zero (e.g., a flat period histogram) and one (all spikes at one phase), and is thus a measure of tight phase-locking. Next, the average firing rate was determined for each modulation frequency and modulation depth. The third measure, power, accounts for both temporal and rate information, but does not depend on phase- locked responses, and is thus an indicator of how well the spike pattern matches the shape of the sAM envelope. Power was measured by a discrete Fourier transform of spike times using a multitaper process (CHRONUX Toolbox, Cold Spring Harbor). This analysis provides the magnitude of the spiking response at the modulation frequency of the sAM stimulus [42], measured as power (spikes/sec2/Hz). For each measure, group differences for MTFs or MDFs were assessed with ANCOVAs. Neurophysiological data analysis: neurometrics. Two neurometrics were applied to those cells whose best modulation frequency was 5 Hz, in order to predict behavioral detection based on the activity of those cells. First, we converted individual neural depth detection into a measure directly comparable with the behavioral data: d9 curves for each cell were calculated based on power at the 5 Hz modulation frequency of the sAM stimuli. Mean z-scores across trials were calculated for each cell at each depth level. For every depth level, d9 was then calculated as d0~z(MDhi){z(MDlow), where MDlow is the lowest depth tested (5%). Therefore, this is a within-cell neurometric, in that the detection ability of each neuron was compared with its own baseline. For each neuron, threshold for detection of the signal was defined as d9 = 1, the same criteria used for the behavioral analysis. Second, a pooling neurometric was applied that assumes a neural architecture of a single downstream neuron that receives input from all cells [43,44]. To measure detection, activity elicited by a baseline stimulus was compared with that elicited by each depth, and the event with maximum likelihood was chosen by a winner-takes-all strategy. Unlike the d9 neurometric, this is an across-cell neurometric, as each neuron did not serve as its own baseline. The neurometric was applied based on power at the 5 Hz modulation frequency of the sAM stimuli. For each experimental group, cells were chosen with replacement from the entire pool of neurons. Rather than sampling the same observed responses repeatedly on each trial, we used the Fano factor r as a measure of neuronal variability to simulate responses drawn from distributions whose mean and variance matched those of the empirical measurements. Thus, for each MD and for baseline (5% MD), the mea nﬃﬃﬃﬃrﬃesponse across trials for each cell was jittered using X ~xz(pxr r), where x is the mean response magnitude, r is the Fano factor, and r is a small random number with mean zero and standard deviation of one. Jittered responses were summed, the summed response magnitudes for baseline versus each MD were compared, and a larger value for the higher MD was considered correct detection. This was repeated 20 times (drawing a random subset for each trial), with each comparison equivalent to a single trial, and performance was measured as average correct detection across trials. The neurometric was run 100 times and depicted as mean 6 standard deviation, as its performance was quite variable when using only 20 trials (a value chosen to reflect the collected neural data). Surgery for Conductive Hearing Loss Bilateral conductive hearing loss was induced prior to hearing onset. Gerbil pups at P10–11 were anesthetized with the halogenated ethyl methyl ether methoxyflurane (Metofane). Conductive hearing loss was induced by tympanic membrane puncture and malleus extirpation [45]. A postauricular skin incision was made, the tympanic membrane was visualized and Hearing Loss Disrupts AM Processing punctured with forceps, and the malleus was removed through this opening. The postauricular wound was closed with cyanoacrylate glue, and the procedure repeated on the other side. At the end of the study, CHL animals were sacrificed and their ears were examined. Malleus removal did not disrupt the stapes in its attachment to the oval window in any of the animals used for this study. Auditory Brainstem Responses (ABRs) After behavioral testing and acquisition of behavioral hearing thresholds, ABRs were measured from a subset of tested animals to measure neural hearing thresholds. Animals were anesthetized with ketamine and chloral hydrate and presented with auditory stimuli as described for neurophysiological recordings. Responses to individual stimuli were conducted using stainless steel needle electrodes inserted subdermally at the dorsal midline between the eyes (non inverting), posterior to the right pinna (inverting), and base of the tail (common ground), and were recorded with a Grass P15 amplifier. Auditory stimuli were 100 ms clicks or 4 ms pure tones with 1 ms rise/fall times, repeated at 11.3/s [45]. Sound level was adjusted in 5 dB steps to obtain a threshold response (i.e., a visually detectable N1 potential). Results Effect of Developmental Conductive Hearing Loss on Behavioral sAM Detection Thresholds Behavioral sAM detection thresholds were obtained from two groups of adult gerbils: animals reared with conductive hearing loss (CHL) induced prior to hearing onset, and age-matched controls (CTR). Animals were trained to detect a 100% sAM depth signal (5 Hz or 100 Hz modulation, noise carrier) in a continuous broadband noise. We obtained an initial assessment of detection threshold immediately after each animal reached criterion performance on the task. Animals were tested with a shuffled set of modulation depths (MD) across a broad range (10–100% MD). Detection threshold was defined as the modulation depth at which an animal’s sensitivity was d9 = 1 (Fig. 1A, ‘‘init’’). To obtain the best detection thresholds, animals were tested over 7–10 days at successively smaller MDs (see Methods). Thus in contrast to the initial testing that spanned a broad range of MDs, each session consisted of a narrow range of MDs that was based on the animal’s performance on the previous session. Thresholds across initial testing and successive testing days (Fig. 1A) show a gradual improvement in performance that did not differ across groups (linear fits for CTR: R = 20.50, p,.0001 and CHL: R = 20.42, p,.0001; ANCOVA interaction measuring differences in slopes of fitted lines: F(8,8) = 0.9, p = 0.53). For the modulation frequency of 5 Hz, CTR animals achieved significantly lower thresholds than CHL animals. Figure 1B shows psychometric functions for each animal during the final three days of testing. The individual thresholds and mean values for the group are in Figure 1C. Thresholds for CHL animals were shifted to higher MDs (CTR: 21.660.8%MD vs CHL: 25.661.1%MD; t(22) = 2.9, p = .009). A performance difference was also observed when comparing each animal’s best performance day (CTR: 17.261.2%MD vs CHL: 21.160.9%MD; t(22) = 2.6, p = 0.016). While the range of thresholds overlapped for the two groups, the distribution of CHL thresholds was clearly shifted towards a larger depth. Thus, bilateral developmental CHL impaired detection thresholds for 5 Hz modulated noise. To determine whether CHL animals are equivalently impaired in detecting higher MFs, we used an identical procedure to test PLoS ONE | www.plosone.org 4 July 2012 | Volume 7 | Issue 7 | e41514