Double-epoch subtraction reveals long-latency mismatch response in urethane-anaesthetized mice

Blind source separation of event-related potentials using a recurrent neural network

Event-related potentials (ERPs) are a superposition of electric potential differences generated by neurophysiological activity associated with psychophysical events. Spatiotemporal dissociation of these signal sources can supplement conventional ERP analysis and improve source localization. However, results from established source separation methods applied to ERPs can be challenging to interpret. Hence, we have developed a recurrent neural network (RNN) method for blind source separation. The RNN transforms input step pulse signals representing events into corresponding ERP difference waveforms. Source waveforms are obtained from penultimate layer units and scalp maps are obtained from feed-forward output layer weights that project these source waveforms onto EEG electrode amplitudes. An interpretable, sparse source representation is achieved by incorporating L1 regularization of signals obtained from the penultimate layer of the network during training. This RNN method was applied to four ERP difference waveforms (MMN, N170, N400, P3) from the open-access ERP CORE database, and independent component analysis (ICA) was applied to the same data for comparison. The RNN decomposed these ERPs into eleven spatially and temporally separate sources that were less noisy, tended to be more ERP-specific, and were less similar to each other than ICA-derived sources. The RNN sources also had less ambiguity between source waveform amplitude, scalp potential polarity, and equivalent current dipole orientation than ICA sources. In conclusion, the proposed RNN blind source separation method can be effectively applied to grand-average ERP difference waves and holds promise for further development as a computational model of event-related neural signals.

Modelling mouse auditory response dynamics along a continuum of consciousness using a deep recurrent neural network

Objective Understanding neurophysiological changes that accompany transitions between anaesthetized and conscious states is a key objective of anesthesiology and consciousness science. This study aimed to characterize the dynamics of auditory-evoked potential morphology in mice along a continuum of consciousness. Approach Epidural field potentials were recorded from above the primary auditory cortices of two groups of laboratory mice: urethane-anaesthetized (A, n = 14) and conscious (C, n = 17). Both groups received auditory stimulation in the form of a repeated pure-tone stimulus, before and after receiving 10 mg/kg i.p. ketamine (AK and CK). Evoked responses were then ordered by ascending sample entropy into AK, A, CK, and C, considered to reflect physiological correlates of awareness. These data were used to train a recurrent neural network (RNN) with an input parameter encoding state. Model outputs were compared with grand-average event-related potential (ERP) waveforms. Subsequently, the state parameter was varied to simulate changes in the ERP that occur during transitions between states, and relationships with dominant peak amplitudes were quantified. Main results The RNN synthesized output waveforms that were in close agreement with grand-average ERPs for each group (r2 > 0.9, p < 0.0001). Varying the input state parameter generated model outputs reflecting changes in ERP morphology predicted to occur between states. Positive peak amplitudes within 25 to 50 ms, and negative peak amplitudes within 50 to 75 ms post-stimulus-onset, were found to display a sigmoidal characteristic during the transition from anaesthetized to conscious states. In contrast, negative peak amplitudes within 0 to 25 ms displayed greater linearity. Significance This study demonstrates a method for modelling changes in ERP morphology that accompany transitions between states of consciousness using a RNN. In future studies, this approach may be applied to human data to support the clinical use of ERPs to predict transition to consciousness.

An Electric Circuit Model of Central Auditory Processing that Replicates Low-level Features of the Mouse Mismatch Response

Neurophysiology research using animals is often necessary to further our understanding of particular areas of medical interest. Human mismatch negativity (MMN) is one such area, where animal models are used to explore underlying mechanisms more invasively and with greater precision than typically possible with human subjects. Computational models can supplement these efforts by providing abstractions that lead to new insights and drive hypotheses. This study aims to establish whether a mouse mismatch response (MMR) analogous to human MMN can be modelled using electric circuit theory. Input to the auditory cortex was modelled as a step function multiplied by a frequency-dependent weighting designed to reflect spectral hearing sensitivity. Afferent sensory responses were modelled using a resistor-capacitor (RC) network, while bidirectional (bottom-up and top-down) responses were modelled using a resistor-inductor-capacitor (RLC) network. Synthetic EEG was combined with RC and RLC circuit currents in response to simulated sequences of auditory input, which comprised duration and frequency oddball paradigms. Two different states of awareness were considered: i) anaesthetized, including only the RC circuit, and ii) conscious, including both RC and RLC circuits. Event-related potential waveforms were obtained from ten simulated experiments for each oddball paradigm and state. These were qualitatively and quantitatively compared with data from a previous in-vivo study, and the model was deemed to successfully replicate low-level features of the mouse central auditory response. Clinical Relevance - Abnormal MMN is believed to reflect pathological changes associated with psychiatric disease. Maximizing the effectiveness of this biomarker will require a greater understanding of the specific cause(s) of these abnormalities. This study presents a computational model that can account for differences between responses to duration and frequency oddball paradigms, which is particularly significant for clinical MMN research.

Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units

The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory‐evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time‐wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound‐level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross‐validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand‐average auditory‐evoked potential waveforms (r² > .9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses and putative ‘safety’ and ‘danger’ units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this could have significant implications for prevailing theories of auditory processing.

Roving oddball paradigm elicits sensory gating, frequency sensitivity, and long-latency response in common marmosets

Mismatch negativity (MMN) is a candidate biomarker for neuropsychiatric disease. Understanding the extent to which it reflects cognitive deviance-detection or purely sensory processes will assist practitioners in making informed clinical interpretations. This study compares the utility of deviance-detection and sensory-processing theories for describing MMN-like auditory responses of a common marmoset monkey during roving oddball stimulation. The following exploratory analyses were performed on an existing dataset: responses during the transition and repetition sequence of the roving oddball paradigm (standard -> deviant/S1 -> S2 -> S3) were compared; long-latency potentials evoked by deviant stimuli were examined using a double-epoch waveform subtraction; effects of increasing stimulus repetitions on standard and deviant responses were analyzed; and transitions between standard and deviant stimuli were divided into ascending and descending frequency changes to explore contributions of frequency-sensitivity. An enlarged auditory response to deviant stimuli was observed. This decreased exponentially with stimulus repetition, characteristic of sensory gating. A slow positive deflection was viewed over approximately 300–800 ms after the deviant stimulus, which is more difficult to ascribe to afferent sensory mechanisms. When split into ascending and descending frequency transitions, the resulting difference waveforms were disproportionally influenced by descending frequency deviant stimuli. This asymmetry is inconsistent with the general deviance-detection theory of MMN. These findings tentatively suggest that MMN-like responses from common marmosets are predominantly influenced by rapid sensory adaptation and frequency preference of the auditory cortex, while deviance-detection may play a role in long-latency activity.