Are We Ready for Real-world Neuroscience?

Real-world environments are typically dynamic, complex, and multisensory in nature and require the support of top-down attention and memory mechanisms for us to be able to drive a car, make a shopping list, or pour a cup of coffee. Fundamental principles of perception and functional brain organization have been established by research utilizing well-controlled but simplified paradigms with basic stimuli. The last 30 years ushered a revolution in computational power, brain mapping, and signal processing techniques. Drawing on those theoretical and methodological advances, over the years, research has departed more and more from traditional, rigorous, and well-understood paradigms to directly investigate cognitive functions and their underlying brain mechanisms in real-world environments. These investigations typically address the role of one or, more recently, multiple attributes of real-world environments. Fundamental assumptions about perception, attention, or brain functional organization have been challenged-by studies adapting the traditional paradigms to emulate, for example, the multisensory nature or varying relevance of stimulation or dynamically changing task demands. Here, we present the state of the field within the emerging heterogeneous domain of real-world neuroscience. To be precise, the aim of this Special Focus is to bring together a variety of the emerging "real-world neuroscientific" approaches. These approaches differ in their principal aims, assumptions, or even definitions of "real-world neuroscience" research. Here, we showcase the commonalities and distinctive features of the different "real-world neuroscience" approaches. To do so, four early-career researchers and the speakers of the Cognitive Neuroscience Society 2017 Meeting symposium under the same title answer questions pertaining to the added value of such approaches in bringing us closer to accurate models of functional brain organization and cognitive functions.

Natural asynchronies in audiovisual communication signals regulate neuronal multisensory interactions in voice-sensitive cortex.

When social animals communicate, the onset of informative content in one modality varies considerably relative to the other, such as when visual orofacial movements precede a vocalization. These naturally occurring asynchronies do not disrupt intelligibility or perceptual coherence. However, they occur on time scales where they likely affect integrative neuronal activity in ways that have remained unclear, especially for hierarchically downstream regions in which neurons exhibit temporally imprecise but highly selective responses to communication signals. To address this, we exploited naturally occurring face- and voice-onset asynchronies in primate vocalizations. Using these as stimuli we recorded cortical oscillations and neuronal spiking responses from functional MRI (fMRI)-localized voice-sensitive cortex in the anterior temporal lobe of macaques. We show that the onset of the visual face stimulus resets the phase of low-frequency oscillations, and that the face-voice asynchrony affects the prominence of two key types of neuronal multisensory responses: enhancement or suppression. Our findings show a three-way association between temporal delays in audiovisual communication signals, phase-resetting of ongoing oscillations, and the sign of multisensory responses. The results reveal how natural onset asynchronies in cross-sensory inputs regulate network oscillations and neuronal excitability in the voice-sensitive cortex of macaques, a suggested animal model for human voice areas. These findings also advance predictions on the impact of multisensory input on neuronal processes in face areas and other brain regions.

Auditory and visual modulation of temporal lobe neurons in voice-sensitive and association cortices.

Effective interactions between conspecific individuals can depend upon the receiver forming a coherent multisensory representation of communication signals, such as merging voice and face content. Neuroimaging studies have identified face- or voice-sensitive areas (Belin et al., 2000; Petkov et al., 2008; Tsao et al., 2008), some of which have been proposed as candidate regions for face and voice integration (von Kriegstein et al., 2005). However, it was unclear how multisensory influences occur at the neuronal level within voice- or face-sensitive regions, especially compared with classically defined multisensory regions in temporal association cortex (Stein and Stanford, 2008). Here, we characterize auditory (voice) and visual (face) influences on neuronal responses in a right-hemisphere voice-sensitive region in the anterior supratemporal plane (STP) of Rhesus macaques. These results were compared with those in the neighboring superior temporal sulcus (STS). Within the STP, our results show auditory sensitivity to several vocal features, which was not evident in STS units. We also newly identify a functionally distinct neuronal subpopulation in the STP that appears to carry the area's sensitivity to voice identity related features. Audiovisual interactions were prominent in both the STP and STS. However, visual influences modulated the responses of STS neurons with greater specificity and were more often associated with congruent voice-face stimulus pairings than STP neurons. Together, the results reveal the neuronal processes subserving voice-sensitive fMRI activity patterns in primates, generate hypotheses for testing in the visual modality, and clarify the position of voice-sensitive areas within the unisensory and multisensory processing hierarchies.

Courtship behaviour reveals temporal regularity is a critical social cue in mouse communication.

While animals navigating the real world face a barrage of sensory input, their brains evolved to perceptually compress multidimensional information by selectively extracting the features relevant for survival. Notably, communication signals supporting social interactions in several mammalian species consist of acoustically complex sequences of vocalisations. However, little is known about what information listeners extract from such time-varying sensory streams. Here, we utilise female mice's natural behavioural response to male courtship songs to identify the relevant acoustic dimensions used in their social decisions. We found that females were highly sensitive to disruptions of song temporal regularity and preferentially approached playbacks of intact over rhythmically irregular versions of male songs. In contrast, female behaviour was invariant to manipulations affecting the songs' sequential organisation or the spectro-temporal structure of individual syllables. The results reveal temporal regularity as a key acoustic cue extracted by mammalian listeners from complex vocal sequences during goal-directed social behaviour.

Interpreting wide-band neural activity using convolutional neural networks.

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors - including a novel representation of head direction - from raw neural activity.

Who is That? Brain Networks and Mechanisms for Identifying Individuals.

Social animals can identify conspecifics by many forms of sensory input. However, whether the neuronal computations that support this ability to identify individuals rely on modality-independent convergence or involve ongoing synergistic interactions along the multiple sensory streams remains controversial. Direct neuronal measurements at relevant brain sites could address such questions, but this requires better bridging the work in humans and animal models. Here, we overview recent studies in nonhuman primates on voice and face identity-sensitive pathways and evaluate the correspondences to relevant findings in humans. This synthesis provides insights into converging sensory streams in the primate anterior temporal lobe (ATL) for identity processing. Furthermore, we advance a model and suggest how alternative neuronal mechanisms could be tested.

Voice cells in the primate temporal lobe.

Communication signals are important for social interactions and survival and are thought to receive specialized processing in the visual and auditory systems. Whereas the neural processing of faces by face clusters and face cells has been repeatedly studied [1-5], less is known about the neural representation of voice content. Recent functional magnetic resonance imaging (fMRI) studies have localized voice-preferring regions in the primate temporal lobe [6, 7], but the hemodynamic response cannot directly assess neurophysiological properties. We investigated the responses of neurons in an fMRI-identified voice cluster in awake monkeys, and here we provide the first systematic evidence for voice cells. "Voice cells" were identified, in analogy to "face cells," as neurons responding at least 2-fold stronger to conspecific voices than to "nonvoice" sounds or heterospecific voices. Importantly, whereas face clusters are thought to contain high proportions of face cells [4] responding broadly to many faces [1, 2, 4, 5, 8-10], we found that voice clusters contain moderate proportions of voice cells. Furthermore, individual voice cells exhibit high stimulus selectivity. The results reveal the neurophysiological bases for fMRI-defined voice clusters in the primate brain and highlight potential differences in how the auditory and visual systems generate selective representations of communication signals.

Hyper-connectivity and hyper-plasticity in the medial prefrontal cortex in the valproic Acid animal model of autism.

The prefrontal cortex has been extensively implicated in autism to explain deficits in executive and other higher-order functions related to cognition, language, sociability and emotion. The possible changes at the level of the neuronal microcircuit are however not known. We studied microcircuit alterations in the prefrontal cortex in the valproic acid rat model of autism and found that the layer 5 pyramidal neurons are connected to significantly more neighbouring neurons than in controls. These excitatory connections are more plastic displaying enhanced long-term potentiation of the strength of synapses. The microcircuit alterations found in the prefrontal cortex are therefore similar to the alterations previously found in the somatosensory cortex. Hyper-connectivity and hyper-plasticity in the prefrontal cortex implies hyper-functionality of one of the highest order processing regions in the brain, and stands in contrast to the hypo-functionality that is normally proposed in this region to explain some of the autistic symptoms. We propose that a number of deficits in autism such as sociability, attention, multi-tasking and repetitive behaviours, should be re-interpreted in the light of a hyper-functional prefrontal cortex.