or disconnected and functionally interacting brain areas, such as posterior cingulate, precuneus, and medial prefrontal lobe [10, 11]. The DMN was involved in the situation when an individual was recalling the past memories, planning for the future, or monitoring the environment [11]. A set of studies were published for the research of DMN in participants with preclinical, mild cognitive impairment, or AD dementia, which indicated that DMN was gradually disrupted along the disease spectrum [12 –15]. Further studies showed that DMN could be divided into subsystems [11, 16], and patterns of functional connectivity between brain regions within these subsystems were different in AD compared with controls. The posterior DMN (pDMN) showed consistently decreased connectivity especially in the precuneus, and in other regions such as posterior cingulate cortex and inferior parietal lobule [14 , 18]. On the other hand, the anterior DMN (aDMN) displayed various patterns, where both increased and decreased functional connectivity were found [14, 17]. The longitudinal study showed that functional connectivity within the aDMN significantly increased at the early disease stage, yet decreased as the disease progressed, which indicated that the different patterns or the extent to which the aDMN subsystem was affected in AD might be dependent on the disease stage [14].

DMN was also disrupted in ADAD. Individuals with known pathogenic gene mutations showed declined functional connectivity of brain areas within the DMN in the symptomatic stage of ADAD. The connectivity decreased in frontal lobe and primarily in the precuneus/posterior cingulate and parietal cortices, which was accompanied by increased Clinical Dementia Rating (CDR) scores [19, 20]. Disruption of DMN was not consistently present in presymptomatic mutation carriers (PMC). While some study showed no change of DMN connectivity [21], some studies showed a decrease in some areas and an increase in some other areas [19 , 23]. Specifically, PMC displayed lower intrinsic connectivity in precuneus or posterior cingulate cortex, and lower or higher connectivity in part of the frontal cortices [20, 23].

Up to now, there is no study looking at subsystems of the DMN in ADAD. Although the DMN has shown disruption and was regarded as a potential biomarker in AD, it is indicated mainly by studies focusing on it as one single network. In this study, we focused on the aDMN and pDMN to explore the functional connectivity within these subsystems in individuals with presymptomatic ADAD. We hypothesize that there would be alterations of the two subsystems in mutation carriers compared with controls. To further understand whether and to what extent functional connectivity changes can be explained by gray matter atrophy, we also assessed the volume of the regions in DMN subsystems showing functional connectivity changes.

MATERIALS AND METHODS

Subjects

PMC of PSEN1, APP, or PSEN2, and mutation non-carriers of cognitively normal controls (NC) from the same family were enrolled in this study. Neuropsychological assessment, clinical examination, and neuroimaging including structural and functional MRI were performed for every participant. Twenty-six mutation carriers, mean age = 33.65 years, SD (standard deviation) = 14.25, including 16 carrying PSEN1 mutation, 9 carrying APP mutation, and one carrying PSEN2 mutation, with a CDR global score of 0, formed the PMC group. Twenty-nine right-handed mutation negative cognitively normal subjects (mean age = 38.36 years, SD = 9.35) matched on age, sex, and years of education comprised the NC group. Each subject’s estimated years from expected symptom onset (EYO) was calculated as the age of subject at the time of the scan minus the mean age at which the subject’s family members first showed symptoms of progressive cognitive decline [24]. Several detailed neuropsychological tests were used to assess the cognitive function of all subjects, by experienced psychologists who were blind of mutation status, such as Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA) [25], and Trail Making Test A and B [26]. Each participant was assessed with the CDR [27], and the score of 0 corresponded to normal cognition. Individuals with neurological, neuropsychiatric disorders, or systematic diseases impacting cognition potentially were excluded. All the participants were from Chinese Familial Alzheimer’s Disease Network study (CFAN, Study ID Number: SYXWJ002; ClinicalTrials.gov Identifier: NCT03657732), and were provided the written informed consent according to the Declaration of Helsinki. This study obtained the ethical approval from the Medical Research Ethics Committee at Xuanwu Hospital.

MRI acquisition

Participants underwent fMRI and structural MRI in a 3T scanner (Siemens, Skyra, Germany) using a head-neck coil with 20-channel phased array. High-resolution structural T1-weighted images covering the whole brain were acquired through the magnetization-prepared rapid acquisition gradient echo sequence (repetition time/echo time = 5000 ms /2.98 ms, flip angle = 4°, 256×256 mm field of view, 1 mm isotropic voxels). fMRI scans were acquired in a gradient echo-planar imaging pulse sequence (43 slices, 144 dynamics, repetition time = 2500 ms, echo time = 30 ms, flip angle = 90°, 210×210 mm field of view, voxel size = 3.0×3.0×3.0 mm, interleaved acquisition). During the scan, subjects were requested to keep their eyes closed and try not to move.

MRI preprocessing

Imaging data were preprocessed using Statistical Parametric Mapping [28] (SPM12, Wellcome Trust Centre for Neuroimaging, http://www.fil.ion.ucl.ac.uk) and Data Processing & Analysis of Brain Imaging [29] (DPABI, Chinese Academy of Sciences, Beijing, China, http://rfmri.org).

For the fMRI data, several preprocessing steps were as follows. The first 10 volumes were removed to allow a stabilization image, then slice time correction was performed to make a compensation for slice-dependent time shifts. Next, motion correction and realignment were done, the results of which were used to exclude subjects with excessive movement. Co-registration and segmentation of T1 image were implemented using “new segment” algorithm including segment structural images into grey matter, white matter, and cerebrospinal fluid [30], and then normalization to a standard Montreal Neurological Institute (MNI) template by the Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra (DARTEL) tool [31]. The reorientation of functional and T1 images was done manually before segmentation and normalization, to be consistent with the standard MNI template and improve the accuracy [29]. Regression of linear trend and the nuisance signals including global signal, white matter, cerebrospinal fluid, and head motion results based on the Friston 24-parameter model [32]. Global signal was regressed out to reduce the global non-neuronal confounds which were largely attributed to the vascular effects [33]. Bandpass filter was set at 0.01–0.08 Hz. All images were spatial smoothed with a 6 mm full width at half maximum (FWHM) Gaussian kernel. Quality control was performed to every step of the preprocessing. Translation or rotation of head motion parameters of subjects were less than 2 mm or 2 degree in any direction. Grey matter and white matter boundary were better overlaid after the spatial normalization. Subjects with functional image voxels coverage less than 2*SD under the group mean mask (threshold: 89.81%), which covered at least 90% of voxels, were excluded in this study.

For the structural MRI data, preprocessing involved segmentation of the brain tissue into gray matter, white matter, and cerebrospinal fluid using the SPM segmentation procedure. Then registration and spatial normalization to standard MNI space was done using the DARTEL tool, which improved the normalization accuracy. Specifically, the parameter files produced by the segmentation would be used by the DARTEL [31] tool to write out rigidly transformed versions of the tissue class images, and create a mean of all the images as a template. This template was then applied to calculate the registration parameters of each individual image. The images were modulated and resampled to 1.5 isotropic voxels. Finally, an 8 mm FWHM kernel was used in the spatial smoothing procedure to make the data be more Gaussian and closer to a continuous random field.

Functional connectivity analysis

Seed-based functional connectivity analysis [34] was used to get individual-level intrinsic connectivity networks. The medial prefrontal cortex (MNI coordinates: x = 12, y = 51, z = 36) corresponding to aDMN, and the precuneus (MNI coordinates: x = –4, y = –68, z = 34) corresponding to pDMN were selected as seed regions of interest based on the independent components analysis results of the previous literature [14, 17]. Binary 6 mm radius spheres centered on these loci were created and used in the connectivity analysis. We extracted mean fMRI time series of the two spherical regions of interest separately, and calculated Pearson’s correlation coefficients of them with the whole brain voxels for each participant. Then the correlation coefficients r values were converted to z-scores using Fisher’s r-to-z transformation.

Structural imaging analysis

Structural data from each subject were analyzed by voxel-based morphometry (VBM) processing. To compare the gray matter volume difference in the subsystems of the DMN, masks of each region of interest were created by AAL templates. Total intracranial volume (ICV) equals to the sum of the volume of grey matter, white matter, and cerebrospinal fluid. The volumes of these three tissues and regions of interest were calculated by MATLAB script “get_totals” (http://www.cs.ucl.ac.uk/staff/g.ridgway/vbm/get_totals.m). Each region of interest was divided by ICV to obtain a normalized volume.

Statistical analysis

Demographics of age, years of education, EYO, and cognitive tests scores were compared between PMC and NC group using independent sample t-test. Categorical variables including gender and APOE ɛ4 frequency were compared using chi-square tests. These statistical analyses were performed in SPSS software version 22.0 (SPSS Inc., Chicago, IL, USA). For the functional imaging data. One-sample t-test (False discovery rate-corrected, p < 0.05) was performed on the two z-score maps for each group to identify the pattern of the intrinsic connectivity maps for aDMN and pDMN. Two-sample t-test was used to compare the group connectivity difference. Age, sex, years of education, and grey matter volume were included as covariates. Results were considered significant at p < 0.05 corrected by Gaussian Random Fields (GRF) family-wise error method. The z-scores of brain areas that showed significant group difference were extracted and correlated against EYO and neuropsychological tests scores using partial correlation analysis, correcting for age, sex, and years of education. Significant difference was set at a level of p < 0.05. Grey matter volume comparisons were performed between the two groups using two-sample t-test including age as a covariate.

RESULTS

Participant characteristics

Study demographics were displayed in Table 1. No significant differences in age, gender, years of education, EYO, cognitive tests scores, and frequency of the APOE ɛ4 allele were observed between the PMC and NC.

Table 1

Participant demographic and neuropsychological characteristics

	NC (n = 29)	PMC (n = 26)	p
Age (y)	38.86±9.34	33.65±4.43	0.111^a
Gender (female/male)	19/10	19/7	0.545^b
Years of education	12.27±3.09	11.23±3.135	0.150^a
EYO (years)	–18.12±10.88	–13.24±12.38	0.126^a
MMSE	29.48±0.82	28.65±2.15	0.075^a
MoCA	27.75±1.61	26.62±3.53	0.139^a
Trail making test A (s)	27.90±12.71	29.47±14.38	0.671^a
Trail making test B (s)	42.69±20.18	47.12±24.26	0.467^a
APOEɛ4 carriers	7/22	1/26	0.064^b

EYO, estimated years from expected symptom onset; MMSE, Mini-Mental State Examination; MoCA, Montreal cognitive Assessment; ^aIndependent two sample t test; ^bChi-square test.

Group functional connectivity differences of subsystems of DMN

The spatial correlation maps of aDMN and pDMN were displayed in Fig. 1. There were only decreased connections in the pDMN in the PMC group compared with NC in this study. The right precuneus and superior frontal gyrus showed significant decreased connectivity with the left precuneus (GRF corrected, voxel p < 0.01, cluster size >44 voxels). The PMC group showed decreased connections within aDMN compared with controls. Difference between groups was mainly located in the frontal lobe, with the peak loci on the middle frontal gyrus (GRF corrected, voxel p < 0.01, cluster size >47 voxels) (Fig. 2). No significant increased connectivity was observed within either network. The peak coordinates of significant brain areas were listed in Table 2. In addition, the z-values of right precuneus showed a significant positive correlation with MMSE scores in the PMC group (r = 0.506, p = 0.016), while no significant correlation in the NC group (r = –0.381, p = 0.055) (Fig. 3). No significant correlations were observed between the connectivity values of other brain areas and EYO or neuropsychological tests scores in either group.

Fig.1

The spatial correlation maps of anterior and posterior default mode network (DMN) in presymptomatic mutation carrier (PMC) group and matched normal control (NC) group. A) aDMN, anterior DMN. B) pDMN, posterior DMN. The correlation maps were calculated by one-sample t test for the two groups separately (false discovery rate–corrected p < 0.05). Montreal Neurological Institute (MNI) coordinates axial slices: z=–12, 18, 48.

Fig.2

Group differences of aDMN and pDMN functional connectivity. A) aDMN, anterior DMN. B) pDMN, posterior DMN. Regions displaying decreased connectivity in the PMC group are in blue (GRF corrected, cluster-level p < 0.05). L, left; R, right.

Table 2

Brain regions with significant decreased functional connectivity within aDMN and pDMN in PMC group

Brain Network	Brain regions	Cluster size (number of voxels)	Peak MNI coordinates (mm)
			x	y	z
pDMN	R precuneus	98	–09	–57	45
	R superior frontal gyrus	58	–21	–42	48
aDMN	L middle frontal gyrus	60	–36	–09	54

aDMN, anterior default mode network; pDMN, posterior default mode network; L, left cerebrum; R, right cerebrum.

Fig.3

Scatter plots of the correlation between precuneus functional connectivity z scores and MMSE total scores in the PMC and NC group. Although partial correlation was conducted, the best-linear-fit regression lines are displayed for the convenience of readers. ^*p < 0.05.

Volume of regions of interest within subsystems of DMN

The normalized volume of regions of interest showed no significant difference between PMC group and NC group (Table 3).

Table 3

Normalized volumes of regions of interest

Normalized volume	NC	PMC	p
L middle frontal gyrus	0.0096±0.0007	0.0098±0.0008	0.311
R precuneus	0.0075±0.0005	0.0076±0.0006	0.495
R superior frontal gyrus	0.0074±0.0005	0.0075±0.0006	0.468

DISCUSSION

To our knowledge, this is the first study to assess functional connectivity within subsystems of DMN in presymptomatic stage of ADAD. In the current study, the PMC individuals showed decreased connectivity of left precuneus with right precuneus and superior frontal gyrus within pDMN compared with controls. There was also decreased connection between medial frontal gyrus and the middle frontal gyrus within aDMN.

The regions within the pDMN that showed disruption were similar to the results of previous studies of DMN in sporadic AD or ADAD [14 , 17–19]. Specifically, precuneus was the brain area that consistently presented decreased connectivity in DMN. The disconnection of precuneus corresponded to the study of asymptomatic young mutation carriers, which showed less precuneus deactivation in the task fMRI [35]. The reduced connectivity between precuneus and frontal lobe was also found in symptomatic individuals with mutations [20]. Studies indicated that the pDMN regions were vulnerable to Aβ toxicity, and precuneus showed early Aβ deposition in ADAD [36], sporadic AD [37], and asymptomatic elderly [38, 39]. Elevated Aβ deposition was associated with decreased DMN functional connectivity, which might be linked to synaptic dysfunction [40, 41]. Although the molecular imaging was not available in this study, one study indicated that Aβ began to accumulate and plateaued at a mean age of 28.2 years and 37.6 years, respectively, in PSEN1 mutation carriers [42]. There were also studies suggesting functional connectivity disruption before evidence of amyloid deposition in individuals carrying mutation genes of ADAD [22]. Consequently, the disruption of the pDMN in presymptomatic stage of AD might be partly explained by pathological molecular events or genetic factors interactively or independently.

For the aDMN, the disconnection was mainly located within frontal lobe. The result of presymptomatic individuals in our study was opposite to that of another study in asymptomatic carriers [20], but similar to that in later stage of sporadic AD or ADAD [14 , 21]. Specifically, the presymptomatic carriers showed increased functional connectivity within the frontal lobe, but symptomatic individuals showed decrease [20]. Although the EYO in our study is similar with the above study, such discrepancy might be due to different sample size or mutation-specific effect. The individuals in our study carried mutations of PSEN1, PSEN2, or APP, while only PSEN1 mutation carriers were included in their study. The increased functional connectivity in their study might be a response to early amyloid accumulation, or reflect a functional compensation mechanism [38 , 44]. Another study of sporadic AD showed that the connectivity within aDMN increased in the early stage of AD dementia, but decreased in the later stage of disease [14]. In another study of sporadic AD, the cascading network failure models combining serial MRI, molecular imaging and other biological markers suggested that the network failure began in the pDMN and cascaded through the brain via increased connectivity in aDMN, then declined as the disease progressed finally [18]. Based on these studies, the decrease of functional connectivity within aDMN in our study might be explained by three reasons: 1) mutation-specific effect of APP or PSEN2; 2) the disease progress, which needs to be explored by longitudinal study; and 3) the different connectivity pattern of DMN subsystems between ADAD and sporadic AD.

In addition, this study indicated that functional connectivity within pDMN but not aDMN was correlated with general cognitive function in PMC group. This result was in agreement with previous studies showing that the pDMN was associated with episodic or autobiographical memory retrieval [14, 45], that was a component of the MMSE test; while the aDMN was mainly involved in self-referential processing [14], not a component of MMSE test. The reduced connectivity of precuneus was correlated with worse cognitive function in the PMC but not the NC group, which was in agreement with previous studies showing positive correlations between posterior regional functional connectivity within DMN and cognition in AD but not in NC group [46]. Furthermore, the functional connectivity within DMN subsystems were not correlated with EYO in PMC, which was similar with the pattern of DMN connectivity change with EYO in the previously proposed imaging marker model [47]. Another study found significant negative correlation of the DMN connectivity with EYO in the whole disease spectrum [19]. Such inconsistency may be explained as that DMN connectivity changes slowly at early stage, and fast at later stage, in a nonlinear way.

In the present study, decreased functional connectivity was displayed in both pDMN and aDMN in some brain areas, and such disruption could not be explained as a result of grey matter atrophy. That there was no volume difference in the brain areas showing significant disruption of functional connectivity in PMC group indicated that the early functional changes within subsystems of DMN occurred without affecting structure. The volume results were consistent with a previous study which indicated that there was detectable disruption of functional connectivity when regional atrophy was not prominent in subjects at risk of AD [48].

This study has several limitations. We only included presymptomatic individuals with ADAD to study the subsystems of DMN in preclinical AD; further studies are needed to understand the longitudinal change patterns of the DMN subsystems along the whole disease spectrum. Furthermore, we only studied DMN in ADAD, but not in sporadic AD, thus it remains uncertain whether the findings in ADAD can be generalizable to sporadic AD at preclinical stage. More advanced methods are needed to build the trajectory model of DMN connectivity changes in ADAD as compared with sporadic AD. In addition, we only focused on the DMN, since it was the first network showing disruption in AD. Several other networks have been studied and compared between ADAD and late onset AD (LOAD), including the dorsal attention network, executive control network, salience network, and sensory motor network [49]. They found similar pattern of connectivity between ADAD and LOAD. However, it remains possible that some specific network connectivity may dissociate ADAD from LOAD. More networks that are impaired in AD or involved in cognitive function might be investigated in the future. Finally, the sample size is relatively small to look at the mutation-specific effects on DMN connectivity in the present study.

In conclusion, both pDMN and aDMN subsystems showed reduced functional connectivity in the presymptomatic stage of ADAD, without affecting the structure, and the functional connectivity within pDMN was associated with general cognitive function. Precuneus connectivity within pDMN may be useful as an imaging biomarker for early identification of general cognitive decline and help with early diagnosis. The connectivity changes in the specific subsystems of DMN that occur in the preclinical stage of AD could also be a potential valuable outcome measure in prevention clinical trials.

Footnotes

ACKNOWLEDGMENTS

This study was supported by the research grants to Jianping Jia from the National Natural Science Foundation of China for the Key Project (81530036), the National Key Scientific Instrument and Equipment Development Project (31627803), Beijing Municipal Administration of Hospitals for Mission Program (SML20150801), Beijing Municipal Human Resources and Social Security Bureau for the Beijing Scholars Program, Beijing Municipal Science & Technology Commission for the Beijing Brain Initiative (Z161100000216137), and Beijing Municipal Commission of Health and Family Planning (PXM2019_026283_000003); and research grants to Meina Quan from Beijing Postdoctoral Research Foundation. We would like to thank all the researchers and participants in the CFAN study ().

Authors’ disclosures available online ().

References

Price

, Morris

(1999) Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol 45, 358–368.

Jack

Jr. , Knopman

, Jagust

, Shaw

, Aisen

, Weiner

, Petersen

, Trojanowski

(2010) Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 9, 119–128.

Wang

, Roe

, Snyder

, Brier

, Thomas

, Xiong

, Benzinger

, Morris

, Ances

(2012) Alzheimer disease family history impacts resting state functional connectivity. Ann Neurol 72, 571–577.

Sherrington

, Rogaev

, Liang

, Rogaeva

, Levesque

, Ikeda

, Chi

, Lin

, Li

, Holman

, Tsuda

, Mar

, Foncin

, Bruni

, Montesi

, Sorbi

, Rainero

, Pinessi

, Nee

, Chumakov

, Pollen

, Brookes

, Sanseau

, Polinsky

, Wasco

, Da Silva

, Haines

, Perkicak-Vance

, Tanzi

, Roses

, Fraser

, Rommens

, St George-Hyslop

(1995) Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature 375, 754–760.

Goate

, Chartier-Harlin

, Mullan

, Brown

, Crawford

, Fidani

, Giuffra

, Haynes

, Irving

, James

, Mant

, Netwon

, Rooke

, Roques

, Talbot

, Pericak-Vance

, Roses

, Williamson

, Rossor

, Owen

, Hardy

(1991) Segregation of a missense mutation in the amyloid precursor protein gene with familial Alzheimer’s disease. Nature 349, 704–706.

Levy-Lahad

, Wijsman

, Nemens

, Anderson

, Goddard

, Weber

, Bird

, Schellenberg

(1995) A familial Alzheimer’s disease locus on chromosome 1. Science 269, 970–973.

Dubois

, Feldman

, Jacova

, Hampel

, Molinuevo

, Blennow

, DeKosky

, Gauthier

, Selkoe

, Bateman

(2014) Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria. Lancet Neurol 13, 614–629.

Bateman

, Xiong

, Benzinger

, Fagan

, Goate

, Fox

, Marcus

, Cairns

, Xie

, Blazey

(2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367, 795–804.

Greicius

, Srivastava

, Reiss

, Menon

(2004) Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proc Natl Acad Sci U S A 101, 4637–4642.

10.

Buckner

, Andrews-Hanna

, Schacter

(2008) The brain’s default network - Anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124, 1–38.

11.

Raichle

(2015) The brain’s default mode network. Annu Rev Neurosci 38, 433–447.

12.

Sheline

, Raichle

(2013) Resting state functional connectivity in preclinical Alzheimer’s disease. Biol Psychiatry 74, 340–347.

13.

Petrella

, Sheldon

, Prince

, Calhoun

, Doraiswamy

(2011) Default mode network connectivity in stable vs progressive mild cognitive impairment. Neurology 76, 511–517.

14.

Damoiseaux

, Prater

, Miller

, Greicius

(2012) Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiol Aging 33, 828e819–830.

15.

Rombouts

, Barkhof

, Goekoop

, Stam

, Scheltens

(2005) Altered resting state networks in mild cognitive impairment and mild Alzheimer’s disease: An fMRI study. Hum Brain Mapp 26, 231–239.

16.

Andrews-Hanna

, Reidler

, Sepulcre

, Poulin

, Buckner

(2010) Functional-anatomic fractionation of the brain’s default network. Neuron 65, 550–562.

17.

Jones

, Machulda

, Vemuri

, McDade

, Zeng

, Senjem

, Gunter

, Przybelski

, Avula

, Knopman

, Boeve

, Petersen

, Jack

(2011) Age-related changes in the default mode network are more advanced in Alzheimer disease. Neurology 77, 1524–1531.

18.

Jones

, Knopman

, Gunter

, Graff-Radford

, Vemuri

, Boeve

, Petersen

, Weiner

, Jack

Jr. , Alzheimer’s Disease Neuroimaging Initiative (2016) Cascading network failure across the Alzheimer’s disease spectrum. Brain 139, 547–562.

19.

Chhatwal

, Schultz

, Johnson

, Benzinger

TLS

, Jack

, Ances

, Sullivan

, Salloway

, Ringman

, Koeppe

, Marcus

, Thompson

, Saykin

, Correia

, Schofield

, Rowe

, Fox

, Brickman

, Mayeux

, McDade

, Bateman

, Fagan

, Goate

, Xiong

, Buckles

, Morris

, Sperling

(2013) Impaired default network functional connectivity in autosomal dominant Alzheimer disease. Neurology 81, 736–744.

20.

Sala-Llonch

, Fortea

, Bartres-Faz

, Bosch

, Llado

, Pena-Gomez

, Antonell

, Castellanos-Pinedo

, Bargallo

, Molinuevo

, Sanchez-Valle

(2013) Evolving brain functional abnormalities in PSEN1 mutation carriers: A resting and visual encoding fMRI study. J Alzheimers Dis 36, 165–175.

21.

, Westman

, Thordardottir

, Stahlbom

, Almkvist

, Blennow

, Wahlund

, Graff

(2017) The effects of gene mutations on default mode network in familial Alzheimer’s disease. J Alzheimers Dis 56, 327–334.

22.

Quiroz

, Schultz

, Chen

, Protas

, Brickhouse

, Fleisher

, Langbaum

, Thiyyagura

, Fagan

, Shah

, Muniz

, Arboleda-Velasquez

, Munoz

, Garcia

, Acosta-Baena

, Giraldo

, Tirado

, Ramirez

, Tariot

, Dickerson

, Sperling

, Lopera

, Reiman

(2015) Brain imaging and blood biomarker abnormalities in children with autosomal dominant Alzheimer disease: A cross-sectional study. JAMA Neurol 72, 912–919.

23.

, Zhang

, Schoepf

, Varga-Szemes

, Stubenrauch

, Liang

, Zheng

, Kong

, Xu

, Wang

, Qi

, Lu

, Zhang

(2017) Lower functional connectivity of default mode network in cognitively normal young adults with mutation of APP, presenilins and APOE epsilon4. Brain Imaging Behav 11, 818–828.

24.

Yau

, Tudorascu

, McDade

, Ikonomovic

, James

, Minhas

, Mowrey

, Sheu

, Snitz

, Weissfeld

, Gianaros

, Aizenstein

, Price

, Mathis

, Lopez

, Klunk

(2015) Longitudinal assessment of neuroimaging and clinical markers in autosomal dominant Alzheimer’s disease: A prospective cohort study. Lancet Neurol 14, 804–813.

25.

Nasreddine

, Phillips

, Bédirian

, Charbonneau

, Whitehead

, Collin

, Cummings

, Chertkow

(2005) The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53, 695–699.

26.

Bowie

, Harvey

(2006) Administration and interpretation of the Trail Making Test. Nat Protoc 1, 2277–2281.

27.

Morris

(1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43, 2412–2414.

28.

Ashburner

(2012) SPM: A history. Neuroimage 62, 791–800.

29.

Yan

, Wang

, Zuo

, Zang

(2016) DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339–351.

30.

Ashburner

, Friston

(2005) Unified segmentation. Neuroimage 26, 839–851.

31.

Ashburner

(2007) A fast diffeomorphic image registration algorithm. Neuroimage 38, 95–113.

32.

Friston

, Williams

, Howard

, Frackowiak

, Turner

(1996) Movement-related effects in fMRI time-series. Magn Reson Med 35, 346–355.

33.

Zhu

, Tarumi

, Khan

, Zhang

(2015) Vascular coupling in resting-state fMRI: Evidence from multiple modalities. J Cereb Blood Flow Metab 35, 1910–1920.

34.

Fox

, Raichle

(2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8, 700–711.

35.

Reiman

, Quiroz

, Fleisher

, Chen

, Velez-Pardo

, Jimenez-Del-Rio

, Fagan

, Shah

, Alvarez

, Arbelaez

, Giraldo

, Acosta-Baena

, Sperling

, Dickerson

, Stern

, Tirado

, Munoz

, Reiman

, Huentelman

, Alexander

, Langbaum

JBS

, Kosik

, Tariot

, Lopera

(2012) Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. Lancet Neurol 11, 1048–1056.

36.

Gordon

, Blazey

, Su

, Hari-Raj

, Dincer

, Flores

, Christensen

, McDade

, Wang

, Xiong

(2018) Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: A longitudinal study. Lancet Neurol 17, 241–250.

37.

Tijms

, Visser

(2018) Capturing the Alzheimer’s disease pathological cascade. Lancet Neurol 17, 199–200.

38.

Mormino

, Smiljic

, Hayenga

, Onami

, Greicius

, Rabinovici

, Janabi

, Baker

, V. Yen

, Madison

, Miller

, Jagust

(2011) Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cereb Cortex 21, 2399–2407.

39.

Mintun

, Larossa

, Sheline

, Dence

, Lee

, Mach

, Klunk

, Mathis

, DeKosky

, Morris

(2006) [11C]PIB in a nondemented population: Potential antecedent marker of Alzheimer disease. Neurology 67, 446–452.

40.

Sheline

, Raichle

, Snyder

, Morris

, Head

, Wang

, Mintun

(2010) Amyloid plaques disrupt resting state default mode network connectivity in cognitively normal elderly. Biol Psychiatry 67, 584–587.

41.

Harris

, Devidze

, Verret

, Ho

, Halabisky

, Thwin

, Kim

, Hamto

, Lo

, Yu

, Palop

, Masliah

, Mucke

(2010) Transsynaptic progression of amyloid-beta-induced neuronal dysfunction within the entorhinal-hippocampal network. Neuron 68, 428–441.

42.

Fleisher

, Chen

, Quiroz

, Jakimovich

, Gutierrez Gomez

, Langois

, Langbaum

, Roontiva

, Thiyyagura

, Lee

, Ayutyanont

, Lopez

, Moreno

, Munoz

, Tirado

, Acosta-Baena

, Fagan

, Giraldo

, Garcia

, Huentelman

, Tariot

, Lopera

, Reiman

(2015) Associations between biomarkers and age in the presenilin 1 E280A autosomal dominant Alzheimer disease kindred: A cross-sectional study. JAMA Neurol 72, 316–324.

43.

Prvulovic

, Bokde

, Faltraco

, Hampel

(2011) Functional magnetic resonance imaging as a dynamic candidate biomarker for Alzheimer’s disease. Prog Neurobiol 95, 557–569.

44.

Filippini

, MacIntosh

, Hough

, Goodwin

, Frisoni

, Smith

, Matthews

, Beckmann

, Mackay

(2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele. Proc Natl Acad Sci U S A 106, 7209–7214.

45.

Whitfield-Gabrieli

, Moran

, Nieto-Castañón

, Triantafyllou

, Saxe

, Gabrieli

JDE

(2011) Associations and dissociations between default and self-reference networks in the human brain. Neuroimage 55, 225–232.

46.

Binnewijzend

, Schoonheim

, Sanz-Arigita

, Wink

, van der Flier

, Tolboom

, Adriaanse

, Damoiseaux

, Scheltens

, van Berckel

, Barkhof

(2012) Resting-state fMRI changes in Alzheimer’s disease and mild cognitive impairment. Neurobiol Aging 33, 2018–2028.

47.

Ewers

, Sperling

, Klunk

, Weiner

, Hampel

(2011) Neuroimaging markers for the prediction and early diagnosis of Alzheimer’s disease dementia. Trends Neurosci 34, 430–442.

48.

Nakamura

, Cuesta

, Kato

, Arahata

, Iwata

, Yamagishi

, Kuratsubo

, Kato

, Bundo

, Diers

, Fernandez

, Maestu

, Ito

(2017) Early functional network alterations in asymptomatic elders at risk for Alzheimer’s disease. Sci Rep 7, 6517.

49.

Thomas

, Brier

, Bateman

, Snyder

, Benzinger

, Xiong

, Raichle

, Holtzman

, Sperling

, Mayeux

, Ghetti

, Ringman

, Salloway

, McDade

, Rossor

, Ourselin

, Schofield

, Masters

, Martins

, Weiner

, Thompson

, Fox

, Koeppe

, Jack

Jr. , Mathis

, Oliver

, Blazey

, Moulder

, Buckles

, Hornbeck

, Chhatwal

, Schultz

, Goate

, Fagan

, Cairns

, Marcus

, Morris

, Ances

(2014) Functional connectivity in autosomal dominant and late-onset Alzheimer disease. JAMA Neurol 71, 1111–1122.