1 23

NeuroradiologyA Journal Dedicated to Neuroimagingand Interventional Neuroradiology ISSN 0028-3940 NeuroradiologyDOI 10.1007/s00234-015-1561-1

Effects of scanner acoustic noise on intrinsicbrain activity during auditory stimulation

Natalia Yakunina, Eun Kyoung Kang,Tae Su Kim, Ji-Hoon Min, Sam Soo Kim& Eui-Cheol Nam

1 23

Your article is protected by copyright andall rights are held exclusively by Springer-Verlag Berlin Heidelberg. This e-offprint isfor personal use only and shall not be self-archived in electronic repositories. If you wishto self-archive your article, please use theaccepted manuscript version for posting onyour own website. You may further depositthe accepted manuscript version in anyrepository, provided it is only made publiclyavailable 12 months after official publicationor later and provided acknowledgement isgiven to the original source of publicationand a link is inserted to the published articleon Springer's website. The link must beaccompanied by the following text: "The finalpublication is available at link.springer.com”.

FUNCTIONAL NEURORADIOLOGY

Effects of scanner acoustic noise on intrinsic brain activityduring auditory stimulation

Natalia Yakunina1,2 & Eun Kyoung Kang3 & Tae Su Kim4,7 & Ji-Hoon Min5 &

Sam Soo Kim2,6 & Eui-Cheol Nam2,7

Received: 19 March 2015 /Accepted: 7 July 2015# Springer-Verlag Berlin Heidelberg 2015

AbstractIntroduction Although the effects of scanner backgroundnoise (SBN) during functional magnetic resonance imaging(fMRI) have been extensively investigated for the brain re-gions involved in auditory processing, its impact on othertypes of intrinsic brain activity has largely been neglected.The present study evaluated the influence of SBN on a num-ber of intrinsic connectivity networks (ICNs) during auditorystimulation by comparing the results obtained using sparsetemporal acquisition (STA) with those using continuous ac-quisition (CA).

Methods Fourteen healthy subjects were presented with clas-sical music pieces in a block paradigm during two sessions ofSTA and CA. A volume-matched CA dataset (CAm) wasgenerated by subsampling the CA dataset to temporally matchit with the STA data. Independent component analysis wasperformed on the concatenated STA–CAm datasets, and voxeldata, time courses, power spectra, and functional connectivitywere compared.Results The ICA revealed 19 ICNs; the auditory, defaultmode, salience, and frontoparietal networks showed greateractivity in the STA. The spectral peaks in 17 networkscorresponded to the stimulation cycles in the STA, while onlyfive networks displayed this correspondence in the CA. Thedorsal default mode and salience networks exhibited strongercorrelations with the stimulus waveform in the STA.Conclusions SBN appeared to influence not only the areas ofauditory response but also the majority of other ICNs, includ-ing attention and sensory networks. Therefore, SBN should beregarded as a serious nuisance factor during fMRI studiesinvestigating intrinsic brain activity under external stimulationor task loads.

Keywords FunctionalMRI . Scanner background noise .

Auditory stimulation . Intrinsic connectivity network . Sparsetemporal acquisition

Introduction

Scanner background noise (SBN), generated from the me-chanical oscillations of the gradient coils placed in a magneticfield, is an integral part of the functional magnetic resonanceimaging (fMRI) process. During auditory stimulation, SBNcan mask auditory target stimuli, hindering their perception,modulate the auditory response to the stimuli, and impair task

Electronic supplementary material The online version of this article(doi:10.1007/s00234-015-1561-1) contains supplementary material,which is available to authorized users.

* Eui-Cheol Nambirdynec@kangwon.ac.kr

1 Institute ofMedical Science, School ofMedicine, KangwonNationalUniversity, Chuncheon, Republic of Korea

2 Neuroscience Research Institute, Kangwon National UniversityHospital, Chuncheon, Republic of Korea

3 Department of Rehabilitation Medicine, Kangwon NationalUniversity Hospital, Chuncheon, Republic of Korea

4 Department of Otolaryngology, Kangwon National UniversityHospital, Chuncheon, Republic of Korea

5 Department of Biopsychology, Cognition, and Neuroscience,University of Michigan, Ann Arbor, MI, USA

6 Department of Radiology, Kangwon National University, School ofMedicine, Chuncheon, Republic of Korea

7 Department of Otolaryngology, Kangwon National University,School of Medicine, Kangwondaehak-gil 1, Chuncheon 200-701,Republic of Korea

NeuroradiologyDOI 10.1007/s00234-015-1561-1

Author's personal copy

performance due to emotional stress [1–3]. Additionally, SBNelevates baseline levels of auditory neural signals, which oftenresults in a decreased (or saturated) aimed response to a trueauditory stimulus [4]. SBN is notorious for its detrimentalinfluence on auditory responses [1, 5–7], but neural mecha-nisms that are not directly involved in auditory processing,such as visual and motor [8–11], working memory [12, 13],and affective neural [14] processes, are affected as well. Tominimize the unwanted effects of SBN, the use of sparse tem-poral acquisition (STA) has become common. This techniquecombines clustered slice acquisition (versus evenly distributedslice acquisition) with long time intervals between scans,which allows the hemodynamic responses induced by SBNto decay or degrade close to baseline level before the nextimage acquisition [15]. The advantages to the STA methodover the conventional continuous acquisition (CA) techniquein terms of reducing SBN and improving the detection ofauditory responses to acoustic stimuli have been shown in anumber of studies [16–18].

Recently, inherent low-frequency spontaneous fluctuationsin the brain that form distinct patterns of intrinsic coherentactivity that are consistent across subjects have been gainingattention in the field of neuroimaging [19–21]. Initially dem-onstrated to be active during rest [22, 23], these intrinsic con-nectivity networks (ICNs) persist under task load [24–26] andpresent a powerful tool for investigating human brain functionand architecture. Although the SBN effect is likely not limitedto auditory areas alone, only one study has investigated SBNeffect on other ICNs during resting state and auditory memorytask [26]. An auditory study using STA revealed that the ma-jority of ICNs were affected by passive acoustic stimulationand that their fluctuation patterns changed to follow the stim-ulus presentation cycles [27]. Constant SBN, as observed dur-ing the continuous sampling commonly used in ICN studies[28–32], could hinder this pattern of stimulus-induced behav-ior and affect the neural response of a multitude of brain net-works, possibly altering their temporal and spatial dynamics.Thus, the present study investigated the effects of SBN on all(identified) ICNs during auditory stimulation in terms of theirspatial organization, functional connectivity, temporal behav-ior, and relation to the stimulus. Two acquisition methodswere used: STA as a Bquiet^ imaging method with reducedSBN effects and CA as a general Bnoisy^ method with thecontinuous presence of scanner noise.

Materials and methods

Subjects

The present study included 14 healthy subjects (eight males,mean age 30.6±4.7 years) with normal hearing (<20 dB hear-ing level in the standard audiometric frequency range of 250–

8000 Hz) who had no auditory, neurological, or neuropsycho-logical disorder. Prior to image acquisition, all subjectsunderwent pure-tone audiometry to evaluate their loudnessdiscomfort level and auditory dynamic range; subjects whomet the criteria for hyperacusis were excluded from the study[33].

The present study was approved by the Institutional Re-view Board of our hospital, and all subjects provided writteninformed consent prior to participation in the study.

Stimuli and design

The auditory stimuli utilized in the present study consisted ofseveral pieces of classical instrumental music that were pre-sented binaurally in a block paradigm (36 s on/off), five timesduring each run. The pieces of classical music were (1) Bach’sMarch Fur Die Arche, (2) Vivaldi’s Concerto No.1 Spring,Allegro, (3) Vivaldi’s The Four Seasons (Summer), ConcertoNo. 2 in G Minor, (4) Mendelssohn’s The Hebrides, Op. 26,BFingal’s Cave^, and (5) Mozart’s Symphony No.25 in GMinor, K.183-1, Allegro Con Brio, repeated in the given orderthroughout each run for every subject in each acquisitionmethod. Auditory stimuli were presented using NordicNeuroLab audiosystem headphones (Nordic NeuroLab AS,Bergen, Norway). During imaging, the subjects wereinstructed to keep their eyes closed throughout each run andlisten intently to the music. Each subject underwent two STAand two CA 6-min auditory stimulation runs. Subjects wore aprotective headset to diminish the scanner noise; this reducedthe SBN from an original level of 115-dB sound pressure level(SPL) to 95-dB SPL, on average. Additionally, the scannercoolant pump was turned off during imaging to further reduceambient noise levels.

Image acquisition

All fMRI scanning was conducted with a Philips Achieva 3.0-T MRI scanner using a 32-channel Philips SENSE head coil.In order to normalize the functional data into a common ste-reotactic space, coronal 3D T1-weighted whole brain anatomyimages were acquired with the following parameters: time ofrepetition (TR)=9.8 ms, echo time (TE)=4.8 ms, fractionalanisotropy (FA)=8°, slice thickness=1.0 mm, matrix=256×256×195, field of view (FOV)=220×220 mm2, and voxelsize=0.94×0.94 mm. Functional images were obtained from30 oblique coronal slices covering the whole brain with a T2*-weighted single shot gradient echo-planar imaging (EPI) se-quence with the following parameters: TE=35 ms, FA=90°,slice thickness=5 mm, 1-mm gap, matrix=80×80, FOV=220×220 mm2, voxel size=2.75×2.75 mm. The 19thanterior-most slice was positioned to intersect the inferiorcolliculi and the cochlear nuclei in the brainstem. In the con-tinuous runs, images were acquired without silent gaps

Neuroradiology

Author's personal copy

between acquisitions (repetition time (TR)=2 s, acquisitiontime (TA)=1.88 s, 180 volumes in each run), and in the sparseruns, the functional acquisitions were separated by silent pe-riods of approximately 10 s that were free of scanner noise(TR=12 s, TA=1.88 s, 30 volumes in each run). The totalscanning time was identical in the STA and CA procedures.

Data preprocessing

The data were preprocessed using SPM8 (WellcomeDepartmentof Cognitive Neurology and Collaborators, Institute of Neurolo-gy, London, UK; http://www.fil.ion.ucl.ac.uk/spm/software) andimplemented in MATLAB (ver. 2009a; MathWorks, Inc.,Natick, MA). The functional images were spatially realigned tothe first image in the run series to correct for the head motion ofeach subject and then coregistered, spatially normalized to thestandardMontreal Neurological Institute (MNI) T1 template, andsmoothed with an 8-mm full-width-at-half-maximum (FWHM)isotropic Gaussian kernel.

Independent component analysis

To account for the different sample sizes of the two datasetsfrom the two acquisition methods, a volume-matched contin-uous dataset (CAm, 60 volumes) was generated by collectingevery sixth volume from the CA dataset that temporallycorresponded to the volumes from the STA (Fig. 1). The twodatasets with the same number of volumes (STA and CAm)were then analyzed to assess the SBN effect. Spatial ICAwasperformed using Group ICA in the fMRI toolbox (GIFT, ver.1.3i) [34]. A higher model order was decided upon for ICA toobtain a detailed representation of the ICNs rather than a gen-eral picture. After decomposing with different model ordersbeginning with the highest possible order, the order of 40components was selected because higher orders resulted in

reduced stability of the components and a greater number ofartifactual networks.

For group ICA, STA and CAm data were concatenatedwithin each subject, and then a principal component analysiswas conducted, which reduced the dimensionality of the datain two stages by retaining only the strongest principal compo-nents [35]. First, each subject’s data were reduced to 40 prin-cipal components, and then the reduced individual data wereconcatenated in time to form a single group-level dataset,which was again reduced to 40 components. The independentcomponents were extracted using the InfoMax algorithm [36],and 20 iterations of ICA were performed using the ICASSOalgorithm [37] to verify the stability of the components. AfterICA estimation, the time courses and maps of the componentswere back reconstructed, which resulted in ordered matchedsets of individual components for each subject. All individualcomponents were scaled to z-scores. Ultimately, 19 compo-nents were selected as of interest for further analysis becauseof (1) their non-artifactual nature determined by visual inspec-tion (components were judged as artifactual if they tended togroup with cerebrospinal fluid (CSF) spaces, border withbrain edges, or form a spatially scattered pattern) and (2) highspatial correlation with existing ICN templates established in aprevious multi-subject resting-state study [28].

Group statistical maps for each of the selected componentswere generated by performing a voxel-wise one-sample t teston the individual independent component maps. The mapswere then thresholded at a false discovery rate (FDR)corrected to q<0.05.

All statistical analyses were performed using SPSS soft-ware (ver. 19.0; IBM Corp., Armonk, NY). The number ofvoxels, average z-score, and maximum z-score were calculat-ed for each selected individual ICN of each subject, and thesevalues were then compared between the STA and CAmdatasets using paired t tests.

Fig. 1 Experimental design and generation of the datasets. a In the sparsetemporal acquisition (STA) method, the images were collected with silentinter-scan intervals of approximately 10 s. b In the continuous acquisition(CA) method, the volumes were continuously acquired with no quiet

intervals between acquisitions. c A volume-matched continuous dataset(CAm) was generated by collecting every sixth volume from the CA andmatching it in time to the volumes of the STA

Neuroradiology

Author's personal copy

Time course analysis

A fast Fourier transform (FFT) was performed on the timecourse of each component, concatenated across subjects,to obtain the frequency power spectrum. Prior to theFFT, a Butterworth high-pass filter was applied to alltime courses, with the lower cutoff frequency set to0.01 Hz [23]. For the correlation analysis, the timecourses were averaged over a single run. The stimuluspresentation timing waveform was obtained by convolv-ing the stimulus boxcar function with the hemodynamicresponse function and then subsampling it at 12-s inter-vals. Pearson correlation between the individual compo-nent time courses and the stimulus waveform was per-formed to determine the degree of correlation betweenthe corresponding components and the stimulus. Thesecorrelation coefficients were then converted to Fisher’sz-scores and compared between the two datasets usingpaired t tests.

Functional connectivity analysis

To estimate the temporal relationships among the ICNs, thetime courses of the 19 selected components were entered intothe Functional Network Connectivity (FNC) toolbox (http://mialab.mrn.org/software). The time courses were first high-pass filtered with a Butterworth filter with a lower cutoff fre-quency of 0.01 Hz, and then the maximal lagged correlationbetween all pairwise combinations of the time courses (171pairs) was estimated [38]. The maximum possible lag betweentime courses was set at 6 s. The resulting Pearson’s correlationcoefficients were transformed to Fisher’s z-scores and com-pared between the two datasets using paired t tests. To controlfor multiple comparisons, the p values were thresholded ac-cording to a FDR of 0.05.

Signal-to-noise ratio calculation

Signal-to-noise ratio (SNR) of the two datasets was assessed,since it may have differed between the two datasets becausethe longer inter-acquisition intervals in the STA allow for bet-ter signal recovery [15]. Two regions of interest (ROIs) weredrawn: one consisted of two spheres (of 10-mm radius) in graymatter of the postcentral gyrus (area that would not be affectedby the auditory stimulation), and another one comprised twospheres of the same size outside of the brain, avoiding areas ofghosting and filter artifacts, which result in an increased signalnear the edge of the image. Normalized EPI images wereaveraged through each run for every subject, which resultedin 28 mean EPIs for each acquisition method (two per sub-ject). SNR was calculated for each mean EPI as the meansignal in gray matter ROI divided by the standard deviationof the signal in the ROI outside of the brain; it was then

multiplied by the factor of 0.655 to account for Rician distri-bution of the background noise [39, 40].

Results

Independent components analysis

All identified components had very high stability indices(mean, 0.968±0.017). Nineteen ICNs were identified amongthe ICA results: auditory (AUD), cerebellum (CBLL), ventral/dorsal/anterior default mode (DMNve/do/an), salience (SAL),left/right frontoparietal (LFPN/RFPN), dorsal attention(DAN), ventral attention (VAN), visuospatial (VISSPT), an-terolateral/posterolateral/anteromedial/posteromedial/right-left lateral sensorimotor (SMNala/pla/ame/pme/rtlt), and pri-mary/extrastriate/lateral visual (VISpr/ex/la; Fig. 2). The STAproduced a greater number of voxels in the SAL network,higher average z-values for the AUD, DMNdo, DMNan,LFPN, and RFPN networks, and greater maximum z-valuesfor the AUD network relative to the CAm. The CAm pro-duced a greater maximum z-score for SMNpme (Table 1).

Time course analysis

In the STA, 17 of the 19 ICNs showed the greatest spectralpeak corresponding to the fundamental frequency of the stim-ulus cycles (five cycles/run, or 0.01389 Hz) or its secondharmonic (0.02778 Hz; Fig. 2, Table 2). In contrast, theCAm dataset had only five networks whose spectral peakswere at the stimulation frequency (all five networks exhibitedstimulation-related peaks in the STA dataset as well). In thecorrelation analysis, the time courses of the DMNdo, SAL,and SMName networks had significantly stronger negativecorrelations with the stimulus waveforms in the STA. Signif-icant differences in the correlation values were also identifiedin the VAN, but both of these values were extremely weak(very close to zero).

Functional connectivity

Pairwise comparisons of the internetwork functional connec-tions between the datasets did not reveal any statistical differ-ences in connectivity patterns among the 19 ICNs betweenSTA and CAm (Supplementary Table 1).

Signal-to-noise ratio calculation

The average SNR of the STA and CAm datasets was 26.98±2.70 and 26.92±2.71, respectively. Paired comparison did notreveal a significant difference (p=0.362, Wilcoxon signedrank sum test).

Neuroradiology

Author's personal copy

Discussion

In the present study, STA was used as a quiet method as op-posed to the common noisy CA. Although proven to be

effective in reducing SBN and facilitating the detection ofstimulus- and task-related brain responses, the STA techniquehas a major drawback of obtaining a significantly smallernumber of functional volumes compared with the CA method

Fig. 2 Spatial maps, time courses, and power spectra of the 19 identifiedintrinsic networks in the two datasets. Spatial maps are thresholded at q<0.05 (FDR corrected) and displayed at the most informative coronal slicefollowing neurological convention (the left side of the image correspondsto the left side of the brain). STA time courses (blue) and CAm timecourses (red) are averaged across subjects and runs. Gray barsrepresent stimulus-on periods, and the length of bars is fixed for all plots(−1.5 to 1.5 in z-scores). The power spectra are presented on a linearscale; the vertical axis represents the power of each frequency (the axislength is fixed at 0 to 0.025 for all plots except the plot of the auditory

network, which is fixed at 0 to 0.18). The greatest spectral peaks aremarked with a black dot. STA power spectra (top panel) and CAm powerspectra (bottom panel). Red dotted lines indicate the frequency of thestimulus cycles and its second harmonic. STA sparse temporal acquisition,CAm matched continuous acquisition, AUD auditory, CBLL cerebellum,DMNve/do/an ventral/dorsal/anterior default mode, SAL salience, LFPN/RFPN left/right frontoparietal, DAN dorsal attention, VAN ventral atten-tion, VISSPT visuospatial, SMNala/pla/ame/pme/rtlt anterolateral/pos-terolateral/anteromedial/posteromedial/right-left sensorimotor, VISpr/ex/la primary/extrastriate/lateral visual networks

Neuroradiology

Author's personal copy

during the same time period. The resulting difference in sam-ple size between the two methods presented a significant con-founding variable that would have affected the results of theICA and the temporal analyses in the present study. Thus, thisdiscrepancy was accommodated by creating the CAm dataset,which was volume matched in time with the STA dataset.

The ICA successfully extracted a number of ICNs that coin-cided well with previous studies [24, 28, 41]. Among the com-ponents in both acquisition methods, the AUD network, whichcomprises the primary and secondary auditory cortices, showedthe strongest correlation with the stimulus waveform and themost prominent spectral peak at the frequency of stimulus cyclesin both acquisition methods. However, the voxel-wise analysisrevealed stronger activity level of this network during STA. Thedetrimental effect of SBN on auditory perception is widely ap-preciated; thus, the more vital behavior of the auditory networkduring STA should be readily expected. Only one other networkexhibited a positive correlation with stimulus timing, namely theCBLL. The involvement of the cerebellum in sensory auditoryprocessing, particularly in the perception of music, has beenestablished by several studies [42–45]. Although the CBLL ex-hibited spectral peaks that corresponded to the stimulation fre-quency and its second harmonic peak in STA, it did not exhibiteither during CA. This behavior may be attributed to SBN’sinterfering with auditory perception.

The DMN is associated with reflection and introspection. Itis active in the absence of any goal-directed behavior or ex-ternal stimuli, and it is believed to be suppressed (deactivated)during attention-demanding tasks or stimulation to supporttask-related processes [46, 47]. Originally thought of as a sin-gle network encompassing the medial prefrontal cortex, pos-terior cingulate cortex (PCC), precuneus, and parietal cortex,the DMN appears to be rather heterogeneous, with differenthubs exhibiting different functional connections that support avariety of functions [48, 49]. In the present study, the DMNwas subdivided into anterior (medial prefrontal cortex), dorsalposterior (dorsal PCC/precuneus), and ventral posterior (ven-tral PCC/precuneus) components. The dorsal and anteriorDMN demonstrated significantly higher z-values in STA. Ad-ditionally, all three DMN networks showed stronger anti-correlation with the stimulus (deactivation) during STA, butthe dorsal DMN had the strongest correlation of the three; thiscorrelation was significantly higher in STA than in CA(Table 2). A similar trend was observed in the spectral analy-sis, where all three networks featured their greatest spectralpeaks consistent with the stimulation cycle in STA and not inCA; the highest peak was displayed by the dorsal DMN. It hasbeen shown that the dorsal and ventral parts of the PCC, whichis the major hub of the posterior DMN, are functionally dis-tinct and behave differently during attentional control: theventral PCC shows increased connectivity with the DMN dur-ing rest and internally focused tasks, whereas the dorsal PCCbecomes more integrated with the DMN as the external taskload increases [50, 51]. This may explain why the dorsalDMN had the strongest reaction to the stimulus among thethree DMN components. Furthermore, the stronger DMN de-activation during auditory stimulation in STA suggests that thebrain is more involved with the stimulus in the quiet environ-ment of STA, more actively suppressing its internally directed

Table 1 Mean and standard deviation of voxel number, average z-score, and maximum z-score for the identified 19 ICNs

ICN Dataset Number of voxels Average z Maximum z

AUD STA 6259.2±493.2 4.10±0.35* 14.96±2.58*

CAm 6431.9±450.4 3.70±0.26* 12.59±1.99*

CBLL STA 8236.1±759.1 2.67±0.07 7.83±1.58

CAm 8214.7±775.8 2.68±0.08 7.95±1.42

DMNve STA 7264.9±556.0 2.85±0.13 8.49±2.22

CAm 7038.6±673.6 2.88±0.10 9.10±1.83

DMNdo STA 6136.3±579.1 3.27±0.29* 11.62±2.84

CAm 6349.6±534.5 3.07±0.21* 10.94±3.02

DMNa STA 8219.9±448.5 3.16±0.11* 9.14±1.21

CAm 8387.9±404.3 2.94±0.12* 8.93±1.11

SAL STA 8185.6±591.3* 2.91±0.13 9.01±1.85

CAm 7582.4±546.6* 2.84±0.12 8.99±1.84

LFTN STA 9383.7±505.7 3.03±0.12* 9.28±1.92

CAm 9128.4±509.6 2.82±0.07* 10.38±2.79

RFPN STA 8436.9±561.3 2.95±0.11* 9.52±1.73

CAm 8009.9±464.8 2.81±0.12* 9.64±1.92

DAN STA 7295.5±625.5 3.12±0.33 9.63±1.99

CAm 7454.6±640.8 3.00±0.23 10.56±1.60

VAN STA 7513.1±821.8 2.70±0.10 7.85±1.17

CAm 7416.5±837.5 2.67±0.09 9.54±3.40

VISSPT STA 5629.9±421.7 2.63±0.07 8.35±2.94

CAm 5561.8±623.4 2.66±0.10 9.14±2.29

SMNala STA 6672.5±573.4 2.87±0.34 8.93±2.09

CAm 6543.1±547.8 2.82±0.25 8.88±1.58

SMNpla STA 7834.5±588.0 2.74±0.09 7.90±1.14

CAm 7526.9±631.1 2.71±0.10 7.85±0.96

SMName STA 7378.1±546.6 2.75±0.12 9.93±2.66

CAm 7108.3±718.7 2.69±0.12 10.17±4.04

SMNpme STA 7777.1±551.5 3.07±0.30 9.15±1.68*

CAm 7850.5±625.1 2.98±0.24 9.98±1.88*

SMNltrt STA 8122.9±589.8 3.02±0.30 9.85±2.52

CAm 8110.9±616.7 2.93±0.26 10.12±2.13

VISpr STA 8210.8±836.3 3.11±0.37 8.51±1.38

CAm 8138.4±847.1 3.06±0.36 8.46±1.67

VISex STA 8435.7±946.6 2.75±0.15 2.75±0.15

CAm 8282.4±930.6 2.79±0.23 2.79±0.23

VISla STA 6824.6±655.0 2.91±0.25 8.13±1.74

CAm 6641.9±593.8 3.02±0.33 9.55±1.89

Significantly different values are marked in bold

*p<0.05 by paired t test

Neuroradiology

Author's personal copy

thought in favor of the attention to the external auditory stim-uli, which results in a stronger DMN deactivation. In supportof these findings, previous studies have shown that SBN re-sults in a weaker DMN activity during auditory tasks [26, 52].

Similar to the DMN, the SAL network was also obviouslyaffected by SBN. It exhibited negative correlation with thestimulation cycle, which was significantly stronger duringSTA, and had a very prominent spectral peak correspondingto the frequency of the stimulation cycle during STA, but notCA. The SAL network comprises the anterior cingulate andanterior insular cortices and has been suggested to function asa behavior guide that constantly monitors both external andinternal stimuli, segregates the most homeostatically relevant(salient) input, and reorients attentional resources accordingly[53–55]. Tightly linked with the DMN, the SAL network me-diates attention between the external and internal worlds andsignals the DMN to reduce its activity when an external eventrequires attention [55, 56]. Leech and colleagues [51] pro-posed that the anterior insula and dorsal PCC form a systemthat regulates attentional focus. Damage to white matter tractswithin the SAL network results in abnormal PCC/DMN func-tion, which corroborates the existence of a connection be-tween the SAL network and the DMN [57, 58]. In the present

study, the deactivation of this network appeared to be moreextensive and more strongly driven by the stimulus in thequiet environment of STA and disrupted by SBN duringCA. Given that the DMN exhibited similar behavior, SBNseemed to hinder stimulus perception, interfere with the inter-action between the DMN and the SAL network, and interruptthe process of regulating and reorienting attentional recourses.

In contrast, the two lateral frontoparietal networks, theLFPN and RFPN, demonstrated different behavior in the pres-ent study. Although both of these networks appeared to bemore extensive during STA, the LFPN did not seem to followthe stimulus cycle during either acquisition method, whereasthe RFPN had a spectral peak at the stimulation frequencyduring STA but not CA, and it was anti-correlated with thestimulus waveform. It has been proposed that these networksare involved in cognitive control and together form the exec-utive control network [59–61]. However, these areas demon-strated distinct functionalities under various task loads, e.g.,the LFPN and RFPN showed opposite modulation during astop-signal task [62] and different levels of activity during aworking memory task [63]; furthermore, the LFPN showedincreased activity under low- versus high-load conditions,whereas the RFPN showed reduced activity under low load

Table 2 Peak frequencies andcorrelation coefficients with thestimulus presentation waveformof the identified ICNs in the twodatasets

ICN Peak frequency Correlation with stimulus timing

STA CAm

Hz Cycle/run Hz Cycle/run STA CAm p

AUD 0.01389 5.0 0.01389 5.0 0.71 0.77 0.109

CBLL 0.02778 10.0 0.0121 4.4 0.25 0.18 0.433

DMNve 0.01389 5.0 0.025 9.0 −0.18 −0.12 0.397

DMNdo 0.01389 5.0 0.02857 10.3 −0.27 −0.09 0.019*

DMNan 0.01389 5.0 0.0373 13.4 −0.13 −0.09 0.730

SAL 0.01389 5.0 0.01429 5.1 −0.30 −0.14 0.035*

LFPN 0.01528 5.5 0.01508 5.4 0.03 −0.01 0.451

RFPN 0.01389 5.0 0.02837 10.2 −0.17 −0.10 0.422

DAN 0.01389 5.0 0.01389 5.0 −0.21 −0.32 0.064

VAN 0.03056 11.0 0.025 9.0 −0.09 0.07 0.006*

VISSPT 0.01389 5.0 0.0129 4.6 0.06 0.02 0.551

SMNala 0.02778 10.0 0.01726 6.2 −0.08 −0.06 0.826

SMNpla 0.02778 10.0 0.02778 10.0 −0.11 −0.05 0.706

SMName 0.01389 5.0 0.01548 5.6 −0.19 −0.10 0.084

SMNpme 0.01389 5.0 0.01389 5.0 −0.17 −0.23 0.433

SMNltrt 0.02778 10.0 0.02698 9.7 −0.07 −0.14 0.397

VISpr 0.02778 10.0 0.04048 14.6 −0.16 −0.20 0.683

VISex 0.02778 10.0 0.01389 5.0 −0.09 −0.18 0.382

VISla 0.02778 10.0 0.04048 14.6 −0.07 0.00 0.331

Frequencies that matched with the frequency of the stimulus cycles (5 cycles/run) or its second harmonic, andsignificantly different correlations are marked in bold

*p<0.05 by paired t test, Bonferroni corrected

Neuroradiology

Author's personal copy

conditions during visual target identification tasks [64]. Thisdegree of heterogeneity within the frontoparietal control net-work may explain its segregated temporal behavior in relationto the stimulus in the present study. Although the exact rela-tionship of the RFPN to the stimulus in the given experimentcan only be hypothesized (e.g., participation in top-down con-trol of attention to the stimulus, spatial localization, memoryretrieval, or reaction to the music familiarity [65–69]), thedetrimental effect of SBN on its reaction to the stimulus isquite evident.

Two additional networks related to attentional control werealso identified in the present study: the DAN, which consistsof the intraparietal sulcus and the junction of the precentraland superior frontal sulcus (frontal eye fields), and the VAN,which comprises the temporoparietal junction and the ventralfrontal cortex. The DAN is thought to be involved in cognitiveselection of sensory information and subsequent top-downvoluntary allocation of attention to the selected stimuli basedupon current goals and expectations, whereas the VAN isthought to be responsible for detection of unanticipated andunattended behaviorally relevant external sensory inputs andinitiation of corresponding (involuntary) attention shifts[70–72]. The DAN did not appear to be affected by the con-stant SBN load; it was one of the few networks whose spectralpeak remained at the frequency of stimulus presentations dur-ing CA, and it was deactivated in both acquisition schemes(Fig. 2, Table 2). The DAN is pre-activated by the expectationof certain stimuli or objects or the preparation of specific re-sponses [71, 73, 74]. Its deactivation in the present study maybe an indicator of pre-activation during the anticipation of themusic because the subjects were instructed to pay attention tothe stimuli. If this was the case, it is not surprising that SBNdid not affect the activity of this network because the expec-tation of the target stimuli would not be hindered by back-ground noise. In contrast, the VAN did not display any spec-tral behavior that would relate it to the stimulus, and althoughthe difference between the correlations with the stimuluswaveforms between the STA and CAm datasets was signifi-cant, both of these correlations were considerably small. It isthought that the VAN did not follow the stimulus in eitheracquisition method because it is responsible for involuntaryreactions to novel stimuli that do not match expectations. Be-cause the present study did not include this type of variable,SBN did not seem to affect the VAN.

Altogether, SBN in the present study appears to have in-fluenced every network that participated in auditory percep-tion as well as the majority of the networks involved in theregulation of attention in relation to the stimulus, except theDAN, VAN, and LFPN. These networks were equally in-volved in both acquisition conditions, or they did not show aresponse to the stimulus in either acquisition condition. Fur-thermore, the sensorimotor and visual networks seemed to beaffected as well; all nine sensory networks (visuospatial, five

sensorimotor, and three visual) exhibited spectral peaks corre-sponding to the stimulus presentation frequency or its secondharmonic during STA, but only three of these correlationswere maintained during CA (Fig. 2, Table 2). The auditoryand visual cortices are known to have functional and structuralconnections [75–77], interacting at the level of multisensoryintegration [78, 79] or at the attentional level [80, 81]. Thevisual cortex has been shown to directly activate following theacoustic stimuli [82, 83]. Along with audiovisual cross-modalprocessing, auditory–somatosensory interactions are also aknown part of multisensory integration [84, 85], with existingevidence for cross-modal attentional effects occurring be-tween the somatosensory and auditory modalities [86, 87].This may explain why visual and somatosensory networksshowed better response to the stimulus in the quiet environ-ment of STA.

The fact that most ICNs followed the stimulation cycle dur-ing STA is consistent with the previous study using STA, whichdemonstrated that auditory stimulation influences the majorityof the identified networks [27]. It was argued that even passiveattending to acoustic stimuli considerably modulates the activ-ity pattern of the entire brain networks due to the reallocation ofprocessing resources. This was clearly not the case with the CAin the present study, suggesting that SBN significantly reducedthe driving power of the stimulus over the whole brain ratherthan only in areas that regulate auditory responses.

No differences in functional connectivity among the ICNswere identified between STA and CA. Although it would bereasonable to expect that continuous SBN might introduceadditional temporal coherence among brain networks or, viceversa, inhibit the existing connections, the present results arein line with previous findings demonstrating that functionalconnectivity measures are rather insensitive to the presence ofSBN [26].

Regarding the limitations of the current study, it can beclaimed that SBN may not be the only discrepant factor be-tween the STA and CAm datasets. The two datasets may havediffered in terms of the signal-to-noise ratio because the longerinter-acquisition intervals in the STA allow for better signalrecovery [15]. However, the impact of the inter-acquisitionduration on signal recovery depends on the T1-relaxationtime, which is the highest for CSF and relatively small forgray matter [88, 89], and the average SNR of STA and CAmdatasets measured at the postcentral gyrus in our study showedno significant difference between the datasets. Thus, the dif-ference in repetition time would not have affected gray matternetworks significantly. Another limitation might be the factthat STA (and volume-matched CAm dataset) has smallernumber of volumes, which results in weaker statistical power.The purpose of the present study, however, was not to find themost optimal method for imaging brain activity but to isolatethe SBN effect, for which STA is a proven method whoseeffectivity has been established by numerous studies.

Neuroradiology

Author's personal copy

Conclusions

During auditory stimulation, SBN affected the stimulus-driven behavior of not only the auditory networks but alsothe majority of other intrinsic networks including the defaultmode, attention, and sensory networks. These findings sug-gest that SBN interfered with auditory perception, the process-es of regulating and reorienting attentional resources, andadapting to current changes in the environment. Consequent-ly, SBN may present a significant nuisance confounder notonly in auditory fMRI studies but also in studies of generalintrinsic brain networks and should be accounted for wheninvestigating brain behavior under stimulation or task loadusing fMRI.

Acknowledgments This research was supported by the Basic ScienceResearch Program through the National Research Foundation of Korea(NRF), funded by the Ministry of Education (2014R1A1A4A01003909).

Ethical standards and patient consent We declare that all human andanimal studies have been approved by the Kangwon National UniversityHospital Institutional Review Board and have therefore been performedin accordance with the ethical standards laid down in the 1964 Declara-tion of Helsinki and its later amendments. We declare that all patientsgave informed consent prior to inclusion in this study.

Conflict of interest We declare that we have no conflict of interest.

References

1. Gaab N, Gabrieli JD, Glover GH (2007) Assessing the influence ofscanner background noise on auditory processing. II. An fMRIstudy comparing auditory processing in the absence and presenceof recorded scanner noise using a sparse design. Hum Brain Mapp28:721–732

2. Talavage TM, Edmister WB, Ledden PJ, Weisskoff RM (1999)Quantitative assessment of auditory cortex responses induced byimager acoustic noise. Hum Brain Mapp 7:79–88

3. Bandettini PA, Jesmanowicz A, Van Kylen J, Birn RM, Hyde JS(1998) Functional MRI of brain activation induced by scanneracoustic noise. Magn Reson Med 39:410–416

4. Baumgart F, Gaschler B, Tempelmann C, Tegeler C, Stiller D,Heinze H, Scheich H (1996) Masking of acoustic stimuli by thegradient noise alters the fMRI detectable activation in a particularhuman auditory cortex. MAGMA 4:185

5. Tamer G, Talavage TM, Ulmer JL (2004) Characterizing the atten-uation and/or saturation effect of the acoustic scanner noise in au-ditory event-related functional magnetic resonance imaging. ConfProc IEEE Eng Med Biol Soc 3:1868–1871

6. Langers DR, Van Dijk P, Backes WH (2005) Interactions betweenhemodynamic responses to scanner acoustic noise and auditorystimuli in functional magnetic resonance imaging. Magn ResonMed 53:49–60

7. Shah NJ, Jancke L, Grosse-Ruyken ML, Muller-Gartner HW(1999) Influence of acoustic masking noise in fMRI of the auditorycortex during phonetic discrimination. J Magn Reson Imaging 9:19–25

8. Cho ZH, Chung SC, Lim DW, Wong EK (1998) Effects of theacoustic noise of the gradient systems on fMRI: a study on auditory,motor, and visual cortices. Magn Reson Med 39:331–335

9. Zhang N, Zhu XH, Chen W (2005) Influence of gradient acousticnoise on fMRI response in the human visual cortex. Magn ResonMed 54:258–263

10. Mazard A, Mazoyer B, Etard O, Tzourio-Mazoyer N, Kosslyn SM,Mellet E (2002) Impact of fMRI acoustic noise on the functionalanatomy of visual mental imagery. J Cogn Neurosci 14:172–186

11. Fuchino Y, Sato H, Maki A, Yamamoto Y, Katura T, Obata A,Koizumi H, Yoro T (2006) Effect of fMRI acoustic noise on sen-sorimotor activation examined using optical topography.Neuroimage 32:771–777

12. Novitski N, Anourova I, Martinkauppi S, Aronen HJ, Naatanen R,Carlson S (2003) Effects of noise from functional magnetic reso-nance imaging on auditory event-related potentials in workingmemory task. Neuroimage 20:1320–1328

13. Tomasi D, Caparelli EC, Chang L, Ernst T (2005) fMRI-acousticnoise alters brain activation during working memory tasks.Neuroimage 27:377–386

14. Skouras S, Gray M, Critchley H, Koelsch S (2013) FMRI scannernoise interaction with affective neural processes. PLoS ONE 8:e80564

15. Hall DA, Haggard MP, Akeroyd MA, Palmer AR, SummerfieldAQ, Elliott MR, Gurney EM, Bowtell RW (1999) BSparse^ tempo-ral sampling in auditory fMRI. Hum Brain Mapp 7:213–223

16. Gaab N, Gabrieli JD, Glover GH (2007) Assessing the influence ofscanner background noise on auditory processing. I. An fMRI studycomparing three experimental designs with varying degrees ofscanner noise. Hum Brain Mapp 28:703–720

17. Zaehle T, Schmidt CF, Meyer M, Baumann S, Baltes C, Boesiger P,Jancke L (2007) Comparison of Bsilent^ clustered and sparse tem-poral fMRI acquisitions in tonal and speech perception tasks.Neuroimage 37:1195–1204

18. Blackman GA, Hall DA (2011) Reducing the effects of backgroundnoise during auditory functional magnetic resonance imaging ofspeech processing: qualitative and quantitative comparisons be-tween two image acquisition schemes and noise cancellation. JSpeech Lang Hear Res 54:693–704

19. Fransson P (2005) Spontaneous low-frequency BOLD signal fluc-tuations: an fMRI investigation of the resting-state default mode ofbrain function hypothesis. Hum Brain Mapp 26:15–29

20. Damoiseaux JS, Rombouts SA, Barkhof F, Scheltens P, Stam CJ,Smith SM, Beckmann CF (2006) Consistent resting-state networksacross healthy subjects. Proc Natl Acad Sci U S A 103:13848–13853

21. Fox MD, Raichle ME (2007) Spontaneous fluctuations in brainactivity observed with functional magnetic resonance imaging.Nat Rev Neurosci 8:700–711

22. Biswal B, Yetkin FZ, Haughton VM, Hyde JS (1995) Functionalconnectivity in the motor cortex of resting human brain using echo-planar MRI. Magn Reson Med 34:537–541

23. Cordes D, Haughton VM, Arfanakis K, Carew JD, Turski PA,Moritz CH, Quigley MA, Meyerand ME (2001) Frequencies con-tributing to functional connectivity in the cerebral cortex in “rest-ing-state” data. AJNR Am J Neuroradiol 22:1326–1333

24. Smith SM, Fox PT, Miller KL, Glahn DC, Fox PM, Mackay CE,Filippini N, Watkins KE, Toro R, Laird AR, Beckmann CF(2009) Correspondence of the brain’s functional architectureduring activation and rest. Proc Natl Acad Sci U S A 106:13040–13045

25. Laird AR, Fox PM, Eickhoff SB, Turner JA, Ray KL, McKay DR,Glahn DC, Beckmann CF, Smith SM, Fox PT (2011) Behavioralinterpretations of intrinsic connectivity networks. J Cogn Neurosci23:4022–4037

Neuroradiology

Author's personal copy

26. Langers DR, van Dijk P (2011) Robustness of intrinsic connectivitynetworks in the human brain to the presence of acoustic scannernoise. Neuroimage 55:1617–1632

27. Yakunina N, Tae WS, Lee KU, Kim SS, Nam EC (2013)Spatiotemporal segregation of neural response to auditory stimula-tion: an fMRI study using independent component analysis andfrequency-domain analysis. PLoS ONE 8:e66424

28. Allen EA, Erhardt EB, Damaraju E, GrunerW, Segall JM, Silva RF,Havlicek M, Rachakonda S, Fries J, Kalyanam R, Michael AM,Caprihan A, Turner JA, Eichele T, Adelsheim S, Bryan AD,Bustillo J, Clark VP, Feldstein Ewing SW, Filbey F, Ford CC,Hutchison K, Jung RE, Kiehl KA, Kodituwakku P, Komesu YM,Mayer AR, Pearlson GD, Phillips JP, Sadek JR, Stevens M,Teuscher U, Thoma RJ, Calhoun VD (2011) A baseline for themultivariate comparison of resting-state networks. Front SystNeurosci 5:2

29. Zuo XN, Kelly C, Adelstein JS, Klein DF, Castellanos FX, MilhamMP (2010) Reliable intrinsic connectivity networks: test-retest eval-uation using ICA and dual regression approach. Neuroimage 49:2163–2177

30. Guo CC, Kurth F, Zhou J, Mayer EA, Eickhoff SB, Kramer JH,Seeley WW (2012) One-year test-retest reliability of intrinsic con-nectivity network fMRI in older adults. Neuroimage 61:1471–1483

31. Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC,Raichle ME (2005) The human brain is intrinsically organized intodynamic, anticorrelated functional networks. Proc Natl Acad Sci US A 102:9673–9678

32. Fukunaga M, Horovitz SG, van Gelderen P, de Zwart JA, JansmaJM, Ikonomidou VN, Chu R, Deckers RH, Leopold DA, Duyn JH(2006) Large-amplitude, spatially correlated fluctuations in BOLDfMRI signals during extended rest and early sleep stages. MagnReson Imaging 24:979–992

33. Goldstein B, Shulman A (1996) Tinnitus-hyperacusis and the loud-ness discomfort level test—a preliminary report. Int Tinnitus J 2:83–89

34. Calhoun VD (2004) Group ICA of fMRI toolbox (GIFT)35. Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for

making group inferences from functional MRI data using indepen-dent component analysis. Hum Brain Mapp 14:140–151

36. Bell AJ, Sejnowski TJ (1995) An information-maximization ap-proach to blind separation and blind deconvolution. NeuralComput 7:1129–1159

37. Himberg J, Hyvarinen A (2003) Icasso: software for investigatingthe reliability of ICA estimates by clustering and visualization.Neural Networks for Signal Processing, 2003 NNSP’03 2003 I.E.13th Workshop on:259-268

38. Jafri MJ, Pearlson GD, Stevens M, Calhoun VD (2008) A methodfor functional network connectivity among spatially independentresting-state components in schizophrenia. Neuroimage 39:1666–1681

39. Henkelman RM (1985) Measurement of signal intensities in thepresence of noise in MR images. Med Phys 12:232–233

40. Reeder SB (2007)Measurement of signal-to-noise ratio and parallelimaging. In: Parallel Imaging in Clinical MR Applications.Springer, pp 49-61

41. De Luca M, Beckmann CF, De Stefano N, Matthews PM, SmithSM (2006) fMRI resting state networks define distinct modes oflong-distance interactions in the human brain. Neuroimage 29:1359–1367

42. Parsons LM (2001) Exploring the functional neuroanatomy of mu-sic performance, perception, and comprehension. Ann N Y AcadSci 930:211–231

43. Petacchi A, Laird AR, Fox PT, Bower JM (2005) Cerebellum andauditory function: an ALE meta-analysis of functional neuroimag-ing studies. Hum Brain Mapp 25:118–128

44. Sens PM, Almeida CI, Souza MM, Goncalves JB, Carmo LC(2011) The role of the cerebellum in auditory processing usingthe SSI test. Braz J Otorhinolaryngol 77:584–588

45. KH E, Chen SH, Ho MH, Desmond JE (2014) A meta-analysis ofcerebellar contributions to higher cognition from PET and fMRIstudies. Hum Brain Mapp 35:593–615

46. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA,Shulman GL (2001) A default mode of brain function. Proc NatlAcad Sci U S A 98:676–682

47. Buckner RL, Andrews-Hanna JR, Schacter DL (2008) The brain’sdefault network: anatomy, function, and relevance to disease. AnnN YAcad Sci 1124:1–38

48. Uddin LQ, Kelly AM, Biswal BB, Xavier Castellanos F, MilhamMP (2009) Functional connectivity of default mode network com-ponents: correlation, anticorrelation, and causality. Hum BrainMapp 30:625–637

49. Laird AR, Eickhoff SB, Li K, Robin DA, Glahn DC, Fox PT (2009)Investigating the functional heterogeneity of the default mode net-work using coordinate-based meta-analytic modeling. J Neurosci29:14496–14505

50. Leech R, Kamourieh S, Beckmann CF, Sharp DJ (2011)Fractionating the default mode network: distinct contributions ofthe ventral and dorsal posterior cingulate cortex to cognitive con-trol. J Neurosci 31:3217–3224

51. Leech R, Sharp DJ (2014) The role of the posterior cingulate cortexin cognition and disease. Brain 137:12–32

52. Gaab N, Gabrieli JD, Glover GH (2008) Resting in peace or noise:scanner background noise suppresses default-mode network. HumBrain Mapp 29:858–867

53. SeeleyWW,Menon V, Schatzberg AF, Keller J, Glover GH, KennaH, Reiss AL, GreiciusMD (2007) Dissociable intrinsic connectivitynetworks for salience processing and executive control. J Neurosci27:2349–2356

54. Eckert MA,Menon V,Walczak A, Ahlstrom J, Denslow S, HorwitzA, Dubno JR (2009) At the heart of the ventral attention system: theright anterior insula. Hum Brain Mapp 30:2530–2541

55. Menon V, Uddin LQ (2010) Saliency, switching, attention and con-trol: a network model of insula function. Brain Struct Funct 214:655–667

56. Sridharan D, Levitin DJ, Menon V (2008) A critical role for theright fronto-insular cortex in switching between central-executiveand default-mode networks. Proc Natl Acad Sci U S A 105:12569–12574

57. Bonnelle V, Leech R, Kinnunen KM, Ham TE, Beckmann CF, DeBoissezon X, Greenwood RJ, Sharp DJ (2011) Default mode net-work connectivity predicts sustained attention deficits after traumat-ic brain injury. J Neurosci 31:13442–13451

58. Jilka SR, Scott G, Ham T, Pickering A, Bonnelle V, Braga RM,Leech R, Sharp DJ (2014) Damage to the Salience Network andinteractions with the Default Mode Network. J Neurosci 34:10798–10807

59. Spreng RN, Sepulcre J, Turner GR, Stevens WD, Schacter DL(2013) Intrinsic architecture underlying the relations among thedefault, dorsal attention, and frontoparietal control networks ofthe human brain. J Cogn Neurosci 25:74–86

60. ColeMW, Repovs G, Anticevic A (2014) The frontoparietal controlsystem: a central role in mental health. Neuroscientist 20:652–664

61. Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL (2008)Evidence for a frontoparietal control system revealed by intrinsicfunctional connectivity. J Neurophysiol 100:3328–3342

62. Zhang S, Li CS (2012) Functional networks for cognitive control ina stop signal task: independent component analysis. Hum BrainMapp 33:89–104

63. Kim DI, Manoach DS, Mathalon DH, Turner JA, Mannell M,Brown GG, Ford JM, Gollub RL, White T, Wible C, Belger A,Bockholt HJ, Clark VP, Lauriello J, O’Leary D, Mueller BA, Lim

Neuroradiology

Author's personal copy

KO, Andreasen N, Potkin SG, Calhoun VD (2009) Dysregulationof working memory and default-mode networks in schizophreniausing independent component analysis, an fBIRN andMCIC study.Hum Brain Mapp 30:3795–3811

64. Xu J, Calhoun VD, Pearlson GD, Potenza MN (2014) Oppositemodulation of brain functional networks implicated at low vs. highdemand of attention and working memory. PLoS ONE 9:e87078

65. Fassbender C, Simoes-Franklin C, Murphy K, Hester R, Meaney J,Robertson I, Garavan H (2006) The role of a right fronto-parietalnetwork in cognitive control. J Psychophysiol 20:286–296

66. Diekhof EK, Biedermann F, Ruebsamen R, Gruber O (2009) Top-down and bottom-up modulation of brain structures involved inauditory discrimination. Brain Res 1297:118–123

67. Brunetti M, Della Penna S, Ferretti A, Del Gratta C, Cianflone F,Belardinelli P, CauloM, Pizzella V, Olivetti BelardinelliM, RomaniGL (2008) A frontoparietal network for spatial attention reorientingin the auditory domain: a human fMRI/MEG study of functionaland temporal dynamics. Cereb Cortex 18:1139–1147

68. Mangano GR, Oliveri M, Turriziani P, Smirni D, Zhaoping L,Cipolotti L (2014) Impairments in top down attentional processesin right parietal patients: paradoxical functional facilitation in visualsearch. Vis Res 97:74–82

69. Iidaka T, Matsumoto A, Nogawa J, Yamamoto Y, Sadato N (2006)Frontoparietal network involved in successful retrieval from epi-sodic memory. Spatial and temporal analyses using fMRI andERP. Cereb Cortex 16:1349–1360

70. Corbetta M, Shulman GL (2002) Control of goal-directed andstimulus-driven attention in the brain. Nat Rev Neurosci 3:201–215

71. Corbetta M, Patel G, Shulman GL (2008) The reorienting system ofthe human brain: from environment to theory of mind. Neuron 58:306–324

72. Vossel S, Geng JJ, Fink GR (2014) Dorsal and ventral attentionsystems: distinct neural circuits but collaborative roles.Neuroscientist 20:150–159

73. Shulman GL, Ollinger JM, Akbudak E, Conturo TE, Snyder AZ,Petersen SE, Corbetta M (1999) Areas involved in encoding andapplying directional expectations to moving objects. J Neurosci 19:9480–9496

74. Connolly JD, Goodale MA, Menon RS, Munoz DP (2002) HumanfMRI evidence for the neural correlates of preparatory set. NatNeurosci 5:1345–1352

75. Eckert MA, Kamdar NV, Chang CE, Beckmann CF, Greicius MD,Menon V (2008) A cross‐modal system linking primary auditoryand visual cortices: evidence from intrinsic fMRI connectivity anal-ysis. Hum Brain Mapp 29:848–857

76. Beer AL, Plank T, Meyer G, Greenlee MW (2013) Combineddiffusion-weighted and functional magnetic resonance imaging re-veals a temporal-occipital network involved in auditory-visual ob-ject processing. Frontiers in integrative neuroscience 7

77. Beer AL, Plank T, Greenlee MW (2011) Diffusion tensor imagingshows white matter tracts between human auditory and visual cor-tex. Exp Brain Res 213:299–308

78. Stein BE, Stanford TR (2008) Multisensory integration: currentissues from the perspective of the single neuron. Nat RevNeurosci 9:255–266

79. Koelewijn T, Bronkhorst A, Theeuwes J (2010) Attention and themultiple stages of multisensory integration: a review of audiovisualstudies. Acta Psychol 134:372–384

80. Spence C, Driver J (1997) Audiovisual links in exogenous covertspatial orienting. Percept Psychophys 59:1–22

81. Driver J, Spence C (1998) Cross-modal links in spatial attention.Philos Trans R Soc Lond B Biol Sci 353:1319–1331

82. McDonald JJ, Green JJ, Stormer VS, Hillyard SA (2012) Cross-modal spatial cueing of attention influences visual perception. In:Murray MM, Wallace MT (eds) The neural bases of multisensoryprocesses. Taylor & Francis Group, LLC, Boca Raton

83. Feng W, Stormer VS, Martinez A, McDonald JJ, Hillyard SA(2014) Sounds activate visual cortex and improve visual discrimi-nation. J Neurosci 34:9817–9824

84. Foxe JJ, Morocz IA, Murray MM, Higgins BA, Javitt DC,Schroeder CE (2000) Multisensory auditory-somatosensory inter-actions in early cortical processing revealed by high-density elec-trical mapping. Brain Res Cogn Brain Res 10:77–83

85. Foxe JJ, Wylie GR, Martinez A, Schroeder CE, Javitt DC,Guilfoyle D, Ritter W, Murray MM (2002) Auditory-somatosensorymultisensory processing in auditory association cor-tex: an fMRI study. J Neurophysiol 88:540–543

86. Spence C, Nicholls ME, Gillespie N, Driver J (1998) Cross-modallinks in exogenous covert spatial orienting between touch, audition,and vision. Percept Psychophys 60:544–557

87. Ghazanfar AA, Schroeder CE (2006) Is neocortex essentially mul-tisensory? Trends Cogn Sci 10:278–285

88. Woodruff PWR (1994) Structural magnetic resonance imaging inpsychiatry—the functional psychoses. In: Dawbarn D, Kerwin R(eds) Cambridge medical reviews: neurobiology and psychiatry.Cambridge University Press

89. Chen L, Berns t e in M, Hus ton J , Fa in S (2001)Measurements of T1 relaxation times at 3.0 T: implicationsfor clinical MRA

Neuroradiology

Author's personal copy