NeuroImage 53 (2010) 534–543

Contents lists available at ScienceDirect

NeuroImage

j ourna l homepage: www.e lsev ie r.com/ locate /yn img

Alcohol increases spontaneous BOLD signal fluctuations in the visual network

Fabrizio Esposito a,⁎, Giuseppe Pignataro a, Gianfranco Di Renzo a, Alessandra Spinali a, Antonella Paccone b,c,Gioacchino Tedeschi b,c,d, Lucio Annunziato a

a Division of Pharmacology, Department of Neuroscience, School of Medicine, University of Naples “Federico II”, Naples, Italyb Neurological Institute for Diagnosis and Care “Hermitage Capodimonte”, Naples, Italyc Magnetic Resonance Imaging Center, Multiple Sclerosis Italian Foundation (FISM), Genoa, Italyd Second Division of Neurology, Department of Neurological Sciences, School of Medicine, Second University of Naples, Naples, Italy

⁎ Corresponding author. Department of NeuroscienceII”, c/o Unit of Audiology and Vestibology, Building 13,Italy. Fax: +39 0817463581.

E-mail address: fabrizio.esposito@unina.it (F. Esposi

1053-8119/$ – see front matter © 2010 Elsevier Inc. Adoi:10.1016/j.neuroimage.2010.06.061

a b s t r a c t

a r t i c l e i n f o

Article history:Received 15 March 2010Revised 22 June 2010Accepted 24 June 2010Available online 28 June 2010

Keywords:Functional magnetic resonance imagingfMRIResting stateIndependent component analysisVisual functionVisual networkVisual perception

Brain activity during resting wakefulness is characterized by slow (b0.1 Hz) fluctuations of bloodoxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals that aretopographically organized in discrete functional connectivity networks (resting-state networks, RSNs). Thepresent study aimed at revealing possible network-specific alcohol-induced changes in resting-state fMRI(RS-fMRI) signals.RS-fMRI was carried out on eight healthy subjects in four consecutive 6-min sessions, one before and threeafter a 0.7 g/kg dose of ethyl alcohol. Control experiments were carried out in different days without alcoholadministration. Independent component analysis (ICA) was performed on all experimental and control scansto extract individual and group-level RSN maps in a dynamic network analysis.Alcohol administration significantly increased the overall strength of the visual network ICA component,reaching the peak at 90 min. Within the visual network, the alcohol-induced increase was more pronouncedin the primary regions of the occipital cortex and less pronounced in the secondary regions of the occipito-temporal cortex. Other major RSN components, such as the default-mode, the fronto-parietal, the sensori-motor, the self-referential and the auditory components, did not exhibit alcohol-induced changes during thesame time window.Alcohol-induced effects on the resting-state functional connectivity of the visual network observed in thepresent study demonstrate that the visual system is a selective and primary target of acute alcoholadministration. The strong enhancement of spontaneous BOLD fluctuations in the primary visual cortex in anacute alcoholic state may impair the normal activation response to visual stimuli and affect visual perception.

, University of Naples “FedericoVia S. Pansini 5, 80131 Naples,

to).

ll rights reserved.

© 2010 Elsevier Inc. All rights reserved.

Introduction

Resting-state functional magnetic resonance imaging (RS-fMRI) isa special fMRI modality to investigate and characterize “in-vivo” theblood oxygen level dependent (BOLD) signals in the absence of input(e.g. sensory) and output (e.g. motor) tasks (Biswal et al., 1995; Loweet al., 1998).

A vast body of literature supports the existence of a structuredbaseline of continuousneuronal activity, fromwhich conventional brain“activations,” i.e. phasic changes induced by a perceptual or cognitivetask, arise (Gusnard and Raichle, 2001). This baseline activity ischaracterized by slow signal fluctuations (b0.1 Hz) and is topologicallyorganized in multiple spatially distributed functional connectivitynetworks called resting-state networks (RSNs) (Damoiseaux et al.,

2006; De Luca et al., 2006; Mantini et al., 2007; van de Ven et al., 2004;van den Heuvel et al., 2009). Mapping the amount of distributedcoherence of the RS-fMRI signals within and between different regionsallows a robust physiological fractionation of the whole-brain sponta-neous activity into a discrete and specific set of RSNs.

Collecting information about brain functions from the fMRI studyof the resting brain is attractive because no explicit performance oraction is required to the subject. Although no specific functions areexplicitly investigated, a whole-brain RS-fMRI study allows to relatethe functional connectivity of different brain systems to a physiolog-ical, clinical or pharmacological factor if one or more RSNs are globallyor regionally modified in their spatial distribution (Damoiseaux et al.,2008; Esposito et al., 2009; Greicius et al., 2007, 2008, 2004; Sorg et al.,2007). Despite the known limitations of BOLD fMRI, which measureshemodynamic rather than neuronal changes, there are at least fourcrucial aspects that render the study of the RSN functional connectivityattractive and important for a better understanding of the physiologicalmechanisms of integration and cooperation between close and remoteneuronal assemblies: (i) RS-fMRI activity cannot be considered a purely

535F. Esposito et al. / NeuroImage 53 (2010) 534–543

vascular effect, but possesses a relevant metabolic component,supporting its functional significance (Fukunaga et al., 2008); (ii) upto nine functional connectivity RSNs have been so far documented,reflecting an underlying cortico-cortical structural connectivity archi-tecture of the human brain, as visible with diffusion tensor imaging(DTI) (Greicius et al., 2009; van den Heuvel et al., 2009); (iii) for at leastsix of the nine known RSNs, the time-course of spontaneous BOLDactivity is correlated with electro-encephalographic (EEG) brainrhythms, which are not dependent on hemodynamics (Mantini et al.,2007); (iv) all reported RSNs spatially overlap with those functionalnetworks that are activated or modulated during passive (sensorial) oractive (motor, cognitive or behavioral) tasks (Smith et al., 2009).

For all the above mentioned aspects, the knowledge of possiblespatially selective drug-induced alteration of RSN functional connec-tivity might be of great interest to enhance our understanding about apharmacological action on the normal functional brain organizationand better predict and characterize drug-induced modulation ofactive and passive task responses in their possible clinical applica-tions. Importantly, RS-fMRI signals do not necessarily reflect aparticular level of activity, but rather slow oscillations betweendifferent levels of neural activity. Therefore, the link between possiblealterations in RSN functional connectivity and task-related brainactivity and functionality can be bi-directional. Previous clinical andnormative studies have linked connectivity dysfunctions (such asimpaired toggling between RSN and active networks) to traitattention deficit hyperactivity disorder (ADHD) and trial-wise taskerror incidents in normal using both directionalities (for review see,e.g., Sonuga-Barke and Castellanos, 2007). Thereby, pharmacological-ly induced increases in coherent RSN signal fluctuations may eitherindicate a better organized functional network or rather an abnor-mally increased “resting” or “idling” state at the expense of task-elicited functioning. In the latter case, RSN signal fluctuations wouldbe enhanced in those regions where the stimulus-related activationresponses are impaired or inhibited during the action of the drug.

The present studywas aimed at revealing possible alcohol-inducedchanges in the RSN functional connectivity in the normal population.In particular, the purpose was to assess if acute alcohol administrationchanged the baseline neural activity as measured by RS-fMRI.

Starting from the assumption that normal RSNs are indicative of awell-ordered mind, it may be of both social and clinical relevance toknow if and how certain RSNs would be affected by alcoholintoxication. For example, impairment of connectivity in the so-calledself-referential RSN could portend increased impulsivity (Doughertyet al., 2000, 2008) and even aggressive behavior to intoxicatedpersons (Giancola, 2002a,b; Miller et al., 2009). In addition,neurocognitive disruption by acute alcohol administration might bemanifested with disrupted connectivity in some other fronto-corticalnetworks (see, e.g., Rzepecki-Smith et al., 2009).

Previous fMRI studies have clearly shown that alcohol reducesvisual cortical activation in response to visual stimuli. This was equallyseen in a simple experiment with photic stimulation (Levin et al.,1998) and in another experiment with a more complex visualperception task, such as the Motor-Free Visual Perception Test-Revised (MVPT-R) (Calhoun et al., 2004a). Thereby, we may expectalcohol would affect the functional connectivity in visual areas in theresting state as well, in spite of affecting skill performance andactivation of these same regions under visuomotor task demands.

The study was designed to have two comparable sessions, onedrug-free and one drug-active. For the analysis, we adopted acompletely data-driven methodology based on independent compo-nent analysis (ICA) (Calhoun et al., 2009; McKeown et al., 1998) toextract RSN functional connectivity maps from all (experimental andcontrol) scans (Damoiseaux et al., 2006; van de Ven et al., 2004). Weused self-organizing group-level ICA (sog-ICA) (Esposito et al., 2008,2005; Koch et al., 2009; Mantini et al., 2007; van de Ven et al., 2009) toproduce “alcohol-free” RSN spatial templates from control scans

(Esposito et al., 2008; Greicius et al., 2004) and, then, analyzed thenetwork-averaged ICA scores from experimental scans. The alcoholadministration was adjusted to achieve a blood concentration slightlyabove the social tolerance (50 mg/dl) according to international laws.

Our results on the effects of alcohol on RSN functional connectivitydemonstrated that acute alcohol administration significantly andstrongly enhances the physiologic baseline fluctuations of the restingbrain selectively in the visual network and more prominently in theprimary visual cortex.

Materials and methods

Subjects and experimental design

The study was approved by the ethical committee of the SecondUniversity of Naples and all participants gave their written informedconsent prior to their inclusion in the study in compliance withnational legislation and the Code of Ethical Principles for MedicalResearch Involving Human Subjects of the World Medical Association(Declaration of Helsinki).

Eight healthy volunteer subjects (5 males and 3 females, age 28±3.2) participated in two fMRI sessions on two separate days. Prospectivevolunteers that currentlymet anydiagnostic criteria for alcohol abuse ordependence, orwhodid not regularly drink alcoholwere excluded fromparticipation. All recruited subjects were “social” drinkers between 21and 35 years of age with no more than 25 g of alcohol per day (theaverage amount of alcohol assumption was 7.5±6.9). On the scanningdays subjects were asked to refrain from smoking and consumingcaffeine and glucose containing drinks and food for at least 5 h prior tothe study. None of the recruited volunteers had a history of alcoholdependence ormedical conditions, or neededmedical treatment and allwere considered reliable and physically and mentally capable of adher-ing to the protocol. Before scanning theywere carefully interviewed andphysically examined by both a neurologist and an anesthesiologist inrelation to the amount and time of alcohol assumption and for possibledrug abuse. All experiments were performed on the mornings ofTuesdays and Thursdays, away from week-end days.

Alcohol administration consisted in letting the subjects stay seatedon a comfortable chair and consume a controlled quantity of theirfavorite alcoholic drink over a 10-min period. The quantity was calcu-lated to reach an estimated blood alcohol concentration of 64 mg/dl.

In session 1, a first RS-fMRI 6-min scan (see below) was taken30 min before the alcohol administration. Then, three more RS-fMRI6-min scans were started at 30, 90 and 180 min after drinking.

Blood alcohol concentration (BAC) wasmeasured prior to each RS-fMRI scan using the enzymatic method of alcohol dehydrogenase thatexploits the changes of florescence intensity by an AxSYM-System(Abbott Laboratories, Abbott Park, IL). This method also known asradiative energy attenuation (REA®-Ethanol Assay, Abbott Laborato-ries), leads to conversion of color during a combined catalytic reactionof alcohol dehydrogenase and diaphorase (Shaffar and Stroup, 1983)and has a detection limit of 2.2 mM (corresponding to 0.1‰).

Fig. 1 illustrates the experiment with example BAC and RS-fMRIdata from a single subject.

In session 2, four identical RS-fMRI 6-min scans were taken exactlyat the same day times to avoid confounds from circadian variability(Bartlett et al., 1988), but no alcoholic drink was administered to thesubjects between the first two time points. The interval betweensession 1 and 2 ranged from 2 to 8 weeks.

Magnetic resonance imaging

Magnetic resonance imaging was performed on a 3-T scanner(HDxt, General Electric Medical System, United States) equipped withan 8-channel parallel head coil.

Fig. 1. Experimental design illustration with exemplary BAC data and RS-fMRI component maps (DMNmaps) from one single subject. Four RS-fMRI 6-min scans were started at−30,30, 90 and 180 min with respect to alcohol administration and BAC was measured prior to each RS-fMRI scans.

536 F. Esposito et al. / NeuroImage 53 (2010) 534–543

Each RS-fMRI scan consisted of 240 volumes of a repeatedgradient-echo echo planar imaging sequence (TR=1508 ms, axialslices=29, matrix=64×64, field of view=256 mm, thick-ness=4 mm, interslice gap=0 mm). During the functional scan,subjects were asked to simply stay motionless, awake and relaxedwith their eyes closed; no visual or auditory stimuli were presented atany time during functional scanning.

Three-dimensional T1-weighted images (FSPGR BRAVO sequence,voxel size=0.9×0.9×1.2 mm3) were acquired in the same session inorder to have high-resolution spatial references for registration andnormalization of the functional images.

The functional and anatomical series were repeated four times ineach scanning day as described in the previous paragraph. Only onesubject missed the fourth scan of the day. During each functional scanthe timing of physiological cycles was recorded by a pulse-oximeterplaced on the left index finger and a pneumatic belt positioned at thelevel of the abdomen of the subjects. The cardiac and respiratorymeasurement devices were integrated into the MRI scanner and datawere automatically synchronized to image acquisition. Physiologicalsignals were displayed on the console and the digital samples (40 Hz)saved to text files.

Resting-state fMRI data preparation

Standard image data preparation, normalization and pre-proces-sing and statistical analysis and visualizationwere performedwith theBrainVoyager QX software (Brain Innovation B.V., The Netherlands)(Goebel et al., 2006). Functional data preprocessing included thecorrection for slice scan timing acquisition, the 3D rigid body motioncorrection and the application of a temporal high-pass filter with cut-off set to 3 cycles per time-course. Structural and functional data wereco-registered and spatially normalized to the Talairach standard spaceusing a 12-parameter affine transformation. In the course of thisprocedure, the functional images were resampled to an isometric

3 mm grid covering the entire Talairach box. To account for possibleBOLD effects due to cardiac pulsation and respiratory cycle (Birn et al.,2008) physiological noise correction was performed on eachfunctional scan using the RETROICOR technique (Glover et al.,2000). Time-courses for components of heart rate, respiration andrespiration volume per time were created from the recordedphysiological signals at the fMRI sampling rate using Matlab scripts(The Mathworks, United States) available from the AFNI suite (Cox,1996) and used, together with the motion estimate time-coursesavailable from the previous 3D rigid body motion correction, aspredictors in single-study general linear model (GLM) analysis(Friston et al., 1995) of each functional scan. Using the residualtime-courses from this GLM allowed us to regress out possible signalfluctuations time-locked with the phase of cardiac and respiratorycycles and residual movement-related signal fluctuations.

Resting-state fMRI data analysis

Single-subject and group-level ICA analyses were performed onthe pre-processed functional time series and the estimated indepen-dent components using two plug-in extensions of BrainVoyager QXimplementing the fastICA algorithm (Hyvarinen, 1999) and the self-organizing group-level ICA (sog-ICA) algorithm (Esposito et al., 2005).

For each subject and each scan, 40 independent components, wereextracted and scaled to spatial z-scores (i.e. the number of standarddeviations of their whole-brain spatial distribution). These valuesexpress the relative amount a given voxel is modulated by theactivation of the component (McKeown et al., 1998) and hence reflectthe amplitude of the correlated fluctuations within the correspondingfunctional connectivity network. The final number of ICA componentsis a free parameter, which has previously been either empiricallydetermined or estimated (Calhoun et al., 2009) as the number ofprincipal components retained in the multivariate data. This numbertypically lies between 20 and 60 depending on the data. In the present

Fig. 2. Analysis of variance (ANOVA) bar graph for the mean BAC measured in the eightstudied subjects across all time points. Statistical significance is reported in for allcomparisons between successive time points.

537F. Esposito et al. / NeuroImage 53 (2010) 534–543

work we chose to keep a number of principal componentscorresponding to one sixth of the number of time points (Greiciuset al., 2007) and accounting for more than 99.9% of the total variance.

In order to select the best-fit individual ICs for the physiologicalRSNs in each experimental run we used a two-step procedure. In thefirst step, all individual component maps from all control runs (i.e. the“drug-free” scans) and all subjects were hierarchically “clustered” firstin the time point and, then, in the subject space using the sog-ICAalgorithm two times (hierarchical sog-ICA) (van de Ven et al., 2009).Thereby, all 40 individual independent components were uniquelyassigned to one out of 40 “clusters” of independent components. Then,group-level statistical maps were obtained by modelling at each voxelthe variance of the individual component values both within subjects(across clusters) and between subjects (within clusters) in a 2-factoranalysis of variance (ANOVA). In the 2-factor ANOVA, the within-subject factor had fixed levels (represented by the clusters ofindependent components) and the between-subject factor hadrandom levels (represented by the random observations of thecluster across all subjects) (Esposito et al., 2008). This produced 40single-group maps that were visually inspected to recognize the mostelectro-physiologically relevant RSN components as reported andillustrated previously (Mantini et al., 2007). All other componentswere excluded from further analysis.

For each selected cluster, the t-maps expressing the whole-brainrandom-effects statistical distribution of the group ICA componentswere one-side thresholded to a Bonferroni corrected significance levelof p=0.05. The thresholded maps were, then, used for creatinginclusive RSN masks (“drug-free” RSN spatial templates). In thesecond step, the template masks were applied to each single-subjectICA component set for all experimental (and control) runs in such away to select one best-fitting RSN component for each subject and foreach time point spanning the observed drug kinetic (Greicius et al.,2008, 2004). To avoid ICA sign ambiguity, each component sign wasadjusted in such a way to have all spatial correlation coefficientspositive. Then, in order to obtain a single figure for quantifying theamount of within-network functional connectivity, the selected RSNcomponent distributions in each subject and at each time point wereselectively averaged across all voxels included in the “drug-free” RSNtemplate masks. The difference between this figure at each time pointand the figure at baseline was considered to evaluate the changes inrelation to the drug kinetic: a positive difference would, thus, resultfrom the increase in the amplitude of the correlated fluctuations andvice versa for a negative difference.

Time point specific random-effects group maps were alsogenerated for each RSN and each time point to visualize the group-level RSN functional connectivity distribution in the “drug-active”state in relation to the drug kinetic and the “drug-free” RSN layout. Inaddition, group-level t-maps resulting from the direct voxel-by-voxelstatistical comparison of the RSN component maps between thealcoholic and control states were also generated to detect possibleregional differences in the RSN component distributions. However,regional differences were only accepted for compact clusterssurviving the joint application of a voxel- and a cluster-level thresholdwhich were chosen using a non-parametric randomization approach.Namely, an initial uncorrected threshold was applied (p=0.01) to allvoxels and, then, a minimum cluster size was calculated thatprotected against false positive clusters at 5% after 500 Montecarlosimulations (Forman et al., 1995; Goebel et al., 2006).

Results

Following alcohol ingestion, a statistically significant change wasobserved in the mean BAC in all eight studied subjects across all timepoints compared to baseline (pb0.0001, Fig. 2). Specifically, BACreached amean value of 37.96 mg/dl at 30 min, peaked at 90 minwithamean value of 63.98 mg/dl and decreased to 41.44 mg/dl at 180 min.

The observed variance in the BAC peak values was not significantlycorrelated with the subjects' drinking history (R=0.034, p=0.94).The most significant change from baseline was at 90 min, that inconsideration of the pharmacokinetic properties of ethanol was theexpectedpeak, and the target BACwas reached in good approximation.The BAC increase from 30 to 90 min and the BAC decrease from 90 to180 were also statistically significant (respectively, p=0.014 andp=0. 0.022), reflecting the transitions from a non-intoxicated (timepoint t0) to an intoxicated (time point t2) status and vice versa.

Considering all scans from the alcohol-free sessions, the sixelectro-physiologically relevant RSN components illustrated previ-ously (Mantini et al., 2007) were extracted in all subjects. A visualinspection of the RSN group-level maps overlaid on the averagednormalized anatomy allowed to identify: (i) the default-modenetwork (DMN), functionally connecting the posterior and anteriorcingulate cortex and, bilaterally, the inferior parietal lobules (Greiciuset al., 2003; Raichle et al., 2001), (ii) the visual network (VIS)involving bilaterally the retinotopic occipital cortex up to thetemporal–occipital junctions and human-MT (Lowe et al., 1998;Wang et al., 2008), (iii) the fronto-parietal network (FPN) including,bilaterally, the intra-parietal cortex and the superior-lateral frontalcortex (Corbetta and Shulman, 2002), (iv) the sensori-motor network(SMN) involving, bilaterally, the pre- and post-central gyri, the medialfrontal gyrus, the primary and supplementary motor and the primaryand secondary sensory areas (Biswal et al., 1995), (v) the auditorynetwork (AUD), involving, bilaterally, the superior and middletemporal cortex (Seifritz et al., 2002) and (vi) the self-referentialnetwork (SFN) involving the ventro-medial prefrontal cortex and theperigenual anterior cingulate cortex (D'Argembeau et al., 2007). Allthe obtained RSN group maps are shown in Fig. 3 using differentcolors to identify the different RSNs.

Using the control group maps as RSN spatial templates, thehomologue RSN components were selected from the experimentalscans and further evaluated for the effect of alcohol administration ontheir scores. Regions of interest (ROIs) were created from the RSNtemplates and used to compute an average z-score for all RSNcomponents at all time points. Fig. 4a reports the time-courses ofthese scores (mean and standard error across subjects, aftersubtraction of the baseline). The visual network in alcohol-treatedsubjects exhibited a statistically significant increase in the amplitudeof the correlated fluctuations which was coincident with the BAC peak(90 min). For the other RSNs, a trend of positive change was noted for

Fig. 3. Spatial templates of the RSN components and correspondence between spatial patterns and colors. Visual component (VIS), default-mode (DMN), fronto-parietal (FPN),sensori-motor component (SMN), auditory (AUD) and the self-referential (SRN) components.

538 F. Esposito et al. / NeuroImage 53 (2010) 534–543

the SMN and of negative change for all other networks, but none ofthese reached statistical significance. Fig. 4b reports the scatterplots (with linear regression fits) of the same scores against the

Fig. 4. (a) Time-courses of the differences (mean and standard error across subjects) betweenactive) time points and all RSNs. (b) Scatter plots with fitted linear regression lines illustratinR statistic is reported near each line.

BAC measures. A statistically significant correlation (R=0.3863, p=0.03185) was found for the visual network but not for all otherRSNs. For none of the RSNs the estimated alcohol-induced functional

the ROI-averaged z-score and the same value at the baseline for all experimental (drug-g the correlation between the individual ROI-averaged z-scores and the BAC values. The

539F. Esposito et al. / NeuroImage 53 (2010) 534–543

connectivity changes at the time point of maximum BAC significantlycorrelated with the subjects' drinking history (pN0.05).

Fig. 5 reports the random-effects group-level t-maps for allexperimental time points in the alcohol condition and illustrate thedistribution of the functional connectivity strength in all RSNs beforeand after alcohol administration. Regional score time-courses are alsoreported for both control and alcohol-treated time points. The visualnetworkmap in alcohol-treated subjects reached themaximal activityacross the regions of the corresponding RSN template at time point t2(90 min. from administration) (Fig. 5a). Moreover, considering thesame regional scores at the same time points, these were significantlyhigher at time point t2 after alcohol administration compared tocontrol scans (p=0.019) in the visual network, whereas this was notthe case for any of the other RSNs. Within the visual network, thecomponent score increase observed in alcohol-treated volunteers wasmore pronounced in the primary visual cortex (early perceptionareas) than in the secondary visual cortex (association and motionareas). This was statistically confirmed by a voxel-by-voxel compar-ison between all maps from time points t2 (90 min after alcoholadministration) and all maps from t0 (baseline), which produced twocompact clusters of significant regional difference (pb0.05, cluster-

Fig. 5. Time-point specific group-level random-effect component t-maps overlaid on thecovering the studied drug kinetic. An uncorrected threshold (p=0.01) was set to better illusRSN template masks (whose borders are highlighted by the while line on the maps). The ti(a) the visual component (VIS), the default-mode (DMN) and the fronto-parietal (FPN) netwself-referential (SRN) networks are shown.

level corrected). One of these clusters was located in the right lingualgyrus (p=0.000017, Talairach coordinates: +10,−96, −10) and theother was located in the left lingual gyrus (p=0.00034, Talairachcoordinates: −16, −90, −9). The DMN and the FPN appeared to bemostly preserved in their functional connectivity during the timeinterval of alcohol effect observation (Fig. 5a).

Discussion

The present study was designed to clarify the behavior of the RSNfunctional connectivity in the normal population following theingestion of a modest (but socially relevant) alcohol dose. To ourknowledge, this is the first human study to explore task-independentchanges in RSN functional connectivity during acute alcohol admin-istration, in a within-subject design, across multiple time points of theexpected drug kinetic and in correlation with individually sampledBACs. Our results demonstrated that acute alcohol administrationsignificantly and strongly enhances the physiologic baseline fluctua-tions of the resting brain by selectively increasing the amplitude of thecorrelated BOLD signals in the visual network and more prominentlyin the primary visual cortex.

average normalized anatomy. All four maps are reported for all six RSN componentstrate the dynamic changes in the RSN functional connectivity maps in comparison to theme-course of the ROI-average z-score is plotted on the right near each series of maps.orks are shown; (b) the sensori-motor component (SMN), the auditory (AUD) and the

Fig. 5 (continued).

540 F. Esposito et al. / NeuroImage 53 (2010) 534–543

Alcohol intoxication determines a variety of neurochemical (Eckardtet al., 1998) and hemodynamic changes (Sano et al., 1993) that end upwith affecting cognition, mood and behavior (Vengeliene et al., 2008).Previous fMRI studies have shown the modulation of neuronalactivation following acute alcohol ingestion in various types of cross-over activation designwith experimental tasks. These included auditory(Seifritz et al., 2000) and visual perception tasks (Calhoun et al., 2004a;Levin et al., 1998), goal-directedvisuo-motor (VanHornet al., 2006) andmemory (Gundersen et al., 2008) tasks and even more complexscenarios like simulated driving (Allen et al., 2009; Calhoun et al.,2004b; Meda et al., 2009). Although previous studies have beenextremely informative for visualizing alcohol effects on brain function-ality and illustrating important implications of fMRI after drugadministration, it has remained difficult in conventional task-basedfMRI studies to determine whether differences in activation and task-by-drug interactionswere due to a directly drug-affected brain functionor to analteredbehavioral performance secondary to analtered baselinestate of the brain due to drug administration. In the present study, weintended to explore possible spatially selective effects of alcohol on thefMRI baseline fluctuations, without stimulating and without assessingchanges in the subject's behavior and cognitive performance.

The multi-factorial nature of the BOLD effect together with theknown vasoactive properties of alcohol seriously limits an uncon-strained interpretation of phenomenological hemodynamic changes

in the RS-fMRI signals. Therefore, we have limited our investigation tosix RSNs already found to be correlated with EEG rhythms (Mantini etal., 2007), thereby gathering the chance to compare our findings toprevious electro- and magneto-enchephalographic (MEG) studieswhere the effect of alcohol was studied under identical restingconditions but without the hemodynamic confound. On the otherhand, this means that, although many of the excluded independentcomponents are often linked to artifacts (see, e.g., (De Martino et al.,2007)), we are not ruling out that other components in the originalunconstrained ICA solution represent RSNs of neurological signifi-cance and that these may be affected by alcohol intoxication.

We found that a moderate dose of alcohol, during the acute phase,strongly and selectively enhanced the functional connectivity of thevisual network, and this effect was statistically significant not only incomparison to baseline but also in comparison to the physiologicalvariability of the same network across different experimental daysand day times. Therefore this effect cannot be explained in terms ofother alcohol-independent physiological variables, such as circadianand sleep rhythms, which are also thought to potentially affect thehuman metabolism in the resting state (Bartlett et al., 1988).

Surprisingly, the effects of acute alcohol administrationwe observedon our subjects were selective for the visual network as all other EEG-relevantRSNs such as the default-mode, the fronto-parietal, the sensori-motor, the auditory and the self-referential networks were not affected.

541F. Esposito et al. / NeuroImage 53 (2010) 534–543

In this regard, although we cannot exclude that some of the studiedRSNs that are intuitively more linked to behavioral and neurocognitivedisruption caused by acute alcohol administration might have reachedsignificance with more subjects, there are other possible explanationsfor these negative findings. Particularly, it should be remarked thatblood alcohol levels reached in the present study (0.7 g/kg) are oftenconsideredmoderate (Dougherty et al., 2000, 2008) and, thus, might benot as highly toxic to, e.g. induce changes in the self-referential networkand, therefore, aggressivebehavior.Moreover, it is possible that alcohol-induced (motor or cognitive) impulsivity is moderated by the so-calledalcoholmyopia (Steele and Josephs, 1990). In linewith this theory, thereis evidence from animal and human studies (Olmstead et al., 2006;Ortner et al., 2003) that alcohol causes people to react more to salientevents, with consequentially less recruitment or reference to long-termconsiderations of behavior. Thereby, if alcohol caused an imbalance thatfavors RSNs responding to acute stimuli over other networks, thiswouldexplainwhy the RSNs that are conceptuallymore linked to behavior andcognition are not affected in the resting state. Lastly, we did not includesubjects with neurocognitive deficits at baseline, who might beespecially prone to alcohol disruption of cognition or behavior(Dougherty et al., 2004). Future studies will possibly address theseaspects using larger cohorts of subjects.

Following the ingestion of an alcoholic drink, the changes of thevisual network functional connectivity were time-related with theBAC changes. In fact, the peak of the average change in the globalvisual network connectivity was coincident with the peak in theaverage BAC and the individual changes in the network scores werepositively correlated with individual BAC peak values. Moreover, wedetected regional differences in the primary visual cortex whencomparing network maps at peak and baseline on a voxel-by-voxelbasis. These consisted of two compact clusters with peaks located inthe right and left lingual gyrus. Therefore, we can state that alcoholprimarily affects the BOLD baseline activity in those cortical regionsthat are fundamental for early visual perception.

In previous fMRI studies using visual stimulation (Levin et al., 1998);(Calhoun et al., 2004a) alcohol administration similar to the presentstudy has been shown to result in a dose-dependent decrease of theBOLD activation contrast over much of the visual perception networkand specifically in the lingual gyrus, which corresponds to the sameregion where, according to our results, spontaneous BOLD signalfluctuations become stronger and maximally coherent in a resting“alcoholic” state. Therefore, alcohol-dependent increasedfluctuations inthe visual cortex during rest are co-localized with alcohol-dependentreduced changes in the BOLD signal following visual stimulation.

The impact of ethanol on the resting brain activity has beenextensively studied with other neuroimaging modalities. The changesin brain spontaneous electrical activity, which is characterized byhigher or lower level of synchronization in the neuronal oscillations,have been investigated in relation to alcohol administration withelectro- (EEG) and magneto-encephalography (MEG). Many EEG andMEG studies have reported an increase of alpha oscillations (~10 Hz)after a moderate dose of alcohol ingestion (Ehlers et al., 1989; Ilan andGevins, 2001; Nikulin et al., 2005; Noldy and Carlen, 1990).Remarkably, resting-state simultaneous EEG-fMRI has shown thatthe time-course of activity of the visual RSN ICA component ispreferentially correlatedwith the trace of the EEG oscillatory power inthe alpha band more than in all other bands (Mantini et al., 2007).Thereby, the observed alcohol effects on the visual network functionalconnectivity may not only phenomenologically indicate strongerspontaneous fluctuations in the BOLD signals, but also indirectly pointto increased power levels of neuronal oscillations in the alpha band.

While Mantini et al. (2007) have established the physiologicalassociation between resting-state fMRI components and EEG rhythmsat a global network level, other simultaneous EEG-fMRI studies havehighlighted the negative polarity of the local coupling between alphaand BOLD waves in the primary visual cortex (Feige et al., 2005;

Goldman et al., 2002; Moosmann et al., 2003): according to thesestudies, phasic positive changes in the alpha power accompany phasicnegative changes in the BOLD signal and vice versa. Taking all theseevidences together, and considering that it is generally believed thatthe presence of alpha oscillations signifies “idling” or inhibition ofcortical processing (Pfurtscheller et al., 1996; Worden et al., 2000),both the global increase in the visual network functional connectivityand the regional increase of the RS-fMRI signal fluctuations inducedby alcohol may point to increased inhibition rather than increasedexcitation of the neural activity in the visual system.

Positron emission tomography (PET) studies using [(18)f]-fluoro-2-deoxy-D-glucose (PET-FDG) have consistently reported large decre-ments in the metabolism of the occipital cortex following acute alcoholintoxication (Volkow et al., 2008; Wang et al., 2000). Given that whenGABA receptors are acutely exposed to ethanol there is a potentiation ofGABA-inhibiting currents (Davies, 2003), the very high concentrationof GABA receptors expressed in the occipital cortex can explain theinhibiting effects of alcohol on themetabolism of the visual system (Hilland Toffolon, 1990; Nicholson et al., 1995; Pearson and Timney, 1998;Watten et al., 1998;Wilson andMitchell, 1983). In addition, it has beenrecently reported that ethanol may exert an inhibitory action on theexcitatory effect of glutamate on the N-methyl-D-Aspartate (NMDA)receptors (Weiner and Valenzuela, 2006). Therefore, all these neuro-chemical evidences suggest that ethanol effects on GABA-ergic andglutamatergic synapses might be related to the observed inhibitoryeffects on visual cortical processing. Importantly, reducedmetabolism inthe visual cortex has also been linked to increased levels ofsimultaneously measured EEG power in the alpha band in normalhuman subjects during rest (Oakes et al., 2004), suggesting that alphapower is inversely related to metabolism and therefore to neuronalactivation, preferentially in the visual cortex.

The present study is mainly descriptive and has some limitationsincluding a small sample size. On the non-alcohol day, we did not usea placebo drink as is required for pharmacological studies involvingbehavioral or task performances. Although this is not critical for aresting-state experiment where subjects are asked to do nothing thanrest motionless with their eyes closed, we cannot rule out theexistence of psychological expectancy effects on 1 day and not theother. Moreover, we did not collect specific personality, impulsivity orcognitive measures that could account for alcohol having divergingeffects on different individuals (Dougherty et al., 2004; Miller et al.,2009), independently of their drinking history.

In conclusion, our findings demonstrate that alcohol increasesspontaneous BOLD signal fluctuations in the visual network and thisphenomenon suggests that alcohol abnormally promotes the “idle”state of the visual function in the resting human brain. The visualcortex, and preferentially the perceptual occipital regions, may thusbe considered a primary target of alcohol-induced inhibition of neuralprocessing. The relevance of the present studymainly consisted in thefact that anatomically selective information about the acute effect of adrug may become visible in-vivo using RS-fMRI. Adding a pharma-cological dimension to this technique is attractive because the drugeffect on the brain function is not detected as a result of a functionalinteraction with the behavioral or cognitive performance or thecharacteristics of the stimulus but as a characterization of the changesinduced during the resting state of a neural network. Of course, thebrain at rest is always highly active and drugs could still affect thebehavior and the cognitive processes that are engaged at rest even ifno changes are manifest in a given RSN.

Acknowledgments

We thank Federico Criscitiello and Biagio Cacciapuoti (GE MedicalSystems) for their expert technical assistance, Mariella Gallinaro,Nunzia Migliozzi, Francesca Trojsi, Rosaria Sacco, Marida Della Corte,Alvino Bisecco for their assistance during blood sampling.

542 F. Esposito et al. / NeuroImage 53 (2010) 534–543

References

Allen, A.J., Meda, S.A., Skudlarski, P., Calhoun, V.D., Astur, R., Ruopp, K.C., Pearlson, G.D.,2009. Effects of alcohol on performance on a distraction task during simulateddriving. Alcohol. Clin. Exp. Res. 33, 617–625.

Bartlett, E.J., Brodie, J.D., Wolf, A.P., Christman, D.R., Laska, E., Meissner, M., 1988.Reproducibility of cerebral glucose metabolic measurements in resting humansubjects. J. Cereb. Blood Flow Metab. 8, 502–512.

Birn, R.M., Murphy, K., Bandettini, P.A., 2008. The effect of respiration variations onindependent component analysis results of resting state functional connectivity.Hum. Brain Mapp. 29, 740–750.

Biswal, B., Yetkin, F.Z., Haughton, V.M., Hyde, J.S., 1995. Functional connectivity in themotor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34,537–541.

Calhoun, V.D., Altschul, D., McGinty, V., Shih, R., Scott, D., Sears, E., Pearlson, G.D., 2004a.Alcohol intoxication effects on visual perception: an fMRI study. Hum. Brain Mapp.21, 15–26.

Calhoun, V.D., Pekar, J.J., Pearlson, G.D., 2004b. Alcohol intoxication effects on simulateddriving: exploring alcohol-dose effects on brain activation using functional MRI.Neuropsychopharmacology 29, 2097–2107.

Calhoun, V.D., Liu, J., Adali, T., 2009. A review of group ICA for fMRI data and ICA for jointinference of imaging, genetic, and ERP data. Neuroimage 45, S163–S172.

Corbetta, M., Shulman, G.L., 2002. Control of goal-directed and stimulus-drivenattention in the brain. Nat. Rev. Neurosci. 3, 201–215.

Cox, R.W., 1996. AFNI: software for analysis and visualization of functional magneticresonance neuroimages. Comput. Biomed. Res. 29, 162–173.

Damoiseaux, J.S., Rombouts, S.A., Barkhof, F., Scheltens, P., Stam, C.J., Smith, S.M.,Beckmann, C.F., 2006. Consistent resting-state networks across healthy subjects.Proc. Natl. Acad. Sci. U. S. A. 103, 13848–13853.

Damoiseaux, J.S., Beckmann, C.F., Arigita, E.J., Barkhof, F., Scheltens, P., Stam, C.J., Smith,S.M., Rombouts, S.A., 2008. Reduced resting-state brain activity in the “defaultnetwork” in normal aging. Cereb. Cortex 18, 1856–1864.

D'Argembeau, A., Ruby, P., Collette, F., Degueldre, C., Balteau, E., Luxen, A., Maquet, P.,Salmon, E., 2007. Distinct regions of the medial prefrontal cortex are associated withself-referential processing and perspective taking. J. Cogn. Neurosci. 19, 935–944.

Davies, M., 2003. The role of GABAA receptors in mediating the effects of alcohol in thecentral nervous system. J. Psychiatry Neurosci. 28, 263–274.

De Luca, M., Beckmann, C.F., De Stefano, N., Matthews, P.M., Smith, S.M., 2006. fMRIresting state networks define distinct modes of long-distance interactions in thehuman brain. Neuroimage 29, 1359–1367.

De Martino, F., Gentile, F., Esposito, F., Balsi, M., Di Salle, F., Goebel, R., Formisano, E.,2007. Classification of fMRI independent components using IC-fingerprints andsupport vector machine classifiers. Neuroimage 34, 177–194.

Dougherty, D.M., Marsh, D.M., Moeller, F.G., Chokshi, R.V., Rosen, V.C., 2000. Effects ofmoderate and high doses of alcohol on attention, impulsivity, discriminability, andresponse bias in immediate and delayed memory task performance. Alcohol. Clin.Exp. Res. 24, 1702–1711.

Dougherty, D.M., Mathias, C.W., Tester, M.L., Marsh, D.M., 2004. Age at first drink relatesto behavioral measures of impulsivity: the immediate and delayed memory tasks.Alcohol. Clin. Exp. Res. 28, 408–414.

Dougherty, D.M., Marsh-Richard, D.M., Hatzis, E.S., Nouvion, S.O., Mathias, C.W., 2008. Atest of alcohol dose effects on multiple behavioral measures of impulsivity. DrugAlcohol Depend. 96, 111–120.

Eckardt, M.J., File, S.E., Gessa, G.L., Grant, K.A., Guerri, C., Hoffman, P.L., Kalant, H., Koob,G.F., Li, T.K., Tabakoff, B., 1998. Effects of moderate alcohol consumption on thecentral nervous system. Alcohol. Clin. Exp. Res. 22, 998–1040.

Ehlers, C.L., Wall, T.L., Schuckit, M.A., 1989. EEG spectral characteristics following ethanoladministration in young men. Electroencephalogr. Clin. Neurophysiol. 73, 179–187.

Esposito, F., Scarabino, T., Hyvarinen, A., Himberg, J., Formisano, E., Comani, S., Tedeschi,G., Goebel, R., Seifritz, E., Di Salle, F., 2005. Independent component analysis of fMRIgroup studies by self-organizing clustering. Neuroimage 25, 193–205.

Esposito, F., Aragri, A., Pesaresi, I., Cirillo, S., Tedeschi, G., Marciano, E., Goebel, R., DiSalle, F., 2008. Independent component model of the default-mode brain function:combining individual-level and population-level analyses in resting-state fMRI.Magn. Reson. Imaging 26, 905–913.

Esposito, F., Aragri, A., Latorre, V., Popolizio, T., Scarabino, T., Cirillo, S., Marciano, E.,Tedeschi, G., Di Salle, F., 2009. Does the default-mode functional connectivity of thebrain correlate with working-memory performances? Arch. Ital. Biol. 147, 11–20.

Feige, B., Scheffler, K., Esposito, F., Di Salle, F., Hennig, J., Seifritz, E., 2005. Cortical andsubcortical correlates of electroencephalographic alpha rhythm modulation. J.Neurophysiol. 93, 2864–2872.

Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., Noll, D.C., 1995.Improved assessment of significant activation in functional magnetic resonanceimaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33, 636–647.

Friston, K.J., Holmes, A.P., Poline, J.B., Grasby, P.J., Williams, S.C., Frackowiak, R.S., Turner,R., 1995. Analysis of fMRI time-series revisited. Neuroimage 2, 45–53.

Fukunaga, M., Horovitz, S.G., de Zwart, J.A., van Gelderen, P., Balkin, T.J., Braun, A.R.,Duyn, J.H., 2008. Metabolic origin of BOLD signal fluctuations in the absence ofstimuli. J. Cereb. Blood Flow Metab. 28, 1377–1387.

Giancola, P.R., 2002a. Alcohol-related aggression in men and women: the influence ofdispositional aggressivity. J. Stud. Alcohol 63, 696–708.

Giancola, P.R., 2002b. Irritability, acute alcohol consumption and aggressive behavior inmen and women. Drug Alcohol Depend. 68, 263–274.

Glover, G.H., Li, T.Q., Ress, D., 2000. Image-based method for retrospective correction ofphysiological motion effects in fMRI: RETROICOR. Magn. Reson. Med. 44, 162–167.

Goebel, R., Esposito, F., Formisano, E., 2006. Analysis of functional image analysiscontest (FIAC) data with brainvoyager QX: from single-subject to cortically alignedgroup general linear model analysis and self-organizing group independentcomponent analysis. Hum. Brain Mapp. 27, 392–401.

Goldman, R.I., Stern, J.M., Engel Jr., J., Cohen, M.S., 2002. Simultaneous EEG and fMRI ofthe alpha rhythm. Neuroreport 13, 2487–2492.

Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in theresting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad.Sci. U. S. A. 100, 253–258.

Greicius, M.D., Srivastava, G., Reiss, A.L., Menon, V., 2004. Default-mode networkactivity distinguishes Alzheimer's disease from healthy aging: evidence fromfunctional MRI. Proc. Natl. Acad. Sci. U. S. A. 101, 4637–4642.

Greicius, M.D., Flores, B.H., Menon, V., Glover, G.H., Solvason, H.B., Kenna, H., Reiss, A.L.,Schatzberg, A.F., 2007. Resting-state functional connectivity in major depression:abnormally increased contributions from subgenual cingulate cortex and thalamus.Biol. Psychiatry 62, 429–437.

Greicius, M.D., Kiviniemi, V., Tervonen, O., Vainionpaa, V., Alahuhta, S., Reiss, A.L.,Menon, V., 2008. Persistent default-mode network connectivity during lightsedation. Hum. Brain Mapp. 29, 839–847.

Greicius, M.D., Supekar, K., Menon, V., Dougherty, R.F., 2009. Resting-state functionalconnectivity reflects structural connectivity in the default mode network. Cereb.Cortex 19, 72–78.

Gundersen, H., Gruner, R., Specht, K., Hugdahl, K., 2008. The effects of alcoholintoxication on neuronal activation at different levels of cognitive load. OpenNeuroimaging J. 2, 65–72.

Gusnard, D.A., Raichle, M.E., 2001. Searching for a baseline: functional imaging and theresting human brain. Nat. Rev. Neurosci. 2, 685–694.

Hill, J.C., Toffolon, G., 1990. Effect of alcohol on sensory and sensorimotor visualfunctions. J. Stud. Alcohol 51, 108–113.

Hyvarinen, A., 1999. Fast and robust fixed-point algorithms for independentcomponent analysis. IEEE Trans. Neural Netw. 10, 626–634.

Ilan, A.B., Gevins, A., 2001. Prolonged neurophysiological effects of cumulative winedrinking. Alcohol 25, 137–152.

Koch,W., Teipel, S., Mueller, S., Buerger, K., Bokde, A.L., Hampel, H., Coates, U., Reiser, M.,Meindl, T., 2009. Effects of aging on default mode network activity in resting statefMRI: does the method of analysis matter? Neuroimage.

Levin, J.M., Ross, M.H., Mendelson, J.H., Kaufman, M.J., Lange, N., Maas, L.C., Mello, N.K.,Cohen, B.M., Renshaw, P.F., 1998. Reduction in BOLD fMRI response to primaryvisual stimulation following alcohol ingestion. Psychiatry Res. 82, 135–146.

Lowe, M.J., Mock, B.J., Sorenson, J.A., 1998. Functional connectivity in single andmultislice echoplanar imaging using resting-state fluctuations. Neuroimage 7,119–132.

Mantini, D., Perrucci, M.G., Del Gratta, C., Romani, G.L., Corbetta, M., 2007.Electrophysiological signatures of resting state networks in the human brain.Proc. Natl. Acad. Sci. U. S. A. 104, 13170–13175.

McKeown, M.J., Makeig, S., Brown, G.G., Jung, T.P., Kindermann, S.S., Bell, A.J., Sejnowski,T.J., 1998. Analysis of fMRI data by blind separation into independent spatialcomponents. Hum. Brain Mapp. 6, 160–188.

Meda, S.A., Calhoun, V.D., Astur, R.S., Turner, B.M., Ruopp, K., Pearlson, G.D., 2009.Alcohol dose effects on brain circuits during simulated driving: an fMRI study. Hum.Brain Mapp. 30, 1257–1270.

Miller, C.A., Parrott, D.J., Giancola, P.R., 2009. Agreeableness and alcohol-relatedaggression: the mediating effect of trait aggressivity. Exp. Clin. Psychopharmacol.17, 445–455.

Moosmann, M., Ritter, P., Krastel, I., Brink, A., Thees, S., Blankenburg, F., Taskin, B., Obrig,H., Villringer, A., 2003. Correlates of alpha rhythm in functional magnetic resonanceimaging and near infrared spectroscopy. Neuroimage 20, 145–158.

Nicholson, M.E., Andre, J.T., Tyrrell, R.A., Wang, M., Leibowitz, H.W., 1995. Effects ofmoderate dose alcohol on visual contrast sensitivity for stationary and movingtargets. J. Stud. Alcohol 56, 261–266.

Nikulin, V.V., Nikulina, A.V., Yamashita, H., Rossi, E.M., Kahkonen, S., 2005. Effects ofalcohol on spontaneous neuronal oscillations: a combined magnetoencephalogra-phy and electroencephalography study. Prog. Neuropsychopharmacol. Biol.Psychiatry 29, 687–693.

Noldy, N.E., Carlen, P.L., 1990. Acute, withdrawal, and chronic alcohol effects in man:event-related potential and quantitative EEG techniques. Ann. Med. 22, 333–339.

Oakes, T.R., Pizzagalli, D.A., Hendrick, A.M., Horras, K.A., Larson, C.L., Abercrombie, H.C.,Schaefer, S.M., Koger, J.V., Davidson, R.J., 2004. Functional coupling of simultaneouselectrical and metabolic activity in the human brain. Hum. Brain Mapp. 21,257–270.

Olmstead, M.C., Hellemans, K.G., Paine, T.A., 2006. Alcohol-induced impulsivity in rats:an effect of cue salience? Psychopharmacology (Berl.) 184, 221–228.

Ortner, C.N., MacDonald, T.K., Olmstead, M.C., 2003. Alcohol intoxication reducesimpulsivity in the delay-discounting paradigm. Alcohol Alcohol. 38, 151–156.

Pearson, P., Timney, B., 1998. Effects of moderate blood alcohol concentrations onspatial and temporal contrast sensitivity. J. Stud. Alcohol 59, 163–173.

Pfurtscheller, G., Stancak Jr., A., Neuper, C., 1996. Event-related synchronization (ERS)in the alpha band—an electrophysiological correlate of cortical idling: a review. Int.J. Psychophysiol. 24, 39–46.

Raichle, M.E., MacLeod, A.M., Snyder, A.Z., Powers, W.J., Gusnard, D.A., Shulman, G.L.,2001. A default mode of brain function. Proc. Natl. Acad. Sci. U. S. A. 98, 676–682.

Rzepecki-Smith, C.I., Meda, S.A., Calhoun, V.D., Stevens, M.C., Jafri, M.J., Astur, R.S.,Pearlson, G.D., 2009. Disruptions in functional network connectivity during alcoholintoxicated driving. Alcohol. Clin. Exp. Res.

Sano,M.,Wendt, P.E.,Wirsen, A., Stenberg, G., Risberg, J., Ingvar, D.H., 1993. Acute effects ofalcohol on regional cerebral blood flow in man. J. Stud. Alcohol 54, 369–376.

543F. Esposito et al. / NeuroImage 53 (2010) 534–543

Seifritz, E., Bilecen, D., Hanggi, D., Haselhorst, R., Radu, E.W., Wetzel, S., Seelig, J.,Scheffler, K., 2000. Effect of ethanol on BOLD response to acoustic stimulation:implications for neuropharmacological fMRI. Psychiatry Res. 99, 1–13.

Seifritz, E., Esposito, F., Hennel, F., Mustovic, H., Neuhoff, J.G., Bilecen, D., Tedeschi, G.,Scheffler, K., Di Salle, F., 2002. Spatiotemporal pattern of neural processing in thehuman auditory cortex. Science 297, 1706–1708.

Shaffar, M., Stroup, S.D., 1983. A general method for routine clinical chemistry in theAbbott TDx® analyzer. Clin. Chem. 29, 1251.

Smith, S.M., Fox, P.T., Miller, K.L., Glahn, D.C., Fox, P.M., Mackay, C.E., Filippini, N.,Watkins, K.E., Toro, R., Laird, A.R., et al., 2009. Correspondence of the brain'sfunctional architecture during activation and rest. Proc. Natl. Acad. Sci. U. S. A. 106,13040–13045.

Sonuga-Barke, E.J., Castellanos, F.X., 2007. Spontaneous attentional fluctuations inimpaired states and pathological conditions: a neurobiological hypothesis.Neurosci. Biobehav. Rev. 31, 977–986.

Sorg, C., Riedl, V., Muhlau, M., Calhoun, V.D., Eichele, T., Laer, L., Drzezga, A., Forstl, H., Kurz,A., Zimmer, C., et al., 2007. Selective changes of resting-state networks in individualsat risk for Alzheimer's disease. Proc. Natl. Acad. Sci. U. S. A. 104, 18760–18765.

Steele, C.M., Josephs, R.A., 1990. Alcohol myopia. Its prized and dangerous effects. Am.Psychol. 45, 921–933.

van de Ven, V.G., Formisano, E., Prvulovic, D., Roeder, C.H., Linden, D.E., 2004. Functionalconnectivity as revealed by spatial independent component analysis of fMRImeasurements during rest. Hum. Brain Mapp. 22, 165–178.

van de Ven, V., Esposito, F., Christoffels, I.K., 2009. Neural network of speechmonitoringoverlaps with overt speech production and comprehension networks: a sequentialspatial and temporal ICA study. Neuroimage 47, 1982–1991.

van den Heuvel, M.P., Mandl, R.C., Kahn, R.S., Hulshoff Pol, H.E., 2009. Functionallylinked resting-state networks reflect the underlying structural connectivityarchitecture of the human brain. Hum. Brain Mapp. 30, 3127–3141.

Van Horn, J.D., Yanos, M., Schmitt, P.J., Grafton, S.T., 2006. Alcohol-induced suppressionof BOLD activity during goal-directed visuomotor performance. Neuroimage 31,1209–1221.

Vengeliene, V., Bilbao, A., Molander, A., Spanagel, R., 2008. Neuropharmacology ofalcohol addiction. Br. J. Pharmacol. 154, 299–315.

Volkow, N.D., Ma, Y., Zhu, W., Fowler, J.S., Li, J., Rao, M., Mueller, K., Pradhan, K., Wong,C., Wang, G.J., 2008. Moderate doses of alcohol disrupt the functional organizationof the human brain. Psychiatry Res. 162, 205–213.

Wang, G.J., Volkow, N.D., Franceschi, D., Fowler, J.S., Thanos, P.K., Scherbaum, N., Pappas,N., Wong, C.T., Hitzemann, R.J., Felder, C.A., 2000. Regional brain metabolism duringalcohol intoxication. Alcohol. Clin. Exp. Res. 24, 822–829.

Wang, K., Jiang, T., Yu, C., Tian, L., Li, J., Liu, Y., Zhou, Y., Xu, L., Song, M., Li, K., 2008.Spontaneous activity associated with primary visual cortex: a resting-state FMRIstudy. Cereb. Cortex 18, 697–704.

Watten, R.G., Magnussen, S., Greenlee, M.W., 1998. Spatial-frequency discrimination,brain lateralisation, and acute intake of alcohol. Perception 27, 729–736.

Weiner, J.L., Valenzuela, C.F., 2006. Ethanol modulation of GABAergic transmission: theview from the slice. Pharmacol. Ther. 111, 533–554.

Wilson, G., Mitchell, R., 1983. The effect of alcohol on the visual and ocular motorsystems. Aust. J. Ophthalmol. 11, 315–319.

Worden, M.S., Foxe, J.J., Wang, N., Simpson, G.V., 2000. Anticipatory biasing ofvisuospatial attention indexed by retinotopically specific alpha-band electroen-cephalography increases over occipital cortex. J. Neurosci. 20, RC63.