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    Neural Generators of Intensity Mismatch Negativity Modelled with a Recurrent Neural Network : A Pilot Study on the Role of Sound Level Transitions
    (2023-01-01)
    Srivastava, Chandan K.
    ;
    Gupta, Rashmi
    ;
    O'Reilly, Jamie A.
    Intensity mismatch negativity (MMN) is an electrophysiological response to auditory oddball stimulation with unexpected changes in sound pressure level. Studies using only quieter deviant stimuli leave uncertainty regarding the nature of resulting MMN, with louder deviant stimuli potentially evoking an opposite polarity amplitude shift due to intensity modulation of the auditory evoked response. To test this hypothesis, we conducted a pilot study with three subjects listening to an intensity oddball paradigm with 80 dB standards and 70 dB and 90 dB deviants. Event-related potentials and deviant-minus-standard difference waveforms were analyzed to determine whether MMN elicited by 70 dB and 90 dB reflects simple intensity modulation. We modelled the resulting neural activity with a recurrent neural network (RNN) to estimate generative signals from a distributed source space computed with a template head co-registered with standard 10-20 system electrode locations. Event-related potential waveforms suggest that intensity MMN elicited by quieter and louder auditory stimuli cannot be explained simply by intensity modulation of the auditory evoked response, given that both produced negative amplitudes from 0.1 to 0.25 s. Results from RNN-based source estimation are consistent with bilateral thalamic and right-prefrontal contributions to intensity MMN evoked by rising and falling sound level transitions.
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    A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks
    (2022-12-01)
    O’Reilly, Jamie A.
    ;
    Wehrman, Jordan
    ;
    Sowman, Paul F.
    In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.