Cognitive neurodynamics of affective disorders

Area of Science:

• Computational psychiatry
• Neuroscience
• Control theory

Background:

• Severe psychiatric illnesses, such as melancholia and bipolar disorder, are characterized by long-term mood and motivation disturbances.
• Existing theories lack a unified explanation for the long-term dynamics of these conditions.
• Solomon's opponent-process theory describes short-term affective responses but not long-term clinical trajectories.

Purpose of the Study:

• To investigate if opponent-process dynamics can be modeled as a computational homeostatic controller.
• To determine if distinct clinical trajectories of mood disorders represent failure modes of this system.
• To explore the potential of a unified dynamical systems framework for understanding severe affective illnesses.

Main Methods:

• Formulated a control-systems model incorporating feedforward 'a-process' and 'b-process' dynamics.
• Simulated the model's behavior across minute-scale and month-scale time regimes.
• Modified parameters such as opponent-process gain, decay, and damping to observe emergent dynamics.

Main Results:

• The model successfully reproduced classical opponent-process responses under healthy parameters.
• Altering gain and decay parameters generated a downward drift, mimicking melancholia's timescale and asymmetry.
• Reducing damping resulted in underdamped oscillations, characteristic of bipolar illness's long period and phase asymmetry.

Conclusions:

• Severe affective illnesses may represent distinct dynamical regimes of a single motivational homeostat.
• This computational framework offers a unified perspective on melancholia and bipolar disorder.
• The model provides testable predictions for quantifying opponent-process dynamics as potential biomarkers.