Attention Modulates the Auditory Cortical Processing of Spatial and Category Cues in Naturalistic Auditory Scenes.

This combined fMRI and MEG study investigated brain activations during listening and attending to natural auditory scenes. We first recorded, using in-ear microphones, vocal non-speech sounds, and environmental sounds that were mixed to construct auditory scenes containing two concurrent sound streams. During the brain measurements, subjects attended to one of the streams while spatial acoustic information of the scene was either preserved (stereophonic sounds) or removed (monophonic sounds). Compared to monophonic sounds, stereophonic sounds evoked larger blood-oxygenation-level-dependent (BOLD) fMRI responses in the bilateral posterior superior temporal areas, independent of which stimulus attribute the subject was attending to. This finding is consistent with the functional role of these regions in the (automatic) processing of auditory spatial cues. Additionally, significant differences in the cortical activation patterns depending on the target of attention were observed. Bilateral planum temporale and inferior frontal gyrus were preferentially activated when attending to stereophonic environmental sounds, whereas when subjects attended to stereophonic voice sounds, the BOLD responses were larger at the bilateral middle superior temporal gyrus and sulcus, previously reported to show voice sensitivity. In contrast, the time-resolved MEG responses were stronger for mono- than stereophonic sounds in the bilateral auditory cortices at ~360 ms after the stimulus onset when attending to the voice excerpts within the combined sounds. The observed effects suggest that during the segregation of auditory objects from the auditory background, spatial sound cues together with other relevant temporal and spectral cues are processed in an attention-dependent manner at the cortical locations generally involved in sound recognition. More synchronous neuronal activation during monophonic than stereophonic sound processing, as well as (local) neuronal inhibitory mechanisms in the auditory cortex, may explain the simultaneous increase of BOLD responses and decrease of MEG responses. These findings highlight the complimentary role of electrophysiological and hemodynamic measures in addressing brain processing of complex stimuli.

Of cats and women: temporal dynamics in the right temporoparietal cortex reflect auditory categorical processing of vocalizations.

Understanding the temporal dynamics underlying cortical processing of auditory categories is complicated by difficulties in equating temporal and spectral features across stimulus classes. In the present magnetoencephalography (MEG) study, female voices and cat sounds were filtered so as to match in most of their acoustic properties, and the respective auditory evoked responses were investigated with a paradigm that allowed us to examine auditory cortical processing of two natural sound categories beyond the physical make-up of the stimuli. Three cat or human voice sounds were first presented to establish a categorical context. Subsequently, a probe sound that was congruent, incongruent, or ambiguous to this context was presented. As an index of a categorical mismatch, MEG responses to incongruent sounds were stronger than the responses to congruent sounds at ~250 ms in the right temporoparietal cortex, regardless of the sound category. Furthermore, probe sounds that could not be unambiguously attributed to any of the two categories ("cat" or "voice") evoked stronger responses after the voice than cat context at 200-250 ms, suggesting a stronger contextual effect for human voices. Our results suggest that categorical templates for human and animal vocalizations are established at ~250 ms in the right temporoparietal cortex, likely reflecting continuous online analysis of spectral stimulus features during auditory categorizing task.

Sound categories are represented as distributed patterns in the human auditory cortex.

The ability to recognize sounds allows humans and animals to efficiently detect behaviorally relevant events, even in the absence of visual information. Sound recognition in the human brain has been assumed to proceed through several functionally specialized areas, culminating in cortical modules where category-specific processing is carried out. In the present high-resolution fMRI experiment, we challenged this model by using well-controlled natural auditory stimuli and by employing an advanced analysis strategy based on an iterative machine-learning algorithm that allows modeling of spatially distributed, as well as localized, response patterns. Sounds of cats, female singers, acoustic guitars, and tones were controlled for their time-varying spectral characteristics and presented to subjects at three different pitch levels. Sound category information--not detectable with conventional contrast-based methods analysis--could be detected with multivoxel pattern analyses and attributed to spatially distributed areas over the supratemporal cortices. A more localized pattern was observed for processing of pitch laterally to primary auditory areas. Our findings indicate that distributed neuronal populations within the human auditory cortices, including areas conventionally associated with lower-level auditory processing, entail categorical representations of sounds beyond their physical properties.

Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns.

In functional brain mapping, pattern recognition methods allow detecting multivoxel patterns of brain activation which are informative with respect to a subject's perceptual or cognitive state. The sensitivity of these methods, however, is greatly reduced when the proportion of voxels that convey the discriminative information is small compared to the total number of measured voxels. To reduce this dimensionality problem, previous studies employed univariate voxel selection or region-of-interest-based strategies as a preceding step to the application of machine learning algorithms. Here we employ a strategy for classifying functional imaging data based on a multivariate feature selection algorithm, Recursive Feature Elimination (RFE) that uses the training algorithm (support vector machine) recursively to eliminate irrelevant voxels and estimate informative spatial patterns. Generalization performances on test data increases while features/voxels are pruned based on their discrimination ability. In this article we evaluate RFE in terms of sensitivity of discriminative maps (Receiver Operative Characteristic analysis) and generalization performances and compare it to previously used univariate voxel selection strategies based on activation and discrimination measures. Using simulated fMRI data, we show that the recursive approach is suitable for mapping discriminative patterns and that the combination of an initial univariate activation-based (F-test) reduction of voxels and multivariate recursive feature elimination produces the best results, especially when differences between conditions have a low contrast-to-noise ratio. Furthermore, we apply our method to high resolution (2 x 2 x 2 mm(3)) data from an auditory fMRI experiment in which subjects were stimulated with sounds from four different categories. With these real data, our recursive algorithm proves able to detect and accurately classify multivoxel spatial patterns, highlighting the role of the superior temporal gyrus in encoding the information of sound categories. In line with the simulation results, our method outperforms univariate statistical analysis and statistical learning without feature selection.

Dynamic premotor-to-parietal interactions during spatial imagery.

The neurobiological processes underlying mental imagery are a matter of debate and controversy among neuroscientists, cognitive psychologists, philosophers, and biologists. Recent neuroimaging studies demonstrated that the execution of mental imagery activates large frontoparietal and occipitotemporal networks in the human brain. These previous imaging studies, however, neglected the crucial interplay within and across the widely distributed cortical networks of activated brain regions. Here, we combined time-resolved event-related functional magnetic resonance imaging with analyses of interactions between brain regions (functional and effective brain connectivity) to unravel the premotor-parietal dynamics underlying spatial imagery. Participants had to sequentially construct and spatially transform a mental visual object based on either verbal or visual instructions. By concurrently accounting for the full spatiotemporal pattern of brain activity and network connectivity, we functionally segregated an early from a late premotor-parietal imagery network. Moreover, we revealed that the modality-specific information upcoming from sensory brain regions is first sent to the premotor cortex and then to the medial-dorsal parietal cortex, i.e., top-down from the motor to the perceptual pole during spatial imagery. Importantly, we demonstrate that the premotor cortex serves as the central relay station, projecting to parietal cortex at two functionally distinct stages during spatial imagery. Our approach enabled us to disentangle the multicomponential cognitive construct of mental imagery into its different cognitive subelements. We discuss and explicitly assign these mental subprocesses to each of the revealed effective brain connectivity networks and present an integrative neurobiological model of spatial imagery.

Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis.

In combination with cognitive tasks entailing sequences of sensory and cognitive processes, event-related acquisition schemes allow using functional MRI to examine not only the topography but also the temporal sequence of cortical activation across brain regions (time-resolved fMRI). In this study, we compared two data-driven methods--fuzzy clustering method (FCM) and independent component analysis (ICA)--in the context of time-resolved fMRI data collected during the performance of a newly devised visual imagery task. We analyzed a multisubject fMRI data set using both methods and compared their results in terms of within- and between-subject consistency and spatial and temporal correspondence of obtained maps and time courses. Both FCM and spatial ICA allowed discriminating the contribution of distinct networks of brain regions to the main cognitive stages of the task (auditory perception, mental imagery and behavioural response), with good agreement across methods. Whereas ICA worked optimally on the original time series, averaging with respect to the task onset (and thus introducing some a priori information on the stimulation protocol) was found to be indispensable in the case of FCM. On averaged time series, FCM led to a richer decomposition of the spatio-temporal patterns of activation and allowed a finer separation of the neurocognitive processes subserving the mental imagery task. This study confirms the efficacy of the two examined methods in the data-driven estimation of hemodynamic responses in time-resolved fMRI studies and provides empirical guidelines to their use.