Horizontal tuning for faces originates in high-level Fusiform Face Area

Recent work indicates that the specialization of face visual perception relies on the privileged processing of horizontal angles of facial information. This suggests that stimulus properties assumed to be fully resolved in primary visual cortex (V1; e.g., orientation) in fact determine human vision until high-level stages of processing. To address this hypothesis, the present fMRI study explored the orientation sensitivity of V1 and high-level face-specialized ventral regions such as the Occipital Face Area (OFA) and Fusiform Face Area (FFA) to different angles of face information. Participants viewed face images filtered to retain information at horizontal, vertical or oblique angles. Filtered images were viewed upright, inverted and (phase-)scrambled. FFA responded most strongly to the horizontal range of upright face information; its activation pattern reliably separated horizontal from oblique ranges, but only when faces were upright. Moreover, activation patterns induced in the right FFA and the OFA by upright and inverted faces could only be separated based on horizontal information. This indicates that the specialized processing of upright face information in the OFA and FFA essentially relies on the encoding of horizontal facial cues. This pattern was not passively inherited from V1, which was found to respond less strongly to horizontal than other orientations likely due to adaptive whitening. Moreover, we found that orientation decoding accuracy in V1 was impaired for stimuli containing no meaningful shape. By showing that primary coding in V1 is influenced by high-order stimulus structure and that high-level processing is tuned to selective ranges of primary information, the present work suggests that primary and high-level levels of the visual system interact in order to modulate the processing of certain ranges of primary information depending on their relevance with respect to the stimulus and task at hand

Neural correlates of phonetic adaptation as induced by lexical and audiovisual context

When speech perception is difficult, one way listeners adjust is by reconfiguring phoneme category boundaries, drawing on contextual information. Both lexical knowledge and lipreading cues are used in this way, but it remains unknown whether these two differing forms of perceptual learning are similar at a neural level. This study compared phoneme boundary adjustments driven by lexical or audiovisual cues, using ultra-high-field 7-T fMRI. During imaging, participants heard exposure stimuli and test stimuli. Exposure stimuli for lexical retuning were audio recordings of words, and those for audiovisual recalibration were audio–video recordings of lip movements during utterances of pseudowords. Test stimuli were ambiguous phonetic strings presented without context, and listeners reported what phoneme they heard. Reports reflected phoneme biases in preceding exposure blocks (e.g., more reported /p/ after /p/-biased exposure). Analysis of corresponding brain responses indicated that both forms of cue use were associated with a network of activity across the temporal cortex, plus parietal, insula, and motor areas. Audiovisual recalibration also elicited significant occipital cortex activity despite the lack of visual stimuli. Activity levels in several ROIs also covaried with strength of audiovisual recalibration, with greater activity accompanying larger recalibration shifts. Similar activation patterns appeared for lexical retuning, but here, no significant ROIs were identified. Audiovisual and lexical forms of perceptual learning thus induce largely similar brain response patterns. However, audiovisual recalibration involves additional visual cortex contributions, suggesting that previously acquired visual information (on lip movements) is retrieved and deployed to disambiguate auditory perception

Sensory substitution information informs locomotor adjustments when walking through apertures

The study assessed the ability of the central nervous system (CNS) to use echoic information from sensory substitution devices (SSDs) to rotate the shoulders and safely pass through apertures of different width. Ten visually normal participants performed this task with full vision, or blindfolded using an SSD to obtain information regarding the width of an aperture created by two parallel panels. Two SSDs were tested. Participants passed through apertures of +0%, +18%, +35%, and +70% of measured body width. Kinematic indices recorded movement time, shoulder rotation, average walking velocity across the trial, peak walking velocities before crossing, after crossing and throughout a whole trial. Analyses showed participants used SSD information to regulate shoulder rotation, with greater rotation associated with narrower apertures. Rotations made using an SSD were greater compared to vision, movement times were longer, average walking velocity lower and peak velocities before crossing, after crossing and throughout the whole trial were smaller, suggesting greater caution. Collisions sometimes occurred using an SSD but not using vision, indicating that substituted information did not always result in accurate shoulder rotation judgements. No differences were found between the two SSDs. The data suggest that spatial information, provided by sensory substitution, allows the relative position of aperture panels to be internally represented, enabling the CNS to modify shoulder rotation according to aperture width. Increased buffer space indicated by greater rotations (up to approximately 35% for apertures of +18% of body width), suggests that spatial representations are not as accurate as offered by full vision

Multiclass fMRI data decoding and visualization using supervised self-organizing maps

When multivariate pattern decoding is applied to fMRI studies entailing more than two experimental conditions, a most common approach is to transform the multiclass classification problem into a series of binary problems. Furthermore, for decoding analyses, classification accuracy is often the only outcome reported although the topology of activation patterns in the high-dimensional features space may provide additional insights into underlying brain representations. Here we propose to decode and visualize voxel patterns of fMRI datasets consisting of multiple conditions with a supervised variant of self-organizing maps (SSOMs). Using simulations and real fMRI data, we evaluated the performance of our SSOM-based approach. Specifically, the analysis of simulated fMRI data with varying signal-to-noise and contrast-to-noise ratio suggested that SSOMs perform better than a k-nearest-neighbor classifier for medium and large numbers of features (i.e. 250 to 1000 or more voxels) and similar to support vector machines (SVMs) for small and medium numbers of features (i.e. 100 to 600voxels). However, for a larger number of features (>800voxels), SSOMs performed worse than SVMs. When applied to a challenging 3-class fMRI classification problem with datasets collected to examine the neural representation of three human voices at individual speaker level, the SSOM-based algorithm was able to decode speaker identity from auditory cortical activation patterns. Classification performances were similar between SSOMs and other decoding algorithms; however, the ability to visualize decoding models and underlying data topology of SSOMs promotes a more comprehensive understanding of classification outcomes. We further illustrated this visualization ability of SSOMs with a re-analysis of a dataset examining the representation of visual categories in the ventral visual cortex (Haxby et al., 2001). This analysis showed that SSOMs could retrieve and visualize topography and neighborhood relations of the brain representation of eight visual categories. We conclude that SSOMs are particularly suited for decoding datasets consisting of more than two classes and are optimally combined with approaches that reduce the number of voxels used for classification (e.g. region-of-interest or searchlight approaches)

Pattern analysis of EEG responses to speech and voice: Influence of feature grouping

Pattern recognition algorithms are becoming increasingly used in functional neuroimaging. These algorithms exploit information contained in temporal, spatial, or spatio-temporal patterns of independent variables (features) to detect subtle but reliable differences between brain responses to external stimuli or internal brain states. When applied to the analysis of electroencephalography (EEG) or magnetoencephalography (MEG) data, a choice needs to be made on how the input features to the algorithm are obtained from the signal amplitudes measured at the various channels. In this article, we consider six types of pattern analyses deriving from the combination of three types of feature selection in the temporal domain (predefined windows, shifting window, whole trial) with two approaches to handle the channel dimension (channel wise, multi-channel). We combined these different types of analyses with a Gaussian Naive Bayes classifier and analyzed a multi-subject EEG data set from a study aimed at understanding the task dependence of the cortical mechanisms for encoding speaker's identity and speech content (vowels) from short speech utterances (Bonte, Valente, & Formisano, 2009). Outcomes of the analyses showed that different grouping of available features helps highlighting complementary (i.e. temporal, topographic) aspects of information content in the data. A shifting window/multi-channel approach proved especially valuable in tracing both the early build up of neural information reflecting speaker or vowel identity and the late and task-dependent maintenance of relevant information reflecting the performance of a working memory task. Because it exploits the high temporal resolution of EEG (and MEG), such a shifting window approach with sequential multi-channel classifications seems the most appropriate choice for tracing the temporal profile of neural information processing

EEG decoding of spoken words in bilingual listeners: from words to language invariant semantic-conceptual representations

Spoken word recognition and production require fast transformations between acoustic, phonological, and conceptual neural representations. Bilinguals perform these transformations in native and non-native languages, deriving unified semantic concepts from equivalent, but acoustically different words. Here we exploit this capacity of bilinguals to investigate input invariant semantic representations in the brain. We acquired EEG data while Dutch subjects, highly proficient in English listened to four monosyllabic and acoustically distinct animal words in both languages (e.g., "paard"-"horse"). Multivariate pattern analysis (MVPA) was applied to identify EEG response patterns that discriminate between individual words within one language (within-language discrimination) and generalize meaning across two languages (across-language generalization). Furthermore, employing two EEG feature selection approaches, we assessed the contribution of temporal and oscillatory EEG features to our classification results. MVPA revealed that within-language discrimination was possible in a broad time-window (~50-620 ms) after word onset probably reflecting acoustic-phonetic and semantic-conceptual differences between the words. Most interestingly, significant across-language generalization was possible around 550-600 ms, suggesting the activation of common semantic-conceptual representations from the Dutch and English nouns. Both types of classification, showed a strong contribution of oscillations below 12 Hz, indicating the importance of low frequency oscillations in the neural representation of individual words and concepts. This study demonstrates the feasibility of MVPA to decode individual spoken words from EEG responses and to assess the spectro-temporal dynamics of their language invariant semantic-conceptual representations. We discuss how this method and results could be relevant to track the neural mechanisms underlying conceptual encoding in comprehension and production

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