Intermodal attention modulates visual processing in dorsal and ventral

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NeuroImage 63 (2012) 1295–1304

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Intermodal attention modulates visual processing in dorsal and ventral streams A.D. Cate a,⁎, T.J. Herron b, X. Kang b, c, E.W. Yund b, D.L. Woods b, c, d, e a

Psychology Department, Virginia Polytechnic Institute and State University, 109 Williams Hall, Blacksburg, VA 24061, USA Human Cognitive Neurophysiology Laboratory, VA Northern California Health Care System, 150 Muir Rd., 151-I, P.O. Box 2339, Martinez, CA 94553, USA c UC Davis Department of Neurology, 4860 Y St., Suite 3700, Sacramento, CA 95817, USA d Center for Neuroscience, UC Davis, 1544 Newton Ct., Davis, CA 95616, USA e UC Davis Center for Mind and Brain, 202 Cousteau Place, Suite 201, Davis, CA 95616, USA b

a r t i c l e

i n f o

Article history: Accepted 5 August 2012 Available online 16 August 2012 Keywords: Vision Attention fMRI Faces Words Parietal lobe Temporal lobe

a b s t r a c t Attending to visual objects while ignoring information from other modalities is necessary for performing difficult visual discriminations, but it is unclear how selecting between sensory modalities alters processing within the visual system. We used an audio-visual intermodal selective attention paradigm with fMRI to study the effects of visual attention on cortical activity in the absence of competitive interactions between multiple visual stimuli. Complex stimuli (faces and words) activated higher visual areas even in the absence of visual attention. These stimulus-dependent activations (SDAs) covered foveal retinotopic cortex, extended ventrally to the anterior fusiform gyrus and dorsally to include multiple distinct foci in the intraparietal sulcus (IPS). Attention amplified the baseline response in posterior retinotopic regions and altered activity in different ways in the extrastriate dorsal and ventral pathways. The majority of the IPS was strongly and exclusively activated by visual attention: attention-related modulations (ARMs) encompassed and spread well beyond the focal SDAs. In contrast, in the fusiform gyrus only a small subset of the regions activated by unattended stimuli showed ARMs. Ventral cortex was also heterogeneous: we found a distinct ventrolateral region in the occipitotemporal sulcus (OTS) that was activated exclusively by attention, showing neither SDAs nor any significant stimulus preferences. Attention-dependent activations in the IPS and the OTS suggest that these regions play critical roles in intermodal visual attention. © 2012 Elsevier Inc. All rights reserved.

Introduction A fundamental issue for understanding the functional specialization of the human visual system is the degree to which higher-level visual processing is dependent on attention. Performing difficult visual discrimination tasks often requires attending to visual objects while ignoring information from other sensory modalities. While much neuroimaging research has addressed the mechanisms of attentional selection among multiple visual stimuli (e.g. Kastner et al., 2001), less is known about the mechanism for selecting stimuli based on their modality: intermodal selective attention. Intra- and intermodal visual attention may rely on similar cortical networks (Talsma and Kok, 2001), but such comparison is difficult because intra- and intermodal studies have tended to focus on different mechanisms of attentional selection. Studies of intramodal attention Abbreviations: ARM, attention-related modulation; BA, bimodal auditory; BV, bimodal visual; CoS, collateral sulcus; ERP, event-related potential; FG, fusiform gyrus; fMRI, functional magnetic resonance imaging; IPS, intraparietal sulcus; LOS, lateral occipital sulcus; MOG, middle occipital gyrus; OTS, occipitotemporal sulcus; ROI, region of interest; RT, reaction time; SDA, stimulus-dependent activation; UA, unimodal auditory; UV, unimodal visual. ⁎ Corresponding author. Fax: +1 540 231 3652. E-mail address: [email protected] (A.D. Cate). 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.neuroimage.2012.08.026

have often examined how attention modulates well-localized activity believed to represent individual stimuli, with special emphasis on the role of competitive target–distractor interactions. Numerous fMRI studies, for example, have investigated how visual cortex activity changes when a stimulus is attended while ignoring similar visual distractor stimuli (e.g. Murray and Wojciulik, 2004; O'Craven et al., 1997; Wojciulik et al., 1998). In contrast, intermodal studies have often examined how attention alters depth of processing. Näätänen's influential model of selective attention (Näätänen, 1992) hypothesizes that the processing of unattended stimuli terminates as soon as their features mismatch those of attended targets. Differences between modalities are more distinctive than any possible within-modality differences, according to this view. Consistent with this model, previous fMRI studies of intermodal selective attention show that activity evoked by unattended auditory stimuli is markedly attenuated in auditory cortex (auditory belt and parabelt fields) (Woods et al., 2010, 2011) and the processing of unattended visual inputs is attenuated throughout visual cortex (Johnson and Zatorre, 2005; Woodruff et al., 1996). Intra- and intermodal methods ultimately investigate the same behavioral phenomenon of selective attention, and indeed intramodal attention fMRI studies provide crucial information for understanding how intermodal selective attention works in the neocortex.

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Ideally, an intermodal selection process is one that will apply equally to any stimulus in a given modality. Intramodal visual attention fMRI studies have distinguished parietal regions that support modality-general processes of selective attention, in contrast to regions whose activity is stimulus feature-specific (Kimberg et al., 2000; Shulman et al., 2002; Wojciulik and Kanwisher, 1999). A remaining question for intermodal fMRI studies, then, is to identify regions that are not only stimulus-general, but modality-specific. The current study investigated how intermodal visual attention modulates cortical activity related to processing complex visual stimuli. We specifically sought to identify regions where modulation generalized across very different kinds of stimuli (faces and words). We used spatially precise cortical surface mapping techniques to characterize the spatial distribution of activations elicited by attended and unattended stimuli. We addressed two central questions: (1) to what extent does higher-level visual cortex respond to unattended visual stimuli? And (2) to what extent does selective attention simply amplify activity in regions showing sensory responses to unattended stimuli, and to what extent does it recruit activity in other regions? Stimulus-dependent activations (SDAs) A key question regarding the function of extrastriate visual regions is the extent to which their activity is driven by sensory attributes of visual stimuli even in the absence of attention. Single-unit recordings in anesthetized animals consistently detected stimulus-specific activity not only in primary visual cortex (Hubel and Wiesel, 1962), but also in ventral temporal visual regions (Desimone et al., 1984; Gross et al., 1969; Hikosaka, 1997; Kobatake and Tanaka, 1994; Tanaka et al., 1991). Since anesthesia presumably prohibits voluntary attention processes (Alkire, Hudetz et al., 2008), these studies suggest that robust stimulus-dependent activations (SDAs) occur throughout ventral visual cortex. In contrast, the extent of SDAs in awake, behaving animals and humans is less clear, mainly because SDAs have been operationalized in different ways stemming from the variety of methods employed to prevent attention to the stimulus in question. Neuroimaging studies of visual selective attention have used intramodal attention paradigms that were often designed to measure inhibitory interactions between sensory representations of same-modality targets and distractors. In a typical study there are multiple visual stimuli present, so that a given stimulus can either be attended (to the exclusion of distractors) or can be ignored (i.e. it is a distractor). Targets and distractors are both visual stimuli, so special task constraints must be used in order to distinguish which stimulus is driving a given neural response, especially in ventral visual cortex (Kastner and Pinsk, 2004). Isolating the neural responses associated with the different stimuli can be performed by presenting stimuli in sufficiently separated retinotopic locations (e.g. several degrees for studies of retinotopic regions; Geng, Ruff et al., 2009; Kastner et al., 2001), or separate hemifields for studies of anterior extrastriate areas (Geng, Eger et al., 2006; Pinsk, Doniger et al., 2004) and then examining responses in the corresponding spatiotopic brain regions. Alternatively, if attended and ignored features are part of the same object (e.g. Wojciulik and Kanwisher, 1999) or are spatially superimposed (e.g. O'craven et al., 1999), one can measure activity in distinct brain regions of interest (ROIs) believed to respond preferentially to one of those features, and then quantify the effects of directing attention to a feature in terms of changes in the ROI response. When such strategies to isolate the source of SDAs in a given brain region, it is still possible for SDAs to reflect inhibitory competition between stimulus representations, in addition to stimulus-evoked excitatory activity. A number of neuroimaging studies have extended a finding from primate single-unit experiments: attending to one visual stimulus can suppress a single neuron's response to another, ignored stimulus in the same extrastriate receptive field (Moran and Desimone, 1985). Presenting multiple visual stimuli simultaneously to human fMRI subjects reduces ventral visual responses relative to

when the same stimuli are presented in temporal succession, bolstering the idea that simultaneously-presented stimuli produce competitive inhibition (e.g. Kastner et al., 2001). The degree of similarity between the simultaneously-presented stimuli can modulate this response suppression (Gentile and Jansma, 2010), which supports the idea that this effect is due to competition between patterns of activity that represent the sensory features of the stimuli, and is not simply an artifact arising from the difference between presenting images simultaneously and sequentially. These kinds of inhibitory effects are spatially broad: even when targets and distractors are presented in separate hemifields to isolate their excitatory effects, there is evidence that suppressive effects on distractor-related activity can cross hemifields (Kastner and Pinsk, 2004; Vanduffel, Tootell et al., 2000). In general, it is often difficult to disentangle neural responses to attended and unattended visual stimuli in anterior ventral regions where cells have large receptive fields (Desimone et al., 1984; Op de Beeck and Vogels, 2000; Tanaka, 1996). The pervasiveness of visual interstimulus inhibition makes it difficult to measure the baseline magnitude (unaffected by biased competition) of SDAs evoked by an ignored stimulus in intramodal studies. Question 1 can therefore be effectively addressed with an intermodal selective attention paradigm because visual sensory responses are unambiguously elicited by a single visual stimulus in the absence of attention. This is accomplished by quantifying activations in visual cortex when subjects are performing difficult auditory tasks. The absence of attention to visual stimuli can be reasonably inferred if the presence of the visual stimuli does not impair auditory task performance. This is especially the case when there is little supramodal (semantic) similarity between the auditory and visual stimuli. For this reason the present study avoided using both visual written words and auditory spoken words, for example. Extent of SDAs in dorsal visual cortex There is some evidence suggesting that posterior parietal neurons might respond to unattended visual stimuli. Many posterior parietal cortex neurons in monkeys respond during passive viewing of simple stimuli (Bushnell et al., 1981; Robinson and Goldberg, 1978; Robinson et al., 1978). Note that “passive” does not necessarily imply “unattended,” since these monkeys were awake and able to perform tasks. However, these early studies did at least demonstrate that parietal neurons showed sensory responses to visual stimuli independent of any movement or planning for movement, and that this activity was lower than when animals actively attended to the stimuli during a demanding task. More recently, fMRI retinotopic mapping studies in both humans and (awake) monkeys have identified multiple distinct regions in the intraparietal sulcus (IPS) that showed retinotopic response patterns even when the mapping stimuli themselves were not task-relevant (Orban, Claeys et al., 2006; Swisher et al., 2007; Wandell, Dumoulin et al., 2007). We hypothesized that intermodal SDAs would exist but be relatively small in dorsal visual regions. Extent of SDAs in ventral visual cortex Single-unit recordings in anesthetized animals consistently detected stimulus-specific activity not only in primary visual cortex (Hubel and Wiesel, 1962), but also in ventral temporal visual regions (Desimone et al., 1984; Gross et al., 1969; Hikosaka, 1997; Kobatake and Tanaka, 1994; Tanaka et al., 1991). We hypothesized that intermodal attention paradigms will yield larger SDAs in ventral occipitotemporal regions than in intramodal studies, due to the absence of intramodal competitive inhibition. Activity related to competitive inhibition can still be expected to occur in intermodal experiments, but the dampening of SDAs by crossmodal inhibition is likely to be smaller than from intramodal inhibition (Ciaramitaro, Buracas et al., 2007), especially in posterior regions. Furthermore, it is expected that any intermodal competitive inhibition will be mediated by supramodal frontoparietal

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networks (Arrington, Carr et al., 2000; Hopfinger, Woldorff et al., 2001; Kastner and Ungerleider, 2001; Schwartz, Vuilleumier et al. 2005) rather than by local, columnar-level competition in ventral visual regions. Effects of intermodal attention on the cortical distribution of activation Extrastriate visual neurons that respond to unattended stimuli can show amplified responses when the stimuli are actively attended, both in single-unit recordings (Luck et al., 1997; Moran and Desimone, 1985; Motter, 1994), and in fMRI (Murray and Wojciulik, 2004; O'Craven et al., 1997; Wojciulik et al., 1998). The cortical spatial distribution of such attention-related modulations (ARMs) is less well understood, however. Several studies have compared the magnitude of ARMs across different early cortical visual ROIs (i.e. v1 through v4; Kastner and Pinsk, 2004; Mehta et al., 2000), but it remains unclear whether ARMs are spatially homogenous within these regions. Studies focusing on posterior ROIs also tend not to discuss whether higher-level ventral visual regions are reliably modulated by selective attention. Furthermore, there is little known about the relative strength of ARMs and SDAs in parietal visual regions, perhaps because the well-demonstrated role of posterior parietal cortex in directing selective attention (e.g. Corbetta, 1998; Rafal, 1996) is presumed to be highly intertwined with its sensory role (Geng and Mangun, 2009; Gottlieb, 2007; Zenon et al., 2010). A question that our study can address, then, is how the cortical distribution of ARMs compares to that of SDAs. Since the cortical topography of the visual system is reflects a functional hierarchy of processing stages (e.g. Riesenhuber and Poggio, 1999; Tanaka, 1996), examining the full extent of ARMs will indicate which parts of the sensory processing hierarchy are modulated during selective attention. Role of ARMs in dorsal visual cortex In the dorsal stream, the activity of neurons that respond to passively-viewed stimuli is enhanced when the stimuli are attended (Bushnell et al., 1981; Colby, Duhamel et al., 1996). Parietal activity has been demonstrated during diverse intramodal attention tasks (Wojciulik and Kanwisher, 1999), which may suggest that parietal activity represents the production of top-down signals that may serve to alter biased competition between stimulus-selective neurons elsewhere, i.e. in the ventral stream. However, electrophysiological studies have suggested that intermodal visual attention, in particular, is characterized by enhanced activity in ventral visual cortex, as opposed to in supramodal parietal regions (Woods et al., 1992). Therefore, the size of dorsal stream ARMs is an important empirical question. We expected that any ARMs identified would be largely independent from SDAs, at least in the spatial sense. That is, we expected to find ARMs at locations not showing SDAs, in accord with the ideas that the posterior parietal lobe's role is primarily altering biased competition between sensory representations elsewhere.

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are stronger in extrastriate ventral regions than in v1 in humans (Avidan, Levy et al., 2003; Kastner and Pinsk, 2004). Additionally, intermodal selective attention studies in monkeys have shown that modulation becomes stronger as successively higher levels of ventral visual cortex, reaching a peak in v4 before becoming smaller in inferior temporal cortex (Mehta et al., 2000). Identifying topographically-organized patterns of cortical ARMs will help to characterize the function and hierarchical structure of the growing number of ventral visual subregions that have been described (Arcaro, McMains et al., 2009; Wandell et al., 2005; Wandell, Dumoulin et al., 2007). We hypothesized that ventral stream ARMs would be greatest in the vicinity of v4, which has been typically localized to fall around the posterior collateral sulcus and near the lingual sulcus in humans (Gallant, Shoup et al., 2000; Rottschy, Eickhoff et al., 2007). Extending from this hypothesis, we also hypothesized that ARMs would co-occur with SDAs to the extent that SDAs did not extend downstream of v4. Testing these hypotheses requires spatially precise cortical surface mapping to compare the distribution of activations elicited by attended and unattended stimuli. Although it has been established that attending to a visual stimulus can amplify the unattended fMRI BOLD response in ventral visual regions selective for that stimulus (Downing et al., 2001; Murray and Wojciulik, 2004; O'Craven et al., 1997; O'craven et al., 1999; Wojciulik et al., 1998) these studies depend on a region of interest (ROI) analysis. ROI analysis is critically dependent on the parameters of localizer scan procedures (Fox et al., 2009) typically in passive viewing conditions where attention is not explicitly controlled. Moreover, ROI extent is somewhat arbitrary: increasing the duration of localizer scanning will increase the size of the ROI (Murphy et al., 2007). Furthermore, ROI methods ignore variation within the ROI so they cannot reveal if attention-related response modulations share the topography of sensory responses to unattended stimuli (Berman et al., 2010; Friston et al., 2006). Here, we analyzed the entire surface of visual cortex to compare the magnitude and distribution of activations produced by stimulus presence alone with those due to the effects of selective attention. Specifically, we analyzed the cortical surface distribution of stimulusdependent activations (SDAs) elicited by unattended stimuli and compared them with the distribution of attention-related modulations (ARMs), enhancements in activations that occurred when the stimuli were attended. The degree to which SDAs and ARMs overlapped allowed us to parcellate visual cortex into three broadly-defined categories: (1) regions showing sensory responses unmodulated by attention, (2) sensory regions where activity was modulated by attention, and (3) attention-dependent regions where activations only occurred to attended stimuli. Parcellating visual cortex in this way may help us to identify functional subregions of visual cortex, and particularly to compare their locations with the distribution of stimulus specificity. Methods Subjects

Location of ARMs in ventral visual cortex Intermodal ARMs pose an especially interesting question in ventral stream regions, where biased competition models of attention propose that attentional modulations serve to arbitrate between multiple active sets of neuronal responses. Do ARMs only occur in regions that show SDAs to unattended stimuli? What would ARMs signify in regions where there SDAs do not occur, i.e. where there is little activity for attention to bias? Would they signify that these regions are involved in generating top-down activity that modulates regions where there are SDAs, in the same way that posterior parietal neurons are thought to do? Several studies have investigated the question of how attentional modulations differ at successive points along the ventral stream hierarchy. fMRI studies using intramodal paradigms have found that ARMs

Nine individuals (aged 18–34 years, 8 male, 2 left-handed) each participated in one orientation session that included task training and anatomical imaging and then underwent six separate fMRI sessions (three with sparse and three with continuous sampling) over a period of 2–6 weeks. All subjects provided informed consent in accordance with the local institutional review board and had normal or corrected-to-normal vision and normal hearing. Stimuli and procedure Functional images were acquired while subjects performed attention-demanding one-back matching tasks in the attended modality (Fig. 1) cued by a partially transparent cue letter (“A” or “V”)

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at fixation indicating the modality to be attended. Stimuli were presented in blocks containing unimodal or bimodal stimulation. In unimodal auditory and visual blocks (UA and UV, respectively), subjects were cued to attend to the presented modality. In bimodal blocks, auditory and visual stimuli were presented concurrently, and subjects were cued to attend to the auditory (BA blocks) or visual (BV) modality. During bimodal sequences auditory and visual stimuli were presented asynchronously with randomized temporal offsets to minimize intermodal integration. Subjects performed a difficult one-back matching task, responding to repeated auditory tone patterns or image categories in auditory and visual attention blocks respectively. Unimodal and bimodal blocks occurred with equal probabilities. Auditory stimuli were triplets of tones varying in mean frequency, intensity and ear of delivery as previously described (Woods et al., 2009, 2010). Visual stimuli in each block were black and white photographs of faces (visual angle 2° × 3°) or words (mean visual angle 2.5°× 0.8°) presented in separate blocks. Faces were eight individuals from the Ekman set (Ekman, 1992), each with four photographs of different emotional expressions (disgust, fear, happiness, and neutral). Targets in the face blocks were successive photographs of the same individual with different emotional expressions. Words were selected from ten different semantic categories (e.g., cities, plants, animals, etc.), each containing four exemplars. Targets in the word blocks were successive words belonging to the same semantic category. Responses were recorded to measure RTs and to permit the calculation of hit and false alarm rates. The difficulty of the auditory and visual tasks was equated based on behavioral pilot testing outside of the scanner. The precise values of the stimulus features (face and word stimulus duration, duration of the individual tones comprising the triplets, frequency of the tones within a triplet) were adjusted to titrate performance level. Retinotopic mapping of the visual cortex was performed with two subjects. The horizontal and vertical meridians were mapped using high-contrast checkerboard wedges (extending from 0.2° to 4.79°, 0.05° wide at inner edge, 0.58° wide at outer edge), and two eccentricities were mapped using central (0.96° eccentricity, 0.19° wide) and peripheral (4.79°, 0.38° wide) rings. Stimulus presentation and response collection were controlled with Presentation software (Version 10, Neurobehavioral Systems, Inc., Albany, CA).

voxel size 0.94× 1.30 × 0.94 mm, TE 4.47 ms, TR 15 ms, flip angle 35°, field of view 240 × 240 mm). Six separate 1 h functional imaging sessions were performed with each subject using an EPI sequence (29 axial slices 4 mm thick plus 1 mm gap, voxel size 1.88× 1.88× 5 mm, TE 39.6 ms, flip angle 90°). All functional scans used a similar blocked design (16 behavioral trials/block). For each subject, three sessions were performed using a sparse imaging sequence (2 functional images acquired per block, TR 10.4 s, 20.8 s/block, sequential slices) and three sessions employed continuous imaging (8 functional images per block, TR 2.9 s, 23.2 s/block, interleaved slices). The rationale for using these two different imaging protocols was that one of our goals for analyzing the auditory cortex data (reported in Woods et al., 2009) was to compare the effects of background acoustic noise on the auditory cortex activations. Sparse imaging produces much less acoustic noise than continuous imaging. Since this was not an important factor for the analysis of the visual activations, we combined the two data sets as described. We used cortical surface analysis procedures to quantify BOLD activations in relation to cortical surface anatomy (Fig. 2). Anatomical image sets were resliced to 1 mm3, segmented, inflated and coregistered to a spherical coordinate system using FreeSurfer (Fischl et al., 1999). Each subject's functional images were coregistered and resampled directly into the high-resolution anatomical space (Kang et al., 2007) after correcting for head movement using SPM5 (Friston et al., 1996). BOLD image values in voxels corresponding to the cortical surface were extracted into the spherical coordinate system, and spatial smoothing was applied to all cortical surface data using a 3-mm FWHM Gaussian filter (Chung et al., 2005). Functional image data were high-pass filtered across time with a cutoff of 0.005 Hz using polynomial detrending. Percent signal change was calculated relative to the overall mean BOLD response for each voxel. Mean BOLD responses associated with each block were calculated by averaging across both functional images in the sparse imaging sessions and across images 2–8 (i.e., beginning 5.8 s after beginning of block) in continuous imaging sessions. Masked mean BOLD values are visualized using an equal-area Mollweide projection to a flat map of the entire cortical surface. Functional activations were depicted on a Mollweide representation of the average cortical surface anatomy of 60 healthy control subjects.

Behavioral data analysis MRI scanning High-resolution T1 anatomical images were acquired from each subject on a 1.5 T Philips Eclipse scanner (matrix size 256 × 212 × 256,

Repeated-measures ANOVAs were performed to examine the differences between auditory and visual task performance. Data from the two bimodal conditions (BA and BV) formed two levels of a modality of attention factor. Since the bimodal conditions both presented identical

Fig. 1. Stimuli and task. Subjects attended to either auditory or visual stimuli in 21 s blocks to detect repeated patterns in the modality cued by a letter at fixation (top row). Auditory and visual stimuli occurred asynchronously at mean stimulus onset intervals of 1.5 s within each modality. Visual stimuli were presented for 700 ms (blue rectangles). Auditory stimuli were tone triplets (250 ms/tone = 750 ms, red rectangles). Visual targets (asterisk) were successive images of the same individual with a different facial expression (shown), or were successive words belonging to the same semantic category.

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Fig. 2. Cortical surface analysis. Functional activations were projected onto maps of the cortical surface. A map of the left cerebral hemisphere is shown partially (top left), fully inflated to a sphere (top right), rotated into an appropriate orientation (bottom right) and displayed using an equal-area Mollweide projection with the occipital pole at the map's center (bottom left). Shading indicates average cortical curvature from 60 individual brains (light: convex; dark: concave) with a functional activation map (left hemisphere stimulus-dependent activation, similar to lower left of Fig. 3) overlaid. Dashed yellow lines indicate the region depicted in the figures. Labels: LOS, lateral occipital sulcus; IPS, intraparietal sulcus; OTS, occipitotemporal sulcus; FG, fusiform gyrus; PHG, parahippocampal gyrus; POS, parieto-occipital sulcus.

stimuli, they were more comparable than the unimodal conditions. Visual stimulus type (faces or words) was also included in a factorial analysis.

fMRI data analysis Stimulus-dependent activations (SDAs; activations produced by unattended visual stimuli; see Fig. 3) were isolated from subtractions of activations in UA blocks from those obtained in BA blocks. Attention-related modulations (ARMs; see Fig. 3) were isolated by subtracting bimodal blocks during auditory attention conditions from bimodal blocks during visual attention conditions (BV–BA). BA and BV blocks contained identical stimuli, and differed only in the modality attended. Functional data from the two cortical hemispheres were aligned using a hemispherically unified anatomically-based coordinate system (as in Cate et al., 2009; Woods et al., 2009). The differences in curvature between the mean spherical maps of the left hemisphere and the reflected right hemisphere were numerically minimized using surface translation and rotation. All analyses used data combined from scans using both imaging protocols (sparse and continuous). Functional data were first analyzed within each imaging protocol separately using F statistics (Clare et al., 1999). The two resulting sets of results (sparse and continuous) were combined via Fisher's method to give z-scores of the activation for each voxel on the map. The envelope masking the activation maps in Fig. 3 was defined by the complete set of visually-responsive surface voxels defined by UV–UA contrast, using the same thresholds applied to the SDAs and ARMs: z greater than 4.0 (p≪ 0.001), signal change greater than 0.1%, cluster size greater than 10 voxels.

Results Behavioral performance The visual and auditory tasks had been equated for difficulty in pilot studies, and accordingly no significant differences were seen of target detection rates during the experiment (visual= 70% and auditory= 62%, F(1,8)= 2.94, p = 0.12 n.s.). However, RTs were significantly faster in visual attention conditions (F(1,8) =531.11, p b 0.001), reflecting the fact that auditory targets required processing of the third tone in the triplet that occurred at 500 ms (Woods et al., 2009). During visual attention blocks, hit rates were equivalent for face and word targets (F(1,8)= 0.01, p = 0.91), but RTs were faster for faces (632 vs. 731 ms, F(1,8)= 36.12, p b 0.001). The presence of unattended auditory stimuli had no effect on visual task performance (hit rate, F(1,8)= 1.50, n.s., RTs F(1,8) = 0.01, n.s.). Similarly, the presence of unattended visual stimuli had no significant effect on auditory task performance (hit rate, F(1,8) = 0.01, n.s.; RT, F(1,8) = 0.12, n.s.). This last result indicates that visual stimuli did not impinge upon auditory task-related attention, since the null effect of visual distraction occurred when auditory performance was well below ceiling (62%). Furthermore, the lack of visual distraction effects were not likely to be due to floor effects because task-irrelevant features of the auditory stimuli (loudness) significantly modulated hit rates (fewer hits with soft sounds: F(1,8) = 17.12, p b 0.005). Activations to unattended stimuli We report the results for activations related to visual modality stimuli and attention here. The auditory modality results have been reported elsewhere (Woods et al., 2009).

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Fig. 3. Visual stimulus-dependent activations (SDAs) and attention-related modulations (ARMs). Upper left: subtractions used to calculate SDAs and ARMs. Upper right: ARMs. A circled cross indicates the occipital pole. Bottom left: SDAs. LOS, lateral occipital sulcus, OTS, occipitotemporal sulcus, IPS, intraparietal sulcus, POS, parietal–occipital sulcus. Functional activations were averaged across hemispheres, which were aligned based on anatomical curvature using a rigid-body rotation, and projected on a map of mean curvature (darker gray = sulcus). All activation maps are triple-thresholded (z >4/p ≪ 0.001, signal change >0.1%, cluster size > 10 voxels). The color scale shows mean percent signal change. Bottom right: ARM/SDA log ratio map.

Fig. 3 (lower left) shows SDAs to unattended visual stimuli revealing that unattended visual stimuli activated not only the occipital pole but also regions of the inferior intraparietal sulcus and ventrolateral occipitotemporal cortex.

beyond the boundaries of SDAs to include part of the precuneus and the superior part of the parieto-occipital sulcus (POS), and extending inferolaterally to the angular gyrus.

Dorsal stream SDAs formed multiple foci along the inferior–superior axis of the inferior intraparietal sulcus (IPS). Dorsal stream SDAs had relatively small magnitudes, below 0.5% and were largely restricted to its caudal extent of the IPS.

Ventral stream Several large ARM clusters occupied the posterior extent of the CoS, LOS and OTS. Smaller clusters were found that extended to the anterior extremity of the FG and laterally in the anterior occipital sulcus.

Ventral stream SDAs covered the occipital pole and extended to surrounding cortex, especially ventrally and laterally. Activated voxels in the pericalcarine region did not appear to extend anteriorly past the 5° eccentricity mapped in one subject (see Fig. 6). SDA magnitudes peaked at approximately 1.0% in the inferior middle occipital gyri (MOG), and covered the lateral occipital sulcus (LOS) and the posterior two-thirds of the fusiform gyrus (FG) as well as the medial bank of the occipitotemporal sulcus (OTS), the posterior collateral sulcus (CoS), and the lingual gyrus (LG). The effects of attention Fig. 3 (upper right) shows the distribution of ARMs. Dorsal stream The inferior IPS superior to the transverse occipital sulcus showed large ARMs, peaking at approximately 0.8% and extending well

The spatial relationship of SDAs and ARMs Fig. 3 (lower right) shows the relative magnitudes of ARMs and SDAs. It plots an attentional modulation index, calculated as the log ratio of ARM to SDA magnitude for each voxel. Blue/cyan colors indicate regions with larger SDAs than ARMs while red/yellow colors indicate regions with larger ARMs than SDAs. Dorsal stream regions generally showed strong attention‐dependence: larger ARMs than SDAs were seen throughout most of the IPS and in surrounding parietal areas. However, there were two small distinct stimulus-dependent regions at the superomedial edge of the activations in IPS. The ventral stream was generally characterized by stimulus-dependence: SDA-dominant responses were seen throughout much of FG, LOS, and the occipital pole. However, attention-dependent regions were evident in the posterior OTS, anterior pericalcarine regions including the lingual gyrus and cuneus, and small regions in the anterior FG and in the anterior occipital sulcus.

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Fig. 4. Response type parcellations. Schematic maps assigning visual cortex voxels to one of three categories, for trials using either faces or words as the visual stimulus. Only voxels in which the contrasts in question represented signal change>0.1% are depicted. SDA-type regions were defined as those surface voxels showing a significant SDA (z>3.09/pb 0.001), but whose ARM contrast did not reach this criterion. ARM-type regions were defined similarly. Mixed-type regions showed significant contrasts in both cases. Cortical surface curvature maps and functional data represent the mean of both hemispheres. Labels: OTS, occipitotemporal sulcus; POS, parietal–occipital sulcus.

A second map (Fig. 4) classified voxels according to the significance of their ARMs and SDAs without regard to their magnitude. A conjunction analysis was used to assign each voxel to one of three categories: SDA significant only, and ARM and SDA both significant, and ARM significant only. These classification patterns were consistent across a broad range of statistical thresholds (see Fig. 5). As Fig. 4 shows, mixed-type regions predominated around the occipital pole. These regions, which include foveal striate cortex and surrounding retinotopic maps of the visual field (see Fig. 6) were activated by unattended face and word stimuli and modulated by intermodal selective attention. As Fig. 3 illustrates, the signal magnitudes of SDAs in these areas generally exceeded those of ARMs. Two small stimulus-dependent regions were also found at the occipital pole for both words and faces, without significant ARMs. Finally, ARM-type regions were evident in anterior pericalcarine area for both word and face stimuli. The dorsal stream could be characterized as a largely ARM-type region. The highest magnitude ARMs were found in the IPS, as seen in Fig. 3, and most IPS voxels showed non-significant SDAs. However, several distinct patches of mixed-type responses were seen in the IPS for both word and face stimuli, and a small cluster of SDA-only voxels capped the superomedial extent of the responsive IPS. In the ventral stream, portions of the anterior and posterior FG showed nearly complete stimulus-dependence both to face and word stimuli, with more stimulus-dependence found for the latter. Finally, a completely attention-dependent region was found in the posterior OTS bordering the FG to both words and faces.

Discussion The intermodal selective attention paradigm permitted the characterization of responses to unattended visual stimuli (stimulus-dependent activations, SDAs) that were uninfluenced by the presence of concurrent visual distractors. We were also able to measure the degree to which selective attention amplified activations (attention-related modulations, ARMs) throughout visual cortex. In our paradigm dorsal parietal regions showed strong visual (and not auditory) ARMs. We also report the novel finding that a region of ventrolateral cortex showed a response pattern identical to that observed in the IPS. This posterior OTS region was attention-dependent and showed no significant stimulus preferences. Intermodal selective attention enhanced face preferences in several extrastriate regions, including the FG.

Activation to unattended stimuli (SDAs) Dorsal stream Our analyses showed several distinct clusters in the IPS that responded to unattended stimuli. Most of these SDA clusters were also modulated by attention. Our results add to the evidence that human dorsal visual stream regions can be stimulated during passive perception, as seen in awake monkey single-unit studies (Bushnell et al., 1981; Robinson and Goldberg, 1978; Robinson et al., 1978) and in more recent reports of multiple distinct retinotopically-organized regions within the IPS (Pitzalis et al., 2006; Schluppeck et al., 2005; Sereno et al., 2001; Swisher et al., 2007; Wandell et al., 2005). Ventral stream We found widespread SDAs that extended well beyond classic retinotopic areas. Surprisingly, large SDA clusters in FG showed no attentional modulation. One likely explanation for the extensive SDAs seen in our study is the absence of competitive interactions between pools of neurons responding to different visual stimuli (Chelazzi et al., 1998; Desimone, 1998; Kastner and Ungerleider, 2001). Neuronal responses to unattended visual stimuli are reduced in the ventral visual stream when multiple stimuli are presented concurrently (e.g. Kastner et al., 2001). The absence of competitive inhibition may have unmasked the intrinsic responses to face and word stimuli within these regions in our study. This suggests that the complex shape selectivity of anterior ventral occipitotemporal neurons might not necessarily depend on voluntary attention, in accord with earlier neurophysiology studies in anesthetized monkeys (Desimone et al., 1984; Gross et al., 1969; Hikosaka, 1997; Kobatake and Tanaka, 1994; Tanaka et al., 1991). Similarly, studies in patients with spatial neglect due to unilateral parietal lesions have found that ventral stream BOLD responses are similar for both unperceived and for successfully detected faces (Rees et al., 2000; Vuilleumier et al., 2001). Attention-related modulations (ARMs) It has been well established that extrastriate visual neurons that respond to unattended stimuli can show enhanced responses when the stimuli are actively attended, both in single-unit recordings (Luck et al., 1997; Moran and Desimone, 1985; Motter, 1994), and in ROI-based fMRI studies (Downing et al., 2001; Murray and Wojciulik, 2004; O'Craven et al., 1997; O'craven et al., 1999; Wojciulik et al., 1998). The

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Fig. 5. Illustration of the stability of response type parcellations across various significance levels. Schematic maps assigning visual cortex voxels to one of three categories, for trials using either faces or words as the visual stimulus. Only voxels in which the contrasts in question represented signal change>0.1% are depicted. SDA-type regions were defined as those surface voxels showing a significant SDA but whose ARM contrast did not reach this criterion. ARM-type regions were defined similarly. Mixed-type regions showed significant contrasts in both cases. Cortical surface curvature maps and functional data represent the mean of both hemispheres. Labels: OTS, occipitotemporal sulcus; POS, parietal–occipital sulcus.

current study compared the spatial distributions of sensory responses and attentional modulations. Dorsal stream Dorsal stream activation was largely attention-dependent: i.e., activations were seen exclusively with attended stimuli. Previous studies have found that attention to empty regions of space is sufficient to activate parietal regions (e.g. the superior parietal lobule or SPL; Kastner et al., 1999), with no additional activation caused by the presence of stimuli. While the overall attention-dependence of the posterior parietal cortex supports its well-demonstrated role in visual selective attention (e.g. Corbetta, 1998; Rafal, 1996; Wojciulik and Kanwisher, 1999) and working memory (Xu and Chun, 2009), the presence of spatiallyrestricted SDAs suggests that its attentional and sensory roles are intertwined to some degree (Geng and Mangun, 2009; Gottlieb, 2007; Zenon et al., 2010). It is worth noting that ARMs, as they were defined in our analyses, signify a strong preference for visual attention versus auditory attention: i.e., greater activation when subjects attended to visual stimuli

than when they attended to sounds. Therefore the robust statistical significance of the ARMs surrounding the IPS emphasizes this region's specialization for visual attention in our study (Degerman et al., 2007; Stevens et al., 2000), although posterior parietal regions have also been shown to be active during spatially-directed attention to other modalities including audition (Shomstein and Yantis, 2006). Ventral stream We discovered a completely attention-dependent region in the OTS, whose pattern of activation resembled those seen in the posterior parietal lobe more than those seen in neighboring ventral regions. This attention-dependent OTS region was immediately lateral to the large, predominantly stimulus-dependent activations seen in the FG. Like attention-dependent regions in the IPS, this region responded to both face and word stimuli (see Fig. 4). The abrupt transition between stimulus-responsive, stimulus-selective regions of the FG and the parietal-type pattern (attention-dependent, stimulus non-specific) seen in the OTS suggests that these regions may play distinct functional roles in visual perception. In particular, the OTS may play a role in

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Fig. 6. Retinotopic mapping from the left hemisphere of one subject. The retinotopic map is overlaid on an activation map from the sparse acquisition scans showing the group-level ARM contrast (bimodal visual minus bimodal auditory), thresholded at p b 0.001. Activation map colors indicate BOLD percent signal change, as illustrated by inset scale.

modulating processing in earlier visual areas (Mehta et al., 2000; Schroeder and Foxe, 2002; Schroeder et al., 2001). Conclusions Our intermodal selective attention experiments revealed several key functional properties of human visual cortex. We found that most regions of the dorsal stream were attention-dependent and stimulus nonspecific. Activation in the dorsal regions that did show responses to unattended stimuli was further enhanced by attention. Different patterns were seen in the ventral stream. Large, relatively anterior regions showed complete stimulus-dependence, i.e. sensory responses were not modified by attention. Posterior occipitotemporal cortex responded to unattended stimuli and showed further attentional enhancement. Surprisingly, we also identified a lateral ventral stream region in the occipitotemporal sulcus that exhibited response properties typical of the dorsal stream (i.e., attention-dependent and stimulus general). Thus, intermodal attention paradigms can provides insights into visual processing that complement those derived from intramodal attention tasks. Acknowledgments This work was supported by the VA Research Service. References Alkire, M.T., Hudetz, A.G., Tononi, G., 2008. Consciousness and anesthesia. Science 322, 876–880. Arcaro, M.J., McMains, S.A., Singer, B.D., Kastner, S., 2009. Retinotopic organization of human ventral visual cortex. J. Neurosci. 29, 10638–10652. Arrington, C.M., Carr, T.H., Mayer, A.R., Rao, S.M., 2000. Neural mechanisms of visual attention: object-based selection of a region in space. J Cogn Neurosci 12 (Suppl 2), 106–117. Avidan, G., Levy, I., Hendler, T., Zohary, E., Malach, R., 2003. Spatial vs. object specific attention in high-order visual areas. Neuroimage 19, 308–318.

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