Distributed representation of tone frequency in highly decodable spatio-temporal activity in the auditory cortex

Stability of spontaneous, correlated activity in mouse auditory cortex

Neural systems can be modeled as complex networks in which neural elements are represented as nodes linked to one another through structural or functional connections. The resulting network can be analyzed using mathematical tools from network science and graph theory to quantify the system’s topological organization and to better understand its function. Here, we used two-photon calcium imaging to record spontaneous activity from the same set of cells in mouse auditory cortex over the course of several weeks. We reconstruct functional networks in which cells are linked to one another by edges weighted according to the correlation of their fluorescence traces. We show that the networks exhibit modular structure across multiple topological scales and that these multi-scale modules unfold as part of a hierarchy. We also show that, on average, network architecture becomes increasingly dissimilar over time, with similarity decaying monotonically with the distance (in time) between sessions. Finally, we show that a small fraction of cells maintain strongly-correlated activity over multiple days, forming a stable temporal core surrounded by a fluctuating and variable periphery. Our work indicates a framework for studying spontaneous activity measured by two-photon calcium imaging using computational methods and graphical models from network science. The methods are flexible and easily extended to additional datasets, opening the possibility of studying cellular level network organization of neural systems and how that organization is modulated by stimuli or altered in models of disease.

Neurons coordinate their activity with one another, forming networks that help support adaptive, flexible behavior. Still, little is known about the organization of these networks at the cellular scale and their stability over time. Here, we reconstruct networks from calcium imaging data recorded in mouse primary auditory cortex. We show that these networks exhibit spatially constrained, hierarchical modular structure, which may facilitate specialized information processing. However, we show that connection weights and modular structure are also variable over time, changing on a timescale of days and adopting novel network configurations. Despite this, a small subset of neurons maintain their connections to one another and preserve their modular organization across time, forming a stable temporal core surrounded by a flexible periphery. These findings represent a conceptual bridge linking network analyses of macroscale and cellular-level neuroimaging data. They also represent a complementary approach to existing circuits- and systems-based interrogation of nervous system function, opening the door for deeper and more targeted analysis in the future.

Tonotopic and Field-Specific Representation of Long-Lasting Sustained Activity in Rat Auditory Cortex

Cortical information processing of the onset, offset, and continuous plateau of an acoustic stimulus should play an important role in acoustic object perception. To date, transient activities responding to the onset and offset of a sound have been well investigated and cortical subfields and topographic representation in these subfields, such as place code of sound frequency, have been well characterized. However, whether these cortical subfields with tonotopic representation are inherited in the sustained activities that follow transient activities and persist during the presentation of a long-lasting stimulus remains unknown, because sustained activities do not exhibit distinct, reproducible, and time-locked responses in their amplitude to be characterized by grand averaging. To address this gap in understanding, we attempted to decode sound information from densely mapped sustained activities in the rat auditory cortex using a sparse parameter estimation method called sparse logistic regression (SLR), and investigated whether and how these activities represent sound information. A microelectrode array with a grid of 10 × 10 recording sites within an area of 4.0 mm × 4.0 mm was implanted in the fourth layer of the auditory cortex in rats under isoflurane anesthesia. Sustained activities in response to long-lasting constant pure tones were recorded. SLR then was applied to discriminate the sound-induced band-specific power or phase-locking value from those of spontaneous activities. The highest decoding performance was achieved in the high-gamma band, indicating that cortical inhibitory interneurons may contribute to the sparse tonotopic representation in sustained activities by mediating synchronous activities. The estimated parameter in the SLR decoding revealed that the informative recording site had a characteristic frequency close to the test frequency. In addition, decoding of the four test frequencies demonstrated that the decoding performance of the SLR deteriorated when the test frequencies were close, supporting the hypothesis that the sustained activities were organized in a tonotopic manner. Finally, unlike transient activities, sustained activities were more informative in the belt than in the core region, indicating that higher-order auditory areas predominate over lower-order areas during sustained activities. Taken together, our results indicate that the auditory cortex processes sound information tonotopically and in a hierarchical manner.

SCoRS--A Method Based on Stability for Feature Selection and Mapping inNeuroimaging [corrected]

Feature selection (FS) methods play two important roles in the context of neuroimaging based classification: potentially increase classification accuracy by eliminating irrelevant features from the model and facilitate interpretation by identifying sets of meaningful features that best discriminate the classes. Although the development of FS techniques specifically tuned for neuroimaging data is an active area of research, up to date most of the studies have focused on finding a subset of features that maximizes accuracy. However, maximizing accuracy does not guarantee reliable interpretation as similar accuracies can be obtained from distinct sets of features. In the current paper we propose a new approach for selecting features: SCoRS (survival count on random subsamples) based on a recently proposed Stability Selection theory. SCoRS relies on the idea of choosing relevant features that are stable under data perturbation. Data are perturbed by iteratively sub-sampling both features (subspaces) and examples. We demonstrate the potential of the proposed method in a clinical application to classify depressed patients versus healthy individuals based on functional magnetic resonance imaging data acquired during visualization of happy faces.

Pre-attentive, context-specific representation of fear memory in the auditory cortex of rat

Neural representation in the auditory cortex is rapidly modulated by both top-down attention and bottom-up stimulus properties, in order to improve perception in a given context. Learning-induced, pre-attentive, map plasticity has been also studied in the anesthetized cortex; however, little attention has been paid to rapid, context-dependent modulation. We hypothesize that context-specific learning leads to pre-attentively modulated, multiplex representation in the auditory cortex. Here, we investigate map plasticity in the auditory cortices of anesthetized rats conditioned in a context-dependent manner, such that a conditioned stimulus (CS) of a 20-kHz tone and an unconditioned stimulus (US) of a mild electrical shock were associated only under a noisy auditory context, but not in silence. After the conditioning, although no distinct plasticity was found in the tonotopic map, tone-evoked responses were more noise-resistive than pre-conditioning. Yet, the conditioned group showed a reduced spread of activation to each tone with noise, but not with silence, associated with a sharpening of frequency tuning. The encoding accuracy index of neurons showed that conditioning deteriorated the accuracy of tone-frequency representations in noisy condition at off-CS regions, but not at CS regions, suggesting that arbitrary tones around the frequency of the CS were more likely perceived as the CS in a specific context, where CS was associated with US. These results together demonstrate that learning-induced plasticity in the auditory cortex occurs in a context-dependent manner.

Sound frequency representation in primary auditory cortex is level tolerant for moderately loud, complex sounds

The distribution of neuronal characteristic frequencies over the area of primary auditory cortex (AI) roughly reflects the tonotopic organization of the cochlea. However, because the area of AI activated by any given sound frequency increases erratically with sound level, it has generally been proposed that frequency is represented in AI not with a rate-place code but with some more complex, distributed code. Here, on the basis of both spike and local field potential (LFP) recordings in the anesthetized cat, we show that the tonotopic representation in AI is much more level tolerant when mapped with spectrotemporally dense tone pip ensembles rather than with individually presented tone pips. That is, we show that the tuning properties of individual unit and LFP responses are less variable with sound level under dense compared with sparse stimulation, and that the spatial frequency resolution achieved by the AI neural population at moderate stimulus levels (65 dB SPL) is better with densely than with sparsely presented sounds. This implies that nonlinear processing in the central auditory system can compensate (in part) for the level-dependent coding of sound frequency in the cochlea, and suggests that there may be a functional role for the cortical tonotopic map in the representation of complex sounds.