Differences in temporal processing speeds between the right and left auditory cortex reflect the strength of recurrent synaptic connectivity — TEI-tagged view

NeophytoupersName, ArribaspersName, ArorapersName, LevypersName, ParkpersName, OviedopersName (2022date). “Differences in temporal processing speeds between the right and left auditory cortex reflect the strength of recurrent synaptic connectivity.” PLOS Biologytitle·j.

Object of study — whether the left and right auditory cortex differ in how long they integrate sound, and whether that difference follows from a difference in recurrent excitatory wiring

Research question — what neural mechanism produces the known asymmetry in temporal integrationterm between the hemispheresterm·var

Contribution — recurrent excitatory connections are stronger in the superficial layersterm of the right auditory cortex, and the network time constantterm·var measured there in awake mice is about 50 percent longer, linking a wiring difference to a timescale difference in the same animal

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Abstract Brain asymmetry in the sensitivity to spectrotemporal modulation is an established functional feature that underlies the perception of speechterm·dom and music. The left auditory cortex (ACx) is believed to specialize in processing fast temporal components of speechterm·dom sounds, and the right ACxterm slower components. However, the circuit features and neural computations behind these lateralized spectrotemporal processes are poorly understood. To answer these mechanistic questions we use mice, an animal model that captures some relevant features of human communication systems. In this study, we screened for circuit features that could subserve temporal integrationterm differences between the left and right ACxterm. We mapped excitatory input to principal neuronsterm in all cortical layersterm and found significantly stronger recurrent connectionsterm·dom in the superficial layersterm of the right ACxterm compared to the left. We hypothesized that the underlying recurrent neural dynamics would exhibit differential characteristic timescales corresponding to their hemispheric specialization. To investigate, we recorded spike trains from awake mice and estimated the network time constantsterm·var using a statistical method to combine evidence from multiple weak signal-to-noiseterm ratio neurons. We found longer temporal integrationterm windows in the superficial layersterm of the right ACxterm compared to the left as predicted by stronger recurrent excitation. Our study shows substantial evidence linking stronger recurrent synaptic connections to longer network timescales. These findings support speechterm·dom processing theories that purport asymmetry in temporal integrationterm is a crucial feature of lateralizationterm in auditory processing.

This study uses the mouse as a model system that captures relevant features of human lateralized processing of speechterm·dom and music, revealing a novel mechanism whereby stronger recurrent activity in the Right Auditory Cortex led to longer temporal integrationterm compared to the Left (consistent with the temporal scales of syllabic and supra-syllabic processing).

Introduction Social communication calls have myriads of constituent sounds that are temporally and spectrally dynamic. The auditory system must have processes in place to quickly decode and encode ethologically relevant features of an auditory signal to elicit an appropriate response. Hemispheric asymmetry in sound processing (i.e., lateralizationterm) has long been proposed to be critical in the dynamic processing of speechterm·dom sounds. Human studies have shown that the left superior temporal gyrus (STGterm) is more capable of integrating information over a shorter timescale and plays a greater role in speechterm·dom perception and phonological processing than the right [ 1 – 3 ]. On the other hand, the right STGterm has longer integration windows to potentially subserve the processing of suprasegmental information [ 4 , 5 ]. The unanswered question remains: What neural mechanisms underlie these differences in temporal integrationterm? One possible mechanism we dissect in this study is interhemispheric differences in excitatory recurrent connectionsterm·dom. This would introduce positive feedback that would counteract the exponential decay of individual neurons within a neural population, leading to effectively slower temporal dynamics with a longer time constantterm.

Studies of animal models can provide more mechanistic insight regarding circuit function. Circuit mapping of the mouse auditory cortex (ACx) has shown that the synaptic organization of Layer 3 (L3) differs between the 2 hemispheresterm·var. In the left ACxterm, principal neuronsterm in L3 receive out-of-column excitatory input from L6 cells located in higher frequency bands, whereas in the right ACxterm, the same pathway has balanced frequency projections across the tonotopic axisterm. This lateralized synaptic organization is in turn associated with differences in sound-evoked activity in L3 [ 6 ]. Behavioral studies in gerbils have reported asymmetries in temporal processing. Lesions in the right ACxterm impact discrimination of frequency sweep direction, suggesting it plays a role in processing global temporal cues. Whereas lesions in the left ACxterm impact discrimination of gap durations, implying a role in processing local temporal cues [ 7 ].

Here, we tested directly whether there are differences in the temporal integrationterm properties between the auditory cortices and dissected the underlying circuit dynamics. To assess cortical circuit mechanisms that could underlie hemispheric asymmetry in temporal processing, we used circuit-mapping techniques to screen for connectivity differences in excitatory pathways. We show novel differences in recurrent connectivity between the hemispheresterm·var, particularly in the superficial layersterm. To examine how these asymmetries in recurrence translate into differences in temporal integrationterm, we recorded spontaneous spikes from awake mice and developed a statistical method to estimate time constantsterm and their uncertainty from spike trains. Our method utilizes the dichotomized Gaussian (DG) spiking model to generate surrogate spike trains with a data matching autocorrelationterm function. The estimated time constantsterm from each neuron recorded in superficial layersterm of the left and right ACxterm were aggregated to form a distribution over the network time constantsterm·var. We found significant differences in temporal integrationterm consistent with the timescale of hypothesized lateralized auditory signal processing. Numerous models of neural architectures have been proposed to account for observed differences in integration timescales throughout the brain. Here, we show, for the first time, compelling evidence of differences in synaptic circuit organization that translate into distinct temporal integrationterm windows.

Results Lateralized connectivity motifs are found in every layer of the auditory cortices To screen for hemispheric differences in the organization of excitatory pathways, we used glutamate uncaging-based laser scanning photostimulation (LSPSterm; [ 8 ]). We performed voltage-clamp recordings on principal neuronsterm in Layers 2 to 6 of the left ( n = 229) and right ( n = 209) ACx, ( Fig 1A and 1B ). The uncaging stimulus gridterm·var covered the entire primary ACx and all cortical layersterm (total area of 1.125*1.125 mm, 256 stimulation sites/map pixelsterm). Hence, we specifically measured only intracorticalterm sources of synaptic inputterm. We focused on excitatory input by holding the membrane potentialterm·var at the reversal for inhibition (-70 mV). To assess potential synaptic connectivity differences in each cortical layerterm, we performed a statistical comparison of the population data underlying corresponding map pixelsterm from each hemisphereterm·var. We found statistically significant differences in the strength and organization of synaptic inputterm between most layers of the auditory cortices ( Fig 1C ). To assess the likelihood that these differences were by chance, we randomly assigned each pixel to the left or right ACxterm. We largely found fewer significant differences between the hemispheresterm·var when the pixels were randomized ( Fig 1D ).

Fig 1. Summary of excitatory pathways in the left and right ACxterm across all cortical layersterm.

(A, B) Patterns of excitatory synaptic inputterm were recorded using LSPSterm as described in the text. Maps were averaged over all cells in each layer and then interpolated for clarity. Masked areas indicate direct hits in >50% of cells for that region. White triangles denote location of somata. Laminar boundaries were defined with respect to the fractional distance from the L1/L2 boundary to the white matter. L5A and L5B were defined as the upper and lower 50% of L5, respectively. N indicates number of recorded cells for each panel. (C) Pixel-wise significance maps ( p < 0.05, unpaired 2-tailed t tests) for response amplitude in left vs. right ACxterm. Red and green denote significantly higher average response in the left and right ACxterm, respectively. Gray denotes no significant difference. (D) Same as C but maps from both hemispheresterm·var were pooled and assigned to 2 groups at random with the same total N shown in A, B. (E) Graphs of the significant pixel counts for measured (C) and random (D) comparisons. (F) Models depicting potential synaptic mechanisms underlying the right ACxterm’s higher excitatory connectivity in superficial layersterm compared to the left ACxterm. The left panel shows both cortices with a similar pool of presynaptic sources of EPSCs (traces) projecting onto postsynaptic targets (triangles), but the distribution of event amplitudesterm differs. The right panel shows the left and right ACxterm differ in their pool of presynaptic inputsterm but have a similar event amplitudeterm distribution. (G) Observed frequency of event amplitudesterm in superficial layersterm of the left and right ACxterm. Inset shows empirical cumulative distribution of event amplitudesterm. The data underlying all the plots in this figure are included in S1 Data . ACx, auditory cortex; LSPSterm, laser scanning photostimulation.

A large source of presynaptic inputterm to L2 in the right ACxterm arose from intralaminar and other superficial layersterm ( Fig 1A–1C ). In contrast, in the left ACxterm, the most influential presynaptic pathway to L2 arose from deep layers (L5/6), as was reported previously [ 9 ]. There were also hemispheric differences in the organization of input to L3, which were also previously described in detail [ 6 ]. Intracorticalterm input to L4 was stronger in the left ACxterm compared to the right ( Fig 1A–1C ). Layers 5A and 5B are functionally distinct in the ACx [ 10 , 11 ], and the organization of their intracorticalterm synaptic inputterm appears to be lateralized. In the left ACxterm, there was greater ascending input to L5A compared to the right ACxterm. Conversely, there was greater descending input to L5B in the right ACxterm compared to the left ACxterm ( Fig 1A–1C ). Finally, L6 had complementary patterns of intralaminar synaptic inputterm along the tonotopic axisterm: The left ACxterm had greater input arising from higher frequency bands, and the right ACxterm from lower frequency bands ( Fig 1A–1C ). A comparison of the number of measured and random pixels for each layer revealed that the most abundant differences in synaptic inputterm between the hemispheresterm·var arose in the superficial layersterm of the left and right ACxterm ( Fig 1E ). Several synaptic mechanisms could underlie the observation of more significant hotspots in superficial layersterm of the right ACxterm compared to the left. One possibility is that each hemisphereterm·var has a similar pool of presynaptic inputsterm in these layers, but individual synaptic events were larger in the right ACxterm compared to the left ( Fig 1F left). Another possibility is that the distribution of the synaptic events’ amplitudes is similar between the hemispheresterm·var, but there is a larger pool of presynaptic inputsterm in the right ACxterm compared to the left ( Fig 1F right). To disentangle these possibilities, we analyzed the distribution of event amplitudesterm in superficial layersterm (L2-4). We randomly chose the same number of cells to analyze from each hemisphereterm·var ( n = 96), and the threshold for synaptic events was set to above the average baseline events. Fewer synaptic events met the threshold criteria in the left ACxterm ( n = 1,090) compared to the right ( n = 1,761, Fig 1G ). Additionally, the distribution of event amplitudesterm was similar between the auditory cortices ( Fig 1G inset). This supports the prediction that there is a larger pool of presynaptic inputsterm in superficial layersterm of the right ACxterm.

Lateralized recurrent connectivity in superficial layersterm of the auditory cortices Neural circuits composed of neurons with short time constantsterm can effectively have long temporal memory and computation by forming a long feedforward chain or concise recurrent feedback loops [ 12 ]. Therefore, we investigated whether the synaptic connectivity differences translate into systematic differences in the recurrent interlaminar feedback. To compare the relative strength of interlaminar pathways between the hemispheresterm·var, we computed connectivity matrices for the left and right ACxterm. These input–output matricesterm·def (presynaptic-postsynaptic) summarize the organization of local excitatory networks [ 13 , 14 ]. We ordered maps according to the cortical depth of the soma and summed the 2D LSPS-derived input maps for each cell over the tonotopic axisterm (across the horizontal dimension), which produced vectors of input strength as a function of cortical depth ( Fig 2A ). Therefore, each neuron’s input vector represents presynaptic inputterm from different laminar locations. Each row in the laminar connectivity matrix represents input to that specific laminar location, and each column represents synaptic output from that laminar location. Very local connections (<50 μm) within each layer lie along the diagonal and were under-sampled due to direct excitation [ 13 ]. In a previous study, we determined that neuronal density and photoexcitability of individual cells did not significantly differ between the hemispheresterm·var; therefore, we did not normalize maps by these factors [ 6 ].

Fig 2. Recurrent connectionsterm·dom are significantly stronger in superficial layersterm of the right ACxterm.

(A) Schematic diagram of the generation of laminar input–outputterm maps [ 14 ]. Left , 2D LSPS-derived input maps for each cell were summed over the anterior-posterior axis to produce 1D maps of input strength vs. cortical depth (vertical strips). Note that anteroposterior information is discarded, such that the 2 L6 input hotspots for the L3 cell (top) collapse to a single spot. Triangles denote soma location. Middle , the 1D maps were rotated 90 degrees (for graphic clarity), sorted by cortical depth of the soma, and binned (bin size = 80 μm). Right , the binned maps were combined into a single 2D map of presynaptic inputterm location (x axis) vs. binned postsynaptic soma location (y axis). Maps were interpolated for display. (B) Input–outputterm map for the left ACxterm, constructed as shown in A. Diagonal line indicates x = y with respect to cortical depth. The diagonal does not span the full x axis because recorded cell bodies (y axis) were confined between L2 and L6, whereas the stimulation grid (x axis) extended more broadly from L1 into the white matter. (C) Same as B but for the right ACxterm. (D) Same map as in B but showing only pathways in the 75th percentile and above in the left ACxterm. (E) Same as D but for the right ACxterm. (F) Summary of loops and pathways in the 75th percentile and above in the left ACxterm and right ACxterm (G). In F and G, the arrow thickness indicates strength of the pathway (thickest in the 99th percentile to thinnest in the 75th percentile), ascending pathways are shown in black, descending in gray, recurrent in violet, and open loop in dashed line. The data underlying all the plots in this figure are included in S1 Data . ACx, auditory cortex; LSPSterm, laser scanning photostimulation.

The most striking asymmetry in the laminar connectivity matrices was the stronger synaptic connections in superficial layersterm of the right ACxterm compared to the left ( Fig 2B and 2C ). To quantify the strengths of the pathways, we calculated fractional input and output. In this analysis, we summed along rows and columns and normalized by the total [ 14 ]. The fractional input and output of Layers 2 to 4 was significantly greater in the right ACxterm ( p = 0.0079 for input, p = 0.0411 for output). In the deeper layers, input to L6 was significantly greater in the left ACxterm ( p <<0.001), but there was no hemispheric difference in the output. Significant hemispheric differences in intralaminar and interlaminar loops were a major theme in the organization of auditory circuits. Using the input/output laminar connectivity matrices of the left and right ACxterm, we examined pathways in the 75th percentile to capture the most significant trends. In the left ACxterm, we observed strong intralaminar recurrent connectivity only in L6 and to a lesser extent in Layers 2 and 5B ( Fig 2D ). The strongest interlaminar recurrent connectionsterm·dom were observed between deep layers, where L5B and 6 form nested loops (intralaminar loops within L5B and 6, which are in turn connected to each other). In contrast to the sparser prevalence of recurrent connectionsterm·dom and loops in the left ACxterm, these were the dominant motifs in the right ACxterm. All cortical layersterm (except L5) in the right ACxterm were part of nested loops: At the innermost level, there were intralaminar loops (input returning to the same layer), followed by local interlaminar loops between neighboring layers, and transcortical loops that coupled superficial and deep layers ( Fig 2E and 2G ). On the whole, we identified 5 recurrent loopsterm in the left ACxterm and 10 in the right ACxterm. Taken together, these widespread loops of recurrent connectionsterm·dom suggest the right ACxterm may have different temporal filtering properties compared to the left ACxterm.

Hemispheric differences in recurrent activity A potential result of stronger recurrent excitatory connectivity in a circuit is the ability to generate longer network eventsterm [ 15 , 16 ]. To test whether the greater recurrent excitatory connectivity observed in the right ACxterm leads to stronger and longer recurrent activity compared to the left ACxterm, we used experimental conditions to disinhibit slices [ 14 ]. The input–outputterm connectivity maps in Fig 1 were obtained in a slice preparation that reduced excitation to prevent polysynapticterm activity [ 8 ]. Therefore, to promote excitation in the slice, we partially blocked inhibitory circuits using the GABA A receptor antagonist SR95531term (see Methods for more experimental details). We performed LSPSterm, recorded in the cell-attached configuration from excitatory neurons in L2-4, and compared network eventsterm in the left and right ACxterm in slices from the same animal. Network eventsterm were only triggered by photostimulation and were largely initiated by stimulating sites in superficial layersterm of the ACx. A representative example of events triggered in the left and right ACxterm of the same animal using the same concentration of SR95531term shows a greater number of action potentialsterm·var generated per stimulation site and more sites evoking action potential firing in the right ACxterm ( Fig 3A and 3B left and middle panels). This difference in activity was significant across animals ( Fig 3A and 3B right panels, n = 16 cells, 4 animals, p < 0.001, Wilcoxon rank sumterm·stat). We also quantified the duration of these network eventsterm: the time from the first photostimulation-evoked action potential to the return of the membrane potentialterm·var back to the pre-stimulus baseline ( Fig 3C ). The duration of network eventsterm·var was significantly longer in the right ACxterm compared to the left ( Fig 3D , p <<0.001 Wilcoxon rank sumterm·stat.) The long duration and variability of these events suggest that the recurrent excitation results from polysynapticterm circuits. In summary, disinhibiting the slice revealed the network-wide impact of stronger recurrent excitatory connectivity in the right ACxterm.

Fig 3. Stronger network eventsterm in the right ACxterm.

(A, left) All traces of cell-attached recording from an L3 excitatory neuron in the left ACxterm with 4 μm concentration of SR95531term in the bath. Black arrow on the x axis marks the onset of photostimulation. (A, middle) Map of action potentials evoked in the cell shown in the left panel and the population of cells recorded in the left ACxterm ( n = 8, right). Triangles show position of the cells recorded. (B, left and middle) Same experimental conditions as in A, but conducted in the right ACxterm of the same animal. (B, right) Population map of all cells recorded in the right ACxterm ( n = 8). (C) Duration of events was calculated from the onset of the first action potential to the return of the membrane potentialterm·var back to baseline period (see Methods for details). (D) The duration of network eventsterm·var is shown using the survival function, which demonstrates that duration times are shorter in the left ACxterm compared to the right ( n = 16). All cells were mapped using the same number of stimulus sites (i.e., same stimulus gridterm·var). The data underlying all the plots in this figure are included in S1 Data . ACx, auditory cortex.

Hemispheric differences in membrane propertiesterm could also contribute to network activity and time constantsterm. To examine this possibility, we compared the current-voltage and current-firing rate relationship of neurons in superficial layersterm of the left and right ACxterm ( S1 Fig ). We found no hemispheric differences between these input–outputterm relationships (I/V, p = 0.71 n = 10; I/F, p = 0.84 n = 10). Hemispheric differences in resting membrane potentialterm were also not significant ( p = 0.86, mean left ACxterm = −73.4 mV, sem = 1.24, n = 28; mean right ACxterm = −73.2 mV, sem = 1.23, n = 28).

Network time constantterm·var is longer in superficial layersterm of the right auditory cortex To determine if hemispheric differences in recurrent connectivity impacted the network dynamics, we measured the time constantterm of the spontaneous neural activity in awake animals. In the first-order approximation of a system, the causal link between recurrent anatomy and time constantterm is based on the fact that excitatory feedbackterm increases the time constantterm, therefore it provides an approximation of the dynamics by measuring how quickly the correlation in the neural activity decays over time. Without recurrent connectivity, the time constantterm of a single neuron’s activity is at the scale of the membrane time constantterm and delays. Whereas with recurrent connectivity, the fluctuations in the network activity can decay more slowly (i.e., longer time constantterm) due to effective self-excitation (see Methods for mathematical explanation). Therefore, we studied whether hemispheric differences in recurrent connectivity translate into a difference in the network time constantsterm·var reflected in a single neuron’s spontaneous activity in the absence of auditory stimuli. We performed cell-attached recordings in superficial layersterm (150 to 500 μm below the cortical surface) of the left and right ACxterm. Because anesthesia can reduce the contribution of network activity [ 17 ], we conducted experiments in head-fixed awake mice. Neurons in both auditory cortices were active in the absence of auditory stimuli and displayed activity that suggested the presence of temporal correlations ( Fig 4C ).

Fig 4. Timescales of neural activity were longer in the right ACxterm.

(A) Schematic of the fitted exponential decay and its parameters. (B) Sketch of the procedure to estimate the biasterm and uncertainty in the time constantterm of single neurons. We used the DG modelterm with neuron-specified exponential autocorrelationsterm to generate surrogate dataterm·mth, re-fit exponentials, and extract surrogate time constantsterm. Finally, we fit a lognormal distribution to the surrogate time constantsterm to estimate the biasterm and variance. (C) Example left and right ACxterm data, autocorrelationsterm, and sample of surrogate dataterm·mth generated from DG modelterm with the extracted autocorrelationterm. (D) Biasterm corrected time constantterm with its uncertainty against mean firing rateterm for each neuron. Time constantterm error bars are the standard deviation of the surrogate lognormal distribution. Firing rateterm error bars are the standard error over trials. (E) Time constantterm posterior distributions obtained by integrating the single neuron observations for each hemisphereterm·var. The total number of cells in panels D and E is 45 (23 right ACxterm, 22 left ACxterm). The data underlying all the plots in this figure are included in S1 Data and S1 File. ACx, auditory cortex; DG, dichotomized Gaussian.

We extracted the time constantterm of individual cells by fitting an exponential decay with a pedestal to their autocorrelograms ( Fig 4A ). However, precise quantification of time constantsterm from spike trains and their autocorrelograms can be challenging [ 18 , 19 ] especially for neurons with low firing ratesterm or low degrees of engagement with the network. In such a low signal-to-noise-ratio (SNR) regime where spike trains (signal + noise) may not be very informative about the network time constantterm·var (signal), the estimated time constantterm can exhibit large biasterm and variance. Unfortunately, bootstrapping or resampling procedures cannot practically correct for these issues. For example, if there are only a handful of spikes, bootstrap samples may consistently provide very small time constantsterm such that the variance of the estimator is close to zero. However, in this low firing rateterm regime, the uncertainty of the estimator should be large if we properly accounted for the spiking noise—resampling low SNR data combined with a strongly biased estimator can create an illusion of a (misleading) high SNR conclusion.

To better estimate the biasterm and variance of the estimated time constantterm itself, we used a method for generating spike trains from a parametric surrogate distribution ( Fig 4B , Methods). Briefly, for each neuron, we used the exponential fit of the data’s autocorrelationterm to build a probabilistic model (see Methods ) that we used to generate random data that reproduces the given autocorrelationterm. We generated surrogate dataterm·mth from this model that matched the length and sampling rate of the recording. By construction, the generated data reproduced the firing rateterm and autocorrelationterm of the original data on average, while single realizations showed variability determined by the assumed noise. We then re-fit exponential decays to the surrogate dataterm·mth and extracted surrogate time constantsterm from each of the realizations. Using this surrogate time constantterm distribution, we corrected the biasterm by assuming that the biasterm is identical between data and surrogate fits and obtained a time constantterm estimate for each neuron together with an uncertainty around this value ( Fig 4D ) that captured the statistical variability of the estimator (e.g., represented by the log-variance of the estimator).

To systematically aggregate the unequally informative neurons, we used a quasi-Bayesianterm method that accounts for the biasterm and uncertainty in the time constantterm estimation for each neuron ( Fig 4B , Methods). Neurons with high uncertainty (low SNR estimate) and relatively weak evidence about the network’s time constantterm, automatically counted for less contribution. Hence, we did not apply arbitrary criteria to discard neurons with weak autocorrelationsterm, low firing ratesterm or very long timescales, and were able to utilize data from all neurons. We then integrated the evidence from multiple neurons assuming that each estimated time constantterm per neuron is a noisy version of a single shared network time constantterm·var per hemisphereterm·var ( Fig 4E , Methods). The extracted network time constantsterm·var were 82 ms (66 ms, 99 ms) for the left ACxterm and 126 ms (113 ms, 138 ms) for the right ACxterm (mean, (95% credible intervalterm·stat)). We found that the time constantterm of the right ACxterm network was significantly longer (Bayes factorterm·stat = 0.002, 1 posterior for all neurons compared to a different posterior for each hemisphereterm·var). Our results suggest that differences in the recurrent connectivity in superficial layersterm of the auditory cortices translate into differences in time constantsterm that might support lateralized computation of auditory stimuli.

Our quasi-Bayesianterm posterior over time constantsterm were consistent with a simple robust average that discarded time constantsterm longer than 300 ms or shorter than 10 ms: 106.1 ms ( n = 13, SEM = 4.03) for the left and 118.8 ms ( n = 14, SEM = 3.71) for the right ACxterm. It is also consistent with the median valuesterm·stat, 192.9 ms ( n = 24) for the left and 250.3 ms ( n = 27) for the right ACxterm. Note that the medians are biased, since the outliers are not symmetrically distributed, being bounded by 0 ms from below.

The baseline firing rateterm·var of neurons from the right hemisphereterm·var tended to be higher. However, weighted linear regressionterm·stat between firing rateterm and time constantterm or log-time constant resulted in low r^2 values (0.0167 and 0.0001), and the hemispheric time constantterm difference explained by the firing rateterm difference is 7.03 ms, less than ⅕ of the difference. Hence, we rule out that the network time constantterm·var difference is due to firing rateterm differences in the data.

Discussion Rodent studies of the last several decades have demonstrated that there are evolutionarily conserved mechanisms of vocalization processing. Behavioral and electrophysiological evidence supports the role of the left ACxterm in the perception of conspecific vocalizations [ 6 , 20 , 21 ] and detecting local features in auditory signals, whereas the right ACxterm specializes in global features [ 6 , 7 ]. Moreover, these processing asymmetries between the auditory cortices were associated with lateralized circuit motifs [ 6 ]. In this study, we show a novel neural mechanism whereby hemispheric differences in recurrent connectivity in superficial layersterm of the ACx translate into distinct temporal integrationterm time windows. We showed that the synaptic organization and network dynamics consistently exhibit lateralizationterm. Using LSPSterm, we compared the organization and strength of excitatory pathways in the left and right ACxterm across all cortical layersterm. For each auditory cortex, we combined the data across cells by building input–outputterm maps. We found significantly stronger intralaminar and interlaminar connectivity between cells in the superficial layersterm in the right ACxterm than in the left, suggesting stronger recurrent loopsterm ( Fig 2 ). Moreover, we showed that this enhanced recurrent connectivity in the right ACxterm leads to stronger recurrent network activity in vitro ( Fig 3 ). Stronger recurrent activity in the right ACxterm suggests a capacity for enhanced echoic memoryterm: holding a brief memory of auditory signals. This feature would be advantageous to extract auditory information at slower temporal timescales, such as prosody and intonation.

Recurrent connectivity and their positive feedback have long been conjectured to play a key role in persistent neural information processing by lengthening the temporal extent of information representation [ 12 , 22 , 23 ]. Although there has been a bevy of theories and computational models [ 24 – 27 ], only indirect evidence exists correlating recurrent anatomical structure with observed persistent activity or longer time constantsterm. Previous work has shown that timescales extracted from single neurons’ activity were related to cortical network organization in the primate visual processing hierarchy [ 28 ], and to the integration of signals in tasks such as working memory [ 29 ], reward guided choice [ 30 , 31 ], and free choice [ 32 ]. But there has not been a comparison of the naturally existing recurrent excitatory synaptic circuits and the corresponding network time constantsterm·var within the same biological model system. In this work, we were able to exploit the lateralized recurrent circuit architecture of the ACx to make the desired direct comparison. The theory suggests that more excitatory recurrent feedback within the superficial layersterm of the right ACxterm should give rise to longer network time constantterm·var reflected in the neural activity. These differences in recurrent connectivity should impact the temporal structure of both evoked and spontaneous activities [ 33 , 34 ]; however, it is easier to interpret the neural dynamics in the absence of stimulus drive because stimulus dynamics influence the measured time constantsterm. To test the hypothesis, we performed cell-attached recordings in superficial layersterm of the left and right ACxterm in awake mice and studied the autocorrelogram of the spontaneous neural activity. Consistent with the connectivity results, our statistical inference showed that the superficial layerterm network of the right ACxterm exhibits an approximately 50% longer network time constantterm·var relative to the left. To the best of our knowledge, this is the first explicit evidence that excitatory synaptic feedback predicts the spontaneous network dynamics that utilize the natural consistent variations provided by lateralizationterm.

The consensus from decades of human language studies has been that speechterm·dom decoding is carried out bilaterally with each hemisphereterm·var optimized for specific functions. Activity in the left STGterm has been consistently associated with processing of formant transitions, and perception of speechterm·dom content is affected by degradation of temporal information; whereas activity in the right STGterm is associated with processing of intonation contours and is affected by degradation of spectral information [ 1 , 35 ]. However, the underlying neural mechanisms have remained controversial and difficult to prove in humans. One proposed mechanism that could subserve lateralized processing is neuronal ensembles in each hemisphereterm·var with different integration time constantsterm: Neurons in the left ACxterm could preferentially integrate information on shorter timescales, and neurons in the right ACxterm could preferentially integrate information on longer timescales [ 4 ]. Our results support this simultaneous multiscale temporal analysis. Mouse vocalizations are composed of syllablesterm (i.e., a sound unit separated from other sound units by silence [ 36 ]), which themselves contain 1 or several pitch trajectories. The duration of individual pitch trajectories within a syllableterm·def emitted by adult male mice can range from 30 to 90 ms [ 37 , 38 ]. This range of durations is in line with the distribution of integration time constantsterm we observed in the left ACxterm ( Fig 4E ). On the other hand, the right ACxterm’s time constantsterm were consistent with intersyllabic intervals in mouse vocalizations (140 ms on average; [ 39 ]). These hemispheric differences in processing timescales suggest that the left and right ACxterm are simultaneously processing segmental and suprasegmental information, respectively, and thereby extracting different acoustic cues from the same signal [ 4 ]. While the results of our study support asymmetries in temporal integrationterm, our previous work suggests that this is not the only neural mechanism underlying lateralized auditory processing. There are hemispheric differences in the tonotopic organization of auditory cortical circuits and sensitivity to the direction of frequency sweeps, which suggest that there are also lateralized specializations for spectral processing [ 6 ]. Overall, the emerging evidence is that there are neural specializations for division labor in both the temporal and spectral domain. In future studies, we plan to use molecular tools to manipulate neural activity in a layer-specific manner to examine how modulating the activity of excitatory networks impacts time constantsterm.

Inhibitory mechanisms have also been proposed to impact network time constantsterm·var. A recent computational study postulated that the differences in electrophysiological properties between excitatory and inhibitory neurons, as well as differences in the balance between excitation and inhibition, could play a role in hierarchical timescale [ 40 ]. Moreover, we have previously reported that inhibitory neuronal populations have lateralized connectivity [ 41 ]. In particular, parvalbumin positive (PV+term) interneurons in superficial layersterm of the right ACxterm can receive excitatory input from deep layers, whereas in the left ACxterm, they only receive input from superficial layersterm. This suggests that excitatory input from deep layers in the right ACxterm could, in principle, modulate the gain of inhibitory PV interneurons in superficial layersterm. A detailed investigation of lateralized inhibitory mechanisms on network time constantsterm·var will be conducted in future studies.

As the list of circuit asymmetries grows in the ACx, it will be important to consider how they arise. Asymmetries in genetic programs that guide circuit assembly during development as well as hearing experience are very likely sources of lateralized specializations. These forces can influence the asymmetric formation of both excitatory and inhibitory networks [ 41 ]. The observation of greater recurrent connectivity in the right ACxterm potentially suggests a lower rate of pruningterm of synaptic contacts during the critical period compared to the left ACxterm. But it is unclear how layer-specificity would be achieved. In future studies, we plan to dissect cellular and molecular mechanisms of enhanced recurrent connectivity in the right ACxterm. Although the results from this study were from an animal model, they support prevailing findings from the human literature, provide detailed neural mechanisms, and therefore can potentially offer insight for investigations in humans [ 42 ].

Methods Slice preparation and electrophysiologyterm·mth CBA/J male mice aged P28-51 were used in accordance with the National Institute of Health guidelines, as approved by the City College of New York Institutional Animal Care and Use Committee. Animals were anesthetized and decapitated, and the brains were transferred to a chilled cutting solution composed of (in mM): 110 choline chloride, 25 NaHCO 3 , 25 D-glucose, 11.6 sodium ascorbate, 7 MgCl 2 , 3.1 sodium pyruvate, 2.5 KCl, 1.25 NaH 2 PO 4 , and 0.5 CaCl 2 . We made horizontal slices to map synaptic connectivity along the anterior-posterior axis of the brain where tonotopy is represented in the ACx [ 43 ]. We sliced using a 15-degree angle between the blade and the medial-lateral axis so that apical dendrites were parallel to the slice in the ACx. Slices were 300-μm thick and were transferred to artificial cerebrospinal fluid (ACSF) containing (in mM): 127 NaCl, 25 NaHCO 3 , 25 D-glucose, 2.5 KCl, 1 MgCl 2 , 2 CaCl 2 , and 1.25 NaH 2 PO 4 , aerated with 95% O 2 5% CO 2 . The slices were incubated at 34°C for 20 to 30 minutes and then kept at room temperature during the experiments. Excitatory neurons located >50 μm below the surface of the slice were visualized using infrared gradient contrast optics and patched with electrodes (6 to 7 MOhm) containing the following intracellular solution (in mM) 128 K-methylsulfate, 4 MgCl, 10 HEPES, 1 EGTA, 4 NaATP, 0.4 NaGTP, 10 Na-phosphocreatine. The pH of the intracellular solution was adjusted to 7.25 and the osmolarity was 300 mOsm. Whole-cell recordings were made using a Multiclamp 700A amplifier (Axon Instruments, Molecular Devices, Sunnyvale, California, United States of America). Excitatory synaptic currents were measured at a holding potential of –70 mV. We used the custom software package ephus ([ 44 ] http://www.ephus.org ) for instrument control and acquisition written in MATLAB (MathWorks, Natick, Massachusetts, USA).

Analysis of IV/FI data Patched neurons were injected with 11 current pulses (0.5 pA steps) to compute current–voltage relationships (I/V) and frequency–current (F-I) curves. Change in membrane potentialterm·var for each current step was calculated and added to the resting membrane potentialterm of the cell to compute the I/V relationship. These values were averages across all cells in a population. Responses for the I/V relationships were only up to the first current step that induced spiking in the cell. Firing rateterm for each current step was also averaged across all cells in a population to calculate F-I curves.

LSPSterm by glutamate uncagingterm The ACSF for uncaging was supplemented with (in mM): 0.2 nitroindolinyl (NI)-caged glutamate (Tocris), 0.005 CPP (Tocris), and a final concentration of 4 CaCl 2 and 4 MgCl 2 . For focal photolysis using UV flash to activate the caged glutamate compound, we used a 1 ms light stimulus consisting of 100 pulses from a pulsed UV laser (wavelength, 355 nm with a repetition rate of 100 kHz; DPSS Lasers, Santa Clara, California, USA). These uncaging conditions have been shown to prevent polysynapticterm activity [ 8 ]; therefore, the synaptic connectivity data shown is monosynaptic. The grid to stimulate the ACx had 16 × 16 uncaging spots with 75 μm spacing, which resulted in a mapping region of 1.125 × 1.125 mm. To avoid revisiting the vicinity of sites recently stimulated, we used a shifting-X pattern that was rotated and/or transposed between map iterations. Each stimulus trial contained a test pulse to measure electrophysiological parameters, and UV flashes were presented every 1 s. The stimulus gridterm·var was consistently aligned for each cell recorded as described previously [ 9 ]. Briefly, the x axis of the grid was centered on the soma and the y axis was aligned with the second row of the grid placed on the L1/2 border. In previous studies, we confirmed that our slice recordings were performed in A1 (primary ACx) using retrograde tracer DiI and glutamate uncagingterm in the MGBv (see supplementary figures in [ 9 ]).

About 10% of all patched neurons across all layers were presumptive inhibitory cells as evidenced by membrane capacitance <100 pF and high levels of spontaneous synaptic inputterm. These were excluded from the data set. Cells with resting Vm > −50 mV, those that exhibited spontaneous repetitive firing or whose passive membrane propertiesterm fluctuated significantly over the course of data collection were also excluded.

Disinhibitionterm·var experiments For experiments comparing network eventsterm in vitro, we used a 2:1 ratio of CaCl 2 and MgCl 2 , did not block NMDA receptors with CPP, and used 1 to 5 μm of SR95531term (Sigma) to block GABA A receptors. Slices from the left and right ACxterm were collected from the same animals in these experiments to directly compare the impact of disinhibitionterm·var. We used an 8 × 16 uncaging grid (75 μm spacing) centered on the cell body. Recordings were performed in the cell-attached configuration. All other photostimulation parameters were the same as described above.

Analysis of LSPSterm data Analysis was conducted as described previously [ 9 ]. Briefly, the mean current amplitudeterm·var of synaptic events was calculated in the 50-ms epoch after the direct response time window (7.5 ms after UV stimulus). Direct responses triggered by stimulation around somata were excluded in our analyses. We recorded 2 to 4 maps for each cell to create an average input map, and these average maps were used for group averages and for all analyses. Like the individual maps, population maps were aligned with respect to the soma on the x axis and the L1/L2 boundary on the y axis. Pixels in the population maps where direct responses were recorded in >50% of cells, i.e., areas proximal to the cell body, were excluded from the analysis.

To create input–outputterm functions from the map data, average maps for each cell were summed over either the antero-posterior axis or the laminar axis to yield 1 × 16 vectors of input strength versus location. These vectors were then binned according to laminar or antero-posterior location, respectively, and combined to yield n × 16 or 16 × n matrices relating presynaptic inputterm to postsynaptic soma location (“output”), where n = number of bins as reported in the individual figure legends. The matrices were 2D-interpolated for display purposes.

To analyze network eventsterm in disinhibitionterm·var experiments, we calculated the number of action potentialsterm·var evoked per stimulation site and the duration of network eventsterm·var. All network eventsterm began with the firing of an action potential, and the event duration was calculated as the return of the membrane potentialterm·var back to the mean of the pre-stimulus baseline period (±1.5 std).

Statistical significance between hemispheresterm·var for population map values was computed pixel-by-pixel, with the average map value for each cell being considered a single data point. Comparisons were made via 2-tailed, unpaired t teststerm·stat. For comparison, randomized data sets were generated by pooling all maps from both hemispheresterm·stat for each layer and drawing maps at random to give 2 pools with the same n as the original data sets.

In vivo recordings Subjects and surgery A total of 14 CBA/J mice, aged P30-60, were used in accordance with the National Institute of Health guidelines, as approved by the City College of New York Institutional Animal Care and Use Committee. We administered ketamine (75 mg kg −1 )/medetomidine (0.5 mg kg −1 ) before a stereotactic surgery was performed to allow for awake recordings. The scalp from the entire top of the skull was removed to reveal bregma and lambda, barely exposing the muscle of the temporal bone. A metal plate was bound to the exposed bone via 2 layers of Metabond and a single layer of Vitribond, followed by a covering of dental cement. Following administration of ketamine (75 mg kg −1 )/medetomidine (0.5 mg kg −1 ), mice were head-fixed on a freely rotating wheel inside a soundproof chamber. We made a craniotomy and durotomy over the auditory cortex, centered at 2.5 mm posterior and 4 mm lateral to bregma. Craniotomies were 0.1 to 0.2 mm in diameter to provide stability to our recordings. Mice were allowed at least 24 hours post-surgery to recover before any recording sessions began.

Electrophysiologyterm·mth We recorded from the left and right ACxterm of awake mice. During the recording sessions, we targeted neurons in superficial layersterm (L2/3, L4; 150 to 500 μm below cortical surface) using the standard blind cell-attached technique [ 45 ]. One hemisphereterm·var was recorded per animal. Electrodes were pulled from a glass borosilicate filament and filled with either physiological saline (0.9% NaCl) or intracellular solution ((in mM) 128 K-methylsulfate, 4 MgCl, 10 HEPES, 1 EGTA, 4 NaATP, 0.3 NaGTP, 10 Na-phos-phocreatine) and had resistances between 4 to 8 MOhms. Recordings were obtained using Axopatch 200B (Axon Instruments) and custom electrophysiological data acquisition software (exper, by Tomas Hromadka) written in MATLAB (Mathworks). We detected the presence of cells in the cortex based on changes in pipette resistance.

A soundproof chamber was used to conduct all recordings. We used a custom-built real-time Linux system (200 kHz sampling rate) driving a high-end Lynx L22 audio card (Lynx Studio Technology, Newport Beach, California, USA) with an ED1 electrostatic speaker (Tucker-Davis Technologies, Alachua, Florida, USA) in free-field configuration (speaker located 6 inches lateral to, and facing, the contralateral ear). The stimuli were generated with custom MATLAB scripts. To compute tuning curves of our cells’ best frequency, we used a set of pure tones that lasted 100 ms long of 16 different frequencies at 3 intensity levels (20dB, 50dB, and 80dB). Tuning curves and timing of tone-evoked responses (40 to 50 ms from stimulus presentation) were used to ensure that the position of our recordings were within the primary ACx.

Time constantterm posterior distributions Link between recurrent anatomy and time constantterm The causal connection between recurrent connectivity and time constantterm is based on the intuitive fact that excitatory feedbackterm increases time constantterm in the first-order approximation of a system. In particular, consider a first-order linear dynamical system that represents the neural population with no feedback with time constantterm τ : d x d t = − 1 τ · x + η , where x ( t ) represents the 1D subspace of the population activity and η denotes white noise representing noise and input drive. The autocorrelationterm of this system has the form: acf ( l a g ) = a e − | l a g | τ . Since we fit the autocorrelationterm function with a single τ, it would correspond to the dominant time constantterm expressed in the neuron’s activity. We assume that the neural autocorrelationterm directly reflects the time constantterm of the population (i.e., the network time constantterm·var). Now if there is positive feedback in the first-order linear system, we get: d x d t = − 1 τ · x + g · x + η , where 0< g <1/τ is a positive constant that captures the strength of the feedback of the population. In this simplified form, we can see that the effective time constantterm increases as a function of g , since d x d t = − ( 1 τ − g ) · x + η = − 1 τ eff · x + η , where the effective time constantterm with the recurrence is τ e f f = τ 1 − τ · g > τ . Hence, under these first-order approximations, excitatory feedbackterm is expected to increase the time constantterm of spontaneous activity.

Autocorrelationterm and single neuron time constantterm estimation For this analysis, we used inter-trial interval recordings in which no auditory stimulus was played, and we were blind to the hemispheric identity of the data. Each inter-trial interval started at least 100 ms after the end of the stimulus and of duration 1,540 ms. We binned the recordings in Δ = 20 ms bins and computed the unbiased autocorrelation functionterm·stat: acf ^ ( l ) = 1 N trial ∑ n = 1 N trial 1 T n − l + 1 ∑ t = 1 T n x n ( t ) x n ( t − l ) , where l denotes the l -th binned lag, x n ( t ) is the binned spontaneous spike trains for trial n , T n is the number of time bins for the corresponding trial, and N trial is the total number of trials.

For each neuron, we performed nonlinear least squares fitsterm·stat to the estimated autocorrelationterm functions, exponential decays of the form C ( l ) = a · e − l τ ^ + b (1) with multiple initializations to extract the values of the parameters τ ^ and a . We used the autocorrelationterm function starting from 20 ms up to 760 ms binned at 20 ms. Disregarding the first 20 ms of the autocorrelationterm removes the dominant very short time constantterm component in the data. We assumed the process is stationary and fixed the value of b to ( λΔ ) 2 where λ is the mean firing rateterm. We then estimated the biasterm and variance of the estimated time constantterm τ ^ for each neuron using the DG modelterm.

Dichotomized Gaussian modelterm·def The DG modelterm is a spiking generative model that can capture correlation structures in spiking neural data [ 46 ]. Sample (binarized) spike trains X ( t ) were generated by thresholding samples of a latent multivariate Gaussian time series V ( t ) X ( t ) = 1 iff V ( t ) > 0 with V ∼ N ( γ , Σ ) with Σ ( t , t ) = 1 without loss of generality. Assuming the process is stationary, γ is a constant and the time covariance matrix Σ ( t , s ) = Σ ( t − s ) is only a function of the lag between time points. The latent parameters γ and Σ were obtained from the spiking rate λ per bin and autocorrelationterm C using γ = Φ − 1 ( λ ) C ( t − s ) − Φ 2 ( λ , λ , Σ ( t − s ) ) = 0 , where Φ and Φ 2 denote the cumulative distribution of the standard normal and the bivariate normal distributions with given correlation [ 46 ]. Since we use the exponential decay form for the autocorrelationterm, Σ ( t − s ) is monotonically decaying as function of the lag ( t − s ), there is a unique solution for γ constrained to be in the range (−1, 1). In this way, the DG modelterm can capture specific spiking rates and temporal autocorrelationsterm in the data.

Biasterm and variance estimation For each neuron, we generated 400 sets of surrogate dataterm·mth replicating the recording conditions and using the DG modelterm with the exponential autocorrelationterm of Eq ( 1 ) and the extracted parameters τ ^ and a . We repeated the estimation procedure for each set and obtained a set of surrogate time constantsterm { τ 1 D G , τ 2 D G , … , τ 400 D G } . We assumed these values are observations from a log-normal distribution l o g ( τ i D G ) ∼ Ν ( l o g ( τ ^ ) + b i a s , σ 2 ) and we estimated their mean and variance by maximum likelihoodterm·stat. The difference between the estimated mean E [ log ( τ D G ) ] and log ( τ ^ ) is an estimate of the biasterm of our estimator in our procedure and σ 2 represents the uncertainty of the estimate.

Evidence integration We assumed that the biasterm corrected time constantsterm l o g ( τ ^ i u ) = l o g ( τ ^ i ) − b i a s i obtained from the neurons in each hemisphereterm·var come from a log-normal distribution l o g ( τ ^ i u ) ∼ Ν ( l o g ( τ ) , σ i 2 ) with τ the network’s time constantterm and σ i 2 the variance of log time constantterm estimated for each neuron. Assuming a uniform prior for τ , we integrate the evidence from the observations of each hemisphereterm·var into the posterior distributions by computing P ( τ | { τ i } ) ∝ ∏ i P ( τ i | τ , σ i 2 ) .

Supporting information S1 Fig. No hemispheric differences in measured membrane propertiesterm. Whole-cell recordings were performed in superficial layersterm (L2-4) of the left and right ACxterm at room temperature (see Methods for experimental details). A series of current steps were injected to test the subthreshold and suprathreshold membrane propertiesterm of excitatory neurons. Left panel shows the relationship between current injected and evoked membrane response measured at steady state. Right panel is the relationship between current injected and firing rateterm, also measured at steady state. The data underlying all the plots in this figure are included in S1 Data .

(TIF) Click here for additional data file. (1.3MB, tif) S1 Data. Excel spreadsheets containing the quantitative data for each experiment as described in the results and figure legends. (XLSX) Click here for additional data file. (129KB, xlsx) S1 File. The 45 figures in this document contain data from all the cells recorded in vivo and analyzed in Fig 4 . The blue raster panels correspond to the data recorded and the pink to the DG modelterm. Data from the left ACxterm have files named Calyx and the right ACxterm Thelo.

(PDF) Click here for additional data file. (2MB, pdf) Abbreviations ACSF artificial cerebrospinal fluid ACx auditory cortex DG dichotomized Gaussian LSPSterm laser scanning photostimulation SNR signal-to-noise-ratio STGterm superior temporal gyrus Data Availability All relevant data are within the paper and its Supporting Information files.

Funding Statement HVO is funded by NSF IOS-1652774 and NIH 1R21DC019737-01. IMP is funded by NSF IIS-1845836 and NIH UF1-NS115779 The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.