It is a matter of perspective: Attentional focus rather than dietary restraint drives brain responses to food stimuli.

Brain responses to food are thought to reflect food's rewarding value and to fluctuate with dietary restraint. We propose that brain responses to food are dynamic and depend on attentional focus. Food pictures (high-caloric/low-caloric, palatable/unpalatable) were presented during fMRI-scanning, while attentional focus (hedonic/health/neutral) was induced in 52 female participants varying in dietary restraint. The level of brain activity was hardly different between palatable versus unpalatable foods or high-caloric versus low-caloric foods. Activity in several brain regions was higher in hedonic than in health or neutral attentional focus (p < .05, FWE-corrected). Palatability and calorie content could be decoded from multi-voxel activity patterns (p < .05, FDR-corrected). Dietary restraint did not significantly influence brain responses to food. So, level of brain activity in response to food stimuli depends on attentional focus, and may reflect salience, not reward value. Palatability and calorie content are reflected in patterns of brain activity.

More complex than you might think: Neural representations of food reward value in obesity.

Obesity reached pandemic proportions and weight-loss treatments are mostly ineffective. The level of brain activity in the reward circuitry is proposed to be proportionate to the reward value of food stimuli, and stronger in people with obesity. However, empirical evidence is inconsistent. This may be due to the double-sided nature of high caloric palatable foods: at once highly palatable and high in calories (unhealthy). This study hypothesizes that, viewing high caloric palatable foods, a hedonic attentional focus compared to a health and a neutral attentional focus elicits more activity in reward-related brain regions, mostly in people with obesity. Moreover, caloric content and food palatability can be decoded from multivoxel patterns of activity most accurately in people with obesity and in the corresponding attentional focus. During one fMRI-session, attentional focus (hedonic, health, neutral) was manipulated using a one-back task with individually tailored food stimuli in 32 healthy-weight people and 29 people with obesity. Univariate analyses (p < 0.05, FWE-corrected) showed that brain activity was not different for palatable vs. unpalatable foods, nor for high vs. low caloric foods. Instead, this was higher in the hedonic compared to the health and neutral attentional focus. Multivariate analyses (MVPA) (p < 0.05, FDR-corrected) showed that palatability and caloric content could be decoded above chance level, independently of either BMI or attentional focus. Thus, brain activity to visual food stimuli is neither proportionate to the reward value (palatability and/or caloric content), nor significantly moderated by BMI. Instead, it depends on people's attentional focus, and may reflect motivational salience. Furthermore, food palatability and caloric content are represented as patterns of brain activity, independently of BMI and attentional focus. So, food reward value is reflected in patterns, not levels, of brain activity.

Cross-validation and permutations in MVPA: Validity of permutation strategies and power of cross-validation schemes.

Multi-Voxel Pattern Analysis (MVPA) is a well established tool to disclose weak, distributed effects in brain activity patterns. The generalization ability is assessed by testing the learning model on new, unseen data. However, when limited data is available, the decoding success is estimated using cross-validation. There is general consensus on assessing statistical significance of cross-validated accuracy with non-parametric permutation tests. In this work we focus on the false positive control of different permutation strategies and on the statistical power of different cross-validation schemes. With simulations, we show that estimating the entire cross-validation error on each permuted dataset is the only statistically valid permutation strategy. Furthermore, using both simulations and real data from the HCP WU-Minn 3T fMRI dataset, we show that, among the different cross-validation schemes, a repeated split-half cross-validation is the most powerful, despite achieving slightly lower classification accuracy, when compared to other schemes. Our findings provide additional insights into the optimization of the experimental design for MVPA, highlighting the benefits of having many short runs.

Reorganization of Sound Location Processing in the Auditory Cortex of Blind Humans.

Auditory spatial tasks induce functional activation in the occipital-visual-cortex of early blind humans. Less is known about the effects of blindness on auditory spatial processing in the temporal-auditory-cortex. Here, we investigated spatial (azimuth) processing in congenitally and early blind humans with a phase-encoding functional magnetic resonance imaging (fMRI) paradigm. Our results show that functional activation in response to sounds in general-independent of sound location-was stronger in the occipital cortex but reduced in the medial temporal cortex of blind participants in comparison with sighted participants. Additionally, activation patterns for binaural spatial processing were different for sighted and blind participants in planum temporale. Finally, fMRI responses in the auditory cortex of blind individuals carried less information on sound azimuth position than those in sighted individuals, as assessed with a 2-channel, opponent coding model for the cortical representation of sound azimuth. These results indicate that early visual deprivation results in reorganization of binaural spatial processing in the auditory cortex and that blind individuals may rely on alternative mechanisms for processing azimuth position.

Methods for computing the maximum performance of computational models of fMRI responses.

Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models. The accuracy of such prediction is used as an indicator of how well the model describes the computations underlying the brain function that is being considered. However, the prediction accuracy is bounded by the proportion of the variance of the brain response which is related to the measurement noise and not to the stimuli (or cognitive functions). This bound to the performance of a computational model has been referred to as the noise ceiling. In previous fMRI applications two methods have been proposed to estimate the noise ceiling based on either a split-half procedure or Monte Carlo simulations. These methods make different assumptions over the nature of the effects underlying the data, and, importantly, their relation has not been clarified yet. Here, we derive an analytical form for the noise ceiling that does not require computationally expensive simulations or a splitting procedure that reduce the amount of data. The validity of this analytical definition is proved in simulations, we show that the analytical solution results in the same estimate of the noise ceiling as the Monte Carlo method. Considering different simulated noise structure, we evaluate different estimators of the variance of the responses and their impact on the estimation of the noise ceiling. We furthermore evaluate the interplay between regularization (often used to estimate model fits to the data when the number of computational features in the model is large) and model complexity on the performance with respect to the noise ceiling. Our results indicate that when considering the variance of the responses across runs, computing the noise ceiling analytically results in similar estimates as the split half estimator and approaches the true noise ceiling under a variety of simulated noise scenarios. Finally, the methods are tested on real fMRI data acquired at 7 Tesla.

Reconstructing the spectrotemporal modulations of real-life sounds from fMRI response patterns.

Ethological views of brain functioning suggest that sound representations and computations in the auditory neural system are optimized finely to process and discriminate behaviorally relevant acoustic features and sounds (e.g., spectrotemporal modulations in the songs of zebra finches). Here, we show that modeling of neural sound representations in terms of frequency-specific spectrotemporal modulations enables accurate and specific reconstruction of real-life sounds from high-resolution functional magnetic resonance imaging (fMRI) response patterns in the human auditory cortex. Region-based analyses indicated that response patterns in separate portions of the auditory cortex are informative of distinctive sets of spectrotemporal modulations. Most relevantly, results revealed that in early auditory regions, and progressively more in surrounding regions, temporal modulations in a range relevant for speech analysis (∼2-4 Hz) were reconstructed more faithfully than other temporal modulations. In early auditory regions, this effect was frequency-dependent and only present for lower frequencies (<∼2 kHz), whereas for higher frequencies, reconstruction accuracy was higher for faster temporal modulations. Further analyses suggested that auditory cortical processing optimized for the fine-grained discrimination of speech and vocal sounds underlies this enhanced reconstruction accuracy. In sum, the present study introduces an approach to embed models of neural sound representations in the analysis of fMRI response patterns. Furthermore, it reveals that, in the human brain, even general purpose and fundamental neural processing mechanisms are shaped by the physical features of real-world stimuli that are most relevant for behavior (i.e., speech, voice).

Active Sound Localization Sharpens Spatial Tuning in Human Primary Auditory Cortex.

Spatial hearing sensitivity in humans is dynamic and task-dependent, but the mechanisms in human auditory cortex that enable dynamic sound location encoding remain unclear. Using functional magnetic resonance imaging (fMRI), we assessed how active behavior affects encoding of sound location (azimuth) in primary auditory cortical areas and planum temporale (PT). According to the hierarchical model of auditory processing and cortical functional specialization, PT is implicated in sound location ("where") processing. Yet, our results show that spatial tuning profiles in primary auditory cortical areas (left primary core and right caudo-medial belt) sharpened during a sound localization ("where") task compared with a sound identification ("what") task. In contrast, spatial tuning in PT was sharp but did not vary with task performance. We further applied a population pattern decoder to the measured fMRI activity patterns, which confirmed the task-dependent effects in the left core: sound location estimates from fMRI patterns measured during active sound localization were most accurate. In PT, decoding accuracy was not modulated by task performance. These results indicate that changes of population activity in human primary auditory areas reflect dynamic and task-dependent processing of sound location. As such, our findings suggest that the hierarchical model of auditory processing may need to be revised to include an interaction between primary and functionally specialized areas depending on behavioral requirements.SIGNIFICANCE STATEMENT According to a purely hierarchical view, cortical auditory processing consists of a series of analysis stages from sensory (acoustic) processing in primary auditory cortex to specialized processing in higher-order areas. Posterior-dorsal cortical auditory areas, planum temporale (PT) in humans, are considered to be functionally specialized for spatial processing. However, this model is based mostly on passive listening studies. Our results provide compelling evidence that active behavior (sound localization) sharpens spatial selectivity in primary auditory cortex, whereas spatial tuning in functionally specialized areas (PT) is narrow but task-invariant. These findings suggest that the hierarchical view of cortical functional specialization needs to be extended: our data indicate that active behavior involves feedback projections from higher-order regions to primary auditory cortex.

How to test for phasic modulation of neural and behavioural responses.

Research on whether perception or other processes depend on the phase of neural oscillations is rapidly gaining popularity. However, it is unknown which methods are optimally suited to evaluate the hypothesized phase effect. Using a simulation approach, we here test the ability of different methods to detect such an effect on dichotomous (e.g., "hit" vs "miss") and continuous (e.g., scalp potentials) response variables. We manipulated parameters that characterise the phase effect or define the experimental approach to test for this effect. For each parameter combination and response variable, we identified an optimal method. We found that methods regressing single-trial responses on circular (sine and cosine) predictors perform best for all of the simulated parameters, regardless of the nature of the response variable (dichotomous or continuous). In sum, our study lays a foundation for optimized experimental designs and analyses in future studies investigating the role of phase for neural and behavioural responses. We provide MATLAB code for the statistical methods tested.

Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs.

Functional Magnetic Resonance Imaging (fMRI) has been successfully used for Brain Computer Interfacing (BCI) to classify (imagined) movements of different limbs. However, reliable classification of more subtle signals originating from co-localized neural networks in the sensorimotor cortex, e.g. individual movements of fingers of the same hand, has proved to be more challenging, especially when taking into account the requirement for high single trial reliability in the BCI context. In recent years, Multi Voxel Pattern Analysis (MVPA) has gained momentum as a suitable method to disclose such weak, distributed activation patterns. Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA. When applicable, block designs are considered the safest choice, but the expectations, strategies and adaptation induced by blocking of similar trials can make it a sub-optimal strategy. Fast event-related designs, in contrast, require a more complicated analysis and show stronger dependence on linearity assumptions but allow for randomly alternating trials. However, they lack resting intervals that enable the BCI participant to process feedback. In this proof-of-concept paper a hybrid blocked fast-event related design is introduced that is novel in the context of MVPA and BCI experiments, and that might overcome these issues by combining the rest periods of the block design with the shorter and randomly alternating trial characteristics of a rapid event-related design. A well-established button-press experiment was used to perform a within-subject comparison of the proposed design with a block and a slow event-related design. The proposed hybrid blocked fast-event related design showed a decoding accuracy that was close to that of the block design, which showed highest accuracy. It allowed for across-design decoding, i.e. reliable prediction of examples obtained with another design. Finally, it also showed the most stable incremental decoding results, obtaining good performance with relatively few blocks. Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of trials of the same type a sub-optimal strategy. Additionally, the blocked fast event-related design is also suitable for applications in which fast incremental decoding is desired, and enables the use of a slow or block design during the test phase.

Cortical tracking of multiple streams outside the focus of attention in naturalistic auditory scenes.

In everyday life, we process mixtures of a variety of sounds. This processing involves the segregation of auditory input and the attentive selection of the stream that is most relevant to current goals. For natural scenes with multiple irrelevant sounds, however, it is unclear how the human auditory system represents all the unattended sounds. In particular, it remains elusive whether the sensory input to the human auditory cortex of unattended sounds biases the cortical integration/segregation of these sounds in a similar way as for attended sounds. In this study, we tested this by asking participants to selectively listen to one of two speakers or music in an ongoing 1-min sound mixture while their cortical neural activity was measured with EEG. Using a stimulus reconstruction approach, we find better reconstruction of mixed unattended sounds compared to individual unattended sounds at two early cortical stages (70 ms and 150 ms) of the auditory processing hierarchy. Crucially, at the earlier processing stage (70 ms), this cortical bias to represent unattended sounds as integrated rather than segregated increases with increasing similarity of the unattended sounds. Our results reveal an important role of acoustical properties for the cortical segregation of unattended auditory streams in natural listening situations. They further corroborate the notion that selective attention contributes functionally to cortical stream segregation. These findings highlight that a common, acoustics-based grouping principle governs the cortical representation of auditory streams not only inside but also outside the listener's focus of attention.

Frequency-specific attentional modulation in human primary auditory cortex and midbrain.

Paying selective attention to an audio frequency selectively enhances activity within primary auditory cortex (PAC) at the tonotopic site (frequency channel) representing that frequency. Animal PAC neurons achieve this 'frequency-specific attentional spotlight' by adapting their frequency tuning, yet comparable evidence in humans is scarce. Moreover, whether the spotlight operates in human midbrain is unknown. To address these issues, we studied the spectral tuning of frequency channels in human PAC and inferior colliculus (IC), using 7-T functional magnetic resonance imaging (FMRI) and frequency mapping, while participants focused on different frequency-specific sounds. We found that shifts in frequency-specific attention alter the response gain, but not tuning profile, of PAC frequency channels. The gain modulation was strongest in low-frequency channels and varied near-monotonically across the tonotopic axis, giving rise to the attentional spotlight. We observed less prominent, non-tonotopic spatial patterns of attentional modulation in IC. These results indicate that the frequency-specific attentional spotlight in human PAC as measured with FMRI arises primarily from tonotopic gain modulation, rather than adapted frequency tuning. Moreover, frequency-specific attentional modulation of afferent sound processing in human IC seems to be considerably weaker, suggesting that the spotlight diminishes toward this lower-order processing stage. Our study sheds light on how the human auditory pathway adapts to the different demands of selective hearing.

Frequency-Selective Attention in Auditory Scenes Recruits Frequency Representations Throughout Human Superior Temporal Cortex.

A sound of interest may be tracked amid other salient sounds by focusing attention on its characteristic features including its frequency. Functional magnetic resonance imaging findings have indicated that frequency representations in human primary auditory cortex (AC) contribute to this feat. However, attentional modulations were examined at relatively low spatial and spectral resolutions, and frequency-selective contributions outside the primary AC could not be established. To address these issues, we compared blood oxygenation level-dependent (BOLD) responses in the superior temporal cortex of human listeners while they identified single frequencies versus listened selectively for various frequencies within a multifrequency scene. Using best-frequency mapping, we observed that the detailed spatial layout of attention-induced BOLD response enhancements in primary AC follows the tonotopy of stimulus-driven frequency representations-analogous to the "spotlight" of attention enhancing visuospatial representations in retinotopic visual cortex. Moreover, using an algorithm trained to discriminate stimulus-driven frequency representations, we could successfully decode the focus of frequency-selective attention from listeners' BOLD response patterns in nonprimary AC. Our results indicate that the human brain facilitates selective listening to a frequency of interest in a scene by reinforcing the fine-grained activity pattern throughout the entire superior temporal cortex that would be evoked if that frequency was present alone.

Fast Gaussian Naïve Bayes for searchlight classification analysis.

The searchlight technique is a variant of multivariate pattern analysis (MVPA) that examines neural activity across large sets of small regions, exhaustively covering the whole brain. This usually involves application of classifier algorithms across all searchlights, which entails large computational costs especially when testing the statistical significance of the accuracies with permutation methods. In this article, a new implementation of the Gaussian Naive Bayes classifier is presented (henceforth massive-GNB). This approach allows classification in all searchlights simultaneously, and is faster than previously published searchlight GNB implementations, as well as other more complex classifiers including support vector machines (SVM). To ensure that the gain in speed for GNB would be useful in searchlight analysis, we compared the accuracies of massive-GNB and SVM in detecting the lateral occipital complex (LOC) in an fMRI localizer experiment (26 subjects). Moreover, this region as defined in a meta-analysis of many activation studies was used as a gold standard to compare error rates for both classifiers. In individual searchlights, SVM was somewhat more accurate than massive-GNB and more selective in detecting the meta-analytic LOC. However, with multiple comparison correction at the cluster-level the two classifiers performed equivalently. Thus for cluster-level analysis, massive-GNB produces an accuracy similar to more sophisticated classifiers but with a substantial gain in speed. Massive-GNB (available as a public Matlab toolbox) could facilitate the more widespread use of searchlight analysis.

Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression.

Major methodological advancements have been recently made in the field of neural decoding, which is concerned with the reconstruction of mental content from neuroimaging measures. However, in the absence of a large-scale examination of the validity of the decoding models across subjects and content, the extent to which these models can be generalized is not clear. This study addresses the challenge of producing generalizable decoding models, which allow the reconstruction of perceived audiovisual features from human magnetic resonance imaging (fMRI) data without prior training of the algorithm on the decoded content. We applied an adapted version of kernel ridge regression combined with temporal optimization on data acquired during film viewing (234 runs) to generate standardized brain models for sound loudness, speech presence, perceived motion, face-to-frame ratio, lightness, and color brightness. The prediction accuracies were tested on data collected from different subjects watching other movies mainly in another scanner. Substantial and significant (QFDR<0.05) correlations between the reconstructed and the original descriptors were found for the first three features (loudness, speech, and motion) in all of the 9 test movies (R¯=0.62, R¯ = 0.60, R¯ = 0.60, respectively) with high reproducibility of the predictors across subjects. The face ratio model produced significant correlations in 7 out of 8 movies (R¯=0.56). The lightness and brightness models did not show robustness (R¯=0.23, R¯ = 0). Further analysis of additional data (95 runs) indicated that loudness reconstruction veridicality can consistently reveal relevant group differences in musical experience. The findings point to the validity and generalizability of our loudness, speech, motion, and face ratio models for complex cinematic stimuli (as well as for music in the case of loudness). While future research should further validate these models using controlled stimuli and explore the feasibility of extracting more complex models via this method, the reliability of our results indicates the potential usefulness of the approach and the resulting models in basic scientific and diagnostic contexts.

Opponent Coding of Sound Location (Azimuth) in Planum Temporale is Robust to Sound-Level Variations.

Coding of sound location in auditory cortex (AC) is only partially understood. Recent electrophysiological research suggests that neurons in mammalian auditory cortex are characterized by broad spatial tuning and a preference for the contralateral hemifield, that is, a nonuniform sampling of sound azimuth. Additionally, spatial selectivity decreases with increasing sound intensity. To accommodate these findings, it has been proposed that sound location is encoded by the integrated activity of neuronal populations with opposite hemifield tuning ("opponent channel model"). In this study, we investigated the validity of such a model in human AC with functional magnetic resonance imaging (fMRI) and a phase-encoding paradigm employing binaural stimuli recorded individually for each participant. In all subjects, we observed preferential fMRI responses to contralateral azimuth positions. Additionally, in most AC locations, spatial tuning was broad and not level invariant. We derived an opponent channel model of the fMRI responses by subtracting the activity of contralaterally tuned regions in bilateral planum temporale. This resulted in accurate decoding of sound azimuth location, which was unaffected by changes in sound level. Our data thus support opponent channel coding as a neural mechanism for representing acoustic azimuth in human AC.

Localization of complex sounds is modulated by behavioral relevance and sound category.

Meaningful sounds represent the majority of sounds that humans hear and process in everyday life. Yet studies of human sound localization mainly use artificial stimuli such as clicks, pure tones, and noise bursts. The present study investigated the influence of behavioral relevance, sound category, and acoustic properties on the localization of complex, meaningful sounds in the horizontal plane. Participants localized vocalizations and traffic sounds with two levels of behavioral relevance (low and high) within each category, as well as amplitude-modulated tones. Results showed a small but significant effect of behavioral relevance: localization acuity was higher for complex sounds with a high level of behavioral relevance at several target locations. The data also showed category-specific effects: localization biases were lower, and localization precision higher, for vocalizations than for traffic sounds in central space. Several acoustic parameters influenced sound localization performance as well. Correcting localization responses for front-back reversals reduced the overall variability across sounds, but behavioral relevance and sound category still had a modulatory effect on sound localization performance in central auditory space. The results thus demonstrate that spatial hearing performance for complex sounds is influenced not only by acoustic characteristics, but also by sound category and behavioral relevance.

The effect of spatial resolution on decoding accuracy in fMRI multivariate pattern analysis.

Multivariate pattern analysis (MVPA) in fMRI has been used to extract information from distributed cortical activation patterns, which may go undetected in conventional univariate analysis. However, little is known about the physical and physiological underpinnings of MVPA in fMRI as well as about the effect of spatial smoothing on its performance. Several studies have addressed these issues, but their investigation was limited to the visual cortex at 3T with conflicting results. Here, we used ultra-high field (7T) fMRI to investigate the effect of spatial resolution and smoothing on decoding of speech content (vowels) and speaker identity from auditory cortical responses. To that end, we acquired high-resolution (1.1mm isotropic) fMRI data and additionally reconstructed them at 2.2 and 3.3mm in-plane spatial resolutions from the original k-space data. Furthermore, the data at each resolution were spatially smoothed with different 3D Gaussian kernel sizes (i.e. no smoothing or 1.1, 2.2, 3.3, 4.4, or 8.8mm kernels). For all spatial resolutions and smoothing kernels, we demonstrate the feasibility of decoding speech content (vowel) and speaker identity at 7T using support vector machine (SVM) MVPA. In addition, we found that high spatial frequencies are informative for vowel decoding and that the relative contribution of high and low spatial frequencies is different across the two decoding tasks. Moderate smoothing (up to 2.2mm) improved the accuracies for both decoding of vowels and speakers, possibly due to reduction of noise (e.g. residual motion artifacts or instrument noise) while still preserving information at high spatial frequency. In summary, our results show that - even with the same stimuli and within the same brain areas - the optimal spatial resolution for MVPA in fMRI depends on the specific decoding task of interest.

Brain-based decoding of mentally imagined film clips and sounds reveals experience-based information patterns in film professionals.

In the perceptual domain, it has been shown that the human brain is strongly shaped through experience, leading to expertise in highly-skilled professionals. What has remained unclear is whether specialization also shapes brain networks underlying mental imagery. In our fMRI study, we aimed to uncover modality-specific mental imagery specialization of film experts. Using multi-voxel pattern analysis we decoded from brain activity of professional cinematographers and sound designers whether they were imagining sounds or images of particular film clips. In each expert group distinct multi-voxel patterns, specific for the modality of their expertise, were found during classification of imagery modality. These patterns were mainly localized in the occipito-temporal and parietal cortex for cinematographers and in the auditory cortex for sound designers. We also found generalized patterns across perception and imagery that were distinct for the two expert groups: they involved frontal cortex for the cinematographers and temporal cortex for the sound designers. Notably, the mental representations of film clips and sounds of cinematographers contained information that went beyond modality-specificity. We were able to successfully decode the implicit presence of film genre from brain activity during mental imagery in cinematographers. The results extend existing neuroimaging literature on expertise into the domain of mental imagery and show that experience in visual versus auditory imagery can alter the representation of information in modality-specific association cortices.

Task-dependent decoding of speaker and vowel identity from auditory cortical response patterns.

Selective attention to relevant sound properties is essential for everyday listening situations. It enables the formation of different perceptual representations of the same acoustic input and is at the basis of flexible and goal-dependent behavior. Here, we investigated the role of the human auditory cortex in forming behavior-dependent representations of sounds. We used single-trial fMRI and analyzed cortical responses collected while subjects listened to the same speech sounds (vowels /a/, /i/, and /u/) spoken by different speakers (boy, girl, male) and performed a delayed-match-to-sample task on either speech sound or speaker identity. Univariate analyses showed a task-specific activation increase in the right superior temporal gyrus/sulcus (STG/STS) during speaker categorization and in the right posterior temporal cortex during vowel categorization. Beyond regional differences in activation levels, multivariate classification of single trial responses demonstrated that the success with which single speakers and vowels can be decoded from auditory cortical activation patterns depends on task demands and subject's behavioral performance. Speaker/vowel classification relied on distinct but overlapping regions across the (right) mid-anterior STG/STS (speakers) and bilateral mid-posterior STG/STS (vowels), as well as the superior temporal plane including Heschl's gyrus/sulcus. The task dependency of speaker/vowel classification demonstrates that the informative fMRI response patterns reflect the top-down enhancement of behaviorally relevant sound representations. Furthermore, our findings suggest that successful selection, processing, and retention of task-relevant sound properties relies on the joint encoding of information across early and higher-order regions of the auditory cortex.