Combining ultra-high-field fMRI adaptation and computational modelling to estimate neuronal frequency selectivity in human auditory cortex

Frequency selectivity is a ubiquitous property of auditory neurons. Measuring it in human auditory cortex may be crucial for understanding common auditory deficits, but current non-invasive neuroimaging techniques can only measure the aggregate response of large populations of cells, thereby overestimating tuning width. Here we attempted to estimate neuronal frequency tuning in human auditory cortex using a combination of fMRI-adaptation paradigm at 7T and computational modelling. We measured the BOLD response in the auditory cortex of eleven participants to a high frequency (3.8 kHz) probe presented alone or preceded by adaptors at different frequencies (0.5 to 3.8 kHz). From these data, we derived both the response tuning curves (the BOLD response to adaptors alone as a function of adaptor frequency) and adaptation tuning curves (the degree of response suppression to the probe as a function of adaptor frequency, assumed to reflect neuronal tuning) in primary and secondary auditory cortical areas, delineated in each participant. Results suggested the existence of both frequency-independent and frequency-specific adaptation components, with the latter being more frequency-tuned than response tuning curves. Using a computational model of neuronal adaptation and BOLD non-linearity in topographically-organized cortex, we demonstrate both that the frequency-specific adaptation component overestimates the underlying neuronal frequency tuning and that frequency-specific and frequency-independent adaptation component cannot easily be disentangled from the adaptation tuning curve. By fitting our model directly to the response and adaptation tuning curves, we derive a range of plausible values for neuronal frequency tuning. Our results suggest that fMRI adaptation is suitable for measuring neuronal frequency tuning properties in human auditory cortex, provided population effects and the non-linearity of BOLD response are taken into account.

In search of different categories of abstract concepts: a fMRI adaptation study

Concrete conceptual knowledge is supported by a distributed neural network representing different semantic features according to the neuroanatomy of sensory and motor systems. If and how this framework applies to abstract knowledge is currently debated. Here we investigated the specific brain correlates of different abstract categories. After a systematic a priori selection of brain regions involved in semantic cognition, i.e. responsible of, respectively, semantic representations and cognitive control, we used a fMRI-adaptation paradigm with a passive reading task, in order to modulate the neural response to abstract (emotions, cognitions, attitudes, human actions) and concrete (biological entities, artefacts) categories. Different portions of the left anterior temporal lobe responded selectively to abstract and concrete concepts. Emotions and attitudes adapted the left middle temporal gyrus, whereas concrete items adapted the left fusiform gyrus. Our results suggest that, similarly to concrete concepts, some categories of abstract knowledge have specific brain correlates corresponding to the prevalent semantic dimensions involved in their representation.

Long term fMRI adaptation depends on adapter response in face-selective cortex

Repetition suppression (RS) reflects a neural attenuation during repeated stimulation. We used fMRI and the subsequent memory paradigm to test the predictive coding hypothesis for RS during visual memory processing by investigating the interaction between RS and differences due to memory in category-selective cortex (FFA, pSTS, PPA, and RSC). Fifty-six participants encoded face and house stimuli twice, followed by an immediate and delayed (48 h) recognition memory assessment. Linear Mixed Model analyses with repetition, subsequent recognition performance, and their interaction as fixed effects revealed that absolute RS during encoding interacts with probability of future remembrance in face-selective cortex. This effect was not observed for relative RS, i.e. when controlled for adapter-response. The findings also reveal an association between adapter response and RS, both for short and long term (48h) intervals, after controlling for the mathematical dependence between both measures. These combined findings are challenging for predictive coding models of visual memory and are more compatible with adapter-related and familiarity accounts.

Getting to Know Someone: Familiarity, Person Recognition, and Identification in the Human Brain

In our everyday life, we continuously get to know people, dominantly through their faces. Several neuroscientific experiments showed that familiarization changes the behavioral processing and underlying neural representation of faces of others. Here, we propose a model of the process of how we actually get to know someone. First, the purely visual familiarization of unfamiliar faces occurs. Second, the accumulation of associated, nonsensory information refines person representation, and finally, one reaches a stage where the effortless identification of very well-known persons occurs. We offer here an overview of neuroimaging studies, first evaluating how and in what ways the processing of unfamiliar and familiar faces differs and, second, by analyzing the fMRI adaptation and multivariate pattern analysis results we estimate where identity-specific representation is found in the brain. The available neuroimaging data suggest that different aspects of the information emerge gradually as one gets more and more familiar with a person within the same network. We propose a novel model of familiarity and identity processing, where the differential activation of long-term memory and emotion processing areas is essential for correct identification.

Repetition enhancement to voice identities in the dog brain

In the human speech signal, cues of speech sounds and voice identities are conflated, but they are processed separately in the human brain. The processing of speech sounds and voice identities is typically performed by non-primary auditory regions in humans and non-human primates. Additionally, these processes exhibit functional asymmetry in humans, indicating the involvement of distinct mechanisms. Behavioural studies indicate analogue side biases in dogs, but neural evidence for this functional dissociation is missing. In two experiments, using an fMRI adaptation paradigm, we presented awake dogs with natural human speech that either varied in segmental (change in speech sound) or suprasegmental (change in voice identity) content. In auditory regions, we found a repetition enhancement effect for voice identity processing in a secondary auditory region – the caudal ectosylvian gyrus. The same region did not show repetition effects for speech sounds, nor did the primary auditory cortex exhibit sensitivity to changes either in the segmental or in the suprasegmental content. Furthermore, we did not find evidence for functional asymmetry neither in the processing of speech sounds or voice identities. Our results in dogs corroborate former human and non-human primate evidence on the role of secondary auditory regions in the processing of suprasegmental cues, suggesting similar neural sensitivity to the identity of the vocalizer across the mammalian order.