Fourteen right-handed subjects (aged 20–35 years, nine females and five males) each participated in three imaging sessions after providing informed consent in accordance with the local Institutional Review Board. All subjects had normal or corrected-to-normal vision and normal hearing by self-report.

Figure 2 illustrates the paradigm. Subjects performed a demanding intermodal selective attention task. Subjects always attended to a single modality that was randomly assigned on each block and cued by a semi-transparent letter at fixation. Different blocks were presented according to a randomized factorial design with both stimulus and attention conditions exhaustively crossed in each experiment. There were four different types of blocks: two unimodal blocks with attended auditory or attended visual stimuli presented in isolation and two bimodal blocks where attention was directed to either auditory or visual stimuli. There were equal numbers of each block type, and block order was randomized with the additional constraint that a unimodal visual condition occurred on every 4th block.

Three 1.2-h functional brain imaging sessions were performed on separate days. In each imaging session, there were six 12-min runs (24 blocks per run). Thus, there were a total of 108 blocks containing auditory stimuli, each containing eight triplet trials with CVCs or AMNBs in separate blocks. AMNBs were modulatations of talker-specific speech spectra had been obtained from the CVCs.

One focus of the current study was to compare the distributions of activations to consonants with different manners (fricatives vs. plosives) and places (front, middle, back) of articulation. In order to examine these consonant-specific differences in more detail two-thirds of the auditory blocks contained CVCs and one-third AMNBs. Thus, 1,728 syllables and 864 AMNBs were presented to each subject during each daily session.

The processing of unattended speech sounds and noise bursts were isolated in stimulus-dependent activations (SDAs) by subtracting activations in unimodal visual attention blocks from activations in bimodal visual attention blocks. Attention-related modulations (ARMs), reflecting enhanced auditory activations during auditory attention blocks, were isolated from differences between bimodal blocks during auditory and visual attention conditions. These blocks contained the same physical stimuli, but differed in whether subjects performed auditory or visual tasks of similar difficulty.

In CVC conditions, subjects listened to CVC syllables containing consonants with place of articulation (front, middle or back) and manner of articulation (plosive or fricative) held constant in each block as shown in Table 1. Thus, there were six different types of CVC blocks based on place of articulation (front, middle, or back) and manner of articulation (plosive or fricative). During each CVC block, two different consonants (one voiced, one unvoiced) with the same place and manner of articulation occurred in both initial and final syllable positions. Consonants were combined with three different vowels (/a/,/i/, and /u/) to create 12 different CVC syllables presented in each block. Two different tokens of each CVC were sampled from each of four talkers (two female). This resulted in 96 different CVC tokens that were randomly sampled during each block. Mean CVC durations ranged from 521 (for frontal plosives) to 717 ms (for back fricatives). CVCs were selected from among 9,600 syllables that had previously been used to characterize consonant confusions and evaluate consonant identification ability (Woods et al., 2010a,b).

Non-speech stimuli were speech-spectrum noise bursts (AMNBs) of 600 ms duration. The spectra had been obtained by averaging the spectra of 2,400 CVC tokens from each talker as described in Woods et al. (2010b). This spectrum was then used to create a finite impulse response (FIR) filter that was used to filter broadband white noise to create 10 different noise segments of 1200-ms duration. Segments of these noise samples were then randomly sampled and amplitude modulated during the testing sessions. Four different types of AMNB tokens per spectrum were created by amplitude modulating the speech-spectrum noise bursts at 0, 4, 8, or 16 Hz. Thus, 16 types of AMNBs were randomly sampled from a set of 160 AMNB tokens that included 10 independent samples of speech spectrum noise from each of four talkers.

Auditory stimuli were presented in groups of three. During attend CVC blocks, subjects performed a one-back syllable matching task searching for repeated syllable triads regardless of variations in the talker's voices (Figure 2). The talker's voice, consonant voicing and vowel content varied randomly within each block. During attend AMNB blocks, subjects performed a similar one-back triad-matching task based on the amplitude modulation rates of the three stimuli. Targets occurred one or two times per block with a mean probability of 18.75%. Stimulus intensity was fixed at 88 dB SPL A-weighted so that stimuli were clearly audible above background scanner noise. Auditory stimuli were delivered monaurally to the left or right ear on separate blocks. Interstimulus intervals within the triad were fixed at 100 ms. Inter-triad intervals (range 1000–1600 ms) were slightly adjusted so that the eight triads were equally spaced over each 29.6 s block.

Visual stimuli were line drawings of rectangular shapes whose properties varied independently of auditory stimuli. Visual stimuli varied in the type of line drawing (solid line, broken line segments forming a closed shape, and misoriented broken line segments forming an exploded shape) and the type of shape variation that occurred within each visual triad (surface area variation only, aspect ratio variation only, or width variation only). There were nine different visual conditions (three types of line drawings × three types of shape variation). Subjects performed a one-back matching task with targets defined by a repeating triad of shape changes. To minimize intermodal integration, visual stimulus onsets in bimodal stimulus blocks occurred at random asynchronies (range ±200–500 ms) with respect to the onset of auditory stimuli. During bimodal sequences, auditory blocks were randomly combined with the different types of visual blocks. Randomization and stimulus presentation and response collection were controlled with Presentation software (NBS, Albany, CA, version 13.0).

EPI-related acoustic noise was measured with an MRI compatible microphone head and torso system (Bruel and Kjaer, 2260) and showed an intensity of 105 dB SPL (A-weighted) with a frequency peak at 642 Hz. Scanner noise amplitude was modulated with the acquisition of each axial slice at a frequency of 10 Hz. Pump noise audible during inter-image acquisitions had an intensity of 65 dB SPL (A-weighted) that was dominated by low frequencies. Stimuli were presented through MRI-compatible electrostatic earbuds (Stax MRI-002, Stax Ltd, Saitama prefecture, Japan) that attenuated external noise over the audible frequency range. Further attenuation of ambient sounds was obtained with circumaural ear protectors (Howard Leight LM-77, Howard Leight Industries, San Diego, CA, USA) that provided 25 dB of additional attenuation at 4000 Hz, 18 dB at 1000 Hz, 6 dB at 250 Hz. Thus, the overall attenuation of external noise varied from 16 to 35 dB with greater attenuation of external sounds at high frequencies.

Each subject underwent 1 h of behavioral training session followed by high-resolution T1 structural brain imaging on a 1.5 T Philips Eclipse scanner (voxel size 0.94 mm × 1.30 mm × 0.94 mm, TR 15 ms, TE 4.47 ms, flip angle 35). Two such anatomical images were re-sliced to 1 mm × 1 mm × 1 mm resolution, averaged, and then inflated to the cortical surface using FreeSurfer (Dale et al., 1999; Fischl et al., 1999a). The inflated cortical surfaces of the left and right hemispheres were then co-registered to a common spherical coordinate system (Fischl et al., 1999b) based on a reference template derived from the average pattern of 40 individual subjects. The differences in curvature between the mean spherical maps of the left hemisphere and the reflected right hemisphere were then numerically minimized using surface translation and rotation to create a hemispherically unified anatomically based coordinate system (Woods et al., 2009). This resulted in an accurate alignment of the principal gyri, including the STG and Heschl's gyrus (HG), across subjects and hemispheres.

On three subsequent days, each subject underwent 1.2-h sessions of functional imaging using a spin-echo EPI sequence (29 axial slices 4 mm thick plus 1 mm gap, voxel size 2.5 mm × 2.5 mm × 5 mm, TR = 2.96 s), TE 39.6 ms, flip angle 90°. On each day 144 stimulus blocks were presented across six 12-min runs, with rests between runs provided upon subject request. Eleven images were obtained during each block, with five additional images preceding the start of the experiment in order to reach saturation.

Head movements were measured using SPM5 (Friston et al., 1996). Each functional image was resampled into higher-resolution (1 mm³) anatomical space before realigning and averaging, so-called anatomical space analysis (Kang et al., 2007). This technique, similar to hyper-resolution techniques developed for spy satellite image analysis, improves the spatial resolution of fMRI due to the fact that the relative locations of functional and anatomical voxels change slightly over the course of experiments (due to minor head movements) and across experimental sessions.

Overall mean voxel image values for each imaging session were computed and equalized across each subject's three sessions. Average percent signal changes in response to auditory stimuli were calculated for each subject relative to the overall mean BOLD response for each voxel. The effects of physiologically implausible BOLD fluctuations (Weisskoff, 2000) were reduced by capping local fluctuations at ±5%, a technique called Winsorization (Marchini and Ripley, 2000). Finally, functional image data were high-pass filtered with a cutoff of 0.006 Hz using orthonormal polynomial detrending.

Auditory activations on the cortical surface were visualized using an equal-area Mollweide projection after spherical inflation of each hemisphere using Freesurfer (Figure 3). The Mollweide projection was centered on HG and oriented so that the superior temporal plane lay on the equator. Population-averaged fMRI data was used to define a rectangular region encompassing areas showing significant activations to non-attended sounds. The rectangle included HG, much of the superior temporal plane and STG, and portions of the STS (Figure 2, bottom left). Grid positioning was identical to that used in our previous study (Woods et al., 2010c).

Activations were assigned to human ACFs based on cortical surface coordinates obtained using tonotopic field mapping (Woods et al., 2010c). Field assignment was based on the Kaas et al. (1999) model of primate auditory cortex. The core included primary auditory cortex (A1), the rostral field (R) and the rostrotemporal (RT) field. The medial belt included the rostrotemporal medial (RTM), rostromedial (RM) and caudomedial (CM) fields. The lateral belt included the caudolateral (CL), middle–lateral (ML), AL, and rostrotemporal-lateral (RTL) fields. The parabelt included the rostral parabelt (RPB) and CPB fields. Auditory activations were compared between field groups and then between fields within each group. ACFs were defined using identical surface coordinates in the left and right hemispheres, following the co-registration of the left and mirror-reversed right hemisphere. Thus, ACF coordinates in both hemispheres shared similar locations relative to HG and other anatomical landmarks.

Auditory CVC and AMNB activations were quantified using a rectangular grid that contained 1,748 1.75-mm × 1.75-mm grid elements as shown in Figure 4. The grid spanned 80.5 mm anterior–posteriorly and 66.5 mm medial–laterally on the cortical surface and covered approximately 5400 mm², i.e., roughly the full spatial extent of auditory response produced during visual attention conditions. Response magnitude (mean percent signal change) was quantified for each grid element in each field. To compare stimulus preferences across fields, we examined the ratio of CVC activations to the mean of CVC + AMNB activations. Thus, fields showing a CVC preference produce CVC/mean ratios above 100%, while fields with AMNB preferences produced CVC/mean ratios below 100%. Stimulus preferences were statistically evaluated using ANOVA for repeated measures with subjects treated as a random factor and ACF or ACF-group, Stimulus-type (CVC or AMNB), Hemisphere (left or right), Ear of delivery and Time after block onset (i.e., image number) included as factors. Individual ACFs were also analyzed with grid position as a factor to determine if there were significant differences in the distribution of activations within ACFs as a function of stimulus type or ear of delivery.

ANOVA analyses for multifactorial repeated measures were performed with the open source CLEAVE program (www.ebire.org/hcnlab). All analyses were based on a random effects model and using the Box–Greenhouse–Geisser correction to control for any lack of statistical independence due to spatially correlated BOLD signals.

Reaction times (RTs) were available for only 11 of 14 subjects due to a problem in response recording. An analysis of the remaining subjects’ data showed that performance in the auditory tasks was superior to performance in the visual tasks [F(1,10) = 8.16, p < 0.02] with hit rates of 84.5% in attend-auditory blocks and 76.8% in attend visual blocks. However, RTs were significantly shorter in visual (644 ms) than auditory (829 ms) blocks [F(1,10) = 249.3, p < 0.0001], due primarily to the fact that auditory target discrimination depended on information at the end of the auditory stimuli (e.g., final consonants).

Performance in the auditory conditions was further examined in 3-way ANOVAs with Stimulus-type (CVCs or AMNBs), Condition (unimodal stimulation vs. bimodal stimulation, auditory attention) and Ear of delivery as factors. Hit rate showed a significant main effect of Stimulus-type [F(1,10) = 68.24, p < 0.0001] due to higher hit rates in CVC (86.3%) than AMNB (74.7%) blocks. However, there were no significant differences in RTs as a function of Stimulus-type, nor were there significant differences in either hit rate or RT as a function of Condition or Ear of delivery.

A second 4-way ANOVA analyzed performance in CVC blocks, with Condition (unimodal stimulation, auditory attention vs. bimodal stimulation, auditory attention), consonant Place of articulation (front, middle, or back), consonant Manner of articulation (fricative or plosive), and Ear of delivery as factors. For RTs, neither Condition [F(1,10) = 0.25], nor Place of articulation [F(1,10) = 1.44] significantly affect performance. However, RTs were significantly affected by Manner of articulation [F(1,10) = 52.31, p < 0.0001], due to shorter RTs for plosives (759 ms) than fricatives (887 ms). This likely reflected differences in mean syllable duration (522 ms for plosive syllables vs. 676 ms for fricative syllables) and was not accompanied by changes in hit rate [F(1,10) = 0.23]. RTs were also shorter to CVC targets in the right ear than to CVC targets in the left ear [812 vs. 832 ms, F(1,10) = 5.45, p < 0.05] and subjects also tended to detect right ear targets more accurately [88.3 vs. 84.4%, F(1,10) = 3.76, p < 0.08] consistent with previous results (Fry, 1974).

Table 2 shows the activation magnitudes and stimulus preferences of different ACF groups. Activation magnitudes were largest in the core and lateral belt fields and significantly smaller in the medial belt and parabelt field groups [F(3,39) = 50.89, p < 0.0001]. Activation magnitudes were not significantly influenced by the presence of visual distractors in any field group.

To further examine stimulus preferences, amplitudes were normalized across all stimulus conditions within each ACF. Different field groups had distinct stimulus preferences that were reflected in a significant Stimulus-Preference × Field-group interaction [F(3,39) = 31.93, p < 0.0001]. As shown in Figure 5, this was due to the fact that medial belt fields showed a preference for AMNBs [F(1,13) = 11.44, p < 0.005], core fields showed no significant preference for either stimulus category [F(1,13) = 2.37, p < 0.15], while lateral belt and parabelt fields showed marked preferences for CVCs [F(1,13) = 34.36, p < 0.0001 and F(1,13) = 32.86, p < 0.0001, respectively]. Importantly, the highly significant stimulus preferences seen in medial belt, lateral belt and parabelt groups were also evident for unattended stimuli [i.e., p < 0.006 for all comparisons].

There was also a highly significant contralaterality effect over auditory cortex [F(1,13) = 98.56, p < 0.0001]: activations were about 30% larger in the hemisphere contralateral to stimulated ear than over the ipsilateral hemisphere. In addition, there was also a significant Contralaterality × Field-group interaction [F(3,39) = 8.42, p < 0.0002] due to greater contralaterality in core and medial belt ACFs than in lateral belt or parabelt ACFs. Although contralaterality was greatest in the core and medial belt ACFs, significant contralaterality was still evident in lateral belt ACFs [F(1,13) = 31.51, p < 0.0001], but contralaterality failed to reach significance in parabelt ACFs [F(1,13) = 3.18, p < 0.10].

Figure 6 shows the effects of attention on activations in each field group. There was a significant Condition × Field-group interaction [F(6,78) = 4.83, p < 0.008]: attentional modulation was relatively reduced in core fields in comparison with other field groups. Nevertheless, attention significantly enhanced responses throughout auditory cortex [F(2,26) = 16.04, p < 0.0001] and within every field group [at least p < 0.002 in all field groups, including the core]. Importantly, there were no significant trends in Condition × Stimulus-type or Condition × Stimulus-type × Field-group analyses. This indicates that the attentional enhancements to CVCs and AMNBs had similar magnitudes and distributions over auditory cortex.

Figure 7 shows the timecourse of SDA and ARMs, for the different field groups. As in previous studies, SDAs peaked shortly after sound onset and showed a gradual adaptation throughout the stimulus block (Petkov et al., 2004). In contrast, ARMs were minimal at block onset but increased in magnitude over the first 21 s of sound delivery, remained at a high level until the end of the stimulation (image 10), and continued during the first image of the next stimulation block. As a consequence, field groups with relatively larger ARMs would be expected to show delays in the timecourse of activations. We did find small but significant differences in the timecourse of amplitude-normalized activations between field groups [F(30,390) = 2.49, p < 0.04], with delays in lateral belt and parabelt fields and increased persistence of activations after sound offset in comparison with activations in the core and medial belt. However, when SDAs alone were analyzed, the differences in time courses of different field groups failed to reach significance [F(30,390) = 1.35].

Activation contralaterality also reflected the relative contributions of SDAs and ARMs in different field groups. As in previous studies (Petkov et al., 2004; Woods et al., 2009), ARMs showed insignificant contralaterality [F(3,39) = 2.25, NS] whereas SDAs showed large contralateral enhancements [F(3,39) = 7.98, p < 0.001]. Thus, fields with large ARM/SDA ratios (e.g., parabelt) would be expected to show reduced contralaterality in comparison with core fields with smaller ARM/SDA ratios, as observed.

Activation magnitudes were similar in the left and right hemisphere [F(1,16) = 0.65, NS] without significant hemisphere asymmetries noted for any ACF group. CVC blocks were further analyzed as function of the place and manner of consonant articulation. No significant main effects or interactions of Place or Manner with ACF-group, Ear of delivery, Condition or Hemisphere were observed.

Table 2 shows activation magnitudes in the three different core fields: A1, R and RT. There was no significant main effect of Stimulus type nor was there a significant Stimulus-type × ACF interaction. However, there was a small but significant Hemisphere × ACF interaction [F(2,26) = 4.94, p < 0.03], due to the fact that activations were larger over the left hemisphere in A1 and RT, but over the right hemisphere in R. In A1 there was also a significant Stimulus-type × Hemisphere interaction [F(1,13) = 6.13, p < 0.03]: CVCs produced larger activations in left hemisphere whereas AMNBs produced larger activations in the right hemisphere. This asymmetry was also seen in SDAs alone [F(1,13) = 9.33, p < 0.01] and did not interact with Ear of stimulation.

Further analysis of activation distributions was performed in A1 and R. In A1, there was no significant overall preference for CVCs [F(1,13) = 2.40, NS]. However, there was a significant variation in stimulus preferences with grid position [F(172,2236) = 3.57, p < 0.007]. Figure 8 shows an analysis of stimulus preferences for different isofrequency contours as estimated from Woods et al. (2010c). Two effects were observed. First, CVC preferences interacted with frequency preferences: high-frequency responsive regions preferred AMNBs, mid-frequency responsive regions slightly preferred CVCs, and low-frequency responsive regions strongly preferred CVCs. Second, there was a medial-to-lateral gradient in stimulus preferences within isofrequency bands: medial regions preferred AMNBs and lateral regions preferred CVCs.

Like A1, R showed a significant Stimulus-type with Grid-position interaction [F(172,2236) = 3.61, p < 0.008]. However, unlike A1, R also showed a small but significant overall preference for CVCs [F(1,13) = 5.26, p < 0.04]. Figure 9 shows an analysis of stimulus preferences along isofrequency contours in R. As in A1, CVC preferences generally increased at the lateral boundary of R, particularly in regions responding to middle and low spectral frequencies. In contrast, no significant Stimulus-type with Grid-position interaction was found in core field RT [F(46,598) = 1.01, NS].

Attention significantly enhanced activations in core fields as a whole [F(2,26) = 11.34, p < 0.003] and in each core field individually [p < 0.05 to p < 0.0001]. However, attention effects were relatively larger in RT than in R or A1, producing a significant Attention-condition × ACF interaction [F(4,52) = 7.13, p < 0.006]. Finally, a further analysis restricted to blocks containing CVCs showed a main effect of Manner of articulation in A1 [F(1,13) = 11.54, p < 0.005]: fricatives produced larger activations than did plosives. No Manner effects were found in other core ACFs nor did Place of articulation affect activation magnitudes in any core ACF.

As shown in Table 2, there was no significant difference in the magnitude of activations in different medial belt ACFs [F(2,26) = 0.62]. AMNBs produced significantly larger activations than CVCs in medial belt ACFs [F(1,13) = 11.44, p < 0.005] and significant AMNB preference was also found to non-attended sounds [F(1,13) = 16.61, p < 0.002]. ARMs did not differ in magnitude for AMNBs and CVCs [F(1,13) = 0.99, NS]. There was also a significant Stimulus-type × ACF interaction [F(2,26) = 6.50, p < 0.02] that reflected an increasing preference for AMNBs in more anterior fields. AMNB-preferences were significantly greater in RTM than RM or CM [p < 0.01 both comparisons], with CM being the only medial belt field that failed to show a significant preference for AMNBs. There was also a significant difference in contralaterality in medial belt fields [F(2,26) = 8.05, p < 0.03] due to increased contralaterality in RM in comparison with RTM and CM.

Attention significantly enhanced medial belt activations [F(2,26) = 11.24, p < 0.002] without significant Attention-condition × ACF interactions [F(4,52) = 0.72, NS]. No significant main effects or 1^st order interactions were found for place or manner of articulation when analyzed in CVC-only blocks either in all medial belt fields or in individual medial belt fields.

As shown in Table 2, activation magnitudes differed significantly among lateral belt fields [F(3,39) = 28.49, p < 0.0001] with larger activations seen in ML (0.42%), AL (0.33%) and CL (0.28%), than in RTL (0.09%). CVCs produced larger activations than AMNBs in all lateral belt fields. However, the differences in activation magnitudes between CVC and AMNB blocks were larger in AL (0.16%) and ML (0.14%) than in CL (0.04%) or RTL (0.06%). Further analysis showed that CVCs produced significantly larger activations than AMNBs in lateral belt ACFs [F(1,13) = 33.36, p < 0.0001]. This preference was also evident in visual attention conditions [F(1,13) = 15.14, p < 0.002]. Although all lateral belt ACFs preferred CVCs [least significant: p < 0.02 for CL], CVC preferences increased in more AL belt ACFs resulting in a significant Stimulus-type × ACF interaction [F(2,26) = 6.83, p < 0.02]. Pairwise comparisons showed the greatest CVC preferences were seen in the AL belt fields, RTL and AL, with significant pairwise differences between AL and ML and between ML and CL.

Attention significantly enhanced activations in all lateral belt fields [F(2,26) = 33.36, p < 0.0001]. There was also an Attention × ACF interactions [F(3,39) = 3.89, p < 0.04] due to the fact that attentional enhancements were relatively larger in more AL belt ACFs. Attention effects tended to be larger for CVCs than AMNBs, but this effect failed to reach significance [F(1,13) = 3.06, p < 0.11]. There were no significant hemisphere asymmetries either in the lateral belt as a whole or in any individual field. The further analysis of CVC-blocks revealed no main effects of Place or Manner of consonant articulation and no significant Consonant-type × Grid-position interactions.

As shown in Table 2, activation magnitudes were small in parabelt ACFs (0.12%) and did not differ between RPB and CPB. CVCs produced significantly larger activations than AMNBs [F(1,13) = 32.86, p < 0.0001], an effect that was also observed during visual attention conditions only [F(1,13) = 9.67, p < 0.01]. The magnitude of attention effects was similar for AMNBs and CVCs [F(1,13) = 1.06, NS]. There were no significant Stimulus-type × ACF interactions. However, there was a Hemisphere × ACF interaction [F(1,13) = 9.40, p < 0.01]: larger activations were seen in the right hemisphere RPB and the left hemisphere CPB. There was no significant Consonant-type × Grid-position interaction in any lateral belt field.

Attention significantly enhanced parabelt activations during CVC-only blocks [F(2,26) = 10.12, p < 0.002] without significant Attention × ACF interactions. We found no significant main effects of place or manner of articulation in CVC blocks, and no significant topographic differences as a function of Place or Manner of articulation.

Figure 10 shows ACFs color coded by relative activation magnitudes to CVCs (red) and AMNBs (green). Significant pairwise differences in response properties for adjacent ACFs are shown for CVC stimulus preference (S), contralaterality (C), Attention (A) and Hemisphere (H). There was a clear medial-to-lateral gradient of CVC-selectivity: medial belt fields preferred AMNBs, core fields showed minimal stimulus preferences, and lateral belt and parabelt fields strongly preferred CVCs. This gradient extended through tonotopic fields A1 and R: medial regions of these fields preferred AMNBs and lateral regions preferred CVCs. There was also a posterior to anterior gradient of increased stimulus selectivity: rostral fields produced small activations with increasingly clear stimulus preferences. The most rostral medial belt field (RTM) showed the greatest preference for AMNBs and the most rostral–lateral belt field (RTL) showed the greatest preference for CVCs.

Significant differences in response properties were particularly evident in pairwise comparisons of adjacent core and belt fields: core fields showed less marked stimulus preferences than adjacent belt fields: i.e., less AMNB preference than adjacent medial belt fields and less CVC preference than adjacent lateral belt fields. As in previous studies (Woods et al., 2010c) core fields also showed increased contralaterality in comparison with adjacent belt fields, as well as reduced attentional lability. At the lateral belt/parabelt boundary, the response properties of RPB were similar to those of adjacent lateral belt fields AL and RTL. However, the CPB field showed a greater CVC-preference and attentional lability than ML and a greater CVC preference than CL.

Several properties of ACFs and ACF field groups were similar to those noted in previous studies using pure tone stimuli (Woods et al., 2010c): (1) overall activation magnitudes were largest in posterior lateral auditory cortex in fields A1 and ML; (2) greater contralaterality was seen in core fields than lateral belt or parabelt fields, with medial belt fields showing an intermediate pattern; and (3) attention effects were enhanced in belt fields in comparison with the core. Thus, belt fields appear to be generally less sensitive to basic acoustic properties of stimuli (e.g., ear of delivery) and more responsive to their behavioral relevance. However, unlike the previous study where stimulus-related differences between activations were largest in core fields to sounds differing in frequency or intensity, in the current experiment sounds differing in more complex features produced larger stimulus-related differences in the medial and lateral belt fields.

We also found differences between sensory responses to non-attended sounds (SDAs) and ARMs that replicated those seen in previous studies. SDAs showed strong contralaterality while ARMs were symmetrically distributed over the hemisphere ipsilateral and contralateral to stimulation (Petkov et al., 2004; Woods et al., 2009). These results contrast with those of selective attention studies using dichotic inputs where ARMs can show a contralateral distribution that reflects the direction of attention (Rinne et al., 2008).

In addition, there were striking differences in the adaptation of SDAs and ARMs similar to those that we have found in previous studies (Petkov et al., 2004). SDAs peaked shortly after stimulation began and showed gradual adaptation through the stimulus block, diminishing to about 60% of peak amplitudes. SDA adaptation occurred in all ACF field groups and was not stimulus specific. For example, in CVC blocks both the syllables and the talkers changed randomly. This pattern could reflect different adaptation of transient and sustained responses (Lehmann et al., 2007) or alternatively transient involuntary attention effects elicited by the onset of auditory stimuli. In contrast, ARMs were initially small, but increased in amplitude throughout the block. This likely reflects delays in the switching of focus of attention from the previous block as well as an improvement in the attentional focus that occurred as subjects gained familiarity with the specific stimulus set being presented.

As in previous studies (Woods et al., 2010c) core fields showed the largest overall activation magnitudes of any field group, with the largest activations seen in A1. Overall activation magnitudes in A1 were similar to CVCs and AMNBs. However, A1 showed two gradients of stimulus-preference. First, there was a medial-to-lateral gradient in stimulus preferences along isofrequency contours: medial regions of A1 showed a preference for AMNBs while lateral regions showed a preference for CVCs. In addition, high frequency responsive regions of A1 generally preferred AMNBs, mid-frequency regions show no clear stimulus preference, and low frequency regions generally preferred CVCs. Since CVCs and AMNBs had identical average frequency spectra, this finding suggests that the low- and mid-frequency sensitive regions of A1 (responding to pure tones in 250–2000 Hz frequency range) are biased for processing the acoustic features of human vocalizations. Unlike A1, the tonotopic field R showed an overall preference for CVCs. However, like A1, R showed a medial-to-lateral stimulus preference gradient: medial regions preferred AMNBs and lateral regions preferred CVCs and regions tuned to low frequencies tended to show the greatest preference for CVCs.

One possible explanation for these results is that human A1 and R contain receptive field gradients within isofrequency bands that are tuned to complex acoustic features. In core fields A1 and R, lateral regions, particularly those tune to low frequencies, respond maximally to sounds with the spectrotemporal features that characterize speech (e.g., harmonic structure, formant characteristics, etc.). The preferential activation of low-frequency regions of auditory cortex by CVCs may reflect the critical role played by consonant voicing and vowels in speech discrimination. In contrast, medial belt ACFs may respond maximally to sounds with other complex spectrotemporal features (e.g., broadband non-periodic noise, envelope amplitude modulation, etc.).

Preferential tuning of regions of human auditory cortex to acoustic features that characterize speech might be expected on evolutionary grounds. In bats, another species with a highly specialized vocalization system, neurons in primary auditory cortex are tuned to vocalization-specific acoustic cues (Kanwal and Rauschecker, 2007) that are refined with experience (Razak and Fuzessery, 2010). The auditory system of songbirds is also highly specialized to process conspecific vocalizations and shares functional and developmental properties with human brain regions implicated in speech processing (Bolhuis et al., 2010). In the zebra finch the A1 homologue is preferentially responsive to conspecific vocalizations in comparison with spectrally matched control stimuli (Grace et al., 2003; Hauber et al., 2007).

The hypothesis that lateral regions of core ACFs are specialized to process speech sounds is consistent with a number of studies that have shown that sound with harmonic pitch structure of the sort that characterize vocalizations preferential activate lateral regions of auditory cortex (Warren et al., 2005; von Kriegstein et al., 2007, 2010; Garcia et al., 2010; Overath et al., 2010; Puschmann et al., 2010). Rapid acoustic transients that provide consonant cues also produce enhanced activations in lateral auditory cortex (Rimol et al., 2005; Hutchison et al., 2008; McGettigan et al., 2010).

However, the stimulus preferences gradients seen within A1 and R may also have reflected the smearing of functional properties at ACF boundaries that occurs as a consequence of population averaging (Woods et al., 2009). Because of intersubject and interhemispheric differences in the size and anatomical locations of ACFs, population averaging based on anatomical coordinates will result in a mixture of the functional properties at field boundaries. For example, lateral regions of A1 in one subject may overlap with medial regions of ML in another. As a result, the functional properties of voxels at the A1/ML border will reflect a blending of properties of A1 and ML from different subjects. While stimulus preference gradients in A1 and R would have reflected the blending of stimulus preferences across core and adjacent belt fields, blending is probably not the only explanation for regional variation in stimulus preferences within core fields. If blending were the sole contributor to selectivity gradients within core fields, the sharpest gradients would have been expected in RT, a small core ACF bounded medially by strongly AMNB-preferring RTM and laterally by strongly CVC-preferring RTL. In fact, RTM failed to show a clear gradient in stimulus selectivity.

Medial belt activations were markedly enhanced to AMNBs. The preference of medial belt fields for AMNBs did not depend upon attention. Thus, medial belt fields preferentially process amplitude-modulated noise that lacks the pitch and formant structure that characterizes speech. AMNB stimuli also revealed functional distinctions among medial belt fields that were not evident in previous studies that used pure tone stimuli (Woods et al., 2010c). For example, RM showed greater contralaterality than other medial belt fields. There was also a posterior to anterior gradient of stimulus preference that was increased in the most anterior medial belt field, RTM. The results confirm suggestions that medial belt fields require acoustically complex stimuli to elucidate their tuning properties (Kusmierek and Rauschecker, 2009).

Human studies have long implicated the STG and STS in the phonological processing of speech sounds (Obleser et al., 2007; Rauschecker and Scott, 2009; Leaver and Rauschecker, 2010; Turkeltaub and Coslett, 2010). Consistent with previous reports, we found strong response preferences for CVCs in lateral belt fields and parabelt fields. This CVC preference was not dependent on attention: similar CVC preferences were seen for unattended sounds.

Although the largest differences in absolute activation magnitudes were found in AL and ML, ML showed large activations to both CVCs and AMNBs while AL showed a greater CVC preference. Additional analysis of the stimulus preferences of lateral belt fields suggested that CVC-preferences increased further in the more anterior field, RTL. Thus, in both medial belt and lateral belt fields there was an increase in stimulus preferences from posterior to anterior regions of auditory cortex. This is consistent with the what/where dichotomy that has been reported in many studies of auditory cortex (Rauschecker and Scott, 2009), with posterior cortical regions more response to the spatial location of stimuli and anterior regions more sharply tuned for the complex acoustic features that characterize particular stimulus categories. Parabelt fields also showed a strong preference for CVCs, with CVC-preferences that equaled or exceeded those seen in adjacent lateral belt fields. As in lateral belt ACFs, CVC preferences in parabelt fields were not dependent on attention.

Although the experimental design was optimized to detect differences in activation distributions associated with different consonant features, no consistent differences were found as a function of the place or manner of articulation. This contrasts with reports of distinct activation distributions that have been associated with the processing of different vowels (Obleser et al., 2006).

Although attention significantly enhanced activation magnitudes in all ACFs, belt ACFs showed greater attentional modulation than core ACFs. Unexpectedly, although stimulus-preferences were clearly evident in SDAs (i.e., medial belt fields preferred AMNBs and lateral belt/parabelt fields preferred CVCs) no stimulus preferences were evident in ARMs: ARMs did not simply reflect the amplification of local sensory processing. Rather, ARM magnitudes to AMNBs and CVCs did not differ significantly in any ACF group. These results suggest that auditory attention in our 1-back triad-matching task engaged a common network of auditory areas regardless of the type of stimuli that are presented.

Unlike previous studies (Turkeltaub and Coslett, 2010), we found no evidence of hemispheric asymmetries in phonological processing in lateral belt and parabelt fields (cf. Leaver and Rauschecker, 2010). These results contrast with the consistent lateral belt asymmetries (right > left) that are observed in the processing of tone stimuli in pitch discrimination tasks (Woods et al., 2010c) or when discriminating musical sounds (Johnsrude et al., 2000; Zatorre and McGill, 2005; Zatorre et al., 2007). Nevertheless, we did find evidence of small but significant hemisphere asymmetries in primary auditory cortex, even in the absence of attention. Left hemisphere A1 showed a preference for CVCs while right hemisphere A1 showed a preference for AMNBs. This may reflect asymmetries in the acoustic feature preferences of A1 or differences in the thalamo-cortical projections to the two hemispheres (Rademacher et al., 2002).