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The role of the MOCR in auditory processing is not well-understood. Various proposals have been made, such as increased speech comprehension in noise (Giraud et al., 1997), protection against loud sounds (Kujawa and Liberman, 1997; Brown et al., 1998), and a possible role in the development of cochlear function (Walsh et al., 1998). Further elucidation of the role of the MOCR requires a combination of behavioral and physiological methods.

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In humans, 3 basic approaches have been used to study the MOCR. Measurement of otoacoustic emissions while presenting contralateral sounds allows a rather direct probing of effects on outer hair cells (Guinan, 2006), but a drawback is that such measurements do not address effects on the cochlear neural output. This concern is alleviated by the measurement of acoustically evoked neural mass potentials while presenting contralateral stimuli (Folsom and Owsley, 1987; Kawase and Takasaka, 1995; Chabert et al., 2002; Lichtenhan et al., 2016), but in turn these techniques have other issues such as signal quality, state of arousal, and role of pathology in patients. Finally, a range of psychoacoustical paradigms have been developed to study efferent effects (see below). The challenge with behavioral paradigms is to know whether the effects observed indeed reflect the MOCR or whether they involve other neural pathways or phenomena. By probing cochlear neural potentials as directly as possible, in normal hearing subjects, and applying stimulus paradigms as used in psychoacoustical studies, we aim to tighten the interpretation of behavioral and physiological responses with respect to efferent function.

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Although in physiological studies the MOCR may be elicited via direct electrical stimulation of the efferent pathway, the MOCR is more naturally activated by sounds to either ear (Gifford and Guinan, 1987). Use of acoustic stimulation of the contralateral ear to trigger efferent activity is appealing because of its technical and interpretational simplicity. However, anatomical and physiological evidence in cat and guinea pig (Liberman and Brown, 1986; Brown, 1989), indicates that the MOCR is more strongly activated by an ipsilateral elicitor than a contralateral one. While this suggests it is important to study ipsilateral elicitors of efferent activation, such elicitors introduce additional effects, such as cochlear suppression and neural adaptation, which complicate the interpretation of the results.

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Under certain circumstances, neural responses to tones in noise may increase in amplitude when the MOCR is elicited. This is known as the anti-masking effect and is thought to reflect a decrease in masking due to a reduction in cochlear gain by the MOCR (Kawase and Liberman, 1993; Kawase et al., 1993). Various psychoacoustical paradigms have been developed to study the effect of the MOCR on masking. For example, in studies of the so-called overshoot or temporal effect (Zwicker, 1965), a precursor sound leads to effects which are qualitatively consistent with the neural anti-masking phenomenon (Strickland, 2001, 2004, 2008). The precursor sound is thought to lead to gain reduction by triggering the MOCR. To tease out the role of gain reduction against other cochlear phenomena (neural adaptation, suppression), psychoacoustic experimenters have developed forward masking paradigms in which masking by a short ON- or OFF-frequency masker is compared with and without a precursor (Roverud and Strickland, 2010). In contrast to the simultaneous masking condition, in forward masking the precursor increases signal threshold. However, the precursor increases signal threshold much more when the masker is well-below the signal frequency than when the masker is at the signal frequency, which would be consistent with a reduction in cochlear gain (Jennings et al., 2009; Jennings and Strickland, 2012; Yasin et al., 2014).

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The interpretation of psychoacoustical results in terms of MOCR activity would be strengthened by linking psychoacoustical paradigms more directly with physiological measurements. Here, we attempt to electrophysiologically assess the mechanism by which a precursor affects the detection of a masked probe tone. Our stimulus paradigm is similar to the psychoacoustical studies, but modified to extract the compound action potential (CAP) from mass-potentials near the round window. The experiments were performed in two awake subjects. We first examine the impact of a precursor on a probe tone of 4 kHz and then explore the effect of an additional masker. Finally, we compare the results with predictions from simulations.

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This study (S56783) was carried out in accordance with the recommendations of good clinical practice (ICH/GCP), Medical Ethics Committee of the University of Leuven with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. The protocol (ECochG-EF-P-2) was approved by the Medical Ethics Committee of the University of Leuven.

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We recruited volunteers between 20 and 30 years of age via an advertisement. Subjects were requested to avoid exposure to loud sounds such as rock concerts in the days preceding the experimental session. The day before or the morning of the experimental session, the subject's hearing was assessed including an inquiry for hearing problems, a pure tone audiogram (thresholds <20 dB nHL, 125 Hz–8 kHz), tympanometry to assess middle ear function, an otomicroscopy by an otolaryngologist, and the determination of the ipsilateral acoustically evoked middle ear reflex threshold for broadband noise and a 1 kHz tone (ZODIAC 901).

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The duration of these experimental sessions varied between 1 and 4 h; subjects could end the session at any time. The experiments were conducted in a double-walled soundproofed and electrically shielded booth (Industrial Acoustics Company, Niederkrüchten, Germany). Subjects chose a comfortable reclined position on a bed and were asked to remain still during the recordings. When in the booth, subjects and experimenters were grounded to the booth via an antistatic wrist strap. During the actual experiment, an observer was present with the subject in the booth to monitor the status of the subject and to act as an intermediary with the experimenters outside the booth. Two female subjects participated in the electrophysiological experiments in this study.

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A trans-tympanic procedure was used to record evoked mass responses from the human middle ear (Verschooten et al., 2013, 2015). For every subject, a custom silicone ear mold (Dentsply, Aquasil Ultra XLV regular) was made which contained two casted openings to hold tubes of 2 mm diameter for needle insertion, visualization, acoustic stimulation, and calibration. The complete acoustic system was calibrated in situ with a probe-microphone (Etymotic Research, ER-7C) close to the tympanic membrane. The earphone-speaker was connected to one of the openings of the ear mold via a plastic T-piece which also served as access port for a rigid endoscope with camera (R. WOLF, 8654.402 25 degree PANOVIEW; ILO electronic GmbH, XE50-eco X-TFT-USB) to visualize the ear canal and tympanic membrane. During the acoustic calibration all openings were sealed with Audalin acrylic impression compound (Microsonic); a tiny opening in one of the tubes prevented static pressure build-up. Before the needle-electrode was inserted, the tympanic membrane and ear canal were locally anesthetized with Bonain's solution (equal amounts of cocaine hydrochloride, phenol and menthol), which was aspirated after about 30 min. A short sterile plastic tube was inserted in the mold to accommodate the sterile needle-electrode. Ground and reference electrodes were connected to the equipment. The needle-electrode (TECA, sterile monopolar disposable, 75 mm × 26G, 902-DMG75-TP), was inserted and gently placed through the tympanic membrane on the cochlear promontory or in the niche of the round window under visual endoscopic control. To maintain its position and to ensure good electrical contact, the needle-electrode was maintained under slight tension with rubber bands supported by a custom frame, which was positioned over the external ear and fastened around the head with Velcro strips. Subjects usually had a short-lasting and vague sensation of touch during insertion of the electrode. The openings of the tubes were sealed with Audalin and the needle-electrode was connected to the preamplifier. The subject's right ear was studied: there was no experimental manipulation of the other ear. The session was terminated within 4 h or when the subject expressed the desire to stop. At the end of the experiment, the needle electrode and ear mold were removed and an otomicroscopic examination was performed. Subjects were requested to keep the ear dry for 10 days following the recording session. An otolaryngologyst was available during the weeks after the experiment to address any worries or for a second checkup.

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Stimuli were generated with custom software and a digital sound system (Tucker-Davis Technologies, system 2, sample rate: 125 kHz/channel) consisting of a digital-to-analog converter (PD1), a digitally controlled analog attenuator (PA5), a headphone driver (HB7) and an electromagnetically shielded earphone-speaker (Etymotic Research, ER2, 20 Hz–16 kHz) connected with plastic tubing to the ear mold. The stimuli were compensated for the in situ calibration.

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Auditory evoked potentials were measured using a low noise differential preamplifier (Stanford Research Systems, SR560). All contacts were made on the ipsilateral side to the recording: the signal input was connected to the needle-electrode; the reference input was connected to an earlobe clamp (with conductive gel) and the ground input was connected to a standard disposable surface electrode placed at the mastoid. For safety, the battery-operated preamplifier was galvanically isolated (A-M systems, Analog stimulus isolator Model 2200) from the mains-powered equipment outside the sound booth. Before the signal was recorded (TDT, RX8, ~100 kHz/channel, max. SNR 96 dB), stored and analyzed (MATLAB), the signal was further amplified (DAGAN, BVC-700A) and band pass filtered (30 Hz–30 kHz, cut-off slopes 12 dB/octave). All stimuli and recorded signals were monitored on-line (LeCroy, WaveSurfer 24Xs) during the session.

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Human acoustically-evoked neural mass responses are smaller than those recorded in common laboratory animals. To improve the signal-to-noise ratio (SNR) of the response, the uncorrelated background noise was reduced by averaging the responses of many repetitions (n = 200). The averaged response was then de-noised (smoothed) with a non-causal low-pass filter using an RLOESS function (MATLAB). The RLOESS is a non-parametric robust local regression function using weighted linear squares and a 2nd degree polynomial model, which assigns lower weight to outliers in the regression (the weights are given by the bisquare function with zero weight for deviations greater than six mean absolute deviations). The span of the filter was chosen such that it corresponded to a low-pass cutoff of ~3 kHz, or ~1 kHz for CAP measurements with low SNR (i.e., heavily masked responses). The magnitude of the CAP was obtained between the first positive and first negative peak (P1-N1).

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The recordings in the awake subjects occasionally contained artifacts due to sporadic head movements. These artifacts had a significant impact on the background noise and thus also on the SNR of the CAP. Single responses were selectively removed by measuring the individual contributions to the CAP (Jackknife method), and rejecting those that deviated in order to optimize the SNR. Note that the stimulus level of the precursor was kept below the subject's middle ear reflex threshold (90 dB SPL for subject 1 and 80 dB SPL for subject 2).

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Our stimulus paradigm is designed to assess the mechanism by which a broadband noise precursor affects the detection of a tonal probe of 4 kHz. It is based on psychoacoustical paradigms, but modified to extract the CAP response from mass-potentials near the round window. A first modification is that we employ alternating stimulus polarity to cancel the cochlear microphonic (CM). Second, considerable attention was paid to remove masker artifacts—especially for simultaneous and strong forward maskers—and also to minimize drift between CAPs with different precursor conditions. Drift was expected due to the nature of the recording conditions (movements of awake subjects; varying state of arousal). Figure 1 illustrates the two paradigms, for simultaneous masking (upper) and forward masking (lower). Only the first half presentation, to one stimulus polarity, is shown; the second half is the same, but with opposite polarity. The temporal sequence is such that each paradigm consists of 4 segments. The first segment (a) contains all 3 stimulus components: a probe with a masker and a precursor. The second segment (b) is the same as (a), but without a precursor. The third segment (c) is also the same as segment (a) but without the probe, and the last segment (d) contains only the masker. The duration of the precursor was 50 ms, which has been found to be the optimal length for maximizing gain reduction in psychoacoustic experiments (Roverud and Strickland, 2013). The probe and simultaneous masker were set at the same duration as the precursor. The forward masker was short (20 ms) in order to avoid activation of the MOCR, but long enough to mask the tone. The silent periods between the segments were chosen to be long enough (>500 ms) to allow the MOC-system to recover in between trials.

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Illustration of the first half presentation of the two stimulus paradigms used in this study. (A) paradigm with simultaneous masker and (B) with forward masker. Each presentation has 4 segments indicated by letters: (a) contains all 3 stimulus components: precursor, masker, and probe; (b) similar but without precursor; (c) similar but without probe; (d) masker only. The probe is always a tone of 4 kHz. The precursor is a broadband noise. The masker can be an ON-frequency (4 kHz) tone; a 2.4 kHz OFF-frequency tone; or a narrowband noise. The second half representation (not shown) is the same as the first, but with all stimuli presented in inverted polarity. A single “condition” consists of the half presentation shown here and the half with opposite polarity. The masker is drawn in dashed lines, indicating the possibility of a condition without masker.

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The probe was always a pure tone of 4 kHz, and the precursor was a Gaussian broadband noise (300–8,000 Hz). The masker was not fixed and changed over experiments and subjects. In the case of forward masking, the masker was either an ON- (4 kHz) or OFF-frequency (2.4 kHz) tone and for simultaneous masking, an OFF-frequency (2.4 kHz) tone or Gaussian narrowband noise (2–6 kHz). The level of the probe was 50, 60, or 70 dB SPL, dependent on subject and masker type. The level of the precursor was fixed to 50 dB SPL and below the subject's threshold of the acoustic reflex. The masker level was the independent variable, but did not exceed 95 dB SPL. Note that measurements with different masker levels were measured in blocks, where the masker level was changed across blocks in arbitrary order.

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The rationale for the stimulus design (Figure 1) is as follows. The precursor is designed to activate the MOCR: comparison of segments (a) and (b) will therefore reveal the effect of this activation. Because the MOCR is hypothesized to reduce simultaneous masking, and to increase masking by an OFF-frequency masker more than for an ON-frequency masker, the effect of the precursor is assessed by examining the response to a masker-probe combination. More specifically, we are interested in the response to the probe, which should be reduced by the presence of a masker, and this reduction should change in the presence of a precursor. However, the response to the precursor-masker-probe combination (Figure 1, segment a) contains not only the CAP to the probe tone, but also an off- or on-set and ongoing response to the forward or simultaneous masker. Thus, to isolate the response to the probe, we add conditions in which there is no probe stimulus: a condition with precursor and masker (c) and one without precursor (d). To remove the masker response from (a) and (b), we subtract the responses to (c) and (d), respectively. A disadvantage of such a subtraction procedure is an increase in noise: the mathematical operation to remove the transient response increased the CAP's background noise by 3 dB (summation of two signals with independent background noise signals). For heavily masked responses, where the transient responses to the masker are the largest, we used as compensation the average of segment c and d, which was still satisfactory to suppress the masker's transient response but with less increase in background noise due to the averaging of the two independent background noises inside the compensation signals; the increase in background noise is only 1.6 instead of 3 dB.

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We examined the effect of an ipsilateral precursor in simultaneous and forward masking paradigms, which have been used in previous physiological and psychoacoustical studies as described in the Introduction. In simultaneous masking, a release from masking (i.e., an increase in probe response) is expected following a precursor, based on previous physiological studies of the CAP (Kawase and Liberman, 1993) and psychoacoustical studies of overshoot (Zwicker, 1965). In forward masking with an OFF-frequency masker, the precursor will decrease the probe response but not the masker response: so more masking is expected for an OFF-frequency masker than for an on-frequency masker, based on previous psychoacoustical studies (Kawase et al., 2000; Jennings et al., 2009; Jennings and Strickland, 2012).

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Forward masking paradigms have the advantage that the different stimulus components do not mutually interact (Figure 1) at the level of the cochlea, and do not induce additional cochlear suppression effects, such as two-tone suppression (e.g., Sachs and Kiang, 1968; Ruggero et al., 1992; van der Heijden and Joris, 2005), which complicate the interpretation of the results.

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A total of five experiments were conducted: 3 in a single session with subject 1, and 2 in a single session with subject 2. The various stimulus conditions used in the two subjects are chronologically listed in Table 1. In all experiments, the masker level was parametrically varied. The first experiment (SM1n) studied masking of a tone in noise using a simultaneous masking paradigm (upper figure in Figure 1), while the other two experiments used OFF- (FM1off) and ON-frequency (FM1on) tonal maskers in a forward masking paradigm (lower figure in Figure 1). In the second session (subject 2), we used only OFF-frequency maskers and compared results with simultaneous (SM2off) and forward (FM2off) maskers. To facilitate comparison between different experiments, CAP responses are expressed as relative values (in %) with respect to the corresponding response without masker.

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The names in the first column identify the experiments: the first two characters indicate whether simultaneous masking (SM) or forward masking (FM) was used; the subsequent number indicates the subject; the last characters indicate the stimulus type of the masker (noise or ON- or OFF-frequency masker).

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The precursor is the experimental variable that is intended to activate the MOCR. A difficulty in the study of ipsilateral effects is that the precursor may not only activate efferents but will also have “lingering” or history effects on responses of the same ear to subsequent stimuli even without efferent activation. For convenience, we group such non-efferent history effects (which may contain mechanical, hair cell, synaptic, and neural components) loosely under the term “neural adaptation.” We first examine conditions, present in all experiments, in which there is no effective masker. This gives a first simple assessment of the effect of the precursor on the probe response. Figure 2 shows CAP responses to 4 kHz tones with and without a precursor, from experiment FM2off. Two effects are visible. The CAP amplitude is reduced by the presence of the precursor. Expressing CAP amplitude as the difference in magnitude between the first positive peak P1 and the first negative peak N1, the precursor reduces the CAP magnitude by approximately 20%. Second, the presence of the precursor causes a small delay of 130 μs of N1.

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An example of the effect of a broad-band noise precursor of 50 dB SPL on the amplitude and time-course of a human CAP response to a 4 kHz (50 dB SPL) tone, based on >600 averages. The CAP amplitude is measured between P1 and N1. Data is from experiment FM2off.

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Using the same precursor, experiments FM1off, FM1on and SM2off showed a very similar reduction of 20%, as shown in Figure 3. Curiously, the only exception is experiment SM1n, which shows a much greater reduction (35%) compared to the others, as well as smaller variability. Importantly, because Figure 3 is for conditions in which there was no masker, and because the probe frequency and precursor were identical in all experiments, the only stimulus differences were in probe level and in the relative timing between precursor and probe. It appears that the high probe level in experiment SM1n somehow caused a larger effect.

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Notwithstanding that the only experiment with somewhat different stimulus conditions gave a deviating result, it is reassuring that the other experiments—where the stimulus conditions were virtually identical—gave rise to very similar effects across experiments and across the two subjects. In the next session, a masker is added to attempt to tease out efferent vs. neural adaptation effects.

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Figure 4 shows data for all experiments. We first discuss the overall effect of increasing masker levels, and then the influence of the precursor on that effect, while making abstraction of the different experimental conditions. The blue symbols and lines indicate the probe CAP responses without a precursor. A cursory look at Figures 4A–E shows that, as expected, for all masking configurations an increase in masker level caused a decrease in response to the probe. These curves, which we refer to as standard masking functions, show three regions—not distinct in all experiments. At low masker level there is a region without masking; then a region of active masking where the response declines with masker level; then a region of saturation at high masker levels.

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(A–E) CAP responses of a 4 kHz masked tone as function of masker levels with (red symbols) and without (blue symbols) BBN precursor, for different experiments. Datapoints on Y-axis are those without masker. (F) CAP response at masking saturation of experiment FM2off, with and without precursor. Signals in background are from Figure 2.

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Given that there is masking of the probe response in all experimental conditions, we can look for anti-masking of CAP responses as was shown in anesthetized animals (Kawase and Liberman, 1993), using similar recordings. These investigators found efferent anti-masking effects on CAP responses to tones-in-noise with both forward and simultaneous maskers, which involved both the ipsi- and contra-driven efferent loops. If the noise precursor used here effectively activates the MOCR, the masked response could be larger in the presence of a precursor. This is however never the case (Figures 4A–E): none of the data pairs at any masker level exhibit an increase in response when there is a precursor, so that the red and blue lines and data never cross each other.

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The absence of a simple anti-masking effect does not imply that there is no differential MOCR involvement between conditions with or without precursor. The data with a precursor have a similar course (red trendline) as the standard masking curves (blue trendline), but do not asymptote toward the same response values at high masker levels. At low masker levels there is the initial CAP reduction due to the presence of the precursor by itself (Figure 3). This reduction, relative to the condition without precursor, persists at active masker levels. Even at high masker levels, where there is a region of saturation, there remains a constant difference in CAP amplitude between conditions with and without precursor (only exception is at 60 dB for SM1n, Figure 4A, which we consider an outlier). This suggests that the effect of the precursor is not simply one of neural adaptation, because in that case the probe response at high, saturated masker levels would not be affected by the presence or absence of a precursor. We will return to this observation with a quantitative treatment in the final section and figure of Results.

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We now zoom in on a more detailed analysis and comparison of the results of the different experiments and exploit the differences in masker configurations to search for the presence of possible MOCR effects. With tonal ON-frequency maskers, cochlear gain changes due to the MOCR can affect both the probe and masker response. Tonal OFF-frequency maskers, of a frequency lower than the probe, perform masking in the tail of the masker's excitation pattern. Of course, with an OFF-frequency masker, higher masker levels are required to reach masking threshold. OFF-frequency maskers are of interest because they behave linearly with masker level, and, at the tonotopic location of the probe, are believed to be unaffected by the MOCR (Kawase et al., 2000; Cooper and Guinan, 2006). If the precursor indeed triggers the MOCR, this activation will cause a gain reduction for both ON-frequency masker and probe. However, with an OFF-frequency masker a gain reduction due to MOCR activation would only affect the probe and not the masker, effectively making the masker more potent. Thus, the expectation is that, when preceded by a precursor, ON-frequency maskers show a smaller response reduction than OFF-frequency maskers.

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Figures 4C,E shows the effect of a precursor on the CAP response to a forward masked 4 kHz tone as a function of masker level. Figure 4E shows the results of the ON-frequency masker (experiment FM1on) and Figure 4C that of the OFF-frequency masker (experiment FM1off). Comparison of the two standard masking curves (blue lines, Figures 4C,E), shows, as expected, a rightward shift of ~40 dB for the OFF-frequency masker (value based on sigmoidal fits, explained in Section Predictions from a simple model). This rightward shift is simply due to the fact that it is only through the tail of its excitation pattern that the masker interferes with the probe. When compensated for this level shift, we observe that at active masker levels (i.e., 70, 80 dB SPL for the OFF-frequency masker and 30, 40 dB SPL for the ON-frequency masker) the CAP reduction by precursor is much larger for the OFF- than for ON-frequency maskers. This is illustrated in Figure 5, which shows the CAP reduction induced by the precursor for both experiments. At low masker levels, the same percentage of CAP reduction is observed for ON- and OFF-frequency maskers. At high masker levels, the percentage of CAP reduction is also similar, and presumably reflects gain reduction of the probe response due to the MOCR (see also Figures 6I,J and the final section of RESULTS). However, at masker levels in between, there is indeed a greater reduction by the precursor for the OFF-frequency masker than for the ON-frequency masker, consistent with a reduction in gain by activation of the MOCR (double arrow).

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Comparison of CAP response reductions by precursor as function of masker level for ON-and OFF-frequency forward maskers. The masker levels are horizontally offset by 40 dB, according to the midpoint of the masking curves. For each curve, the CAP reduction (in %) is calculated as (CAP response without precursor—CAP responses with precursor)/CAP response without precursor. The datapoint for FM1on at 20 dB is considered an outlier (see also Figure 4E).

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(A–E) Predicted masked CAP responses in case the reduction by precursor is from masking (dashed red) or due to the activation of the MOCR (solid red). The data points of the masked responses with precursor are indicated by the red squares; those without precursor by the blue dots. These data points were fit by the blue curve, which was used for the predictions. The dashed gray lines indicated the bias level, Lprec. (F–J) Predicted response reductions obtained from the red and blue curves in (A–E). Green dashed curve is for the prediction by masking; the solid green line is the predicted attenuation by the MOCR. The experimental data points are indicated by the black squares.

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In our discussion of Figure 4 (Section Anti-masking), we remarked that standard masking curves saturate to a certain asymptotic level. At these saturated masker levels, a further decrease in probe response is obtained when a precursor is present. We refer to this as a “residual reduction.” This observation is important because it goes against the reasoning that any contribution by the precursor to neural adaptation can be overwhelmed by a stronger forward masker so that in the limit, at high masker levels, the curves with and without precursor should converge. The residual reduction at saturation suggests an MOCR effect. In the next section, we put this reasoning on a more quantitative footing.

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The clearest examples of residual reduction are for Experiments SM2off and FM2off (Figures 4B,D double arrows). CAP responses for FM2off at saturation, with (red) or without (blue) precursor, are illustrated in Figure 4F. For comparison, overlaid in the background, are non-masked responses to these conditions. The masked responses exhibit the same precursor effects as the non-masked responses: a reduction in size and presence of a delay for N1 and P1 (red vs. blue traces). Note also the large delay accompanying the size reduction between non-masked and masked conditions (i.e., the delay between the two red curves and the delay between the two blue curves). Similar residual reductions are present at the highest masker levels in experiments SM1n, FM1off, and FM1on, but for these experiments saturation may not have been reached yet.

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Examination of Figure 4 suggests that the size of residual masking by the precursor is related to the size of the remaining response at saturation: the larger the response at saturation (i.e., the larger the blue datapoints at high masker levels), the larger the residual adaptation (i.e., the larger the length of the double arrows). More generally, at all masker levels, the reduction in CAP response between non-precursor and precursor conditions seems to be a constant fraction (between 20 and 30%) across experimental conditions. The observation that this fraction extends to saturated levels of masking suggests that the precursor triggers a constant attenuation of the probe response, consistent with a gain reduction by the MOCR. In Figure 6, we explore this with a phenomenological model and further analysis of the data.

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Our model examines the effect of the precursor on the standard masking curve, which is fit by a function. For simplification, only the two most important mechanisms are considered, neural adaptation and reduction in gain. Two important assumptions we make are that the MOCR is modeled by an attenuation due to a reduction in gain; and both mechanisms (MOCR and neural adaptation) are assumed to be independent. We consider 3 situations: Case 1, a response reduction due to neural adaptation by the precursor; Case 2, a gain reduction by the MOCR which affects only the probe but not the masker (cf. OFF-frequency masker); and Case3, the same as Case2 but with an additional “masker release” due to the MOC i.e., an MOC effect on both probe and masker (cf. ON-frequency maskers).

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Figures 6A–E shows the trend lines from the model, together with the data points. The blue traces are sigmoidal model fits through the standard masking curves, i.e., data points of the masked responses without a precursor (blue symbols). These fits are obtained with an automated fitting procedure using a modified logistic function (Equation 1).

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Here, RCAP is the masked response (in %), Lmid the level of the sigmoid midpoint (dB SPL), k determines the steepness of the sigmoid (dB SPL−1), Rmax is the unmasked CAP response (in %), Rsat is the response at masking saturation (in %), α is an attenuation factor determining the gain reduction by the MOC, and Lmask is the effective masker input level. For the automatic fitting procedure, MATLAB function “fminsearch” was used in search for the parameters (i.e., Lmid, Rmax, Rsat, k) that minimized the RMS-error. Data points were weighted according to their SEM. The data point on the y-axis (Figures 6A–E) is the CAP response without masker (cf. Figure 3): for convenience these are inserted 20 dB below the lowest masker level.

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For the standard masker curve, the attenuation (α) was set to 1. In general, the fit to the experimental data is good (Figures 6A–E, blue traces). Note that the data point at the highest masker level in SM1n (Figure 6A) is considered an outlier and was excluded from the dataset. In experiment FM1off (Figure 6D), there were not enough data points in the region of saturation for a proper automated fit, and parameter Rsat was manually chosen based on experiment FM1on.

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The red dashed traces in Figures 6A–E represent the predicted trends with precursor for Case 1, thus only including neural adaptation. The same function and fitting parameters were used as for the standard masking curve (blue lines), but with recalculated effective masker input levels (Lm) to include neural masking by the precursor. Masking by the precursor is simply considered as an additional bias on the existing masking. The bias level was obtained from the standard masking curve as the masker level (Lprec) generating a CAP response of the same amplitude as a condition with precursor but without masker (Rprec; see Figure 3). Lmask was then recalculated as the square root of the power of Lmask and Lprec. This is illustrated by the gray dashed lines in Figure 6A. The RCAP function so obtained (Figures 6A–E, dashed red line) matched the observed CAP values quite well for SM2off, but not in the other experiments. Clearly, neural adaptation is not adequate to model the effect of the precursor.

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The red solid traces (Figures 6A–E) represent the predictions for Case 2, under the assumption that the MOCR induces a gain reduction of the probe only, matching the experimental conditions with OFF-frequency maskers. The same function and fitting parameters were used as for the standard masking curve (blue lines), but with an additional attenuation (α, constant within an experiment) equal to the initial reduction by the precursor, Rprec. This prediction clearly outperforms that of Case1 and gives a good fit to the masking data with precursor, except for experiment FM1off, where the predicted masking curve is too far to the right.

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Finally, the red dashed-dotted traces (Figures 6A–E) represent the predictions of Case 3, where both masker and probe are affected by a gain reduction caused by the MOCR elicited by the precursor—the situation thought to arise with ON-frequency forward maskers. The same function and fitting parameters were used as for Case 2, but with an additional offset to the masker input level (Lmask) to incorporate a gain reduction by the MOC. The size of this additional offset is unknown: we estimate it based on the reduction of the CAP response by the precursor only, as follows. We first determine the maximal slope of the standard masking curve (at Lmid of solid blue line): this slope tells us how to translate a change in CAP response to a change in masker level. We then apply this slope to the reduction of the precursor only (1 – Rprec) as follows: offset = (1 – Rprec)/absmax(slope of the standard making curve). This offset is the masker threshold shift assuming similar gain reduction as for the probe. Note that—whatever the exact estimate of offset—a reduction in gain of the masker will always shift the masker curve to the right, to higher masker levels (Figures 6A–E, red dashed-dotted lines). A rightward shift actually brings the model prediction further from the observed datapoints than for Case2. Thus, whatever the estimated effect of a gain reduction on the masker, a combined reduction of both masker and probe (Case3) does not give better predictions than gain reduction just of the probe (Case2).

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To illustrate the effect of the precursor more directly for these three cases, Figures 6F–J show the percent CAP reductions due to the precursor for the model and the data as a fractional change (% reduction with precursor – % reduction without precursor)/(% reduction without precursor). For Case2, the prediction is simply a horizontal line representing an attenuation or constant gain reduction. For the other two cases, the predicted reductions are strongly dependent on masker level. By and large, the horizontal trend of a constant gain reduction seems to best capture the data.

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We assessed the ipsilateral sound-evoked MOCR in humans using CAPs recorded transtympanically in the middle ear using stimulus paradigms similar to previous MOC studies. We measured CAP responses to forward- or simultaneously-masked 4 kHz tones, preceded in some trials by a precursor designed to trigger the MOCR. Some, but not all, of the findings are consistent with MOCR effects as opposed to effects of neural adaptation. First, a noise precursor has a clear reducing effect on unmasked CAP responses (Figures 2, 3). The reduction observed does not seem entirely explainable in terms of neural adaptation. Second, we find residual masking at high masker levels, i.e., while masking saturates at high stimulus levels, a precursor causes further reduction in CAP responses (Figure 4). The behavior of this residual masking is consistent with a gain reduction due to MOCR activation (Figure 6). Third, a comparison between ON- and OFF-frequency maskers showed a clear difference in response reduction by the precursor, consistent with a gain reduction by the MOCR (Figures 4, 5).

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Previous CAP recordings in anesthetized animals show that the MOCR can produce an anti-masking effect, in the sense that CAP responses to a probe tone masked by ipsilateral noise increase in amplitude due to MOCR activation (Kawase and Liberman, 1993). In the latter study, involvement of efferents driven by the ipsilateral ear was detected by sectioning of the olivocochlear bundle which carries efferent fibers from the brainstem to the cochlea. A simple prediction for paradigms as employed in the present study, where the MOCR is triggered by a precursor in the ipsilateral ear, would be that masked CAP responses would increase when preceded by a precursor, relative to the responses without precursor. In the present study, such simple anti-masking effect was not found in any of the stimulus configurations (Figure 4): the datapoints with precursor (red) are always below the datapoints without precursor (blue). However, the absence of such simple anti-masking in the paradigms used in human but not in animals is not very informative and it is misleading to make this comparison. Cutting the olivocochlear bundle allows a clean comparison between responses of a system with and without efferents. The same is not true for the responses with and without precursor: the precursor can affect the responses by mechanisms which are separate from the efferent system. More specifically, the precursor also causes neural adaptation. A more pertinent question therefore is: does the presence of the precursor cause less reduction in masked responses than expected? Answering this question requires a means to disentangle effects of neural adaptation from effects of efferent activation.

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Perhaps the most convincing evidence of the presence of an MOCR triggered by the precursor, is the residual reduction of the CAP response at high masker levels. Our reasoning is that exhaustion of neural adaptation manifests itself as saturation of the masking curve at high masker levels (Figure 4). We refer to this as residual reduction, and argue that it is due to a triggering of the MOCR by the precursor. A concern is the reliability of the CAP measurements at high masker levels. Most of the saturated CAPs are quite small and have poor SNR (Figure 4). We took the peak-to-peak amplitude of the CAP to reduce contributions of the summating potential, and also observed that a reduction in amplitude was accompanied by a time delay (Figures 2,4F). Moreover, the presence of residual masking was quite consistent across experiments and across the two subjects. In summary, the data argue that the precursor triggers a process besides neural adaptation which reduces CAP responses.

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One technique used in psychoacoustical experiments to identify an efferent effect is to compare the effectiveness of ON- and OFF-frequency forward maskers. The underlying reasoning is that efferent activity maximally affects basilar membrane vibration near the cochlear location of maximal vibration (active region with gain), and less at more apical or more basal locations with a more linear behavior (Robles and Ruggero, 2001). Thus, while an ON-frequency masker will be rendered less effective by efferent activation, this is less the case for an OFF-frequency masker. We compared the two masker configurations (FM1on and FM1off). Figure 5 shows indeed that the OFF-frequency masker is less affected (remains a stronger masker) by the precursor than for the ON-frequency masker, consistent with a gain reduction for the ON-frequency masker.

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Nevertheless, review of the different experiments and quantitative comparisons with predictions from a simple model (Figure 6) reveals a pattern of results that is more complex than anticipated. If the precursor triggers the MOCR so that only the gain to the probe tone (and not to the masker) is affected, a constant CAP reduction is expected across masker levels (horizontal solid line in Figures 6G–I): this is the prediction for an OFF-frequency masker. There is however a tendency in the three experimental conditions with OFF-frequency maskers to display more reduction in fractional change with increasing masker level (i.e., datapoints above the solid horizontal lines in Figures 6G–I). Paradoxically, for the two experiments with ON-frequency maskers, the data very closely do follow the horizontal lines (Figures 6F,J), rather than the prediction for this condition (dash-dotted lines). To put it simply: the results for ON-frequency maskers look as expected for OFF-frequency maskers. The data therefore suggest that in all experiments there is an additional source of reduction of the probe response, which is not adequately modeled by a constant, MOCR-induced, reduction in gain at the probe frequency.

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We surmise that a dependency exists between activation of the MOCR and masker level and/or masker type. For example, the shape of the masking curve with precursor might be influenced by the masker level via additional activation of the MOCR by the masker itself. In preliminary experiments (not shown) we have observed that efferent activation seems to be biased toward low-frequency stimuli. Although the short masker and slow MOCR activation make it unlikely, there is still a possibility that the presence of a low-frequency, OFF-frequency, masker increasingly contributes to activation of the MOCR with increasing masker level. This would cause additional reduction of the CAP response to the probe (note that the start of the masker always precedes that of the probe, Figure 1, even in the simultaneous masking paradigm). Such increased MOCR activation may explain why there tends to be more reduction of the CAP response with increasing masker level of OFF-frequency maskers (Figures 6G–I) than predicted by the model. With ON-frequency maskers (Figures 6F,J), we modeled the effect of the precursor as a constant attenuation of masker and probe by the MOCR, resulting in the dash-dotted lines, but again the data show more reduction in fractional change than the model. Increased MOCR activation by the increasing masker may be the cause of this additional reduction.

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Other factors may add to the complexities of the results, which have more to do with technical aspects of the recorded signals. One issue is that, as masker level increases and CAP amplitude decreases, the nature of the recorded signal may change with a larger reflection of an IHC summating potential. A hint that this may be the case is that the masking curves do not always asymptote to the typically low values seen in animal experiment (Verschooten et al., 2012). Also, there is a possibility that a reflex contraction of the middle ear muscles (MEM) may have affected the recordings, even though the stimuli were below the clinical reflex threshold. We have several reasons to doubt that this was the case. First, muscle activity generates a large signal that is easily detected through the recording electrode, both during online visual and auditory monitoring of the recorded signal, and in the offline analysis (rejection of samples with artifacts). In another study (other subjects), where we used a more intense and longer broadband noise masker, we sometimes observed muscle activity at sound levels which were consistent with the reflex threshold measured with the clinical apparatus. However, in the subjects in this study, such sound-driven MEM artifacts were not observed. Second, another indicator for MEM activation is a significant and systematic decrease in CM amplitude, which is larger for low frequencies but still significantly present for mid and high frequencies (Pang and Guinan, 1997). In our data we did not find a consistent change in CM amplitude over any of the masker levels, including the highest levels at 95 dB SPL. Third, the masker is the stimulus component that reaches the highest levels, and it is present in all stimulus segments (see Figure 1). Considering the short duration of both the masker (20 ms) and its interval to the probe, and the slowness of MEM activation, it is improbable that MEM activation triggered by the masker would differentially affect the responses obtained with and without precursor. To conclude, we think there are sufficient arguments to rule out the possibility that the MEM-reflex rather than the MOCR underlies the effects observed.

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Overshoot is a phenomenon observed in psychoacoustics, which refers to the enhanced detection of a simultaneously-masked pip-tone in the presence of a precursor. The most common hypotheses are that the overshoot is caused by a reduction in gain due to the MOCR (Strickland, 2004; Jennings et al., 2011; Fletcher et al., 2013) or by a reduction in masking due to the adaptive effect of the precursor (Fletcher et al., 2015). As already mentioned (Section Anti-masking effect), none of our electrophysiological experiments revealed an increase in response by the presence of a precursor. We subjected six subjects to a psychoacoustical experiment with a paradigm identical to SM1n, except that the probe tone was shortened to 6 ms. All subjects showed a clear psychoacoustical overshoot, with a consistent masker level increase of ~5 dB (not shown). The absence of an effect in the physiological recordings but not in the psychoacoustical testing does not provide support for the hypothesis that overshoot is caused by a simple gain reduction due to the MOCR, nor by an adaptive effect of the precursor. Rather, in line with conclusions based on psychoacoustical studies (Fletcher et al., 2013, 2015), it is possible that overshoot is a product of central auditory processing operating on peripheral changes that are not detected by our recording methods.

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The CAP waveform reflects the summed synchronized discharge of a population of auditory nerve fibers (AN-fibers; Goldstein and Kiang, 1958; Kiang, 1984). Changes in acoustic input or in the processes leading up to the AN responses can affect this summed synchronized population discharge and thereby affect the waveform of the CAP. The most obvious example is the combined change in the waveform's amplitude and latency with input level (Eggermont, 1976; Chabert et al., 2002; Verschooten et al., 2012). In the present study, we focused on effects of the MOCR on CAP amplitude, but, as shown in Figures 2,4F, the precursor also affects latency and shape of the CAP. Particularly the difference in latency at high masker levels, between conditions with and without precursor, suggests that these temporal aspects of the response may help in disambiguating effects of forward masking vs. MOCR (Figure 4F).

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The processes of gain reduction by the MOCR and of neural adaptation affect AN firing and consequently also the CAP waveform. The overall impact of neural adaptation on the CAP waveform is similar to a reduction in input level (Eggermont, 1979). The solid lines in Figure 7A show indeed that with increasing masker level, CAP amplitudes decrease and latencies increase. Formulating an expectation regarding the effect of an MOCR-induced gain reduction on latency, is more difficult. On the one hand, a reduction in gain is expected to cause a decrease in amplitude and an increase in latency similar to a reduction in input level. On the other hand, several studies report that efferent activation only causes a decrease in CAP amplitude but does not cause a change in latency (e.g., Desmedt et al., 1971; Chabert et al., 2002; Elgueda et al., 2011). In our data, the reduction in CAP amplitude caused by a precursor is accompanied by an increase in latency (Figures 4F,7A: compare solid and dashed lines for a given masker level). While this may at first sight suggest that the CAP reductions caused by the precursor do not reflect activation of the MOCR, but rather neural adaptation, it is important to note that other studies have demonstrated latency effects secondary to efferent activation (e.g., Liberman, 1989; Kawase and Liberman, 1993; Aedo et al., 2015). Possibly, these different outcomes in different studies are related to the type of CAP-evoking stimulus, where studies using clicks show no latency effects but studies using tones do. In any case, it is not clear that examination of the effects on latency allow a better disambiguation of effects of neural adaptation vs. effects of the MOCR.

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CAP waveforms of experiment FM2off with and without precursor. (A) Different masker levels. (B) Comparison of CAP waveforms for different conditions: masker-only (blue), precursor-only (red), and without either (dashed). The waveform for the masking-only condition was obtained by interpolating the CAPs for masker levels 70 and 75 dB SPL, such that the CAP magnitude was equal to that of the precursor-only condition.

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Neural adaptation and gain reduction by the MOCR operate at different peripheral stages and affect AN-fibers differently. These differences may be reflected not only in amplitude and latency, but also in the precise shape of the CAP waveforms. To illustrate, Figure 7B shows an example of a masker-only (blue line) and precursor-only (red line) responses, that resulted in CAPs identical in amplitude and latency but not in exact waveform shape. The CAP without masker or precursor (dashed line) shows several late waves (e.g., N3,P3): such late features are present in the masker-only condition (blue line) but are more subtle in the precursor-only condition. Possibly, examination of such later features may help to reveal the presence of an MOCR, but a better SNR and availability of additional stimulus conditions would be required for such an effort.

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Our expectation was to find an anti-masking effect in CAPs, similar to that observed in anesthetized cat by Kawase and Liberman (1993). Three further points merit consideration. First, especially regarding the comparison of our physiological recordings with psychoacoustical results, it should be remembered that the CAP response only captures a certain aspect of auditory nerve activity (synchronous onset responses). Changes in neural activity that are important for behavioral detection of a probe are not necessarily reflected in the CAP response to this probe. Second, there is a possibility that for some reason (e.g., related to the transtympanic procedure) the MOCR was continuously active during the recording sessions, and that the effect of the presence of the precursor cannot be equated to a simple on or off switching of the MOCR. Third, species differences may be important. In experimental animals, the ipsilateral MOC pathway and reflex is about double in size relative to the contralateral component (Warr, 1992; Guinan, 2011). Anatomical data support the existence of both a lateral and MOC system in humans (Arnesen, 1984; Moore et al., 1999) and, more generally, in primates (Bodian and Gucer, 1980; Thompson and Thompson, 1986), but there is to our knowledge no human anatomical data that addresses anatomical size differences between ipsi- and contralateral MOC systems. Human OAE data suggest that there is little difference between the size of ipsilateral and contralateral MOC reflexes (Guinan, 2006), although more recent data show larger effects for ipsilateral elicitors under certain conditions (Lilaonitkul and Guinan, 2009, 2012).

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It appears that the expected difference between reduction by neural masking and reduction in gain by the MOCR is more subtle and less clear than expected. However, we found several indications of MOC involvement, despite the absence of an anti-masking for tone in noise. Comparison between ON- and OFF-frequency maskers showed a larger reduction by a precursor for OFF than for ON-frequency, consistent with gain reduction. An inconsistency between our model and the data suggests a relationship between the masker level and gain reduction by the MOCR. The most convincing evidence of the presence of a MOCR is the residual response by the precursor at high masker levels.

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To conclude, the results in this study show that the response reduction by the precursor is approximately 20–30%. We found that the reduction is fairly independent of masker type, masker level and probe level. These results support psychoacoustical paradigms that are designed to probe the efferent system as indeed activating that system.

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Synaptic adhesion molecules play important roles in the regulation of various processes involved in synapse development and function, including early axo-dendritic contacts, maturation of early synapses, synaptic transmission and plasticity, and synapse maintenance and elimination (Dalva et al., 2007; Biederer and Stagi, 2008; Han and Kim, 2008; Sanes and Yamagata, 2009; Woo et al., 2009b; Shen and Scheiffele, 2010; Siddiqui and Craig, 2011; Krueger et al., 2012; Missler et al., 2012; Valnegri et al., 2012; Takahashi and Craig, 2013; Um and Ko, 2013, 2017; Bemben et al., 2015; Ko J. et al., 2015; de Wit and Ghosh, 2016; Cao and Tabuchi, 2017; Jang et al., 2017; Krueger-Burg et al., 2017; Sudhof, 2017; Yuzaki, 2018). Prototypical examples of such molecules are neuroligins and neurexins (Sudhof, 2017). Subsequent studies have identified a large number of other synaptic molecules, suggesting that diverse synaptic adhesion molecules may act in concert to regulate synapse specificity, maturation and plasticity.

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Synaptic adhesion-like molecules (SALMs), also known as leucine-rich repeat (LRR) and fibronectin III domain-containing (LRFN) proteins, are a family of synaptic adhesion molecules originally identified independently by three groups as novel cell adhesion-like molecules that bind through their C-terminal tails to the PDZ domains of PSD-95 (Ko et al., 2006; Morimura et al., 2006; Wang et al., 2006; Nam et al., 2011), an abundant excitatory postsynaptic scaffolding protein (Sheng and Kim, 2011). A total of five members of the SALM family have been identified: SALM1/Lrfn2, SALM2/Lrfn1, SALM3/Lrfn4, SALM4/Lrfn3 and SALM5/Lrfn5 (Ko et al., 2006; Morimura et al., 2006; Wang et al., 2006; Nam et al., 2011).

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These molecules share a similar domain structure, containing six LRRs, an immunoglobulin (Ig) domain, and a fibronectin type III (FNIII) domain in the extracellular side, followed by a transmembrane domain and a cytoplasmic region that ends with PDZ domain-binding motif (Figure 1A). The PDZ domain-binding motif is present in SALMs 1–3, but not SALM4 or SALM5. In contrast to the extracellular domains of SALMs, which share high amino acid sequence identities, especially in adhesion domains, the cytoplasmic regions lack shared domains and substantially differ in length as well as amino acid sequence, suggesting that they may have distinct functions.

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Domain structure of Synaptic adhesion-like molecules (SALMs) and LAR-RPTPs. (A) Domain structure of SALMs 1–5. Note that the PDZ domain-binding motif (PDZ-BD) is present in SALMs 1–3 but not in SALM4 or SALM5. FNIII, fibronectin III domain; Ig, immunoglobulin domain; LRR, leucine-rich repeats; NT and CT, N-terminal and C-terminal LRR. Note that the number of LRRs in this diagram is seven, although it was suggested to be six in early studies based on amino acid sequence analyses (Ko et al., 2006; Morimura et al., 2006; Wang et al., 2006; Nam et al., 2011). Recent X-ray crystallographic studies have identified seven LRRs in SALM5 (Lin et al., 2018) and eight LRRs in SALM2 and SALM5 (Goto-Ito et al., 2018), which may reflect different ways of defining LRRs. (B) Domain structure of LAR-RPTPs (LAR, PTPσ and PTPδ). D1 and D2, membrane-proximal and -distal tyrosine phosphatase domains of LAR-RPTPs; meA/B/C; mini-exon A/B/C.

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Our previous review of SALMs summarized basic and functional characteristics of SALMs, including chromosomal locations of the corresponding genes and exon-intron structures, mRNA and protein expression patterns, protein–protein interactions, and involvement in regulating neuronal and synapse development (Nam et al., 2011). One prominent function of SALMs is to regulate neurite outgrowth and branching through mechanisms including lipid raft-associated flotillin proteins (Wang et al., 2006, 2008; Swanwick et al., 2009, 2010; Seabold et al., 2012). SALMs also regulate synapse development and function through mechanisms involving interactions with PSD-95 and glutamate receptors (Ko et al., 2006; Wang et al., 2006; Mah et al., 2010).

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Notably, these functional features of SALMs have been identified mainly through in vitro studies. Recently, however, additional studies on SALMs using in vivo approaches, such as genetic mouse models, have provided intriguing insights into the physiological functions of SALMs (Li et al., 2015; Lie et al., 2016; Morimura et al., 2017). In addition, SALM3 and SALM5, which unlike other SALMs possess synaptogenic activities (Mah et al., 2010), have been found to interact trans-synaptically with presynaptic LAR family receptor tyrosine phosphatases (LAR-RPTPs; Li et al., 2015; Choi et al., 2016), a group of adhesion molecules with cytoplasmic phosphatase activity that are critically involved in various aspects of neuro- and synapse development across many species (Johnson and Van Vactor, 2003; Takahashi and Craig, 2013; Um and Ko, 2013; Figure 1B). Moreover, two independent X-ray crystallography studies have determined the stoichiometry and molecular details of the interaction of SALM5 with LAR-RPTPs (Goto-Ito et al., 2018; Lin et al., 2018). Lastly, recent clinical studies have additionally identified associations of SALMs with diverse neurodevelopmental disorders (Nho et al., 2015; Rautiainen et al., 2016; Thevenon et al., 2016; Farwell Hagman et al., 2017; Morimura et al., 2017; Bereczki et al., 2018). This review article will summarize these new findings and discuss how SALMs regulate synapse development and function.

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As implied by the name “synaptic adhesion-like molecule”, it was initially unclear whether SALMs are indeed localized at neuronal synapses and regulate synapse development and function through cis/trans-synaptic adhesion. The first, albeit indirect, evidence came from the fact that some SALMs directly interact with well-known excitatory synaptic proteins, such as PSD-95, N-methyl-D-aspartate receptors (NMDARs), and α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors (AMPARs; Ko et al., 2006; Morimura et al., 2006; Wang et al., 2006). Functionally, SALM2, artificially clustered on neuronal dendrites by antibody-coated beads, was shown to be able to recruit PSD-95 and NMDARs/AMPARs (Ko et al., 2006). In addition, SALM3 and SALM5 expressed in heterologous cells was shown to induce presynaptic differentiation in contacting axons of cocultured neurons in mixed culture assays (Mah et al., 2010), in which synaptogenic activity is tested by coculturing neurons with heterologous cells exogenously expressing synaptic adhesion molecules (Scheiffele et al., 2000; Biederer and Scheiffele, 2007).

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More direct evidence for synaptic localization of SALMs has come from electron microscopy, immunocytochemistry, biochemical and proteomic analyses. One early study using immunocytochemistry detected endogenous SALM2 signals at excitatory, but not inhibitory, synapses in cultured rat hippocampal neurons (Ko et al., 2006). A subsequent electron microscopy study detected endogenous SALM4 signals at various subcellular locations in rat brain hippocampal neurons, including synaptic and extra-synaptic sites, pre- and postsynaptic sites, and dendrites and axons (Seabold et al., 2008). Biochemical experiments further demonstrated that SALMs are enriched in the postsynaptic density (PSD)—electron-dense multiprotein complexes at excitatory postsynaptic sites that contain neurotransmitter receptors, adaptor/scaffolding proteins and signaling molecules (Sheng and Sala, 2001; Sheng and Hoogenraad, 2007); SALM1 (Wang et al., 2006), SALM2 (Ko et al., 2006), SALM3 (Mah et al., 2010), SALM4 (Lie et al., 2016) and SALM5 (Mah et al., 2010).

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Trans-synaptic, cis-, and cytoplasmic interactions of SALMs. SALMs interact trans-synaptically with presynaptic LAR-RPTPs (LAR, PTPσ and PTPδ), in cis with AMPA/NMDA receptors and other SALM proteins, and cytoplasmically with the postsynaptic scaffolding protein PSD-95 (in the case of SALMs 1–3 but not SALM4 or SALM5). Protein interactions are indicated by the close proximity of the indicated proteins/domains or by dotted lines. Whether SALMs directly interact with NMDA/AMPA receptors remains to be determined. The trans-synaptic interactions between postsynaptic SALM3/5 and presynaptic LAR-RPTPs are known to promote presynaptic differentiation, although the function of the newly identified SALM2–LAR-RPTP (PTPδ) interaction is unclear. SALM4 interacts in cis with SALM3 to suppress the binding of SALM3 to presynaptic LAR-RPTPs and SALM3-dependent presynaptic differentiation. Postsynaptic SALM5 can also interacts with presynaptic SALM5 in a homophilic manner, which may interfere with the trans-synaptic interaction between presynaptic LAR-RPTPs and postsynaptic SALM5. The cis-interactions between different postsynaptic SALMs are based on both in vitro and in vivo results, and may be mediated by the SALM–SALM dimerization revealed by X-ray crystallographic studies. Although not shown here, some LAR-RPTPs are thought to be present and function at postsynaptic sites, in addition to presynaptic sites.

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Like SALM3, SALM5 also interacts with LAR-RPTPs (Choi et al., 2016; Figure 2). In this case, the meB splice insert in LAR-RPTPs suppresses SALM5–LAR-RPTP interactions, an effect opposite that of meB on SALM3–LAR-RPTP interactions. Therefore, both SALM3 and SALM5 interact with LAR-RPTPs in a splicing-dependent manner, although the polarity of the modulatory effect of the insert appears to differ (but see below for conflicting results and related structural and biochemical data).

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An early study reported that SALM3 and SALM5, but not other SALMs, expressed in heterologous cells induce presynaptic differentiation in contacting axons of cocultured neurons (Mah et al., 2010). However, it has remained unclear which presynaptic adhesion molecules mediate SALM3/5-dependent presynaptic differentiation.

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A recent study found that SALM3 interacts with presynaptic LAR-RPTPs to promote presynaptic differentiation (Li et al., 2015; Figure 2). This conclusion is supported by several lines of evidence, including protein binding, cell aggregation, and coculture assays. All three known member of the LAR-RPTP family (LAR, PTPσ and PTPδ) can interact with SALM3. Importantly, these interactions are regulated by alternative splicing of LAR-RPTPs. Specifically, the splice B insert (termed mini-exon B or meB), but not the splice A insert (meA), both of which are located in the N-terminal three Ig domains of LAR-RPTPs, is required for the interaction with SALM3 (Table 1).

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More recently, an elegant study using proximity biotinylation, a method combining an engineered enzyme and proteomic mapping of biotinylated proteins within 10–50 nm of a particular bait protein in a subcellular environment (Han et al., 2017), identified SALMs among a large number of synaptic cleft proteins (Loh et al., 2016). Specifically, SALM1/Lrfn2 and SALM3/Lrfn4 were found to be present in the vicinity of LRRTM2 and LRRTM3, the reference excitatory synaptic adhesion molecules used in this study. Another study also using proximity biotinylation detected SALM1/Lrfn2 in close proximity to PSD-95 (Uezu et al., 2016). However, SALMs were not found to be close neighbors of the inhibitory adhesion molecules, neuroligin-2 and Slitrk3, or gephyrin (Loh et al., 2016; Uezu et al., 2016), a major inhibitory synaptic scaffolding protein (Tyagarajan and Fritschy, 2014; Choii and Ko, 2015; Krueger-Burg et al., 2017). These results suggest that some SALMs are important components of excitatory synapses; however, they do not preclude their possible presence at inhibitory synapses, since the biotinylation approach used is likely biased toward identification of more abundant proteins.

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Collectively, these previous observations suggest that SALMs are present or enriched at synaptic sites, but also highlight important details that still remain to be determined, including excitatory vs. inhibitory synaptic localization of SALMs, pre- vs. postsynaptic localization, and changes in synaptic localization during development and activity. Addressing these additional questions could be aided by knockout (KO) animals combined with high-quality antibodies, as well as advanced methodologies, such as proximity biotinylation and endogenous protein tagging using CRISPR/Cas9-mediated homology-independent targeted integration (Suzuki et al., 2016).

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It can also be expected that postsynaptic LAR-RPTP ligands would be differentially expressed in specific brain regions and cell types. In addition, each postsynaptic LAR-RPTP ligand apparently has a unique preference for particular splice variants of LAR-RPTPs. For instance, meB is required for (or positively regulates) LAR-RPTP binding to SALM3, Slitrks, IL1RAPL1 and IL-1RAcP (Yoshida et al., 2011, 2012; Takahashi et al., 2012; Yim et al., 2013; Li et al., 2015), but inhibits LAR-RPTP binding to TrkC (Takahashi et al., 2011). Notably, NGL-3 differs from other postsynaptic LAR-RPTP-binding proteins in that it binds to the first two FNIII domains of LAR-RPTPs (Woo et al., 2009a), whereas all other such proteins bind to the N-terminal Ig domains of LAR-RPTPs (Takahashi et al., 2011, 2012; Yoshida et al., 2011, 2012; Yim et al., 2013; Li et al., 2015; Choi et al., 2016). This suggests the intriguing possibility that LAR-RPTPs form ternary protein complexes with NGL-3 and other postsynaptic LAR-RPTP binders, and hints at the potential interplay among these complex components. Therefore, interactions of trans-synaptic LAR-RPTPs with their postsynaptic partners likely occur in a precisely regulated manner.

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It is thought that LAR-RPTPs are present mainly at presynaptic sites, because LAR proteins expressed in heterologous cells do not induce presynaptic protein clustering at contacting axons of cocultured neurons, but do induce postsynaptic protein clustering in contacting dendrites (Woo et al., 2009a). However, although some light microscopy-level immunostaining has been performed (Takahashi et al., 2011; Farhy-Tselnicker et al., 2017), clear pre- vs. postsynaptic localization of endogenous LAR-RPTPs has not been determined at the electron microscopy level. In addition, postsynaptic LAR-RPTPs have been shown to regulate dendritic spines and AMPAR-mediated synaptic transmission (Dunah et al., 2005). More recently, PTPδ coexpressed with IL1RAPL1 in cultured hippocampal neurons was found to inhibit IL1RAPL1-dependent suppression of dendritic branching, suggesting that postsynaptic PTPδ interacts in cis with, and inhibits, IL1RAP1 (Montani et al., 2017). Therefore, it is possible that SALM3/5-LAR-RPTP interactions also occur at postsynaptic sites in a cis manner.

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Presynaptic LAR-RPTPs are known to interact with several other postsynaptic adhesion molecules in addition to SALM3/5, including NGL-3, Slitrks, TrkC, IL1RAPL1 and IL-1RAcP (Woo et al., 2009a,b; Kwon et al., 2010; Takahashi et al., 2011, 2012; Valnegri et al., 2011; Yoshida et al., 2011, 2012; Yim et al., 2013; Li et al., 2015); also see reviews by Craig, Ko and colleagues (Takahashi and Craig, 2013; Um and Ko, 2013) for further details. These results give rise to a number of obvious questions: Why are there multiple LAR-RPTP-binding postsynaptic adhesion molecules? Does a single synapse contain all, or a majority, of the postsynaptic LAR-RPTP ligands? If so, do they compete with each other for mutually exclusive LAR-RPTP binding, or do they act in concert to fine-tune synapse regulation? These questions can also be applied to the three presynaptic LAR-RPTPs, LAR, PTPσ and PTPδ.

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First, it seems unlikely that all three LAR-RPTPs are present in the same synapses, in part because LAR, PTPσ and PTPδ are differentially expressed in distinct brain regions (Kwon et al., 2010). In addition, evidence suggests that LAR, PTPσ and PTPδ differentially localize to and regulate excitatory and inhibitory synapses, with PTPσ and PTPδ being more important at excitatory and inhibitory synapses, respectively (Takahashi et al., 2011, 2012; Takahashi and Craig, 2013; Um and Ko, 2013); however, additional details remain to be determined. Splice variants of LAR-RPTPs are tightly regulated in a spatiotemporal manner (O’Grady et al., 1994; Pulido et al., 1995a,b; Zhang and Longo, 1995). In particular, each LAR-RPTP protein’s mini-exon profile, which strongly influences interactions with their postsynaptic partners (Takahashi and Craig, 2013; Um and Ko, 2013), appears to be distinct in specific brain regions. For instance, the meB splice insert in the rat hippocampus is almost always present in PTPδ, but is rarely found in LAR and is only present in about half of PTPσ molecules (Li et al., 2015), suggesting that hippocampal SALM3 is likely to interact with LAR-RPTPs in the rank order, PTPδ > PTPσ ≫ LAR (Li et al., 2015). Similarly, the majority of PTPδ splice variants in the mouse hippocampus contain the meB splice insert (Yoshida et al., 2011). Therefore, LAR-RPTPs are likely to interact with their postsynaptic partners in a spatiotemporally and molecularly regulated manner.

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Although previous studies have identified interactions between SALM3/5 and LAR-RPTPs, the molecular stoichiometry and mechanistic details of these interactions have remained unclear. Two recent X-ray crystallography studies have been instrumental in resolving many of these uncertainties.

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The first revealed that SALM5 can form a dimeric structure, in which dimerization is mediated mainly by the N-terminal LRR domain, and that this dimer forms a complex with two PTPδ monomers (Lin et al., 2018; Figures 3A,B). In this 2:2 stoichiometry, a SALM5 dimer bridges two PTPδ monomers, which are positioned at opposite sides of the SALM5 dimer. The overall shape of the complex has two components: a central platform-like structure formed by two antiparallel LRR domains of SALM5 with a concave core in its center, and four leg-like structures formed by two Ig domains of SALM5 and two Ig3 domains of PTPδ.

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X-ray crystal structure of SALM5 in complex with PTPδ in a 2:2 heterotetrameric format. (A) Side view of the structure (surface representation). (B) Top-down view of the structure (ribbon diagram). These images were borrowed without modification from Figures 1B,C of a recent report on the crystal structure of SALM5 in complex with PTPδ (Lin et al., 2018), which are under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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It was found that the specific molecular interfaces that mediate the SALM5–PTPδ interaction are the LRR domain of SALM5, which interacts with the second Ig domain of PTPδ, and the Ig domain of SALM5, which interacts with both the second and third Ig domains of PTPδ. Importantly, mutations in the LRR domain of SALM5 that disrupt dimerization were shown to abolish SALM5–LAR-RPTPs interactions and SALM5-dependent presynaptic differentiation. Therefore, SALM5 dimerization is critical for both the trans-synaptic adhesion and synaptogenic activity of SALM5.

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Experiments using heterologous cells and cultured neurons have shown that SALM5 can engage in both transcellular and homophilic adhesions (Seabold et al., 2008). This suggests that presynaptic SALM5 may compete with presynaptic LAR-RPTPs for binding to postsynaptic SALM5. Alternatively, these two interactions may occur in a spatiotemporally distinct manner.

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Lastly, heparan sulfate proteoglycans interact with LAR-RPTPs in the presynaptic membrane to regulate their interactions and functions (Aricescu et al., 2002; Johnson et al., 2006; Song and Kim, 2013; Coles et al., 2014; Ko J. S. et al., 2015; Farhy-Tselnicker et al., 2017; Won et al., 2017), and thus may regulate SALM–LAR-RPTP interactions and functions. In addition, LAR proteins associate with netrin-G1, a glycosylphosphatidylinositol-anchored presynaptic adhesion molecule (Nakashiba et al., 2000), at the presynaptic side when netrin-G1 is coupled with its cognate postsynaptic ligand NGL-1 (Song et al., 2013), suggesting the possibility that trans-synaptic SALM3/5–LAR-RPTP interactions is regulated by a neighboring trans-synaptic netrin-G1-NGL-1 interaction.

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These conclusions are further confirmed by a second study, which reported a SALM5 dimer in complex with two PTPδ monomers (Goto-Ito et al., 2018). This study identified eight LRRs whereas the other study identified seven LRRs; notably, both values differ from the number predicted in previous studies (six) based on amino acid sequence analyses (Ko et al., 2006; Morimura et al., 2006; Wang et al., 2006; Nam et al., 2011). These differences appear to reflect the specific criteria authors used to define LRRs in the different studies.

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Intriguingly, this second study also solved the 2:2 structure of PTPδ in complex with SALM2 (Goto-Ito et al., 2018), a member of the SALM family that, unlike SALM3 and SALM5, has little or no synaptogenic activity (Mah et al., 2010). It is possible that SALM2 actually has synaptogenic activity that has gone unidentified in previous studies employing coculture assays and neuronal overexpression (Ko et al., 2006). Alternatively, SALM2 may interact with PTPδ to regulate other aspects of neuronal synapses. For instance, SALM2 is capable of associating with PSD-95 and NMDA/AMPARs (Ko et al., 2006). Therefore, the PTPδ–SALM2 interaction may promote postsynaptic protein clustering rather than presynaptic differentiation.

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The findings of these two X-ray crystallography studies are largely similar to those investigating other LAR-RPTP interactions, which showed that meB is required for (or promotes) interactions between Slitrk1 and PTPσ (Um et al., 2014), Sltrk2 and PTPδ (Yamagata et al., 2015a), and IL1RAPL1/IL-1RAcP and PTPδ (Yamagata et al., 2015b). Therefore, these interactions, if present in the same synapse together with the SALM4–LAR-RPTP complex, are likely to be simultaneously regulated by meB.

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The 2:2 stoichiometry of SALM5–LAR-RPTP interactions that involves an antiparallel LRR dimerization, something that is not observed in other LAR-RPTP-related crystal structures (Coles et al., 2014; Um et al., 2014; Yamagata et al., 2015a,b; Won et al., 2017), carries multiple potential functional implications. One possibility is that this stoichiometry could increase the affinity of the trans-synaptic SALM5–LAR-RPTP interaction. Indeed, the Kd values for the SALM5–PTPδ interaction determined in two independent studies ranged from 0.07 μM to 14.4 μM, indicating weaker interactions than those for LAR-RPTPs with Slitrk1, Slitrk2, IL1RAPL1, IL-1RAcP or TrkC, which are in the sub-micromolar range (0.15–0.55 μM). However, it remains unclear whether the SALM5–PTPδ interactions measured under the surface plasmon resonance condition involves the 2:2 stoichiometry.

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These two studies have also provided significant molecular insights into how alternative splicing regulates SALM-LAR-RPTP interactions. Specifically, they show that the meB, but not meA, splice insert is located in the junctional region between Ig2 and Ig3 domains of PTPδ, both of which are engaged in SALM5 interactions. The meB splice insert, although not directly interacting with SALM5, appears to function as a flexible linker that optimizes the position of the PTPδ-Ig3 domain for its high-affinity interaction with the SALM5-Ig domain (Goto-Ito et al., 2018; Lin et al., 2018). This conclusion is further supported by surface plasmon resonance assays that used purified PTPδ proteins, with or without meB, and demonstrated that the presence of meB increases the affinities between SALM5 and PTPδ by ~7–30 fold (Goto-Ito et al., 2018; Lin et al., 2018; Table 1).

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Overall, these results are in apparent contrast with an earlier report that meB suppresses the interaction between LAR-RPTPs and SALM5 (Choi et al., 2016). A possible reason for this discrepancy is differences in the method used to assess binding—cell aggregation assays in this earlier report (Choi et al., 2016) and binding assays using purified proteins in the more recent studies (Goto-Ito et al., 2018; Lin et al., 2018). Indeed, the effects of meB on SALM3–LAR-RPTP interactions were substantially weakened in cell aggregation assays relative to protein binding assays (Li et al., 2015).

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What advantages might accrue to SALMs because they are able to achieve an appropriate trans-synaptic affinity through dimerization—a property lacking in other LAR-RPTP ligands? It is possible that a SALM5 dimer brings two PTPδ molecules close to each other to more efficiently promote presynaptic differentiation through liprin-α. Liprin-α belongs to a family of LAR-RPTP-binding scaffolding/adaptor proteins whose members are known to form homodimers and bridge LAR-RPTPs with their phospho-tyrosine protein substrates, such as β-catenin (Serra-Pagès et al., 1995, 1998; Dunah et al., 2005; Stryker and Johnson, 2007; de Curtis, 2011).

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On the postsynaptic side, SALM2 dimers, which are clearly revealed in crystal structures (Goto-Ito et al., 2018), may efficiently interact with PSD-95 and PSD-95–associated proteins known to form dimeric/multimeric structures, such as Shank and Homer (Kim et al., 1996; Hsueh et al., 1997; Xiao et al., 1998; Naisbitt et al., 1999; Hayashi et al., 2009). These multimeric interactions may facilitate the formation of platform-like multi-protein structures in the PSD.

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Cis-interactions of diverse synaptic adhesion molecules have received increasing attention because they often regulate trans-synaptic interactions as well as receptor-mediated synaptic transmission (Jang et al., 2017). For example, neuroligin-1 interacts in cis with the GluN1 subunit of NMDARs through extracellular domains to increase the synaptic abundance of NMDARs (Budreck et al., 2013). In addition, postsynaptic neurexin-1β interacts in cis with neuroligin-1 to suppress the trans-synaptic interaction of neuroligin-1 with presynaptic neurexins (Taniguchi et al., 2007). More recently, MDGAs (MAM domain-containing glycosylphosphatidylinositol anchors) have been found to interact in cis with neuroligins to modulate trans-synaptic neuroligin–neurexin interactions (Lee et al., 2013; Pettem et al., 2013; Elegheert et al., 2017; Gangwar et al., 2017; Kim et al., 2017; Thoumine and Marchot, 2017).

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SALMs are involved in cis-interactions in addition to trans-interactions. The first clue came from the original study on SALMs, which reported that SALM1 associates with and promotes surface expression and clustering of NMDARs (Wang et al., 2006; Figure 2). This required the C-terminal tail of SALM1, which interacts with PSD-95 and subsequently with GluN2B subunits of NMDARs, suggesting that SALM1 indirectly interacts with and clusters NMDARs through PSD-95. However, SALM1 can also associate with GluN1, a subunit of NMDARs that lacks the cytoplasmic region, suggesting that SALM1 can directly interact with NMDARs. Additional clues for cis-interactions of SALMs came from the finding that bead-mediated direct clustering of SALM2 on the dendritic surface of cultured neurons induces secondary clustering of PSD-95 as well as AMPA/NMDARs (Ko et al., 2006), although whether this is mediated by direct interactions remains unclear.

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What might be the molecular mechanisms underlying the cis-interactions of SALMs? Perhaps, the aforementioned dimeric nature of SALMs revealed by X-ray crystallography may explain some of these interactions. The fact that the LRR domain of SALM4 is important for its cis-complex formation with SALM2/5 is consistent with the critical role of LRR domains in SALM dimerization. However, the SALM4–SALM3 cis-interaction, which requires the transmembrane domain, is unlikely to involve LRR dimerization.

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What could be the possible functions of cis-interactions in SALMs? If heteromeric dimerization occurs, these interactions may increase the diversity of the subunit composition of SALM dimers. For instance, a SALM2–SALM5 dimer might bring SALM5 into proximity with the SALM2–PSD-95 complex and promote SALM5-dependent presynaptic differentiation at excitatory synapses, thereby shifting the balance of excitatory and inhibitory synapses towards excitation. In addition, these interactions may increase the diversity of non-SALM proteins, including trans-synaptic adhesion proteins, cis-neighboring membrane proteins, and cytoplasmic adaptor/signaling proteins around SALM complexes. This, in turn, could influence the synaptic trafficking and synapse-modulatory actions of SALMs.

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A careful examination of cis-interactions between different SALM family members showed that all SALM members coimmunoprecipitate with each other in both a homomeric and heteromeric manner in heterologous cells (Seabold et al., 2008; Figure 2). The extracellular domains of SALMs are important for these cis-interactions, as evidenced by the fact that a SALM1 mutant lacking the entire cytoplasmic domain can form homo- and heteromultimers. In the brain, however, heteromeric SALM complexes are formed between SALMs 1–3, but not SALM4 or SALM5. The ability of SALM4 and SALM5, but not other SALMs, to mediate homophilic trans-synaptic adhesion suggests that postsynaptic SALMs can be segregated into three subgroups: (1) SALMs 1–3; (2) SALM4; and (3) SALM5.

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However, a recent study has complicated this picture, reporting that SALM4 can coimmunoprecipitate with SALM2 in the mouse brain (Lie et al., 2016). This study further showed that SALM4 can also form complexes with SALM3 and SALM5 in heterologous cells. Additional domain-mapping experiments revealed that the LRR domain of SALM4 is important for its interactions with SALM2/5, whereas the transmembrane domain is important for its interaction with SALM3. Thus, cis-interactions between SALMs may be more complex than previously thought.

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As noted above, SALM1 was previously shown to be involved in surface expression and dendritic clustering of NMDARs (Wang et al., 2006). More recently, immunogold electron microscopy has revealed strong colocalization of SALM1 with the GluN1 subunit of NMDARs (Thevenon et al., 2016). These results suggest that SALM1 promotes synaptic clustering of NMDARs, although in vivo support for these findings has been lacking.

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A recent study reported a mouse line that lacks exon 2 of the Lrfn2 gene encoding SALM1 (Lrfn2–/– mice; Morimura et al., 2017; Table 2). Contrary to the expectation that Lrfn2 KO would suppress synaptic NMDAR function, Lrfn2–/– mice displayed normal NMDAR-mediated synaptic transmission in the hippocampus. Instead, many SALM1-lacking synapses also lacked AMPARs, as evidenced by the slightly reduced number of dendritic spines, but markedly reduced frequency of miniature excitatory postsynaptic currents (mEPSCs), as well as altered failure rates with minimal stimulation of NMDA/AMPA-evoked postsynaptic currents (EPSCs). This suggests that many Lrfn2–/– excitatory synapses are silent synapses, an immature form of excitatory synapse that harbors NMDARs, but not AMPARs (Isaac et al., 1995; Liao et al., 1995). Therefore, Lrfn2 KO appears to suppress synaptic delivery of AMPARs to NMDAR-only synapses during developmental synapse maturation, rather than acting at the previous step to suppress synaptic levels of NMDARs. In line with this change, Lrfn2 KO causes an increase in NMDAR-dependent long-term potentiation (LTP), likely because silent synapses have more room to accommodate incoming AMPARs. In addition to these functional changes at excitatory synapses, Lrfn2–/– mice show morphological changes, including reduced spine head size and increased spine length (Morimura et al., 2017), suggesting that Lrfn2 KO suppresses normal development of dendritic spines. Collectively, these findings suggest that Lrfn2 KO suppresses both morphological and functional maturation of excitatory synapses.

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Behaviorally, Lrfn2–/– mice display autistic-like behavioral abnormalities, including suppressed social interaction and enhanced repetitive behaviors. They also show enhanced acoustic startle and suppressed prepulse inhibition, suggestive of impaired sensory-motor gating. Furthermore, using targeted gene sequencing, this study identified point mutations of the LRFN2 gene in individuals with autism spectrum disorders (ASDs), and demonstrated that a missense mutation inhibits the association of SALM1 with PSD-95. Interestingly, Lrfn2–/– mice show enhanced spatial learning and fear memory, consistent with the enhanced LTP observed in these mice and a report that some individuals with LRFN2 mutations show enhanced memory together with delayed speech development (Thevenon et al., 2016).

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