Distinct higher-order representations of natural sounds in human and ferret auditory cortex
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ABSTRACT:
SUBMITTER: Landemard A
PROVIDER: S-EPMC8601661 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Recent studies have challenged the traditional notion of modality-dedicated cortical systems by showing that audition and touch evoke responses in the same sensory brain regions. While much of this work has focused on somatosensory responses in auditory regions, fewer studies have investigated sound responses and representations in somatosensory regions. In this functional magnetic resonance imaging (fMRI) study, we measured BOLD signal changes in participants performing an auditory frequency discrimination task and characterized activation patterns related to stimulus frequency using both univariate and multivariate analysis approaches. Outside of bilateral temporal lobe regions, we observed robust and frequency-specific responses to auditory stimulation in classically defined somatosensory areas. Moreover, using representational similarity analysis to define the relationships between multi-voxel activation patterns for all sound pairs, we found clear similarity patterns for auditory responses in the parietal lobe that correlated significantly with perceptual similarity judgments. Our results demonstrate that auditory frequency representations can be distributed over brain regions traditionally considered to be dedicated to somatosensation. The broad distribution of auditory and tactile responses over parietal and temporal regions reveals a number of candidate brain areas that could support general temporal frequency processing and mediate the extensive and robust perceptual interactions between audition and touch.
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Project description:Functional neuroimaging research provides detailed observations of the response patterns that natural sounds (e.g. human voices and speech, animal cries, environmental sounds) evoke in the human brain. The computational and representational mechanisms underlying these observations, however, remain largely unknown. Here we combine high spatial resolution (3 and 7 Tesla) functional magnetic resonance imaging (fMRI) with computational modeling to reveal how natural sounds are represented in the human brain. We compare competing models of sound representations and select the model that most accurately predicts fMRI response patterns to natural sounds. Our results show that the cortical encoding of natural sounds entails the formation of multiple representations of sound spectrograms with different degrees of spectral and temporal resolution. The cortex derives these multi-resolution representations through frequency-specific neural processing channels and through the combined analysis of the spectral and temporal modulations in the spectrogram. Furthermore, our findings suggest that a spectral-temporal resolution trade-off may govern the modulation tuning of neuronal populations throughout the auditory cortex. Specifically, our fMRI results suggest that neuronal populations in posterior/dorsal auditory regions preferably encode coarse spectral information with high temporal precision. Vice-versa, neuronal populations in anterior/ventral auditory regions preferably encode fine-grained spectral information with low temporal precision. We propose that such a multi-resolution analysis may be crucially relevant for flexible and behaviorally-relevant sound processing and may constitute one of the computational underpinnings of functional specialization in auditory cortex.
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Project description:Life or death in hostile environments depends crucially on one's ability to detect and gate novel sounds to awareness, such as that of a twig cracking under the paw of a stalking predator in a noisy jungle. Two distinct auditory cortex processes have been thought to underlie this phenomenon: (i) attenuation of the so-called N1 response with repeated stimulation and (ii) elicitation of a mismatch negativity response (MMN) by changes in repetitive aspects of auditory stimulation. This division has been based on previous studies suggesting that, unlike for the N1, repetitive "standard" stimuli preceding a physically different "novel" stimulus constitute a prerequisite to MMN elicitation, and that the source loci of MMN and N1 are different. Contradicting these findings, our combined electromagnetic, hemodynamic, and psychophysical data indicate that the MMN is generated as a result of differential adaptation of anterior and posterior auditory cortex N1 sources by preceding auditory stimulation. Early ( approximately 85 ms) neural activity within posterior auditory cortex is adapted as sound novelty decreases. This alters the center of gravity of electromagnetic N1 source activity, creating an illusory difference between N1 and MMN source loci when estimated by using equivalent current dipole fits. Further, our electroencephalography data show a robust MMN after a single standard event when the interval between two consecutive novel sounds is kept invariant. Our converging findings suggest that transient adaptation of feature-specific neurons within human posterior auditory cortex filters superfluous sounds from entering one's awareness.
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Project description:A variety of attention-related effects have been demonstrated in primary auditory cortex (A1). However, an understanding of the functional role of higher auditory cortical areas in guiding attention to acoustic stimuli has been elusive. We recorded from neurons in two tonotopic cortical belt areas in the dorsal posterior ectosylvian gyrus (dPEG) of ferrets trained on a simple auditory discrimination task. Neurons in dPEG showed similar basic auditory tuning properties to A1, but during behavior we observed marked differences between these areas. In the belt areas, changes in neuronal firing rate and response dynamics greatly enhanced responses to target stimuli relative to distractors, allowing for greater attentional selection during active listening. Consistent with existing anatomical evidence, the pattern of sensory tuning and behavioral modulation in auditory belt cortex links the spectrotemporal representation of the whole acoustic scene in A1 to a more abstracted representation of task-relevant stimuli observed in frontal cortex.
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Project description:Associative learning of pure tones is known to cause tonotopic map expansion in the auditory cortex (ACx), but the function this plasticity sub-serves is unclear. We developed an automated training platform called the "Educage," which was used to train mice on a go/no-go auditory discrimination task to their perceptual limits, for difficult discriminations among pure tones or natural sounds. Spiking responses of excitatory and inhibitory parvalbumin (PV+) L2/3 neurons in mouse ACx revealed learning-induced overrepresentation of the learned frequencies, as expected from previous literature. The coordinated plasticity of excitatory and inhibitory neurons supports a role for PV+ neurons in homeostatic maintenance of excitation-inhibition balance within the circuit. Using a novel computational model to study auditory tuning curves, we show that overrepresentation of the learned tones does not necessarily improve discrimination performance of the network to these tones. In a separate set of experiments, we trained mice to discriminate among natural sounds. Perceptual learning of natural sounds induced "sparsening" and decorrelation of the neural response, consequently improving discrimination of these complex sounds. This signature of plasticity in A1 highlights its role in coding natural sounds.
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Project description:We investigate how the neural processing in auditory cortex is shaped by the statistics of natural sounds. Hypothesising that auditory cortex (A1) represents the structural primitives out of which sounds are composed, we employ a statistical model to extract such components. The input to the model are cochleagrams which approximate the non-linear transformations a sound undergoes from the outer ear, through the cochlea to the auditory nerve. Cochleagram components do not superimpose linearly, but rather according to a rule which can be approximated using the max function. This is a consequence of the compression inherent in the cochleagram and the sparsity of natural sounds. Furthermore, cochleagrams do not have negative values. Cochleagrams are therefore not matched well by the assumptions of standard linear approaches such as sparse coding or ICA. We therefore consider a new encoding approach for natural sounds, which combines a model of early auditory processing with maximal causes analysis (MCA), a sparse coding model which captures both the non-linear combination rule and non-negativity of the data. An efficient truncated EM algorithm is used to fit the MCA model to cochleagram data. We characterize the generative fields (GFs) inferred by MCA with respect to in vivo neural responses in A1 by applying reverse correlation to estimate spectro-temporal receptive fields (STRFs) implied by the learned GFs. Despite the GFs being non-negative, the STRF estimates are found to contain both positive and negative subfields, where the negative subfields can be attributed to explaining away effects as captured by the applied inference method. A direct comparison with ferret A1 shows many similar forms, and the spectral and temporal modulation tuning of both ferret and model STRFs show similar ranges over the population. In summary, our model represents an alternative to linear approaches for biological auditory encoding while it captures salient data properties and links inhibitory subfields to explaining away effects.
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Project description:The acoustic dimensions that distinguish speech sounds (like the vowel differences in "boot" and "boat") also differentiate speakers' voices. Therefore, listeners must normalize across speakers without losing linguistic information. Past behavioral work suggests an important role for auditory contrast enhancement in normalization: preceding context affects listeners' perception of subsequent speech sounds. Here, using intracranial electrocorticography in humans, we investigate whether and how such context effects arise in auditory cortex. Participants identified speech sounds that were preceded by phrases from two different speakers whose voices differed along the same acoustic dimension as target words (the lowest resonance of the vocal tract). In every participant, target vowels evoke a speaker-dependent neural response that is consistent with the listener's perception, and which follows from a contrast enhancement model. Auditory cortex processing thus displays a critical feature of normalization, allowing listeners to extract meaningful content from the voices of diverse speakers.
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Project description:Despite numerous studies of auditory cortical processing in the ferret (Mustela putorius), very little is known about the connections between the different regions of the auditory cortex that have been characterized cytoarchitectonically and physiologically. We examined the distribution of retrograde and anterograde labeling after injecting tracers into one or more regions of ferret auditory cortex. Injections of different tracers at frequency-matched locations in the core areas, the primary auditory cortex (A1) and anterior auditory field (AAF), of the same animal revealed the presence of reciprocal connections with overlapping projections to and from discrete regions within the posterior pseudosylvian and suprasylvian fields (PPF and PSF), suggesting that these connections are frequency specific. In contrast, projections from the primary areas to the anterior dorsal field (ADF) on the anterior ectosylvian gyrus were scattered and non-overlapping, consistent with the non-tonotopic organization of this field. The relative strength of the projections originating in each of the primary fields differed, with A1 predominantly targeting the posterior bank fields PPF and PSF, which in turn project to the ventral posterior field, whereas AAF projects more heavily to the ADF, which then projects to the anteroventral field and the pseudosylvian sulcal cortex. These findings suggest that parallel anterior and posterior processing networks may exist, although the connections between different areas often overlap and interactions were present at all levels.
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Project description:Sounds in our environment like voices, animal calls or musical instruments are easily recognized by human listeners. Understanding the key features underlying this robust sound recognition is an important question in auditory science. Here, we studied the recognition by human listeners of new classes of sounds: acoustic and auditory sketches, sounds that are severely impoverished but still recognizable. Starting from a time-frequency representation, a sketch is obtained by keeping only sparse elements of the original signal, here, by means of a simple peak-picking algorithm. Two time-frequency representations were compared: a biologically grounded one, the auditory spectrogram, which simulates peripheral auditory filtering, and a simple acoustic spectrogram, based on a Fourier transform. Three degrees of sparsity were also investigated. Listeners were asked to recognize the category to which a sketch sound belongs: singing voices, bird calls, musical instruments, and vehicle engine noises. Results showed that, with the exception of voice sounds, very sparse representations of sounds (10 features, or energy peaks, per second) could be recognized above chance. No clear differences could be observed between the acoustic and the auditory sketches. For the voice sounds, however, a completely different pattern of results emerged, with at-chance or even below-chance recognition performances, suggesting that the important features of the voice, whatever they are, were removed by the sketch process. Overall, these perceptual results were well correlated with a model of auditory distances, based on spectro-temporal excitation patterns (STEPs). This study confirms the potential of these new classes of sounds, acoustic and auditory sketches, to study sound recognition.
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