Persistent neural activity in auditory cortex is related to auditory working memory in humans and nonhuman primates — TEI-tagged view

HuangpersName, MatysiakpersName, HeilpersName, KönigpersName, BroschpersName, KingpersName (2016date). “Persistent neural activity in auditory cortex is related to auditory working memory in humans and nonhuman primates.” eLifetitle·j.

Object of study — whether persistentterm changes in auditory cortexterm activityterm reflect WMterm, or the other mental processes that accompany doing a taskterm

Research question — whether auditory cortexterm exhibits persistentterm changes in neural activityterm related to auditory WMterm, which the paper calls still an open question

Contribution — three designs that separate working-memory activityterm from expectationterm, preparationterm and stimulus-evoked activityterm, by varying memory loadterm while holding the sounds identical

Full text — tagged

Persistentterm neural activityterm in auditory cortexterm is related to auditory working memory in humans and nonhuman primates eLife 5 :e15441.

https://doi.org/10.7554/eLife.15441 Copy to clipboard Download BibTeX Download .RIS Abstract Introduction Results Discussion Materials and methods References Article and author information Metrics Working memory is the cognitive capacity of short-term storageterm of information for goal-directed behaviors. Where and how this capacity is implemented in the brain are unresolved questions. We show that auditory cortexterm stores information by persistentterm changes of neural activityterm. We separated activityterm related to working memory from activityterm related to other mental processes by having humans and monkeysterm perform different tasksterm with varying working memory demands on the same sound sequences. Working memory was reflected in the spiking activityterm of individual neurons in auditory cortexterm and in the activityterm of neuronal populationsterm, that is, in local field potentials and magnetic fields. Our results provide direct support for the idea that temporary storageterm of information recruits the same brain areas that also process the information. Because similar activityterm was observed in the two species, the cellular bases of some auditory working memory processes in humans can be studied in monkeysterm.

https://doi.org/10.7554/eLife.15441.001 Working memory (WM)term·def has been defined as a cognitive process for temporary storageterm of task-relevant information for goal-directed behaviors ( D’Esposito, 2007 ; Sreenivasan et al., 2014 ). The information can be related to past sensory events (e.g., short-term sensory memory), or to future sensory events (e.g., sensory prediction) or actions. The two types of information have been termed the retrospective and prospective codes, respectively, and both can be stored in WMterm to bridge sensory events or their contingent behavioral actions ( Curtis et al., 2004 ; D’Esposito, 2007 ; Postle, 2006 ; Sreenivasan et al., 2014 ). Research on WMterm has promoted the view that the storageterm is accomplished by sustained attention to internal representations of information ( Awh and Jonides, 2001 ; Chun, 2011 ; Gazzaley and Nobre, 2011 ; Kiyonaga and Egner, 2013 ; Postle, 2006 ; Zimmermann et al., 2016 ). It is further assumed that WMterm arises through the coordinated recruitment, via attention, of brain areas in a broad network ( Constantinidis and Procyk, 2004 ; Postle, 2006 ; Ranganath and D'Esposito, 2005 ) and that the task-relevant information is stored in some of the same brain areas that also process it, such as sensory cortexterm, whereas prefrontal cortex (PFC) is thought to aid storageterm of the information in sensory cortexterm ( D’Esposito, 2007 ; Postle, 2006 ; Sreenivasan et al., 2014 ).

In the auditory modality, lesion ( Colombo et al., 1990 , 1996 ; Fritz et al., 2005 ), imaging ( Brechmann et al., 2007 ; Grimault et al., 2014 ; Guimond et al., 2011 ; Kumar et al., 2016 ; Linke and Cusack, 2015 ; Linke et al., 2011 ; Nolden et al., 2013 ; Rämä et al., 2004 ), and recording studies ( Bigelow et al., 2014 ; Gottlieb et al., 1989 ; Sakurai, 1994 ; Scott et al., 2014 ) have revealed that auditory cortexterm (AC) is involved in the performance of auditory WMterm tasksterm. Although some of these studies have demonstrated neural activityterm in AC that is persistently elevated or suppressed during the period when information needs to be held in WMterm, we argue here that such persistentterm changes in activityterm do not unequivocally reflect WMterm. Many studies have not controlled for potential long-lasting neural activityterm evoked by a stimulusterm and for mental processes other than WMterm that are associated with performing a taskterm, such as general attention, reward expectationterm or preparationterm for behavioral responses. These processes can also be associated with changes in AC activityterm that last for seconds ( BroschpersName et al., 2011 ). For example, in some of the imaging ( Kumar et al., 2016 ; Linke and Cusack, 2015 ) and recording studies ( Bigelow et al., 2014 ; Gottlieb et al., 1989 ; Scott et al., 2014 ), persistentterm changes in activityterm were revealed by comparing activityterm during the WMterm period with that during a baselineterm period. However, these two periods differed not only in WMterm but also with respect to the expectationterm of upcoming stimuli and of rewards, and with respect to preparationterm for behavioral responses. Therefore, the persistentterm changes in activityterm revealed in these studies do not necessarily reflect WMterm. They could reflect expectationterm and preparationterm. In some other studies ( Grimault et al., 2014 ; Nolden et al., 2013 ), the persistentterm changes in activityterm were revealed by comparing activityterm in experimental conditions with different WM loadterm·var. However, in these studies, the auditory stimuli always co-varied with the WM loadterm·var across conditions. Therefore, differences in the persistentterm activityterm revealed in these studies could reflect differences in activityterm evoked by different stimuli rather than differences in WM loadterm·var. Moreover, it is unclear whether persistentterm changes in neural activityterm in AC are stimulusterm specific ( Gottlieb et al., 1989 ; Kumar et al., 2016 ; Lemus et al., 2009 ; Linke and Cusack, 2015 ; Linke et al., 2011 ; Rämä et al., 2004 ; Scott et al., 2014 ), even though stimulusterm specificity has been traditionally considered a hallmark of WMterm in sensory cortexterm ( Curtis and Lee, 2010 ). Furthermore, with few recent exceptions ( Grimault et al., 2014 ; Nolden et al., 2013 ), human studies have not attempted to localize neural activityterm related to the performance of auditory WMterm tasksterm (e.g., Guimond et al., 2011 ; Kaiser et al., 2009 ; Lu et al., 1992 ). Thus, it is still an open question whether AC exhibits persistentterm changes in neural activityterm related to auditory WMterm.

To address this issue, we conducted three studies on humans and nonhuman primates in which they performed different tasksterm on sequences of two sounds, S1 and S2, separated by a delayterm ( Figure 1 ). The tasksterm and the sequences differed in the extent to which WMterm was required. They were designed to separate neural activityterm in AC related to WMterm from potential late activityterm·def evoked by S1 or activityterm related to mental processes other than WMterm. They also allowed examination of the stimulusterm specificity of WM-related activityterm in AC. In humans, we assessed neural activityterm in AC by means of magnetoencephalography (MEGterm·mth), through which we determined the strengths of regional sourcesterm·var ( Scherg and Ebersole, 1993 ). In monkeysterm, we recorded local field potentials (LFPsterm) and spiketerm activityterm from core fields of AC. To track potential contributions from other brain structures, particularly from PFC, we not only compared LFPsterm and spiketerm activityterm in core fields of AC but also recorded spiketerm activityterm in PFC.

Figure 1 with 3 supplements see all Download asset Open asset Experimental paradigms used to identify working-memory related neural activityterm.

A sequence of two stimuli, S1 and S2, separated by a delayterm, was presented on every trial. Tasksterm associated with these sequences differed. For tasksterm 1, 2, 3 and 6, four sequences (AA, AB, BA and BB) were used. A and B represent tonesterm of different frequencies. For taskterm 4, S1 and S2 were tonesterm of three different frequencies, A, B or C. For taskterm 5, S1 was also a toneterm (of frequency A, B or C), whereas S2 was a noise burst (D), a frequency-modulated toneterm (E) or a click train (F). ( a ) Tasksterm 1 and 2 required a go response for sequence AA and a nogo response for the other sequences. The WM loadterm·var was higher when S1 was A (thick orange traces) than when it was B (thin blue traces). Tasksterm 1 and 2 differed with respect to the hand used for the responses. Putative WM-related activityterm was identified by a within-task comparison. ( b ) Taskterm 6 required a go response for sequence BB and a nogo response for the other sequences. The partial changes of stimulus-response associations relative to those of taskterm 1 enabled WM-related activityterm to be revealed also by between-task comparisons. ( c ) Taskterm 3, a delayed-response taskterm, required a go response when S1 was A and a nogo response when it was B, irrespective of the frequency of S2. Potential differences in activityterm related to preparationterm for go and nogo responses could be revealed by comparing activityterm during the delayterm of trials requiring go responses (thin orange traces) and nogo responses (thin blue traces). ( d ) Taskterm 4, a delayed-match-to-sample taskterm, required a go response when S1 and S2 were identical and a nogo response otherwise. ( e ) Taskterm 5, a sound-discrimination taskterm, required a go response when S2 was the noise burst and a nogo response when S2 was the click train or the frequency-modulated toneterm, irrespective of the frequency of S1. WM-related activityterm was revealed by comparing activityterm during the delayterm in tasksterm 4 and 5. Feedback of whether the response was correct or not was provided to the human subjects in tasksterm 4 and 5 and to the monkeysterm in tasksterm 1 and 6. The human subjects were shown a smiling face immediately after a correct response and a frowning face after an incorrect response. For the monkeysterm, a drop of water was provided immediately after correct go and nogo responses.

https://doi.org/10.7554/eLife.15441.002 Human subjects performed three tasksterm on an identical set of four sequences of tonesterm. The tasksterm and the sequences differed in the extent to which WMterm was required. Each sequence was composed of two tonesterm, S1 and S2, separated by a delayterm ( Figure 1a,c ). The tonesterm could be of frequency A or B, resulting in the sequences AA, AB, BA, and BB.

Tasksterm 1 and 2 both required a go response (a button press within a limited time window after S2) to the sequence AA and a nogo response (no button press) to the other three sequences ( Figure 1a ). The two tasksterm differed with respect to the index finger used for the go response: the right for taskterm 1 and the left for taskterm 2. They also differed with respect to the ear stimulated: the left for taskterm 1 and the right for taskterm 2, contralateral to the finger used. The tasksterm thus controlled for potential differential effects of the side of auditory stimulation and execution of the behavioral response and allowed the investigation of whether both the left and right AC are involved in auditory WMterm. In order to solve tasksterm 1 and 2, subjects first had to identify the frequency of S1 because it determined how the remainder of the trial needed to be processed. When S1 was B, a nogo response was required, irrespective of S2, and nothing else needed to be held in WMterm. When S1 was A, however, subjects remained uncertain whether a go or a nogo response would be required after S2. To cope with this uncertainty, they could use different strategies involving WMterm. For example, they could hold in WMterm the two possible associations between S2 and the behavioral response (a go response when S2 was A and a nogo response when it was B), and then make the appropriate response after identifying S2. Or they could hold in WMterm the frequency of S1, compare it with that of S2, and then make a go or a nogo response depending on whether S2 was identical to or different from S1, respectively. Irrespective of the strategies subjects utilized, WMterm was involved during the delayterm between S1 and S2 in each trial. The random presentation of the four sequences, AA, AB, BA and BB, prevented the predictability of the sequence in the upcoming trial and, consequently, the information required for making a correct response needed to be stored and updated on a trial-by-trial basis, that is, in WMterm on a scale of seconds ( Goldman-Rakic, 1995 ). Because more information needed to be stored during the delayterm in trials when S1 was A than when it was B, the WM loadterm·var during delayterm was higher in the former than in the latter trials. In the following, these trials are referred to as high- and low-WM-load trials, respectively.

If AC were involved in WMterm, there should be differences in AC activityterm during the delayterm between high- and low-WM-load trials, but the presence of such differences would not allow the conclusion that they are due to differences in WM loadterm·var. They could also result from differences in WM-unrelated activityterm that is evoked by S1 but outlasts it ( late activityterm·def ) or from differences related to preparing for a possibly required go response within a limited time window after S2 in high-WM-load trials rather than for the nogo response in low-WM-load trials ( preparatory activityterm·def ). To distinguish between these possibilities, the same subjects also performed taskterm 3 on the same stimuli and used the same finger as in taskterm 1 ( Figure 1c ). Taskterm 3 was a delayed-response taskterm in which subjects had to hold in WMterm that, irrespective of the identity of S2, a go response after S2 was required when S1 was A and a nogo response was required when S1 was B. If differences in activityterm during the delayterm in taskterm 1 were only due to differences in late activityterm·def or in preparatory activityterm·def, then the differences in activityterm between the high- and low-WM-load trials in taskterm 1 should be very similar to the differences between the go and nogo trials in taskterm 3. If, however, such a similarity is not observed, then the differences in activityterm during the delayterm in taskterm 1 (and by inference taskterm 2) are at least partly due to differences in WM loadterm·var.

Subjects performed tasksterm 1, 2 and 3 in three separate blocks within one experimental session of MEGterm·mth measurements. Each block consisted of 240 trials (60 trials for each sequence) and lasted ~20 min. The order of the blocks was randomized across subjects. The frequencies of tonesterm A and B were 1.5 and 1.6 kHz, respectively. The toneterm duration was 100 ms and the stimulus-onset interval 2000 ms. Neural activityterm in AC was estimated by determining the strengths of two regional sources. They were seeded in the left and right AC, respectively, based on the anatomical MR image of each subject’s brain. To obtain the grand mean source waveforms, individual source waveforms were geometrically averaged across subjectsterm·stat, for reasons given elsewhere ( KönigpersName et al., 2015 ; MatysiakpersName et al., 2013 ; Zacharias et al., 2011 ). Source strengths were analyzed for correct trials (∼90%) only.

We found neural activityterm in the human AC that was related to WMterm. Figure 2a and b show that in taskterm 1, the strength of the regional sources seeded in the left and right AC increased in response to S1 and, throughout the entire delayterm, remained at a level higher than that before S1 (p<0.001, permutation testterm·stat). In addition, sources in the left and right AC were significantly stronger in high- (thick orange traces) than in low-WM-load trials (thin blue traces) during the final 500 ms of the delayterm (p<0.05 with Bonferroni correction for multiple comparisonsterm·stat, permutation testterm·stat; for p-values, see the numbers above the abscissae in Figure 2a,b ). In contrast, the peak source strengths around the time of the M100, the most prominent component of the early responses evoked by the two different S1s, were very similar in the high- and low-WM-load trials. Similar results were obtained in taskterm 2 ( Figure 2c,d ) where the sounds were presented to the opposite ear and subjects used the opposite index finger for the go responses, although the differences between the high- and low-WM-load trials during the final 500 ms of the delayterm were only significant at the level of 0.05 without Bonferroni correction.

Figure 2 Download asset Open asset Working-memory related neural activityterm in human auditory cortexterm identified in study 1.

Strengths of the sources in AC in taskterm 1 ( a , b ), taskterm 2 ( c , d ) and taskterm 3 ( e , f ). Data from the left and right hemisphere are shown in the left and right column, respectively. Each trace represents the grand geometric meanterm·stat source waveform derived from MEGterm·mth recordings in 12 subjects. Thick orange and thin blue traces in a–d represent source strengths in high- and low-WM-load trials, respectively. Thin orange and thin blue traces in e–f represent source strengths in go and nogo trials, respectively. The empty bars on the abscissae represent the timing and duration of S1 and S2. The numbers on the abscissae are the p-values of permutation teststerm·stat for the ratios of the source strength in high- to that in low-WM-load trials or in go to that in nogo trials during the three 500-ms periods of the delayterm. Note that the ordinates have logarithmic scaling. Also note that the differences in the left and right AC in tasksterm 1 and 2 reflect differences in WM loadterm·var.

https://doi.org/10.7554/eLife.15441.006 It is likely that the differences in source strength between the high- and low-WM-load trials in tasksterm 1 and 2 were due to differences in WM loadterm·var but presumably not due to differences in preparatory activityterm·def or late activityterm·def. This conclusion is supported by the results of taskterm 3 ( Figure 2e,f ). In this taskterm, differences between go and nogo trials were not significant (p>0.05, permutation testterm·stat), neither in the left AC ( Figure 2e ) nor in the right AC ( Figure 2f ).

Study 2 aimed at finding independent support for the existence of WM-related activityterm in the human AC with new subjects and with an alternative approach that allowed the direct demonstration of WM-related activityterm by comparing trials that differed only in WM loadterm·var and not in other characteristics including frequencies of S1 and preparationterm for motor responses. We contrasted a conventional delayed-match-to-sample taskterm (taskterm 4) with a sound-discrimination taskterm (taskterm 5). In taskterm 4 ( Figure 1d ), tonesterm of three different frequencies (A, B and C) were used as S1 and S2. A go response was required when the frequencies of S1 and S2 were identical and a nogo response otherwise. Subjects could solve this taskterm by using similar strategies as in tasksterm 1 and 2 of study 1 for trials starting with A. All trials in taskterm 4 constituted high-WM-load trials and a third of them required go responses. Taskterm 5 ( Figure 1e ) required a go response when S2 had a specific identity and a nogo response otherwise, irrespective of the identity of S1. This specific S2 occurred on one third of the trials so that the proportions of go and nogo trials and, thus, the proportion of trials on which subject prepared for one or the other motor responses, were the same as in taskterm 4. The WM loadterm·var during the delayterm between S1 and S2 in taskterm 5 was lower than that in taskterm 4 and therefore all trials in taskterm 5 were considered to be low-WM-load trials. For S1 in taskterm 5, the same tonesterm A, B and C were used as in taskterm 4. Unlike in taskterm 4, acoustic stimuli different from pure tonesterm were used as S2: a noise burst (D), a click train (E), and a frequency-modulated toneterm (F). Subjects were instructed to make a go response when S2 was a noise burst (AD, BD and CD).

In both tasksterm, the frequencies of A, B and C were set to 1.5, 0.789 and 2.85 kHz, respectively. Subjects performed tasksterm 4 and 5 in separate blocks within one experimental session. Each block consisted of 360 trials (40 trials for each of the nine sequences) and lasted ∼30 min. The order of the blocks was randomized across subjects. Source strengths were computed for correct trials (∼95%) only.

We found WM-related activityterm in both the left and right AC by comparing the source strengths during the delayterm in tasksterm 4 and 5. Figure 3 shows, for each hemisphere, the grand mean source waveforms from all trials in taskterm 4 (thick orange traces) and taskterm 5 (thin blue traces). As in study 1, activityterm in AC was stronger than before S1 throughout the entire delayterm (p<0.001, permutation testterm·stat) in both the delayed-match-to-sample (taskterm 4) and the sound-discrimination taskterm (taskterm 5). In addition, in both ACs, the sources were stronger in taskterm 4 than in taskterm 5 during the entire delayterm, being significant during many portions of the delayterm (p<0.05 with Bonferroni correction, permutation testterm·stat; for p values, see the numbers above the abscissae). In contrast, the peak source strengths during the M100 component evoked by S1 were very similar in the two tasksterm. This suggests that levels of general attention in tasksterm 4 and 5 were similar during the presentation of S1 because the peak amplitude of the M100 component is strongly modulated by attention ( Kauramäki et al., 2012 ; Woldorff et al., 1993 ). Because tasksterm 4 and 5 differed in WM loadterm·var, but not in S1 or in the proportions of go and nogo trials, we conclude that the differences in source strengths during the delayterm between the two tasksterm were due to differences in WM loadterm·var and that activityterm in both ACs can be related to WMterm.

Figure 3 Download asset Open asset Working-memory related neural activityterm in human auditory cortexterm identified in study 2.

Sources in the left ( a ) and right ( b ) AC were stronger in taskterm 4 in which WM loadterm·var was high (thick orange traces) than in taskterm 5 in which WM loadterm·var was low (thin blue traces). Each trace represents the grand geometric meanterm·stat source waveform across 15 subjects. Other conventions as in Figure 2 .

https://doi.org/10.7554/eLife.15441.007 In order to search for WM-related activityterm in core fields of AC at the level of single neurons and small groups of neurons and also in a different species, two monkeysterm (L and C) were each trained to perform two different tasksterm (tasksterm 1 and 6) on the same set of stimuli. Taskterm 1 was equivalent to taskterm 1 performed by the human subjects. It required a go response (a bar release) for the sequence AA and a nogo response (withholding a bar release) for the other three sequences AB, BA and BB ( Figure 1a ). Our analyses of the behavioral data ( Figure 1—figure supplement 2 ) indicated that the monkeysterm solved the taskterm utilizing WMterm and not solely by means of other strategies. Therefore, the WM loadterm·var during the delayterm was higher in trials when S1 was A than when it was B (for detailed reasoning, see the second paragraph in Section Experimental overview and rationale).

To rule out differences in late activityterm·def evoked by S1, or preparatory activityterm·def for the behavioral responses as potential confounds (see Section Experimental overview and rationale), the two monkeysterm also performed taskterm 6, which was equivalent to taskterm 1 except that the WM loadsterm·var of the A and B tonesterm were reversed. Taskterm 6 required a go response for the sequence BB and a nogo response for the other three sequences ( Figure 1b ). We contrasted taskterm 1 with taskterm 6, rather than with a delayed-response taskterm (taskterm 3), because the reversal of WM loadterm·var also allowed addressing the stimulus-specificity of WM-related activityterm. To further examine potential sources of differences in activityterm during the delayterm of high- and low-WM-load trials, the same set of stimuli was also presented to the two monkeysterm while they did not perform the tasksterm ( passive trained condition ). The stimuli were also presented to a third, untrained, monkeyterm ( passive untrained condition ).

As in study 1, we identified WM-related activityterm by comparing activityterm in trials from the same taskterm that started with A versus those that started with B ( within-task comparison ). If, at a given recording site in core fields of AC, differences in activityterm during the delayterm between these trials were only due to late activityterm·def that was evoked by S1 but outlasted it, one would expect very similar differences in taskterm 1, in taskterm 6, and in the passive conditions. If the differences were only due to preparatory activityterm·def, one would expect opposite differences in tasksterm 1 and 6 and no differences in the passive conditions. Therefore, if the differences were observed in only one taskterm but not in the other, they would be due to differences in WM loadterm·var and thus indicate WM-related activityterm. As a second method of identifying WM-related activityterm, we compared trials starting with the same toneterm but taken from different tasksterm ( between-task comparison ). Results of this method ( Figure 4—source data 1 ) were consistent with those of the first method. As a third method of identifying WM-related activityterm, we compared high- and low-WM-load trials with regard to activityterm differences between the delayterm and the baselineterm within the same trial. In addition to WM-related activityterm, this method also revealed how the neuronal activityterm varied in an ongoing trial with WM loadterm·var.

A and B were tonesterm of 3 kHz and 1 kHz, respectively, with a duration of 200 ms. In most experimental sessions, the stimulus-onset interval between S1 and S2 was 1000 ms, resulting in an 800-ms delayterm, defined as the interval from the offset of S1 to the onset of S2. In other sessions, it was 1300 ms, resulting in an 1100-ms delayterm. Within each session a monkeyterm performed tasksterm 1 and 6 in separate, alternating blocks (2–8 per session). A block consisted of ~140 trials and lasted ~25 min. Microelectrode recordings of single-unit and multiunit activityterm and of local field potentials from core fields of the right AC and, later in separate experimental sessions, also from the left prefrontal cortex started once the monkeysterm responded correctly to each sequence in ≥65% of the trials in both tasksterm during 10 consecutive sessions.

The spiketerm rates of many neurons in core fields of AC during the delayterm differed between high- and low-WM-load trials while the monkeysterm performed taskterm 1 or taskterm 6. Figure 4a and b show the average spiketerm rates of two representative multiunits recorded from monkeysterm C and L while they performed taskterm 6 correctly. In both examples, the differences in spiketerm rate emerged shortly after the offset of S1 and persisted until the end of the delayterm. We compared the responses by computing the ratio of the spiketerm rate in high- to that in low-WM-load trials ( high-/low-load spike-rate ratioterm·def ). Comparisons of the responses during the delayterm were limited to the final 500 ms to minimize the potential difference in late activityterm·def that was evoked by the S1s. Permutation teststerm·stat revealed that the spike-rate ratios during the delayterm differed significantly from 1 (p<0.05; green bars on the abscissae in Figure 4a,b ). This was not the case during the 500-ms period directly before S1 ( baselineterm ). The spiketerm rate during the delayterm was higher in the high- than in the low-WM-load trials in the example of Figure 4a and lower in the example of Figure 4b . In the following, we argue that such differences were due to differences in WM loadterm·var.

Figure 4 with 1 supplement see all Download asset Open asset Working-memory related spiketerm activityterm in monkeyterm auditory cortexterm.

( a , b ) The spiketerm rates of two representative multiunits recorded in AC while the monkeysterm performed taskterm 6 correctly. Thick and thin traces represent spiketerm rates in high- and low-WM-load trials, respectively. S1 was 1 kHz (blue) in high-WM-load trials and 3 kHz (orange) in low-WM-load trials. The green bars on the abscissae represent the final 500 ms of the delayterm during which the high-/low-load spike-rate ratiosterm·def differed significantly from 1. In the example in panel a, the spiketerm rate is higher, and in the example in panel b lower, in high- than in low-WM-load trials. The empty bars on the abscissae represent the timing and duration of S1 and S2. ( c ) The high-/low-load spike-rate ratiosterm·def during the final 500 ms of the delayterm (green) and during the 500 ms directly before S1 ( baselineterm ; black) for all multiunits. Significant ratios are marked by dots. The proportions of units with significant ratios are also provided. Left and right panels show the ratios obtained when the monkeysterm performed taskterm 1 and taskterm 6 correctly. The horizontal dashed lines mark the median cumulative probability and the vertical dashed lines the ratio of 1. ( d ) Taskterm and stimulusterm specificity of high-/low-load spike-rate ratiosterm·def during the final 500 ms of the delayterm. Ratios obtained from a given unit in tasksterm 1 and 6 are connected by a line. Spike-rate ratios which are significantly different from 1 are represented by green dots, those which are not by gray dots. The left, middle, and right panels show the data from all units where the ratios were significantly different from 1 in taskterm 1 only, in taskterm 6 only, or in tasksterm 1 and 6, respectively. The 65% of units with significant ratios in one taskterm only are considered to be involved in WMterm. The proportions and the numbers of units in the three groups are also provided. ( e ) Dependence of the occurrence of significant high-/low-load spike-rate ratiosterm·def on the behavioral context. The green bars show the probability of units with significant ratios during the delayterm of correct trials, of error trials, and during passive conditions in the two trained monkeysterm and in the untrained monkeyterm. The black bar shows the probability of units with significant ratios during the baselineterm of correct trials. The asterisks indicate significant differences between the conditions (chi-square test, one-tailed, p<0.001). ( f ) The mean spiketerm rates in high- (thick traces) and low-WM-load trials (thin traces) for units with high-/low-load spike-rate ratiosterm·def significantly >1 (left panel) or <1 (right panel). They were obtained by geometric averaging after normalizing each response to baselineterm. ( g ) Dominance of delayterm enhancement in AC. Distributions of the delayterm/baselineterm spike-rate ratios are shown separately for high- and low-WM-load trials and for taskterm 1 and taskterm 6. Ratios >1 represent delayterm enhancement and ratios <1 delayterm suppression. Significant ratios are marked by dots. Analyses were performed only on units with significant high-/low-load spike-rate ratiosterm·def during the final 500 ms of the delayterm (panel c ). The proportions of units with significant delayterm/baselineterm spike-rate ratios are also provided for high- and low-WM-load trials, respectively.

https://doi.org/10.7554/eLife.15441.008 Figure 4—source data 1 Working-memory related neuronal activityterm identified by between-task comparison.

The table shows numbers and percentages (in brackets) of recording sites where the neuronal activityterm during the final 500 ms of the delayterm was significantly different in high- and low-WM-load trials with the same S1s (p<0.05, permutation testterm·stat). Numbers of sites with significant differences in spiketerm activityterm in core fields of AC, in the LFPterm in core fields of AC, and in spiketerm activityterm in vlPFCterm are presented in rows. Numbers of sites with a significant difference only when S1 was A (toneterm A only), only when S1 was B (toneterm B only), or when S1 was A and when it was B (tonesterm A and B), are presented in separate columns. The total number of recording sites was 307 for spiketerm activityterm in core fields of AC, 310 for the LFPterm in core fields of AC, and 238 for spiketerm activityterm in vlPFCterm.

https://doi.org/10.7554/eLife.15441.009 Download elife-15441-fig4-data1-v2.xlsx Figure 4c shows the high-/low-load spike-rate ratiosterm·def for all multiunits (124 in monkeyterm L and 183 in monkeyterm C) from the trials in which the two monkeysterm performed the two tasksterm correctly. The ratios ranged from 0.75 to 1.9. Ratios significantly different from 1 ( significant ratios ) were obtained from 27.4% and 34.5% of the multiunits during the delayterm in tasksterm 1 and 6, respectively (green dots), with the majority of ratios >1 (71 of 84 in taskterm 1; 99 of 106 in taskterm 6). These proportions are higher than those expected by chance with a criterion of p<0.05 used in the permutation testterm·stat and higher than the proportions of significant high-/low-load spike-rate ratiosterm·def during the baselineterm (black dots in Figure 4c ; ∼6.0%; p<0.001, chi-square test, one-tailed). Significant ratios during the delayterm were observed both in multiunits responding to the two S1s and in multiunits not responding to the two S1s, but more frequently in the former populationterm (28.9% vs. 21.3% in taskterm 1; 38.2% vs. 19.7% in taskterm 6). Similar findings were obtained from the 152 single units isolated from the multiunit recordings, although significant high-/low-load spike-rate ratiosterm·def during the delayterm were observed less often (15.1% in taskterm 1; 10.5% in taskterm 6; Figure 4—figure supplement 1 ).

Comparison of the responses in the two tasksterm revealed that the differences in the spiketerm rate during the delayterm of high- and low-WM-load trials could not simply be explained by differences in late activityterm·def evoked by S1 with frequencies A and B or by differences in preparatory activityterm·def for the behavioral responses. Significant high-/low-load spike-rate ratiosterm·def occurred in only one of the two tasksterm in the majority of the units and in both tasksterm in the minority of the units (65% vs. 35% for multiunits and 92% vs. 8% for single units; Figure 4d and Figure 4—figure supplement 1b ). These proportions were significantly different from those expected if the significant spike-rate ratios were only due to differences in late or preparatory activityterm·def (i.e., 0% vs. 100%; p<0.001, chi-square test, one-tailed). The occurrence of significant spike-rate ratios in only one of the two tasksterm was related to the units’ best frequencies (for details of the assessment of the best frequency, see Sections Behavioral paradigms and Electrophysiological recordings and data analysis). Significant spike-rate ratios only in taskterm 1 occurred more frequently in units with a best frequency of ∼3 kHz (2.67–3.37 kHz) than in other units (26.7% vs. 9.4%; p<0.05, chi-square test, one-tailed). Significant spike-rate ratios only in taskterm 6 occurred more frequently in units with a best frequency of ∼1 kHz (0.89–1.12 kHz) than in other units (30.1% vs. 15.4%; p<0.05). Tasksterm 1 and 6 had reversed WM loadsterm·var for sequences starting with A and with B. Therefore, the taskterm specificity of the occurrence of significant spike-rate ratios in most units is equivalent to a stimulusterm specificity, that is, the differences emerged only when either A or B (or the S2-response associations) had to be held in WMterm. We thus argue that in the majority of units, those displaying the taskterm and stimulusterm specificity, the differences in the spiketerm rate during the delayterm of high- and low-WM-load trials were due to differences in WM loadterm·var. Thus, 30.0% (92/307) of the multiunits and 21.7% (33/152) of the single units in core fields of AC were considered to exhibit WM-related spiketerm activityterm.

In summary, task-relevant information is stored in core fields of AC by temporary changes of the spiketerm rates of specific neurons. For the majority of multiunits, spiketerm rates increased with increasing WM loadterm·var (yielding ratios >1) and for the minority spiketerm rates decreased with increasing WM loadterm·var (yielding ratios <1; Figure 4c,d ). To scrutinize the responses of the two sub-populations, we averaged the responses from the units with significant ratios >1 or <1 separately for high- and low-WM-load trials. These analyses revealed that in both sub-populations the differences in spiketerm activityterm between high- and low-WM-load trials persisted throughout the delayterm ( Figure 4f ).

The following observations also exclude the possibility that the differences in the spiketerm rate during the delayterm between high- and low-WM-load trials could simply be explained by differences in late activityterm·def evoked by S1 with frequencies A or B. In both tasksterm, significant high-/low-load spike-rate ratiosterm·def during the delayterm were observed more often (p<0.001, chi-square test, one-tailed) in multiunit recordings obtained when the monkeysterm performed the tasksterm correctly (27.4% in taskterm 1 and 34.5% in taskterm 6) than when they made errors (9.8% and 9.5%) or in the responses to the corresponding sequences recorded when the monkeysterm did not perform the tasksterm (5.5% and 7.5%; Figure 4e ). Significant ratios were also rarely observed in the responses to the corresponding sequences recorded in the untrained naive monkeyterm (0.0% and 3.5%; Figure 4e ). Furthermore, in additional 15 sessions, monkeyterm L performed tasksterm 1 and 6 also on sequences where S1 and S2 were separated by the longer delayterm of 1100 ms. Significant ratios during the final 500 ms of the 1100-ms delayterm (23.2% and 18.8% of 69 recordings in tasksterm 1 and 6) were observed slightly, but not significantly more frequently (p>0.05 for tasksterm 1 and 6, chi-square test, one-tailed) than during the final 500 ms of the 800-ms delayterm (17.7% and 15.3% of 124 recordings). This result is inconsistent with that expected if significant ratios are due to differences in late activityterm·def because such differences would be expected to decay over time.

Two ways of temporarily storing task-relevant information by cortical neurons have been identified: persistentterm enhancement or suppression of the spiketerm rate during the delayterm relative to baselineterm ( Fuster and Jervey, 1982 ; Goldman-Rakic, 1995 ; Wilson et al., 1994 ; Zhou and Fuster, 1996 ). We also observed both persistentterm enhancement and suppression in core fields of AC but enhancement clearly dominated in both tasksterm, and most prominently in high-WM-load trials. This was demonstrated by computing the ratio between the spike rate during the final 500 ms of the delayterm·var and that during the 500-ms period before S1 ( delayterm/baselineterm spike-rate ratio ). Delayterm/baselineterm spike-rate ratios >1 represent enhancement and ratios <1 suppression. Figure 4g shows these ratios, separately for high- and low-WM-load trials, for each of the multiunits with significant high-/low-load spike-rate ratiosterm·def during the delayterm (84 in taskterm 1 and 106 in taskterm 6; see above). Delayterm/baselineterm spike-rate ratios ranged from 0.5 to 1.6, with the majority of significant ratios occurring in high-WM-load trials (65 of 84 in taskterm 1 and 77 of 106 in taskterm 6) and the minority in low-WM-load trials (21 of 84 in taskterm 1 and 13 of 106 in taskterm 6). In both high- and low-WM-load trials, and in both tasksterm, the majority of the significant delayterm/baselineterm ratios were >1 ( Figure 4g ).

We asked whether WM-related spiketerm activityterm arose from local processing within core fields of AC or whether other brain structures contributed to it. For this purpose, we analyzed LFPsterm in core fields of AC, which were simultaneously recorded along with the spiketerm activityterm in core fields of AC, and spiketerm activityterm in PFC, which was recorded in separate sessions in monkeyterm C. LFPsterm in core fields of AC partly reflect synaptic inputs to core fields of AC, and PFC has been hypothesized to exert a top-down modulation of activityterm in sensory cortexterm to aid storageterm of information ( Sreenivasan et al., 2014 ).

During the delayterm, differences in WM loadterm·var were also reflected in the LFPsterm recorded in core fields of AC. Figure 5a and b show two representative sites where the LFPsterm during the final 500 ms of the delayterm of high- and low-WM-load trials were significantly different when the monkeysterm performed taskterm 1 correctly. These differences emerged shortly after S1 and persisted until the end of the delayterm. LFPterm differences were computed by subtracting the amplitude in the low-WM-load trials from that in the high-WM-load trials ( high- – low-load LFPterm difference ). Figure 5c shows the distributions of these differences during the final 500 ms of the 800-ms delayterm (green dots and traces) and during baselineterm (black dots and traces) for all 310 sites from both monkeysterm combined. Similar to the results obtained with spiketerm activityterm, significant LFPterm differences occurred more frequently during the delayterm than during baselineterm (p<0.001, chi-square test, one-tailed). The frequency of occurrence of significant LFPterm differences during the delayterm also depended on the behavioral context, being significantly higher (p<0.001, chi-square test, one-tailed) when the monkeysterm performed the tasksterm correctly (25.5% and 27.1% in tasksterm 1 and 6, respectively) than when they made errors (13.5% and 7.9%) or than in the responses to the corresponding sequences when the monkeysterm did not perform the tasksterm in the passive condition (8.3% and 9.3%; Figure 5e ). Significant LFPterm differences during the delayterm were also observed more often when the monkeysterm performed the tasksterm correctly than in the responses to the corresponding sequences in the untrained naive monkeyterm (3.5% and 1.7%; Figure 5e ). Also mirroring the spiketerm activityterm, significant LFPterm differences occurred in only one of the two tasksterm at the majority of sites (107 of the 135 sites with a significant LFPterm difference in one or both tasksterm; 79%) and in both tasksterm at the minority of sites (28 of 135; 21%; Figure 5d ). Based on these results, we conclude that at 107 of 310 sites (34.5%) the LFPterm differences between high- and low-WM-load trials were due to differences in WM loadterm·var. Thus, task-relevant information is also stored in the synaptic potentials in core fields of AC. As with spiketerm activityterm, we found that LFPsterm were persistently different during the delayterm, such that at some sites LFPsterm were more positive in high- than in low-WM-load trials (left panel in Figure 5f ), and at other sites more negative (right panel in Figure 5f ). Both effects were observed at many sites ( Figure 5c,d ), unlike in the case of the spiketerm activityterm where increases in spiketerm rate with WM loadterm·var were much more common than decreases ( Figure 4c,d ).

Figure 5 with 1 supplement see all Download asset Open asset Working-memory related potentials in monkeyterm auditory cortexterm.

( a , b ) Representative local field potentials (LFPsterm) recorded at two different sites in AC of the two monkeysterm while they performed taskterm 1 correctly. The organization of the figure and other conventions are equivalent to those of Figure 4 . LFPsterm were compared by subtracting the LFPterm amplitude in low-WM-load trials from that in high-WM-load trials ( high- – low-load LFPterm difference ). In panel g, LFPsterm were compared by subtracting the mean LFPterm amplitude during the 500-ms period before S1 ( baselineterm ) from that during the final 500 ms of the delayterm ( delayterm–baselineterm LFPterm difference ).

https://doi.org/10.7554/eLife.15441.011 Another difference between spiketerm activityterm and LFPsterm was revealed when we examined how LFPsterm during the delayterm differed from baselineterm. Figure 5g plots the delayterm–baselineterm LFPterm differences , defined as the differences between the mean LFPterm amplitude during the final 500 ms of the delayterm and that during the 500-ms period before S1. This measure was calculated for the 79 sites in taskterm 1 and the 84 sites in taskterm 6 with a significant high- – low-load LFPterm difference. In both tasksterm, significant delayterm–baselineterm LFPterm differences occurred at many of these sites, both in high- and in low-WM-load trials (60.8% and 58.3%, respectively, in taskterm 1; 53.6% and 73.9% in taskterm 6). Figure 5g shows that negative delayterm–baselineterm LFPterm differences prevailed over positive ones in both high- (31.7% vs 29.1% in taskterm 1; 41.7% vs 11.9% in taskterm 6) and low-WM-load trials (38.0% vs 20.3% in taskterm 1; 69.1% vs 4.8% in taskterm 6). At many of the 79 sites in taskterm 1 (41.8%) and the 84 sites in taskterm 6 (67.9%), LFPsterm during the delayterm changed more strongly relative to baselineterm in low- than in high-WM-load trials. It is conceivable that the LFPsterm at these sites still have the more common relationship to WM loadterm·var, i.e., change more strongly in high- than in low-WM-load trials. This relationship could be disguised by other mental processes if they had a large effect on LFPsterm in the direction opposite to that of WM loadterm·var ( Figure 5—figure supplement 1 ). This conjecture also fits the observation that there was a very small number of sites at which the LFPsterm during the delayterm changed in opposite directions in high- and low-WM-load trials.

Because differences in WM loadterm·var were reflected in LFPsterm, it is possible that synaptic inputs to core fields of AC contribute to the differences in spiketerm activityterm between high- and low-WM-load trials in core fields of AC. To clarify whether some of these inputs arise from PFC, we also recorded spiketerm activityterm from the ventrolateral part of PFC (vlPFCterm) in monkeyterm C ( Figure 1—figure supplement 3 ). This part of PFC has been shown to play an essential role in auditory WMterm tasksterm ( Hwang and Romanski, 2015 ; Plakke and Romanski, 2014 ; Plakke et al., 2015 ). We analyzed 238 multiunits recorded during 70 sessions in which the monkeyterm performed tasksterm 1 and 6. Like in core fields of AC, neurons in vlPFCterm could discharge significantly more spikesterm (p<0.05, permutation testterm·stat) during the delayterm of high- than of low-WM-load trials ( Figure 6a ) or vice versa ( Figure 6b ). We observed that, compared to core fields of AC, a similar proportion of multiunits in vlPFCterm displayed high-/low-load spike-rate ratiosterm·def during the delayterm differing significantly from 1. Also, there was a similar dependence of the occurrence of significant high-/low-load spike-rate ratiosterm·def on the behavioral context (cf. Figures 6c and 4c , Figures 6e and 4e ), and a similar taskterm or stimulusterm specificity (cf. Figures 6d and 4d ). These results indicate WM-related activityterm also in vlPFCterm. Unlike in core fields of AC, where overall enhancement during the delayterm dominated and spiketerm rates predominantly changed in high-WM-load trials ( Figure 4f,g ), in vlPFCterm suppression was as common as, or even more common than, enhancement during the delayterm ( Figure 6d,f,g ). Spiketerm rates also frequently changed in low-WM-load trials, and sometimes even more strongly than in high-WM-load trials ( Figure 6f,g ). The conjecture in Figure 5—figure supplement 1 fits this finding.

Figure 6 Download asset Open asset Working-memory related spiketerm activityterm in monkeyterm ventrolateral prefrontal cortex.

( a , b ) The spiketerm rates of two representative multiunits recorded in vlPFCterm from monkeyterm C while he performed taskterm 6 correctly. The organization of the figure and all other conventions are identical to those of Figure 4 .

https://doi.org/10.7554/eLife.15441.013 Core fields of AC and vlPFCterm also differed in the time course of WM-related spiketerm activityterm (cf. Figure 4f and Figure 6f ). This issue was quantified by determining for each multiunit the earliest time, relative to the offset of S1, at which the spiketerm rates during the delayterm of high- and low-WM-load trials differed significantly ( differential latencyterm·def ; permutation testterm·stat, p<0.05, two-tailed). This analysis was conducted in consecutive 10-ms bins and restricted to those multiunits with significant high-/low-load spike-rate ratiosterm·def during the delayterm. Figure 7 shows the distributions of the differential latencyterm·def of units in core fields of AC (dotted traces) and in vlPFCterm (solid traces). In both tasksterm, differential latencies were significantly shorter in core fields of AC than in vlPFCterm, with a median difference of ∼100 ms (p<0.001, permutation testterm·stat, two-tailed). This finding is difficult to reconcile with the notion that PFC exerts a top-down modulation of activityterm in sensory cortexterm ( Sreenivasan et al., 2014 ) and thus triggers the storageterm of information in core fields of AC on a trial-by-trial basis.

Figure 7 Download asset Open asset Working-memory related spiketerm activityterm emerges earlier in auditory cortexterm than in prefrontal cortex.

Differential latencies (see Results for definition) of multiunits recorded in AC (dashed traces) and vlPFCterm (solid traces) in tasksterm 1 ( a ) and 6 ( b ). The dashed horizontal lines mark the median cumulative probability. Data were obtained from monkeyterm C.

https://doi.org/10.7554/eLife.15441.014 The current report provides converging evidence from humans and monkeysterm that neural activityterm in AC can reflect WMterm. This was demonstrated in three independent studies in which subjects performed different tasksterm on the same sequences of sounds, allowing separation of activityterm related to WMterm from activityterm related to other mental processes. Changes in WM loadterm·var resulted in persistentterm changes of spiking activityterm, LFPsterm and magnetic fields, suggesting that AC stores information in spikesterm and synaptic potentials. Our results thus support the idea that early sensory cortexterm stores information for WMterm ( Sreenivasan et al., 2014 ).

An auditory taskterm·def has been defined as an operation requiring the production of an intentional connection between sounds, other context-relevant stimuli, and behavioral actions, which are executed because subjects are motivated to do so ( Scheich and BroschpersName, 2012 ). Activityterm in AC is not only related to processing the sounds and the other context-relevant stimuli but also to mental processes associated with performing a taskterm such as selection of and preparationterm for behavioral actions, and expectationterm of rewards ( BroschpersName et al., 2011 ). In the current report, we confirmed that activityterm in AC can persistently change for several seconds relative to baselineterm in a sound-discrimination taskterm (taskterm 5; Figure 3 ). Thus, persistentterm changes of neural activityterm observed during the performance of WMterm tasksterm in previous studies are, on their own, not sufficient for demonstrating that AC is involved in WMterm ( Bigelow et al., 2014 ; Gottlieb et al., 1989 ; Grimault et al., 2014 ; Kumar et al., 2016 ; Lemus et al., 2009 ; Linke and Cusack, 2015 ; Linke et al., 2011 ; Ng et al., 2014 ; Nolden et al., 2013 ; Scott et al., 2014 ).

In the current report, we used three approaches to show that at least some portion of persistentterm changes in activityterm in AC was related to WMterm and not to other concurrent mental processes. In the first approach (study 1), we compared differences in activityterm between trials that differed in WM loadterm·var in one taskterm with differences in activityterm between trials that did not differ in WM loadterm·var in another taskterm. In the second approach (study 3), we compared differences in activityterm between trials that differed in WM loadterm·var in one taskterm with differences in activityterm between trials that also differed in WM loadterm·var, but in a reversed fashion, in another taskterm. This further enabled the determination of the stimulus-specificity of the effect of WM loadterm·var. In the third approach (study 2), we directly compared activityterm in trials which differed only in WM loadterm·var. Our approaches have the potential to single out other types of WM-related activityterm including rhythmic activities and spatiotemporal activation patterns and could be used in other sensory modalities. They can also be adapted to tease out activityterm related to other cognitive and motivational processes. However, note that the second approach requires stimulus-specificity of the neural activityterm under consideration. It may be difficult to demonstrate such stimulus-specificity in gross measures of the electrical activityterm of large neural populationsterm, such as EEG and MEGterm·mth.

Our converging results from intracranial recordings in monkeysterm and extracranial recordings in humans demonstrated that information is temporarily stored in AC by persistentterm changes in spiketerm rates and slow potentials. This was reflected in enhancement or suppression of spiketerm rates, positive or negative changes of LFPsterm, and increases in the strengths of regional sourcesterm·var during the delayterm relative to baselineterm. These ways of storing information in AC are similar to those considered for visual ( Fuster and Jervey, 1982 ), somatosensory ( Zhou and Fuster, 1996 ), and prefrontal cortex ( Goldman-Rakic, 1995 ; Reinhart et al., 2012 ; Wilson et al., 1994 ). Enhancement of activityterm in AC during the delayterm may serve to refresh representations of information to keep them active while suppression of activityterm may serve to protect representations from internal or external interference ( Linke et al., 2011 ; Scott et al., 2014 ). The latter has been found to be particularly relevant when recoding and rehearsal strategies were not used by subjects ( Linke et al., 2011 ). Information could also be stored in AC in other ways ( Stokes, 2015 ), such as in spatiotemporal activation patterns, synaptic plasticity, or rhythmic activities, but these were not analyzed here.

Although our approaches revealed sites where neural activityterm was related to WMterm, it is likely that other mental processes contribute to the persistentterm changes in activityterm during the delayterm relative to baselineterm. In the spiketerm activityterm, effects of other mental processes seem to be relatively small, because the spiketerm rate barely changed during the delayterm relative to baselineterm in low-WM-load trials ( Figure 4f,g ). In the LFPsterm, effects of other mental processes seem larger, because at many sites LFPsterm changed during the delayterm even in low-WM-load trials ( Figure 5g ). This is consistent with the large increase of source strength during the delayterm relative to baselineterm in our MEGterm·mth studies, which was only slightly smaller in low- than in high-WM-load trials ( Figures 2 , 3 ). LFPsterm and magnetic fields have been considered to partially reflect inputs to a region of tissue, in contrast to spiketerm activityterm, which has been considered to preferentially reflect output to connected areas ( Buzsáki, 2004 ; Buzsáki et al., 2012 ; Hämäläinen et al., 1993 ). Our observations that mental processes other than WMterm were represented differently at the input to and at the output from AC therefore suggest that selective input-output transformations occur in AC such that, at the output (spikesterm), the representation of WMterm is favored over that of other mental processes.

In our experiments, subjects could have used two behavioral strategies to solve the tasksterm which required WMterm. They could have stored either the frequency of S1 during the delayterm or the stimulus-response associations of S2. Which information was stored in an individual trial is difficult to assess and cannot be inferred from the behavioral response in that trial. Therefore, we cannot determine what information is reflected in the neural activityterm in AC. This question can be addressed in future experiments with tasksterm that can only be solved with a single strategyterm. For instance, increasing the number of frequencies for S1 in study 2 would bias the subject to solve the tasksterm by storing the information of the past S1, as tested in previous studies on animals (e.g., Gottlieb et al., 1989 ).

The WM-related activityterm in the core fields of AC, as measured in monkeysterm, was stimulusterm specific, i.e., it was observed only when a specific frequency of S1 (or specific stimulus-response associations of S2) had to be stored in WMterm. Since only two tonesterm (1 and 3 kHz) were tested, the stimulusterm selectivity could not be assessed accurately. Our study confirms the stimulus-specificity of WM-related activityterm in AC revealed in fMRI studies of humans ( Kumar et al., 2016 ; Linke et al., 2011 ) but contrasts with studies of nonhuman primates which reported stimulus-specificity to be missing in AC ( Lemus et al., 2009 ; Scott et al., 2014 ).

Our report provides evidence for the sensory-recruitment hypothesis of WMterm·def ( Sreenivasan et al., 2014 ), which posits that storageterm of information recruits the same neurons in sensory cortexterm that represent the information. This hypothesis has mainly been based on multivariate analysis of fMRI signal patterns in early visual cortex, thus, on a signal that is only indirectly related to brain activityterm ( Logothetis and Wandell, 2004 ), but not on direct measurements of electric activityterm in sensory cortexterm. Our results are also compatible with the static populationterm coding hypothesis for WMterm which posits that storageterm of information recruits neurons without high selectivity ( Sreenivasan et al., 2014 ), because we also observed WM-related activityterm in neurons that did not respond to the two tonesterm (1 and 3 kHz).

Our results imply that single neurons in AC are capable of temporarily storing information by changing their spiketerm rates. Such neurons were found in the core fields of AC, where they comprised 22% of all recorded neurons. It is conceivable that these neurons are spatially organized in clusters preferentially exhibiting either enhancement or suppression during the delayterm. This speculation is consistent with our finding that WM-related activityterm in multiunits was as common as in single units (30% vs. 22%). This would not have been observed if the neurons exhibiting enhancement or suppression were not clustered. Following similar reasoning, spatial clustering may also be present for the slow potentials, otherwise changes of LFPsterm in positive or negative directions would not have been observed during the delayterm.

While recordings of spiketerm activityterm provide clear evidence for WM-related activityterm in the core fields of AC, we cannot clarify whether other fields of AC are also involved in WMterm. Because the slow potentials analyzed here can spread over millimeters, it is possible that synaptic potentials in adjacent AC fields contributed to the WM-related LFPsterm measured in the core fields. An involvement of AC fields outside the core in the performance of WMterm tasksterm has been demonstrated for rostral superior temporal cortex ( Scott et al., 2014 ) and temporal pole ( Ng et al., 2014 ). Our MEGterm·mth studies clarified that activityterm in both the left and right AC was related to WMterm. Involvement of both ACs has been seen in previous studies for simple WMterm tasksterm with other stimuli ( Grimault et al., 2014 ; Kumar et al., 2016 ; Nolden et al., 2013 ), for complex WMterm tasksterm that, e.g., required manipulations of WMterm content ( Brechmann et al., 2007 ), or for WMterm tasksterm in which subjects utilized other strategies, such as articulatory rehearsal ( Linke et al., 2011 ).

Our report also revealed WM-related activityterm in vlPFCterm. Such activityterm is consistent with previous studies showing that vlPFCterm is involved in non-spatial auditory WMterm tasksterm ( Hwang and Romanski, 2015 ; Plakke and Romanski, 2014 ; Plakke et al., 2015 ). Although based on one monkeyterm only, our observation of persistentterm enhancement and suppression of spiketerm rates during the delayterm suggests that vlPFCterm stores information in the same ways as AC. Our observation that WM-related activityterm started ∼100 ms later in vlPFCterm than in AC suggests that vlPFCterm does not trigger the storageterm of information in AC on a trial-by-trial basis. Although PFC has been traditionally hypothesized to exert a top-down modulation to aid storageterm of information in sensory cortexterm ( Curtis and D’Esposito, 2003 ; D’Esposito and Postle, 2015 ; Fuster, 2001 ; Miller and Cohen, 2001 ; Miller et al., 1996 ; Sreenivasan et al., 2014 ), our finding challenges vlPFCterm as the source of top-down signals to auditory cortexterm.

The present study reported a sizable number of neurons in AC with activityterm related to auditory WMterm. The storageterm in AC of information about sounds over several seconds may be an essential component of the analysis of temporal patterns of sounds including speech and communication sounds ( May et al., 2015 ; Petkov and Jarvis, 2012 ). The WM-related persistentterm activityterm changes described here reflect active processes recruited when subjects are engaged in WMterm tasksterm. Because the change of the neural activityterm persisted throughout most of the period during which information needed to be stored, it is conceivable that the memory trace carried by this activityterm remains relatively unaltered throughout the memory period. Whether persistentterm activityterm changes in AC also support the storageterm of information for even longer periods and other WMterm processes, including spatial aspects and processes requiring modifications of stored information, is not clear and is under debate ( Fritz et al., 2005 ; Scott and Mishkin, 2016 ). Because very similar persistentterm WM-related activityterm was observed in our studies in the AC of humans and monkeysterm, the cellular bases of some auditory WMterm processes in humans can well be studied in monkeysterm.

Fifteen subjects (7 males, 8 females, mean age = 26 years) participated in study 1 and twenty-six (14 males, 12 females, mean age = 27 years) in study 2. All subjects gave their written informed consent to participate in the studies which were approved by the ethics committee of the Otto-von-Guericke University, Magdeburg. Data from three subjects in study 1 and from 11 subjects in study 2 were excluded prior to analysis because of strong artifacts or technical problems during measurements. The studies were designed and conducted following the guidelines for basic MEGterm·mth research in the field of neuroscience ( Gross et al., 2013 ).

For study 1, subjects were instructed to perform tasksterm 1, 2 and 3 on an identical set of four two-tone sequences (AA, AB, BA and BB) within the same experimental session. Each trial started with a soft noise burst (a 25-ms burst of white noise with a sound pressure level of 16 dB), accompanied by the appearance of a fixation cross on a screen ∼1 m in front of the subject’s head. One second later, the first toneterm (S1) was presented followed by the second toneterm (S2) two seconds later. Both tonesterm had durations of 100 ms. The delayterm between S1 and S2, defined as the time from the offset of S1 to the onset of S2, was therefore 1.9 s. The frequencies of the two tonesterm were either A (1.5 kHz) or B (1.6 kHz) and their sensation levels were adjusted to 80 dB SL. Stimuli were conducted to the subject’s ear via a plastic tube and ear mold which fitted comfortably into the external ear canal. When a go response was required, as explained below, subjects had to make it within 2 ± 0.25 s after the onset of S2 when the next trial started (see Zacharias et al., 2011 for details of the experimental setup).

For tasksterm 1 and 2 ( Figure 1a ), subjects were instructed to make a go response (i.e., press a response button) when both S1 and S2 were A and a nogo response (no button press) for the other three sequences (AB, BA and BB). Sequences starting with A were high-WM-load trials and those starting with B were low-WM-load trials. Tasksterm 1 and 2 differed only with respect to the ear of stimulation and the hand used to make the response. For taskterm 1, stimuli were conducted to the subject’s left ear and the go response was made with the right index finger, contralateral to the stimulated ear. For taskterm 2, stimuli were conducted to the right ear and the go response was made with the left index finger, also contralateral to the stimulated ear. For taskterm 3 ( Figure 1c ), subjects were instructed to make a go response when S1 was A and a nogo response when S1 was B, irrespective of the frequency of S2. As for taskterm 1, the stimuli were conducted to the left ear and the right index finger was used for the go response. Each subject performed the three tasksterm in three separate blocks. The order of the tasksterm was randomized across subjects.

For study 2, subjects were instructed to perform tasksterm 4 and 5 on two different sets of two-stimulus sequences which differed in S2 but not in S1. All stimuli had durations of 100 ms and were presented at a sound pressure level of 80 dB. Taskterm 4 ( Figure 1d ) was a delayed-match-to-sample taskterm, for which the same three tonesterm were used as S1 and S2 (A, B and C with frequencies of 1.5, 0.789 and 2.85 kHz, respectively). The frequencies of B and C were selected such that the ratios A/B and C/A were equal to 1.9. Subjects had to make a go response when S1 and S2 were identical (AA, BB, CC) and a nogo response when S1 and S2 were different (AB, AC, BA, BC, CA, CB). For taskterm 5 ( Figure 1e ), the same three tonesterm were used as S1, whereas S2 was different. S2 was either a uniform-white-noise burst (D), a toneterm modulated in frequency from 0.789 to 2.850 kHz (E), or a train of 25 clicks of 1-ms duration each and presented at a rate of 250 clicks per second (F). Regardless of S1, subjects had to make a go response when S2 was the noise burst (AD, BD, CD), and a nogo response when S2 was the frequency modulated toneterm or the click train (AE, BE, CE, AF, BF, CF). All sequences in taskterm 4 were high-WM-load trials and those in taskterm 5 were low-WM-load trials.

For both tasksterm, the delayterm between S1 and S2 was 1.9 s. Subjects had to make their go responses within 1.5 s after the onset of S2. Each trial started with a visual cue (a fixation cross). A visual feedback was provided immediately after the subject’s response (a smiling face for a correct response and a frowning face for an incorrect response). The inter-trial interval was 2 s. Stimuli were conducted to the subject’s left ear and go responses were made with the right index finger, contralateral to the stimulated ear. Each subject performed the two tasksterm in two separate blocks within one experimental session. The order of the tasksterm was randomized across subjects.

MEGterm·mth measurements were performed in a magnetically shielded chamber by means of a 248-channel whole-head magnetometer system (4-D Neuroimaging, San Diego, USA). The magnetic field was acquired continuously at a sampling rate of 678.17 Hz in study 1 and of 1017.25 Hz in study 2. Signals were filtered online between DC and 100 Hz in study 1 and between DC and 200 Hz in study 2. Note that high-pass filtering signals at 4 Hz revealed no differences between high- and low-WM-load trials. In addition, vertical and horizontal electrooculograms were recorded to track eye movements and blinks.

For further processing, the raw data were exported into the BESA software package (BESA Software GmbH, Gräfelfing, Germany). We first performed a heartbeat artifact correction ( Ille et al., 2002 ). The corrected signal was then segmented into 3-s epochs, ranging from 500 ms before the onset of S1 to 500 ms after the onset of S2. Epochs contaminated with other artifacts were discarded (for details, see Zacharias et al., 2011 ). For each subject, taskterm and sensor, the remaining artifact-free epochs were band-pass filtered (high-pass DC, low-pass 30 Hz, zero phase, 24 dB/octave). Average waveforms were obtained by arithmetically averaging the signals from epochs of trials with correct responses, separately for each subject, taskterm and sensor. The use of the arithmetic average for this purpose is justified ( Zacharias et al., 2011 ; KönigpersName et al., 2015 ). The average waveforms were then baseline-offset corrected. The value for the baselineterm offset correction was determined from the mean signal amplitudes over the 300-ms interval directly before S1 and was subtracted from the waveform.

The baseline-corrected average waveforms from all sensors formed the input to the source analysis for which each dataset was co-registered with the T1-weighted anatomical MR image (3T, 192 slices) of the subject’s brain using Brainvoyager QX (Brain Innovation B.V., Maastricht, The Netherlands). Separately for each subject and taskterm, we modeled the neuronal sources underlying the magnetic field distribution around the peak of the M100 component evoked by S1 by two regional sources, one in the left and one in the right AC. These regional sources were seeded at the border of Heschl’s gyrus and planum temporale ( Näätänen and Picton, 1987 ). In addition, we seeded three pairs of regional sources roughly symmetrically in left and right central, frontal and occipital cortical areas ( Figure 1—figure supplement 1 ) to model background activityterm, that is, to exclude potential confounds from other brain areas. These three pairs of sources were located close to the cortical surface such that their distances to the source in the AC of the same hemisphere were relatively large. We did so to minimize the mutual influence between the signals of these sources and of the sources in AC because the mutual influence decreases with increasing distance between the sources. In contrast to the positions of the sources, their orientations and amplitudes were fitted to best account for the magnetic field distribution of the average waveforms at all sensors using the spherical volume conductor model as head model ( Scherg, 1990 ). Results of this source analysis are source waveforms, one for each subject and condition. These waveforms were then exported to MATLAB (MathWorks, Natick, MA) for further analysis.

The comparison of grand mean waveforms across tasksterm requires homogeneous variance and a normal distribution of the underlying MEGterm·mth data. We found that this could be achieved by a log-transformation of individual source waveformsterm·stat, consistent with earlier studies ( KönigpersName et al., 2015 ; MatysiakpersName et al., 2013 ; Zacharias et al., 2011 ). To obtain the grand mean waveform, the log-transformed source waveforms were arithmetically averaged across subjects and this mean waveform was then back-transformed (by exponentiation) and plotted along a logarithmic axis.

Statistical analyses were performed by permutation tests.term·stat The significance level was set to 0.05.

Three macaque monkeysterm (Macaca fascicularis) participated in this study. Two of them (monkeysterm C and L) were trained to perform tasksterm 1 and 6, on an identical set of four two-tone sequences (AA, AB, BA and BB) within the same experimental sessions. Taskterm 1 was equivalent to taskterm 1 performed by human subjects. Each trial started with the illumination of a LED, which signaled that the monkeysterm had to grasp a bar within 4 s. Shortly after grasping the bar (1–2s), two tonesterm S1 and S2 were presented. Both tonesterm had durations of 200 ms. The stimulus-onset interval between S1 and S2 was 1000 ms in most sessions, resulting in an 800-ms delayterm, defined as the interval from the offset of S1 to the onset of S2. In other sessions, the stimulus-onset interval between S1 and S2 was 1300 ms, resulting in an 1100-ms delayterm. The frequencies of tonesterm A and B were 3 kHz and 1 kHz, respectively, and their sound pressure level was 67 dB SPL. Each toneterm was presented through two loudspeakers (Karat 720), located on the left and right side of the monkeyterm. When a go response was required, monkeyterm L had to release the bar within 240–1360 ms after the onset of S2, and monkeyterm C within 240–1960 ms. When a nogo response was required, the monkeysterm had to continue holding the bar beyond the end of these time windows. Upon correct responses, the monkeysterm were rewarded with a drop of water (0.2–0.3 ml). The monkeysterm were given at least 5 s for drinking water before the start of the next trial. The monkeysterm performed the tasksterm using their left hand.

For taskterm 1 ( Figure 1a ), the monkeysterm had to make a go response when both S1 and S2 were A and a nogo response for the other three sequences (AB, BA and BB). For taskterm 6 ( Figure 1b ), the monkeysterm had to make a go response when both S1 and S2 were B and a nogo response for the other three sequences (BA, AB and AA). In each experimental session, the monkeysterm performed the two tasksterm in separate, alternating blocks. In each block, trials requiring a go or nogo response were presented in random order, with go responses in ∼60% of the trials and nogo responses in ∼40% of the trials. In taskterm 1, the LED was green and located on the right side. In taskterm 6, the LED was red and located on the left side.

The same set of stimuli was also presented in blocks to the two trained monkeysterm and to a third untrained naïve monkeyterm (monkeyterm M) when they did not perform the tasksterm in the way that the LEDs were not illuminated and the touch bar was not functional. There were blocks in which the AA sequence occurred in ∼60% of the trials and the other three sequences ∼40% of the trials, as in taskterm 1. In other blocks, the BB sequence occurred in ∼60% of the trials and the other three sequences ∼40% of the trials, as in taskterm 6. For the two trained monkeysterm, these passive-condition measurements were made after they had performed the behavioral tasksterm, or on days in which they did not perform the behavioral tasksterm at all.

In an additional passive block, pure tonesterm with 40 different frequencies were presented at ∼60 dB SPL to the monkeysterm to assess each unit’s best frequency. Frequencies were equidistantly spaced on a logarithmic scale over a range of 8 octaves (0.0625–16 kHz). Toneterm duration was 100 ms. Each toneterm was repeated 10 times. All tonesterm were presented in a pseudorandom order with a stimulus-onset interval of 500 ms. These tonesterm were presented before the monkeysterm performed the behavioral tasksterm, or before the passive-condition measurements on days in which the monkeysterm did not perform the tasksterm.

The experiments were approved by the authority for animal care and ethics of the federal state of Saxony-Anhalt (No. 28-42502-2-1129IfN), and conformed to the rules for animal experimentation of the European Communities Council Directive (86/609/EEC).

For simultaneous recordings of action potentials and local field potentials (0.1–140 Hz) from right AC core area (mainly from A1) and left ventrolateral prefrontal cortex (vlPFCterm; Figure 1—figure supplement 3 ), we used a seven-channel microelectrode manipulator (2–2.5 MΩ; for details, see BroschpersName et al., 2005 ). By means of the built-in spiketerm detection tools of the data acquisition systems (threshold crossings and spiketerm duration), we discriminated the action potentials of a few neurons (multiunit) from each electrode and stored the time stamp and the waveform of each action potential. Single unit activityterm was extracted off-line from the multiunit recordings by means of principal component analysisterm·stat. The position of A1 was estimated using the spatial distribution of the units’ best frequencies and the penetration traces of the electrodes, i.e., electrodes passed through parietal cortex into the auditory cortexterm ( BroschpersName et al., 2005 ; Kaas and Hackett, 2000 ). The vlPFCterm was identified by its anatomical location (below the principal sulcus and anterior to the arcuate sulcus; Romanski and Goldman-Rakic, 2002 ) and by the latency and selectivity of neuronal responses for complex auditory stimuli (animal and human vocalizations, natural sounds, man-made sounds, and white noise). This follows the observation of Romanski and Goldman-Rakic (2002) that auditory neurons were tightly clustered in a small region of vlPFCterm outside of which no auditory neurons were found. We also found that neuronal activityterm in the vlPFCterm could be synchronized with that in auditory cortexterm, following the observation of Romanski et al., (1999) that the vlPFCterm reciprocally connected with auditory cortexterm.

Data analysis was conducted off-line using MATLAB (MathWorks, Natick, MA). For each unit or at each site, statistical analyses were performed by permutation teststerm·stat of the neuronal activityterm during the final 500 ms of the delayterm. High-pass filtering the local field potentials at 4 Hz revealed no differences between high- and low-WM-load trials. For each unit, the best frequency was assessed by analyzing the responses to the 40 pure tonesterm as described by BroschpersName et al., (1999) .

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https://doi.org/10.1016/j.brainres.2015.11.032 Google Scholar Author details Ying HuangpersName Special Lab Primate Neurobiology, Leibniz Institute for Neurobiology, Magdeburg, Germany Contribution YH, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents For correspondence Ying.HuangpersName@ifn-magdeburg.de Competing interests The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-6471-8009 Artur MatysiakpersName Special Lab Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, Magdeburg, Germany Contribution AM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article, Contributed unpublished essential data or reagents Competing interests The authors declare that no competing interests exist.

Peter HeilpersName Department Systems Physiology of Learning, Leibniz Institute for Neurobiology, Magdeburg, Germany Center for Behavioral Brain Sciences, Otto-von-Guericke-University, Magdeburg, Germany Contribution PH, Conception and design, Analysis and interpretation of data, Drafting or revising the article Competing interests The authors declare that no competing interests exist.

Reinhard KönigpersName Special Lab Non-Invasive Brain Imaging, Leibniz Institute for Neurobiology, Magdeburg, Germany Contribution RK, Conception and design, Analysis and interpretation of data, Drafting or revising the article Competing interests The authors declare that no competing interests exist.

Michael BroschpersName Special Lab Primate Neurobiology, Leibniz Institute for Neurobiology, Magdeburg, Germany Center for Behavioral Brain Sciences, Otto-von-Guericke-University, Magdeburg, Germany Contribution MB, Conception and design, Analysis and interpretation of data, Drafting or revising the article Competing interests The authors declare that no competing interests exist.

Peter HeilpersName Reinhard KönigpersName Michael BroschpersName Peter HeilpersName Reinhard KönigpersName Michael BroschpersName Peter HeilpersName Michael BroschpersName Peter HeilpersName Reinhard KönigpersName Michael BroschpersName The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

We thank Norman Zacharias, Nadia Abu Eid, and Aida Hajizadeh for assistance in MEGterm·mth data collection and analysis, Cornelia Bucks for technical assistance of monkeyterm experiments, and Drs. André Brechmann, Patrick May, Dexter Irvine, Erich Schröger and Brian Scott for their valuable comments on the manuscript. We also acknowledge the support by an Alexander von Humboldt Polish Honorary Research Scholarship by the Foundation for Polish Science. This research was supported by a LIN Special Project to PH, RK, and MB, and by the Deutsche Forschungsgemeinschaft (He 1721/10-1, He 1721/10-2, and SFB TR31, A4).

Human subjects: All subjects gave their written informed consent to participate in the studies which were approved by the ethics committee of the Otto-von-Guericke University, Magdeburg.

Animal experimentation: The experiments were approved by the authority for animal care and ethics of the federal state of Saxony-Anhalt (No. 28-42502-2-1129IfN), and conformed to the rules for animal experimentation of the European Communities Council Directive (86/609/EEC).

© 2016, HuangpersName et al.

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