Dataset Information

Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach.

ABSTRACT:

Objective

Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion.

Method

To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sustained reward responsiveness, were associated with anhedonia. Two experiments were conducted in outpatients diagnosed with SZ (n = 28; n = 102) and healthy controls (n = 28; n = 71) who completed EMA reports of emotional experience at multiple time points in the day over the course of several days. Markov chain analyses were applied to the EMA data to evaluate stochastic dynamic changes in emotional states to determine processes underlying failures in sustained reward responsiveness.

Results

In both studies, Markov models indicated that SZ had deficits in the ability to sustain positive emotion over time, which resulted from failures in augmentation (ie, the ability to maintain or increase the intensity of positive emotion from time t to t+1) and diminution (ie, when emotions at time t+1 are opposite in valence from emotions at time t, resulting in a decrease in the intensity of positive emotion over time). Furthermore, in both studies, augmentation deficits were associated with anhedonia.

Conclusions

These computational findings clarify how abnormalities in the temporal dynamics of emotion contribute to anhedonia.

SUBMITTER: Strauss GP

PROVIDER: S-EPMC7505187 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach.

Strauss Gregory P GP Esfahlani Farnaz Zamani FZ Granholm Eric E Holden Jason J Visser Katherine Frost KF Bartolomeo Lisa A LA Sayama Hiroki H

Schizophrenia bulletin 20200901 5

<h4>Objective</h4>Anhedonia, traditionally defined as a diminished capacity for pleasure, is a core symptom of schizophrenia (SZ). However, modern empirical evidence indicates that hedonic capacity may be intact in SZ and anhedonia may be better conceptualized as an abnormality in the temporal dynamics of emotion.<h4>Method</h4>To test this theory, the current study used ecological momentary assessment (EMA) to examine whether abnormalities in one aspect of the temporal dynamics of emotion, sust ...[more]

PMID: 32103266

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Project description:The prediction of extreme events, from avalanches and droughts to tsunamis and epidemics, depends on the formulation and analysis of relevant, complex dynamical systems. Such dynamical systems are characterized by high intrinsic dimensionality with extreme events having the form of rare transitions that are several standard deviations away from the mean. Such systems are not amenable to classical order-reduction methods through projection of the governing equations due to the large intrinsic dimensionality of the underlying attractor as well as the complexity of the transient events. Alternatively, data-driven techniques aim to quantify the dynamics of specific, critical modes by utilizing data-streams and by expanding the dimensionality of the reduced-order model using delayed coordinates. In turn, these methods have major limitations in regions of the phase space with sparse data, which is the case for extreme events. In this work, we develop a novel hybrid framework that complements an imperfect reduced order model, with data-streams that are integrated though a recurrent neural network (RNN) architecture. The reduced order model has the form of projected equations into a low-dimensional subspace that still contains important dynamical information about the system and it is expanded by a long short-term memory (LSTM) regularization. The LSTM-RNN is trained by analyzing the mismatch between the imperfect model and the data-streams, projected to the reduced-order space. The data-driven model assists the imperfect model in regions where data is available, while for locations where data is sparse the imperfect model still provides a baseline for the prediction of the system state. We assess the developed framework on two challenging prototype systems exhibiting extreme events. We show that the blended approach has improved performance compared with methods that use either data streams or the imperfect model alone. Notably the improvement is more significant in regions associated with extreme events, where data is sparse.

Dataset Information

Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach.

Objective

Method

Results

Conclusions

Publications

Mathematically Modeling Anhedonia in Schizophrenia: A Stochastic Dynamical Systems Approach.

Similar Datasets

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