Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia

Abstract

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Keywords: computational psychiatry; connectivity; decoding; mismatch negativity; schizophrenia.

Figure 1.

Overview of modeling techniques. In applying computational models to the study of MMRs in schizophrenia, two broadly complementary approaches can be used. Going from empirical data to theoretical/computational models, computational approaches include connectivity modeling (inferring effective connectivity patterns mediating mismatch signaling in patients and controls) and decoding techniques (eg, allowing the classification of study participants into patients and controls based on multivariate MMR features). Going from models to empirical data, computational approaches include neural networks (which, similar to decoding, can allow classifying study participants, but also encompass eg MMR simulation studies) and cognitive models (typically quantifying stimulus sequences in computational terms based on eg probability and surprise).

Figure 2.

Qualitative overview of effective connectivity results. The graph shows the reported modulatory effects of Sz-relevant groups versus controls, mapped onto the widely used dynamic causal model (DCM) of auditory mismatch responses. The most consistent effects include IFG and A1 disinhibition, right STG inhibition, as well as increased right-to-left STG connectivity in patients or related groups. While the reviewed studies show qualitatively heterogeneous results (largely dependent on the investigated cohort and paradigm), please note that this overview should not be interpreted as a direct comparison of posterior parameter estimates between studies, as different studies may select different winning models. Sz: schizophrenia diagnosis; FEP: first-episode psychosis; CAPE: community assessment of psychic experiences (quantifying psychotic-like experiences); inpat.: inpatients without psychosis. In case of multiple groups investigated, asterisk denotes the modulatory effect on connectivity associated with membership of a specific group.

Figure 3.

Possible applications of decoding and neural network models. (A) Example of SVM application to decode schizophrenia diagnosis. By applying SVM to a multivariate set of MMR features (eg, EEG amplitudes in the MMR window vs a later time window), it is possible to classify participants based on diagnosis. The SVM creates a hyperplane which separates the data into classes with up to 90-98% accuracy. (B) Possible application of DNN models in MMR/schizophrenia research. Two recurrent neural network (RNN) models are created, with their mechanisms altered in a fashion that represents a given hypothesis in schizophrenia research (In this example: schizophrenia patients exhibit impaired top-down feedback). Then, neuroconnectionist methods (here: RSA) are used to compare the dynamics or representations between the model variations and neurophysiological data from healthy and schizophrenia-diagnosed participants. If the representational dissimilarity matrix (RDM) of the hypothesis-altered model better fits the RDM of schizophrenia patients than the RDM of the standard model (and vice versa for healthy participants), the altered underlying mechanism can be taken as a better model for the corresponding neural mechanisms of schizophrenia.

Figure 4.

Cognitive models. (A) Observation probabilities include probabilistic quantities related to stimulus occurrence, alternation/repetition, and transitions between stimuli. These probabilistic quantities are subject to different read-out functions based on surprise. (B) Modeling MMRs using the HGF indicated that the MMN and P3 can be mapped onto different hierarchical levels of predictions and PEs. The directed graph shows a typical HGF architecture, tracking probability estimates over time. The highest level relates to volatility estimates and has been linked to the P3, while the lower level relates to transition probability estimates and has been linked to the MMN.^, Both levels have been shown to be altered in schizophrenia.^,