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Neural substrates of individual differences in human fear learning: evidence from concurrent fMRI, fear-potentiated startle, and US-expectancy data.

van Well S, Visser RM, Scholte HS, Kindt M - Cogn Affect Behav Neurosci (2012)

Bottom Line: Fear learning was evident from the differential expressions of fear (CS(+) > CS(-)) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula).We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation.This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS-US contingency or hippocampal activation).

View Article: PubMed Central - PubMed

Affiliation: University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
To provide insight into individual differences in fear learning, we examined the emotional and cognitive expressions of discriminative fear conditioning in direct relation to its neural substrates. Contrary to previous behavioral-neural (fMRI) research on fear learning--in which the emotional expression of fear was generally indexed by skin conductance--we used fear-potentiated startle, a more reliable and specific index of fear. While we obtained concurrent fear-potentiated startle, neuroimaging (fMRI), and US-expectancy data, healthy participants underwent a fear-conditioning paradigm in which one of two conditioned stimuli (CS(+) but not CS(-)) was paired with a shock (unconditioned stimulus [US]). Fear learning was evident from the differential expressions of fear (CS(+) > CS(-)) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula). We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation. This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS-US contingency or hippocampal activation). Our study provides the first evidence for the potential of examining startle potentiation in concurrent fMRI research on fear learning.

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Electromyographic (EMG) traces, along with a schematic representation of the stimulus presentation during scanning. The upper panels show typical EMG recordings before (a) and after (b) magnetic resonance imaging artifact correction. The lower panels illustrate the presentation of the conditioned stimulus (CS), the unconditioned stimulus (US), and the startle probe (panels c, d and e, respectively) and indicate the start of the acquisition of a brain volume (f)
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Fig1: Electromyographic (EMG) traces, along with a schematic representation of the stimulus presentation during scanning. The upper panels show typical EMG recordings before (a) and after (b) magnetic resonance imaging artifact correction. The lower panels illustrate the presentation of the conditioned stimulus (CS), the unconditioned stimulus (US), and the startle probe (panels c, d and e, respectively) and indicate the start of the acquisition of a brain volume (f)

Mentions: The raw EMG data set was offline corrected for scanner artifacts using the Brain Vision Analyzer software (version 1.05, Brain Products GmbH, Munich, Germany). First, each EMG data file was up-sampled to 20480 Hz and slice volumes were aligned (for synchronizing the clocks of the EMG and fMRI devices). Thereafter, gradient and pulse artifacts were removed, according to the algorithm proposed by Allen et al. (2000), by subtracting an artifact template from the EMG data using a baseline-corrected sliding average of 25 consecutive volumes. Each corrected EMG signal was then down-sampled to the initial sampling frequency (2048 Hz) and low-pass filtered (512 Hz) to reduce residual scanner artifacts. In addition, a 50-Hz notch filter was applied. Visual inspection of the raw and corrected EMG data indicated that the average template subtraction method was effective in removing MRI artifacts from the EMG signal (see Fig. 1a and b). Subsequently, to compute fear-potentiated startle responses, peak amplitudes were identified from the corrected EMG data over the period of 200 ms following startle probe onset by using the home-built VSRRP98 software (VSRRP98: www.test.uva.nl/ozi_psychology/index.php?Page=Software). To adjust for between-participants differences in startle responsivity, startle responses were converted to t scores, within participants for each day separately. Finally, missing data (M = 5.6%, SD = 6.6%) were replaced by using the linear-trend-at-point method.Fig. 1


Neural substrates of individual differences in human fear learning: evidence from concurrent fMRI, fear-potentiated startle, and US-expectancy data.

van Well S, Visser RM, Scholte HS, Kindt M - Cogn Affect Behav Neurosci (2012)

Electromyographic (EMG) traces, along with a schematic representation of the stimulus presentation during scanning. The upper panels show typical EMG recordings before (a) and after (b) magnetic resonance imaging artifact correction. The lower panels illustrate the presentation of the conditioned stimulus (CS), the unconditioned stimulus (US), and the startle probe (panels c, d and e, respectively) and indicate the start of the acquisition of a brain volume (f)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3400034&req=5

Fig1: Electromyographic (EMG) traces, along with a schematic representation of the stimulus presentation during scanning. The upper panels show typical EMG recordings before (a) and after (b) magnetic resonance imaging artifact correction. The lower panels illustrate the presentation of the conditioned stimulus (CS), the unconditioned stimulus (US), and the startle probe (panels c, d and e, respectively) and indicate the start of the acquisition of a brain volume (f)
Mentions: The raw EMG data set was offline corrected for scanner artifacts using the Brain Vision Analyzer software (version 1.05, Brain Products GmbH, Munich, Germany). First, each EMG data file was up-sampled to 20480 Hz and slice volumes were aligned (for synchronizing the clocks of the EMG and fMRI devices). Thereafter, gradient and pulse artifacts were removed, according to the algorithm proposed by Allen et al. (2000), by subtracting an artifact template from the EMG data using a baseline-corrected sliding average of 25 consecutive volumes. Each corrected EMG signal was then down-sampled to the initial sampling frequency (2048 Hz) and low-pass filtered (512 Hz) to reduce residual scanner artifacts. In addition, a 50-Hz notch filter was applied. Visual inspection of the raw and corrected EMG data indicated that the average template subtraction method was effective in removing MRI artifacts from the EMG signal (see Fig. 1a and b). Subsequently, to compute fear-potentiated startle responses, peak amplitudes were identified from the corrected EMG data over the period of 200 ms following startle probe onset by using the home-built VSRRP98 software (VSRRP98: www.test.uva.nl/ozi_psychology/index.php?Page=Software). To adjust for between-participants differences in startle responsivity, startle responses were converted to t scores, within participants for each day separately. Finally, missing data (M = 5.6%, SD = 6.6%) were replaced by using the linear-trend-at-point method.Fig. 1

Bottom Line: Fear learning was evident from the differential expressions of fear (CS(+) > CS(-)) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula).We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation.This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS-US contingency or hippocampal activation).

View Article: PubMed Central - PubMed

Affiliation: University of Amsterdam, Amsterdam, The Netherlands.

ABSTRACT
To provide insight into individual differences in fear learning, we examined the emotional and cognitive expressions of discriminative fear conditioning in direct relation to its neural substrates. Contrary to previous behavioral-neural (fMRI) research on fear learning--in which the emotional expression of fear was generally indexed by skin conductance--we used fear-potentiated startle, a more reliable and specific index of fear. While we obtained concurrent fear-potentiated startle, neuroimaging (fMRI), and US-expectancy data, healthy participants underwent a fear-conditioning paradigm in which one of two conditioned stimuli (CS(+) but not CS(-)) was paired with a shock (unconditioned stimulus [US]). Fear learning was evident from the differential expressions of fear (CS(+) > CS(-)) at both the behavioral level (startle potentiation and US expectancy) and the neural level (in amygdala, anterior cingulate cortex, hippocampus, and insula). We examined individual differences in discriminative fear conditioning by classifying participants (as conditionable vs. unconditionable) according to whether they showed successful differential startle potentiation. This revealed that the individual differences in the emotional expression of discriminative fear learning (startle potentiation) were reflected in differential amygdala activation, regardless of the cognitive expression of fear learning (CS-US contingency or hippocampal activation). Our study provides the first evidence for the potential of examining startle potentiation in concurrent fMRI research on fear learning.

Show MeSH
Related in: MedlinePlus