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. 2012;7(1):e29965.
doi: 10.1371/journal.pone.0029965. Epub 2012 Jan 9.

Multidimensional characterization and differentiation of neurons in the anteroventral cochlear nucleus

Marei Typlt  1 , Bernhard Englitz, Mandy Sonntag, Susanne Dehmel, Cornelia Kopp-Scheinpflug, Rudolf Ruebsamen
Affiliations

Multidimensional characterization and differentiation of neurons in the anteroventral cochlear nucleus

Marei Typlt et al. PLoS One. 2012.

Abstract

Multiple parallel auditory pathways ascend from the cochlear nucleus. It is generally accepted that the origin of these pathways are distinct groups of neurons differing in their anatomical and physiological properties. In extracellular in vivo recordings these neurons are typically classified on the basis of their peri-stimulus time histogram. In the present study we reconsider the question of classification of neurons in the anteroventral cochlear nucleus (AVCN) by taking a wider range of response properties into account. The study aims at a better understanding of the AVCN's functional organization and its significance as the source of different ascending auditory pathways. The analyses were based on 223 neurons recorded in the AVCN of the Mongolian gerbil. The range of analysed parameters encompassed spontaneous activity, frequency coding, sound level coding, as well as temporal coding. In order to categorize the unit sample without any presumptions as to the relevance of certain response parameters, hierarchical cluster analysis and additional principal component analysis were employed which both allow a classification on the basis of a multitude of parameters simultaneously. Even with the presently considered wider range of parameters, high number of neurons and more advanced analytical methods, no clear boundaries emerged which would separate the neurons based on their physiology. At the current resolution of the analysis, we therefore conclude that the AVCN units more likely constitute a multi-dimensional continuum with different physiological characteristics manifested at different poles. However, more complex stimuli could be useful to uncover physiological differences in future studies.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Schematic of aggregation procedure in hierarchical cluster analysis.
Left column: The position of single units in the n-dimensional space. In each step (A–D) units or groups of units with the smallest distance to each other are grouped together. Right column: The resulting developing dendrogram of the cluster analysis. The linkage distance indicates the similarity of the units. The higher the linkage distance the more differ the units in their properties.
Figure 2
Figure 2. Classification of AVCN units based on PSTH types.
A1–A4: Response patterns during tone burst stimulation (100 ms, 80 dB SPL at CF) of four units; dot raster to 50 stimulus presentations above, PSTH below (bin width 0.5 ms). A1: primary-like (PL), A2: primary-like with notch (PLN), A3: transient chopper (CT), A4: sustained chopper (CS). The insets show the inter-spike interval (ISI) distribution during the first 20 ms of the stimulus of the respective units (bin width 0.1 ms); B1: Relation between mean ISI and S.D. of ISI for the different unit types (symbols as indicated in the figure) B2: Relation between the first spike latency (FSL) and the jitter of the FSL. Note that both types of chopper units have lower ISIs and a lower variation in ISI than PL and PLN units. Shortest latencies are observed in PLN units.
Figure 3
Figure 3. Waveform analysis.
A1–A4: Averaged and normalized waveforms of single units sorted according to their PSTH types (numbers of units indicated in the graphs). The inset in A1 shows the mean waveform of the action potentials (black line, grey lines: S.D.) of a PL unit displaying the presynaptic component P and the two postsynaptic components ‘A’ and ‘B’. B1: Normalized average waveforms of all units of the respective PSTH types. C1: Signal-to-noise ratio and C2: duration from the maximum to the minimum of the waveforms of the respective PSTH types. Each symbol represents the value of a single AVCN unit. Significant differences (p≤0.05) between PSTH types are indicated with asterisks.
Figure 4
Figure 4. Tuning characteristics and spontaneous activity.
A: Distribution of characteristic frequencies and threshold values for units of different PSTH type (dotted line: behavioural audiogram of the Mongolian gerbil, Ryan 1976). B: Frequency selectivity of the units quantified by Q10-values (symbols as in A). C1 and C3: Distribution and C2 and C4: cumulative frequency of the spontaneous (C1 and C2) and of the maximum (C3 and C4) discharge rates of the respective PSTH types. Note that PL units have the highest spontaneous but low maximum discharge rates. Highest discharge rates were observed in CS units. D1–D4: Rate-level functions of the units of different PSTH types. The histograms on the right side of each graph show the fraction the different rate-level function types: steady monotonic (ms), monotonic with plateau (mp), non-monotonic (nm). Note that all types of rate-level functions have their share in the different PSTH groups.
Figure 5
Figure 5. Responses to SAM.
Synchronization indices (left column) and entrainment (right column) as a function of modulation frequency for the different PSTH types. The respective bottommost plots show the average transfer functions for each PSTH type. The horizontal line indicates the 0.3 cut-off criterion that was chosen to classify responses as being phase-locked. Note that CS units show best and PL units worst ability to comodulate with fast fluctuations in stimulus amplitude.
Figure 6
Figure 6. Cluster analyses considering all evaluated response properties.
A: Dendrogram illustrating the result of hierarchical cluster analysis. The units (n = 233) are lined up at the bottom of the graph. The analysis suggests five clusters characterized by a specific distribution of parameter values. B: Mean of the respective parameter values for each property in the resulted clusters I–V. The values are standardized and normalized to the respective maxima (for original data and statistical analyses see table 2). Note that for almost all individual properties significant differences exist between the clusters, and some properties also correlated across clusters. C: However, principal component analysis gives no indication for clearly separated groups of units, neither for the different clusters gathered from hierarchical cluster analysis (C1) nor for the different PSTH types (C2). In both cases units establishing different groups tend to accumulate in different regions of the plot. Still, the different groups strongly overlap, especially in the centre of the plot. Thus, with respect to their physiological properties the AVCN neurons form a continuum rather than distinct groups.
Figure 7
Figure 7. Cluster analyses based on a restricted set of parameters.
See text for the reasons of the restriction. The parameter considered are indicated in B. Design of the graphs is the same as in figure 6. A: The present cluster analysis suggests a distinction of four clusters (a–d) with B: specific properties. Note that there is some correspondence between this restricted analysis and the analysis given in figure 6: Cluster ‘a’ relates to cluster V; ‘b’ to I, ‘c’ to II, and ‘d’ to IV. C: The principal component analysis arranges the units in one big coherent cluster. Units establishing different clusters (C1) and different PSTH types (C2) still could not be separated.
Figure 8
Figure 8. Principal component analysis employing three principal components and recheck of PSTH classification.
Same sample of units (n = 174) and analysis as in figure 7. A1: Principal component analysis with assignment of the units to the different clusters gathered from hierarchical cluster analysis. A2: Principal component analysis with assignment of the units to the different PSTH types. Note that even a visualization based on the three dominant principal components does not indicate a clear separation of unit types. B: PSTHs of units in regions of the plot which are mainly occupied by other PSTH types, i.e. a PLN (B1), a CS (B2) and a unit which is not unambiguously to classify (B3). This unit was classified as CT, but it could also be a PL.
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References

    1. Thompson AM, Schofield BR. Afferent projections of the superior olivary complex. Microsc Res Tech. 2000;51:330–354. doi: 10.1002/1097-0029(20001115)51:4<330::AID-JEMT4>3.0.CO;2-X. - DOI - PubMed
    1. Eggermont JJ. Between sound and perception: reviewing the search for a neural code. Hear Res. 2001;157:1–42. - PubMed
    1. Cant NB, Benson CG. Parallel auditory pathways: projection patterns of the different neuronal populations in the dorsal and ventral cochlear nuclei. Brain Res Bull. 2003;60:457–474. - PubMed
    1. Osen KK. Cytoarchitecture of the cochlear nuclei in the cat. J Comp Neurol. 1969;136:453–484. doi: 10.1002/cne.901360407. - DOI - PubMed
    1. Brawer JR, Morest DK, Kane EC. The neuronal architecture of the cochlear nucleus of the cat. J Comp Neurol. 1974;155:251–300. doi: 10.1002/cne.901550302. - DOI - PubMed

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