Anxiety and depression: A top-down, bottom-up model of circuit function

Area of Science:

• Neuroscience
• Computational Psychiatry
• Systems Neuroscience

Background:

• Brain function relies on integrating sensory input (bottom-up) with internal goals (top-down).
• These processes can be conceptualized as dynamic attractor states within neural networks.
• Altered processing dynamics are implicated in psychiatric conditions like anxiety and depression.

Purpose of the Study:

• To explore the relationship between bottom-up and top-down processing in the context of anxiety and depression.
• To apply Marr's computational framework to understand these processing modalities.
• To model the neural dynamics underlying mood disorders.

Main Methods:

• Utilizing a dynamical systems model to represent brain attractor states.
• Analyzing bottom-up processing via basolateral amygdala signaling.
• Examining top-down processing through medial prefrontal cortex signaling.

Main Results:

• Bottom-up and top-down processing are key to environmental responses.
• Attractor state transitions depend on stability, neuromodulation, and plasticity.
• Specific neural pathways (amygdala, prefrontal cortex) exemplify these processing types.

Conclusions:

• Understanding attractor state dynamics is crucial for modeling mood disorders.
• The interplay of bottom-up and top-down processing provides a framework for anxiety and depression research.
• Computational models can illuminate the neural basis of psychiatric conditions.