Using mathematical models to find hidden brain states

  Individual outcomes cannot be accurately predicted, and symptoms alone can only be used to diagnose mental health disorders. Using mathematical models, a scientist at the ETH hopes to change that.

Why do we feel what we do? Before providing an answer, Klaas Enno Stephan, a professor at the University of Zurich and ETH Zurich, carefully considers the question: It seems very plausible that the emotions are meant to alert us to body processes that are unconscious.

Stephan is particularly interested in how the body and brain interact as a doctor and researcher. He uses the example of how our blood sugar rises before we even take a bite of food, when insulin is released at the sight of it.

Information from the world around us is constantly interpreted and updated in our brain.

According to Stephan, “the brain constructs models of the world and uses them to make predictions.” The basis for anticipatory corrective actions, such as releasing insulin prior to eating, is based on these predictions.

The internal balance that the body tries to maintain through the regulation of parameters like blood pressure, core body temperature, blood sugar levels, and acid-base balance is known as homeostasis. The brain takes action to restore this equilibrium when it is disturbed, typically without our awareness.

However, it makes sense for us to be aware of an immediate threat to homeostasis when our body is confronted with one.

According to Stephan, “It’s very plausible that emotions are states of consciousness connected to very specific actions aimed at maintaining certain bodily functions.” Fear and anxiety, for instance, make us conscious of the urgent need to respond to a threat.

Expectations management, but not all fear is acute; In fact, some people constantly experience increased anxiety. Predictions that are too precise could be one reason for this.

In such instances, we perceive our healthy body as constantly in danger and interpret even natural, minor deviations as a threat.

To address this apparent disruption of homeostasis, corrective measures are initiated. However, attempting to control the heart’s rhythm can cause it to beat faster and more irregularly than before.

The sympathetic nervous system, which is a part of the autonomic nervous system that helps the body prepare for stressful situations, then accelerates this downward spiral.

Stephan devised an inventive experiment with his coworker Olivia Harrison to test the hypothesis that overly precise predictions of bodily states in a specific brain region, the anterior insula, accompany heightened anxiety.

The researchers examined the brain activity of individuals with varying levels of anxiety using functional magnetic resonance imaging (fMRI). The members lay in the X-ray scanner while wearing a sort of snorkel that could be utilized to increment breathing opposition unexpectedly.

In the primary phase of the examination, members discovered that the presentation of specific pictures was an indicator of whether they would have the option to inhale typically or whether breathing would become more enthusiastically. In a subsequent stage, the relationship among’s pictures and breathing opposition was switched.

The researchers were able to examine the degree to which measured brain activity mirrored both learned expectations and changes in expectations by employing mathematical models. Their hypothesis that signals for predictive accuracy are clustered in the anterior insula was supported by this. The scans also showed that this part of the brain is active at different levels depending on how anxious the person is.

Underlying mechanisms, according to Stephan, “Our ultimate goal is always clinical application.” He explains that symptoms alone are currently used to diagnose mental health disorders: Quantitative tests and measurement tools are simply not available to psychiatrists for the purpose of determining the underlying mechanisms or causes.

This is why he is so enthusiastic about using mathematical models to infer hidden—that is, states of neuronal populations that are not directly measurable. On a basic level, such models could be valuable for recognizing conceivable organic components of problems, like changes in the strength of specific synaptic associations.

Stephan asserts, “We can also use these models to apply them to specific clinical problems and use them to predict individual outcomes.” He refers to the case of a fMRI concentrate on in which patients with discouragement were given pictures of countenances communicating various feelings.

The researchers were able to predict with 80% accuracy whether individuals would recover from their depression within two years or would remain chronically depressed by employing a mathematical model of how various brain regions communicate with one another when viewing emotional faces.

Although Stephan’s lab’s methods are not yet ready for clinical use, his enthusiasm remains unwavering. He asserts that mathematical models can assist us in gaining access to occluded brain states.

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