Modular organization of intrinsic connections associated with spectral tuning in cat auditory cortex

Abstract

Many response properties in primary auditory cortex (AI) are segregated spatially and organized topographically as those in primary visual cortex. Intensive study has not revealed an intrinsic, anatomical organizing principle related to an AI functional topography. We used retrograde anatomic tracing and topographic physiologic mapping of acoustic response properties to reveal long-range (>1.5 mm) convergent intrinsic horizontal connections between AI subregions with similar bandwidth and characteristic frequency selectivity. This suggests a modular organization for processing spectral bandwidth in AI.corticocortical projections ͉ cortical modules L ittle is known about how intrinsic connections in cat primary auditory cortex (AI) are organized and what their functions are. These connections form a discontinuous and elongated spatial pattern (1-3) different from that in the primary visual cortex (4 -6). After artificial rerouting of visual thalamic afferents into AI, intrinsic connectivity takes on spatial and functional characteristics of primary visual cortex (VI) (7). However, the relationship between acoustic response properties and intracortical connectivity in AI is unknown. Studies of the spatial organization of frequency (2,3,8) and binaurality (8 -11) show that these features cannot account for the topography of AI horizontal connections. Injections of AI with anterograde tracers that involve supragranular cortical layers find many intrinsic terminal patches in the dorsoventral (isofrequency) axis and few that cross the caudorostral (cochleotopic) dimension of AI. The frequency preference of the patches was believed to match the injection site because most labeling followed the dorsoventral axis (2, 3). However, it has been suggested that the dorsal patches of labeling are tuned preferentially to higher frequencies (1); hence, the frequency alignment of intrinsic connections in AI is still unresolved. AI neurons also segregate according to binaural properties, and the spatial pattern of excitatory and inhibitory binaural bands covaries with bands of commissural terminal labeling (8 -11). However, the spatial arrangement of AI horizontal intrinsic connections differs from that of binaural bands (1).Features other than characteristic frequency (CF) and binaurality that are represented in AI include intensity dependence, excitatory bandwidth, preferred speed of frequency sweeps, and response latency (12)(13)(14)(15)(16)(17)(18)(19). Each feature has a slightly different topographic organization. However, most align dorsoventrally to a central cluster of neurons with narrow bandwidth (NB) tuning (19). This region crosses all frequencies and is flanked above and below (along the isofrequency axis) by clusters of broadbandwidth (BB) neurons (12,14,19). We mapped bandwidth topography (14) to target deposits of a tracer in the central NB region. We found multiple patches of retrograde labeling at consistent and predictable locations within AI. One, and perhaps all, of these patches contain neurons with spectra...

Spectral integration plasticity in cat auditory cortex induced by perceptual training

“…In this study, care was taken to sample the targeted frequency region evenly to avoid spatial biases due to sharpness of tuning clusters. A spatial analysis of the distribution of sharpness of pure-tone tuning (not shown) suggested an organization compatible with previous reports (Schreiner and Mendelson, 1990;Schreiner and Sutter, 1992;Read et al, 2001) but ripple maps were quite noisy and a significant correlation between ripple and pure-tone responses was evident in only two cases. This suggests that pure-tone estimates of spectral integration may be more robust and less influenced by other contributing elements, such as depth of modulation, overall intensity, frequency position etc, than broad-band measures.…”

“…In each animal, a uniform spatial sample of locations across AI avoided sampling biases due to differences in the distribution of local spectral integration aggregates along the iso-frequency axis (e.g. Schreiner and Mendelson, 1990;Read et al, 2001). The sampled range of CFs was 2.3 to 11.5 kHz with no statistically significant differences among the cases (Kolmogorov-Smirnov test).…”

“…In cats and New World monkeys, sharpness of tuning, or local frequency specificity, to puretones represents an important functional organization principle reflected in a modular or patchy cortical organization associated with specific horizontal connections (Recanzone et al, 1999;Cheung et al, 2001;Schreiner et al, 2000;Read et al, 2001). The spatial organization of tuning to broad-band ripple spectra should be related to sharpness of tuning assessed with pure-tones since both reflect aspects of spectral filtering (Calhoun and Schreiner, 1998;Wang and Shamma, 1995).…”

Asynchronous inputs alter excitability, spike timing, and topography in primary auditory cortex

“…Cortical response maps were reconstructed using the Voronoi tessellation procedure (Kilgard and Merzenich, 1999;Read et al, 2001) (see Fig. 1).…”

Spectral and Temporal Processing in Rat Posterior Auditory Cortex

“…Collectively, these studies lend support to the notion that acoustic signal processing occurs in both parallel and serial fashion throughout the auditory cortex and that cortical fields in rats can in large part be distinguished on physiological criteria. As in other modalities, a combined approach using physiological mapping and neuroanatomical tracing studies will be crucial in delineating the modular and hierarchical organization of information processing in the auditory cortex (Luethke et al 1988;Rouiller et al 1991;Read et al 2001;Lee et al 2004). Additionally, the perceptual role that each of these functionally distinct fields plays in processing the acoustic biotope will need to be determined with behavioral studies.…”