Two-Stage Processing of Sounds during Attention Figure 4. M100 peak amplitudes and latencies. (A) M100 source strengths (+SEM) were clearly modulated by masker type and attention. (B) M100 peak latencies (+SEM) show strong effect of masker type, nearly doubling in magnitude from the widest notch (+500 Hz, or 0.5 above) to the white noise masker. Attention-induced differences in latency occurred only for the no masker and white noise (0) stimuli in the left hemisphere. Significant changes between AttAud and AttVis are indicated by asterisks (* pv0:05, ** pv0:01, *** pv0:001). doi:10.1371/journal.pone.0046872.g004 during AttVis condition (620 ms vs. 582 ms for AttAud vs. AttVis, F1,13~8:83, p~0:011). For the RT analysis, 11/224 (4.9%) data were missing with the narrowest notches (+100 Hz, +50 Hz, 0 Hz) because some subjects did not detect any deviants. For statistical analysis, these values were imputed by Expectation Maximization (EM) algorithm with a Matlab implementation [28]. Common alternative of mean value imputation was tested as well, and due to small sample size and amount of missing data the results were practically identical to the chosen method. Addition- ally, it should be noted that in Figure 8, the SEMs were calculated with the real number of data points. When data from the two attention conditions were tested separately, the main effect of MASKERTYPE for d’ remained significant both for AttAud (F6,78~47:68, pv0:0001, e~0:68, ranging from 1.11 to 3.54) and AttVis conditions (F6,78~16:34, pv0:0001, e~0:87, range 2.63–3.61). The main effect of masker for RT was observed only for AttAud condition (F6,78~20:44, pv0:0001, e~0:77). The main effect in d’ for AttVis condition is due to enhanced performance with +150 Hz and +50 Hz maskers (see Fig. 8A). The origin of this effect is unknown, as the video clips including standards and deviants used with those maskers were similarly pseudorandomized from ten precalculated video types (see Materials and Methods) as with other masker types. Further, we did not find a correlation between AttVis task performance and precalculated video type (r~0:0097, p~0:92 for hit rate, r~0:060, p~0:53 for d’), nor was there an order effect due to these masker types always occurring first or last in the whole session. The order effect, however, could in part explain the enhanced performance during AttVis condition with no masker stimulus, as this was always presented first in the session. Correlations between behavioral and neurophysiological data Task performance, measured in d’, was inversely proportional to RT during AttAud (r~{0:65, pv0:0001) but not with AttVis condition (r~0:051, p~0:59). Further, for ECD data, significant correlations were found between d’ and left (r~0:37, p~0:0004) and right (r~0:34, p~0:0012) ECD peak M100 amplitude during AttAud condition, and between RT and right ECD amplitude (r~{0:22, p~0:041). No significant correlations were observed for AttVis data. ECD M100 peak latency turned out to be even more robust correlate for behavioral data, showing significant correlations with AttAud RT (left hemisphere peak latency: r~0:63, pv0:0001; right: r~0:71, pv0:0001) and d’ (left: r~{0:73, pv0:0001; right: r~{0:75, pv0:0001). Again, no significant correlations were observed for AttVis data. When a similar analysis was done for the MNE data, vertex-by- vertex correlation at M100 peak latency with behavioral data confirmed the significant d’ and MNE amplitude dependence during auditory attention. Importantly, after thresholding the data with pv0:01, a region of interest was revealed posterior to left HG, in planum temporale, where the MNE showed highest amplitudes and the M100 peak was localized (Fig. 9). With the same threshold, no continuous regions were evident in the right hemisphere, thus further suggesting that left hemisphere activity is of more behavioral relevance in our demanding tone-in-noise detection task. Interestingly, reaction times showed negative PLOS ONE | www.plosone.org 5 October 2012 | Volume 7 | Issue 10 | e46872