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From Anxious to Reckless: A Control Systems Approach Unifies Prefrontal-Limbic Regulation Across the Spectrum of Threat Detection

View Article: PubMed Central - PubMed

ABSTRACT

Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction.

No MeSH data available.


Related in: MedlinePlus

Physiological negative feedback loops show outputs with characteristic dynamic signatures; dysregulation of the circuit causes a shift in dynamics that can be characterized by autocorrelation—either stronger or weaker, depending upon the type of dysregulation. To illustrate a shift towards autocorrelation that is stronger than optimal, here we show three age and gender-matched subjects’ glucose time-series using an implantable MedTronic device, sampled every 5 min over 6.25 days. The glucose time-series produced by the Type 1 diabetic patients are more auto-correlated (self-similar, fractal) than those of the healthy control, in this case reflecting impaired negative feedback as glucose boluses trigger excitatory responses that are only weakly suppressed by insufficient insulin. As shown, detection sensitivity for differences in glucose amplitude varied dramatically during the day, as well as between days; thus, acquisition of random mean values over short periods of time (as typical for functional magnetic resonance imaging (fMRI) experiment, 10 min with TR = 2000 ms yields ~300 samples, which is roughly equivalent to 1 day of glucose measurements) would yield highly variable accuracy. However, even over this same period, patients showed markedly less complexity in their time-series than the healthy control. Using the Hurst exponent, in which maximum complexity is achieved at H = 0.5 with >H corresponding to stronger auto-correlation, our healthy control showed H = 0.68, with patients showing H = 0.82 and H = 0.83, respectively. A similar shift towards autocorrelation is seen in heart rate variability of heart disease patients, for whom the vagus nerve only weakly suppresses sympathetic excitatory responses. In contrast to the two above examples, in which circuit dysregulation is caused by changes in feedback strength, our neurobiological results suggest different, more complex, types of control circuit dysregulation caused by changes in gating and anatomical connectivity that affect feedback lag. These result in time-series with autocorrelations that are weaker than optimal, as shown in Figures 4, 6.
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Figure 1: Physiological negative feedback loops show outputs with characteristic dynamic signatures; dysregulation of the circuit causes a shift in dynamics that can be characterized by autocorrelation—either stronger or weaker, depending upon the type of dysregulation. To illustrate a shift towards autocorrelation that is stronger than optimal, here we show three age and gender-matched subjects’ glucose time-series using an implantable MedTronic device, sampled every 5 min over 6.25 days. The glucose time-series produced by the Type 1 diabetic patients are more auto-correlated (self-similar, fractal) than those of the healthy control, in this case reflecting impaired negative feedback as glucose boluses trigger excitatory responses that are only weakly suppressed by insufficient insulin. As shown, detection sensitivity for differences in glucose amplitude varied dramatically during the day, as well as between days; thus, acquisition of random mean values over short periods of time (as typical for functional magnetic resonance imaging (fMRI) experiment, 10 min with TR = 2000 ms yields ~300 samples, which is roughly equivalent to 1 day of glucose measurements) would yield highly variable accuracy. However, even over this same period, patients showed markedly less complexity in their time-series than the healthy control. Using the Hurst exponent, in which maximum complexity is achieved at H = 0.5 with >H corresponding to stronger auto-correlation, our healthy control showed H = 0.68, with patients showing H = 0.82 and H = 0.83, respectively. A similar shift towards autocorrelation is seen in heart rate variability of heart disease patients, for whom the vagus nerve only weakly suppresses sympathetic excitatory responses. In contrast to the two above examples, in which circuit dysregulation is caused by changes in feedback strength, our neurobiological results suggest different, more complex, types of control circuit dysregulation caused by changes in gating and anatomical connectivity that affect feedback lag. These result in time-series with autocorrelations that are weaker than optimal, as shown in Figures 4, 6.

Mentions: Injuries have singular onsets, with anatomically defined damaged loci. In contrast, diseases are inherently dynamic: resulting from dysregulation of the negative feedback loops that, in a healthy individual, function to maintain allostasis in the face of chaotic environmental inputs. These negative feedback loops are necessary because biological processes typically only are able to function within a narrow window of upper and lower limits for water, sodium, glucose, temperature, etc. Because the environment often includes perturbations that exceed those thresholds, the body maintains homeostasis by negative feedback loops that correct the system towards baseline. For example, an acute bolus of glucose, unopposed, would lead to a hyperglycemic coma. Therefore, the metabolic control circuit responds by secreting the hormone insulin, sending the system into postprandial reactive hypoglycemia. Because hypoglycemia is just as dangerous to the body as hyperglycemia, the metabolic control circuit then secretes a different hormone, glucagon, which releases glucose back into the bloodstream. In a healthy person, the negative feedback loop as whole functions as a damped oscillator, with multiple excitatory (e.g., glucose, glucagon, cortisol) and inhibitory (insulin) responses acting in series to maintain glucose within acceptable limits. In a person with diabetes, however, the same perturbation is inadequately controlled—leading to extreme oscillations between hyper and hypoglycemia (Figure 1).


From Anxious to Reckless: A Control Systems Approach Unifies Prefrontal-Limbic Regulation Across the Spectrum of Threat Detection
Physiological negative feedback loops show outputs with characteristic dynamic signatures; dysregulation of the circuit causes a shift in dynamics that can be characterized by autocorrelation—either stronger or weaker, depending upon the type of dysregulation. To illustrate a shift towards autocorrelation that is stronger than optimal, here we show three age and gender-matched subjects’ glucose time-series using an implantable MedTronic device, sampled every 5 min over 6.25 days. The glucose time-series produced by the Type 1 diabetic patients are more auto-correlated (self-similar, fractal) than those of the healthy control, in this case reflecting impaired negative feedback as glucose boluses trigger excitatory responses that are only weakly suppressed by insufficient insulin. As shown, detection sensitivity for differences in glucose amplitude varied dramatically during the day, as well as between days; thus, acquisition of random mean values over short periods of time (as typical for functional magnetic resonance imaging (fMRI) experiment, 10 min with TR = 2000 ms yields ~300 samples, which is roughly equivalent to 1 day of glucose measurements) would yield highly variable accuracy. However, even over this same period, patients showed markedly less complexity in their time-series than the healthy control. Using the Hurst exponent, in which maximum complexity is achieved at H = 0.5 with >H corresponding to stronger auto-correlation, our healthy control showed H = 0.68, with patients showing H = 0.82 and H = 0.83, respectively. A similar shift towards autocorrelation is seen in heart rate variability of heart disease patients, for whom the vagus nerve only weakly suppresses sympathetic excitatory responses. In contrast to the two above examples, in which circuit dysregulation is caused by changes in feedback strength, our neurobiological results suggest different, more complex, types of control circuit dysregulation caused by changes in gating and anatomical connectivity that affect feedback lag. These result in time-series with autocorrelations that are weaker than optimal, as shown in Figures 4, 6.
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Figure 1: Physiological negative feedback loops show outputs with characteristic dynamic signatures; dysregulation of the circuit causes a shift in dynamics that can be characterized by autocorrelation—either stronger or weaker, depending upon the type of dysregulation. To illustrate a shift towards autocorrelation that is stronger than optimal, here we show three age and gender-matched subjects’ glucose time-series using an implantable MedTronic device, sampled every 5 min over 6.25 days. The glucose time-series produced by the Type 1 diabetic patients are more auto-correlated (self-similar, fractal) than those of the healthy control, in this case reflecting impaired negative feedback as glucose boluses trigger excitatory responses that are only weakly suppressed by insufficient insulin. As shown, detection sensitivity for differences in glucose amplitude varied dramatically during the day, as well as between days; thus, acquisition of random mean values over short periods of time (as typical for functional magnetic resonance imaging (fMRI) experiment, 10 min with TR = 2000 ms yields ~300 samples, which is roughly equivalent to 1 day of glucose measurements) would yield highly variable accuracy. However, even over this same period, patients showed markedly less complexity in their time-series than the healthy control. Using the Hurst exponent, in which maximum complexity is achieved at H = 0.5 with >H corresponding to stronger auto-correlation, our healthy control showed H = 0.68, with patients showing H = 0.82 and H = 0.83, respectively. A similar shift towards autocorrelation is seen in heart rate variability of heart disease patients, for whom the vagus nerve only weakly suppresses sympathetic excitatory responses. In contrast to the two above examples, in which circuit dysregulation is caused by changes in feedback strength, our neurobiological results suggest different, more complex, types of control circuit dysregulation caused by changes in gating and anatomical connectivity that affect feedback lag. These result in time-series with autocorrelations that are weaker than optimal, as shown in Figures 4, 6.
Mentions: Injuries have singular onsets, with anatomically defined damaged loci. In contrast, diseases are inherently dynamic: resulting from dysregulation of the negative feedback loops that, in a healthy individual, function to maintain allostasis in the face of chaotic environmental inputs. These negative feedback loops are necessary because biological processes typically only are able to function within a narrow window of upper and lower limits for water, sodium, glucose, temperature, etc. Because the environment often includes perturbations that exceed those thresholds, the body maintains homeostasis by negative feedback loops that correct the system towards baseline. For example, an acute bolus of glucose, unopposed, would lead to a hyperglycemic coma. Therefore, the metabolic control circuit responds by secreting the hormone insulin, sending the system into postprandial reactive hypoglycemia. Because hypoglycemia is just as dangerous to the body as hyperglycemia, the metabolic control circuit then secretes a different hormone, glucagon, which releases glucose back into the bloodstream. In a healthy person, the negative feedback loop as whole functions as a damped oscillator, with multiple excitatory (e.g., glucose, glucagon, cortisol) and inhibitory (insulin) responses acting in series to maintain glucose within acceptable limits. In a person with diabetes, however, the same perturbation is inadequately controlled—leading to extreme oscillations between hyper and hypoglycemia (Figure 1).

View Article: PubMed Central - PubMed

ABSTRACT

Here we provide an integrative review of basic control circuits, and introduce techniques by which their regulation can be quantitatively measured using human neuroimaging. We illustrate the utility of the control systems approach using four human neuroimaging threat detection studies (N = 226), to which we applied circuit-wide analyses in order to identify the key mechanism underlying individual variation. In so doing, we build upon the canonical prefrontal-limbic control system to integrate circuit-wide influence from the inferior frontal gyrus (IFG). These were incorporated into a computational control systems model constrained by neuroanatomy and designed to replicate our experimental data. In this model, the IFG acts as an informational set point, gating signals between the primary prefrontal-limbic negative feedback loop and its cortical information-gathering loop. Along the cortical route, if the sensory cortex provides sufficient information to make a threat assessment, the signal passes to the ventromedial prefrontal cortex (vmPFC), whose threat-detection threshold subsequently modulates amygdala outputs. However, if signal outputs from the sensory cortex do not provide sufficient information during the first pass, the signal loops back to the sensory cortex, with each cycle providing increasingly fine-grained processing of sensory data. Simulations replicate IFG (chaotic) dynamics experimentally observed at both ends at the threat-detection spectrum. As such, they identify distinct types of IFG disconnection from the circuit, with associated clinical outcomes. If IFG thresholds are too high, the IFG and sensory cortex cycle for too long; in the meantime the coarse-grained (excitatory) pathway will dominate, biasing ambiguous stimuli as false positives. On the other hand, if cortical IFG thresholds are too low, the inhibitory pathway will suppress the amygdala without cycling back to the sensory cortex for much-needed fine-grained sensory cortical data, biasing ambiguous stimuli as false negatives. Thus, the control systems model provides a consistent mechanism for IFG regulation, capable of producing results consistent with our data for the full spectrum of threat-detection: from fearful to optimal to reckless. More generally, it illustrates how quantitative characterization of circuit dynamics can be used to unify a fundamental dimension across psychiatric affective symptoms, with implications for populations that range from anxiety disorders to addiction.

No MeSH data available.


Related in: MedlinePlus