"Doctor" or "darling"? Decoding the communication partner from ECoG of the anterior temporal lobe during non-experimental, real-life social interaction

Human brain processes underlying real-life social interaction in everyday situations have been difficult to study and have, until now, remained largely unknown. Here, we investigated whether electrocorticography (ECoG) recorded for pre-neurosurgical diagnostics during the daily hospital life of epilepsy patients could provide a way to elucidate the neural correlates of non-experimental social interaction. We identified time periods in which patients were involved in conversations with either their respective life partners (Condition 1; C1) or attending physicians (Condition 2; C2). These two conditions can be expected to differentially involve subfunctions of social interaction which have been associated with activity in the anterior temporal lobe (ATL), including the temporal pole (TP). Therefore, we specifically focused on ECoG recordings from this brain region and investigated spectral power modulations in the alpha (8–12 Hz) and theta (3–5 Hz) frequency ranges, which have been previously assumed to play an important role in the processing of social interaction. We hypothesized that brain activity in this region might be sensitive to differences in the two interaction situations and tested whether these differences can be detected by single-trial decoding. Condition-specific effects in both theta and alpha bands were observed: the left and right TP exclusively showed increased power in C1 compared to C2, whereas more posterior parts of the ATL exhibited similar (C1 > C2) and also contrary (C2 > C1) effects. Single-trial decoding accuracies for classification of these effects were highly above chance. Our findings demonstrate that it is possible to study the neural correlates of human social interaction in non-experimental conditions. Decoding the identity of the communication partner and adjusting the speech output accordingly may be useful in the emerging field of brain-machine interfacing for restoration of expressive speech.

From speech to thought: the neuronal basis of cognitive units in non-experimental, real-life communication investigated using ECoG

Exchange of thoughts by means of expressive speech is fundamental to human communication. However, the neuronal basis of real-life communication in general, and of verbal exchange of ideas in particular, has rarely been studied until now. Here, our aim was to establish an approach for exploring the neuronal processes related to cognitive “idea” units (IUs) in conditions of non-experimental speech production. We investigated whether such units corresponding to single, coherent chunks of speech with syntactically-defined borders, are useful to unravel the neuronal mechanisms underlying real-world human cognition. To this aim, we employed simultaneous electrocorticography (ECoG) and video recordings obtained in pre-neurosurgical diagnostics of epilepsy patients. We transcribed non-experimental, daily hospital conversations, identified IUs in transcriptions of the patients' speech, classified the obtained IUs according to a previously-proposed taxonomy focusing on memory content, and investigated the underlying neuronal activity. In each of our three subjects, we were able to collect a large number of IUs which could be assigned to different functional IU subclasses with a high inter-rater agreement. Robust IU-onset-related changes in spectral magnitude could be observed in high gamma frequencies (70–150 Hz) on the inferior lateral convexity and in the superior temporal cortex regardless of the IU content. A comparison of the topography of these responses with mouth motor and speech areas identified by electrocortical stimulation showed that IUs might be of use for extraoperative mapping of eloquent cortex (average sensitivity: 44.4%, average specificity: 91.1%). High gamma responses specific to memory-related IU subclasses were observed in the inferior parietal and prefrontal regions. IU-based analysis of ECoG recordings during non-experimental communication thus elicits topographically- and functionally-specific effects. We conclude that segmentation of spontaneous real-world speech in linguistically-motivated units is a promising strategy for elucidating the neuronal basis of mental processing during non-experimental communication.

Intention concepts and brain-machine interfacing

Intentions, including their temporal properties and semantic content, are receiving increased attention, and neuroscientific studies in humans vary with respect to the topography of intention-related neural responses. This may reflect the fact that the kind of intentions investigated in one study may not be exactly the same kind investigated in the other. Fine-grained intention taxonomies developed in the philosophy of mind may be useful to identify the neural correlates of well-defined types of intentions, as well as to disentangle them from other related mental states, such as mere urges to perform an action. Intention-related neural signals may be exploited by brain-machine interfaces (BMIs) that are currently being developed to restore speech and motor control in paralyzed patients. Such BMI devices record the brain activity of the agent, interpret (“decode”) the agent’s intended action, and send the corresponding execution command to an artificial effector system, e.g., a computer cursor or a robotic arm. In the present paper, we evaluate the potential of intention concepts from philosophy of mind to improve the performance and safety of BMIs based on higher-order, intention-related control signals. To this end, we address the distinction between future-, present-directed, and motor intentions, as well as the organization of intentions in time, specifically to what extent it is sequential or hierarchical. This has consequences as to whether these different types of intentions can be expected to occur simultaneously or not. We further illustrate how it may be useful or even necessary to distinguish types of intentions exposited in philosophy, including yes- vs. no-intentions and oblique vs. direct intentions, to accurately decode the agent’s intentions from neural signals in practical BMI applications.