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. 2015 Sep 14;36(12):4808–4818. doi: 10.1002/hbm.22951

Resting‐state functional connectivity and presynaptic monoamine signaling in Alcohol Dependence

Xi Zhu 1,†, Nisha Dutta 2,†, Sarah G Helton 2, Melanie Schwandt 2, Jia Yan 3, Colin A Hodgkinson 4, Carlos R Cortes 1, Mike Kerich 1, Samuel Hall 2, Hui Sun 2, Monte Phillips 2, Reza Momenan 1, Falk W Lohoff 2,✉
PMCID: PMC4715530  NIHMSID: NIHMS716943  PMID: 26368063

Abstract

Alcohol Dependence (AD) is a chronic relapsing disorder with high degrees of morbidity and mortality. While multiple neurotransmitter systems are involved in the complex symptomatology of AD, monoamine dysregulation and subsequent neuroadaptations have been long postulated to play an important role. Presynaptic monoamine transporters, such as the vesicular monoamine transporter 1 (VMAT1), are likely critical as they represent a key common entry point for monoamine regulation and may represent a shared pathway for susceptibility to AD. Excessive monoaminergic signaling as mediated by genetic variation in VMAT1 might affect functional brain connectivity in particular in alcoholics compared to controls. We conducted resting‐state fMRI functional connectivity (FC) analysis using the independent component analysis (ICA) approach in 68 AD subjects and 72 controls. All subjects were genotyped for the Thr136Ile (rs1390938) variant in VMAT1. Functional connectivity analyses showed a significant increase of resting‐state FC in 4 networks in alcoholics compared to controls (P < 0.05, corrected). The FC was significantly positively correlated with Alcohol Dependence Scale (ADS). The hyperfunction allele 136Ile was associated with a significantly decreased FC in the Default Mode Network, Prefrontal Cortex Network, and Executive Control Network in alcohol dependent participants (P < 0.05, corrected), but not in controls. Our data suggest that increased FC might represent a neuroadaptive mechanism relevant to AD that is furthermore mediated by genetic variation in VMAT1. The hyperfunction allele Thr136Ile might have a protective effect that is, in particular, relevant in AD by mechanism of increased monoamine transport into presynaptic storage vesicles. Hum Brain Mapp 36:4808–4818, 2015. Published 2015. This article is a U.S. Government work and is in the public domain in the USA

Keywords: genetics, monoamine transporter, presynaptic, resting‐state, mPFC, SLC18A1

Abbreviations

ACC

Anterior cingulate cortex

AD

Alcohol Dependence

a‐DMN

Anterior DMN

ADS

Alcohol Dependence Scale

AIM

Ancestry informative marker

BET

Brain Extraction Tool

DMN

Default Mode Network

ECN

Executive Control Network

FLIRT

FMRIB's Linear Image Registration Tool

FMRIB

Functional MRI of the Brain

FP

Frontal pole

ICA

Independent component analysis

IFG

Inferior frontal gyrus

MELODIC

Multivariate Exploratory Linear Decomposition into Independent Components

MFG

Middle frontal gyrus

NOI

Networks of interest

OFC

Orbitofrontal cortex

p‐DMN

Posterior DMN

PFCN

Prefrontal Cortex Network

PICA

Probabilistic Independent Component Analysis

ROI

Region of interest

SCID

Structured Clinical Interview for DSM‐IV Axis I Disorders

SN

Salience Network

TFCE

Threshold‐free cluster enhanced

VMAT1

Vesicular monoamine transporter 1

MNI

Montreal Neurological Institute

FP

Frontal Pole

mPFC

Medial Frontal Cortex

SFG

Superior Frontal Gyrus

ACC

Anterior Cingulate Cortex

PCC

Posterior Cingulate Cortex

PCG

Posterior Cingulate Gyrus

OFC

Orbital Frontal Cortex

AG

Angular Gyrus

MFG

Middle Frontal Gyrus

PreCG

Precentral Gyrus

IFG

Inferior Frontal Gyrus

MTG

Middle Temporal Gyrus

LOC

Lateral Occipital Cortex

PG

Paracingulate Gyrus

INTRODUCTION

Alcohol dependence (AD) is characterized by excessive and continued use of alcohol despite physiological, psychological and societal negative consequences [Association, 2013; Stahre et al., 2014]. Chronic alcohol exposure leads to a variety of neurochemical and neuroanatomical adaptations in the brain [Becker and Mulholland, 2014]. These include adaptations in the amino acid neurotransmitter systems, monoamine systems, neuropeptide systems and ion channels, as well as direct and indirect changes in neurocircuitries [Becker and Mulholland, 2014]. The resulting changes in neural activity are thought to reflect a new allostatic state, most of which occurs in the brain's reward, emotional and stress systems [Koob and Le Moal, 2001]. The monoamine system is one of the brain systems involved in the pathophysiology of AD [Charlet et al., 2013; Fujimoto et al., 1983; Yang et al., 2014], and monoamine‐related neuroadaptations account for a large proportion of the symptoms seen in patients with AD [Becker and Mulholland, 2014]. While multiple studies have investigated the role of monoaminergic neurotransmission in AD, focusing on plasma membrane transporters and postsynaptic receptors, relatively little is known about presynaptic components. Presynaptic monoamine transporters are likely critical, as they represent a key common entry point for monoamine regulation and may represent a shared pathway for susceptibility to a range of psychiatric and intermediate phenotypes, particularly for AD, given the involvement of multiple monoamine neurotransmitter systems.

The vesicular monoamine transporters (VMATs) are membrane transport proteins that are located presynaptically. Two similar but functionally distinct transporters exist, VMAT1 and VMAT2, which are both encoded by separate genes, SLC18A1 and SLC18A2 respectively. Both transporters are capable of moving dopamine, serotonin, norepinephrine and histamine into presynaptic storage vesicles. While several studies have provided preclinical and clinical support for a role of VMAT2 in AD [Fehr et al., 2013; Fon et al., 1997; Lin et al., 2005; Schwab et al., 2005; Takahashi and Uhl, 1997; Wang et al., 1997], no data with regards to VMAT1 and AD exist today. VMAT1 has recently emerged as an interesting new candidate gene, given its plausible neurobiological function and potentially distinct expression patterns in the brain during development and adult life [Hansson et al., 1998]. In preclinical studies, the deletion of VMAT1 in knockout mice results in a decrease in dopamine in the frontal cortex that translates behaviorally to cognitive deficits [Multani et al., 2012]. Furthermore, human genetic studies have found positive case–control associations between VMAT1 common missense variants and neuropsychiatric disorders, including bipolar disorder, schizophrenia, anxiety phenotypes and cognitive phenotypes related to schizophrenia, suggesting pleiotropy [Bly, 2005; Chen et al., 2007; Lohoff et al., 2006; Lohoff et al., 2008a; Lohoff et al., 2008b; Need et al., 2009]. More recently, Lohoff et al. found that the minor allele (136Ile) of the common missense variant Thr136Ile in VMAT1 (rs1390938) significantly increases the transport of monoamines into synaptic vesicles in vitro [Lohoff et al., 2014]. This same variant also was found to alter medial prefrontal cortex (mPFC) and amygdala function using fMRI tasks measuring emotionality [Lohoff et al., 2014].

Given the functional role of the VMAT1 Thr136Ile variant, we hypothesize that an increase in presynaptic loading of vesicles with monoamines leads to chronic adaptation in monoamine signaling with resulting genotype‐specific effects on brain functional connectivity (FC) in vivo that might be particularly relevant to patients with AD.

MATERIALS AND METHODS

Participants

A total of 140 (72 controls and 68 ADs) right‐handed individuals were recruited to the National Institute on Alcohol Abuse and Alcoholism at the National Institutes of Health, USA (see Table 1). There were no significant differences (P < 0.05) in age, gender, and years of education between Thr136Thr and Thr136Ile/Ile136Ile in cases and controls. Written informed consent to the study was obtained from all the subjects, which was approved by the Institutional Review Board of the National Institute on Alcohol Abuse and Alcoholism and was in accordance with the Declaration of Helsinki and the NIH Combined Neuroscience Institutional Review Board. Participants were compensated for their time.

Table 1.

Demographic and clinical characteristics of 68 cases and 72 controls separated by genetic groups in the imaging genetics sample

Cases Controls
Thr136Thr Thr136Ile/Ile136Ile Total p Thr136Thr Thr136Ile/Ile136Ile Total p
Number of Participants 35 33 68 37 35 72
Sex, N (%)
Male 28 (80) 19 (57.6) 47 (69) 0.08 22 (59.5) 13 (37.1) 35 (48.6) 0.06
Female 7 (20) 14 (42.4) 21 (31) 15 (40.5) 22 (62.9) 37 (51.4)
Age, mean years (SD) 41.0 (8.7) 36.4 (10.8) 38.7 (9.7) 0.1 29.3 (8.2) 29.2 (6.6) 29.3 (7.4) 1
Race, N (%)
Caucasian 10 (28.6) 24 (72.7) 34 (50.0) 23 (62.2) 27 (77.1) 50 (69.4)
African‐ American 23 (65.7) 3 (9.1) 26 (38.2) 8 (21.6) 2 (5.7) 10 (13.9)
Asian 0 (0.0) 0 (0.0) 0 (0.0) 4 (10.8) 6 (17.1) 10 (13.9)
Others 2 (5.7) 6 (18.2) 8 (11.8) 2 (5.4) 0 (0.0) 2 (2.8)
Smokers (%) 74.3 63.6 69.1 2.7 2.9 2.8
Education, mean years (SD) 13.2 (2.8) 13 (3) 13.1 (2.9) 1 16 (2) 17 (2.3) 16.5 (2.2) 0.1
ADS, mean score (SD) 19.8 (7.8) 20 (8) 19.9 (7.9) 0.8 n.a. n.a. n.a.
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Abbreviations: ADS, Alcohol Dependence Scale.

p not applicable for number of participants, race and percentage of smokers in sample.

The Structured Clinical Interview for DSM‐IV Axis I Disorders (SCID) [First et al., 1996] was administered to all participants. A smoking questionnaire helped to determine the amount and frequency of a participant's cigarette use. The Alcohol Dependence Scale (ADS) was administered to determine the severity of alcohol dependence in AD participants. Exclusionary criteria included pregnancy, claustrophobia and significant neurological or medical diagnoses. Participants were instructed not to consume any alcohol for 24 hours and no more than half a cup of caffeinated beverages 12 hours prior to each scanning visit. Participants could not participant if they had a positive alcohol breathalyzer or positive urine drug screen on the day of the scan. Controls were excluded if they met criteria for any current or past AD. All subjects were required to be right‐handed and deemed physically healthy by a clinician. At the time of the MRI, AD participants could not be exhibiting severe symptoms of alcohol withdrawal, as determined by a Clinical Institutes Withdrawal Assessment‐Alcohol revised (CIWA‐Ar) score of greater than 8.

MRI Data Acquisition and Pre‐Processing

Whole brain anatomical images and five minutes of closed‐eyes resting‐state (rs)‐fMRI were collected using 3T General Electric and 3T SIEMENS MRI scanners. High resolution T1‐weighted 3‐D structural scans were acquired for each subject using an MPRAGE sequence (128 axial slices, TR = 1,200 ms TE = 30 ms, 256 × 256 matrix). Resting‐state fMRI datasets were collected using a single‐shot gradient echo planar imaging pulse sequence with 36 axial slices acquired parallel to the anterior/posterior commissural line (TR = 2,000 ms, TE = 30 ms, flip angle = 90°, 3.75 mm × 3.75 mm × 3.8 mm voxels).

rs‐fMRI Data Pre‐Processing

Using Functional MRI of the Brain (FMRIB)'s Software Library (http://www.fmrib.ox.ac.uk/fsl) we applied; (1) slice timing correction for interleaved acquisitions using Sinc interpolation with a Hanning windowing kernel; (2) motion correction using Motion Correction using FMRIB's Linear Image Registration Tool (MCFLIRT); [Jenkinson et al., 2002] (3) non‐brain removal using Brain Extraction Tool (BET); [Smith, 2002] (4) spatial smoothing using a Gaussian kernel of full width at half maximum 5 mm; (5) grand‐mean intensity normalization of the entire 4D dataset by a single multiplicative factor, the size of the voxels is 4 × 4 × 4 mm3; (6) high‐pass temporal filtering (Gaussian‐weighted least‐squares straight line fitting, with sigma = 50s); and (7) registration to high resolution structural MNI standard space images using the FMRIB's Linear Image Registration Tool (FLIRT) [Jenkinson et al., 2002; Jenkinson and Smith, 2001].

rs‐fMRI Connectivity Analysis

We applied Probabilistic Independent Component Analysis (PICA) [Beckmann and Smith, 2004] by using the Multivariate Exploratory Linear Decomposition into Independent Components (MELODIC) toolbox of the FSL package.

A temporal concatenation tool in MELODIC was used to derive group level components across all subjects. Pre‐processed data were whitened and projected into a 36‐dimensional subspace using PICA where the number of dimensions was estimated using the Laplace approximation to the Bayesian evidence of the model order [Beckmann and Smith, 2004]. The whitened observations were decomposed into sets of vectors which describe signal variation across the temporal domain (time‐courses) and across the spatial domain (maps) by optimizing for non‐Gaussian spatial source distributions using a fixed‐point iteration technique [Hyvarinen, 1999]. These functional connectivity (FC) components maps were standardized into z statistic images via a normalized mixture model fit, thresholded at z > 7 [Beckmann and Smith, 2004]. Two criteria were used to remove biologically irrelevant components: (1) those representing known artifacts such as motion, and high‐frequency noise; and (2) those with connectivity patterns not located mainly in gray matter. Networks of interest (NOI) were identified as anatomically and functionally classical resting‐state networks (RSNs) [Biswal et al., 2010] upon visual inspection.

The between‐subject analysis was carried out using dual regression, a regression technique which back‐reconstructs each group level component map at the individual subject level. Next, a non‐parametric permutation test (FSL's randomize tool, with 5,000 permutations), that utilizes a threshold‐free cluster enhanced (TFCE) thresholding, was used to assess statistically significant differences in FC between the groups. To minimize the potential confounding influence of age, gender, ancestry informative marker (AIM) scores and scanner types in these results, these parameters were used as nuisance covariates for each NOI. Finally, the resulting statistical maps were threshold at P < 0.05 familiy‐wise error (FWE)‐corrected for the main group effect. The Harvard‐Oxford cortical and subcortical atlases incorporated in FSL were used to identify the anatomical regions of the resulting PICA maps. The FSL Cluster tools were used to report information about clusters in the selected maps. The FC maps were overlaid onto the mean standardized structural T1 1‐mm MNI template and visualized using MRICROGL (http://www.mccauslandcenter.sc.edu/mricrogl/).

VMAT1 Thr136Ile Genotyping

Samples (n = 140) were genotyped for the Thr136Ile variant (rs1390938), in which the Threonine allele (G) is the major allele and the Isoleucine allele (A) is the minor allele. Genotyping for rs1390938 was performed at the NIAAA Laboratory of Neurogenetics using the Illumina OmniExpress BeadChip (Illumina, San Diego, CA). Ancestry informative markers (AIMs, n = 2500) were extracted from the Illumina array to calculate ancestral proportions for all study participants. Using methods described previously for an AIMs panel including 186 markers [Hodgkinson et al., 2008], which were not available for our data set, the ancestry assessment identified six ethnic factors (Africa, Europe, Asia, Far East Asia, Oceania, and Americas).

RESULTS

Demographic Data and Genotyping Results

Demographic characteristics and clinical characteristics of the 140 participants are shown in Table 1. None of the genotype counts deviated significantly from those expected from Hardy‐Weinberg equilibrium for cases or controls, combined or separated by ethnicity. There was no statistically significant difference in genotype and allele frequencies between cases and controls for the combined sample or when separated by ethnic background.

Imaging Results

Consistent with our previous study [Zhu et al., 2015], we identified four networks (NOI), including Default Mode Network (DMN) which split into anterior DMN (a‐DMN) and posterior DMN (p‐DMN); Executive Control Network (ECN) which split into left ECN (L‐ECN) and right ECN (R‐ECN); Salience Network (SN) containing insula, anterior cingulate cortex (ACC), amygdala, thalamus, putamen and caudate; and Prefrontal Cortex Network (PFCN) containing mPFC, orbitofrontal cortex (OFC) and frontal pole (FP). When compared with controls, AD individuals showed significantly (P < 0.05, FWE‐corrected) increased FC within the DMN, ECN, SN and PFCN (Fig. 1a). The detailed information is given in Table 2. The FC was significantly positive correlated with ADS in DMN (P = 0.0004), ECN (P < 0.0001), SN (P < 0.0001) and PFCN (P < 0.0001) (Fig. 2).

Figure 1.

Figure 1

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Functional connectivity differences between cases and controls (A) and between Thr136Thr and Ile136 Carriers in Cases (B). (A) FC differences between cases and controls. Compared with controls, cases showed increased within‐network FC in a‐DMN, p‐DMN, L‐ECN, R‐ECN, SN and PFCN. Group‐level Networks were obtained using Probabilistic Independent Component Analysis. Subject‐specific maps were obtained using dual regression technique. Then, cluster‐based two‐sample t‐test was carried out to assess statistically significant differences in FC between the groups. Age, gender and ancestry informative marker (AIM) were used as nuisance covariates for each NOI. The statistical threshold was set at P < 0.05, FWE‐corrected for multiple comparisons. The FC maps were overlaid onto the mean standardized structural T1 1‐mm MNI template and visualized using Mricrogl. (B) In AD individuals (cases), compared with Thr136Thr individuals, Thr136lle/Ile136Ile carriers showed decreased FC in a‐DMN, p‐DMN, L‐ECN, R‐ECN and PFCN. The statistical threshold was set at P < 0.05, corrected for multiple comparisons through FWE. There were no significant differences in controls.

Table 2.

Functional connectivity differences between cases and controls in DMN, ECN, SN and PFCN (P < 0.05, FWE corrected)

Cluster index Voxels Max X (mm) Max Y (mm) Max Z (mm) Atlas Structure
a‐DMN 1 3,193 26 22 −28 FP, mPFC, SFG, insula, ACC, PCC, OFC
p‐DMN 1 1,603 −10 −54 0 PCC, Precuneous, AG
2 53 −14 26 44 SFG, MFG
LECN 1 1,951 −54 14 −16 MFG, PreCG, SFG, FP, IFG, insula
RECN 1 1,307 50 34 0 FP, SFG, MFG, IFG, PreCG, OFC
2 252 66 −54 −4 MTG, AG, LOC
SN 1 6,821 −26 18 −16 FP, insula, SFG, PreCG, ACC, PCC, OFC, Thalamus, Caudate, Putamen, Amygdala, NAcc
PFCN 1 1,681 42 54 −12 FP, mPFC, OFC, PG, insula
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Cluster index, number of voxels in each network, peak MNI coordinates and atlas structure in each network are also presented.

Abbreviations: MNI, Montreal Neurological Institute; FP, Frontal Pole; mPFC, Medial Frontal Cortex; SFG, Superior Frontal Gyrus; ACC, Anterior Cingulate Cortex; PCC, Posterior Cingulate Cortex; PCC, Posterior Cingulate Gyrus; OFC, Orbital Frontal Cortex; AG, Angular Gyrus; MFG, Middle Frontal Gyrus; PreCG, Precentral Gyrus; IFG, Inferior Frontal Gyrus; MTG, Middle Temporal Gyrus; LOC, Lateral Occipital Cortex; PG, Paracingulate Gyrus.

Figure 2.

Figure 2

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Relationship between FC in ADS Scores in Cases. Scatterplot of ADS and FC in cases. Positive correlations were found between ADS and FC within DMN (P = 0.0004), ECN (P < 0.0001), SN (P < 0.0001) and PFCN (P < 0.0001) in cases. The parameter estimate images from stage 2 of the dual regression were used to calculate the mean FC within each NOI in AD. Linear regression was used in SAS to model the relationship between the two variables and blue lines represent the regression line. [Color figure can be viewed in the online issue, which is available at http://wileyonlinelibrary.com.]

Imaging‐Genetics Results

When compared with Thr136Thr, minor allele carriers (Ile136 carriers) showed significantly (P < 0.05, corrected) decreased FC in DMN including mPFC, OFC, FP, and ACC; ECN including FP, middle frontal gyrus (MFG), inferior frontal gyrus (IFG), OFC etc.; and PFCN including mPFC, FP and OFC in cases (Fig. 1b). The detailed information is given in Table 3. Interestingly, there is no significant difference between Thr136Thr and Thr136lle/Ile136Ile in FC in controls.

Table 3.

Functional connectivity differences between Thr136Thr and Thr136Ile/Ile136Ile in DMN, ECN and PFCN in Cases (P < 0.05, FWE corrected)

Cluster Index Voxels Max X (mm) Y (mm) Z (mm) Atlas Cortical Structure
a‐DMN 1 1261 0.998 30 26 −16 mPFC, FP, OFC, ACC
2 157 0.994 6 −38 32 PCC, Precuneous
p‐DMN 1 87 0.989 10 −50 24 PCC, Precuneous
2 23 0.978 −22 14 40 MFG, SFG
3 19 0.973 54 −70 28 LOC, AG, MTG
LECN 1 517 0.996 −54 38 12 FP, IFG, MFG, PreCG, PoCG, OFC
2 6 0.963 −14 6 56 SMC, SFG
3 6 0.964 −10 30 48 SFG, ParacingulateG
RECN 1 54 0.988 54 26 20 IFG, MFG, FP
2 11 0.971 54 −38 12 SG, MTG, STG, AG
PFCN 1 747 0.998 22 50 −24 mPFC, FP, OFC
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Cluster index, number of voxels in each networks, peak MNI coordinates and atlas structure in each network are also presented.

Abbreviations: MNI, Montreal Neurological Institute; FP, Frontal Pole; mPFC, Medial Frontal Cortex; OFC, Orbital Frontal Cortex; ACC, Anterior Cingulate Cortex; PCC, Posterior Cingulate Cortex; LOC, Lateral Occipital Cortex; AG, Angular Gyrus; MTG, Middle Temporal Gyrus; IFG, Inferior Frontal Gyrus; MFG, Middle Frontal Gyrus; PreCG, Precentral Gyrus; PoCG, Postcentral Gyrus; SMC, Supplementary Motor Cortex; SFG, Superior Frontal Gyrus; PG, Paracingulate Gyrus; SG, Supramarginal Gyrus; STG, Superior Temporal Gyrus.

Although AIM scores were used as a covariate in our analyses, in order to address any potential confounding factors on FC based on different allele frequencies between ethnic groups, we conducted additional analysis of the networks associated with the Thr136Ile polymorphism (DMN, left and right ECN, PFCN) separated by ethnicity. Our data show that we can reproduce the same statistically significant results when comparing FC in Caucasians with Thr136Thr and Caucasians with Thr136lle/Ile136Ile. In the African American group, the results showed a trend that African Americans with Thr136lle/Ile136Ile showed decreased FC compared to African Americans with Thr136Thr, consistent with what is expected with a smaller sample size.

To ensure that any significant differences between cases and controls were not the result of age and gender, we performed a group wise post‐hoc analysis of covariance for each NOI in SPM8. We did not detect any effects of age or gender in any of the NOIs (P < 0.05 FWE‐corrected). In addition, we repeated our analyses with 44 age and gender‐matched subjects from each group (AD: 44 subjects; Age: 33.9 ± 7.29; Gender: 28 males. Control: 44 subjects; Age: 32.2 ±7.21; Gender: 28 males. P = 0.65) and were able to reproduce the same statistically significant results. This result points to the fact that adding more subjects in both groups that did not match in age and gender has not altered the results but has increased the power of the analysis.

DISCUSSION

In this study, we examined the neuronal consequences of chronic alcohol on brain connectivity and examined the effect of inter‐individual differences in the presynaptic VMAT1 function on resting‐state FC in alcoholics and controls. We found increased resting‐state FC in alcoholics when compared to controls in four networks, including the DMN, ECN, PFCN and SN. Additionally, we show that the minor allele of the common VMAT1 missense variant Thr136Ile, which leads to increased monoamine neurotransmitter transport into the vesicle in vitro [Lohoff et al., 2014], is associated with reduced connectivity in networks that show a general increased connectivity in alcoholics, indicating a potential protective effect.

Resting‐state fMRI, measured by temporal coherence in low‐frequency fluctuations in distinct regions, provides insight into the intrinsic properties of functional brain organization [Biswal et al., 1995]. Thus, evaluating RSNs can provide information regarding inherent brain function that may help identify networks responsible for addiction‐related behaviors, which may be diagnostically or therapeutically useful. Previous studies have examined resting‐state FC in AD [Camchong et al., 2013a; Chanraud et al., 2011; Muller‐Oehring et al., in press; Orban et al., 2013; Weiland et al., 2014]. Compared with controls, recently (short‐term) abstinent alcoholic patients showed reduced fronto‐cerebellar FC derived from a motor task [Rogers et al., 2012] and reduced fronto‐striatal connectivity during response inhibition [Courtney et al., 2013]. Long‐term abstinent alcoholic subjects showed reduced resting‐state synchrony between the nucleus accumbens (NAcc) and thalamus, caudate, postcentral, and parietal regions, yet increases between the NAcc and frontal regions [Camchong et al., 2013b]. Further, AD subjects showed lower FC in the L‐ECN, basal ganglia and primary visual networks when compared with controls [Weiland et al., 2014].

Interestingly, our results are contrary to other studies in that we show increased FC in four important brain networks (Fig. 1a). One explanation for this difference is the use of different methodologies for FC analysis. Previous studies have used pre‐defined region of interest (ROI) or seed‐based approaches, which have been known to have potential biases related to seed‐selection [Cole et al., 2010]. Additionally, seed‐based methods are affected by the structured spatial confounds (other RSNs) and structured noise (residual head motion or scanner‐induced artifacts). This approach, while hypothesis‐driven, limits the ability to identify network abnormalities beyond the predefined framework. In this study, we used independent component analysis (ICA), which is a data driven technique that does not require a priori specification of ROI to analyze FC, thus allowing the evaluation of the entire RSNs [Beckmann et al., 2005; van den Heuvel et al., 2009; Zuo et al., 2010]. Similar to our initial work using ICA in FC analysis in a small sample of AD (n = 25) [Zhu et al., 2015], the current data confirm the previously identified networks, including DMN, ECN, SN and PFCN, in a larger cohort of AD (n = 68). Increased FC was also found in the L‐ECN in individuals with substance dependence [Krmpotich et al., 2013] and between NAcc and ACC, between NAcc and OFC, and between amygdala and OFC in chronic heroin users when compared with controls [Ma et al., 2010].

Our finding of increased FC in intrinsic networks implicated in addictions could represent a neuronal mechanism of compensation/adaptation after chronic alcohol exposure. In fact, previous studies have shown long‐term effects of alcohol on both gray and white matter in human brains [Durkee et al., 2013; Grodin et al., 2013; Momenan et al., 2012; Pfefferbaum et al., 2007]. Task‐based fMRI studies have provided evidence for a link between white matter damage and functional activity in the brain. Hyperactivation in a variety of areas including the PFC, insula, thalamus, striatum, and ACC was observed in AD patients and was associated with decreased white matter integrity [Dager et al., 2013; Jansen et al., 2015; Seo et al., 2013]. This enhanced recruitment of task‐relevant areas may be interpreted as an attempt of the brain to recruit additional resources in order to compensate for white matter integrity and neural impairment, indicating an enhanced neural effort in cortical computation due to the structural damage of the brain. In this study, we found increased resting‐state FC in a large sample of AD patients, which may reflect the widespread structural disturbances. Also, the FC was positively correlated with severity of AD. Therefore, related networks such as ECN, DMN and PFCN are utilized to provide a secondary level of restoring the compromised functionality.

In our imaging‐genetics analysis, we found that FC is influenced by inter‐individual differences in VMAT1. This effect was only observed in the AD group, and 136Ile allele carriers showed significantly decreased functional connectivity in the DMN, PFCN and ECN in AD, similar to controls. This finding might represent long‐standing neuroadaptations as a result of increased monoamine signaling in the 136Ile carriers, which might be protective against the damaging neuronal changes observed in chronic alcoholics.

There are several limitations that should be carefully taken into account for the interpretation of the imaging part of our study. First, 69% of the patients were smokers; however, no difference was observed in smoking status based on genotype. To isolate the effect of alcohol from that of smoking on FC, we performed additional analysis on AD with smoking and AD without smoking. No significant group difference was found in any of the NOIs. Thus, no influence was found by this confounding factor in our case samples. Second, clinical heterogeneity could have confounded results, as some cases have current or past use of other drugs, such as cocaine, cannabis, or opioid etc. and psychiatric comorbidities (Table 4). However, different subjects had different types of drug use or psychiatric disorders, and it was not consistent across the subjects. Alcohol use may still be the dominant factor contributing to the FC difference between cases and controls. Third, the case and control groups were not matched exactly regarding age and gender, although there were no differences between the Thr136Thr and the Thr136Ile/Ile136Ile group within cases or controls in this regard. Despite our efforts to recruit young adults with AD to perform meaningful comparisons with controls subjects who, on average, tend to be younger, the average age of the case group was still higher than that of the control group. Consequently, we statistically controlled for age, in addition to gender, in our analysis. In addition, analyses on subgroups of participants with age and gender matched were able to reproduce the same statistically significant results. Future research with case and control groups matched for age and gender is recommended.

Table 4.

Total counts of comorbidities in the imaging genetics sample (n = 140)

(a) Comorbidity of Other Drug Use
Drug Cases (n = 68) Controls (n = 72)
Dependence Abuse Dependence Abuse
Current count Past count Current count Past count Current count Past count Current count Past count
Cannabis 6 18 2 15 0 0 0 0
Cocaine 8 20 1 12 0 0 0 0
Opioid 3 7 0 5 0 0 0 0
Hallucinogen 0 5 5 0 0 0 0 0
Sedative 0 5 1 3 0 0 0 0
Phencyclidine 1 0 0 3 0 0 0 0
Amphetamine 1 3 0 4 0 0 0 0
Other Substance Dependence 0 1 0 0 0 0 0 0
(b) Comorbidity of Psychiatric Disorders
Cases (n = 68) Controls (n = 72)
Psychiatric disorder Current count Past count Current count Past count
Dysthymic disorder 2 1 0 0
Eating disorder 2 1 0 0
Generalized anxiety disorder 10 4 0 0
Obsessive compulsive disorder 1 1 0 0
Panic disorder with agoraphobia 0 1 0 0
Panic disorder without agoraphobia 0 3 0 1
PTSD 20 13 0 1
Social phobia 6 1 0 0
Specific phobia 4 1 0 0
Major depression disorder recurrent 10 6 0 2
Major Depression Disorder Single 1 3 0 2
Agoraphobia 2 1 0 0
Alcohol induced mood 15 11 0 0
Anorexia 0 1 0 0
Anxiety disorder 2 0 0 0
Bereavement 0 1 0 0
Other substance induced anxiety disorder 1 0 0 0
Other substance induced mood disorder 3 2 0 0
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In summary, our data support a functional role of the Thr136Ile variant in neuroadaptations of neuronal networks relevant to AD and might provide an important new target for the investigation of the pathophysiology of AD.

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